Precipitation Dynamical Downscaling Over the Great Plains

Abstract

Detailed, regional climate projections, particularly for precipitation, are critical for many applications. Accurate precipitation downscaling in the United States Great Plains remains a great challenge for most Regional Climate Models, particularly for warm months. Most previous dynamic downscaling simulations significantly underestimate warm‐season precipitation in the region. This study aims to achieve a better precipitation downscaling in the Great Plains with the Weather Research and Forecast (WRF) model. To this end, WRF simulations with different physics schemes and nudging strategies are first conducted for a representative warm season. Results show that different cumulus schemes lead to more pronounced difference in simulated precipitation than other tested physics schemes. Simply choosing different physics schemes is not enough to alleviate the dry bias over the southern Great Plains, which is related to an anticyclonic circulation anomaly over the central and western parts of continental U.S. in the simulations. Spectral nudging emerges as an effective solution for alleviating the precipitation bias. Spectral nudging ensures that large and synoptic‐scale circulations are faithfully reproduced while still allowing WRF to develop small‐scale dynamics, thus effectively suppressing the large‐scale circulation anomaly in the downscaling. As a result, a better precipitation downscaling is achieved. With the carefully validated configurations, WRF downscaling is conducted for 1980–2015. The downscaling captures well the spatial distribution of monthly climatology precipitation and the monthly/yearly variability, showing improvement over at least two previously published precipitation downscaling studies. With the improved precipitation downscaling, a better hydrological simulation over the trans‐state Oologah watershed is also achieved.

Evaluation of the CABLEv2.3.4 Land Surface Model Coupled to NU‐WRFv3.9.1.1 in Simulating Temperature and Precipitation Means and Extremes Over CORDEX AustralAsia Within a WRF Physics Ensemble

“…Therefore, it is likely that the GF configurations, avoided the dry bias for the wrong reasons, instead due to problems with the timing and magnitude of precipitation events. Previous evaluations of the different cumulus parameterizations over North America (e.g., Hu et al, ) also find that the GF scheme is wetter than the other cumulus schemes that we have tested. Note that the GF scheme performs better at finer‐scale resolutions, as suggested by Fowler et al ().…”

“…Specifically, comparing the green configurations that use YSU PBL/KF cumulus/WSM5 microphysics the difference between WRF‐LIS‐CABLE and WRF‐Noah are comparable to the difference between these models where the microphysics also differs. Similar conclusions about the impact of microphysics selection at resolutions >10 km also conclude that this has minimal impact on precipitation amounts compared to the selection of PBL and cumulus parameterization schemes (e.g., Evans et al, ; Hu et al, ).…”

Dynamical Downscaling of CO2 in 2016 Over the Contiguous United States Using WRF‐VPRM, a Weather‐Biosphere‐Online‐Coupled Model

“…In June, convection over the Great Plains tapers off as the monsoon ridge sets in. July–September is a challenging period for precipitation downscaling over the Great Plains due to mesoscale vertical circulations, eastward propagating convection, and large‐scale moisture advection (Dai et al, 1999; Findell et al, 2011; Hu, Xue, et al, 2018; Liang et al, 2006; Martynov et al, 2013; Qiao & Liang, 2015; Schumacher et al, 2013). Still, the downscaling correctly reproduces the spatial distributions of precipitation during this period in the region and the whole CONUS, though with lower correlation coefficients than the cold season.…”

“…In this study, calibrated VPRM parameters by Hilton et al (2013) using eddy covariance tower data over North America were first implemented into WRF‐VPRM. The updated WRF‐VPRM was then used to simulate CO 2 over CONUS with a resolution of 12 km for the year 2016 in a continuous run using an optimal downscaling configuration justified in Hu, Xue, et al (2018), with NCEP/DOE R2 and CT2017 outputs providing initial and boundary conditions of meteorology and CO 2 , respectively. The downscaled fields are evaluated using the PRISM meteorological data and CO 2 data from OCO‐2 and ACT‐America, in addition to long‐term surface‐based XCO 2 observations from four TCCON sites (Park Falls [WI], Lamont [OK], Caltech [CA], and Dryden [CA]).…”

Dynamical downscaling the impact of spring Western US land surface temperature on the 2015 flood extremes at the Southern Great Plains: effect of domain choice, dynamic cores and land surface parameterization

“…The under-prediction of precipitation over the CONUS, in particular the SGP, seems to be a common weakness of many regional climate models (Xue et al 2007Mearns et al 2012;De Sales and Xue 2013;Saini et al 2016;Sun et al 2016). Recently, Hu et al (2018) suggested the spectral nudging approach as an effective solution to alleviate the precipitation dry bias. In opposition to Case D1 and Case D2, the dryness is somewhat reduced in both spatial extent and magnitude in Case D3, leading to a better simulation of the anomalous positive precipitation over the SGP (32% of observed anomaly) due to the LST/SUBT effect, in agreement with the more accurate simulation of the WUS positive land surface temperature anomaly (see Fig.…”