GEWEX Radiation Panel LandFlux Initiative

1. GEWEX Radiation Panel LandFlux Initiative Raghuveer Vinukollu, Remi Meynadier, Eric Wood, Matthew McCabe, Carlos Jimenez, Joshua Fisher and numerous data providers… 2nd Pan-GEWEX Meeting, Seattle, USA

2. GEWEX Goals • GEWEX objectives include: • Produce consistent research quality data sets (with error description) of the Earth’s water and energy budget (and their variability) for process studies at decadal time scales (and beyond); • Enhance understanding of how energy and water cycle processes function, and quantify their contribution to climate feedbacks; • Improve the predictive capability for key water and energy cycle variables and feedbacks – determine geographical and seasonal characteristics of these.

3. GRP LandFlux Initiative • GRP initiated LandFlux around 2007 to “produce a global, multi‐decadal surface turbulent flux data product”. • 2007, LandFlux Assessment and Organization Workshop (Toulouse) • 2008, International Workshop on the Retrieval and Use of Land Surface Temperature: Bridging the Gaps (Asheville) • 2009, GEWEX-WATCH International Symposium on Global Land-surface Evaporation and Climate (Wallingford) • 2009, GEWEX-iLEAPSLandFlux Workshop (Melbourne) • 2010, Joint Meeting for GSWP/GLASS, AsiaFlux/FLUXNET and LandFlux-EVAL at HESSS2 (Tokyo)

4. GEWEX-iLEAPS LandFlux Workshop • In recent years an increasing number of global multi-year ET datasets have been derived: • Satellite-based estimates (RS-ET) • Estimates based on empirical upscaling of point observations • LSM driven with observation-based forcings • Reanalysis datasets & output from GCMs • Atmospheric water balance estimates (inferred ET)

5. GEWEX-iLEAPS LandFlux Workshop • In recent years an increasing number of global multi-year ET datasets have been derived: • Satellite-based estimates (RS-ET) • Estimates based on empirical upscaling of point observations • LSM driven with observations-based forcing • Reanalysis datasets & output from GCMs • Atmospheric water balance estimates (inferred ET) • But ALSO… • Activities happening in a disconnected manner • Groups largely unaware of what others are doing • No clear/consistent approach to evaluate products • No metrics, benchmarking or good-quality forcing data sets with which to evaluate the various products

6. LandFlux the first to do ‘multi-method’ intercomparison exercices including multi-scale (spatial and temporal) datasets, assessment over long time-periods and identifications of specific regions for focused analysis. • Recently completed analyses: • Jimenez et al. (2010) “Global inter-comparison of 12 land surface heat flux estimates” (JGR) • Mueller et al. (2010) “Evaluation of global evapotranspiration datasets: first results of the LandFlux-EVAL project” (GRL)

7. Presents a global inter-comparison of 12 land surface monthly averaged heat fluxes (1993-1995) • Aim is to quantify the range of uncertainty • NO ATTEMPT to quantify the accuracy of the products • NO CLAIMS on the superiority of one over another

9. Homogenized 1994 Product Ensembles LE-flux Rnet mean std relative std More relative variability in LE than in Rn1. Large absolute LE variability over tropical regions, but in relative terms comparable with other regions (e.g. some southern regions in USA). 1 Some products share common radiative terms LE all product global average ~45 W/m2 spread ~ 20 W/m2

10. @ Princeton University : E.F. Wood, R.K. Vinukollu, R. Meynadier, M. Pan, J. Sheffield Multi-model Multi-Platform analyses of Evapotranspiration at the Global Scale SEBS Surface Energy Budget Systems, Su 2002 PM-Mu Modified Penman-Monteith Cleugh et al., 2007; Mu., et al 2007 PT-Fi Modified Priestley-Taylor Fisher et al., 2008

11. Input Data Sources

12. @ Princeton University : E.F. Wood, R.K. Vinukollu, R. Meynadier, M. Pan, J. Sheffield Cold regions (2003-2004) differences with all-product average Mid-latitude basins • uncertainties in ET-models • uncertainties in input forcing datasets

13. Impact of input forcings on RS-ET estimates over Amazon (1984-2007) 20 Rnet W/m2 monthly mean anomaly SRBqc 0 SRB ISCCP -25 20 ET PM (mm/mon) monthly mean anomaly 0 Same surface meteorological forcing = ISCCP -25 1 W/m2 = 1.05 mm/month

14. Impact of input forcings on ET estimates over Amazon 1984-2007 monthly mean anomaly Shum (g/kg) Tair (K) Wind speed (m/s) ISCCP MERRA ET-PM (mm/mo) ET-PT (mm/mo) ET-SEBS (mm/mo) Same radiation fluxes forcing = SRBqc

15. ET sensitivity to WIND Annual ET (mm/year) SEBS PM-Mu PT-Fi 0 300 600 900 1200 1500 1800

16. ET Sensitivity - Amazon Control Run Black lines show the sensitivity runs using a 5% change in the input forcings: Air temperature, Skin temperature, Specific humidity, Wind speed, Surface pressure and Precipitation.

17. Could Evapotranspiration be regarded as an Essential Climate Variable (ECV) ? Impact of change in satellite sensors on long term serie albedo SRB / SRB QC Amazon Surface Albedo monthly mean anomalies 1984 – 2007 SRB -SRBQC SRB SRBQC Surface albedos are derived with a parameterization using monthly climatological clear-sky TOA albedos which are based on 46 months of CERES terra observations The value of the surface albedo depends on the ISCCP DX clear sky composite radiance and the initial guess aerosol optical depth. 1 2 3 4 5 6 1/100 1- Feb 1985 NOAA-7 replaced by NOAA-9 6- Oct 2001: NOAA-14 replaced by NOAA-16 2- Nov 1988: NOAA-9 replaced by NOAA-11 7- Jan, 2006: NOAA-16 replaced by NOAA-18 3- Sep 1994: NOAA-11 goes out of service Need ECV for Rnet 4- Feb 1995: NOAA-14 goes into service

18. Summary • Spread in ET estimates are likely due to : • Huge uncertainties exist among the net radiation estimates from ISCCP, SRB and SRBqc. These uncertainties need to be addressed. This includes problems with surface albedo estimates that impact Rn. No good, long term global albedo products exist --> Need ECV for radiation should precede a need for an ECV for ET • Inconsistency among the met forcings (between Tair and Tsurf for SEBS model) • - Uncertainties in the ET-model (improvements in the surface resistance using high temporal resolution tower data; improvements in the aerodynamic resistance (improves the impact of atmospheric stability effects)

19. LandFlux-Eval will provide multi-product averages with uncertainty ranges (a first guess?)Will identify possible common biases, remove obvious non-physical outliers, and assess different measures of spread and uncertainty.Next Phase:Assessment of inter-annual variability & extremesProduction of ensemble of surface fluxes?Focus on candidate basins (need common forcing data)Evaluate with ground-based observations (multi-types)

20. Extra slides …

21. SEBS veg_data Roughness Parameters (z0m, z0h) NDVI / fc Vegetation Rnet Flux LAI Land cover Resistance (surface + aerodynamic) LE Flux Wind Energy Balance Tsurf H Flux Meteorology Tair Humidity G Flux Pressure Ground heat (G/Rnet) Emissivity Radiative Albedo SWR / LWR Process Output Input Intermediate Result Legend

22. PM-Mu NDVI / fc Plant Transpiration Rnet Flux (to plant) Vegetation LAI Plant Stress Land cover Wind LE Flux Tsurf Ground heat (G/Rnet) Meteorology Tair Humidity Scalar Pressure Soil Evaporation Potential Soil Evaporation Emissivity Radiative Albedo Rnet Flux (to soil) SWR / LWR Process Output Input Intermediate Result Legend

23. PT-Fi NDVI / fc Plant Transpiration Vegetation Eco- physiological constraints LAI Land cover Priestly Taylor  LE Flux Tsurf Ground heat (G/Rnet) Tair Meteorology Soil Moisture constraint Humidity Pressure Soil Evaporation Emissivity Radiative Albedo Rnet Flux (to soil) SWR / LWR Process Output Input Intermediate Result Legend

24. Basins derived from Oki et al. (1998) “Design of Total Runoff Integrating Pathways (TRIP): a global river channel network” Earth Interactions, 2: 1–37.

25. Mueller-Seneviratne Analysis: 1989-1995 Mueller GRL analysis also includes comparison against 10 IPCC AR4 (20c3m) models: ECHAM, INMCM, IPSL, HadGEM, NCAR, HadCM, MRI, GISS, MIROC (medium resolution), CCCMA.

26. Standard deviation Diagnostic Datasets (89-95) Mean Diagnostic Datasets (89-95)