Simulating diurnal variations over the southeastern United States

Abstract

The diurnal variations from a high-resolution regional climate model (Regional Spectral Model; RSM) are analyzed from six independent decade long integrations using lateral boundary forcing data from the National Centers for Environmental Prediction Reanalysis 2 (NCEPR2), European Center for Medium-Range Weather Forecasts 40-year Reanalysis and the Twentieth Century Reanalysis (20CR). With each of these lateral boundary forcing data, the RSM is integrated separately using two convection schemes: Relaxed Arakawa-Schubert and Kain-Fritsch. The results show that RSM integrations forced with 20CR have the least fidelity in depicting the seasonal cycle and diurnal variability of precipitation and surface temperature over the Southeastern United States. The remaining four model simulations show comparable skill. The differences in the diurnal amplitude of rainfall during the summer months of the 20CR forced integration from the corresponding NCEPR2 forced integration, for example, is found to be largely from the transient component of the moisture flux convergence. The root mean square error (RMSE) of the seasonal cycle of precipitation and surface temperature of the other four simulations (not forced by 20CR) were comparable to each other and highest in the summer months. But the RMSE of the diurnal amplitude of precipitation and the timing of its diurnal zenith were largest during winter months and least during summer and fall months in the four model simulations (not forced by 20CR). The diurnal amplitude of surface temperature in comparison showed far less fidelity in all models. The phase of the diurnal maximum of surface temperature however showed significantly better validation with corresponding observations in all of the six model simulations.

The oceanic influence on the rainy season of Peninsular Florida

“…This bias in the onset date of the wet season in the RCM simulations stem partly from the misrepresentation of the land cover type in the Everglades region as perennial ground cover [cf. Selman and Misra , , Figure 1] as swamp or saturated land mass type does not exist in the land surface model [ Ek et al ., ]. Furthermore, bias in the clouds and associated surface fluxes (not shown) and their consequent impact on bias of surface temperature, moisture flux convergence, and convection (shown later) also affect the onset date simulation in the RCM.…”

“…A brief outline of the physics of the RSM used in this study is provided in Table . The choice of these schemes stems from previous successful simulations over the region [ DiNapoli and Misra , ; Selman et al ., ; Selman and Misra , ].…”

Diurnal temperature range in CMIP5 models and observations on the Tibetan Plateau

“…Moreover, as the TP is the highest plateau in the world with low air density, model differences in DTR are significantly affected by clouds, long‐wave and short‐wave fluxes, consistent with the previous study (Lindvall and Svensson, ). Meanwhile, the DTR is the result of a complex interplay of many processes, involving the land surface and the atmospheric boundary layer as well as the atmosphere circulation (Lindvall and Svensson, ; Sayemuzzaman et al ., ; Selman and Misra, ). Cloud cover is important but other factors are also considered to explain a large proportion of the geographic variations in DTR (Jackson and Forster, ), and numerical models have underestimated the observed DTR decrease on the global scale, suggesting that the inadequate representation of model cloud cover leads to unrepresentative maximum/minimum temperature (Braganza et al ., ; Lauritsen and Rogers, ).…”

“…Most GCMs are hardly able to simulate the interannual variations of DTR, and cannot reproduce the seasonal variations of observed DTR on the TP, which is related to the assimilation scheme for the maximum/minimum temperature. These findings are in general agreement with numerous analyses on the global and regional scales (Lewis and Karoly, ; Lindvall et al ., ; Cattiaux et al ., ; Lindvall and Svensson, ; Selman and Misra, ). It is shown that model bias for DTR is greater for winter than summer, which may be related to changes in snow cover (land surface property) and cloud that alter the albedo during the cool period of the year (You et al ., ).…”

“…The DTR is influenced by maximum and minimum temperature at daytime/night‐time. The maximum temperature is largely determined by the solar radiative forcing and the partitioning between sensible, latent and ground heat flux, and the minimum temperature by the downward long‐wave radiation (Cattiaux et al ., ; Lindvall and Svensson, ; Sayemuzzaman et al ., ; Selman and Misra, ). This study confirms that the model differences in DTR on the TP are mainly determined by differences in maximum/minimum temperature, through changes in cloud properties in the models (Dai et al ., ; IPCC, ; Lewis and Karoly, ; Lindvall and Svensson, ).…”