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Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles. [ABSTRACT FROM AUTHOR]

The availability of reliable meteorological records is crucial for the development of a number of environmental studies. Unfortunately, these records are not always complete, usually show errors and/or have an insufficient length. This paper presents a gap filling and data record extension methodology for minimum temperature, maximum temperature, and precipitation. It uses climatic information from the NCEP-NCAR Reanalysis project, identifying pixels (grid cells) within a Reanalysis domain that have the highest Pearson's correlation coefficient with the variable of interest. Nine stations in the Maipo River basin (Santiago, Chile) were selected for a reconstruction experiment (from 1950 to 1970) and a subsequent gap filling experiment (from 1970 to 2012). A generalized linear mixed model with a bidirectional stepwise fit procedure was used to model temperature, whereas precipitation occurrence was represented using a generalized linear mixed model with binomial distribution, and precipitation amount used an exponential generalized linear model. The performance of the algorithm was compared with inverse distance weighting and spline interpolation methods and further evaluated using the Standardized Precipitation Evapotranspiration Index, contrasting real versus modeled data. Values of the coefficient of determination averaged 0.76 (0.74–0.84) minimum temperature, 0.73 (0.73–0.81) for maximum temperature, and 0.68 (0.51–0.78) for precipitation. Root-mean-squared error was around 1.5 °C and 5 mm for temperature and precipitation, respectively. The model explains local variation of climatic variables and indicators and can be replicated anywhere, as the Reanalysis data are easily accessible and have a worldwide coverage. [ABSTRACT FROM AUTHOR]

This paper examines an asymmetric spatiotemporal connection and climatic impact between the winter atmospheric blocking activity in the Euro-Atlantic sector and the life cycle of the North Atlantic Oscillation (NAO) during the period 1950-2012. Results show that, for positive NAO (NAO+) events, the instantaneous blocking (IB) frequency exhibits an enhancement along the southwest-northeast (SW-NE) direction from the eastern Atlantic to northeastern Europe (SW-NE pattern, hereafter), which is particularly evident during the NAO+ decaying stage. By contrast, for negative NAO (NAO−) events, the IB frequency exhibits a spatially asymmetric southeast-northwest (SE-NW) distribution from central Europe to the North Atlantic and Greenland (SE-NW pattern, hereafter). Moreover, for NAO− (NAO+) events, the most marked decrease (increase) in the surface air temperature (SAT) in winter over northern Europe is in the decaying stage. For NAO+ events, the dominant positive temperature and precipitation anomalies exhibit the SW-NE-oriented distribution from western to northeastern Europe, which is parallel to the NAO+-related blocking frequency distribution. For NAO- events, the dominant negative temperature anomaly is in northern and central Europe, whereas the dominant positive precipitation anomaly is distributed over southern Europe along the SW-NE direction. In addition, the downward infrared radiation controlled by the NAO’s circulation plays a crucial role in the SAT anomaly distribution. It is further shown that the NAO’s phase can act as an asymmetric impact on the European climate through producing this asymmetric spatiotemporal connection with the Euro-Atlantic IB frequency. [ABSTRACT FROM AUTHOR]