On Uncertainty in Global Terrestrial Evapotranspiration Estimates from Choice of Input Forcing Datasets*

Grayson Badgley Department of Global Ecology, Carnegie Institution for Science, Stanford, California

Search for other papers by Grayson Badgley in
Current site
Google Scholar
PubMed
Close
,
Joshua B. Fisher Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

Search for other papers by Joshua B. Fisher in
Current site
Google Scholar
PubMed
Close
,
Carlos Jiménez Laboratoire d’Études du Rayonnement et de la Matière en Astrophysique, Centre National de la Recherche Scientifique, Observatoire de Paris, Paris, France

Search for other papers by Carlos Jiménez in
Current site
Google Scholar
PubMed
Close
,
Kevin P. Tu Theiss Research, Davis, California

Search for other papers by Kevin P. Tu in
Current site
Google Scholar
PubMed
Close
, and
Raghuveer Vinukollu Swiss Re, Armonk, New York

Search for other papers by Raghuveer Vinukollu in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Evapotranspiration ET is a critical water, energy, and climate variable, and recent work has been published comparing different global products. These comparisons have been difficult to interpret, however, because in most studies the evapotranspiration products were derived from models forced by different input data. Some studies have analyzed the uncertainty in regional evapotranspiration estimates from choice of forcings. Still others have analyzed how multiple models vary with choice of net radiation forcing data. However, no analysis has been conducted to determine the uncertainty in global evapotranspiration estimates attributable to each class of input forcing datasets. Here, one of these models [Priestly–Taylor JPL (PT-JPL)] is run with 19 different combinations of forcing data. These data include three net radiation products (SRB, CERES, and ISCCP), three meteorological datasets [CRU, Atmospheric Infrared Sounder (AIRS) Aqua, and MERRA], and three vegetation index products [MODIS; Global Inventory Modeling and Mapping Studies (GIMMS); and Fourier-Adjusted, Sensor and Solar Zenith Angle Corrected, Interpolated, Reconstructed (FASIR)]. The choice in forcing data produces an average range in global monthly evapotranspiration of 10.6 W m−2 (~20% of global mean evapotranspiration), with net radiation driving the majority of the difference. Annual average terrestrial ET varied by an average of 8 W m−2, depending on choice of forcings. The analysis shows that the greatest disagreement between input forcings arises from choice of net radiation dataset. In particular, ISCCP data, which are frequently used in global studies, differed widely from the other radiation products examined and resulted in dramatically different estimates of global terrestrial ET.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JHM-D-14-0040.s1.

Corresponding author address: Grayson Badgley, Department of Global Ecology, Carnegie Institution for Science, 260 Panama St., Stanford, CA 94305. E-mail: badgley@stanford.edu

Abstract

Evapotranspiration ET is a critical water, energy, and climate variable, and recent work has been published comparing different global products. These comparisons have been difficult to interpret, however, because in most studies the evapotranspiration products were derived from models forced by different input data. Some studies have analyzed the uncertainty in regional evapotranspiration estimates from choice of forcings. Still others have analyzed how multiple models vary with choice of net radiation forcing data. However, no analysis has been conducted to determine the uncertainty in global evapotranspiration estimates attributable to each class of input forcing datasets. Here, one of these models [Priestly–Taylor JPL (PT-JPL)] is run with 19 different combinations of forcing data. These data include three net radiation products (SRB, CERES, and ISCCP), three meteorological datasets [CRU, Atmospheric Infrared Sounder (AIRS) Aqua, and MERRA], and three vegetation index products [MODIS; Global Inventory Modeling and Mapping Studies (GIMMS); and Fourier-Adjusted, Sensor and Solar Zenith Angle Corrected, Interpolated, Reconstructed (FASIR)]. The choice in forcing data produces an average range in global monthly evapotranspiration of 10.6 W m−2 (~20% of global mean evapotranspiration), with net radiation driving the majority of the difference. Annual average terrestrial ET varied by an average of 8 W m−2, depending on choice of forcings. The analysis shows that the greatest disagreement between input forcings arises from choice of net radiation dataset. In particular, ISCCP data, which are frequently used in global studies, differed widely from the other radiation products examined and resulted in dramatically different estimates of global terrestrial ET.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JHM-D-14-0040.s1.

Corresponding author address: Grayson Badgley, Department of Global Ecology, Carnegie Institution for Science, 260 Panama St., Stanford, CA 94305. E-mail: badgley@stanford.edu

Supplementary Materials

    • Supplemental Materials (PDF 1.10 MB)
Save
  • Armanios, D. E., and Fisher J. B. , 2014: Measuring water availability with limited ground data: Assessing the feasibility of an entirely remote-sensing-based hydrologic budget of the Rufiji basin, Tanzania, using TRMM, GRACE, MODIS, SRB, and AIRS. Hydrol. Processes, 28, 853867, doi:10.1002/hyp.9611.

    • Search Google Scholar
    • Export Citation
  • Brubaker, K. L., and Entekhabi D. , 1996: Analysis of feedback mechanisms in land–atmosphere interaction. Water Resour. Res., 32, 13431357, doi:10.1029/96WR00005.

    • Search Google Scholar
    • Export Citation
  • Brutsaert, W., 2006: Indications of increasing land surface evaporation during the second half of the 20th century. Geophys. Res. Lett., 33, L20403, doi:10.1029/2006GL027532.

    • Search Google Scholar
    • Export Citation
  • Ferguson, C. R., Sheffield J. , Wood E. F. , and Gao H. , 2010: Quantifying uncertainty in a remote sensing–based estimate of evapotranspiration over continental USA. Int. J. Remote Sens., 31, 38213865, doi:10.1080/01431161.2010.483490.

    • Search Google Scholar
    • Export Citation
  • Fisher, J. B., Tu K. P. , and Baldocchi D. D. , 2008: Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sens. Environ., 112, 901919, doi:10.1016/j.rse.2007.06.025.

    • Search Google Scholar
    • Export Citation
  • Friedl, M. A., Sulla-Menashe D. , Tan B. , Schneider A. , Ramankutty N. , Sibley A. , and Huang X. , 2010: MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ., 114, 168182, doi:10.1016/j.rse.2009.08.016.

    • Search Google Scholar
    • Export Citation
  • Jiménez, C., and Coauthors, 2011: Global intercomparison of 12 land surface heat flux estimates. J. Geophys. Res., 116, D02102, doi:10.1029/2010JD014545.

    • Search Google Scholar
    • Export Citation
  • Jung, M., and Coauthors, 2010: Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467, 951954, doi:10.1038/nature09396.

    • Search Google Scholar
    • Export Citation
  • Kandel, R., 2012: Understanding and measuring Earth’s energy budget: From Fourier, Humboldt, and Tyndall to CERES and beyond. Surv. Geophys., 33, 337350, doi:10.1007/s10712-011-9162-y.

    • Search Google Scholar
    • Export Citation
  • Lawrence, M. G., 2005: The relationship between relative humidity and the dewpoint temperature in moist air: A simple conversion and applications. Bull. Amer. Meteor. Soc., 86, 225233, doi:10.1175/BAMS-86-2-225.

    • Search Google Scholar
    • Export Citation
  • Lettenmaier, D. P., and Famiglietti J. S. , 2006: Hydrology: Water from on high. Nature, 444, 562563, doi:10.1038/444562a.

  • Los, S. O., and Coauthors, 2000: A global 9-yr biophysical land surface dataset from NOAA AVHRR data. J. Hydrometeor., 1, 183199, doi:10.1175/1525-7541(2000)001<0183:AGYBLS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mitchell, T. D., and Jones P. D. , 2005: An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol., 25, 693712, doi:10.1002/joc.1181.

    • Search Google Scholar
    • Export Citation
  • Mu, Q., Heinsch F. A. , Zhao M. , and Running S. W. , 2007: Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ., 111, 519536, doi:10.1016/j.rse.2007.04.015.

    • Search Google Scholar
    • Export Citation
  • Mu, Q., Zhao M. , and Running S. W. , 2011: Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ., 115, 1781–1800, doi:10.1016/j.rse.2011.02.019.

    • Search Google Scholar
    • Export Citation
  • Mueller, B., and Seneviratne S. I. , 2014: Systematic land climate and evapotranspiration biases in CMIP5 simulations. Geophys. Res. Lett., 41, 128–134, doi:10.1002/2013GL058055.

    • Search Google Scholar
    • Export Citation
  • Mueller, B., and Coauthors, 2011: Evaluation of global observations–based evapotranspiration datasets and IPCC AR4 simulations. Geophys. Res. Lett., 38, L06402, doi:10.1029/2010GL046230.

    • Search Google Scholar
    • Export Citation
  • New, M., Hulme M. , and Jones P. , 2000: Representing twentieth-century space–time climate variability. Part II: Development of 1901–96 monthly grids of terrestrial surface climate. J. Climate, 13, 22172238, doi:10.1175/1520-0442(2000)013<2217:RTCSTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Pan, M., Sahoo A. K. , Troy T. J. , Vinukollu R. K. , Sheffield J. , and Wood E. F. , 2012: Multisource estimation of long-term terrestrial water budget for major global river basins. J. Climate, 25, 31913206, doi:10.1175/JCLI-D-11-00300.1.

    • Search Google Scholar
    • Export Citation
  • Peel, M. C., and McMahon T. A. , 2006: Continental runoff: A quality-controlled global runoff data set. Nature, 444, E14, doi:10.1038/nature05480.

    • Search Google Scholar
    • Export Citation
  • Peterson, T., Golubev V. , and Groisman P. Ya. , 1995: Evaporation is losing its strength. Nature, 377, 687688, doi:10.1038/377687b0.

  • Pinzon, J., Brown M. E. , and Tucker C. J. , 2005: EMD correction of orbital drift artifacts in satellite data stream. Hilbert-Huang Transform and Its Applications, N. E. Huang and S. S. Shen, Eds., World Scientific, 167–186.

  • Prentice, I., and Coauthors, 1992: A global biome model based on plant physiology and dominance, soil properties and climate. J. Biogeogr., 19, 117134, doi:10.2307/2845499.

    • Search Google Scholar
    • Export Citation
  • Priestley, C. H. B., and Taylor R. J. , 1972: On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Wea. Rev., 100, 8192, doi:10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sellers, P. J., and Coauthors, 1996: Comparison of radiative and physiological effects of doubled atmospheric CO2 on climate. Science, 271, 14021406, doi:10.1126/science.271.5254.1402.

    • Search Google Scholar
    • Export Citation
  • Shuttleworth, J. W., 1992: Evaporation. Handbook of Hydrology, D. R. Maidment, Ed., McGraw-Hill, 4.1–4.53.

  • Stackhouse, P. W., Gupta S. K. , Cox S. J. , Chiacchio M. , and Mikovitz J. C. , 2000: The WCRP/GEWEX Surface Radiation Budget Project Release 2: An assessment of surface fluxes at 1 degree resolution. Current Problems in Atmospheric Radiation: Proc. Int. Radiation Symp., St. Petersburg, Russia, International Radiation Symposium.

  • Teuling, A. J., and Coauthors, 2009: A regional perspective on trends in continental evaporation. Geophys. Res. Lett., 36, L02404, doi:10.1029/2008GL036584.

    • Search Google Scholar
    • Export Citation
  • Tucker, C. J., Pinzon J. E. , Brown M. E. , Slayback D. , Pak E. W. , Mahoney R. , Vermote E. , and El Saleous N. , 2005: An extended AVHRR 8-km NDVI data set compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens., 26, 44855598, doi:10.1080/01431160500168686.

    • Search Google Scholar
    • Export Citation
  • Vinukollu, R. K., Meynadier R. , Sheffield J. , and Wood E. F. , 2011: Multi-model, multi-sensor estimates of global evapotranspiration: Climatology, uncertainties and trends. Hydrol. Processes, 25, 39934010, doi:10.1002/hyp.8393.

    • Search Google Scholar
    • Export Citation
  • Wang, K., and Dickinson R. E. , 2012: A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Rev. Geophys., 50, RG2005, doi:10.1029/2011RG000373.

    • Search Google Scholar
    • Export Citation
  • Wetherald, R. T., and Manabe S. , 2002: Simulation of hydrologic changes associated with global warming. J. Geophys. Res., 107, 4379, doi:10.1029/2001JD001195.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y. C., Rossow W. B. , Lacis A. A. , Oinas V. , and Mishchenko M. I. , 2004: Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data. J. Geophys. Res., 109, D19105, doi:10.1029/2003JD004457.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 2355 980 61
PDF Downloads 1455 238 14