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Clouds processes and climate

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Presentation on theme: "Clouds processes and climate"— Presentation transcript:

1 Clouds processes and climate
Robin Hogan Anthony Illingworth Andrew Barrett Nicky Chalmers Julien Delanoe Lee Hawkness-Smith Ewan O’Connor Kevin Pearson Nicola Pounder Jon Shonk Thorwald Stein Chris Westbrook

2 Cloud feedbacks IPCC (2007) Main uncertainty in climate prediction arises due to the different cloud feedbacks in models Very 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 models Cloud microphysics (Westbrook, Illingworth) Evaluation of simulated clouds from space (Delanoe, Pounder) Single column models (Barrett, O’Connor) Challenges Clouds feedbacks associated with specific cloud types “Analogues” for global warming

4 Cloud structure and radiation
Current models: Plane-parallel TOA Shortwave CRF TOA Longwave CRF Fix only overlap Fix only inhomogeneity New 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 effects Global effects to be calculated using a new fast method in a current NERC project

5 Evaluating models from space
80 60 40 20 -20 -40 -60 -80 90S 0.05 0.10 0.15 0.20 0.25 Latitude Vertically integrated cloud water (kg m-2) AMIP: massive spread in model water content Global evaluation of ice water content in models Variational CloudSat-Calipso retrieval (Delanoe & Hogan 2008/9) ESA+NERC funding for EarthCARE preparation Devleopment of “unified” cloud, aerosol and precipitation from radar, lidar and radiometer (Hogan, Delanoe & Pounder)

6 Ice cloud microphysics
Wilson & Ballard Fix ice density Fix density and size distribution Radar reflectivity (dBZ) Unified Model Doppler velocity (m s-1) Ice fall-speed controls how much cirrus present Radar obs reveal factor-of-two error in current Unified Model New theories for fall speed of small ice (Westbrook 2008) and large ice (Heymsfield & Westbrook 2010) Ice capacitance controls growth rate by deposition Spherical 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 various problems in clouds of seven models (Illingworth et al, BAMS 2007) US Dept of Energy “FASTER” project ( ) We are implementing Cloudnet processing at ARM sites Rapid testing of new cloud parameterizations: run many single-column models for many years with different physics Barrett PhD: similar approach to target mixed-phase clouds

8 Key cloud feedbacks Should we target the feedback problem directly?
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)

9 “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)

10 Summary and some challenges
Complex cloud fields starting to be represented for radiation Much work required to exploit new satellite observations Large errors in cloud microphysics still being found in GCMs SCM-testbed promising to develop new cloud parameterizations Challenges Observational constraints on aerosol-cloud interaction How 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?

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