surface wind-wave climate of the pacific region: variability, trends and future projections

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A Partnership between the Bureau of Meteorology and CSIRO

The Centre for Australian Weather and Climate Research:

Surface wind-wave climate of the Pacific region: Variability, trends and future projections

Mark Hemer, Jack Katzfey and Galina Kelareva

SOPAC STAR Conference 2010

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Talk outline

• Wind-waves in the climate context

• Project aims

• Phase 1: Climate drivers of historical wave climate variability

• Phase 2: Wave climate projections under future scenarios

• Summary


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Coastal impacts and climate change

Warming Atmosphere and Oceans

Sea-level rise(0.2 – 0.8 m by 2100, IPCC AR4)

Changes to weather systems and storms

CHANGING RISK OF COASTAL IMPACTS

Chapter 6: Coastal systems and low-lying areas.

6.8 Key uncertainties, research gaps and priorities

[On climate change impact assessments in the coastal zone]…There also remains a strong focus on sea-level rise, which needs to be broadened to include all the climate drivers in the coastal zone (Table 6.2).

Nicholls, R.J. et al. (2007) Coastal systems and low-lying areas. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Parry, M.L. et al. (eds.)]. Cambridge University Press, Cambridge, Uniited Kingdon and New York, NY, USA.

IPCC AR4 (WG-2)


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.auINUNDATION: wind-wave setup is the dominant

contributor to coastal flooding eventsEROSION: wind-waves drive coastal sediment budgets. A shift in direction may lead to erosion.

LAGOON CIRCULATION: wind-wave setup drives lagoon circulation. Changing wave conditions may influence flushing.

Wind-wave influences on the Pacific Islands and Territories

DISTURBANCE: Marine habitats are characterised by the wave climate (energy) at that site.

Steep slope grid

30 56 wind speed (m/s)

Shallow slope grid


Sto

rm s

urg

e (

m)


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Project Aims:

Phase 1:

Make use of existing available wave data in the Pacific basin, characterise mean seasonal conditions, the historical inter-annual

variability and/or trends, and the key climatological drivers of variability in the present day wave climate.

Phase 2: Develop wave climate projections under future climate scenarios, for the Pacific basin, consideration of near-term future (2026-2045) and

end of century (2070-2099).


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Phase 1 (Available data)

Satellite altimeter data(8 Missions)

Waverider buoy data (NDBC and SOPAC)

Global wave reanalysesECMWF ERA-40

ERA-Interim

Global wave models CSIRO (Phase 2 of project)


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Phase 1: Preliminary Results (ERA-Interim: 1989-2009)

Mean Annual Significant Wave Height (m)

High latitude storm belt

High latitude storm belt


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Correlation Coefficient MapEFC-ERA40 components vs SOI (All monthly means)

-0.5 0 0.5Pearson’s correlation coefficient, R.

Bounded regions indicate significant correlation at 95% confidence level.

EF = E.cg = F (Hs, Tm, Dm), EF is a vector (eastwards component, EFu, northwards component EFv)

Hemer et al. (2010)


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Comparisons of HS trends in Satellite Era.

-0.05 0 0.05

HS Trend (m/yr)

Ongoing work: Test robustness of these results using other available datasets,

with the focus being on the full Pacific basin


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Phase 2: Wave climate projections

IPCC AR4 (WG1) Box 11.5: Coastal Zone Climate Change

Introduction

…. There is insufficient information on changes in waves or near-coastal currents to provide an assessment of the effects of climate change on erosion.

Christensen, J.H. et al. (2007) Regional Climate Projections. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S. et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.


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Map of current regional projections

Global projections: Wang & Swail, 2006 Mori et al., 2009


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Regional projections (methodology)

GCM1Scenario A

GCM2Scenario A

GCM1Scenario B

GCM2Scenario B

Regional Climate Model

RCM1Scenario A

RCM2Scenario A

RCM1Scenario B

RCM2Scenario B

Near surface winds force wave modelTypically for time slices (present, future)

Wave1Scenario A

Wave2Scenario A

Wave1Scenario B

Wave2Scenario B

Ensemble mean wave projectionScenario A

Ensemble mean wave projectionScenario B

Subset of CMIP outputs


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Dynamical downscaling (PCCSP) and wave projection methodology

Bias correctedSST-only

GCM

Global 60 km

Using multiple global climate models (GCMs) to capture uncertainty of future climate change. (SRES A2 scenario)

1. CSIRO Mk3.5 4. ECHAM5

2. MIROC 5. HadCM3

3. GFDLcm2.0 6. GFDLcm2.1

The method used is:

1. Correct sea surface temperature biases from global climate models (GCMs)

2. Downscale to 60 km resolution (CSIRO CCAM model)

3. Use 60km resolution surface winds to force global 1 degree wave model

Surface Winds and Sea-Ice only


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Global wave model

H

S (

m)

CCAMECHAM5 – ERA-Interim: 10yr mean

WAVE MODELLING

Global 1 degree wave model

WaveWatch III (v3.14, default configuration)

Forced with global CCAM winds• 1st run. CCAMECHAM5 SRES A2

Two time-slices • Present – 1979-2009

• Future - 2080-2099

Mean Hs (m)


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Phase 2: ongoing work

Validation of climate model forced wave climate for present time-slice

Repeat runs with other climate model forcing (assess uncertainty)

Aiming for follow-on project to provide detailed coastal assessments for specific islands. PCCSP climate model downscaling to 8km at selected PICTs. Use these projections to generate high res wave projections


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IPCC AR4 projections

Figure 3.2. Surface warming for SRES scenarios. Best estimates, and 2090-2099 likely rangesIPCC AR4 (2007) Synthesis Report

Likely ranges include uncertainties between: - climate models (multi-model ensembles), and- model versions (perturbed physics ensembles)


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Existing Global projectionsWang & Swail, 2006

Statistical Hs projn.Ensemble mean: CGCM2 (3PPE); HADCM3 (1PPE); ECHAM4/OPYC3 (1PPE)

SRES A2 scenario

2080-1990 diff.

Mori et al., 2009

Dynamical (SWAN) Hs projn.20km MRI/JMA AGCM IPCC AR4 (CMIP3) ensemble mean SST as BBforcing

SRES A1B scenario

2075-2099 mean – 1979-2003 mean diff

0.2

0.12

0.04 m-0.04

-0.12

-0.2

(m)

Available Global Wave Projections:


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SRES Scenario

Climate Modelling Centre A

Climate Modelling Centre B

Climate Modelling Centre C

…

PPE1 PPE2 PPE3 PPE1 PPE2 PPE1 PPE2 PPE3 …

Emission Scenario

Multi-ModelEnsembles

Perturbed Physics Ensembles

Wave Projection Ensembles

Statistical wave projection

Dynamical wave projection

Wave Modelling Group A (model1)

Wave Modelling Group B (model2)

??

e.g., 1. Raw or corrected forcing/covariate,2. Perturbed physics in dynamic wave model, …

?? ??

Towards a coordinated approach to global wave projections (Hemer et al., 2010)

WCRP/JCOMM workshop on coordinated wave climate projections (April 2011, Geneva)http://www.jcomm.info/cowclip


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Summary

Important to understand climatological influence on wind-waves for Pacific Island coastal impact assessments

Phase 1 of project ongoing investigating key climatological drivers of historical wave climate variability

Phase 2 of project ongoing projecting wave climate for the Pacific basin, with the framework of internationally coordinated global wave climate projections.


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Thankyou

Dr. Mark Hemer

[email protected]

A Partnership between the Bureau of Meteorology and CSIROThe Centre for Australian Weather and Climate Research:


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IPCC AR4 (WG-2)

Nicholls, R.J. et al. (2007) Coastal systems and low-lying areas. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Parry, M.L. et al. (eds.)]. Cambridge University Press, Cambridge, Uniited Kingdon and New York, NY, USA.

Chapter 6: Coastal systems and low-lying areas.

6.8 Key uncertainties, research gaps and priorities

[On climate change impact assessments in the coastal zone]…There also remains a strong focus on sea-level rise, which needs to be broadened to include all the climate drivers in the coastal zone (Table 6.2).


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Dominant contribution to coastal inundation on Pacific Islands is from wave setup

Wave driven inundation event, Cyclone Meena, Rarotonga, Feb 2005

Potential Impacts: FloodingInfrastructure damagefreshwater contamination, etc.


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Steep slope grid


Shallow slope gridSteep slope grid


Shallow slope grid

Steep slope grid


Shallow slope grid



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Waves are a key driver of geomorphological response

An Island with sandy beaches: No waves


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Waves are a key driver of geomorphological response (erosion and accretion) of islands

An Island with sandy beaches: Southerly waves

Wave front Wave front


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Waves are a key driver of geomorphological response (erosion and accretion) of islands

An Island with sandy beaches: Wave climate rotates to South-Westerly

Wave front

Wave front

Erosion

Accretion

Potential impacts: loss of useable land


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Disturbance/Sediment supply

Marine habitats are characterised by the energy of the site. Wave climate characterises large scale reef

morphology, species distributions and nutrient uptake.


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.au Waves drive circulation within reef lagoons. Changed

conditions may alter flushing times, water quality and sand budgets.

From Lowe et al., 2009


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Phase 1: Available Data

Dataset Variables Time-span Resolution

Reanalyses:

ERA-40

(Uppala et al. 2005)

Hs, Tm, Dm 1957-2002 6-hr, 2.5° lat-lon

C-ERA-40 (Sterl and Caires, 2005)

Hs 1957-2002 6-hr, 1.5° lat-lon

ERA-Interim (ECMWF)

Hs, Tm, Dm 1989-2010 6-hr, 1.5° lat-lon

Buoy records:

US NDBC Hs, Tm, Tp (Dm, Dp) 1973-2010 Hourly, 5 buoys

SOPAC Hs, Tm, Tp XXXX-XXXX Hourly, X buoys

Satellite Altimeter

8 Missions

Hs 1985-2010 Variable

Global Wave Models

CSIRO (Phase II)

+ potential others

Hs, Tm, Dm 1979-2009 6-hr, 1° lat-lon


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Directional Distribution (ERA-40)

100

50 %

0


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Directional Distribution Trends (ERA-40: 1957-2002)

1

0 %/yr

-1


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Map of current regional projections

Global projections: Wang & Swail, 2006 Mori et al., 2009

Maldives

Marshall Islands

Tuvalu

Bahamas, Belize Cayman Islands, Turk and Caicos

Islands

Guyana, Suriname

Netherlands

Bangladesh

Djibouti

Egypt Vietnam

Gambia

Countries with highest share of population within Low Elevation Coastal Zone (all countries)

Countries with most population within Low Elevation Coastal Zone, McGranahan et al. (2007)

China, Japan

USA

India

Indonesia

Thailand

Philippines


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8 Km region selection criteria

Region Criterion 1 Criterion 2 Criterion 3 Criterion 4 Likely

impact Climate regime

Mountains Population

PNG high monsoon many 6,732,000 East Timor moderate monsoon many 1,134,000

Fiji moderate SPCZ many 849,000 Solomon Is. moderate SPCZ some 523,000

Vanuatu moderate SPCZ some 240,000 Samoa moderate SPCZ some 179,000 FSM low ITCZ few 111,000

Tonga low SPCZ few 104,000 Kiribati low ITCZ few 98,000

Marshall Is. low ITCZ few 62,000 Palau low monsoon few 20,000

Cook Is. low SPCZ few 20,000 Nauru low ITCZ few 10,000 Tuvalu low SPCZ few 10,000 Niue low SPCZ few 1,500


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8 Km domains

8 km domains in red Extra DDS domain in green

surface wind-wave climate of the pacific region: variability, trends and future projections

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