Robin Hogan

1
Clouds processes and climate

• Robin Hogan
• Anthony Illingworth
• Andrew Barrett
• Nicky Chalmers
• Julien Delanoe
• Lee Hawkness-Smith

Ewan OConnor 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, OConnor)
• 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)

5
Evaluating models from space
AMIP massive spread in model water content

• Global evaluation of ice water content in models
• Variational CloudSat-Calipso retrieval (Delanoe
  Hogan 2008/9)
• ESANERC 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 density and size distribution
Fix ice density
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 (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?