Regional climate models (RCMs) include both terrestrial and atmospheric compartments and thereby allow studying land-atmosphere feedback, in particular, the impact of land-use land cover driven by biogeophysical processes on regional climate. In this study, a method is developed to separate the signals from the noise in RCM simulations of the effects of changes in land use, using perturbed initial boundary conditions (PICs). We want to know how many ensemble members are required to identify robust and statistically significant land-use land cover change (LULCC) effects from RCM LULCC studies. The method is applied to a case study of urbanization and deforestation, for which LULCC scenarios are implemented in the RCM Weather Research and Forecasting (WRF). Based on WRF ensemble simulations with PICs for 2010, the signal-to-noise ratio (SNR) is used to identify areas with pronounced effect of an LULCC or, rather, the parametrization of the land-use classes. While in the urbanization scenarios clear and statistically significant signals are found for air temperature and for both latentand sensible heat (SNR values up to 24), the effects are less pronounced for precipitation, and for deforestation in general (SNR values < 1). For the case study of urbanization and precipitation, the impact of the ensemble size is studied in order to derive robust conclusions about the effects of LULCC on precipitation. We conclude that single RCM realizations of different land-use representations are not sufficient to derive LULCC-induced signals, particularly not for precipitation. Small ensemble sizes led to concluding there were significant LULCC-induced precipitation signals, but these disappeared when the ensemble size was increased. Our regional analysis suggests the need for ensemble sizes well above 10 for precipitation.

To investigate the ability of dynamical seasonal climate predictions for Vietnam, the RegCM4.2 is employed to perform seasonal prediction of 2 m mean (T2m), maximum (Tx), and minimum (Tn) air temperature for the period from January 2012 to November 2013 by downscaling the NCEP Climate Forecast System (CFS) data. For model bias correction, the model and observed climatology is constructed using the CFS reanalysis and observed temperatures over Vietnam for the period 1980–2010, respectively. The RegCM4.2 forecast is run four times per month from the current month up to the next six months. A model ensemble prediction initialized from the current month is computed from the mean of the four runs within the month. The results showed that, without any bias correction (CTL), the RegCM4.2 forecast has very little or no skill in both tercile and value predictions. With bias correction (BAS), model predictions show improved skill. The experiment in which the results from the BAS experiment are further successively adjusted (SUC) with model bias at one-month lead time of the previous run showed further improvement compared to CTL and BAS. Skill scores of the tercile probability forecasts were found to exceed 0.3 for most of the target months.

Cộng, trừ có nhớ trong phạm vi 100 là một chủ đề kiến thức cơ bản và quan trọng trong Toán 2. Các công thức, kỹ thuật làm tính của chủ đề là cơ sở để thực hiện tất cả các phép cộng, trừ có nhớ với số có nhiều chữ số. Cấu trúc nội dung của từng bài và của chủ đề có những điểm đặc biệt. Việc khai thác đặc điểm cấu trúc nội dung, để tổ chức dạy học cộng, trừ có nhớ trong phạm vi 100 theo hướng tích cực là vấn đề cần được quan tâm đúng mức khi dạy học Toán 2.