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Clouds and climate

Clouds and climate. Robin Hogan (with input from Anthony Illingworth, Keith Shine, Tony Slingo and Richard Allan). Overview. The importance of clouds feedbacks Feedbacks associated with specific cloud types Getting clouds right in current climate models

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Clouds and climate

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  1. Clouds and climate Robin Hogan (with input from Anthony Illingworth, Keith Shine, Tony Slingo and Richard Allan)

  2. Overview • The importance of clouds feedbacks • Feedbacks associated with specific cloud types • Getting clouds right in current climate models • Evaluation of simulated clouds (e.g. using A-train data) • Accurate radiation schemes (e.g. cloud inhomogeneity) • Tackling feedbacks and model cloud schemes • “Analogues” for global warming • Using new observations as a tight constraint on model development • Convection and high-resolution modelling

  3. Cloud feedbacks • Main uncertainty in climate prediction arises due to the different cloud feedbacks in models that are not associated with aerosols! IPCC (2007)

  4. Key cloud feedbacks • Boundary-layer clouds • Many studies show these to be most sensitive for climate • Not just stratocumulus: cumulus actually cover larger area • Properties annoyingly dependent on both large-scale divergence and small-scale details (entrainment, drizzle etc) • Mid-level and supercooled clouds • Potentially important negative feedback (Mitchell et al. 1989) but their occurrence is underestimated in nearly all models • Mid-latitude cyclones • Expect pole-ward movement of storm-track but even the sign of the associated radiative effect is uncertain (IPCC 2007) • Deep convection and cirrus • climateprediction.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)

  5. Evaluating models Observed ice water content 0.25 0.20 Vertically integrated cloud water (kg m-2) 0.15 UM ice water content 0.10 0.05 Delanoe and Hogan (2008) 90N 80 60 40 20 0 -20 -40 -60 -80 90S Latitude • A-Train can now provide this via new techniques combining the radar and lidar AMIP: massive spread in model water content - need some observations!

  6. July 2006 global IWC comparison A-Train Model • Too little spread in model • Better than AMIP comparison implied! Temperature (˚C) • Much more detailed evaluation of models (including high resolution ones) will proceed within NCEO and CASCADE… • NCAS should be involved in using these comparisons to improve the model

  7. Cloud structure in radiation schemes Fixing just overlap would increase error, fixing just inhomogeneity would over-compensate error! SW overlap and inhomogeneity biases cancel in tropical convection Main LW effect of inhomogeneity in tropical convection Main SW effect of inhomogeneity in Sc regions Fix only inhomogeneity Tripleclouds (fix both) Plane-parallel Fix only overlap TOA Shortwave CRF TOA Longwave CRF Current models: Plane-parallel Fix only overlap Fix only inhomogeneity Tripleclouds minus plane-parallel (W m-2) New Tripleclouds scheme: fix both! With help from NCAS CMS, Jon Shonk shortly to implement interactively in Met Office climate model …next step: apply Tripleclouds in Met Office climate model

  8. “Analogues” for global warming Models with most positive cloud feedback under climate change • A model that predicts cloud feedbacks should also predict their dependence with other cycles, e.g. tropical regimes • Tropical boundary-layer clouds in suppressed conditions cause greatest difference in cloud feedback • IPCC 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? Observations Other models Convective Suppressed Bony and Dufresne (2005)

  9. Mixed-phase clouds Entrainment of nucleating aerosol Radiative transfer Turbulent mixing Freezing Sublimation Key processes • Suggested approach: single column modelling over Chilbolton with different parameterizations • Evaluate against radar/lidar observations • Potentially strong negative feedback • Warmer climate  more clouds in liquid phase  more reflective& longer lifetime (Mitchell et al. 1989) • But mid-level clouds underestimated in nearly all models

  10. Further activities required • Using observations in model development • Climate models in NWP mode (or single column version forced by large-scale tendencies – preferred by Pier Siebesma) • Re-run many times with different physics and compare to single radar/lidar sites (or A-train observations for global runs) • Remove unjustified complexity (e.g. double-moment ice?) • Deep convection • Need to bite the bullet and modify the convection scheme in the light of cloud-resolving runs (e.g. CASCADE)? • Observational constraint on water vapour detrained from convection, e.g. combination of AIRS and CloudSat? • Even more tricky areas • 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?

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