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University of Reading Surface radiative fluxes: comparison of NWP/Climate models/reanalyses with.

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Presentation on theme: "University of Reading Surface radiative fluxes: comparison of NWP/Climate models/reanalyses with."— Presentation transcript:

1© University of Reading Surface radiative fluxes: comparison of NWP/Climate models/reanalyses with remote sensing estimates Richard P. Allan Environmental Systems Science Centre, University of Reading, UK

2© University of Reading Earths energy balance Kiehl and Trenberth, 1997; Also IPCC 2007 tech. summary, p.94

3© University of Reading Determinants of surface radiation FieldClimatologyDiurnaldecadal change Insolation Cloud Aerosol Ozone Water vapour Temperature Water vapour Cloud aerosol GHG SW LW

4© University of Reading Methods of model surface flux evaluation NWP/Climate model Surface flux observations Physics Reanalyses Satellite data Conventional observations RT models Empirical models Other models

5© University of Reading Ground Based Observations Evaluation of NWP/Climate models ARM siteARMModel Barrow Lamont++ Darwin Manus ** Peter Henderson et al. Atmospheric emissivity Column water vapour (cm)

6© University of Reading Bodas et al. (2008) J. Climate (see also e.g., Wild et al. (2001) J Climate, etc) Excellent time resolution Direct observations Scaling up issues Poor spatial coverage Instrumental uncertainty

7© University of Reading Empirical estimates Based on physics Use surface observations to calibrate –e.g. Prata (1996) QJ Royal Meteorol Soc Clear-sky surface down longwave Column integrated water vapour Screen-level temperature Atmospheric emissivity

8© University of Reading NCEP clear and cloudy surface down longwave and Prata empirical estimate using observed T 2m and column integrated water vapour Niamey, Niger Empirical formulas are valuable tools in understanding physical processes determining radiative flux variations

9© University of Reading Good quality clear-sky fluxes? Range in estimates of clear- sky surface net longwave radiation… SRB (82 Wm -2 ) > NCEP (80 Wm -2 ) > ERA40 (73 Wm -2 ) > SSM/I empirical Reanalyses Allan (2006) JGR

10© University of Reading Robust relationship between clear-sky net surface LW flux (SNLc) and column water vapour (CWV) ERA40 NCEP Allan (2006) JGR dCWV (mm) ~1.3 Wm -2 mm -1 Clear ~1.5 Wm -2 mm -1 CWV (cm) SNL (Wm -2 ) Slingo et al (2008) JGR Global: reanalysesSahel, Africa: observations

11© University of Reading Interannual/Decadal changes: Homogeneity an issue Surface fluxes available globally on model grids Observational basis through data assimilation Model/observational errors; require validation Changes in quality of observing system may lead to spurious variability Allan (2007) Tellus

12© University of Reading Reanalysis cloud properties unrealistic Cloud components of surface fluxes poor ERA40-ISCCP total cloud difference ERA40 – satellite data (below) Allan et al. (2004) JGR

13© University of Reading Remote sensing of surface fluxes Use satellite (and other) retrievals of important parameters (e.g. cloud, T, q) Input to radiative transfer codes Surface fluxes on model/satellite grids –e.g. ISCCP clouds/reanalysis atmosphere: Zhang et al. (2004) JGR, Stackhouse et al (GEWEX), Pavlakis et al. (2004) Atmos Chem Phys

14© University of Reading Bodas et al. (2008) J. Climate HadGAM1-Obs: Albedonet SW Surface Down LWColumn Water Vapour

15© University of Reading Spurious changes in ISCCP clouds Surface fluxes: Issues with cloud-overlap, calibration and coverage/angular effects Norris and Slingo (2008) FIAS

16© University of Reading Remote sensing of surface fluxes e.g. surface longwave What the surface sees Cloud base Column water vapour Tair IR satellite Cloud top Tskin (when clear) microwave satellites Humidity temperature (when clear) Cloud liquid water precipitation, wind. Atmospheric temperature / water vapour

17© University of Reading Comparisons of NWP model and satellite estimates of: Cloud liquid water Water vapour Indirect evaluation of surface fluxes –Parameters important for surface LW (and SW) radiation –Allan et al. (2008) QJRMS

18© University of Reading Constraining model (based on remote sensing estimates) using surface/satellite observations Work with: Nicky Chalmers & Robin Hogan Model v GERB/MSG Model v ARM

19© University of Reading RADAGAST/AMMA case study 1200GMT, 8 March 2006 RADAGAST project:

20© University of Reading Shortwave fluxesLongwave fluxes Diurnal cycle in surface fluxes –Solar/geometry; Temperature response; Atmosphere response Daily variability –Advection of air-masses; Aerosol cloud effects Important to simulate in models Important to correct for in remote sensing estimates

21© University of Reading Radiative transfer models underestimate the solar absorption in the atmosphere during March 2006 dust storm Slingo et al. (2006) GRL, 33, L24817

22© University of Reading Special issue on RADAGAST under review for JGR-Atmospheres (A. Slingo et al.) Using surface observations (and models) improves understanding of physical processes; An indirect method of model evaluation

23© University of Reading Evaluating model climate change responses CMIP3 CMIP3 volcanic NCEP ERA40 SSM/I-derived ~ +0.7 Wm -2 decade -1 SNLc (Wm -2 ) Changes in clear-sky surface net longwave flux in coupled climate models, reanalyses and empirical estimates

24© University of Reading Linear fit dSNLc/dTs ~ 3.5±1.5 Wm -2 K -1 dCWV/dTs ~ 3.0±1.0 mm K -1 CMIP3 non-volcanicCMIP3 volcanic Reanalyses/ ObsAMIP3 Models, reanalyses and observations show increased surface net downward longwave with warming due to increased water vapour

25© University of Reading Increases in water vapour enhance clear- sky longwave radiative cooling of atmosphere to the surface This is offset by enhanced absorption of shortwave radiation by water vapour Changes in greenhouse gases, aerosol and cloud alter this relationship… Tropical oceans

26© University of Reading Sensitivity test: tropical oceans Clear-sky Longwave shortwave TOA SFC ATM ATM 1K increase in tropospheric T, constant RH Greenhouse gas changes from 1980 to 2000 assuming different rates of warming

27© University of Reading Conclusions Evaluation of surface fluxes in models crucial but problematic (climatology, diurnal cycle, trends) Surface observations: –Excellent time-resolution –Upscaling issues, spatial coverage poor Reanalyses limitations: clouds/variability Remote sensing estimates –Good spatial (and temporal) coverage –Measure accurately quantities important for surface fluxes; need to consider variety of time-scales Analysis of surface/satellite data can help to improve physical processes in models better surface fluxes

28© University of Reading Extra Slides

29© University of Reading Evaluation of diurnal cycle in NWP model using surface observations Milton et al. (2008) JGR accepted Niamey ARM station (RADAGAST/AMMA)

30© University of Reading Diurnal effects: near surface temperature NightDay Temperature Altitude

31© University of Reading Near surface temperature: diurnal cycle error Missing physics?

32© University of Reading Diurnal skin temperature effects are also apparent for oceans (clear, calm conditions) Allan (2000) J.Climate

33© University of Reading Surface downward LW sensitive to moisture changes in lowest levels and temperature changes close to the surface Sensitivity of surface downwelling LW to temperature and moisture changes in 50 hPa vertical levels 1K temperature increase; moisture increased to conserve Relative Humidity

34© University of Reading Window region crucial in determining changes in surface net LW flux Spectral signatude of clear-sky surface net longwave radiation

35© University of Reading Increased moisture enhances atmospheric radiative cooling to surface ERA40 NCEP Allan (2006) JGR 111, D22105 SNLc = clear-sky surface net down longwave radiation CWV = column integrated water vapour dCWV (mm) ~1.4 Wm -2 mm -1

36© University of Reading Evaluation of climate model sensitivity SNLc = clear-sky surface net down longwave radiation CWV = column integrated water vapour dSNLc/dCWV ~ 1 1.5 W kg -1

37© University of Reading Also true for unique meteorological environments (e.g. Niamey, Radagast project, Slingo et al.) –Here water vapour & temperature anti-correlated over the seasonal cycle Clear ~1.5 Wm -2 mm -1

38© University of Reading Impact of clouds on surface LW radiation Smaller cloud LW effect in cloudy deep tropics due to water vapour path

39© University of Reading Surface cloud LW effect: observations and NWP model - Higher water path: smaller cloud effect - More cloud, lower/warmer cloud-base: higher cloud effect

40© University of Reading

41© University of Reading Dimming to brightening simulated in HadGEM1 climate model (Bodas et al.)

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43© University of Reading Direct evaluation of models using surface observations Allan (2000) J Climate Barrow, Alaska

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50© University of Reading Bodas et al. (2008) J. Climate

51© University of Reading SST Water vapour Clear net LW down at surface Testing climate model simulations of current variability (tropical oceans)

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