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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]

Effective selection of climate predictors is a fundamental aspect of climate modeling research. Predictor Screening (PS) plays a crucial role in identifying regional climate drivers, reducing noise, expediting convergence, and minimizing time consumption, ultimately leading to the development of robust models. This review delves into the complex landscape of PS techniques within the context of Numerical Climate Modeling (NCM), with a specific focus on their applicability across various Köppen climate classifications and PS model structures. The analysis revealed substantial variations in the performance of PS methods, shedding light on their ability to capture –and prioritize predictors related to precipitation and temperature within distinct climate contexts. Furthermore, the provided methods have been categorized into two subsections: Feature Selection (FS) and Feature Extraction (FE), with FS encompassing filter, wrapper, embedded, and ensemble/hybrid techniques, and FE covering Linear Feature Extraction (LFE), Time-Domain Analysis (TDA), deep learning, and clustering methods. The initial compilation of papers, acquired through a keyword search on Scopus, consisted of 3650 documents. Following a meticulous evaluation process, 206 papers were identified as fitting for inclusion in the literature review, covering the time frame from 1974 to November 3, 2023. In conclusion, the results provide a detailed understanding of the strengths and limitations of each approach, establishing a hierarchy of effectiveness contingent upon the specific climate context. Additionally, insights into promising avenues for future research in this field are offered. This review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) standard as its foundation. [ABSTRACT FROM AUTHOR]

In 2017 the paper 'The Sustainable Development Oxymoron: Quantifying and Modelling the Incompatibility of Sustainable Development Goals' was published, showing that there is a conflict between socio-economic development goals and ecological sustainability goals using cross-country time-series data. The authors looked at production-based CO2 emissions to measure and model the 13th SDG goal addressing climate change. Their models showed that production-based CO2 emissions were stalling or even decreasing in rich countries, which suggests that other countries are also likely to see stalling and decrease in their CO2 emissions once they become rich. However, this conclusion can be challenged when accounting for consumption-based CO2 emissions rather than production-based CO2 emissions. In this follow-up paper, we re-run some of the analyses performed in the original paper making use of consumption-based CO2 emissions. The analysis confirms the inherent SDG conflict between socio-economic and ecological SDGs. But, this new analysis demonstrates that from a consumption perspective the trend of stalling or decreasing CO2 emissions is reversed, with natural depletion costs being exported to poorer countries. Despite this new perspective on CO2 emissions, the conflict between SDG goals can still be avoided by making investments in public health, education and renewable energy, as suggested in the original paper. [ABSTRACT FROM AUTHOR]

Artificial neural networks are widely applied for different forecasting applications including rainfall forecasting. The climate input features and parameters for neural networks highly affect the overall performance of the prediction model. Therefore, an appropriate approach for the selection of features and parameters is needed. In this paper, a novel approach is proposed to select the input features and neural network parameters. A hybrid genetic algorithm that combines natural reproduction and particle swarm optimization characteristics was developed to select the best input features and network parameters for each month. The developed model was compared against alternative climate and network parameters feature selection model, climate feature selection model and climatology where a better accuracy was recorded with the proposed model. The skill score against the three alternative climate models was 17.41, 21.68 and 32.12%, respectively. The aggregated time series of the proposed model showed a root-mean-square error of 141.67 mm for a location with 3553.00 mm annual average. [ABSTRACT FROM AUTHOR]

Prediction of aerosol optical thickness (AOT) is important to study worldwide climate changes. Researchers have built multiple AOT prediction models. However, few researches were focused on the validation of input attributes for AOT regression. In this paper, we proposed a support vector regression (SVR) model-based sensitivity analysis approach to order 35 MODIS input attributes according to their sensitivity to prediction outputs. Next, the attribute sensitivity orders are used for feature selection in the context of regression by removing insensitive attribute one at a time or by removing attributes whose sensitive orders are larger than number k. The experimental results based on the collocated data between MODIS and AERONET from 2009 to 2011 showed that the top 10 insensitive attributes can be screened to speed up prediction model computation with very little loss of accuracy. The results also suggested that the top sensitive attributes are the most informative attributes, requiring the highest precision for accurate AOT prediction. Thereby, our approach will be valuable for remote sensing scientists or atmospheric scientists to optimize the design precision of top sensitive attributes in scanning equipment like MODIS and therefore improve AOT retrieval accuracy. [ABSTRACT FROM AUTHOR]