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A critical evaluation of modeled solar irradiance over California for hydrologic and land surface modeling
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2017
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Source: JGR Atmospheres 122(1): 299-317, 2017
Details:
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Journal Title:Journal of Geophysical Research: Atmospheres
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Description:Studies of land surface processes in complex terrain often require estimates of meteorological variables, i.e., the incoming solar irradiance (Qsi), to force land surface models. However, estimates of Qsi are rarely evaluated within mountainous environments. We evaluated four methods of estimating Qsi: the CERES Synoptic Radiative Fluxes and Clouds (SYN) product, MTCLIM, a regional reanalysis product derived from a long‐term Weather Research and Forecast simulation, and Mountain Microclimate Simulation Model (MTCLIM). These products are evaluated over the Central Valley and Sierra Nevada mountains in California, a region with meteorology strongly impacted by complex topography. We used a spatially dense network of Qsi observations (n = 70) to characterize the spatial characteristics of Qsi uncertainty. Observation sites were grouped into five subregions, and Qsi estimates were evaluated against observations in each subregion. Large monthly biases (up to 80 W m−2) outside the observational uncertainty were found for all estimates in all subregions examined, typically reaching a maximum in the spring. We found that MTCLIM and SYN generally perform the best across all subregions. Differences between Qsi estimates were largest over the Sierra Nevada, with seasonal differences exceeding 50 W m−2. Disagreements in Qsi were especially pronounced when averaging over high‐elevation basins, with monthly differences up to 80 W m−2. Biases in estimated Qsi predominantly occurred with darker than normal conditions associated with precipitation (a proxy for cloud cover), while the presence of aerosols and water vapor was unable to explain the biases. Users of Qsi estimates in regions of complex topography, especially those estimating Qsi to force land surface models, need to be aware of this source of uncertainty.
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Source:JGR Atmospheres 122(1): 299-317, 2017
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