92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Tuesday, 24 January 2012: 9:30 AM
Developing Long-Term Climate Data Records for Global Land Evapotranspiration
Room 352 (New Orleans Convention Center )
Eric F. Wood, Princeton University, Princeton, NJ; and R. Meynadier, J. Sheffield, and R. K. Vinukollu

Quantifying reliable estimates of evapotranspiration (ET) over land is an important part of the larger effort to develop long-term Earth System Data Records (ESDRs) for the major components (fluxes and storages) of the terrestrial water cycle, and is a particular focus of the GEWEX Landflux project. Recent progress has involved the development of global ET data sets using a variety of data sources: objective interpolation of Fluxnet tower data, land surface model estimates and remote sensing approaches that allows for the evaluation of uncertainties across these datasets. However, uncertainties in the input data sets (e.g. inhomogeneities in long-term satellite record) and uncertainty in ET retrieval models parameters (e.g. the surface resistance parameterizations) results in a wide range of estimates, spurious trends and uncertainty of individual retrieved ET datasets. In the current study, a long-term global ET data set has been developed for the period 1984-2007 using three process-based, remote sensing models forced using a combination of input from remote sensing, in-situ and reanalysis products. The retrieval models considered are a modified Penman-Monteith (PM-Mu), Priestley-Taylor (PT-Fi), and the Surface Energy Balance System (SEBS). The three models adjust the surface resistances using aerodynamic principles or provide ecophysiological constraints to account for changing environmental factors; thus scaling ET from its potential value to the actual estimate. The presentation focuses on three aspects: (1) Evaluation of the input forcings and a check of consistency across the variables and time period considered in this study, (2) Inter-comparisons of the surface resistance parameterizations and estimates across the three process models, and their consistency, and (3) Sensitivity analysis of the three models to the various input forcings.

Supplementary URL: