Robin HoganDepartment of Meteorology

University of Reading

Cloud and Climate StudiesCloud and Climate Studies using the Chilbolton Observatoryusing the Chilbolton Observatory

IntroductionIntroduction• Cloud feedbacks remain the largest source of uncertainty in

predicting the global warming arising from increased CO2 (IPCC 2007)– Better observations of clouds are needed to tackle this

problem

• More than a decade of observations at Chilbolton have been used to– Directly evaluate cloud representation in weather & climate

models– Improve understanding of physical processes in clouds– Develop algorithms for spaceborne radar (CloudSat and

EarthCARE)

• This has involved the combination of– Near-continuous vertically pointing radar and lidar

observations (e.g. ESA C2 project, EU Cloudnet project)– Focussed field campaigns together with meteorological aircraft

(e.g. CLARE’98, CWVC, CSIP)

Cloud observations at Cloud observations at ChilboltonChilbolton

• Cloud radars– 35-GHz since 1994 (Rabelais then

Copernicus)– 94-GHz since 1996 (Galileo)– Can also use 3-GHz CAMRa for clouds

• Cloud lidars– 905-nm since 1996 (CT75K)– 1.5-m Doppler lidar since 2006 (HALO)– 355-nm RAMAN and polarization lidars

…plus many other passive instruments! – Chilbolton has led the way in methods to

combine instruments at different wavelengths to retrieve cloud properties

Cloud radar

Cloud lidar

Target classificationTarget classification• First task: use different radar and lidar sensitivities to identify

different types of clouds and other atmospheric targets• From this we can estimate cloud fraction and other model variables

Ice

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Aeros

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Observations

Cloud fraction comparison for a Cloud fraction comparison for a monthmonth

Met Office

Mesoscale Model

ECMWF

Global Model

Meteo-France

ARPEGE Model

Swedish RCA model

Evaluation of 7 forecast Evaluation of 7 forecast modelsmodels

• Cloud fraction and ice water content for 2004

Bulletin of the American Meteorology Society, in press

Good news: ECMWF and Met Office ice water contents are within observational errors at all heights

Bad news: all models except DWD underestimate mid-level cloud fraction, and there is a wide range of low-cloud amounts

Cloud overlapCloud overlap

• Cloud fraction and water content alone is not enough: climate models need to know how clouds overlap

Most models assume “maximum-random” overlap

Radar observations show that in reality overlap is more random:

total cloud cover is higher for the same cloud fraction profile

Warm front observed at Chilbolton

Cloud overlap: global impactCloud overlap: global impactChilbolton overlap retrievals were tested in the ECMWF model: effect on radiation budget is significant, particularly in the tropics

ECMWF model run by Jean-Jacques Morcrette

Difference in outgoing infrared radiation between “maximum-random” overlap and new approach~5 Wm-2

globally

Mixed-phase cloudsMixed-phase clouds• Clouds containing a mixture of super-cooled liquid droplets and

ice particles are a major headache in climate prediction:– In a warmer atmosphere these clouds are more likely to be liquid,

making them more reflective and longer lasting, a negative feedback

• Chilbolton can identify them using lidar and radar – Liquid droplets are much smaller and much more numerous than

ice, so are much more reflective to lidar than to radar

Small supercooled liquid droplets

Large falling ice particles

Small supercooled liquid droplets

Large falling ice particles

35-GHz radar

905-nm lidar

Supercooled water Supercooled water occurrenceoccurrence

• Chilbolton lidar was used to estimate occurrence of supercooled water over a 1-year period– 15% of mid-level ice clouds

contain significant liquid water, decreasing with temperature

– Similar results were obtained from a lidar in space

– Radiative transfer calculations reveal that the liquid water interacts much more strongly with solar and infrared radiation than ice, so it is crucial to get the phase right

• These results are informing the development of models, which poorly represent this behaviour

ECMWF model

Met Office model

The futureThe future• Information for high-resolution models

– Both forecast and climate models are becoming more sophisticated in their representation of clouds… but not necessarily more accurate!

– Use Chilbolton to evaluate model representation of turbulence intensity, cloud particle fall speeds, cloud variability etc.

– Cloud processes need to be understood in more detail, e.g. the interaction of aerosols with clouds (NERC APPRAISE project)

– Assimilation of cloud radar data into forecast models?

• Exciting new technology for cloud observations– E.g. development of the first “cheap”, continuously operating Doppler

lidar for cloud and boundary-layer studies, now at Chilbolton

• Spaceborne cloud radar and lidar– Algorithms developed at Chilbolton will be used by the CloudSat and

Calipso satellites (launched a year ago)– Chilbolton observations have been used to build the science case for

the ESA “EarthCARE” satellite (to be launched in the next 5 years)