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

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)

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

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

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)

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

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

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)

“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

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?