ALBERT

Description: 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.

Description: 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.

Description: Although plant photosynthetic capacity as determined by the maximum carboxylation rate (i.e., Vc, max25) and the maximum electron transport rate (i.e., Jmax25) at a reference temperature (generally 25 °C) is known to vary considerably in space and time in response to environmental conditions, it is typically parameterized in Earth system models (ESMs) with tabulated values associated with plant functional types. In this study, we have developed a mechanistic model of leaf utilization of nitrogen for assimilation (LUNA) to predict photosynthetic capacity at the global scale under different environmental conditions. We adopt an optimality hypothesis to nitrogen allocation among light capture, electron transport, carboxylation and respiration. The LUNA model is able to reasonably capture the measured spatial and temporal patterns of photosynthetic capacity as it explains  ∼  55 % of the global variation in observed values of Vc, max25 and  ∼  65 % of the variation in the observed values of Jmax25. Model simulations with LUNA under current and future climate conditions demonstrate that modeled values of Vc, max25 are most affected in high-latitude regions under future climates. ESMs that relate the values of Vc, max25 or Jmax25 to plant functional types only are likely to substantially overestimate future global photosynthesis.

Description: Climate and terrestrial biosphere models consider nitrogen an important factor in limiting plant carbon uptake, while operational environmental models view nitrogen as the leading pollutant causing eutrophication in water bodies. The community Noah land surface model with multi-parameterization options (Noah-MP) is unique in that it is the next-generation land surface model for the Weather Research and Forecasting meteorological model and for the operational weather/climate models in the National Centers for Environmental Prediction. In this study, we add a capability to Noah-MP to simulate nitrogen dynamics by coupling the Fixation and Uptake of Nitrogen (FUN) plant model and the Soil and Water Assessment Tool (SWAT) soil nitrogen dynamics. This model development incorporates FUN's state-of-the-art concept of carbon cost theory and SWAT's strength in representing the impacts of agricultural management on the nitrogen cycle. Parameterizations for direct root and mycorrhizal-associated nitrogen uptake, leaf retranslocation, and symbiotic biological nitrogen fixation are employed from FUN, while parameterizations for nitrogen mineralization, nitrification, immobilization, volatilization, atmospheric deposition, and leaching are based on SWAT. The coupled model is then evaluated at the Kellogg Biological Station – a Long Term Ecological Research site within the US Corn Belt. Results show that the model performs well in capturing the major nitrogen state/flux variables (e.g., soil nitrate and nitrate leaching). Furthermore, the addition of nitrogen dynamics improves the modeling of net primary productivity and evapotranspiration. The model improvement is expected to advance the capability of Noah-MP to simultaneously predict weather and water quality in fully coupled Earth system models.