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

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Presentation on theme: "Robin Hogan (with input from Anthony Illingworth, Keith Shine, Tony Slingo and Richard Allan) Clouds and climate."— Presentation transcript:

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

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 – 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 AMIP: massive spread in model water content - need some observations! 90N806040200-20-40-60-8090S 0.05 0.10 0.15 0.20 0.25 Latitude Vertically integrated cloud water (kg m -2 ) Delanoe and Hogan (2008) Observed ice water content UM ice water content A-Train can now provide this via new techniques combining the radar and lidar

6 July 2006 global IWC comparison Too little spread in model Better than AMIP comparison implied! Temperature (˚C) A-TrainModel 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 Fix only inhomogeneity Tripleclouds (fix both) Plane-parallel Fix only overlap TOA Shortwave CRFTOA Longwave CRF Tripleclouds minus plane-parallel (W m -2 ) Main SW effect of inhomogeneity in Sc regions Fixing just overlap would increase error, fixing just inhomogeneity would over- compensate error! Main LW effect of inhomogeneity in tropical convection SW overlap and inhomogeneity biases cancel in tropical convection …next step: apply Tripleclouds in Met Office climate model Current models: Plane-parallel Fix only overlap Fix only inhomogeneity New Tripleclouds scheme: fix both! With help from NCAS CMS, Jon Shonk shortly to implement interactively in Met Office climate model

8 Analogues for global warming 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? ConvectiveSuppressed Bony and Dufresne (2005) Models with most positive cloud feedback under climate change Other models Observations

9 Mixed-phase clouds 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 Suggested approach: single column modelling over Chilbolton with different parameterizations Evaluate against radar/lidar observations Radiative transfer Turbulent mixing Freezing Sublimation Entrainment of nucleating aerosol Key processes

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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