A critical evaluation of modeled solar irradiance over California for hydrologic and land surface modeling

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• 作者：Karl E. Lapo ; Laura M. Hinkelman ; Edwin Sumargo ; Mimi Hughes ; Jessica D. Lundquist
• 刊名：Journal of Geophysical Research: Atmospheres
• 出版年：2017
• 出版时间：16 January 2017
• 年：2017
• 卷：122
• 期：1
• 页码：299-317
• 全文大小：4.7MB
• ISSN：2169-8996

文摘

Studies of land surface processes in complex terrain often require estimates of meteorological variables, i.e., the incoming solar irradiance (Q_si), to force land surface models. However, estimates of Q_si are rarely evaluated within mountainous environments. We evaluated four methods of estimating Q_si: 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 Q_si observations (n = 70) to characterize the spatial characteristics of Q_si uncertainty. Observation sites were grouped into five subregions, and Q_si 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 Q_si estimates were largest over the Sierra Nevada, with seasonal differences exceeding 50 W m⁻². Disagreements in Q_si were especially pronounced when averaging over high-elevation basins, with monthly differences up to 80 W m⁻². Biases in estimated Q_si 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 Q_si estimates in regions of complex topography, especially those estimating Q_si to force land surface models, need to be aware of this source of uncertainty.