© Crown copyright Met Office Evaluation of cloud regimes in climate models Keith Williams and Mark Webb (A quantitative performance assessment of cloud.

Slides:

Advertisements

Similar presentations

Met Office Hadley Centre, FitzRoy Road, Exeter, Devon, EX1 3PB United Kingdom Tel: +44 (0) Fax: +44 (0)

Advertisements

Page 1© Crown copyright 2007 Initial tendencies of cloud regimes in the Met Office Unified Model Keith Williams and Malcolm Brooks Met Office, Hadley Centre.

© Crown copyright Met Office Towards understanding the mechanisms responsible for different cloud-climate responses in GCMs. Mark Webb, Adrian Lock (Met.

The Cloud Feedback Model Intercomparison Project Plans for CFMIP-2

Page 1© Crown copyright 2007 Constraining the range of climate sensitivity through the diagnosis of cloud regimes Keith Williams 1 and George Tselioudis.

Robin Hogan, Julien Delanoë, Nicky Chalmers, Thorwald Stein, Anthony Illingworth University of Reading Evaluating and improving the representation of clouds.

Clouds processes and climate

1 Evaluating climate model using observations of tropical radiation and water budgets Richard P. Allan, Mark A. Ringer Met Office, Hadley Centre for Climate.

Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

1 Using Reanalyses in combination with Earth Radiation Budget data to evaluate climate model simulated cloud radiative properties Richard P. Allan, Tony.

Robin Hogan (with input from Anthony Illingworth, Keith Shine, Tony Slingo and Richard Allan) Clouds and climate.

GERB/CERES Meeting 2006 Exeter Evaluation of clouds and radiation in the Met Office global forecast model using GERB/SEVIRI data Richard Allan, Tony Slingo.

The limited contribution of model uncertainty to the uncertainty in observationally-constrained estimates of anthropogenic warming Nathan Gillett, Canadian.

Evening Discussion: Toward a better understanding of PBL cloud feedbacks on climate sensitivity Some introductory material Chris Bretherton University.

© Crown copyright Met Office Atmospheric Blocking and Mean Biases in Climate Models Adam Scaife, Tim Hinton, Tim Woollings, Jeff Knight, Srah Keeley, Gill.

The importance of clouds. The Global Climate System

© Crown copyright Met Office Using stability composites to analyse cloud feedbacks in the CMIP3/CFMIP-1 slab models. Mark Webb (Met Office) CFMIP-GCSS.

Towards stability metrics for cloud cover variation under climate change Rob Wood, Chris Bretherton, Matt Wyant, Peter Blossey University of Washington.

Cloud Biases in CMIP5 using MISR and ISCCP simulators B. Hillman*, R. Marchand*, A. Bodas-Salcedo, J. Cole, J.-C. Golaz, and J. E. Kay *University of Washington,

TEMPLATE DESIGN © Total Amount Altitude Optical Depth Longwave High Clouds Shortwave High Clouds Shortwave Low Clouds.

© Crown copyright Met Office Climate Projections for West Africa Andrew Hartley, Met Office: PARCC national workshop on climate information and species.

© Crown copyright Met Office CFMIP-2 techniques for understanding cloud feedbacks in climate models. Mark Webb (Met Office Hadley Centre) CFMIP/GCSS BLWG.

High Resolution Climate Modelling in NERC (and the Met Office) Len Shaffrey, University of Reading Thanks to: Pier Luigi Vidale, Jane Strachan, Dave Stevens,

CAUSES (Clouds Above the United States and Errors at the Surface) "A new project with an observationally-based focus, which evaluates the role of clouds,

Evaluation of CMIP5 Simulated Clouds and TOA Radiation Budgets in the SMLs Using NASA Satellite Observations Erica K. Dolinar Xiquan Dong and Baike Xi.

© Crown copyright Met Office Providing High-Resolution Regional Climates for Vulnerability Assessment and Adaptation Planning Joseph Intsiful, African.

Synoptic variability of cloud and TOA radiative flux diurnal cycles Patrick Taylor NASA Langley Research Center Climate Science Branch

Dataset Development within the Surface Processes Group David I. Berry and Elizabeth C. Kent.

Clouds in the Southern midlatitude oceans Catherine Naud and Yonghua Chen (Columbia Univ) Anthony Del Genio (NASA-GISS)

MJO simulations under a dry environment Marcela Ulate M Advisor: Chidong Zhang (… in a Nudging World)

Arctic Cloud Biases in CCSM3 Steve Vavrus Center for Climatic Research University of Wisconsin.

The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Southern Ocean cloud biases in ACCESS.

Aerosol-Cloud Interactions and Radiative Forcing: Modeling and Observations Graham Feingold 1, K. S. Schmidt 2, H. Jiang 3, P. Zuidema 4, H. Xue 5, P.

TESTING THE REALISM OF THE MMF (or any GCM) REPRESENTATION OF THE MJO William B. Rossow Eric Tromeur City College of New York CMMAP Meeting July.

C LOUD R ADIATIVE K ERNELS : C LOUDS IN W/m 2 with T. Andrews, J. Boyle, A. Dessler, P. Forster, P. Gleckler, J. Gregory, D. Hartmann, S. Klein, R. Pincus,

Atmospheric Hydrological Cycle in the Tropics in Twentieth Century Coupled Climate Simulations Hailan Wang and William Lau Laboratory for Atmospheres,

1 Hadley Centre Using Earth radiation budget data for climate model evaluation Mark Ringer Hadley Centre, Met Office, UK GIST 19: Aug 27-29, 2003.

Seasonal Cycle of Climate Feedbacks in the NCAR CCSM3.0 Patrick Taylor CLARREO Science Team Meeting July 7, 2010 Patrick Taylor CLARREO Science Team Meeting.

Simple tropical models and their relationship to GCMs Adam Sobel, Columbia Chris Bretherton, U. Washington.

Yuying Zhang, Jim Boyle, and Steve Klein Program for Climate Model Diagnosis and Intercomparison Lawrence Livermore National Laboratory Jay Mace University.

Met Office GPCI simulations Adrian Lock. © Crown copyright UK Met Office simulations in GPCI  HadGAM1 climate – for IPCC AR4  38 levels (~300m at 1km),

Thomas Ackerman Roger Marchand University of Washington.

Observed & Simulated Profiles of Cloud Occurrence by Atmospheric State A Comparison of Observed Profiles of Cloud Occurrence with Multiscale Modeling Framework.

Ocean Surface heat fluxes Lisan Yu and Robert Weller

Cloud-climate feedbacks: what we think we know and why we think we know it David Mansbach 14 April 2006 T 1

Climatic implications of changes in O 3 Loretta J. Mickley, Daniel J. Jacob Harvard University David Rind Goddard Institute for Space Studies How well.

Evaluation of radiative properties of low and high clouds in different regimes using satellite measurements Bing Lin 1, Pat Minnis 1, and Tai-Fang Fan.

Lazaros Oreopoulos (NASA-GSFC)

Eric Tromeur and William B. Rossow NOAA/CREST at the City College of New York Interaction of Tropical Deep Convection with the Large-Scale Circulation.

Stratocumulus-topped Boundary Layer

RT5, WP5.2 : Evaluation of processes and phenomena Objectives : Analyse the capability of the models to reproduce and predict the major modes of variations.

Downscaling Global Climate Model Forecasts by Using Neural Networks Mark Bailey, Becca Latto, Dr. Nabin Malakar, Dr. Barry Gross, Pedro Placido The City.

The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Southern Ocean Clouds Characterization.

Sea Ice, Solar Radiation, and SH High-latitude Climate Sensitivity Alex Hall UCLA Department of Atmospheric and Oceanic Sciences SOWG meeting January 13-14,

Cloud fraction in midlatitude cyclones: observations vs models Catherine Naud, Columbia University Collaborators: James Booth (Columbia), Tony Del Genio.

© Crown copyright Met Office Southern Ocean surface flux biases in GCMs Keith Williams, Alejandro Bodas-Salcedo & Patrick Hyder SOCRATES workshop 18/03/14.

Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

The Solar Radiation Budget, and High-latitude Climate Sensitivity Alex Hall UCLA Department of Atmospheric and Oceanic Sciences University of Arizona October.

The Properties of Cirrus over the Atlantic Basin Revealed by A-Train and SEVIRI data Betsy Dupont and Jay Mace, University of Utah Pat Minnis and Rabi.

Important data of cloud properties for assessing the response of GCM clouds in climate change simulations Yoko Tsushima JAMSTEC/Frontier Research Center.

Cloud, radiation, and precipitation changes with dynamic regime: An observational analysis and model evaluation study PI: George Tselioudis Co-PI: Chris.

LIMITLESS POTENTIAL | LIMITLESS OPPORTUNITIES | LIMITLESS IMPACT Copyright University of Reading CURRENT CHANGES IN EARTH’S ENERGY IMBALANCE

© Crown copyright Met Office From the aircraft to climate prediction Anthony Baran, Met Office, Exeter, UK. OBR conference, December 2012.

Issues surrounding NH high- latitude climate change Alex Hall UCLA Department of Atmospheric and Oceanic Sciences.

Using satellite simulators for cloud evaluation

Project Title: The Sensitivity of the Global Water and Energy Cycles:

Surface Fluxes and Model Error An introduction

Richard Allan Department of Meteorology, University of Reading 1

CURRENT Energy Budget Changes

Climatic implications of changes in O3

Presentation transcript:

© Crown copyright Met Office Evaluation of cloud regimes in climate models Keith Williams and Mark Webb (A quantitative performance assessment of cloud regimes in climate models, Clim. Dyn., In press) ISCCP Symposium, New York, 25/07/08

© Crown copyright Met Office ISCCP tropical cloud regimes (Rossow et al., 2005)

© Crown copyright Met Office Observed and simulated tropical cloud regimes

© Crown copyright Met Office How robust are the observed cloud regimes?

© Crown copyright Met Office Evaluation of regime properties Regime histogram Geographical location Relative frequency of occurrence (RFO) Shortwave cloud radiative forcing (SCRF) Longwave cloud radiative forcing (LCRF) Net cloud radiative forcing (NCRF)

© Crown copyright Met Office Uncertainty in the radiative response under climate change Contribution of each regime to the inter- model variance in the change in net cloud forcing.

© Crown copyright Met Office Climate change response In the cloud regime framework, the mean change in cloud radiative forcing can be thought of as having contributions from: A change in the RFO (Relative Frequency of Occurrence) of the regime A change in the NCRF (Net Cloud Radiative Forcing) within the regime (i.e. a change in the tau-CTP space occupied by the regime/development of different clusters).

© Crown copyright Met Office Uncertainty in the radiative response under climate change 2xCO2 change in CRF of tropical anvil cirrus Actual Obs. adjusted 2xCO2 change in CRF of tropical transition (stratocu->cu) cloud

© Crown copyright Met Office Cloud Regime Error Metric CREM r = Cloud regime error metric for regime r aw = area weight of region (tropics, extra-tropics, polar) NCRF r = Net cloud radiative forcing of regime RFO r = Relative frequency of occurrence of regime W RFOr and W NCRFr = Regime weights ' = Difference from observations

© Crown copyright Met Office Cloud Regime Error Metrics

© Crown copyright Met Office Composite cyclone (based on Field and Wood, 2007) for the Southern Ocean

© Crown copyright Met Office Initial tendencies in cloud regime properties Williams and Brooks (2008)

© Crown copyright Met Office Synoptic conditions for (the lack of) mid- level cloud c/o Bureau of Meteorology, Australia

© Crown copyright Met Office Synoptic conditions for (the lack of) mid- level cloud c/o Bureau of Meteorology, Australia

© Crown copyright Met Office Conclusions Most of the variance in the global cloud radiative response between GCMs is due to low cloud (47% from stratocumulus, 18% from transition). Shallow cumulus has a smaller response and acts to reduce the variance between GCMs. GCMs share a bias for stratocumulus and transition regimes to be overly reflective. If this were corrected (and all other aspects of the response remain the same), the variance in the cloud radiative response would reduce. Some of the high cloud-top regimes are simulated too infrequently in some GCMs. If this were corrected (and all other aspects of the response remain the same), the variance in the cloud radiative response from high-top cloud would increase. The ISCCP simulator diagnostics required for this study/metric will be requested within the standard output for AR5 – requires updated ISCCP simulator.

© Crown copyright Met Office Congratulations to Julia Slingo, the new Met Office chief scientist