Presentation on theme: "Clouds processes and climate"— Presentation transcript:
1 Clouds processes and climate Robin HoganAnthony IllingworthAndrew BarrettNicky ChalmersJulien DelanoeLee Hawkness-SmithEwan O’ConnorKevin PearsonNicola PounderJon ShonkThorwald SteinChris Westbrook
2 Cloud feedbacksIPCC (2007)Main uncertainty in climate prediction arises due to the different cloud feedbacks in modelsVery difficult to resolve: is NERC funding any research on this precise problem at the moment?Starting point is to get the right cloud radiative forcing in the current climate...
3 Overview Radiative transfer and clouds Cloud inhomogeneity, overlap and 3D radiation (Shonk, Hogan)Evaluating and improving clouds in modelsCloud microphysics (Westbrook, Illingworth)Evaluation of simulated clouds from space (Delanoe, Pounder)Single column models (Barrett, O’Connor)ChallengesClouds feedbacks associated with specific cloud types“Analogues” for global warming
4 Cloud structure and radiation Current models:Plane-parallelTOA Shortwave CRFTOA Longwave CRFFix only overlapFix only inhomogeneityNew Tripleclouds scheme: fix both!What is radiative effect of cloud structure?Fast method for GCMs (Shonk & Hogan 2008)Global effects (Shonk & Hogan 2009)Interaction in climate model (nearly completed)3D radiative effectsGlobal effects to be calculated using a new fast method in a current NERC project
5 Evaluating models from space 80604020-20-40-60-8090S0.050.100.150.200.25LatitudeVertically integrated cloud water (kg m-2)AMIP: massive spread in model water contentGlobal evaluation of ice water content in modelsVariational CloudSat-Calipso retrieval (Delanoe & Hogan 2008/9)ESA+NERC funding for EarthCARE preparationDevleopment of “unified” cloud, aerosol and precipitation from radar, lidar and radiometer (Hogan, Delanoe & Pounder)
6 Ice cloud microphysics Wilson & BallardFix ice densityFix density and size distributionRadar reflectivity (dBZ)UnifiedModelDoppler velocity (m s-1)Ice fall-speed controls how much cirrus presentRadar obs reveal factor-of-two error in current Unified ModelNew theories for fall speed of small ice (Westbrook 2008) and large ice (Heymsfield & Westbrook 2010)Ice capacitance controls growth rate by depositionSpherical assumption used by all current models overestimates growth rate by almost a factor of two (Westbrook et al 2008)Ongoing work in “APPRAISE-CLOUDS”...
7 NWP and SCM testbeds Cloudnet project NWP model evaluation from ground-based radar & lidar revealed variousproblems in clouds of seven models(Illingworth et al, BAMS 2007)US Dept of Energy “FASTER” project ( )We are implementing Cloudnet processing at ARM sitesRapid testing of new cloud parameterizations: run many single-column models for many years with different physicsBarrett PhD: similar approach to target mixed-phase clouds
8 Key cloud feedbacks Should we target the feedback problem directly? Boundary-layer cloudsMany studies show these to be most sensitive for climateNot just stratocumulus: cumulus actually cover larger areaProperties annoyingly dependent on both large-scale divergence and small-scale details (entrainment, drizzle etc)Mid-level and supercooled cloudsPotentially important negative feedback (Mitchell et al. 1989) but their occurrence is underestimated in nearly all modelsMid-latitude cyclonesExpect pole-ward movement of storm-track but even the sign of the associated radiative effect is uncertain (IPCC 2007)Deep convection and cirrusclimateprediction.net showed that convective detrainment is a key uncertainty: lower values lead to more moisture transport and a greater water vapour feedback (Sanderson et al. 2007)But some ensemble members unphysical (Rodwell & Palmer ‘07)
9 “Analogues” for global warming Models with most positive cloud feedback under climate changeA model that predicts cloud feedbacks should also predict their dependence with other cycles, e.g. tropical regimesTropical boundary-layer clouds in suppressed conditions cause greatest difference in cloud feedbackIPCC models with a positive cloud feedback best match observed change to BL clouds with increased T (Bony & Dufresne 2005)Apply to other cycles (seasonal, diurnal, ENSO phase…)?Can we use such analysis to find out why BL clouds better represented?Novel compositing methods?Can we “throw out” bad models?ObservationsOther modelsConvectiveSuppressedBony and Dufresne (2005)
10 Summary and some challenges Complex cloud fields starting to be represented for radiationMuch work required to exploit new satellite observationsLarge errors in cloud microphysics still being found in GCMsSCM-testbed promising to develop new cloud parameterizationsChallengesObservational constraints on aerosol-cloud interactionHow can we improve convection parameterization based on high-resolution simulations and new observations?Observational constraint on water vapour detrained from convection, e.g. combination of AIRS and CloudSat?Is there any hope of getting a reliable long-term cloud signal from historic datasets (e.g. satellites)?How do we get cloud feedback due to storm-track movement?Coupling of clouds to surface changes, e.g. in the Arctic?