Observed and Simulated Decadal Variability in Clouds and Water Vapour Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.

Slides:

Advertisements

Similar presentations

WGCM CFMIP/IPCC Climate Sensitivity Meeting, Exeter, April 2004 Decadal Variability in Water Vapour Richard Allan, Tony Slingo Environmental Systems Science.

Advertisements

Dust and vapour cloud the view Richard Allan Environmental Systems Science Centre, University of Reading, UK Thanks to Tony Slingo, Ruth Comer, Sean Milton,

© University of Reading Chapman Conference on Atmospheric Water Vapor and Its Role in Climate Tropospheric Water Vapour and.

University of Reading Improved understanding of how rainfall responds to a warming world Richard Allan Environmental Systems.

University of Reading 2007www.nerc-essc.ac.uk/~rpa Monitoring satellite observations and model simulations of changes in the atmospheric.

IRS2004, Busan, August 2004 Using Satellite Observations and Reanalyses to Evaluate Climate and Weather Models Richard Allan Environmental Systems Science.

© University of Reading 2009 EUMETSAT Monitoring changes in precipitation and radiative energy using satellite data and.

University of Reading 2007www.nerc-essc.ac.uk/~rpa Present day changes in tropical precipitation extremes in models and observations.

3) Empirical estimate of surface longwave radiation Use an empirical estimate of the clear-sky surface downward longwave radiation (SDLc) to estimate the.

Current Changes in the Global Water Cycle Richard P. Allan Department of Meteorology, University of Reading Thanks to Brian Soden, Viju John, William Ingram,

1 Evaluating water vapour in HadAM3 using 20 years of satellite data Richard Allan, Mark Ringer Met Office, Hadley Centre for Climate Prediction and Research.

Changes in clear-sky longwave radiative cooling in the atmosphere Richard Allan Environmental Systems Science Centre, University of Reading,

NCAS-Climate Talk 15 th January 2010 Current Changes in the Global Water Cycle Richard P. Allan Diffusing slowly to Met Department/NCAS-Climate from ESSC.

Links Between Clear-sky Radiation, Water Vapour and the Hydrological Cycle Richard P. Allan 1, Viju O. John 2 1 Environmental Systems Science Centre, University.

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

Implications of trends and variability in low-level water vapour Richard P. Allan Department of Meteorology/NCAS climate, University of Reading Thanks.

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

EUMETSAT 2006 Helsinki Exploitation of GERB/SEVIRI data for evaluation of the Met Office global forecast model Richard Allan, Tony Slingo Environmental.

RMS 18 th June 2003 Evaluating moisture in the Hadley Centre Climate Model using 20 years of satellite data Richard Allan Environmental Systems Science.

Atmospheric clear-sky longwave radiative cooling and precipitation Richard Allan Environmental Systems Science Centre, University of Reading, UK.

ESSC Seminar, October 18, 2007© University of Reading 2007www.reading.ac.uk Intensification of the tropical hydrological cycle? Richard P. Allan Environmental.

Trends in Water Cycle meeting, Paris, November 2004 Cloud and water vapour variability: models, reanalyses and observations Richard P. Allan and Tony Slingo.

University of Reading 2007www.nerc-essc.ac.uk/~rpa Observed and simulated changes in water vapour, precipitation and the clear-sky.

1 03/0045a © Crown copyright Evaluating water vapour in HadAM3 with 20 years of satellite data Richard P. Allan Mark A. Ringer Met Office, Hadley Centre.

SYSTEMATIC BIASES 3-hourly comparisons of top of atmosphere radiation from GERB and the Met Office global forecast model Systematic biases in model fluxes.

Changes in Water Vapour, Clear-sky Radiative Cooling and Precipitation

IPCC Workshop on Climate Sensitivity, Paris, July 2004 Exploiting observations of water vapour to investigate simulations of water vapour feedback processes.

1 Changes in low-latitude radiative energy budget - a missing mode of variability in climate models? Richard P. Allan, Tony Slingo Hadley Centre for 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.

University of Reading 2007www.nerc-essc.ac.uk/~rpa Surface radiative fluxes: comparison of NWP/Climate models/reanalyses with.

Understanding Feedback Processes Outline Definitions Magnitudes and uncertainties Geographic distributions and priorities Observational requirements.

Recent Evidence for Reduced Climate Sensitivity Roy W. Spencer, Ph.D Principal Research Scientist The University of Alabama In Huntsville March 4, 2008.

Water Vapor and Cloud Feedbacks Dennis L. Hartmann in collaboration with Mark Zelinka Department of Atmospheric Sciences University of Washington PCC Summer.

TOA radiative flux diurnal cycle variability Patrick Taylor NASA Langley Research Center Climate Science Branch NEWS PI Meeting.

Richard P. Allan 1 | Brian J. Soden 2 | Viju O. John 3 | Igor I. Zveryaev 4 Department of Meteorology Click to edit Master title style Water Vapour (%)

Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University.

Observational and model evidence for positive low-level cloud feedback Robert J. Burgman and Amy C. Clement Rosenstiel School of Marine and Atmospheric.

3 May 2007 GIST May Professor John Harries, Professor John Harries, Space and Atmospheric Physics group, Blackett Laboratory, Imperial College,

© University of Reading Chapman Conference on Atmospheric Water Vapor and Its Role in Climate Tropospheric Water Vapour and.

The diurnal cycle in GERB data Analysis of the diurnal cycle of outgoing longwave radiation (OLR) One month of GERB top of atmosphere OLR flux data from.

3rd Indo-French Workshop on Megha-Tropiques under ISRO-CNES Programme on Atmosphere, Climate and Oceanography, Ahmedabad, India October 17-20, Convection,

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.

© University of Reading Hadley Centre Workshop: Improving predictions of the large- scale hydrological cycle Agreement/discrepancies.

Trends in Tropical Water Vapor ( ): Satellite and GCM Comparison Satellite Observed ---- Model Simulated __ Held and Soden 2006: Robust Responses.

CLARREO Science Briefing 11/14/08 1 Reflected Solar Accuracy Science Requirements Bruce Wielicki, Dave Young, Constantine Lukashin, Langley Zhonghai Jin,

Using GERB and CERES data to evaluate NWP and Climate models over the Africa/Atlantic region Richard Allan, Tony Slingo, Ali Bharmal Environmental Systems.

Heating of Earth's climate continues in the 2000s based upon satellite data and ocean observations Richard P. Allan 1, N. Loeb 2, J. Lyman 3, G. Johnson.

1 Changes in global net radiative imbalance Richard P. Allan, Chunlei Liu (University of Reading/NCAS Climate); Norman Loeb (NASA Langley); Matt.

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

Current changes in precipitation and moisture Richard P. Allan, Chunlei Liu, Matthias Zahn Department of Meteorology, University of Reading Thanks to George.

A Brief Introduction to CRU, GHCN, NCEP2, CAM3.5

Current and future changes in precipitation and its extremes

Current changes in Earth’s energy budget

Influence of climate variability and

Radiative biases in Met Office global NWP model

Current changes in Earth’s ENERGY imbalance

Dust, vapour & cloud in greenhouse Earth

Global hydrological forcing: current understanding

Monitoring current changes in precipitation and Earth’s radiative energy balance using satellite data, reanalyses and models Richard P. Allan1 | Viju.

Reconciling Ocean Heating and

Changes in global net radiative imbalance

Current global and regional changes in atmospheric water vapour

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

New energy budget estimates at top of Earth’s atmosphere and surface

New energy budget estimates at top of Earth’s atmosphere and surface

Richard Allan ESSC, Reading University Mark Ringer

Environmental Systems Science Centre, University of Reading

TEMPERATURE anomaly (degC)

CURRENT Energy Budget Changes

Presentation transcript:

Observed and Simulated Decadal Variability in Clouds and Water Vapour Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading Mark Ringer Hadley Centre, Met Office

INTRODUCTION Climate Sensitivity and Feedback How does cloud and water vapour respond to changes in surface temperature? How well are interannual variations in cloud and water vapour simulated by a climate model? HadAM3 Simulations (1979-1998) Explicitly calculate “UTH” radiances Satellite-like clear-sky diagnostics Evaluation of HadAM3 using satellite data

EXPERIMENTS [Allan et al. 2003, accepted QJ] Ensemble of AMIP-type HadAM3 runs Standard res, 19 levels, 1978-1999. HadISST SST/sea ice forcing Radiance code [see Ringer et al., 2002, QJ, 129, 1169-1190] Modified clear-sky diagnostics: OLRc & water vapour radiance Additional “all-forcings” run SATELLITE DATA - column water vapour, CWV [SMMR 1979-84, SSM/I 1987-99] - clear-sky OLR [ERBS 1985-89, ScaRaB 1994/5, CERES 1998] - Water Vapour channel brightness temperature, T6.7 [HIRS 1979-1998]

Interannual variability of Column Water vapour 1980 1985 1990 1995 See also Soden (2000) J.Clim 13

Can we use reanalysis CWV? ERA-40

Upper tropospheric moisture Clear-sky OLR sensitive to Ts and RH 6.7 mm cloud cleared radiance sensitive to upper tropospheric Relative Humidity Explicitly simulate 6.7 mm radiance Modified “satellite-like” clear-sky diagnostics

Model-obs differences & Clear-sky Sampling Type II HadAM3-OBS Type-I DT 6.7 DOLRc

EOF analysis of spatio-temporal variability in water vapour radiance

Interannual monthly anomalies: tropical oceans ga=1-(OLRc/sTs4)

(+additional forcings)

Clear-sky sampling: interannual variability Light blue: Type I (weighted by clear-sky fraction) Dark Blue: Type II (unweighted mean)

Large changes in OLR from 7 independent satellite instruments (Wielicki et al, 2002)

Allan & Slingo 2002, GRL, 29(7) OLR Clear-sky OLR RSW

EITHER: (1) satellite data wrong: cannot measure decadal changes in tropical radiation budget (and hence cloudiness variations) using most well-calibrated, stable radiometers OR: (2) Our understanding of what determines decadal changes in clouds and radiation is overly simplistic REGARDLESS: measurements of tropical radiation budget provide vital information on tropical cloudiness changes

Summary Climate model simulates low-level water vapour changes Clear-sky sampling important for infrared channel climatologies but not interannual variability Simulations of satellite brightness temperatures: Consistent decadal variability suggests small DRH realistic Climate models cannot capture the large decadal changes in the tropical radiation budget (1979-1998) More wide-field of view instruments (simple yet well-calibrated & stable) Note of caution: can multiple satellite intercalibration artificially remove decadal trends in the UTH radiances? Changes in atmospheric T also influences T6.7 decadal fluctuations

Climatological mean over 60oS-60oN oceans

Info on upper tropospheric water vapour 500mb omega Clear-sky OLR

Water vapour feedback: recent advances (1) Insensitive to resolution (Ingram 2002, J Climate, 15, 917-921) (2) Consistent with observations following post-Pinatubo cooling (Soden et al 2002, Science, 296, 727)

Is water vapour feedback really consistent between models? dOLRc/dTs ~ 2 Wm-2K-1 dOLR/dTs uncertain (Cess et al. 1990, JGR, 95, 16601) Allan et al. 2002, JGR, 107(D17), 4329. doi: 10.1029/2001JD001131. - Temperature lapse rate (Gaffen et al 2000, Science, 287, 1242) - Tropical Cloudiness (Wielicki et al, 2002, Science, 295, 841)