Abstract
It is important to assess the reliability of high-resolution climate variables used as input to hydrologic models. High-resolution climate data is often obtained through the downscaling of Global Climate Models and/or historical reanalysis, depending on the application. In this study, the performance of dynamically downscaled precipitation from the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) reanalysis data (NCEP/NCAR reanalysis I) was evaluated at point scale, watershed scale, and regional scale against corresponding in situ rain gauges and gridded observations, with a focus on Northern California. Also, the spatial characteristics of the simulated precipitation and wind fields, with respect to various grid sizes, were investigated in order to gain insight to the topographic effect on the atmospheric state variables. To this end, dynamical downscaling was performed using the mesoscale atmospheric model MM5, and the synoptic scale reanalysis data were downscaled to a 3 km grid spacing with hourly temporal resolution. The results of comparisons at point scale and watershed scale over a 50-year time period showed that the MM5-simulated precipitation generally produced the timing and magnitude of the observed precipitation in Northern California. The spatial distributions of MM5-simulated precipitation matched the corresponding observed precipitation reasonably well. Furthermore, the statistical goodness of fit tests of the MM5-simulated precipitation against the corresponding observed precipitation showed the reliability and capability of MM5 simulations for downscaling precipitation. A comparison of the spatial characteristics of the results with respect to various grid sizes indicated that precipitation and wind fields are significantly affected by the local topography. In particular, the banded structures and orographic effects on precipitation and wind fields can be well described by a mesoscale model at 3 km and 9 km grid resolutions while 27 km and 81 km grid model simulation may not be sufficient for watershed-scale or sub-watershed-scale studies.
Highlights
IntroductionGCMs (global climate models) are the primary tools for assessing the magnitude and impacts of climate change on water resources, floods, water quality, sea level rise, ecosystems, etc
GCMs are the primary tools for assessing the magnitude and impacts of climate change on water resources, floods, water quality, sea level rise, ecosystems, etc
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Summary
GCMs (global climate models) are the primary tools for assessing the magnitude and impacts of climate change on water resources, floods, water quality, sea level rise, ecosystems, etc. As such, downscaling of the coarse-resolution global datasets becomes necessary to resolve the scale discrepancy between coarse-resolution GCMs (or reanalysis data) and the resolution required for an assessment at a regional scale or a watershed scale. The major disadvantage of RCMs is the cost of their numerical computations that place restrictions on the simulation period, and the computational grid size For this reason, most of the RCM-related studies have been restricted to applications on the assessment of model performance (e.g., [19,20,21,22,23,24]), or on future climate change impact assessment with a relatively coarse grid resolution (normally more than 20 km) over specified time slices (e.g., [25,26,27]), with finer resolution over specified time slices (e.g., [28]), or with relatively coarse grid resolution over a long-term period [29,30]. The spatial characteristics of the simulated precipitation and wind fields, with respect to tvoarvioaurisogursidgsrizdess,iazrees,inavrestiingvaetesdtiginatoerddeinr torgdaeinr itnosiggahitntointhsiegthotptoogrtahpehitcopefofgecrtaponhitcheefaftemctosopnhethriec astmateosvpahriearbiclesst.ate variables
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