The Reliability of Global Remote Sensing Evapotranspiration Products over Amazon

As a key component of terrestrial water cycle, evapotranspiration (ET), specifically over the Amazon River basin, is of high scientific significance. However, due to the sparse observation network and relatively short observational period of eddy covariance data, large uncertainties remain in the spatial-temporal characteristics of ET over the Amazon. Recently, a great number of long-term global remotely sensed ET products have been developed to fill the observation gap. However, the reliabilities of these global ET products over the Amazon are unknown. In this study, we assessed the consistency of the magnitude, trend and spatial pattern of Amazon ET among five global remotely sensed ET reconstructions. The magnitudes of these products are similar but the long-term trends from 1982 to 2011 are completely divergent. Validation from the eddy covariance data and water balance method proves a better performance of a product grounded on local measurements, highlighting the importance of local measurements in the ET reconstruction. We also examined four hypotheses dealing with the response of ET to brightening, warming, greening and deforestation, which shows that in general, these ET products respond better to warming and greening than to brightening and deforestation. This large uncertainty highlights the need for future studies focusing on ET issues over the Amazon.

Evaluation of Reanalysis Surface Incident Solar Radiation Data in China

Surface incident solar radiation (R_s) of reanalysis products is widely used in ecological conservation, agricultural production, civil engineering and various solar energy applications. It is of great importance to have a good knowledge of the uncertainty of reanalysis R_s products. In this study, we evaluated the R_s estimates from two representative global reanalysis (ERA-Interim and MERRA-2) using quality- controlled surface measurements from China Meteorological Administration (CMA) and Multi-layer Simulation and Data Assimilation Center of the Tibetan Plateau (DAM) from 2000 to 2009. Error causes are further analyzed in combination radiation products from the Earth's Radiant Energy System (CERES) EBAF through time series estimation, hotspot selection and Geodetector methods. Both the ERA-Interim and MERRA-2 products overestimate the R_s in China, and the MERRA-2 overestimation is more pronounced. The errors of the ERA-Interim are greater in spring and winter, while that of the MERRA-2 are almost the same in all seasons. As more quality-controlled measurements were used for validation, the conclusions seem more reliable, thereby providing scientific reference for rational use of these datasets. It was also found that the main causes of errors are the cloud coverage in the southeast coastal area, aerosol optical depth (AOD) and water vapor content in the Sichuan Basin, and cloud coverage and AOD in the northeast and middle east of China.

Evaluation of Multi-Reanalysis Solar Radiation Products Using Global Surface Observations

Solar radiation incident at the Earth’s surface is an essential driver of the energy exchange between the atmosphere and the surface and is also an important input variable in the research on the surface eco-hydrological process. The reanalysis solar radiation dataset is characterized by a long time series and wide spatial coverage and is used in the research of large-scale eco-hydrological processes. Due to certain errors in their production process of the reanalysis of solar radiation products, reanalysis products should be evaluated before application. In this study, three global solar-radiation reanalysis products (ERA-Interim; JRA-55; and NCEP-DOE) in different temporal scales and climate zones were evaluated using surface solar-radiation observations from the National Meteorological Information Center of the China Meteorological Administration (CMA, Beijing, China) and the Global Energy Balance Archive (GEBA, Zürich, Switzerland) from 2000 to 2009. All reanalysis products (ERA-Interim; JRA-55; and NCEP-DOE) overestimated with an annual bias of 14.86 W/m2, 22.61 W/m2, and 31.85 W/m2; monthly bias of 15.17 W/m2, 21.29 W/m2, and 36.91 W/m2; and seasonal bias of 15.08 W/m2, 21.21 W/m2, and 36.69 W/m2, respectively. In different Köppen climate zones, the annual solar radiation of ERA-Interim performed best in cold regions with a bias of 10.30 W/m2 and absolute relative error (ARE) of 8.98%. However, JRA-55 and NCEP-DOE showed the best performance in tropical regions with a bias of 20.08 W/m2 and −0.12 W/m2, and ARE of 11.00% and 9.68%, respectively. Overall, through the evaluations across different temporal and spatial scales, the rank of the three reanalysis products in order was the ERA-Interim, JRA-55, and NCEP-DOE. In addition, based on the evaluation, we analyzed the relationship between the error (ARE) of the reanalysis products and cloud cover, aerosol, and water vapor, which significantly influences solar radiation and we found that cloud was the main cause for errors in the three solar radiation reanalysis products. The above can provide a reference for the application and downscaling of the three solar radiation reanalysis products.

Geophysics and nutritional science: toward a novel, unified paradigm

This article discusses a few basic geophysical processes, which collectively indicate that several nutritionally adverse elements of current Western diets also yield environmentally harmful food consumption patterns. We address oceanic dead zones, which are at the confluence of oceanography, aquatic chemistry, and agronomy and which are a clear environmental problem, and agriculture's effects on the surface heat budget. These exemplify the unknown, complex, and sometimes unexpected large-scale environmental effects of agriculture. We delineate the significant alignment in purpose between nutritional and environmental sciences. We identify red meat, and to a lesser extent the broader animal-based portion of the diet, as having the greatest environmental effect, with clear nutritional parallels.

Simulation of Crop Yields Using ERA-40: Limits to Skill and Nonstationarity in Weather–Yield Relationships

Reanalysis data provide an excellent test bed for impacts prediction systems, because they represent an upper limit on the skill of climate models. Indian groundnut (Arachis hypogaea L.) yields have been simulated using the General Large-Area Model (GLAM) for annual crops and the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis (ERA-40). The ability of ERA-40 to represent the Indian summer monsoon has been examined. The ability of GLAM, when driven with daily ERA-40 data, to model both observed yields and observed relationships between subseasonal weather and yield has been assessed. Mean yields were simulated well across much of India. Correlations between observed and modeled yields, where these are significant, are comparable to correlations between observed yields and ERA-40 rainfall. Uncertainties due to the input planting window, crop duration, and weather data have been examined. A reduction in the root-mean-square error of simulated yields was achieved by applying bias correction techniques to the precipitation. The stability of the relationship between weather and yield over time has been examined. Weather–yield correlations vary on decadal time scales, and this has direct implications for the accuracy of yield simulations. Analysis of the skewness of both detrended yields and precipitation suggest that nonclimatic factors are partly responsible for this nonstationarity. Evidence from other studies, including data on cereal and pulse yields, indicates that this result is not particular to groundnut yield. The detection and modeling of nonstationary weather–yield relationships emerges from this study as an important part of the process of understanding and predicting the impacts of climate variability and change on crop yields.