1. Robin Hogan Department of Meteorology University of Reading Cloud and Climate Studies using the Chilbolton Observatory

2. Introduction Cloud feedbacks remain the largest source of uncertainty in predicting the global warming arising from increased CO 2 (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 C 2 project, EU Cloudnet project) –Focussed field campaigns together with meteorological aircraft (e.g. CLARE98, CWVC, CSIP)

3. Cloud observations at Chilbolton 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

4. Basics of radar and lidar Radar/lidar ratio provides information on particle size Detects cloud base Penetrates ice cloud Strong echo from liquid clouds Detects cloud top Radar: Z~D 6 Sensitive to large particles (ice, drizzle) Lidar: ~D 2 Sensitive to small particles (droplets, aerosol)

5. Cloud radar Cloud lidar Target 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 Liquid Rain Aerosol Insects

6. Observations Cloud fraction comparison for a month Met Office Mesoscale Model ECMWF Global Model Meteo-France ARPEGE Model Swedish RCA model

7. Evaluation of 7 forecast models 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

8. Liquid water content LWC derived using the scaled adiabatic method –Lidar and radar provide cloud boundaries, adiabatic LWC profile then scaled to match liquid water path from microwave radiometers –Met Office mesoscale tends to underestimate supercooled water occurrence –ECMWF has far too great an occurrence of low LWC values –KNMI RACMO identical to ECMWF: same physics package! 0-3 km

9. Cloud 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

10. Cloud overlap: global impact Chilbolton 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

11. Mixed-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

12. Supercooled water occurrence 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

13. Mixed-phase clouds Physics very uncertain Represented very crudely in models Layers detected during CLARE98 experiment: Highly reflective to lidar optically thick Low depolarisation spherical particles Invisible to radar very small particles In situ confirmation of liquid water droplets Lidar depolarisation (from aircraft above) Lidar backscatter (from aircraft above) Radar reflectivity C-130 liquid water (-7ºC)

14. We use radar and lidar to derive profiles of IWC and effective radius, used in radiation calculations –Supercooled water most significant in short-wave –Can reduce net absorbed radiation by more than 100 Wm -2 –In daylight, usually more important than any ice present Radiative effects of ice and liquid Hogan et al. (QJ 2003a) Liquid water layer

15. The 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)