robin hogan anthony illingworth andrew barrett nicky chalmers julien delanoe lee hawkness-smith...
TRANSCRIPT
![Page 1: Robin Hogan Anthony Illingworth Andrew Barrett Nicky Chalmers Julien Delanoe Lee Hawkness-Smith Clouds processes and climate Ewan OConnor Kevin Pearson](https://reader036.vdocuments.net/reader036/viewer/2022062417/55160cda55034694308b5148/html5/thumbnails/1.jpg)
Robin HoganRobin Hogan
Anthony IllingworthAnthony Illingworth
Andrew BarrettAndrew Barrett
Nicky Chalmers Nicky Chalmers
Julien DelanoeJulien Delanoe
Lee Hawkness-Smith Lee Hawkness-Smith
Clouds processes Clouds processes and climateand climate
Ewan O’Connor Ewan O’Connor
Kevin PearsonKevin Pearson
Nicola PounderNicola Pounder
Jon ShonkJon Shonk
Thorwald SteinThorwald Stein
Chris WestbrookChris Westbrook
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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)
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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
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Cloud structure and radiationTOA Shortwave CRF TOA Longwave CRF
Current models:Plane-parallel
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
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Evaluating models from
spaceAMIP: massive spread in model water content
90N 80 60 40 20 0 -20 -40 -60 -8090S
0.05
0.10
0.15
0.20
0.25
Latitude
Ver
tical
ly in
tegr
ated
cl
oud
wat
er (
kg m
-2)
• 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)
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Ice cloud microphysics
• 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”...
Rad
ar re
flect
ivity
(dB
Z)
Doppler velocity (m s-1)
Wilson & Ballard Fix ice density Fix density and size distribution
UnifiedModel
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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 (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
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Key cloud feedbacksShould 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)
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“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?
Convective Suppressed
Bony and Dufresne (2005)
Models with most positive cloud
feedback under climate change
Other models
Observations
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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?