Clouds processes and climate

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

2. Cloud feedbacks • 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... IPCC (2007)

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. 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) 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! • 3D radiative effects • Global effects to be calculated using a new fast method in a current NERC project

5. Evaluating models from space 0.25 0.20 Vertically integrated cloud water (kg m-2) 0.15 0.10 0.05 90N 80 60 40 20 0 -20 -40 -60 -80 90S Latitude • 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) AMIP: massive spread in model water content

6. Ice cloud microphysics Wilson & Ballard Fix density and size distribution Fix ice density • 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”... Radar reflectivity (dBZ) Unified Model Doppler velocity (m s-1)

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 (2009-2014) • 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 • 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)

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 • Summary • 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?