surface ocean – lower atmosphere studies ireland · rachel r. cave & tiernan henry coastal...

Surface Ocean – Lower Atmosphere Studies

Ireland

Edited by

Colin D. O’Dowd

Centre for Climate & Air Pollution Studies

Environmental Change Institute & Department of Physics

National University of Ireland, Galway

Proceedings of the SOLAS Ireland Workshop 2006

Table of Contents

1. Executive Summary 1

2. The Goals of SOLAS 1

3. List of Attendees of SOLAS Ireland workshop 2006 4

4. List of workshop presentations 5

5. Met Éireann Research Overview 6 Ray Mc Grath

Oceanic Whitecaps and Production of Sea Spray Aerosol 8 Gerrit de Leeuw, Leo Cohen & Adrian Callaghan

Marine Aerosol Production: Primary & Secondary Marine Aerosol Production from Natural Sources (MAP) 13

Colin D. O’Dowd

Global warming impacts on storminess 20 Tido Semmler, Saji Varghese, Ray McGrath, Paul Nolan, Shiyu Wang, Peter Lynch & Colin D. O’Dowd Regional Model Estimates of Aerosol Exchanges to and from the NE Atlantic 28 Bärbel Langmann, Saji Varghese, Colin D. O’Dowd & Claire Scannell

Predictive North-East Atlantic and Irish Sea models – hydrodynamic, surge and wave modelling 34 Indiana Olbert & Michael Hartnett

Algal responses to environmental change: seaweed-environment interactions and their applications 40 Dagmar Stengel Groundwater Inputs to the Coastal Zone of south County Galway and north and west County Clare 46 Rachel R. Cave & Tiernan Henry

Coastal CO2 eddy-covariance measurements 52 Colin D. O’Dowd & Philip McVeigh Coastal Biogenic Aerosol Flux – BIOFLUX 57 Colin D. O’Dowd, Karine Selligri & Young Jun Yoon

Seaweed, Iodine and Health 62 Peter Smyth

The Irish Weather Buoy Network: Platforms for sustained ocean- atmosphere measurements 68 Glenn Nolan Observations of the effect of fetch on whitecap coverage in the Irish Sea 70 Adrian Callaghan & Martin White

SOLAS – Ireland Proceedings 2006

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1. Executive Summary

The 2nd Irish SOLAS meeting was held in the National University of Ireland, Galway on December 1st 2006. The objective of the meeting, as in the case of the 1st Irish SOLAS meeting, was to bring together the Irish scientific research communities relevant to SOLAS objectives; to consolidate our ocean-atmosphere research into an Irish SOLAS framework; and to serve as a platform to generate awareness of related research across different disciplines. An increased awareness is evidenced by an increase in the workshop attendance from 14 in 2005 to 31 in 2006. The meeting demonstrated an increase in activities over the year between 2005 and 2006 particularly in terms of more developed ocean-atmosphere research. In particular, regional scale models, both for atmospheric predictions and oceanic predictions have been shown to be in an impressive state after development over the last 3-4 years. In addition, process based research is also at an advanced level, both in terms of individual group activities and also in terms of international collaborations and the execution of large scale international research projects. The observational capability for continuous monitoring of ocean and atmospheric projects has also been significantly increased over the period. Overall, the inter-linkages between physical, chemical and biological processes and systems is becoming more evident, as is the need to conduct inter-disciplinary research across these thematic areas in the context of ocean-atmosphere interactions, climate change, and impacts on ecosystems.

2. The Goal of SOLAS

SOLAS (Surface Ocean - Lower Atmosphere Study) is a new international research initiative that has as its goal:

To achieve quantitative understanding of the key biogeo-chemical-physical interactions and feedbacks between the ocean and atmosphere, and of how this coupled system affects and is affected by climate and environmental change. The scope of the study is illustrated in Figures 1- 4 and described in detail in this Science Plan and Implementation Strategy. The Science Plan parts of this document are largely based on the results of the International SOLAS Open Science Meeting held in Damp, near Kiel, Germany in February 2000 which involved more than 250 scientists from 22 different countries. The International Geosphere-Biosphere Programme (IGBP), Scientific Committee on Oceanic Research (SCOR), Commission on Atmospheric Chemistry and Global Pollution (CACGP) and the World Climate Research Programme (WCRP) have approved SOLAS and are sponsors of it.


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Figure 1: The Scope of SOLAS

Figure 2: Key Processes in the ocean-atmospheric system


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Figure 4: Research foci in SOLAS

Figure 3: SOLAS Structure


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3. List of Attendees of SOLAS Ireland workshop 2006

SOLAS (Surface Ocean Lower Atmosphere Studies) Workshop

Martin Ryan Institute, National University of Ireland, Galway Attended: Colin O’Dowd National University of Ireland, Galway Ireland

(SOLAS National Coordinator) Gerard Jennings National University of Ireland, Galway Ireland

(Director, Environmental Change Institute) Frank McGovern Environmental Protection Agency Ireland (Climate Change Unit) Aodhagan O’Roddy National University of Ireland, Galway Ireland

(Chair, Irish Committee on Climate Change) Gerrit de Leeuw Finnish Meteorological Institute Finland (International SOLAS Committee Member)

Ray McGrath Met Éireann Ireland (C4I Project Leader)

Dagmar Stengel National University of Ireland, Galway Ireland Mike Hartnett National University of Ireland, Galway Ireland Indiana Olbert National University of Ireland, Galway Ireland Glenn Nolan Marine Institute Ireland Darius Ceburnis National University of Ireland, Galway Ireland

Robert Devoy University College Cork Ireland Ned Dwyer University College Cork Ireland Rowan Fealy National University of Ireland, Maynooth Ireland Michael Guiry National University of Ireland, Galway Ireland

(Martin Ryan Institute) Jeff Hare University of East Anglia UK (Executive Officer, SOLAS International Project Office) Peter Heffernan Marine Institute Ireland Tiernan Henry National University of Ireland, Galway Ireland Baerbel Langmann National University of Ireland, Galway Ireland Philip McVeigh National University of Ireland, Galway Ireland Tom Neary National University of Ireland, Galway Ireland Martina Prendergast National University of Ireland, Galway Ireland Tido Semmler Met Éireann Ireland Peter Smyth University College Dublin Ireland John Sweeney National University of Ireland, Maynooth Ireland Jenny Ullgren National University of Ireland, Galway Ireland Xiaochen Liu National University of Ireland, Maynooth Ireland Phillip O’Brien Environmental Protection Agency Ireland Rodney Teck National University of Ireland, Maynooth Ireland Claire Scannell National University of Ireland, Galway Ireland Martin White National University of Ireland, Galway Ireland


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5. List of workshop presentations

1st December 2006

Agenda

11:00 – 11:20 Welcome & Coffee 11:20-11:30 SOLAS-Ireland & Welcome from MRI & ECI O’Dowd, Michael Guiry, Gerard Jennings 11:30-11:40 International Activities & Updates from SOLAS Jeff Hare SOLAS 11:40-11:50 EPA Perspective on Air-Sea Exchange Frank McGovern EPA 11:50-12:00 Marine Institute Research Strategy & FP7 Peter Heffernan Marine Institute 12:00-12:10 Met Éireann Research Overview Ray McGrath Met Éireann 12:10-12:20 Oceanic Whitecaps and Production of Sea-Spray Aerosol Gerrit de Leeuw FMI 12:20-13:20 Lunch 13:20-13:30 Marine Aerosol Production Colin O’Dowd ECI/NUIG 13:30-13:40 Global Warming Impacts on Storminess Tido Semmler Met Éireann 13:40-13:50 Regional Model Estimates of Aerosol Exchange to and Bärbel Langmann ECI/MRI from the NE Atlantic 13:50-14:00 Modelling N. Atlantic Ocean Dynamics Indiana A. Olbert MRI/NUIG 14:00-14:10 Algae Response to Environmental Change Dagmar Stengel MRI/NUIG 14:10-14:20 Ground Water Inputs to Coastal Zones Tiernan Henry EOS/NUIG 14:20-14:30 Wave Dynamics and Coastal Processes Robert Devoy CMRC/UCC 14:30-14:40 CO2 Fluxes in the Coastal Zone Philip McVeigh ECI/NUIG 14:40-14:50 Iodine Fluxes from inter-tidal algae Colin O’Dowd ECI/NUIG 14:50-15:00 Seaweed, Iodine & Health Peter Smyth UCD 15:00-15:20 Coffee 15:20-15:30 The Irish weather buoy network, platforms for sustained Glenn Nolan Marine Institute

ocean-atmosphere measurements 15:30-15:40 An Investigation into the Effect of Sea State on Whitecap Martin White EOS/NUIG

Coverage in the Irish Sea 15:40-16:45 Discussion: developing an integrated research approach for SOLAS-Ireland.

Acronymns ACCENT Atmospheric Composition & Change – A European Network of Excellence C4I Community Climate Change Consortium for Ireland SOLAS Surface Ocean Lower Atmosphere Studies GEMS Global Monitoring for Environment and Security GEO Global Earth Observing GEOSS Global Observing System of Systems MAP Marine Aerosol Production (from natural sources) ECI Environmental Change Institute MRI Martin Ryan (Marine Science) Institute SFI Science Foundation of Ireland RFP Research Frontiers Programme EOS Earth & Ocean Sciences


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Met Éireann Research Overview Ray Mc Grath Met Éireann, Ireland 1 Monitoring Activities In cooperation with NUIG the monitoring of pollutants such PM10 and PM2.5 will be enhanced. Measurements of total column ozone will also be increased with an additional sonde launch each week from Valentia during the winter months; with EPA funding this period may be extended to cover all of 2007. Interest in air quality issues is increasing. Dispersion modelling capability will be upgraded by using community models such as AERMOD and CALPUFF in-house. In cooperation with the Radiological Protection Institute of Ireland (RPII) an emergency system will be installed in Met Éireann’s Headquarters as part of the National Emergency Plan for Nuclear Accidents to model the spread and deposition of radioactive materials arising from nuclear incidents using the Danish DERMA model. 2 Operational Developments 2.1 Numerical Weather Prediction Met Éireann’s operational Numerical Weather Prediction suite, based on the HIRLAM system, will be enhanced in 2007. Currently, the system is run on a dedicated computer but it is likely that production runs will be outsourced early in the year. The resolution of the HIRLAM analysis and forecast models will be increased: the main model will continue to have a horizontal resolution of about 15km but the number of vertical levels will increase from 31 to 60; the “nested” version will be run on a 5km grid (with 60 vertical levels). Currently, the data assimilation system is based on a three-dimensional variational method (3DVAR); a more advanced four-dimensional system (4DVAR) is currently being tested within the HIRLAM community and will be tested operationally by Met Éireann in late 2007. 4DVAR will enable us to make more effective use of observational data. The HIRLAM forecast model has a hydrostatic dynamical core. Work is presently underway within the HIRLAM and ALADIN consortia to develop jointly a meso-scale non-hydrostatic model targeted for operational running at scales ~2km. Met Éireann is contributing to this development work. 2.2 Marine Activities Currently, we provide operational wave forecasts in coastal areas using the WAM wave model (horizontal resolution ~30km). In 2007, a nested PRO-WAM wave model will be used: the outer mesh with a resolution of 15km, the inner mesh with a resolution of 5km.


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Recently, Met Éireann has been running the ROMS ocean model (~9km horizontal grid) to produce semi-operational forecasts of tidal surges in Irish coastal areas (see Figure 1). This will continue in 2007, possibly developing into a full operational system. 3 Climate Research The Community Climate Change Consortium for Ireland (C4I) project is the main vehicle for climate research in Met Éireann. The project is focused on dynamically downscaling GCM outputs to determine the future Irish climate at a regional level. So far, a small ensemble of simulations have been run (see Figure 2). The output data are used to drive application models (e.g. catchment flooding due to runoff). Currently, the project is performing a long simulation with the ROMS ocean model to investigate the impact of climate change on tidal surges and coastal flooding. C4I is also a partner in the EU-funded ENSEMBLES project and has run a 100-year simulation of the European climate. Project funding for C4I will terminate at the end of 2007 but it is likely that the climate research activities will continue within Met Éireann.

Figure 1 Example of tidal surge forecast using ROMS model driven with HIRLAM data

J F M A M J J A S O N D

(2061-2100) - (1961-2000)

FFiigguurree 22:: MMeeaann PPrreecciippiittaattiioonn ((mmmm)) Change (2021-2060) - (1961-2000) Based on dynamical downscaling of different GCMs and GHG emission

scenarios


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Oceanic Whitecaps and Production of Sea Spray Aerosol Gerrit de Leeuw1, Leo Cohen2 and Adrian Callaghan3

1Finnish Meteorological Institute, R&D, ClimateChange Unit, Helsinki, Finland & University of Helsinki, Dept. of Physical Sciences, Helsinki, Finland & TNO Build Environment, Apeldoorn, The Netherlands 2TNO Defence, Security and Safety, The Hague, The Netherlands 3NUIG, Dept. of Earth and Ocean Sciences, Galway, Ireland 1 Introduction

Primary marine aerosol production is produced by the interaction between wind and waves. The wind drag at the sea surface creates waves. When the wind speeds exceeds a certain threshold, between 4 and 7 ms-1, the drag becomes so high that the waves break and air is entrained into the water. The air breaks up into bubbles which rise to the surface where they burst and produce sea spray aerosol. The bubble plume undergoes various stages from formation, injection, rise and senescence with associated changes in the bubble size distribution [Leifer, and De Leeuw, 2006]. The larger bubbles rise fastest and thus most of the air is released back into the atmosphere very fast after wave breaking. The largest bubbles produce film drops due to breaking of the bubble surface film after protruding the surface [Spiel, 1998]. Bubbles smaller than 1.7 mm also produce jet drops when the cavity left after rupture of the bubble film shoots up [Spiel, 1997]. When wind speeds exceeds about 9 ms-1 the drag becomes strong enough to directly tear off sea spray droplets from the wave tops which are called spume drops. Laboratory experiments indicate that 1-6 jet drops and up to 1000 film drops are produced from each bubble, depending on the bubble size. Laboratory experiments on simulated breaking waves provide quantitative information on the number of sea spray droplets produced from a breaking wave and the experimental set up determines whether only bubble-mediated (film and jet) droplets are measured, or whether spume drops are studied as well.

The sea spray source function (S3F) describes the sea spray flux per unit area and per unit of time, parameterized as function of environmental parameters such as wind speed and/or water temperature. Several formulations are available, based on different physical and experimental approaches. One of the approaches is to parameterize the S3F in terms of whitecap cover, i.e. the fraction of the ocean surface covered with whitecaps [Monahan et al., 1986]:

,...),(,...)(3 10 wtrfuWFS = (1)

where W is the whitecap cover formulated in terms of wind speed u10 and possibly other parameters, f is the production of number of sea spray aerosol droplets per unit whitecap area and per unit of time as a function of radius r, water temperature tw and possibly other parameters. Detailed measurements of f(r, tw) are available from Mårtensson et al. [2003].

One of the goals EU FP6 project MAP (Marine Aerosol Production) coordinated by Professor Colin O’Dowd (NUIG) is “to quantify the number and size flux of primary inorganic and organic marine sea-spray aerosol (PMA)”. Data to achieve this objective were collected during the MAP cruise with the Celtic Explorer (Figure 1), 11 June – 5 July, 2006. Various types of experiments were conducted to provide data to derive the S3F. Here we report on whitecap measurements and the first preliminary results from the analysis.


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(a) (b) Figure 1. (a) Celtic Explorer in the port of Killybegs. The mast on the foredeck was constructed and mounted to measure sea spray fluxes using eddy covariance. Containers on the foredeck contained sophisticated instrumentation for characterization of atmospheric components relevant for the assessment of primary and secondary aerosol production: atmospheric trace gases and aerosol chemical and physical properties; (b) Whitecap camera mounted to the railing of the Monkey deck.

2 Experimental set-up

The whitecap measurements were made with a video camera mounted on the monkey deck of the Celtic Explorer, (Figure 1b). The video signal was grabbed and recorded on the hard disk of a pc in the Celtic Explorer dry lab. Data were processed when the video frame neither included the horizon, nor looked very close to the ship. The recording was automatically started after day break and stopped before dusk. Frames were continuously recorded, with a frequency of 50 Hz, during 55 minutes each hour.

3 Results

The Celtic Explorer cruise took place on the North Atlantic Ocean, NW of Ireland. A variety of conditions was encountered. Of particular interest was a gale on 20 and 21 June, 2007, with sustained wind speeds of about 27 ms-1, see Figure 2. Data recorded with the video camera throughout this gale, from the beginning until the wind subsided, was used for the initial analysis of the whitecap cover because a wide range of wind speeds was encountered, including periods with increasing and decreasing wind speed. Results are presented in Figure 3 where we show both the wind speed and the percentage whitecap cover as time series on 20 and 21 June, 2007.


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Figure 3 clearly shows the increase in whitecap cover as wind speed increases. However, when the storm was over its top and the wind speed decreased, the decrease in the whitecap cover appears to be slower. This is clearly illustrated in Figure 4 which shows the whitecap cover as a function of wind speed for increasing wind speed (Figure 4a) and decreasing wind speed (Figure 4b). The regression


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W vs Increasing Wind Speed

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4 Conclusions

Preliminary results have been presented from whitecap measurements during the gale which was experienced during the MAP cruise on 20 and 21 June, 2006. The results clearly show the difference between the effects of increasing and decreasing wind speed. Only a limited selection of all available data has thus far been processed and analyzed. The processing and analysis is on-going and results will be related to direct measurements of sea spray and sea salt aerosol fluxes using the eddy co-variance method to derive a parameterization for the sea spray aerosol flux based on detailed analysis of processes described by a variety of environmental parameters.

Acknowledgements

The MAP project is financially supported by the European Commission (FP6, project number 018332) and the participating institutes; the Celtic Explorer cruise was supported by the Marine Institute. The captain and the crew of the Celtic Explorer made the cruise to a success thanks to their able seamen ship, which made it possible to continue collecting valuable data during the storm.

References

Leifer, I and de Leeuw, G (2006). Bubbles generated from wind-steepened breaking waves: 1. Bubble plume bubbles, J. Geophys. Res., 111, C06020, doi:10.1029/2004JC002673.

Mårtensson, M., Nilsson, E.D., de Leeuw, G., Cohen, L.H., and Hansson, H-C (2003). Laboratory simulations of the primary marine aerosol generated by bubble bursting, JGR-Atmospheres 108 (D9), 10.1029/2002JD002263.

Monahan, E.C. and O'Muircheartaigh, I.G. (1986). Whitecaps and the passive remote sensing of the ocean surface. Int. J. Remote Sensing 7, 627-642.


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Spiel, D.E. (1997). More on the births of jet drops from bubbles bursting on seawater surfaces, J. Geophys. Res., 102, 5815–5821.

Spiel, D.E. (1998). On the birth of film drops from bubbles bursting on seawater surfaces, J. Geophys. Res., 103, 24,907–24,918.


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Marine Aerosol Production: Primary & Secondary Marine Aerosol Production from Natural Sources (MAP) Colin D. O’Dowd School of Physics & Centre for Climate & Air Pollution Studies, Environmental Change Institute, National University of Ireland, Galway, Ireland

Abstract. Marine aerosol contributes significantly to the global radiative budget and consequently, changes in marine aerosol abundance and/or chemical composition will impact on climate change. Various climate feedback mechanisms have been proposed involving the sulphur, sea-salt, iodine and organic sea-spray cycles; however, all cycles and their impacts on aerosol haze and cloud layers remains poorly quantified. MAP will consolidate the current state-of-the-art in the fields of aerosol nucleation and growth and primary marine aerosol (PMA) production to quantify the key processes associated with primary and secondary marine aerosol (SMA) production from natural sources. MAP focuses on the newly identified aerosol formation mechanisms involving iodine oxides, for secondary aerosol production, and the primary production of marine organic matter aerosols produced by plankton and transferred to the atmosphere via the bubble bursting process at the ocean surface. Key processes are being identified, parameterized and implemented in a Global/Regional-scale chemical transport model and in a regional climate model. Combining the knowledge gathered on key processes with satellite-derived information on oceanic and meteorological parameters, an algorithm is being developed to produce a Sea-Spray Source Function (S3F) which will subsequently be used in large scale models to quantify the impacts of marine aerosols. The algorithm and its application will be a product contributing to GMES/GEOSS. Similarly, an organo-iodine source function will also be developed. The impact of marine aerosol on atmospheric chemistry, radiative forcing and climate will be evaluated using the large-scale models.

1 Introduction Marine aerosols contribute to a global heat shield through the formation of aerosol haze layers and cloud layers. The haze layers directly scatter incoming solar energy back out to space and this contributes to the Earths albedo. Since these haze layers overly dark and absorbing oceans, their influence to the radiative budget is significant. Similarly, and perhaps more importantly, they provide the nuclei for cloud formation. Clouds provide event greater reflective layers that haze layers and could be considered to have an even greater global cooling effect. The cloud reflectance is determined by the availability of cloud nuclei, a water-soluble subset of aerosol. Although it is generally considered (Twomey, 1974) that an increase in cloud nuclei availability leads to the formation of brighter clouds (since the liquid water has to be shared out amongst more nuclei, it results in a reduction in mean cloud droplet size which results in an increase in scattering efficiency), work has shown that this effect is not necessarily the case and depends on a complex, non-linear relationship


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between meteorological dynamics and chemical composition of the aerosol, in particular, sea-salt produced by wind wave interaction and sulphate aerosol produced by gaseous plankton emissions (O’Dowd et al., 1999). For a number of decades now, it has been postulated dimethylsulphide, a major plankton emission is the main source of marine aerosols – it is oxidised to SO2 and then further oxidised to aerosol sulphate (so called secondary aerosol production) – could control climate through a negative feedback effect (Charlson et al., 1987). This assumption is based on a minimal role for mechanically-generated sea-spray production (so called primary aerosol production). However, recent work has demonstrated that sea-spray production can significantly influence marine aerosol (O’Dowd et al., 1997) and cloud fields (O’Dowd et al., 1999), and, more recently, that its chemical composition can be significantly influenced by marine biogenic organic matter (O’Dowd et al., 2004). Moreover, the role of sulphur species in dominating secondary aerosol formation has been questioned by the identification of iodine species in the production of new, secondary marine aerosol particles (O’Dowd et al., 2002). The recent findings have set the scene for a large-scale EU project MAP to attempt to further elucidate the formation of marine aerosols, the relative roles of dynamical and biological processes, and the impact of marine aerosol on regional-scale climate. The Specific MAP Objectives are:

(1) To elucidate the dominant condensable vapours driving secondary marine aerosol (SMA) formation.

(2) To quantify the number and size flux of primary inorganic and organic marine sea-spray aerosol (PMA)

(3) To produce a PMA and iodo-carbon source function using integrated Global Earth Observing satellite data and in-situ data.

(4) To quantify the impact of SMA and PMA on radiative forcing and atmospheric chemistry.

Consortium Partners Coordinator National University of Ireland, Galway NUIG Ireland Colin O'Dowd

Participant 2 Netherlands Organization for Applied Research TNO Netherlands Gerrit de Leeuw Participant 3 Institute of Atmosphere Science & Climate CNR-ISAC Italy Maria Cristina Facchini Participant 4 University of Helsinki UHel Finland Markku Kulmala Participant 5 University of Kuopio UKU Finland Ari Laaksonen Participant 6 Finnish Meteorological Institute FMI Finland Veli-Matti Kerminen Participant 7 University of Manchester UMAN UK Gordon McFiggans Participant 8 University of York UoY UK Lucy Carpenter Participant 9 University of East Anglia UEA UK Peter Liss Participant 10 Stockholm University SU Sweden Douglas Nilsson Participant 11 University of Heidelberg UHEI.IP Germany Ulrich Platt Participant 12

Max Planck Institute for Nuclear Physics

MPG-MPIK

Germany Frank Arnold

Participant 13 University of Mainz JGUM Germany Thorsten Hoffmann Participant 14

Joint Research Centre

JRC

CEC Frank Dentener (Jean-Philip Putaud)

Participant 15

University of Crete

UoC

Greece Euripides Stephanou (Maria Kanakidou)

Participant 16 Polytechnic University of Marche UNIVPM Italy Roberto Danovaro


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MAP Structure and Work Package Outline.

MAP comprises 5 research Work Packages (WP). WP1 focuses on quantifying the seasonality in marine aerosol properties on the North Atlantic coastline and in the Mediterranean. Advanced aerosol physics and chemistry measurements were conducted from January 2006 and continue through to December 2006. WP2 focuses on an intensive winter and summer campaign where the most advanced instruments available for aerosol and gas characterisations and air-sea exchange fluxes were deployed both at Mace Head and offshore on the Celtic Explorer Research Vessel. WP3 focuses on developing detailed process models to elucidate key process leading to the formation of marine aerosol and its subsequent evolution. In addition, it focused on developing appropriate parameterisations for inclusion in global and regional chemical transport and climate models.

WP4 aims to develop two GEOSS (Global Earth Observations System of Systems) products – one to produce a global sea-spray source function (S3F) for combined organic-inorganic sea-spray, the other to produce a global organo-halogen source function. The WP integrates field and laboratory flux and air-sea exchange experiments, along with satellite products, into large scale models.

WP5 focuses on initially upgrading the large-scale models to the currently most advanced aerosol and gas schemes, and following the development of new parameterisations under WP3 and source functions under WP4, will implement these schemes to produce a quantification of the impact of marine aerosols on regional climate and the global radiative budget.

2 Progress to date During the first year (September 2005 – September 2006), the MAP consortium conducted the majority of its field studies. These included three intensive campaigns over the North Atlantic during winter and summer in order to capture the main aerosol properties during periods of low and high biological activities. In addition, advanced measurements, albeit to a lesser extend, were conduced at the Mediterranean station Finokalia in Crete. The North Atlantic campaigns were conduced at the Mace Head Atmospheric Research Station (Figure 1) and off-shore on the Celtic Explorer (Figure 2). In addition, a year-long programme of advanced measurements was initiated at Mace Head, starting January 2006. The most challenging component of the intensive field studies was the massive deployment of instruments on the Celtic Explorer and to mobilise this deployment into the peak of the plankton bloom on the continental shelf (Figure 3 and 4). Almost the whole consortium participated on the cruise, deploying the most advanced aerosol and gas mass spectrometers, detailed aerosol physics and off-line chemical samplers, air-sea exchange experiments and flux measurements of micro-


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meteorology, aerosols, and gases. Along with atmospheric and water column sampling, extensive bubble-mediated exchange experiments were conducted in the shops wet labs. Significant advances were also made on the model development. In particular, the process models have been upgraded to handle specific processes relating to marine aerosol production and marine atmospheric chemistry, while the large scale models have been upgraded to handle new aerosol modal dynamics modules and improved atmospheric chemistry schemes. Initial developments of schemes to produce a sea-spray-source-function has been developed as a first attempt to produce a combined organic-inorganic source function and distribution over the North Atlantic. A wide range of conditions were encountered, as expected for the seasonality measurements, but also for the Celtic Explorer campaign. During the ship campaign, two extensive periods of very high winds were encountered with one event gusting up to 30 m s-1. Such conditions proved excellent for the eddy-covariance flux measurements deployed. In addition, good periods of clear sky irradiance under clean air conditions were also encountered - these are ideal for quantifying open ocean atmospheric chemistry processes. As mentioned above, the ship encountered an extensive plankton bloom and, combined with in-situ and satellite measurement guidance, performed many transects into and out of regions of high biological activity. A detailed web-site was set up and used for project management and field study forecasting studies. In addition, it serves as the database for data products. The location is http://macehead.nuigalway.ie/map. A full work programme can be found here.

Figure 1. The Mace Head Atmospheric Research Station operated by the National University of Ireland, Galway. This station was the host of a winter and summer intensive field campaign and a year long programme of advanced measurements to determine seasonality in aerosol properties.

Figure 2. The Celtic Explorer Research Vessel operated by Ireland’s Marine Institute. This platform was the host of extensive and advanced measurements into air-sea exchange fluxes of marine aerosols and gases (CO2 and organo-halogens). The vessel sampled within and outside the North Atlantic summer plankton bloom.


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Figure 3. Visible satellite (MIRIS) image of the extensive plankton bloom which was the focus of the MAP ship campaign.

Figure 4: Chlorophyll concentration from MODIS during MAP intensive summer campaign. Blank areas are either cloud contaminated or land areas.

Figure 5. Celtic Explorer transects through plankton bloom during leg 1 of the cruise

Figure 6. Celtic Explorer transects through plankton bloom during leg 2 of the cruise

3 Main Results to Date MAP has produced an extensive and unique database on marine aerosol and gas properties as well as air-sea exchange products. At this short time after the end of the main campaign, and with the year-long seasonality measurement programme still running, only a glimpse of the results are available. One of the main underlying objectives of all work packages was to quantify the organic fraction of sea-spray aerosol. Previous measurements have indicated a significant organic fraction as a function of season, or biological activity, over the North Atlantic. Continuous measurements confirm increased organic carbon during periods of increasing biological activity. This is summarised in Figure 7 which shows the increase in the baseline of organic carbon (OC) up until September 2006. The peaks represent polluted air incursions. While the baseline of OC increases during spring to summer there is no similar increase in elemental carbon (EC) suggesting a biogenic source for the OC. Detailed size


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segregated aerosol chemical samples illustrate (Figure 8), during the summer intensive campaign, an increasing fraction of water soluble organic carbon (WSOC) for the submicron size fraction. Water insoluble organic carbon, which is expected to be greater than WSOC, has yet to be analysed. Larger than 1 micron, the majority of the mass is due inorganic sea-salt, although the absolute OC mass concentrations are similar for sub-and super-micron modes. The main question relating to the organic fraction of marine aerosol is the fraction of OC produced by primary processes (i.e. bubble-bursting from whitecaps) and what is produced by secondary gas-to-particle production processes. This question was tackled by conducted controlled bubble-mediated aerosol production experiments in the plankton bloom onboard the Celtic Explorer. Initial indications suggest significant organic mass fractions of primary aerosol produced inside the bloom.

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Figure 7. Continuous measurements of organic carbon (OC) and elemental carbon (EC) in aerosols arriving at Mace Head.

Figure 8 Size segregated relative chemical composition of the water soluble fraction of the clean sector sample collected from 19th to 28th June 2006; stage 1 (0.06-0.125µm) not detectable.

Initial flux data indicate an excellent database for aerosol and gas exchange processes, however, at this stage; the dataset is under-going a correction for the ship motion before accurate fluxes can be calculated. Another critical question relating to marine aerosol formation is whether or not new particle production can occur over the option ocean. During clear sky conditions, evidence of at least two particle production events was observed. The full range of initial results is just emerging from the experimental and modelling studies but these are reported in detail in the Activity Progress Report. 4 Conclusions MAP conducted a very successful first 12 months of extensive field and laboratory studies and multi-scale model developments. The first year was very challenging in terms of the scale of the project and objectives over the reporting period, and, although challenging, the work programme was managed and executed very successfully. The next year will produce an analysis of the main datasets as well as key parameterisations from the process models to be implemented in the large scale models.


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References Charlson, R.J., Lovelock, J.E., Andreae, M.O. and Warren, S.G (1987). Oceanic phytoplankton,

atmospheric sulfur, cloud albedo and climate. Nature 326, 655-661. O’Dowd, C.D., Smith, M.H., Consterdine, I.E. and Lowe, J.A. (1997). Marine aerosol, sea salt, and the

marine sulphur cycle: a short review, Atmospheric Environment 31, 73-80. O'Dowd, C.D., Lowe, J., Smith, M.H. and Kaye, A.D. (1999). The relative importance of sea-salt and

nss-sulphate aerosol to the marine CCN population: An improved multi-component aerosol-droplet parameterisation. Q. J. Roy. Met. Soc., 125, 1295-1313.

O’Dowd, C.D., Jimenez, J.L., Bahreini, R., Flagan, R.C., Seinfeld, J.H., Hämeri, K., Pirjola, L., Kulmala, M., Jennings, S.G. and Hoffman, T. (2002a). Marine aerosol formation from biogenic iodine emissions. Nature 417, 632-636.

O'Dowd, C.D., Facchini, M.C., Cavalli, F., Ceburnis, D., Mircea, M., Decesari, S., Fuzzi, S., Yoon, Y.J. and Putaud, J.P. (2004). Biogenically-driven organic contribution to marine aerosol, Nature, doi:10.1038/nature02959.

Twomey, S.A., (1974). Pollution and the Planetary albedo. Atmos. Environ., 8, 1251-1256.


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Global warming impacts on storminess Tido Semmler 1, Saji Varghese 2, Ray McGrath 1, Paul Nolan 1, Shiyu Wang 1, Peter Lynch 3 & Colin O'Dowd 2

1 Met Eireann, Glasnevin Hill, Dublin 9 2 School of Physics & Centre for Climate & Air Pollution Studies, Environmental Change Institute, National University of Ireland, Galway, Ireland 3 University College Dublin, School of Mathematical Sciences Abstract The influence of an increased sea surface temperature (SST) on the frequency and intensity of cyclones over the North Atlantic is investigated using four data sets from simulations with the Rossby Centre regional climate model RCA3. The model domain comprises large parts of the North Atlantic and the adjacent continents. The first pair of simulations with RCA3 is driven by reanalysis data for May to December 1985-2000 at the lateral and lower boundaries, once using original SST and lateral boundary temperature and once increasing these temperatures by 1 K. The second pair of RCA3 simulations is driven by general circulation model (GCM) data for May to December 1985-2000 and May to December 2085-2100 assuming the SRES-A2 emission scenario. The setup of an idealized sensitivity experiment versus a GCM driven experiment ensures that we can separate different factors influencing the development of cyclones. In both pairs of experiments an increase in the frequency of hurricanes is simulated. In the first pair there is not much change in the location of hurricanes, whereas in the second pair there is a pronounced shift in November and December: more hurricanes can be found over the Gulf of Mexico, the Caribbean Sea and the western Sargasso Sea and less over the southern North Atlantic. Contrasting and generally weaker changes are seen in the extratropical region and for the less extreme events. Increases of 9% in the number of extratropical cyclones and 39% in the number of tropical cyclones with wind speeds of at least 18 m/s can be found in the first pair of experiments, whereas decreases by 18% and 13% are simulated in the second pair due to a strongly increased atmospheric stability. 1 Introduction According to climate simulations the SST is predicted to increase in most regions. If an increased SST would lead to more intense cyclones the probability of severe weather events such as heavy precipitation and strong wind storms could increase over Ireland. If an increased SST would lead to an environment favourable for tropical cyclones in more northern latitudes Western Europe could be affected by a larger number of transitioned tropical cyclones. Thus it is very relevant to study changes purely induced by an increased SST as well as changes induced by other influence factors. The influence of SST on the development of extratropical and tropical cyclones has been widely studied already. Gyakum and Danielson (2000) found that the combination of warm SST anomalies with cold air masses over the Western North Pacific leads to enhanced sensible and latent heat fluxes from the ocean into the atmosphere, favoring explosive cyclogenesis. According to Sanders and Gyakum (1980) explosive extratropical cyclogenesis is found preferably over regions with strong SST gradients. Emanuel (1987) showed using a Carnot cycle approach that the maximum possible pressure drop towards the eye of a hurricane can be expressed as a function of the SST, the ambient relative humidity and the thermodynamic efficiency, which is proportional to the difference between the SST


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and a weighted mean temperature in the upper atmosphere. All of these studies as well as many other studies suggest that the SST is one of the key factors influencing the development of cyclones. Even though numerous studies have been conducted on the influence of the SST on the frequency and intensity of cyclones, the results of previous work using GCMs to study future changes in the frequency and intensity of extratropical cyclones due to increased greenhouse gas concentrations differ dependent on the methodology and the GCM used (König et al., 1993, Hall et al., 1994, Zhang and Wang, 1997, Sinclair and Watterson, 1999). It is very important to reduce the sources of these uncertainties to achieve a better understanding of cyclone characteristics in a future climate. 2 Model setup In this study the regional climate model RCA3 (Rossby Centre regional Atmospheric Climate model version 3) (Kjellström et al., 2005; Jones et al., 2004) is used on a model domain including large parts of the North Atlantic to allow the increased SST to have an impact on the development of cyclones. Western Europe and North West Africa are included in the east of the model domain with Ireland, the UK and the Iberian Peninsula being far enough away from the lateral boundaries (Figure 1). The south of North America, Central America, the north of South America, the Gulf of Mexico and the Caribbean Sea are included in the west of the model domain to allow the simulation of tropical cyclones making landfall or traveling across the North Atlantic towards Western Europe while transforming into extratropical cyclones.

Figure 1: Orography [m] in the simulation domain Two pairs of simulations have been performed. The first pair has been driven by the 40-year reanalysis data (ERA-40: Uppala et al., 2005) from the European Centre for Medium-Range Weather Forecasts (ECMWF) at the lateral and lower boundaries. The time period simulated with RCA3 is May to December 1985 to 2000. The model has been initialized each year on the 1st of May and continuously run until end of December to ensure that we simulate the strongest tropical storms and their journey across the North Atlantic including their transition into extratropical storms. The model has been run in a horizontal resolution of 0.25° or around 28 km with 31 non-equally spaced vertical levels on a rotated latitude/longitude grid. One simulation of the first pair uses the original SST from ERA40 as lower boundary values (standard experiment), whereas the other one uses an SST increased by 1 K (sensitivity experiment). In the sensitivity experiment the atmospheric temperature has been increased by 1K in the lateral boundary data at all model levels to maintain the vertical structure of the atmosphere. The specific humidity has also been increased such that the relative humidity remains the same as in the standard experiment at all model levels. According to model projections (Knutson and


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Tuleya, 2004) it is realistic to assume the relative humidity in a warmer climate to be similar to the one observed today, whereas the vertical temperature structure is likely to change towards a more stable stratification in the troposphere. The second pair of simulations has been run on the same model domain and has been driven by data from the GCM ECHAM5-OM1 (Roeckner et al., 2003) from May to December 1985-2000 (control experiment) and from May to December 2085-2100 assuming the SRES-A2 emission scenario (scenario experiment). This second pair of simulations has been carried out to investigate differences to the idealized first pair, which could arise from changes in the general circulation, the vertical structure of the atmosphere or the spatial pattern of SST. The pure influence of a homogeneous warming all over the model domain can be separated from other influences. 3 Results 3.1 Standard/sensitivity experiments Figure 2 shows the number of extratropical and tropical cyclones grouped into intensity classes for the standard and the sensitivity experiment. All cyclones are counted every 3-hour interval. The frequency of the less intense extratropical cyclones does not change very much with increasing SST while the occurrence of more intense extratropical cyclones is increased. An increase in the number of tropical cyclones can be seen in nearly all intensity classes except for the weak classes. A 9% and 39% increase in the number of major extratropical cyclones and tropical cyclones respectively with maximum wind speeds of more than 18 m/s can be found in the sensitivity experiment compared to the standard experiment. The differences are most evident in the very strong intensity classes. The number of extratropical cyclones attaining maximum wind speeds of more than 34 m/s in the sensitivity and the standard experiment are 84 and 21 respectively. The number of hurricanes with wind speeds of more than 42 m/s (category 2 on the Saffir-Simpson Hurricane Scale) increases from 196 to 570 which is a factor of nearly 3 in the sensitivity experiment compared to the standard experiment. The most obvious increase occurs for tropical cyclones over the Gulf of Mexico, the Caribbean Sea and the western Sargasso Sea, where their frequency is more than doubled. a) b)

Figure 2: Total number of (a) extratropical and (b) tropical cyclones as in the standard and in the sensitivity experiment for May to December 1985-2000. All cyclones are counted in each 3-hourly time interval and categorized in intensity classes with a width of 4 m/s maximum wind speed (2-6 m/s, 6-10 m/s, ... , 50-54 m/s).


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In addition more simulated tropical cyclones undergo ET (48% in the sensitivity experiment compared to 44% in the standard experiment) and substantially more cyclones re-intensify after their ET (24% compared to 12%). Therefore more major cyclones with tropical origin reach the vicinity of Western European coastal areas and more major tropical cyclones and extratropical cyclones with tropical origin reach the Eastern US according to the sensitivity experiment compared to the standard experiment (Figure 3). a) b)

Figure 3: Cyclone tracks for all storms in May to December 1985-2000 undergoing extratropical transition during their lifetime with a maximum wind speed of 18 m/s or more from (a) the standard experiment and (b) the sensitivity experiment (red arrows: cyclone classified as tropical; blue arrows: cyclone classified as extratropical). Only cyclones which sustain a minimum wind speed of 18 m/s over at least two time steps are tracked. 3.2 Control/scenario experiments In the ECHAM5-OM1-A2 driven scenario experiment for 2085 to 2100 where factors other than the SST are considered, the number of extratropical cyclones decreases for many intensity compared to the ECHAM5-OM1 driven control experiment for 1985 to 2000 (Figure 4). For the stronger intensity classes beyond 34 m/s increases in the extratropical cyclone numbers are simulated. However these increases are smaller than in the first pair of experiments. Whereas a 4-fold increase in the number of extratropical cyclones is simulated in the first pair, the increase here is slightly more than 2-fold. Because of a weakening of the thermohaline circulation in the ECHAM5-OM1 simulation the SST does not increase very much over the Northeastern North Atlantic – in a small region southwest of Ireland and northwest of Spain it even mildly decreases. But even in the region of tropical cyclones, where the SST clearly increases by more than 2 K in large regions, the increase in the cyclone frequency is smaller than in the first pair for most intensity classes – especially for the strong intensity classes. 207 hurricanes with maximum wind speeds of more than 42 m/s are simulated in the control experiment; 227 such hurricanes in the scenario experiment. For intermediate intensity classes and even for hurricanes with wind speeds up to 42 m/s there is a slight decrease in the frequency. The number of all major extratropical cyclones with maximum wind speeds of at least 18 m/s decreases by 18% and by 13% in the case of tropical cyclones. Here factors other than the SST play a role.


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a) b)

Figure 4: Total number of (a) extratropical and (b) tropical cyclones as in the control experiment for May to December 1985-2000 and in the scenario experiment for May to December 2085-2100. All cyclones are counted in each 3-hourly time interval and categorized in intensity classes with a width of 4 m/s maximum wind speed (2-6 m/s, 6-10 m/s, ... , 50-54 m/s). The vertical temperature profile change in the future according to ECHAM5-OM1 is reflected in the second pair of RCA3 experiments as well. Both in the tropical and in the extratropical regions the atmosphere is getting more stable according to ECHAM5-OM1. The stabilization is even stronger in the extratropical regions compared to the tropical regions mainly because of the lesser warming of the near surface layers. The stabilization of the atmosphere and in addition generally drier conditions seem to be responsible for the smaller increase in the number of very intense tropical cyclones and for the decrease in the numbers of tropical cyclones of intermediate intensity compared to the first pair of experiments. This is also true for the extratropical cyclones, where the stabilization is even stronger due to a weaker SST increase. Since the conditions are less favorable for the development of both tropical and extratropical cyclones in the scenario experiment compared to the control experiment, the total number of tropical cyclones undergoing extratropical transition is reduced (Figure 5). a) b)

Figure 5: Cyclone tracks for all storms undergoing extratropical transition during their lifetime with a maximum wind speed of 18 m/s or more from (a) the control experiment (May to December 1985-2000) and (b) the scenario experiment (May to December 2085-2100; red arrows: cyclone classified as tropical; blue arrows: cyclone classified as extratropical). Only cyclones which sustain a minimum wind speed of 18 m/s over at least two time steps are tracked.


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In terms of the proportion of cyclones undergoing ET to all tropical cyclones, a 2% increase is found similar to the first pair of experiments. 49% of all tropical cyclones undergo ET in the scenario experiment compared to 47% in the control. Furthermore, in the scenario experiment 24% of the cyclones reintensify after their ET compared to 15% in the control which is again consistent with the first pair of experiments. A considerable shift in the location of major cyclones undergoing ET can be seen. Whereas less of these cyclones reach the vicinity of Western Europe, more can be found over the Gulf of Mexico and the Caribbean Sea. Most of this frequency increase is taking place in November and December. Therefore also a strong increase of more than 200% in the 99.9 percentile of daily precipitation is simulated in large portions of this region for November and December (Figure 6).

Figure 6: Ratio of the 99.9 percentile of daily precipitation in November-December (scenario experiment divided by control experiment). 4 Summary and Conclusions In this study two pairs of RCM experiments have been conducted to separate different influencing factors on tropical and extratropical cyclones. The first pair has been driven by ERA-40 data with the original SST (standard experiment) and with an SST increased by 1 K with adapted lateral boundary values (sensitivity experiment) whereas the second pair has been driven by ECHAM5-OM1 data for present day climate (control experiment) and by ECHAM5-OM1-A2 scenario data for future climate (scenario experiment). The results of the first pair of experiments suggest that higher SSTs alone lead to more frequent cyclones over most parts of the North Atlantic at least during the extended hurricane season. In particular, the number of very intense tropical and extratropical cyclones is strongly increasing as well as the number of tropical cyclones undergoing extratropical transition (ET), re-intensifying thereafter and affecting continental areas. Connected extreme precipitation is mainly increasing in regions of tropical cyclones. However, according to most GCM simulations, not only is the SST predicted to increase but also the vertical atmospheric temperature structure is predicted to change towards more stable conditions in the future (Knutson and Tuleya, 2004). Also according to the ECHAM5-OM1-A2 scenario the atmospheric temperature structure will clearly stabilize in the future which counteracts the SST influence. In addition, troposphere above the boundary layer will get slightly drier limiting the available latent heat. It seems that a stabilization of the atmosphere leads to a decrease in the numbers of cyclones for all


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intensity classes similarly, whereas the SST seems to influence the intense cyclones more than the less intense ones. As in the first pair of experiments, re-intensification after ET occurs more often. In the second pair, this is due to a change in location of tropical cyclones and possibly more favorable conditions for post ET re-intensification over the east coast of North America compared to the Central North Atlantic. The SST seems to influence the intense cyclones more than the less intense cyclones. In the two pairs of experiments, the important differences are the drastic change in precipitation and its spatial and temporal distribution which is linked to the cyclone genesis and its path. It is interesting to note that according to both pairs of experiments increases in extreme precipitation are particularly strong over the hurricane prone region Gulf of Mexico, Caribbean Sea, Sargasso Sea and adjoining land areas. There is also a change in the location of occurrence of maximum wind speeds which is again largely associated to the shift in location and intensity of tropical cyclones. Most of the changes in wind speed, precipitation increase and its distribution can be attributed to the tropical cyclones. The increase in storm intensity and near storm precipitation increase is consistent with that of Knutson and Tuleya (2004) and other studies. A similar agreement can be seen in the increase in number of high intensity cyclones although the total number of cyclones is reduced compared to control experiment. The changes as simulated in the scenario experiment compared to the control experiment can not be regarded as a prediction for future climate because of the uncertainty in the GCM prediction of the different influence factors on extratropical and tropical cyclones. The scope of this study is to separate different influence factors on the development of tropical and extratropical cyclones. To conduct a prediction for the future, regional ensemble simulations using different GCM projections as driving data could be performed. Acknowledgements This work was carried out under the Community Climate Change Consortium for Ireland (C4I) Project, funded by the following Irish agencies: Environmental Protection Agency (under the National Development Plan), Met Éireann, Sustainable Energy Ireland, and the Higher Education Authority. The work was also supported by the CosmoGrid project, funded under the Programme for Research in Third Level Institutions (PRTLI) administered by the Irish Higher Education Authority under the National Development Plan and with partial support from the European Regional Development Fund. References Emanuel, K. A. (1987). The dependence of hurricane intensity on climate, Nature, 326, 483-485. Gyakum, J. R., and Danielson, R.E. (2000). Analysis of Meteorological Precursors to Ordinary and

Explosive Cyclogenesis in the Western North Pacific, Monthly Weather Review, 128, 851-863. Hall, N.M.J., Hoskins, B.J., Valdes, P.J. and Senior, C.A. (1994). Storm tracks in a high-resolution

GCM with doubled carbon dioxide, Quarterly Journal of the Royal Meteorological Society, 120, 1209-1230.

Jones, C.G., Willén U., Ullerstig, A. and Hansson, U. (2004). The Rossby Centre regional atmospheric climate model part I: model climatology and performance for the present climate over Europe, Ambio, 33, 199-210.

Kjellström, E., Bärring, L., Gollvik, S., Hansson, U., Jones, C., Samuelsson, P., Rummukainen, M., Ullerstig, A., Willén, U., and Wyser, K. (2005). A 140-year simulation of European climate with the


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new version of the Rossby Centre regional atmospheric climate model (RCA3), SMHI Reports Meteorology and Climatology, 108, SMHI, SE-60176 Norrköping, Sweden, 54 pp.

Knutson, T.R., and Tuleya, R.E. (2004). Impact of CO2-induced warming on simulated hurricane intensity and precipitation: sensitivity to the choice of climate model and convective parameterization, Journal of Climate, 17, 3477-3495.

König, W., Sausen, R., and Sielmann, F. (1993). Objective Identification of Cyclones in GCM Simulations, Journal of Climate, 6, 2217-2231.

Roeckner, E., Bäuml, G., Bonaventura, L., Brokopf, R., Esch, M., Giorgetta, M., Hagemann, S., Kirchner, I., Kornblueh, L., Manzini, E., Rhodin, A., Schlese, U., Schulzweida, U. and Tompkins, A. (2003). The atmospheric general circulation model ECHAM5. Part I: model description, Max-Planck-Institute for Meteorology Report, 349, Hamburg, Germany, ISSN 0937-1060.

Sanders, F. and Gyakum, J.R. (1980). Synoptic-dynamic climatology of the “Bomb”, Monthly Weather Review, 108, 1589-1606.

Sinclair, M.R., and Watterson, I.G. (1999). Objective assessment of extratropical weather systems in simulated climates, Journal of Climate, 12, 3467-3485.

Uppala, S. M., Kållberg, P.W., Simmons, A.J., Andrae, U., da Costa Bechtold, V., Fiorino, M., Gibson J.K., J. Haseler, A. Hernandez, G. A. Kelly, X. Li, K. Onogi, S. Saarinen, N. Sokka, R. P. Allan, E. Andersson, K. Arpe, M. A. Balmaseda, A. C. M. Beljaars, L. van de Berg, J. Bidlot, N. Bormann, S. Caires, F. Chevallier, A. Dethof, M. Dragosavac, M. Fisher, M. Fuentes, S. Hagemann, E. Hólm, B. J. Hoskins, L. Isaksen, P. A. E. M. Janssen, R. Jenne, A. P. McNally, J.-F. Mahfouf, J.-J. Morcrette, N. A. Rayner, R. W. Saunders, P. Simon, A. Sterl, K. E. Trenberth, A. Untch, D. Vasiljevic, P. Viterbo, and J. Woollen (2005). The ERA-40 reanalysis, Quarterly Journal of the Royal Meteorological Society, 131, 2961-3012.

Zhang, Y., and Wang, W.-C. (1997). Model-simulated northern winter cyclone and anticyclone activity under a greenhouse warming scenario, Journal of Climate, 10, 1616-1634.


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Regional Model Estimates of Aerosol Exchanges to and from the NE Atlantic Bärbel Langmann, Saji Varghese, Colin O’Dowd and Claire Scannell School of Physics & Centre for Climate & Air Pollution Studies, Environmental Change Institute, National University of Ireland, Galway, Ireland Abstract This paper describes numerical model simulation results of the exchange of short living gases and aerosols between the ocean surface and the marine atmosphere carried out with a regional atmospheric climate-chemistry/aerosol model. The major goal is to contribute to an improved understanding of the formation, chemical composition and climate impact of the marine aerosol. 1 Introduction Recent publications (Meskhidze and Nennes, 2006; O’Dowd et al., 2004) emphasise the importance of the contribution of organic matter to submicron marine aerosols which are observed in connection with enhanced biological activity of the ocean. These aerosols act as efficient cloud condensation nuclei (CCN) and can significantly influence cloud properties and thereby modify the climate of the earth atmosphere. Generally, it is expected that modified global climate conditions will impact on the today’s interdependencies of the phytoplankton content at the ocean surface, the marine aerosol concentration and marine clouds. However, even today these interactions are far away from being fully understood.

Concerning to the organic contribution of the marine aerosol it is not clear until now, if these are primary aerosols which are directly released from the ocean or secondary aerosols which form in the atmosphere through chemical reactions of gases (e.g. isoprene) released at the ocean surface (Meskhidze and Nennes, 2006; O’Dowd et al., 2004). Available source functions of sea spray aerosols (e.g. Geever et al., 2005; Martenssen et al., 2003; Smith et al., 1993; Monahan et al., 1986) reveal considerable differences of the aerosol size distribution and dependency on wind speed resulting in huge variations of the estimated global sea salt emissions to the atmosphere (Schulz et al., 2004).

On the other hand the atmosphere is a source of nutrient (e.g. Fe, NH4NO3) for the ocean via dry and wet deposition of gases and aerosols. These nutrients increase phytoplankton production in costal regions. Too much nutrients, however, can lead to strong oxygen reduction in the surface water.

Through the combination of field experiments, laboratory studies and numerical modelling an improved understanding of the above mentioned chemical, physical and biological processes at the ocean surface and the marine atmosphere can be achieved. The application of three-dimensional atmospheric climate-chemistry/aerosol models allows to study such simultaneous processes over large regions and long periods of time. The today’s knowledge can be integrated into such models, even though usually in a simplified way and assumption of potentially important processes can be tested. Here we describe first and preliminary results from model simulations which consider the ocean as a source of primary and secondary organic aerosols. Until now, regional and global atmospheric climate models do not take these emissions into account. Additional model results of wet deposition fluxes are presented to demonstrate the potential of atmospheric climate models to deliver information of fluxes of nutrients to the ocean.


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2 Model set-up We use the regional three-dimensional on-line climate-chemistry/aerosol model REMOTE (Regional Model with Tracer Extension, http://www.mpimet.mpg.de/en/wissenschaft/modelle/remote.html) (Langmann, 2000) which is one of the few regional climate models which determines the physical, photochemical and aerosol microphysical state of the model atmosphere at every model time step. The physical parameterisations of the global ECHAM-4 model (Roeckner et al., 1996) are used for the current study. Photochemical and aerosol trace species undergo transport processes (horizontal and vertical advection, transport in convective clouds, vertical turbulent diffusion) and are removed from the atmosphere by sedimentation, dry and wet deposition (Langmann, 2000). For the determination of aerosol dynamics and thermodynamics we use the M7 module (Vignati et al., 2004; Stier et al., 2005). Five aerosol components sulphate (SO4), black carbon (BC), organic carbon (OC), sea salt (SS) and mineral dust (DU) are considered in M7 either as compounds with no or low solubility or as internal mixture of insoluble and soluble compounds. The aerosol size spectrum is subdivided into a nucleation, aitken, accumulation and coarse mode. Each mode can be described by three moments: aerosol number N, number median radius r, and standard deviation σ. Standard deviations are prescribed in M7, so that the median radius of each mode can be calculated from the corresponding aerosol number and aerosol mass, which are transported as 25 tracers. Thus, the total number of prognostic trace species in REMOTE is 63, where 38 ones are included to describe photochemical transformations (Langmann, 2000).

Temporally variable anthropogenic emissions of SOx, NOx, NH3, CO, VOC’s, BC and POC are taken from the EMEP emission inventory. For primary anthropogenic aerosol emissions, number mean radius and number concentration of the respective size mode is related the mass concentration based on Stier et al. (2005). In addition to terrestrial biogenic terpene and isoprene emissions from forests (Langmann, 2000) we consider isoprene emissions from the ocean based on Meskhidze and Nennes (2006). The net accumulation sea spray flux from Geever et al. (2005) is modified into a power law fit in order to represent more realistically the flux at wind speeds below 5 m/s and above 20 m/s and is used as an organic-inorganic source function for the mixture of POC and sea salt aerosols. Based on clean sector measurements of the marine aerosol chemical composition at the Mace Head station (Yoon et al., 2006), we set up a linear relationship between chlorophyll-a concentration and the percentage contribution of POC to sea spray. Chlorophyll-a concentration in the surface ocean water measured from MODIS (http://oceancolor.gsfc.nasa.gov/) serves as proxy for the ocean biological activity (Fig. 1c). Further details will be given in Langmann et al. (2007).

REMOTE is applied with 20 vertical layers of increasing thickness between the Earth’s surface and the 10 hPa pressure level using terrain following hybrid pressure-sigma coordinates. The model domain covers Europe and the Northeast Atlantic (see Fig. 1). At the first time step, REMOTE is initialised using meteorological analysis data of the European Centre for Medium Range Weather Forecast (ECMWF), which are updated at the lateral boundaries every 6h. Here we describe model results for the months of June and December 2002. 3 Model Results Preliminary REMOTE model estimates of the contribution of accumulation mode POC aerosols from the ocean are illustrated in Fig.1 for June 2002. Without marine POC emissions (Fig. 1a) near surface accumulation mode POC aerosols show elevated mass concentrations mainly over land in the vicinity of urban and industrial centres, and at oil platforms off shore the Norwegian coast. By taking into account marine POC emissions as described in section 2, near surface accumulation mode POC aerosol mass concentration is considerably increased over the ocean (Fig.1b), in particular over the Baltic Sea,


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the Black Sea and the North Atlantic west of Norway, but remains nearly unchanged over land. The very high POC concentrations over the Baltic Sea and Black Sea result from high monthly mean chlorophyll-a concentration during June (Fig.1c) whereas over the North Atlantic the wind speed influence in the source function modulates the direct connection between near surface accumulation mode POC aerosol mass concentration and chlorophyll-a concentration at the ocean surface. Isoprene shows the highest near surface concentration over the Baltic Sea as well (Fig. 1d). The isoprene distribution over the North Atlantic is different form the POC distribution in Fig. 1b, because isoprene undergoes fast photochemical oxidation in the presence of OH, O3 and NO3 and is believed to be a major precursor of secondary organic aerosols. During December 2002 negligible marine POC fluxes occur due to the low ocean biological activity. Fig. 1: Preliminary REMOTE model results of monthly mean near surface POC in the accumulation mode for June 2002 without marine POC emissions (a) and including marine POC emissions (b). For comparison the monthly mean chlorophyll-a content interpolated from MODIS satellite data to the REMOTE area is shown (c) and near surface monthly mean isoprene concentration from marine and terrestrial sources (d).

REMOTE model results at the Mace Head measurement facility at the west coast of Ireland are shown in Fig. 2 for June and December 2002. For the comparison the nearest model grid cell to Mace Head was chosen. The land-sea-mask of this grid cell is covered half by land, half by the ocean. Taking into account the horizontal model resolution of 0.5° and the more efficient deposition over land, it might be more representative to choose the next grid point west for a comparison of model results with Mace Head measurements, where slightly higher concentrations are expected.

During June 2002, westerly wind directions dominate at Mace Head with wind speeds between about 4 to 10 m/s. BC mass concentrations in the accumulation mode reach only two times 50 ng/m3, reflecting the very small influence of continental air masses at Mace Head during June 2002. Sea salt and POC mass concentration are variable with maximum concentrations reaching 0.15 ug/m3. The marine contribution to near surface POC mass concentration clearly dominates over the anthropogenic contribution.

During December 2002 the weather situation at Mace Head is characterised by a long period with easterly winds due of the influence of a high pressure system over Europe, and westerly winds at the beginning and end of the month. Except the first few days, wind speed is only between about 2 to 8 m/s. Elevated concentrations of BC (and POC) up to 250 ng/m3 point to the transport of European continental air masses to Mace Head. Due to the unusually low wind speeds for this season of the year sea salt aerosol mass concentration remains pretty low.

REMOTE: June 2002 near surface accumulation mode POC [ug/m3]

(a) without marine POC (b) with marine POC (c) Chlorophyll-a [mg/m3] (d) Isoprene [ug/m3]


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Fig. 2: REMOTE model results at the Mace Head measurement facility at the west coast of Ireland for June 2002 (upper row) and December 2002 (lower row). Near surface aerosol mass concentrations are shown for the accumulation (accum.) mode size interval only. Fig. 3 shows exemplarily wet deposition fluxes of HNO3 and H2SO4 during December 2002. The spatial distribution and magnitude of the two species wet deposition fluxes differ considerably. This results from the different distribution of the emission sources of the precursor gases, different time scales of chemical reactions and transport processes. A considerable amount of HNO3 is removed from the atmosphere by wet deposition over the Atlantic Ocean, whereas H2SO4 is mainly deposited over the eastern Mediterranean Sea. Fig. 3: REMOTE model results for wet deposition of HNO3 and H2SO4 in December 2002.

Wind speed Wind direction BC Sea salt POC in 10m in 10m accum. mode accum. mode accum. mode

REMOTE: December 2002: wet deposition [mg/m2/month]

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4 Conclusions and Outlook

The model simulation results of the exchange of short living gases and aerosols between the ocean surface and the marine atmosphere presented in this paper are the first steps towards the inclusion of other marine emissions than sea salt in atmospheric climate models. Over the ocean, a considerable impact on climate can be expected from these preliminary simulation results. Evaluation of these model simulation results by comparison with measurement data is a next important step. Further analysis in higher atmospheric layers will be necessary to investigate the transport of marine aerosols from the ocean to land areas. Formation of secondary OC aerosols from marine isoprene emissions will be studied to distinguish between the contribution of primary and secondary organic aerosols, and water soluble and insoluble organic aerosols. It should be noted that the REMOTE model is not restricted to applications over Europe; it is applicable in any focus region worldwide up to a horizontal resolution of 10 km.

For further investigations, e.g., under climate change conditions it will be necessary to develop a coupled biogeochemical ocean-atmosphere model system. The ocean model should be able to provide sea surface temperature, chlorophyll-a concentration at the ocean surface and white cap coverage to better determine trace species fluxes from the ocean to the atmosphere. On the other hand, the atmospheric model should be able to provide heat, water and nutrient fluxes for the ocean model. Coupled model simulations would contribute to improve our understanding on the release and formation of primary and secondary marine aerosols and their impact on marine photochemistry. In addition, investigations of cloud processes, precipitation formation and the solar radiation budget in the marine atmosphere with feedbacks to the ocean would be possible.

References Geever, M., O’Dowd, C., van Ekeren, S., Flanagan, R., Nilsson, E.D., de Leeuw, G. and Rannik, Ü.,

Submicron sea spray fluxes (2005). Geophys. Res. Lett., 32, doi: 10.1029/2005GL023081. Langmann, B. (2000). Numerical modelling of regional scale transport and photochemistry directly

together with meteorological processes. Atmos. Environ. 34, 3585-3598, Langmann, B., O’Dowd, C., Varghese, S., Scannell, C. and Ceburnis, D. (2007). Regional model

estimates of organic matter in sea spray aerosols, in preparation for Geophys. Res. Lett., Lohmann, U. and Feichter, J. (2005). Global indirect aerosol effects: a review, Atmos. Chem. Phys. 5,

725-737. Martensson, E. M., Nilsson, E. D., de Leeuw, G., Cohen, L.H. and Hansson, H.-C. (2003). Laboratory

simulation and parameterization of the primary marine aerosol production, J. Geophys. Res. 108, doi: 10.1029/2002JD002263.

Meskhidze, N. and Nenes, A. (2006). Phytoplankton and Cloudiness in the Southern Ocean, Science 314, 1419-1423.

Monahan, E.C., Spiel, D.E. and Davidson, K.L. (1986). A model of marine aerosol generation via whitecaps and wave disruption, in Oceanic Whitecaps and their Role in Air-Sea Exchange, ed. E. C. Monahan and G. Mac Niocaill, D. Reidel, 167-174.

O’Dowd, C., Facchini, M.C., Cavalli, F., Ceburnis, D., Mircea, M., Decesari, S., Fuzzi, S., Yoon, Y.J. and Putaud, J.-P. (2004). Biologically driven organic contribution to marine aerosol, Nature 431, 676-680.


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Roeckner, E., Arpe, K., Bengtsson, L., Christoph, M., Claussen, M., Dümenil, L., Esch, M., Giorgetta, M., Schlese, U. and Schulzweida, U. (1996). The atmospheric general circulation model ECHAM-4: Model description and simulation of present-day climate, Report No. 218, Max Planck Institute for Meteorology, Hamburg, Germany,

Schulz, M., de Leeuw, G. and Balkanski, Y. (2004). Sea-salt aerosol source functions and emissions, in: Emission of the Atmospheric Trace Compounds, Kluwer, 333-359.

Smith, M.H., Park, P.M. and Consterdine, I.E. (1993). Marine aerosol concentrations and estimated fluxes over the sea, Q. J. R. Meteorol. Soc. 119, 809-824.

Stier, P. et al. (2005.) The aerosol-climate model ECHAM5-HAM, Atmos. Chem. Phys. 5, 1125-1156. Vignati, E., Wilson, J. and Stier, P. (2004.). M7: An efficient size-resolved aerosol microphysics

module for large-scale aerosol transport models, J. Geophys. Res. 109, doi:10.1029/2003JD004485. Yoon, Y.J., Ceburnis, D., Cavalli, F., Jourdan, O., Putaud, J.P., Facchini, M.C., Decesari, S., Fuzzi, S.,

Jennings, S.G. and O’Dowd, C. (2006). Seasonal characteristics of the physico-chemical properties of North Atlantic marine atmospheric aerosols, J. Geophys. Res. submitted.


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Predictive North-East Atlantic and Irish Sea models – hydrodynamic, surge and wave modelling Indiana Olbert and Michael Hartnett Marine Modelling Group, MRI, NUI Galway, Ireland. Climate change and its potential impact on the coastline of Ireland is a problem of high urgency. Among the most significant challenges to be faced are rising sea level coupled with a trend towards increased storminess. These phenomena are now considered to have a major effect on the stability of beaches and offshore sandbanks. The greatest concern is focused on the erosion of dune systems with the devastating effect on a shoreline habitat. The long-term observations show the increased erosion due to the action of wave energy generated by winter storms. This is often accompanied a lowering of the beach level as sand is carried offshore – and uncertainty surrounds the importance of offshore sandbanks and the role that they play in both protecting the shoreline from wave action and in replenishing the beach with sand following a storm event. The remobilisation of sediment, storminess and rising sea level are also responsible for local flooding. In 2001 the Intergovernmental panel on Climate Change (IPCC, 2001) reported based on records since 1861 that the average surface temperatures are projected to rise 1.4ºC to 5.8ºC between 1990 and 2100. In the same period mean sea levels are projected to rise by 0.09 to 0.88 m primarily due to thermal expansion and the loss of ice from glaciers and ice cups. To provide insight into the mechanism acting on coastal locations due to extreme weather events, high-resolution numerical models have been developed within PRISM project and used to study hydrodynamic processes, wave action, water quality and movement of sand in the near-shore region. In particular, these covered the influence of changing sea levels, impact of increased wind and storm direction, precipitation rate, freshwater discharge and heat exchange at air-sea interface. Although, the PRISM project is focused on the Irish Sea region, the hydrodynamic and wave models developed cover the North-East Atlantic region in order to provide the large-scale Atlantic forcing to the Irish Sea zone within which the local-scale tidal model with water quality and sediment transport mode have been imbedded. The PRISM project has resulted in the development of 5 numerical models giving hydrodynamic and wave dynamics outputs for 4 regions namely: North-East Atlantic, Continental Shelf, Irish Sea and Dublin Bay. The scheme of dependencies between models has one-way cascade structure using nesting technique. For hydrodynamics, the Dublin Bay model is nested within the Irish Sea model, while Irish Sea model is nested with the North-East Atlantic model. Similarly for wave simulation, the North-East Atlantic wave model provides boundary conditions for Continental Shelf wave model. The domain of North-East Atlantic is defined by the geographic region: -30ºE ÷ 0ºE longitude and 40ºN ÷ 60ºN latitude as presented in Figure 1. The numerical model chosen for the system is the Princeton Ocean Model (POM) developed originally by Blumberg and Mellor (Mellor, 1988). The computation includes hydrodynamic and surge residual fields and is carried out on high-resolution 5’x 2.5’ horizontal grid. The three-dimensional hydrodynamics is forced by baroclinic conditions on top of variable water elevations due to tides that consist of 5 constituents. Meteorological conditions covering the whole region are derived from Met Eireann HIRLAM model at two horizontal resolutions and one hour time average. The results form both the fine and coarse grid models are integrated together and interpolated on POM mesh to provide the optimum surface meteorological boundary conditions to the POM surge model. The surge residual outputs were extensively compared against 40 storm events data.


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The overall model validation was found to be successful. Exemplary results from the comparison are presented in Figure 2. The NE Atlantic grid outputs of water elevations are interpolated down to 1/24º of latitude and 1/12º of longitude (~5 km) scale to be fed at the open boundary of Irish Sea model. This one-way nesting allows a transfer of boundary tidal and surge energy from coarse grid into fine grid of the Irish Sea. Coordinates -7ºE ÷ -2.625ºE and 51ºN ÷ 56ºN bound the model area shown in Figure 3. The POLCOMS 3D numerical model of the Irish Sea is used to provide three-dimensional hydrodynamic, temperature and conservative tracer fields for web display. The temperature validation graphs are presented in Figure 4 and Figure 5. The Dublin Bay model (Figure 6) is nested within the Irish Sea model from which it extracts elevation conditions and feeds them at the open boundaries. Depth-averaged DIVAST numerical model developed by Professor Falconer (Falconer and Liu,1988) is employed to run the hydrodynamics and solute transport within the shallow Dublin Bay as the code unlike any other is adapted to periodical wetting and drying of coastline. The high resolution model resolves hydrodynamic fields on 50m x 50m square grid. In modelling of wave dynamics the WAM wave model is adopted for the North East Atlantic region within which the Continental Shelf zone is resolved using the SWAN wave numerical model. Both WAM (Wave Model) developed by the WAMDI Group (Hasselmann et al., 1987) and the SWAN (Simulation of WAves Nearshore) model (Booij et al., 1999) solve the non-linear interaction processes by incorporating time varying meteorological conditions, currents, tides and bottom conditions. The wave energy conditions extracted from large NE Atlantic are implemented to high-resolution Continental Shelf SWAN model so that the shallow water physics can be resolved without loosing the importance of small-scale processes. The significant height, direction and period of waves in the Irish Sea are uploaded to the website. Figure 7 shows the comparison between SWAN model and data buoy. All numerical models developed under PRISM project run operationally to provide 48 hour forecasts of meteorological and oceanographic conditions. Data are available in form of 6 hour interval snapshots covering the Irish Sea domain or as timeseries for 13 specified locations. The forecasts are displayed on a dedicated website www.prism.ie where they can be accessed by the public. The website provides map information on wind strength and direction, tides, surface currents and temperatures as well as wave heights and periods. Additionally, timeseries data also contain surge residual forecasts. This range of information gives conditions that are essential to promote activities such as sailing and surfing. For end users, the data set is enlarged by surge snapshots as well as 3-dimensional fields of conservative tracer, temperature and currents. These data are protected by password and allow make a ‘what if’ calculations on flooding and/or water quality issues. On that basis the public can be aware in case of marine danger. The extensively tested and validated PRISM models have become a data source for Department of Communications, Marine and Natural Resources to issue their warning letters for storm surge forecast in Irish Waters. Recent researches show that the Atlantic Ocean is very sensitive to climate change. The PRISM models can be now the basis for climate change studies and can serve as a tool in prediction of reliable changes in the coming decades. Particularly, when coupled with atmospheric model it may provide valuable information on moving spatio-temporal changes in stratification and distribution of vertical fluxes. This would further allow exploring and analysing the effect of changes in solar radiation, clouds cover and ocean stratification on sea-air exchange.


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Figure 1. Bathymetry model of NE Atlantic constructed from the 2004 GEBCO data set and the Irish National Seabed Survey data (INNS). Depth in meters.

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(a) (b)

Figure 6. Mid flood (a) and high water (b) currents in Dublin Bay

Figure 7. Comparison between SWAN numerical prediction of significant wave height and M2 Buoy data.

References

Booij, N., Ris R.C. and Holthuijsen, L.H. (1999). A Third Generation Wave Model for Coastal Regions Part 1. Model Description and Validation. Journal of Geophysical Research, Vol. 104, pp. 7649-7666.

Falconer, R.A. and Liu, S. (1988) Modelling of Solute Transport Using QUICK Scheme. Journal of Environmental Engineering, Vol. 14, No. 1, pp. 3-20;

Hasselmann, S. (1987) The WAM model – A Third Generation Ocean Wave Prediction Models. Journal of Physical Oceanography, Vol. 18, pp. 1775-1810;

IPCC (2001) Climate Change 2001, The Scientific Basis. Ed: Houghton, J.T., Cambridge University Press, pp. 994.

Mellor, G.L. (1988) The Three-Dimensional, Primitive Equation, Numerical Ocean Model. Program in Atmospheric and Ocean Sciences, Princeton University.

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Algal responses to environmental change: seaweed-environment interactions and their applications Dagmar Stengel Department of Botany, National University of Ireland, Galway As primary producers upon which marine food webs depend, marine macro- and microalgae are at the centre of a number of current strategic research foci. As photoautotrophic organisms algae are impacted by their immediate ambient environment, and particularly intertidal macroalgae influenced by naturally fluctuating osmotic, temperature and nutrient regimes imposed by tides and seasons. Responses to environmental factors, and also biotic stresses such as grazer attack, endo- and epibiontic microbial and viral infection, are species-specific and have resulted in a range of natural stress- and defence mechanisms. Eco-typic specificity and phenotypic plasticity allow small-scale adjustments of metabolites to optimise productivity or survival, depending on life-strategy of the particular species.

Research in Dr Stengel’s group at NUIG is currently integrating a number of aspects regarding

the application of seaweeds in environmental monitoring, and is investigating some key issues related to climate change. Most research has focused on quantifying the effects of environmental parameters on physiological responses and biochemical compounds with a view to develop bioassays, biomarkers and protocols for assessing environmental change using seaweeds. Additionally, experimental investigations into the environmental control of bioactive compounds are underway. An improved understanding, through careful R&D, of the control of the production of bioactive properties can be manipulated and optimised simply by modifying growth conditions in controlled environments. For example, the production of naturally produced substances with certain biochemical properties can be induced or enhance by exposing algae to certain environmental conditions. Such substance may be natural defence-compounds against UV-radiation (polyphenols) or osmotic stress (e.g. mannitol). As some of these compounds have significant bioactive properties, the investigation of the metabolic function and environmental control has implications in the future use as biomarkers for environmental impacts, but also in applied industrial research (Fig. 1).

Algae, and marine macroalgae in particular, at the forefront of marine research in Ireland as

their biological compounds have applications in biomedical and pharmaceutical research through the screening of natural marine resources for new bioactive products, some of them natural stress-induced defence products, with pharmaceutical applications (‘biodiscovery’). An improved understanding of the mechanisms of compound production and function will potentially allow their optimisation and the maximisation of their yield. Ecophysiological research at NUIG in Dr Stengel’s group bridges the gap in knowledge between applying environmental stressors and observing the presence of the compounds by trying to understand the biological mechanisms involved in their production.


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Fig. 1. Seaweed environment-interactions and their applications in environmental monitoring and biodiscovery research. In addition, fast, sensitive measurable physiological responses of algae can be used in biomonitoring, as biomarkers and ecological indicators: as algal functional physiology is directly controlled by the ambient environment, seaweed responses can be used to sensitively assess and monitor environmental condition. The relationship between environmental triggers and responses can then be used quantitatively, and ecologically relevant, to measure environmental quality. Such applications of algae in environmental monitoring have included their use in monitoring of eutrophication and pollution in coastal ecosystems. In particular, as bioavailable fractions of metals are ad- or absorbed by seaweeds and subsequently will bioaccumulate in the marine food chain; their application in biomonitoring has many advantages over traditional methods. Current and recent research at NUI Galway has included the development of biomarkers in the assessment of metal contamination, the development and application of bioassays, with particular emphasis on chlorophyll fluorescence and compared these methods with traditional biomonitoring approaches (e.g., Stengel et al. 2004, 2005)

General key issues regarding the effects of global change on algae include effects of solar

radiation, temperatures and CO2-availability which is linked to potential future ocean acidification. Table 1 summarises some key questions of algal-environment interactions with relevance to global change. Algal biological processes of particular relevance to SOLAS include their carbon metabolism (as part of the photosynthetic process) including short-term acclimation and long-term adaptations of populations to elevated CO2 concentrations, the release of organic matter into the sea, as was as the production and release of DMS (DMSP) and halocarbons which have received particular attention.


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• Solar radiation:– Decrease: decreased productivity?– Increase: increased productivity in deeper water? Decreased at surface and

in intertidal?– Change in diffuse: species-specific?– UV: no effect in subtidal? Severe effect in intertidal/surface?

• Temperature: – Enhanced productivity; reduced productivity? Incomplete life-cycles; change

in species composition? Change in distribution and ranges, adaptations.

• Nutrients (N, Fe):– Increased productivity?– Changes in metabolism likely: adaptations?– Change in species composition?

• CO2:– Enhanced productivity? C bioavailable? Carbon concentration

mechanisms? C:N ratios; adaptations?– Distinguish between intertidal and subtidal/phytoplankton; community

changes?

Table 1: Key issues of algal research: potential algal responses to global change.

Recent research has focused on algal physiological and chemical responses to O3, UV-radiation and elevated temperatures. Algae-environment interactions are complex, marine environments are characterised by natural fluctuations and many algal responses are group- or even species-specific so that generalisations of algal responses should be treated with caution. Particular ‘unknowns’ and areas of research that need attention are short- and long-term adaptations of populations and species, and their responses to potential interactive effects of environmental parameters.

Important issues concerning algae and climate change include currently unpredictable effects of

changes in solar radiation, including UV and PAR and changes in diffuse radiation, on productivity of species occupying different marine, coastal and off-shore, habitats. For example, productivity of intertidal and subtidal species may be affected differently; in particular UV radiation is likely to have significant effects in intertidal and shallow subtidal waters, but less pronounced impacts at greater depths. Some preliminary observations from Galway Bay suggest that significant pigment bleaching events take place in red algae during spring tides in early winter. These appear to be recent phenomena and potentially represent direct evidence of recent climate change impacts on local marine species. Further research in this field is needed to consolidate these results. Also, temperatures responses are difficult to predict in that they affect both photosynthesis and respiration, and both short- and long-term adjustments can be expected, at least in some species, and productivity of certain species may be reduced, while that of others increased. Similarly, different life stages of algae may respond differently to temperature changes, and the completion of life cycles that are often controlled, or their initiation triggered, by combinations of photoperiod and temperature, could be under threat. As regards phytoplankton, changes in ocean currents but also overall changes in surface water temperatures are likely to alter species distribution patterns. Little is known about long-term adaptations of species to changes in temperature and abundances and distributions of algae, e.g. those with particular ecological functions, or, in the context of SOLAS, those emitting marine aerosols. Similarly little is known about any temperature effects on iodine emissions or those of other halogens and halocarbons.

Although the major form of inorganic carbon utilised by algae is CO2, several species of both

macro- and microalgae have developed carbon-concentrating mechanisms which allows them to fix (and later convert) other forms of inorganic carbon. The driving, or limiting, factors of carbon assimilation by algae in situ directly influence the abundance of algal biomass; this ultimately absorbs more (or less) carbon and also results in the production of compounds of ecological importance,


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including aerosols. However these essential processes are poorly understood and will require significant attention in the near future. Specific algae-environment interactions relevant to SOLAS include their contribution to biogeochemical cycling in coastal and off-shore waters, with basic metabolic functions of algae, both micro- and macroalgae, controlled by light, nutrients and temperatures. A range of organic compounds, including non-methane hydrocarbons (alkenes) that could be detected in marine systems have been related to the presence of algae, both macro and microalgae. Some of these are considered of algal origin and substantial temporal fluctuations have been observed (Gist and Lewis, 2006). Also, isoprene and alkyl halides were recently measured near Mace Head, Co. Galway, and appeared to be linked to the presence of large amounts of macroalgal biomass (Greenberg, 2005). Both planktonic single-celled algae and seaweeds contribute significantly to ethane production, with a particularly high production in coastal areas suggesting a macroalgal origin (Broadgate et al. 2004). Recent evidence suggests that algal metabolic compounds, often those produced under stress, are released into the water in potentially large quantities, significantly contributing to the pool of dissolved organic matter. Such compounds include polyphenols from brown algae, but also mycosporine-like amino acids from red algae. Our understanding of the origin, production as well as the mechanisms by which these compounds are produced is limited. For example, polyphenols appear to have a range of ecological functions, and their production has been linked to a number of factors including UV-radiation, nutrient and temperature conditions and grazing activity. Some aspects of the environmental control of this are currently under investigation in Dr Stengel’s group. Stability of DOM, including that of brown algal origin in seawater is photo- and UV- sensitive (e.g. Ratte et al. 1998; Riemer et al. 2000). On the other hand, light (including UV radiation) also directly influences algal organic matter production, as well as the rate and degree of its release, as well as algal growth and photosynthetic performance (e.g. Bishof et al. 1998; Davison et al. 2007). This emphasises the importance of improving our understanding of the mechanisms by which such compounds are produced and released into the sea, in addition to attempting to quantify their actual amounts. In the past several publications have high-lighted emissions into the atmosphere either by algae directly or by coastal and off-shore systems where algae were growing; of particular interest, and significance, were iodocarbons and different forms of iodine, including I2. For example, these were emitted from brown macroalgae (mainly Laminaria and Fucus species, possibly due to their greatest abundance) and these have particular significance in aerosol formation (McFiggans 2004; Greenberg et al. 2005; Palmer et al. 2005).

Other research at NUI Galway, as part of EPA (ERTDI)-funded project (WP4 in Cluster-project

BioChange; www.BioChange.ie), focuses on the characterisation of natural productivity of a model keystone species (Ascophyllum nodosum) under today’s climatic conditions and the assessment and modelling of the effects of predicted global change on Ascophyllum nodosum as a habitat-forming keystone primary producer in intertidal Irish environments. It aims to predict potential changes in standing crop and productivity that could affect its role as a habitat facilitator for high coastal biodiversity. Further, potential effects of harvesting in the future under increased environmental pressure and habitat loss due to climate change, eutrophication and sea level rise are under investigation. Also, the potential for Ascophyllum nodosum as a biological indicator for global change is being evaluated and its possible role as a carbon sink is considered. This work encompasses field and laboratory studies in selected sites in Galway Bay (Counties Galway and Clare). Currently measurements of productivity (measured as in vivo Pulse Amplitude Modulated chlorophyll fluorescence), O2-evolution and CO2-uptake are underway under a range of environmental conditions, including in air and water. Correlations of results obtained using different methods to measure productivity are established. Fig. 2 illustrates some preliminary results of changes in CO2-uptake rates of a plant undergoing desiccation using an Infra-red Gas Analyser.


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Fig 2. CO2-uptake rate over 2h of A. nodosum (Fucales, Phaeophyceae) from Galway Bay undergoing dehydration (measured in air, 20ºC, December 2006). Data: McGrath and Stengel, (unpublished data)

Another application of current knowledge in algal ecophysiology is in the field of natural sunscreens produced by brown algae such as fucoids (Fucales) and kelps (Laminariales). Photoprotective compounds in brown algae include carotenoid pigments that absorb excess energy, as well as phlorotannins. Current research focuses on the distribution of polyphenols in selected algal species, their seasonal and spatial variation, and their potential application as sunscreens in cosmetic industries. This project is funded by the Marine Institute under the Marine RTDI Measure (Strategic Programme 2005).

Polyphenols, and particularly the environmental control of their production and exudation, are

also the focus of another, EPA-funded, project in the group. Given that they can bind significant amounts of metals under natural conditions, besides algal polysaccharides they have an important role to play in the development of biomonitoring protocols for marine systems in Ireland. As this project concentrates on the influences of light, temperature and nutrients on the production and release of phenols which represents a significant contribution of DOM in coastal waters, it is also of direct relevance, and importance, to SOLAS. In this project a dual approach is used by combining field monitoring of phlorotannins in algae from selected sites, and relating these to site-specific environmental features, with laboratory-based research under controlled conditions.

References

Bishof, K., Hanelt, D., Tüg, H., Karsten, U., Patty E., Brouwer, P.E.M. and Wiencke, C. (1998). Acclimation of brown algal photosynthesis to ultraviolet radiation in Arctic coastal waters (Spitsbergen, Norway). Polar Biology, 20, 388-395.

Broadgate, W.J., Malin, G., Küpper, F.C., Thompson, A. and Liss, P.S. (2004). Isoprene and other non-methane hydrocarbons from seaweeds: a course of reactive hydrocarbons to the atmosphere. Marine Chemistry 88, 61-73.

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Time (min) after emersion


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Davison, I.R., Jordan, T.L., Fegley, J.C. and Grobe, C.W. (2007). Response of Laminaria saccharina (Phaeophyta) growth and photosynthesis to simultanous ultraviolet radiation and nitrogen limitation. Journal of Phycology, in press.

Gist, N. and Lewis, A.C. (2006). Seasonal variations in dissolved alkenes in coastal waters. Marine Chemistry 100, 1-10.

Greenberg, J., Guenther, A.B., and Turnipseed, A. (2005). Marine organic halide and isoprene emissions near Mace Head, Ireland. Environmental Chemistry 2, 291-294.

McFiggans, Coe, H., Burgess, R., Allan, J., Cubison, M., Alfarra, M.R., Saunders, R., Siaz-Lopez, A., Plane, J.M.C., Wevill, D.J., Carpenter, L.J., Rickard, A.R. and Monks, P.S. (2004). Direct evidence for coastal particles from Laminaria macroalgae – linkage to emissions of molecular iodine. Atmospheric Chemistry Research 4, 701-713.

Palmer, C.J., Anders, T.L., Carpenter, L.J., Kuepper, F.C. and McFiggans, G.B. (2005). Iodine and halocarbon response of Laminaria digitata to oxidative stress and links to atmospheric new particle production. Environmental Chemistry 2, 282-290.

Ratte, M., Bujok, O., Spitzy, A. and Rudolph, R. (1998). Photochemical alkene formation in seawater from dissolved organic carbon: results from laboratory experiments. Journal of Geophysical Research 103, 5707-5717.

Riemer, D.D., Milne P.J., Zika, R.G. and Pos, W.H. (2000). Photoproduction of nonmethane hydrocarbons (NMHCs) in seawater. Marine Chemistry 71, 177-198.

Stengel, D.B., Macken, A., Morrison, L. and Morley, N. (2004). Zn concentrations in marine macroalgae and a lichen from western Ireland in relation to phylogenetic grouping, habitat and morphology. Marine Pollution Bulletin 48: 902-909.

Stengel, D.B., McGrath, H., and Morrison, L.J. (2005). Tissue Cu, Fe and Mn concentrations in different-aged and different functional thallus regions of three brown algae from western Ireland. Estuarine, Coastal and Shelf Science 65(4): 687-696.


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Groundwater Inputs to the Coastal Zone of south County Galway and north and west County Clare Rachel R. Cave & Tiernan Henry Department of Earth & Ocean Sciences, NUI, Galway 1 Introduction

There are two distinct freshwater discharge regimes to Galway Bay: predominantly surface water on the north side of the Bay; and predominantly groundwater on the south side of the Bay. This study focuses on the groundwater discharges from the catchment that covers an area of approximately 1000km2 from Kilchreest/Loughrea in the east to Slievecarran in the west and below Lough Cutra in the south.

The hydrogeology of the catchment is well understood (Drew & Daly, 1993) and has been well documented but there has been no direct quantification of discharges to the coastal zone (Jennings O’Donovan & Southern Global Water, 1997). Indeed, little work has been done in Ireland in quantifying groundwater discharges in coastal zones. Therefore there is scant data on the nutrient and contaminant loads delivered by groundwater to our coastal waters. Such studies are common in other regions and locations (Bayari & Kurttas, 2002; Beddows, 2004).

The objectives of this ongoing study are:

• To measure/quantify groundwater discharges;

• To sample levels of nutrients in the discharge;

• To determine loads entering coastal waters from groundwater; and,

• To compare results with river inputs.

2 Background

An explanation of the catchment is important in understanding water movement.

2.1 Geology

The upland area in the south east of the catchment (Slieve Aughty) is composed of a mixture of silt, sand and mudstones (Pracht et al, 2004). The lower ground along the western flank of the hills is made up of Carboniferous Lower-Limestone Shales and muddy fossiliferous limestones.

The remainder of the catchment is made up of Carboniferous Limestone.

2.2 Hydrology

The surface drainage network in the catchment is entirely reflective of the underlying geology. There is a well-developed surface water stream and river network in the Slieve Aughty area. Runoff from the


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upland area discharges to the lowlands and eventually to Galway Bay. The surface drainage features in the lowland area are intermittent and/or seasonal.

Rainfall runoff from the upland area is rapid. There is little storage of water in the soils or in the rock. Rainfall amounts are elevated in the upland areas. The lowland area has a very low gradient (from Limepark to Kinvarra the gradient is less than 10-3 – one metre/kilometre) so this tends to drain less quickly than the upland areas. If input is greater than output then storage in the system must increase, leading to flooding.

This flooding was most pronounced in the winter of 1994/1995 when almost 70 percent of the annual average rainfall fell between December and February (Jennings O’Donovan & Southern Global Water, 1997). The volume of water generated could not drain as quickly as it fell leading to flooding. This was exacerbated by a series of very high tides in Galway Bay driven by westerly winds (Smyth, 1998).

2.3 Hydrogeology

The geology of the catchment determines the nature of the groundwater flow regime. This catchment has two distinct lithological settings (the sandstones and impure limestones in the south east, and the pure limestones in the remainder of the catchment) that have very distinct flow regimes.

Groundwater will flow through all material, but the rates of flow can vary over 12 orders of magnitude, expressed by the hydraulic conductivity (K). Sandstones and impure limestones tend to have low conductivity values, indicating that groundwater will move relatively slowly through these formations. Unweathered pure limestone tends to have low conductivity values too, but where the rock is weathered the conductivity values increase dramatically.

Flow occurs most readily through openings or conduits in rock. Rock subjected to weathering or faulting can develop high values of secondary porosity in localised areas in the rock. While the pure limestone has a low porosity and conductivity, if conduits are open in the rock the porosity and conductivity increases.

Almost all drinking water in the limestone area is derived from groundwater sources (principally bored wells). Although the limestone has a low conductivity, where dissolution has occurred there is potential for rapid movement of large volumes of water. These areas are the targets for the placement of large yield wells.

Wells bored into karst conduits can readily supply large volumes of water. Kinvarra’s drinking water is supplied from wells located at Loughcurra South (approximately 1.5km southeast of the village). These wells (a duty and standby) yield 60-70m3/hour, 24-hours a day (Cave et al, 2005). In the years of operation the wells have never dried out.

Karst areas can therefore be excellent water supply aquifers. The pure limestone in this area is classed as Regionally Important Kart Aquifer (Rk) (DoELG, 1999). The upland area in the southeast of the catchment is classed as a Poor Aquifer, generally unproductive except for local zones (Pl). The lower limestone shales form Locally Important aquifers, moderately productive in local zones only (Ll).

The vulnerability of the groundwater to contamination varies as a function of geology and soil cover. Through most of the catchment the soil cover is thin or non-existent and the entire area underlain by the pure limestone is classed as Extremely Vulnerable.


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Contaminants entering the conduits can be transported long distances very rapidly, so the high yield wells are very vulnerable to contamination.

The catchment is largely agricultural and stocking densities are low. The principal contaminants of concern in the area are nitrate, phosphate and micro-organisms. Non-aqueous phase liquids (NAPLs) are of local concern in limited areas (at petrol stations and where fuels are stored). Intrusion by seawater is also a major water quality issue in this catchment.

Far more water flows through the aquifer than is abstracted from, or stored in, the system. Most of the water flows into the sea. A large part of the flow from the Gort-Kinvarra catchment flows into the sea between the town of Kinvarra and Dunguaire Castle on the Galway Road (Drew, 2001). Some of it flows into the sea further south along the coast, into Aughinish Bay, Muckinish Bay (Bell Harbour) and Ballyvaughan Bay. Many of the flows are very strong, and at low tide it appears as though a river is running into the sea, but one that springs straight out of the rock. It is likely than when sea level was lower, some of these 'springs' that are in the intertidal zone today were the source springs for rivers flowing out to sea across large areas of the exposed continental shelf.

This flow into the coastal waters is very important, because many of the bays around Galway support aquaculture of fish and shellfish, and harvesting of seaweeds. If the groundwater is contaminated in any way, these contaminants are carried out into the coastal waters (Valiela et al, 1990; UNESCO, 2004). If the groundwater is rich in nutrients, for example, it may lead to 'blooms' of algae, which can strip oxygen from the water when they die, killing fish and other aquatic life (Slomp & Cappellen, 2004; Giblin & Gaines, 1990). Some of these blooms may be toxic, and as phytoplankton are the food for filter feeders such as mussels and oysters which are cultivated in these bays, they then become dangerous to eat (Johannes, 1980). In either of these cases, the aquaculture sites may have to be closed, representing a significant economic loss to the owners and to the local economy.

The presence of large conduits in the intertidal zone also means that seawater can find its way into the groundwater system (Capone, & Bautista, 1985; Saltrans, 2003; Marin & Perry, 1994). This can happen at times of spring tides, when the tide comes high up the shore, especially if the spring tide coincides with a dry spell, where rainfall and therefore water flow through the system is low. Often this happens in the summer holiday season, when lots of tourists are about, and the demand for fresh water is high, which exacerbates the problem. Contamination of an aquifer by seawater can be serious, as the water from wells close to the coast will be unfit to drink, and it may take days or even weeks to flush all the salt out of the system (Moore, 1996).

3 Determination of Discharges

Discharges from the Shannon and the Corrib are recorded with accuracy and show how the disposition of rainfall is expressed in river discharges. Simple water balance calculations show that in both these large catchments surface water discharge dominates (see Table No. 1). Using annual data it can be shown that measured and calculated discharges are of the same order of magnitude for both rivers.


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Table No. 1: Water balance calculations for the River Shannon and River Corrib

Catchment Name

Area (km2)

AAR (mm)

PET (mm)

ER (mm)

Calculated Discharge

(m3/s)

Measured Discharge

(m3/s)

Shannon 11,903 1024 450 574 215 213

Corrib 3,111 1331 450 881 87 99

The Kinvarra catchment is part of Hydrometric Area No. 29 and the West Clare coast is part of Hydrometric Area No. 28. If the above exercise is repeated for these systems the importance of groundwater discharges becomes immediately apparent (see Table No. 2).

Table No. 2: Water balance calculations for the Kinvarra Catchment & Hydrometric Area No. 29

Catchment Name

Area (km2)

AAR (mm)

PET (mm)

ER (mm)

Calculated Discharge

(m3/s)

Measured Discharge

(m3/s)

Kinvarra 1,000 1320 450 870 28 0

Hydrometric Area 29 900 1320 450 870 25 6

Surface water discharges in the Kinvarra catchment are negligible, so on average 28m3/s discharges to Galway Bay from this catchment through groundwater. In Hydrometric Area No. 29, approximately three quarters of the discharge to the sea is in the form of groundwater.

3.1 Study Data

Staff and students of the Department of Earth & Ocean Sciences (EOS) have been measuring discharges and nutrient inputs in the Kinvarra area since 2004 (Cave et al, 2005). In the immediate vicinity of Kinvarra groundwater discharges to the sea are of the order of 8m3/s. A number of other discharges have been identified along the north coast but it is estimated that 50 percent of the potential discharge is as yet unaccounted for. It is likely that this discharge is entering the sea further out in the bay in the form of Submarine Groundwater Discharge (SGD).

3.2 Water Quality & Nutrient Loads

One of the most striking things about the Kinvarra catchment is the nutrient loading as a function of discharge.


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Table No. 3: Nutrient loading in River Corrib and in Kinvarra groundwater

River Corrib

July 05

Dunguaire Castle July 05

River Corrib Nov 01

Kinvarra Bay Nov 06

Discharge (m3/s) 90 3 115 6

NO2 + NO3 (tonnes/d)

4.0 0.3 4.4 2.3

Measured discharges at Dunguaire Castle and in Kinvarra Bay show nutrient loading of 0.3 and 2.3 tonnes/day; while discharges from the Corrib are one or two orders of magnitude greater the nutrient loading is comparable. The nutrient loading from the Corrib may be affected by Lough Corrib (acting as a sink) but it is apparent that nutrient loading through groundwater is significant along the south side of the bay.

4 Conclusions

Groundwater discharges make up a significant portion of freshwater discharges to the coastal zone along the south side of Galway Bay and along the west coast of Clare. These discharges are not monitored (either quantitatively or qualitatively) and only indirect determination of discharge volumes has been completed to date.

In the context of the Water Framework Directive the nutrient loading presents significant management problems. Under the terms of the WFD all Irish waters (surface, ground and coastal) are to be of good qualitative and quantitative status by 2015, and must be maintained as such into the future. The groundwater discharges along the south side of Galway Bay are adding significant nutrient loads to coastal waters and mitigating their entry to the groundwater system (and therefore the coastal zone) will be hugely problematic, given the geological and hydrogeological regime in the region.

Much work has been completed in the United States, the western Mediterranean, Australia and Japan on the impact and importance of Submarine Groundwater Discharges; no such studies have been completed in Ireland to date.

Given the data collected to date as part of this ongoing study it would appear that groundwater may be a significant source of nutrients to certain Irish coastal waters.

5 Recommendations

The work of the Department of Earth & Ocean Sciences in this area continues and will cover:

• Field mapping of inter-tidal groundwater discharge sites throughout Galway Bay • Flow measurements at selected sites • Sampling for nutrients • GIS database of results • Future proposals • Irish coastline survey for groundwater discharge • SGD investigations in Galway bay


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References

Bayari, C.S. & Kurttas, T., (2002). Coastal and submarine karstic discharges in the Gokova Bay, SW Turkey. Quarterly Journal of Engineering Geology and Hydrogeology 35, 381-390.

Beddows, P. (2004), Hydrology, microbiology and geochemistry of a density stratified conduit flow carbonate aquifer: Caribbean Coast, Yucatan Peninsula, Mexico. PhD Thesis, Bristol University School of Geographical Sciences

Burnett, W.C., Bokuniewicz, H., Huettel, M., Moore, W.S., Taniguchi, M.,(2003). Groundwater and pore water inputs to the coastal zone. Biogeochemistry 66, 3-33.

Capone, D.G., Bautista, M.F., (1985). A Groundwater Source of Nitrate in Nearshore Marine-Sediments. Nature 313, 214-216.

Cave, R.R., Henry, T. and O’Connor. T. (2005). The impact of groundwater on Irish coastal waters – a preliminary study. Environ ’05 Conference, 28th-30th Jan 2005, IT Sligo

Department of the Environment & Local Government. (1999). Groundwater Protection Schemes. Department of the Environment & Local Government, Dublin.

Drew, D. (2001). Classic Landforms of the Burren Karst. The British Geomorphological Research Group.

Drew, D.P. & Daly, D.,(1993). Groundwater and karstification in mid-Galway, south Mayo and north Clare. Geological Survey of Ireland. GSI report series RS93\3. Dublin.

Giblin, A.E., Gaines, A.G., (1990). Nitrogen Inputs to a Marine Embayment - the Importance of Groundwater. Biogeochemistry 10, 309-328.

Gobler, C.J., Sanudo-Wilhelmy, S.A., (2001). Temporal variability of groundwater seepage and brown tide blooms in a Long Island embayment. Marine Ecology-Progress Series 217, 299-309

O’Donovan, J., & Southern Global Water, (1997). An Investigation of the Flooding Problems in the Gort-Ardrahan Area of South Galway, Final Report, Volumes 1-3. For the OPW, Dublin.

Johannes, R.E., (1980). The Ecological Significance of the Submarine Discharge of Groundwater. Marine Ecology-Progress Series 3, 365-373.

Marin, L.E. & Perry, E.C. (1994), The hydrogeology and contamination potential of northwestern Yucatan, Mexico. Geofisica Internacional, 33(4), 619-23

Moore, W.S., (1996). The subterranean estuary: A reaction zone of groundwater and sea water. Marine Chemistry 65(1-2), pp.111-125.

Pracht, M., Lees, A., Leake, B., Feely, M., Long, B., Morris, J. & McConnell, B.(2004). Geology of Galway Bay: A Geological Description to accompany the Bedrock Geology 1:100000 Scale Map Series, Sheet 14, Galway Bay. Geological Survey of Ireland, Dublin.

Saltrans, (2003). Methods For Assessing Salt Intrusion and Transport In Heterogeneous And Fractured Aquifers, Fifth Framework Programme. http://www.weizmann.ac.il/ESER/Saltrans/

Slomp, C.P & Cappellen.,(2004). Nutrient inputs to the coastal ocean through submarine groundwater discharge: controls and potential impact. Journal of Hydrology 295 pp. 64-86.

Smyth, T.,(1998). A case study in the Gort- Ardrahan area of south Galway: A summary and introduction in Floodaware Final Report. EU Commission, Directorate General XII. Office of Public Works, Dublin.

Valiela, I., Costa, J., Foreman, K., Teal, J.M., Howes, B., Aubrey, D.,(1990). Transport of Groundwater-Borne Nutrients from Watersheds and Their Effects on Coastal Waters. Biogeochemistry 10, 177-197.

UNESCO,(2004). Submarine groundwater discharge: management implications, measurements and effects. UNESCO. Report no. IHP-6 Series on Groundwater no 5/IOC Manuals and Guides no. 44. Paris, 35 pp.


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Coastal CO2 eddy-covariance measurements Colin D. O’Dowd and Philip McVeigh Centre for Climate & Air Pollution Studies, Environmental Change Institute, and School of Physics, National University of Ireland, Galway, Ireland. Abstract. Oceanic micrometeorological and CO2 fluxes from a coastal station (Mace Head) have been derived using an eddy-covariance technique. While the data are in a stage of first-look analysis, it can be concluded that CO2 fluxes are found to increase with wind speed and reach a maximum of -0.005-0.035 micromoles m-2 s-1. The magnitude of the coastal flux is significantly lager that that reported for open ocean conditions 1 Introduction It is estimated that about half of the anthropogenic CO2 emitted to the atmosphere since the industrial revolution has been absorbed by the oceans (Sabine et al., 2004). This has the beneficial effect of regulating atmospheric levels of the greenhouse gas. However, this can impact biogeochemical cycling and there are many feedback mechanisms which may reduce ocean CO2 uptake, for example due to the reduced buffering capacity due to decreasing alkalinity. It is essential to fully elucidate the processes governing oceanic CO2 flux. In recent years there has been increasing concern that the change in seawater CO2 concentration in the world’s oceans could well have major and long lasting consequences, particularly due to the predictable reduction in the alkalinity of seawater (ocean acidification), (The Royal Society, 2005, OSPAR, 2006). Only in recent years did direct ship-borne eddy-covariance flux techniques to measure CO2 flux is deployed (McGillis et al., 2007) with promising results. In this study we present first results from coastal water CO2 fluxes using a similar technique. 2 Experimental The flux package was deployed on the Mace Head 22 m tower and comprised the following components: Gill, Omnidirectional R3 Ultrasonic Anemometer, TSI, 3762 Condensation Particle Counter (CPC), Li-COR, LI 7500 open path H2O/C2O gas analyser, Young anemometer, Data Logging system (Figure 1).


53

Figure 1a. Flux package components.

Figure 1b. Close-up of sonic and Licor on extended boom from tower.


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3 Eddy Covariance Theory The characteristic spectral gap in boundary layer motions separates slowly changing large-scale motions from the rapid fluctuations caused by micro scale turbulence. Turbulent fluctuations of a meteorological variable can be decomposed into a mean and fluctuating part, known as the Reynolds decomposition, and can be used to quantify turbulence. For example, the macroscopic wind speed, U, can be separated into its mean and fluctuating part, as well as its three orthogonal components, u, v and w, e.g.:

The over bar denotes an average, and the prime denotes a fluctuation. The variance of U is then got by multiplying averaging the product of its prime over a chosen time interval.

The covariance of two variables is obtained in a similar way:

This means that we can find fluxes from covariances. Specifically, we find vertical turbulent fluxes from the covariances of w’ with the variable of interest. The eddy covariance technique assumes that there are no horizontal concentration gradients, and that a scalar species comprises a mean and fluctuating component. It also assumes that there are no low frequency trends in the data, nevertheless, this is rarely the case, so the data must be detrended, and departures from a short term mean can then be used. A running mean will accomplish this task. For this work, a running mean of 30mins was selected. The running mean simply involves taking an ensemble of all the points either side of every point and finding their average. Variances and covariances are calculated from the running means. These variances and covariances are then averaged using a fixed mean, and then co-ordinate rotation transforms are applied to them.

The equation above is used to rotate the covariances of a w and a scalar, c.

Where: and Separate equations are used to rotate the means and variances, as well as the covariances between two of the wind components. Many other parameters can be derived from this combination of basic raw data and subsequent calculations. A useful parameter is the Friction velocity, u*, which can be used for open-water fetch quality control:


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4 Results Data were first screened for wind sector to ensure an open water fetch and secondly were screened for friction velocity deviations as a function of wind speed. Invariably, from a coastal station, influenced by tides, significant filtering of data is required and results in a reduced dataset. Figure 2 (a) illustrates the relationship between u* and wind speed and, for the filtered data selection, demonstrates a relationship typical for an open water fetch, thus suggesting the selected data were valid for oceanic flux product derivation. For example, McGillis et al., (2001) found u* values of between 0.7 and 0.8 for wind speeds of 15 m s-1 over the open ocean, as did Geever et al., (2005) for Mace Head during high tide conditions. For the selected high-tide and marine sector filtered data CO2 fluxed range from -0.0001 micromoles m-2 s-1 at wind speeds of 2-4 m s-1 to -0.005-0.035 micromoles m-2 s-1 at wind speed of up to 16 m s-1. The flux values reported here demonstrate a net oceanic sink for CO2, as observed over the open ocean, however, the magnitude of the coastal CO2 sink is significantly higher.

Figure 2. (Top) u* as a function of wind speed for filter open ocean data. (Bottom) CO2 flux as a function of wind speed for open ocean data.


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5 Conclusions This study has demonstrated, from a micrometeorological flux perspective that open water fluxes of scaler parameters such as CO2 can be derived from a coastal site such as Mace Head. The derived CO2 fluxes are observed to increase with wind speeds and demonstrate a net oceanic sink for CO2. The magnitude of the sink is significantly greater than that reported for open ocean sinks. References Geever, M., O'Dowd, C.D., van Ekeren, S., Flanagan, R., Nilsson, D. E., de Leeuw, G., Rannik, Ü.

(2005), Sub-Micron Sea-Spray Fluxes, Geophys. Res. Letts., doi:10.1029/2005GL023081,. McGillis, W. R., Edson, J. B., Hare, J. E. and Fairall, C. W. (2001a), Direct covariance air-sea CO2

fluxes, J. Geophys. Res., 106, 16,729– 16,745. OSPAR (2006). Effects on the Marine Environment of Ocean Acidification resulting from elevated

levels of CO2 in the atmosphere. Biodiversity Series. ISBN 1-905859-23-6. OSPAR Commission London.

Sabine C. L. et al. (2004). The oceanic sink for anthropogenic CO2. Science 305, 367 – 371.


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Figure 1. Flow-through chamber for chamber experiments at Mace Head

Coastal Biogenic Aerosol Flux - BIOFLUX Colin O’Dowd, Karine Selligri, Young Jun Yoon School of Physics & Centre for Climate & Air Pollution Studies, Environmental Change Institute, National University of Ireland, Galway, Ireland Abstract. Experiments on coastal new particle formation have been conducted at Mace Head and nearby hotspots in order to elucidate the role of molecular iodine in the formation of new particles and the associated fluxes of these aerosol particles. Molecular iodine concentrations were taken in air samples during nucleation events and during algae chamber experiments under pseudo-natural conditions. Concentrations of the order of 35-125 ppt were found during atmospheric particle production events and up to 800 ppt in controlled chamber events. In the chamber experiments, I2 concentrations were strongly correlated to algae biomass, although saturating at the highest concentrations. In addition, a linear correlation was found between I2 concentrations and particle production rate. For a algae coverage range of 1-5 kg m2 biomass, it was calculated that the production of new particles, in the 3-3.5 nm range, was between 1 x 1010 and 5 x 1010 particles m-2 s-1. A coastal emission inventory for the Mace Head / MRI Carna region was derived and input into the RAMS chemical transport model to evaluation horizontal and vertical dispersion of the coastal aerosol plume. Significant particle concentrations were predicted for 10 km inland and being vertically-mixed up to a height of 600 m. 1 Introduction New particle formation events have been studied previously at Mace Head and during these events, where the background aerosol concentration can increase from 500 cm-3 to more than 1,000,000 cm-3, iodine oxides have been implicated as the main source of the condensable material (O’Dowd et al., 2002a,b). The precursor of IO has been an open question as initial studies suggested the source was CH2I2 (O’Dowd et al., 2002b Hoffmann et al., 2001) although this organo-halogen may not be able to provide sufficient material at a high enough rate to account for the production rate. As a result, molecular iodine (I2) has been suggested as an alternative source of IO in the coastal environment. A series of experiments were designed to measure the concentration of both new particles and I2 at Mace Head, nearby hotspots (near MRI-Carna) and in chamber experiments in order to explore its role in particle production. 2 Experimental I2 concentrations were determined over 30 minute periods using a starch denuder technique. Aerosol concentrations were determined using a Scanning Mobility Particle Sizer covering sized from 3.5 nm – 50 nm. Ambient samples were taken at Mace Head and in a mobile lab at hotspots nearby (i.e. close to the MRI-Carna labs). Chamber experiments were undertaken in a 2 m3 perspex flow-through chamber which allowed 50%


58

ozone was not removed thus allowing a natural source of ozone for oxidation. The experiments were conduced on local algae such as Laminarie and Fucus. The algae chamber is illustrated in Figure 1. 3 Results The first set of experiments were conducted with no flow through the chamber and under these conditions, nucleation pulses, flowed by rapid aerosol growth were seen (Figure 2.). Large increases in particle concentration are initially seen at 3 nm sizes and the peak in concentration grows rapidly to 20-30 nm via condensation growth. The initial burst increases aerosol surface area (vapour condensation sink), reducing the gas-phase concentration, and thus inhibiting nucleation of further new particles. After rapid growth, the condensation sink eventually reduces due to particle deposition processes and a subsequent nucleation pulse results. The second phase of the chamber experiments centred around pseudo-steady-state conditions where the aerosol size distribution did not evolve with time. These experiments were repeated for four different levels of biomass (5 kg, 9 kg, 16 kg and 25 kg algae mass per square meter) with a resultant aerosol concentration of up to 107 cm-3 for these scenarios. The I2 concentrations were found to range from 130 to 380 ppt depending on the biomass concentration. At higher concentrations of biomass, the I2 concentration seemed to top out (Figure 3a). However, a clear linear relationship was found between particle concentration and molecular iodine concentration as shown in Figure 3b.

Figure 2. 2-D contour plot of aerosol concentration and growth under conditions of no throughput flow in chamber.


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For a range of 1-5 kg m2 biomass, it was calculated that the production of new particles, in the 3-3.5 nm range, was between 1 x 1010 and 5 x 1010 particles m-2 s-1. The corresponding estimated iodine flux in the chamber was of the order of 0.5-1.5 x 109 cm-3 s-1. In terms of particle production per unit of molecular iodine, the production rate corresponded to 2800 particles cm-3 ppt-1.

Figure 3 (a) I2 concentrations as a function of sea weed biomass.

(b) Particle concentration as a function of I2 concentration.

Figure 4. Range of I2 concentrations observed at MRI Carna, bridge nearby MRI Carna and in the chamber. Figure 4 highlights the range of I2 concentrations encountered at MRI Carna, the more exposed bridge about 1 km from MRI Carna, and in the chamber. Concentrations at MRI Carna were of the order of 35 ppt while at the nearby bridge hotsport, they were 121-125 ppt. By comparison, the chamber concentration ranged from 519-846 ppt. The meso-scale atmospheric chemical transport model RAMS was used to simulate particle concentrations resulting from new particle emissions in the tidal regions upwind. Boundary conditions were taken from the ECMWF reanalysis database. RAMS can operate at a resolution from 1 km to


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1000 km and includes nested grids. For this study, simulations are preformed for three nested grids simultaneously to take account of the synoptic and local meteorological circulations. Grid 1 covers the region with a horizontal resolution of 15 km2, Grid 2 covers an intermediate domain of 5 km2, while Grid 3 consists of a 1 km2 domain. A time step of 10 s and 35 vertical levels, 15 in the lower 1500 m was used. An emission inventory for particle emissions was developed based on the chamber derived fluxes as a function of seaweed biomass and applied to the calculated percentage seaweed biomass derived from tidal maps and visual observations of mass loadings. Figure 5 illustrates the spatial distribution of particle concentration fields in the Mace Head (coordinates 0,2) and MRI Carna (coordinates 4, -2) region. Peak concentrations are seen at MRI Carna with concentrations approaching 350,000 cm-3. These concentrations are in excellent agreement with the observed concentrations. The results, as evident from the concentration distribution maps, indicate significant horizontal dispersion of the coastal aerosol plumes. The Section A and Section B lines in Figure 5 mark regions where vertical distributions are extracted from the model simulations and shown in Figure 6. In both sections, significant vertical distribution of coastal new particles is seen up to 600 m which corresponds to the typical boundary/surface layer observed in the region (Kunz et al., 2002). The coastal aerosol plume reaches 300 m after 4 km and 600 m after 10 km.

Figure 5. Surface level distribution of particle concentration around Mace Head and MRI Carna. Section A and B mark regions for extraction of vertical distributions in Figure 6.


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Figure 6. Vertical distributions of particle concentration around Mace Head and MRI Carna as marked along Section B and A, respectively, in Figure 5. 4 Conclusions Chamber studies were used to elucidate the emissions of I2 and new particles from coastal macro-algae. In addition, the inter-relationship between I2 and biomass, and new particles and I2 was investigated. I2 concentrations were positively correlated with algae biomass, although at high concentrations, I2 concentrations seemed to saturate. Strikingly, a linear relationship between particle concentration and I2 was observed. For a range of 1-5 kg m2 biomass, it was calculated that the production of new particles, in the 3-3.5 nm range, was between 1 x 1010 and 5 x 1010 particles m-2 s-1. Peak I2 concentrations under atmospheric conditions were of the order of 125 ppt while chamber conditions led to peak concentrations of > 800 ppt I2. Emission inventories of new particles as a function of coastal biomass were derived and input to a chemical transport model to examine the spatial distribution of the coastal aerosol plumes. The coastal aerosol plume becomes vertically mixed to 600 m after 10 km horizontal advection. References Kunz, G., de Leeuw, G., Becker, E. and O’Dowd, C.D. (2002), Lidar observations of atmospheric

boundary layer structure and sea spray aerosol plumes generation and transport at Mace Head, Ireland (PARFORCE experiment). J. Geophys. Res., 107, 10.1029/2001JD001240.

O’Dowd, C.D., K. Hämeri, J.M. Mäkelä, L. Pirjola, M. Kulmala, S.G. Jennings, H. Berresheim, H.-C. Hansson, G. de Leeuw, A. G. Allen, C. N. Hewitt, A. Jackson, Y. Viisanen, T. Hoffmann (2002). A dedicated study of new particle formation and fate in the coastal environment (PARFORCE): Overview of objectives and initial achievements. J. Geophys. Res., 107, 10.1029/2001000555.

O’Dowd, C.D., Jimenez, J.L., Bahreini R., Flagan, R.C. Seinfeld, J.H., Pirjola, L., Kulmala, M., Jennings, S.G. and Hoffmann, T. (2002). Marine particle formation from biogenic iodine emissions, Nature, 417, 632-636.


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Seaweed, Iodine and Health Peter Smyth UCD Conway Institute of Biomolecular and Biomedical Research University College Dublin 1 Introduction Iodine forms one of the most important mineral constituents of seaweeds. The iodine content and its chemical form varies with different species but is generally greater in brown seaweeds (kelps) than the green or red varieties (Morrissey et al 2001). Seaweeds form an important part of daily diet in many Asian countries such as Japan, Korea and coastal parts of China. Various seaweed species are widely used in sushi, soups, salads and in powdered form as condiments. Japan is the world’s largest seaweed market and is highly regulated with seaweeds divided into 21 categories (www.seaweed.ie). Apart from being treasured as a food supplement, many health benefits have been attributed to seaweed consumption and Japan even has a “National Seaweed Day” (Feb 6) (Watts 2004). The increasing spread of ethnic restaurants and interest in natural foods has also lead to an upsurge in seaweed/sea vegetable consumption in the West. Many curative properties have been attributed to iodine particularly its possible involvement in redox reactions; as an antioxidant or antiseptic agent and in circulatory illness (Winkler and Kliber 1998, Smyth 2004). Despite many centuries of seaweed use as both foodstuffs and medicines (in modern parlance “nutriceuticals”), little is known of the mechanism through which seaweed consumption might exert its effect on mammalian cell function or growth. An exception is the case of seaweeds as a source of iodine nutrition in iodine deficiency and associated disorders of the thyroid gland (WHO 2004). In recent times there has been much speculation on a role for seaweed consumption in protecting against the onset of breast cancer (Smyth 1997, Cann et al 2001, Funahashi et al 2001, Smyth 2003, Aceves et al 2005, Garcia Solis et al 2005 ). This has arisen from epidemiological data showing that the incidence of breast cancer is much lower in countries where seaweed consumption is highest. Incidence rates in Korea were 8.6/100000 females, in Japan 10.9; 21.2 USA; 26.0 UK but 27.5 Ireland (Parkin et al 2002). Whether this relationship reflects the iodine content of seaweeds or some other factor is not clear but both iodine and seaweed extracts have been shown to inhibit carcinogen induced breast tumour growth in experimental animals (Funahashi et al 2001). The ability of stable (nonradioactive) iodine to block uptake of radioactive iodine by the thyroid gland and thus offer protection following a nuclear accident is well established and forms the theoretical background for the distribution of the much discussed nationwide distribution of iodine tablets. Although ingestion of radioactive iodine can have harmful effects it is also used as a therapeutic ablative agent in the treatment of thyroid cancer and hyperthyroidism (overactive thyroid gland). The effectiveness of this form of therapy depends on the ability of the tumour tissue to both accumulate and retain therapeutically administered radioactive iodine. This catatonic effect of iodine may not be confined to its radioactive form as larger doses of nonradioactive iodine can prevent proliferation of both thyroid and other cells and suggest a wider role for iodine as a therapeutic agent (Smyth et al 2003, 2004, Garcia Solis et al 2005). This communication outlines some experience in measuring seaweed iodine content and the ability of seaweeds to take up and retain radioactive iodine 125I in vitro. In addition, the possible contribution to human iodine intake of gaseous iodine released by seaweeds is discussed.


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2 Materials and Methods Seaweeds Samples of 10 seaweed species (Chandra’s crisps, Palm aria palmate, Porphyry, Ulna lactic, Heteromorphy intestinal is, Laminar digitise, Laminar sac carina, Laminar hyperborean, Malaria esculent and Himanthalia elongata) were collected off the Spiddal coast, Co Galway every three months to cover all seasons. When ultra fresh samples were required collections were also made from Dublin Bay (Forty Foot, Sandycove, Co Dublin). Fresh seaweed was dried at 50ºC for 48 hours, after which it was stored in airtight containers at room temperature. Iodine Analysis Total iodine in seaweeds was measured by dry alkaline ashing. This involves preliminary drying of samples and standards at 150˚C followed by incineration for 3 hours at 600˚C in the presence of 1ml strong alkali (KOH). The ash was reconstituted in 5 ml distilled water. Potassium iodate (KIO3) was used to generate a standard curve in the range 0 – 120 ng I. The iodide content was measured colorimetrically at 420nm using the Sandell-Kolthoff reaction. Isolation of protoplasts The seaweed plant is surrounded by an external cuticle and internal cell walls. In order to clarify whether uptake of iodide is a cell wall-mediated process, whether the iodine present is only in the cell wall or if it is transported across the cell membrane into the cytoplasm and possibly the vacuole, an enzyme- mediated protoplast generating protocol was established for L. digitata using a cellulase-alginate lyase mixture. Purification of these protoplasts allowed viability to be assessed and permitted investigation of radioiodide efflux in the absence of a cell wall. This facilitated verification of the intracellular localisation of 125I accumulated iodide and iodo-compounds 125I Uptake and Efflux Studies 125I uptake – tissue/protoplasts incubated at room temperature in seawater spiked with 10,000cpm 125I. CPM of supernatant measured. 125I efflux – radioactive seaweed tissue/protoplasts transferred to fresh seawater and CPM of supernatant measured after fixed time points

Seaweed Iodine Thyroid Seaweed Iodine Thyroid

•Thyroid gland takes up iodide• Accumulates it against a concentration gradient

• Concentrated to 20-40 times that of the bloodstream•Organifies iodide to iodine•Leads to formation of thyroid hormones

I -

Blood I Blood I --

I -

Seawater I Seawater I --

•Seaweed takes up iodide/iodine•Accumulates it against a concentration gradient

••Can accumulate iodines up to 10Can accumulate iodines up to 1066

times that in seawatertimes that in seawater•Role of iodines in seaweed uncertain

Fig 1 The ability to transport iodine defines the relationship between seaweeds and the mammalian thyroid


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3 Results The common ability of both the mammalian thyroid and seaweed plants to accumulate and retain iodine forms the basis for the work presented in this communication. Figure 1 shows a comparison between the abilities of seaweeds and the human thyroid to accumulate iodide from seawater/bloodstream. The main difference is that seaweed has much greater ability to accumulate iodide from its surrounding fluid than does the thyroid (up to 106 in some species. While the main function of iodide in the thyroid is to provide a source of iodine for the formation of thyroid hormones, its role in seaweed is much less certain as is its chemical composition within the plant ( Hou et al 1997 ; Kupper et al 1998 ; Shah et al 2005). Similarly the bioavailability of iodine stored in seaweeds is not fully understood (Hou et al 1997; Aquaron et al 2002). Table 1 shows the median iodine content (ppppmm)) of the three different classes of seaweed and when available their seasonal variation. It can be seen that the iodine content of the brown seaweeds was in general greater than that of green or red with higher values observed in the summer months.

Fig 2 Uptake (A) and Efflux (B) of 125I by different seaweed species.

Species Autumn/ Winter Spring/ SummerChondrus crispus 251 282Palmaria palmata 88 69Porphyra umbilicalis N/A 44

Enteromorpha intestinalis 325 N/AUlva lactuca 144 150

Alaria esculenta 308 N/AHimanthalia elongata 150 188Laminaria digitata 7470 5120Laminaria hyperborea 6343 5106Laminaria saccharina 5714 5260


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Figure 2 shows the effect of incubating samples of different seaweeds with radioactive 125I for varying times and measuring both uptake and efflux of radioactivity. In Fig 2A It can be seen that uptake of 125I increased with time. Highest uptakes were observed with Chondrus Crispus (plateau at 60 mins) while others continued to rise with time. Because of its ready availability and relatively high uptake the seaweed Laminaria Digitata was selected for future experiments. Efflux of accumulated 125I at varying times from different seaweed species is shown in Fig 2 B. It can be seen that efflux from the various seaweeds is much slower than that from the thyroid cell line. In fact less than 10% of accumulated 125I was lost at 60 minutes and in most cases at 120 minutes. Thus approximately 90% of accumulated 125I was retained by the plant at these times. Localisation of 125I in seaweed plants. As shown Fig 3 A and B both uptake and retention of 125I was shown in the protoplasts demonstrating that iodide enters the cell and is not just bound to the cell wall.

Fig 3. Uptake (A) and Efflux (B) of 125I in whole seaweed (L. digitata) and isolated protoplasts

Iodide Uptake through Respiration

Since iodovolatilisation can account for a higher iodine content in the air over seaweed rich areas (Pirjola et al 2005; Sellegri et al 2005), a rough calculation was made bases on the figures of this group to determine if volatile iodine released from seaweeds could exert a significant influence on iodine intake in humans or animals. Table 2 shows approximations of various respiratory indices demonstrating that respiration in an adult could vary from a resting level of 6L per minute to an exercise level of 50L /minute. Correspondingly air samples taken at coastal areas where seaweed mass was low, at “hot spots” where it was high or in an enclosed seaweed containing chamber. Assuming that I2 was absorbed quantitatively as suggested by Morgan et al 1968, respiration could account for an increment in daily iodine intake from as low as 1.5 μg/day in a resting subject (6L/min) at low atmospheric iodine content to 13.0 μg/hr at exercise (50L/min) at the highest atmospheric iodine content. Higher intakes would arise following vigorous exercise with correspondingly increased respiration. These are arbitrary figures, subject to many variables, and require experimental confirmation.


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Lung Capacity Examples of different iodine emissions from seaweeds

Resting 6L/min μg/24 hrs

Exercise 50L/min Low iodine Hot Spots Seaweed Chamber

Iodine ingestion

Resting 24 hrs 1.5 5.3 37.0

Exercise (1.0 hr) 0.54 1.80 13.0

Table 2 Estimate of iodine intakes arising from gaseous I2 emitted from seaweeds from different seashores and from an experimental seaweed chamber (Sellegri et al 2005; Pirjola et al 2005).

4 Discussion

The daily intake of iodine for adults recommended by the World Health organisation (WHO) is 150 μg (WHO 2004). This intake can be readily achieved by consuming marine products but unfortunately due to cost, lack of availability or dietary preference this source is not always availed of. In many countries adequate dietary iodine intake is provided by iodisation of table salt and indeed the WHO recommends a policy of universal salt iodisation to achieve this goal. In the case of Ireland, only approximately 4% of table salt is iodised with the result that dietary iodine intake is almost entirely opportunistic exposing a large part of the population to the dangers of iodine deficiency, in particular infants born to iodine deficient mothers (Smyth et al 2006).It is generally believed that iodine deficiency is less prevalent in populations living near the sea and this was thought to be a major reason why South Tipperary had a high goitre rate (O’Donovan 1950). However this is not a universal finding as iodine deficiency has been frequently reported in coastal communities. On the basis of published values for marine aerosol iodine as extrapolated in this communication into daily iodine intakes, the contribution of respired air to dietary intake is probably not significant. However earlier reports on the high biological availability of inspired iodine vapour (Morgan et al 1968) this pathway requires further investigation.

The study of iodine in seaweeds, its uptake into, formulation within and efflux from the plants has provided an exciting model for the study of mammalian tissue interactions with iodine and of improving its usefulness as a therapeutic agent. Recent demonstrations of atmospheric particle formation induced by iodine emanating from seaweeds offer the possibility of extending this work to studying the effects of gaseous iodine ingestion through respiration.

Acknowledgements

George Cloughley, Emma Burbridge, UCD Conway Institute, M D Guiry, Stefan Kraan, Martin Ryan Institute, NUI (G), Germain Levielle, UCD School of Biological and Environmental Science. Supported by Marine Institute Postdoctoral Fellowship Ref No: PDOC/01/005 Title: Iodine in Commercially used Irish seaweeds.

References Morrissey, J., Kraan, S. and Guiry, M.D. (2001). A guide to commercially important seaweeds on the

Irish Coast. Bord Iascaigh Mhara, Dublin. Watts, J. (2001). Seaweed dries up in Japan The Guardian Feb 8th

www.guardian.co.uk/print/0,3858,4133519,00.html


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Winkler, R and Klieber, M. (1998). Important results of about 45 years balneo-medical research in Bad Hall. Wien MedWorchenschrSupp 148 3-11.

Smyth, P.P.(2003). Role of iodine in antioxidant defence in thyroid and breast disease. Biofactors 19(3-4):121-30

Cann, S.A, van Netten, J.P., and van Netten, C. (2000). Hypothesis: iodine, selenium and the development of breast cancer. Cancer Causes Control. 11:121-7.

Funahashi H, Imai T, Mase T, Sekiya M, Yokoi K, Hayashi H, Shibata A, Hayashi T, Nishikawa M, Suda N, Hibi Y, Mizuno Y, Tsukamura K, Hayakawa A, and Tanuma, S. (2001). Seaweed prevents breast cancer? Jpn J Cancer Res. 5:483-7.

Smyth, P.P.( 2003). The thyroid, iodine and breast cancer. Breast Cancer Res.;5(5):235-8 Aceves, C., Anguiano, B. and Delgado, G. (2005). Is iodine a gatekeeper of the integrity of the

mammary gland? J Mammary Gland Biol Neoplasia. Apr;10(2):189-96 Garcia-Solis P, Alfaro Y, Anguiano B, Delgado G, Guzman R.C, Nandi S, Diaz-Munoz M, Vazquez-

Martinez O and Aceves C (2005). Inhibition of N-methyl-N-nitrosourea-induced mammary carcinogenesis by molecular iodine (I2) but not by iodide (I-) treatment Evidence that I2 prevents cancer promotion.Mol Cell Endocrinol. 236:49-57.

Parkin D.M, Bray F, Ferlay J and Pisani P. Global Cancer Statistics 2002 CA Cancer J Clin. 2005 55:74-108.

Hou, X, Yan, X, and Chai, C. (2000). Chemical Species of iodine in some [email protected] seaweeds II. Iodine-bound biological macromolecules. J. Radioanal. Nucl.Chem., 245(3), 461-467

Kupper FC, Schweigert N,et al.Iodine Uptake In Laminarials Involves Extracellular,Haloperoxidase-Mediated Oxidation of Iodide. Planta(1998) 207:163-171

Shah M, Wuilloud R.G, Kannamkumarath S.S, Caruso J.A. (2005). Iodine speciation studies in commercially available seaweed by coupling different chromatographic techniques with UV and ICP-MS detectioin. J Anal At Spectrom 20: 176-182.

Aquaron R, Delange F, Marchal P, Lognone V, and Ninane L. (2002) . Bioavailability Of Seaweed Iodine In Human Beings.Cell Mol Biol (Noisy-Le-Grand). 48:563-9.

Sellegri, K., Yoon Y. J., Jennings S.G., O’Dowd C.D., Pirjola L., Cautenet S., Chen H.and Hoffmann T., (2005). Quantification of Coastal New Ultra-Fine Particles Formation from In situ and Chamber Measurements during the BIOFLUX Campaign. Environ. Chem. 2, 260. doi:10.1071/EN05074

Pirjola, L., O’Dowd C., Yoon Y.J.and Sellegri K. (2005). Modelling Iodine Particle Formation and Growth from Seaweed in a Chamber. Environ. Chem. 2, 271. doi:10.1071/EN05075

Morgan A, Morgan DJ, Black A (1968). A study of the deposition , translocation and excretion of radioiodine inhaled as iodine vapour. Health Physics 15: 313-322.

WH0, 004. Iodine status worldwide. WHO Global Database on iodine deficiency. (http://whqlibdoc.who.int/publications/2004/9241592001.pdf) Geneva.

O’ Donovan D.K. (1950). The problem of endemic goitre in Ireland. Irish J Med Science 161-171. Nawoor Z., Burns R, Smith D.F., Sheehan S., O’Herlihy C. and Smyth P.P.A. (2006). Iodine intake in

pregnancy in Ireland –A cause for concern? Irish J Med Science 175: 21-24.


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The Irish Weather Buoy Network: Platforms for sustained ocean-atmosphere measurements Glenn Nolan Marine Institute 1 Introduction A network of 6 offshore weather buoys is maintained by the Oceanographic Services team at the Irish Marine Institute at the locations shown in figure 1. The buoys were deployed in response to recommendations from the Fishing Vessel Safety review (1996) outlining the need for offshore platforms to enhance sea area weather forecasts. Some of the other drivers for marine observations from these platforms include:

Weather forecasting/safety at sea Water Framework Directive Research (oceans and atmosphere) Aquaculture: early warning Baseline climatic conditions

Figure 1 Locations of the buoys within the Irish National Weather Buoy Network (FS1 is a gas platform where observations are available periodically) 2 Recent developments The most significant recent development in the buoy network was the deployment of a new deep water buoy at the M6 location (west of Porcupine Bank). This presented a significant challenge to the Oceanographic Services team in that the location is remote and the water depth is ca. 3000m. Expertise


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had to be drawn from the Woods Hole Oceanographic Institution in the U.S. to design and build an inverse catenary mooring for the M6 buoy that is similar in design to buoys deployed in the Gulf Stream and in the Tropical Pacific Ocean. The data acquisition systems currently on the weather buoys are somewhat outdated technology in that only one-way communication is possible and the sensors the system can interrogate is limited. During 2006 and 2007 the acquisition systems will be upgraded to a modern system that has two-way communications between the buoy and the shore and the ability to interrogate advanced sensors such as directional wave sensors and solid state wind sensors. The new system will also be compatible with a wide range of oceanographic instruments including CTDs and Doppler Current Profilers.

Figure 2 Cleaning and maintenance of the M1 weather buoy aboard the RV Celtic Explorer. 3 Future opportunities As highlighted above there is scope to include additional sensors on the weather buoy platforms in the coming years. This could include the gathering of offshore PCO2 data sets as an example. Oceanographic Services are open to assisting with proposed deployments of other sensors from researchers on the weather buoy network.


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Observations of the effect of fetch on whitecap coverage in the Irish Sea Adrian Callaghan and Martin White Department of Earth and Ocean Sciences, National University of Ireland, Galway 1 Introduction One of the most obvious features of any sea or ocean on a windy day is the large presence of whitecaps on the sea surface. Breaking waves and the resulting whitecap coverage have a significant impact on a number of oceanographic processes. The trapping of air and enhancement of interfacial area between air and water as a result of the wave breaking greatly enhances air-sea fluxes of heat and gas. Whitecapping is also responsible for the injection of seawater droplets into the air which is the primary mechanism controlling the rate of sea-salt aerosol generation over the ocean (Stramska and Petelski, 2003). The high albedo of all stages of whitecap evolution introduces the need to include whitecap reflectance in atmospheric corrections for the remote sensing of ocean colour from satellites. Knowledge of the surface coverage of whitecaps gives a means to quantify these resulting effects of breaking waves.

Whitecap coverage measurements have largely been made in the open ocean and the simplest empirical parameterisations of whitecap coverage and wave breaking are in terms of wind speed alone (Woolf, 2005). While it is generally considered that there exists an approximate cubic relationship between wind speed and whitecap coverage (e.g. Monahan and O’Muircheartaigh, 1980) different relationships have been reported. Zhao and Toba (2001) reported a relationship to the fourth power between whitecap coverage and wind speed. Wind speed is the most readily studied factor controlling the fraction of the sea surface covered by whitecaps but other factors such as fetch are also important. The lack of inclusion of the effect of fetch, in whitecap coverage models may explain the differences in reported relationships.

In this study, we investigate the effect of fetch on whitecap coverage. Fetch is the distance over

which the wind acts on the sea surface from the coastline in the direction of the wind. For a given wind speed, variations in fetch can lead to the development of different wave fields. According to the models of Zhao and Toba (2003) and Woolf (2005), at a given wind speed whitecap coverage increases with increasing fetch. The geographical nature of the Irish Sea lends itself to a study of the effect of fetch on whitecap coverage due to its rectangular shaped basin. Conditions of largest fetch occur when the wind blows from the south-west or the north-east. Winds from the west or the east result in conditions of much shorter fetch. Here we present measurements of whitecap coverage from ship borne images that show whitecap coverage does increase with increasing fetch for a given wind speed, supporting the hypotheses of Zhao and Toba (2003) and Woolf (2005). 2 Methods and Study Area

Sea surface images were collected using the Irish Ferries car ferry, “The Ulysses” in the summer of 2004. The ferry made 2 daylight crossings each day between Dublin port situated on the north side of Dublin Bay ( 53.35º N, 353.87º E ) and Holyhead port situated on the island of Holy to the west of Anglesey ( 53.32º N, 355.27º E ), indicated by the black line in figure 1. The morning crossing was from Dublin to Holyhead, with the return crossing in the afternoon. The Irish Sea is


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approximately 102 kilometres wide at this point. The deepest point along this line is approximately 150 metres which occurs roughly halfway between Dublin port on the west and Holyhead port on the east.

Two digital CCD cameras were mounted on the top deck of the ferry (approximately 32 metres above the sea surface), one facing port and the other facing starboard. The cameras were connected to a computer which controlled the data acquisition. The cameras collected video footage of the sea surface at a rate of one frame per second which was later decomposed into individual frames. Video acquisition was confined to three 15 minute periods and images were taken on both crossings. Each continuous 15 minute period corresponded to a different geographical location in the Irish Sea. The first and last periods were near the beginning and end of each crossing and will be referred to as the Dublin and Holyhead stations respectively, and the middle period took place mid crossing and will be referred to as the Middle station.

Whitecaps were detected and quantified in images using an automated threshold method developed during this project. Video footage of the sea surface was separated into its constituent frames, and each frame constituted one image. Each resultant image was changed from its original RGB format to a greyscale format. In the greyscale format, each pixel in the image had a single value ranging from 0 to 1 with 0 representing black and 1 representing white. All levels of grey are represented by numbers between these two extremes. The image processing method identified a suitable threshold for each image. All pixels above this threshold were considered whitecap pixels and all other pixels were considered background water pixels. The whitecap pixels were counted and a percentage value for each image was calculated. The automation of the process meant that large numbers of images could be analysed in a short time and without the subjectivity of a human analyst.

Figure 1. Relief Map of the Irish Sea. Black line showing ferry track.


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3 Results Results presented below are from the analysis of images which were taken in the Irish Sea in summer 2004. Wind speed during this period was low; values were less than 8 m/s for more than 75% of the measurement period. Figure 2 shows percentage whitecap coverage (W) values for three sampling stations on the morning Dublin to Holyhead crossings when the wind direction was only from the southern quadrants. Figure 2. Timeseries of W at 3 different stations along the morning Dublin to Holyhead crossing, Summer 2004.

Values of W are generally largest at the station closest to Holyhead and smallest at the station closest to Dublin. Wind for this period was largely from the south-west quadrant. With a south westerly wind, fetch conditions are larger at the Holyhead station than at the Dublin station. However, on the 10th June and the 20th July, values for W are greater on the western side relative to the eastern side of the Irish Sea. On these days the wind direction had changed from south west to south east thus changing conditions of greatest fetch from the Holyhead station to the Dublin station. In all cases wind speed was steady and an increase in W during any one particular voyage cannot be attributed to an increase in wind speed. Figure 3 shows a log-log relationship between W and wind speed which resulted in the following empirical relationship: W = 4.93 * 10 -6 (U10 3.90) (1) W = Percentage whitecap coverage. U10 = Wind speed at 10 metres above the sea surface.

Perc

enta

ge W

hite

cap

Cov

erag

e


73

Figure 3. Relationship of W with wind speed, Irish Sea, summer 2004.

The exponent (3.90) of Eq.1 above agrees well with a relationship between W and wind speed reported by Zhao and Toba (2001) : W = 2.98 * 10-5 (U10

4.04) (2)

However, the coefficient, 4.93 * 10 -6, of equation 1 is an order of magnitude less than that of equation 2. While Lafon et al (2006) also report smaller values of W in coastal fetch limited regions the values given by equation 1 are lower than expected. Possible causes for the low values reported here are offered in the discussion section.

Figures 4 (a), (b) and (c) display observations of W, wind speed and wind direction at the Dublin station, the middle station and the Holyhead station respectively. Wind speed is measured radially from the origin. Wind speed in the north south direction is on the y axis and east west wind speed is given on the x axis. Largest values of W are seen for south-westerly and north-easterly winds. Both these directions also correspond to conditions of largest fetch for the Irish Sea. When the wind is from the north east, W is larger at the Dublin station than at the Holyhead station, corresponding to larger fetch conditions at the Dublin station. W values are larger at the Holyhead station than at the Dublin station for winds from the south west, corresponding to larger fetch conditions at the Holyhead station. Figures 4 (d), (e) and (f) display the modelled W for each of these stations for the measured wind speed as given by equation 2. As expected the wind speed only model does not reflect the effect differences in fetch have on W.

Perc

enta

ge W

hite

cap

Cov

erag

e

Wind Speed (m/s)


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4 Discussion and Conclusions An increasing positive relationship between wind speed and W was found. It has been shown that W exhibits a large spatial variability for a given wind speed which appears to depend on fetch. For a south westerly wind, values of W increased from lower values at the Dublin station to higher values at the Holyhead station, corresponding to increases in fetch conditions. South easterly winds resulted in higher values of W at the Dublin station than the Holyhead station, corresponding to higher fetch conditions at the Dublin station. When the wind is from the north east quadrant, W was larger at the Dublin station where fetch conditions are greatest for this wind direction than at the Holyhead station which has smaller fetch. These findings that W increases with increasing fetch are in agreement with other studies (e.g. Zhao and Toba (2003) and Woolf (2005)).

The exponent of the resulting W to wind speed power law relationship for this study agrees well with that of Zhao and Toba (2001) although the coefficient is an order of magnitude less. This results in lower than expected values for W which may be due to a number of reasons. Values of W characteristically vary greatly for any given wind speed especially at low wind speeds therefore a large

Win

d Sp

eed

(m/s

) W

ind

Spee

d (m

/s)

Figure 4. (a) - (c) show whitecap coverage as a function of wind speed and wind direction at the three sampling points between Dublin and Holyhead. (d) to (f) show the percentage whitecap coverage from the Zhao and Toba(2001) model for each of the three locations.

(a) (d)

(c) (f)

(e)(b)

Win

d Sp

eed

(m/s

)


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range of values of W can be expected. In addition, the wave field is often unsteady in the coastal zone and W can be small even for relatively high wind speeds (Lafon et. al. 2004). At lower wind speeds and for a given fetch, it takes longer for the wave field to become in equilibrium with the wind than at larger wind speeds. It is also plausible that the spatial coverage for each individual estimate of W was not large enough. The footprint for each image was approximately 130 m2 which may not have been enough to adequately capture wave breaking events, which at low wind speeds can be spatially inhomogeneous and sparse. Subsequent images were taken with a much larger field of view and a forthcoming analysis may reveal an effect of image footprint area on measured values for W. We are confident, however, that the image processing method developed is robust as recent unpublished data shows a relationship between wind speed and W that closely resembles published values.

The variability of W with varying fetch has implications for a number of processes. A whitecap correction term, based on wind speed alone, is applied to the retrieval of ocean colour from satellite remote sensing which is used to obtain estimates of chlorophyll. Fetch by its definition varies most significantly in the coastal zone where phytoplankton blooms are common. Wind speed-derived values for W are also used in models for sea salt aerosol generation. Relationships between gas transfer velocities and wind speed also exhibit a large scatter but W may provide a more appropriate parameterisation. The results presented here represent the first analysis of data taken from a much larger dataset which spans 2 years of images from the Irish Sea. Follow up work is underway to try to quantify the effect of fetch on values of W which would help improve estimates of processes affected by whitecapping. Acknowledgements This project was funded through the INTERREG III A programme. I would like to thank Irish Ferries for the use of their vessel M.V. “The Ulysses”. I am pleased to acknowledge the expertise of the technicians from the workshops in both the Department of Earth and Ocean Sciences at NUI,Galway and the School of Ocean Sciences at UWB, Menai Bridge, Wales for the construction and maintenance of the waterproof camera housings. I would like to thank H. Graven for manuscript comments. References Lafon, C., Piazzola, J., Forget, P., Le Calve, O. and Despiau, S. (2004). Analysis of the variation of the

whitecap fraction as measured in a coastal zone. Boundary-Layer Meteorology. 111, 339-360. Monahan, E.C. and O’Muircheartaigh, I.G.(1980). Optimal power-law description of oceanic whitecap

coverage dependence on wind speed. J. Physical Oceanography. 10. 2094-2099. Stramska, M. and Petelski, T. (2003). Observations of oceanic whitecaps in the north polar waters of

the Atlantic. J. Geophysical Research. 108, 3086. Woolf, D.K. (2005). Parameterisation of gas transfer velocities and sea-state-dependent wave breaking.

Tellus. 57B. 87-94. Zhao, D. and Toba, Y. (2001). Dependence of whitecap coverage on wind and wind-wave properties. J.

Oceanography. 57, 603-616. Zhao, D., Toba, Y., Suzuki, Y. and Komori, S. (2003). Effect of wind waves on air-sea gas exchange:

proposal of an overall CO2 transfer velocity formula as a function of breaking wave parameter. Tellus 55 B, 478-487.

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