The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project aims to advance the development of land evaporation estimates on global and regional scales. Its main objective is the derivation, validation, and intercomparison of a group of existing evaporation retrieval algorithms driven by a common forcing data set. Three commonly used process-based evaporation methodologies are evaluated: the Penman–Monteith algorithm behind the official Moderate Resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Global Land Evaporation Amsterdam Model (GLEAM), and the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL). The resulting global spatiotemporal variability of evaporation, the closure of regional water budgets, and the discrete estimation of land evaporation components or sources (i.e. transpiration, interception loss, and direct soil evaporation) are investigated using river discharge data, independent global evaporation data sets and results from previous studies. In a companion article (Part 1), Michel et al. (2016) inspect the performance of these three models at local scales using measurements from eddy-covariance towers and include in the assessment the Surface Energy Balance System (SEBS) model. In agreement with Part 1, our results indicate that the Priestley and Taylor products (PT-JPL and GLEAM) perform best overall for most ecosystems and climate regimes. While all three evaporation products adequately represent the expected average geographical patterns and seasonality, there is a tendency in PM-MOD to underestimate the flux in the tropics and subtropics. Overall, results from GLEAM and PT-JPL appear more realistic when compared to surface water balances from 837 globally distributed catchments and to separate evaporation estimates from ERA-Interim and the model tree ensemble (MTE). Nonetheless, all products show large dissimilarities during conditions of water stress and drought and deficiencies in the way evaporation is partitioned into its different components. This observed inter-product variability, even when common forcing is used, suggests that caution is necessary in applying a single data set for large-scale studies in isolation. A general finding that different models perform better under different conditions highlights the potential for considering biome- or climate-specific composites of models. Nevertheless, the generation of a multi-product ensemble, with weighting based on validation analyses and uncertainty assessments, is proposed as the best way forward in our long-term goal to develop a robust observational benchmark data set of continental evaporation.

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.

The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project has compiled a forcing data set covering the period 2005–2007 that aims to maximize the exploitation of European Earth Observations data sets for evapotranspiration (ET) estimation. The data set was used to run four established ET algorithms: the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), the Penman–Monteith algorithm from the MODerate resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Surface Energy Balance System (SEBS) and the Global Land Evaporation Amsterdam Model (GLEAM). In addition, in situ meteorological data from 24 FLUXNET towers were used to force the models, with results from both forcing sets compared to tower-based flux observations. Model performance was assessed on several timescales using both sub-daily and daily forcings. The PT-JPL model and GLEAM provide the best performance for both satellite- and tower-based forcing as well as for the considered temporal resolutions. Simulations using the PM-MOD were mostly underestimated, while the SEBS performance was characterized by a systematic overestimation. In general, all four algorithms produce the best results in wet and moderately wet climate regimes. In dry regimes, the correlation and the absolute agreement with the reference tower ET observations were consistently lower. While ET derived with in situ forcing data agrees best with the tower measurements (R² = 0.67), the agreement of the satellite-based ET estimates is only marginally lower (R² = 0.58). Results also show similar model performance at daily and sub-daily (3-hourly) resolutions. Overall, our validation experiments against in situ measurements indicate that there is no single best-performing algorithm across all biome and forcing types. An extension of the evaluation to a larger selection of 85 towers (model inputs resampled to a common grid to facilitate global estimates) confirmed the original findings.

A method has been developed to extend the Global Inundation Estimate from Multiple Satellites (GIEMS). The method presented here is based on retrieval principals similar to GIEMS but with an updated estimation of microwave emissivity in order to be less dependent on ancillary data and with some changes to the final surface water estimation to correct a known overestimation over low vegetation areas. The new methodology, GIEMS‐2, provides monthly estimates of surface water extent, including open water, wetlands, or rice paddies, and it has been applied to the Special Sensor Microwave/Imager and the Special Sensor Microwave Imager Sounder intercalibrated observations to produce a global data record of surface water extent from 1992 to 2015, on an equal area grid of 0.25° × 0.25° at the equator (∼25 km). The time series have been thoroughly evaluated: they are seamless and do not show any obvious artifact related to changes in satellite instrumentation over the ∼25 years. Comparisons with precipitation estimates show good agreement, displaying expected patterns related to surface conditions and precipitation regimes. The temporal variability of basin‐averaged estimates has also been compared with altimeter river height, showing a reasonable agreement. Production will be continued up to current time as soon as the observations become available, with efforts to improve the spatial and temporal resolutions of the estimates currently underway.

Determining the spatial distribution and temporal development of evaporation at regional and global scales is required to improve our understanding of the coupled water and energy cycles and to better monitor any changes in observed trends and variability of linked hydrological processes. With recent international efforts guiding the development of long-term and globally distributed flux estimates, continued product assessments are required to inform upon the selection of suitable model structures and also to establish the appropriateness of these multi-model simulations for global application. In support of the objectives of the Global Energy and Water Cycle Exchanges (GEWEX) LandFlux project, four commonly used evaporation models are evaluated against data from tower-based eddy-covariance observations, distributed across a range of biomes and climate zones. The selected schemes include the Surface Energy Balance System (SEBS) approach, the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model, the Penman–Monteith-based Mu model (PM-Mu) and the Global Land Evaporation Amsterdam Model (GLEAM). Here we seek to examine the fidelity of global evaporation simulations by examining the multi-model response to varying sources of forcing data. To do this, we perform parallel and collocated model simulations using tower-based data together with a global-scale grid-based forcing product. Through quantifying the multi-model response to high-quality tower data, a better understanding of the subsequent model response to the coarse-scale globally gridded data that underlies the LandFlux product can be obtained, while also providing a relative evaluation and assessment of model performance. Using surface flux observations from 45 globally distributed eddy-covariance stations as independent metrics of performance, the tower-based analysis indicated that PT-JPL provided the highest overall statistical performance (0.72; 61 W m⁻²; 0.65), followed closely by GLEAM (0.68; 64 W m⁻²; 0.62), with values in parentheses representing the R², RMSD and Nash–Sutcliffe efficiency (NSE), respectively. PM-Mu (0.51; 78 W m⁻²; 0.45) tended to underestimate fluxes, while SEBS (0.72; 101 W m⁻²; 0.24) overestimated values relative to observations. A focused analysis across specific biome types and climate zones showed considerable variability in the performance of all models, with no single model consistently able to outperform any other. Results also indicated that the global gridded data tended to reduce the performance for all of the studied models when compared to the tower data, likely a response to scale mismatch and issues related to forcing quality. Rather than relying on any single model simulation, the spatial and temporal variability at both the tower- and grid-scale highlighted the potential benefits of developing an ensemble or blended evaporation product for global-scale LandFlux applications. Challenges related to the robust assessment of the LandFlux product are also discussed.

Climate change resulting in ocean warming, sea level rise, and sea ice melting has consequences for the global economy, navigation, and security. The Copernicus Imaging Microwave Radiometer (CIMR) mission is a high priority candidate mission within the European Copernicus Expansion program. CIMR is designed to observe the ocean and sea ice and more particularly the Arctic environment. Sea surface temperature (SST), ocean wind speed, sea surface salinity (SSS), and sea ice concentration (SIC) are fundamental variables for understanding, monitoring, and predicting the state of the ocean and sea ice. CIMR is a conically scanning microwave radiometer imager that includes channels at 1.4, 6.9, 10.65, 18.7, and 36.5 GHz, in a Sun‐synchronous polar orbit, to provide SST, ocean wind speed, SSS, and SIC with an increased accuracy and/or spatial resolution. Here we analyze the performances of the CIMR mission in terms of theoretical retrieval precision and spatial resolution on the SST, SSS, and SIC products. A careful information content analysis is conducted. The CIMR performances are compared with the Advanced Microwave Scanning Radiometer 2 and the Soil Moisture Active Passive current missions. Maps of the retrieval precision based on realistic conditions are computed. CIMR will provide SST, SSS, and SIC with a spatial resolution of 15, 55, and 5 km and a precision of 0.2 K, 0.3 psu, and 5%, respectively. The SST and SIC will be retrieved at better than 30 km from the coast. CIMR is currently in preparatory phase, and if selected, it is for a launch in the 2025+ time frame.