Richard P. Allan Environmental Systems Science Centre, University of Reading, UK

1. 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. Earth’s energy balance Kiehl and Trenberth, 1997; Also IPCC 2007 tech. summary, p.94

3. Determinants of surface radiation SW LW

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

5. Ground Based ObservationsEvaluation of NWP/Climate models Atmospheric emissivity Column water vapour (cm) Peter Henderson et al.

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

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

8. NCEP clear and cloudy surface down longwave and Prata empirical estimate using observed T2m and column integrated water vapour Niamey, Niger Empirical formulas are valuable tools in understanding physical processes determining radiative flux variations

9. Reanalyses • 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 Allan (2006) JGR

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

11. 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. 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. 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. HadGAM1-Obs: Albedo net SW Surface Down LW Column Water Vapour Bodas et al. (2008) J. Climate

15. Spurious changes in ISCCP cloudsSurface fluxes: Issues with cloud-overlap, calibration and coverage/angular effects Norris and Slingo (2008) FIAS

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

17. 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. 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. 1200GMT, 8 March 2006 RADAGAST/AMMA case study RADAGAST project: http://radagast.nerc-essc.ac.uk

20. Shortwave fluxes Longwave 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. Radiative transfer models underestimate the solar absorption in the atmosphere during March 2006 dust storm Slingo et al. (2006) GRL,33, L24817

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

24. dCWV/dTs ~ 3.0±1.0 mm K-1 Linear fit dSNLc/dTs ~ 3.5±1.5 Wm-2K-1 Models, reanalyses and observations show increased surface net downward longwave with warming due to increased water vapour CMIP3 non-volcanic CMIP3 volcanic Reanalyses/ Obs AMIP3

25. Tropical oceans 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…

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

27. 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. Extra Slides

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

30. Diurnal effects: near surface temperature Night Day Altitude  Altitude  Temperature  Temperature 

31. Near surface temperature: diurnal cycle error Missing physics?

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

33. 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. Spectral signatude of clear-sky surface net longwave radiation Window region crucial in determining changes in surface net LW flux

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

36. 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. 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. Impact of clouds on surface LW radiation Smaller cloud LW effect in cloudy deep tropics due to water vapour path

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

41. Dimming to brightening simulated in HadGEM1 climate model (Bodas et al.)

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

50. Bodas et al. (2008) J. Climate