[1] This review surveys the basic theories, observational methods, satellite algorithms, and land surface models for terrestrial evapotranspiration, E (or lE, i.e., latent heat flux), including a long-term variability and trends perspective. The basic theories used to estimate E are the Monin-Obukhov similarity theory (MOST), the Bowen ratio method, and the Penman-Monteith equation. The latter two theoretical expressions combine MOST with surface energy balance.Estimates of E can differ substantially between these three approaches because of their use of different input data.Surface and satellite-based measurement systems can provide accurate estimates of diurnal, daily, and annual variability of E. But their estimation of longer time variability is largely not established. A reasonable estimate of E as a global mean can be obtained from a surface water budget method, but its regional distribution is still rather uncertain. Current land surface models provide widely different ratios of the transpiration by vegetation to total E. This source of uncertainty therefore limits the capability of models to provide the sensitivities of E to precipitation deficits and land cover change.

Abstract. Land evapotranspiration (ET) estimates are available from several global data sets. Here, monthly global land ET synthesis products, merged from these individual data sets over the time periods 1989–1995 (7 yr) and 1989–2005 (17 yr), are presented. The merged synthesis products over the shorter period are based on a total of 40 distinct data sets while those over the longer period are based on a total of 14 data sets. In the individual data sets, ET is derived from satellite and/or in situ observations (diagnostic data sets) or calculated via land-surface models (LSMs) driven with observations-based forcing or output from atmospheric reanalyses. Statistics for four merged synthesis products are provided, one including all data sets and three including only data sets from one category each (diagnostic, LSMs, and reanalyses). The multi-annual variations of ET in the merged synthesis products display realistic responses. They are also consistent with previous findings of a global increase in ET between 1989 and 1997 (0.13 mm yr−2 in our merged product) followed by a significant decrease in this trend (−0.18 mm yr−2), although these trends are relatively small compared to the uncertainty of absolute ET values. The global mean ET from the merged synthesis products (based on all data sets) is 493 mm yr−1 (1.35 mm d−1) for both the 1989–1995 and 1989–2005 products, which is relatively low compared to previously published estimates. We estimate global runoff (precipitation minus ET) to 263 mm yr−1 (34 406 km3 yr−1) for a total land area of 130 922 000 km2. Precipitation, being an important driving factor and input to most simulated ET data sets, presents uncertainties between single data sets as large as those in the ET estimates. In order to reduce uncertainties in current ET products, improving the accuracy of the input variables, especially precipitation, as well as the parameterizations of ET, are crucial.

[1] A global intercomparison of 12 monthly mean land surface heat flux products for the period 1993-1995 is presented. The intercomparison includes some of the first emerging global satellite-based products (developed at Paris Observatory, Max Planck Institute for Biogeochemistry, University of California Berkeley, University of Maryland, and Princeton University) and examples of fluxes produced by reanalyses (ERA-Interim, MERRA, NCEP-DOE) and off-line land surface models (GSWP-2, GLDAS CLM/ Mosaic/Noah). An intercomparison of the global latent heat flux (Q le ) annual means shows a spread of ∼20 W m −2 (all-product global average of ∼45 W m −2 ). A similar spread is observed for the sensible (Q h ) and net radiative (R n ) fluxes. In general, the products correlate well with each other, helped by the large seasonal variability and common forcing data for some of the products. Expected spatial distributions related to the major climatic regimes and geographical features are reproduced by all products. Nevertheless, large Q le and Q h absolute differences are also observed. The fluxes were spatially averaged for 10 vegetation classes. The larger Q le differences were observed for the rain forest but, when normalized by mean fluxes, the differences were comparable to other classes. In general, the correlations between Q le and R n were higher for the satellite-based products compared with the reanalyses and off-line models. The fluxes were also averaged for 10 selected basins. The seasonality was generally well captured by all products, but large differences in the flux partitioning were observed for some products and basins.

[1] Quantification of global land evapotranspiration (ET) has long been associated with large uncertainties due to the lack of reference observations. Several recently developed products now provide the capacity to estimate ET at global scales. These products, partly based on observational data, include satellite-based products, land surface model (LSM) simulations, atmospheric reanalysis output, estimates based on empirical upscaling of eddycovariance flux measurements, and atmospheric water balance datasets. The LandFlux-EVAL project aims to evaluate and compare these newly developed datasets. Additionally, an evaluation of IPCC AR4 global climate model (GCM) simulations is presented, providing an assessment of their capacity to reproduce flux behavior relative to the observations-based products. Though differently constrained with observations, the analyzed reference datasets display similar large-scale ET patterns. ET from the IPCC AR4 simulations was significantly smaller than that from the other products for India (up to 1 mm/d) and parts of eastern South America, and larger in the western USA, Australia and China. The inter-product variance is lower across the IPCC AR4 simulations than across the reference datasets in several regions, which indicates that uncertainties may be underestimated in the IPCC AR4 models due to shared biases of these simulations. Citation: Mueller, B., et al. (2011), Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations, Geophys. Res. Lett., 38, L06402,

Visibility in the clear sky is reduced by the presence of aerosols, whose types and concentrations have a large impact on the amount of solar radiation that reaches Earth's surface. Here we establish a global climatology of inverse visibilities over land from 1973 to 2007 and interpret it in terms of changes in aerosol optical depth and the consequent impacts on incident solar radiation. The aerosol contribution to "global dimming," first reported in terms of strong decreases in measured incident solar radiation up to the mid-1980s, has monotonically increased over the period analyzed. Since that time, visibility has increased over Europe, consistent with reported European "brightening," but has decreased substantially over south and east Asia, South America, Australia, and Africa, resulting in net global dimming over land.

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Existing studies have shown that observed surface incident solar radiation (R s ) over China may have important inhomogeneity issues. This study provides metadata and reference data to homogenize observed R s , from which the decadal variability of R s over China can be accurately derived. From 1958 to 1990, diffuse solar radiation (R sdif ) and direct solar radiation (R sdir ) were measured separately, and R s was calculated as their sum. The pyranometers used to measure R sdif had a strong sensitivity drift problem, which introduced a spurious decreasing trend into the observed R sdif and R s data, whereas the observed R sdir did not suffer from this sensitivity drift problem. From 1990 to 1993, instruments and measurement methods were replaced and measuring stations were restructured in China, which introduced an abrupt increase in the observed R s . Intercomparisons between observation-based and model-based R s performed in this research show that sunshine duration (SunDu)-derived R s is of high quality and can be used as reference data to homogenize observed R s data. The homogenized and adjusted data of observed R s combines the advantages of observed R s in quantifying hourly to monthly variability and SunDu-derived R s in depicting decadal variability and trend. R s averaged over 105 stations in China decreased at À2.9 W m À2 per decade from 1961 to 1990 and remained stable afterward. This decadal variability is confirmed by the observed R sdir and diurnal temperature ranges, and can be reproduced by high-quality Earth System Models. However, neither satellite retrievals nor reanalyses can accurately reproduce such decadal variability over China.

[1] Satellite remote sensing is a promising technique for estimating global or regional evapotranspiration (ET). A simple and accurate method is essential when estimating ET using remote sensing data. Such a method is investigated by taking advantage of satellite measurements and the extensive ground-based measurements available at eight enhanced surface facility sites located throughout the Southern Great Plains (SGP) area of the United States from January 2002 to May 2005. Data analysis shows that correlation coefficients between ET and surface net radiation are the highest, followed by temperatures (air temperature or land surface temperature, T s ), and vegetation indices (enhanced vegetation index (EVI) or normalized difference vegetation index (NDVI)). A simple regression equation is proposed to estimate ET using surface net radiation, air or land surface temperatures and vegetation indices. ET can be estimated using daytime-averaged air temperature and EVI with a root mean square error (RMSE) of $30 W m À2 and a correlation coefficient of 0.91 across all sites and years. ET can also be estimated with comparable accuracy using NDVI and T s . More importantly, the daytime-averaged ET can also be estimated using only one measurement per day of temperatures (the daytime maximum air temperature or T s ) with comparable accuracy. A sensitivity analysis shows that the proposed method is only slightly sensitive to errors of temperatures, vegetation indices and net surface radiation. An independent validation was made using the measurements colleted by the eddy covariance method at six AmeriFlux sites throughout the United States from 2001 to 2006. The land cover associated with the AmeriFlux sites varies from grassland, to cropland and forest. The results show that ET can be reasonably predicted with a correlation coefficient that varies from 0.84 to 0.95 and a bias that varies from 3 W m À2 to 15 W m À2 and RMSE varying from $30 W m À2 to $40 W m À2 . The positive bias partly comes from the energy imbalance problem encountered in the eddy covariance method. The proposed method can predict ET under a wide range of soil moisture contents and land cover types.Citation: Wang, K., P. Wang, Z. Li, M. Cribb, and M. Sparrow (2007), A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature,