Transcript Document
Cloud and Climate Studies using the Chilbolton Observatory Robin Hogan Department of Meteorology University of Reading Introduction • 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 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 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 Cloud radar Cloud lidar Cloud fraction comparison for a month Observations Met Office Mesoscale Model ECMWF Global Model Meteo-France ARPEGE Model Swedish RCA model Evaluation of 7 forecast models • Cloud fraction and ice water content for 2004 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 Bulletin of the American Meteorology Society, in press Most models assume “maximumrandom” overlap Cloud overlap • Cloud fraction and water content alone is not enough: climate models need to know how clouds overlap Warm front observed at Chilbolton Radar observations show that in reality overlap is more random: total cloud cover is higher for the same cloud fraction profile Cloud overlap: global impact Chilbolton overlap retrievals were tested in the ECMWF model: effect on radiation budget is significant, particularly in the tropics Difference in outgoing infrared radiation between “maximumrandom” overlap and new approach ~5 Wm-2 globally ECMWF model run by Jean-Jacques Morcrette 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 35-GHz radar Small supercooled liquid droplets Large falling ice particles 905-nm lidar Small supercooled liquid droplets Large falling ice particles Supercooled water occurrence • Chilbolton lidar was used to estimate occurrence of supercooled water over a 1year 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 ECMWF model Met Office model • These results are informing the development of models, which poorly represent this behaviour 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)