ABSTRACT:In this study, dynamical downscaling was performed using the Weather Research and Forecast (WRF) model to attain fine-resolution gridded meteorological information capable of reflecting Mongolia's complex topographical effect. Mongolia's sparse station network, with an average inter-station distance 107 km, is incapable of representing the spatial distribution of climate variables, such as temperature, over the country's complex topography. In order to reproduce fine-scale air temperature in Mongolia, the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis II data with 6-h intervals from 1981 to 2010 were used as the initial and boundary conditions of the WRF model. A one-way nesting system was applied for two nested domains with horizontal grid spaces of 60 and 20 km. For correction of the systematic biases induced by dynamical downscaling, a statistical correction method was used for the downscaled results simulated by the WRF model. The bias was divided into two parts: the mean and the perturbation. The former was corrected by using a weighting function and a temperature inversion, and the latter by using the selforganizing maps method. In the former correction, the temperature inversion, characterized by an inverted lapse rate, in which temperature increases with increasing height in the lower atmosphere, was considered only when the temperature inversion occurred. According to our result, the domain-averaged Root Mean Square Difference of the model-simulated annual mean temperature was decreased from 3.7 • C to 2.1 • C after the statistical and temperature inversion corrections. On the basis of our study, we suggested that the area-averaged, fine-resolution, annual mean temperature of Mongolia was 1.1 • C (station mean temperature 0.5 • C). Our correction method improves not only spatial patterns with fine resolution but also the time-varying temperature variance over Mongolia.

The hindcast data of Pusan National University coupled general circulation model (PNU CGCM), a participant model of the Asia‐Pacific Economic Cooperation Climate Center (APCC) Multi‐Model Ensemble Climate Prediction System, and August–October sea‐surface temperature (SST) in the northern Barents–Kara Sea (BKI) and the sea‐ice extent (SIE) in the Chukchi Sea (East Siberian Sea index [ESI]) are used for predicting 20 × 20‐km‐resolution anomalous surface air temperature at 2‐m height (aT2m) over Mongolia for boreal winter. For this purpose, area‐averaged surface air temperature (TI) and sea‐level pressure (SLP) over Mongolia are defined. Then four large‐scale indices, TImdl and SHImdl obtained from PNU CGCM, and TIMLR and SHIMLR obtained from multiple linear regressions on BKI and ESI, are incorporated using the artificial neural network (ANN) method for the prediction and statistical downscaling to obtain the monthly and seasonal 20 × 20‐km‐resolution aT2m over Mongolia in winter. An additional statistical method, which uses BKI and ESI as predictors of TI and SHI together with dynamic prediction by the CGCM, is used because of the relatively low skill of seasonal predictions by most of the state‐of‐the‐art models and the multi‐model ensemble systems over high‐latitude landlocked Eurasian regions such as Mongolia. The results show that the predictabilities of monthly and seasonal 20 × 20‐km‐resolution aT2m over Mongolia in winter are improved by applying ANN to both statistical and dynamical predictions compared to utilizing only dynamic prediction. The predictability gained by the proposed method is also demonstrated by the probabilistic forecast implying that the method forecasts aT2m over Mongolia in winter reasonably well.