Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities

Sillmann, Jana; Thorarinsdottir, Thordis L.; Keenlyside, Noel; Schaller, Nathalie; Alexander, Lisa V.; Hegerl, Gabriele C.; Seneviratne, Sonia I.; Vautard, Robert; Zhang, Xuebin; Zwiers, Francis W.

doi:10.1016/j.wace.2017.10.003

Cited by 245 publications

(190 citation statements)

References 80 publications

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“…Overall, coupled climate models are preferred when evaluating the impact of soil moisture and vegetation on large‐scale droughts and heatwaves; regional climate models are often applied to reproduce the effect on land conditions on particular events, while global climate models can be used to evaluate the impact of land surface conditions on the statistics of climate extremes . The latter can be done within a detection‐and‐attribution framework to jointly assess the influence of anthropogenic emissions . These coupled model experiments are often done in pairs, with one simulation serving as a control run, and a second with prescribed land conditions (i.e., soil moisture, leaf area index, and surface albedo).…”

Section: Findings From Coupled Climate Modelsmentioning

confidence: 99%

“…These coupled model experiments are often done in pairs, with one simulation serving as a control run, and a second with prescribed land conditions (i.e., soil moisture, leaf area index, and surface albedo). As such, the comparison of both simulations allows the discrimination of the impact of land conditions on climate . Regional climate simulations—in which the boundary conditions are typically prescribed based on reanalysis data of temperature, humidity, and wind profiles—are frequently used to investigate single events .…”

Section: Findings From Coupled Climate Modelsmentioning

confidence: 99%

“…Nonetheless, it is not the purpose of this perspective paper to review the progress in what relates to the future projection of global temperature and precipitation extremes, thus the reader is directed to, for example, Sillmann et al . and references therein for an overview on that topic . The following section focuses on the regional‐scale simulation of specific drought and heatwave events.…”

Section: Findings From Coupled Climate Modelsmentioning

confidence: 99%

“…); this situation is more likely to occur in regions that rely heavily on rainfall of terrestrial origin, such as large parts of North America, Eurasia, and West Africa . Furthermore, the accompanied impact of land desiccation on long‐range heat and water vapor circulation may favor (or disfavor) the occurrence of new extreme events in other locations . However, efforts so far have concentrated either on land feedbacks on rainfall in situ (Fig.…”

Section: Current Challengesmentioning

confidence: 99%

“…Therefore, these remote interactions, which are ultimately regulated by land conditions, remain largely unstudied, and their role in the propagation of droughts and heatwaves remains unknown. The conceptual diagram in Figure expands upon the processes involved in the (local) self‐intensification illustrated in Figure to embrace this hypothetical (remote) self‐propagation of a drought event.…”

Section: Current Challengesmentioning

confidence: 99%

See 4 more Smart Citations

Land–atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges

Miralles

Gentine

Seneviratne

et al. 2018

Annals of the New York Academy of Sciences

Self Cite

588

434

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Droughts and heatwaves cause agricultural loss, forest mortality, and drinking water scarcity, especially when they occur simultaneously as combined events. Their predicted increase in recurrence and intensity poses serious threats to future food security. Still today, the knowledge of how droughts and heatwaves start and evolve remains limited, and so does our understanding of how climate change may affect them. Droughts and heatwaves have been suggested to intensify and propagate via land-atmosphere feedbacks. However, a global capacity to observe these processes is still lacking, and climate and forecast models are immature when it comes to representing the influences of land on temperature and rainfall. Key open questions remain in our goal to uncover the real importance of these feedbacks: What is the impact of the extreme meteorological conditions on ecosystem evaporation? How do these anomalies regulate the atmospheric boundary layer state (event self-intensification) and contribute to the inflow of heat and moisture to other regions (event self-propagation)? Can this knowledge on the role of land feedbacks, when available, be exploited to develop geo-engineering mitigation strategies that prevent these events from aggravating during their early stages? The goal of our perspective is not to present a convincing answer to these questions, but to assess the scientific progress to date, while highlighting new and innovative avenues to keep advancing our understanding in the future.

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Section: Findings From Coupled Climate Modelsmentioning

confidence: 99%

Section: Findings From Coupled Climate Modelsmentioning

confidence: 99%

Section: Findings From Coupled Climate Modelsmentioning

confidence: 99%

Section: Current Challengesmentioning

confidence: 99%

Section: Current Challengesmentioning

confidence: 99%

See 3 more Smart Citations

Land–atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges

Miralles

Gentine

Seneviratne

et al. 2018

Annals of the New York Academy of Sciences

Self Cite

588

434

View full text Add to dashboard Cite

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Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

Bhagavathi

Thavasimuthu

Murugesan

et al. 2021

Int J Communication

138

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In this research, a weather forecasting model based on machine learning is proposed for improving the accuracy and efficiency of forecasting. The aim of this research is to propose a weather prediction model for short-range prediction based on numerical data. Daily weather prediction includes the work of thousands of worldwide meteorologists and observers. Modernized computers make predictions more precise than ever, and earth-orbiting weather satellites capture pictures of clouds from space. However, in many cases, the forecast under many conditions is not accurate. Numerical weather prediction (NWP) is one of the popular methods for forecasting weather conditions. NWP is a major weather modeling tool for meteorologists which contributes to more accurate accuracy. In this research, the weather forecasting model uses the C5.0 algorithm with K-means clustering. The C5.0 is one of the better decision tree classifiers, and the decision tree is a great alternative for forecasting and prediction. The algorithm for clustering the K-means is used to combine identical data together. For this process, the clustering of K-means is initially applied to divide the dataset into the closest cluster of K. For training and testing, the meteorological data collection obtained from the database Modern-Era Historical Analysis for Research and Applications (MERRA) is used. The model's performance is assessed through MAE mean absolute error (MAE) and root mean square error (RMSE). And the proposed model is assessed with accuracy, sensitivity, and specificity for validation. The results obtained are compared with other current machine learning approaches, and the proposed model achieved predictive accuracy of 90.18%.

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Modeling monthly and seasonal Michigan snowfall based on machine learning: A multiscale approach

Meng

Zhu

2022

Preprint

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Snowfall has important significance in water resources management and disaster prevention worldwide. Accurate prediction of both mean and extreme snowfall is challenging because of multiple controlling mechanisms at different spatial and temporal scales. By using a 65 years long in-situ snowfall observation, we evaluated seven different machine learning algorithms for predicting monthly snowfall in the Lower Peninsula of Michigan (LPM). The Bayesian Additive Regression Trees (BART) demonstrates the best fitting (R2 = 0.88) and out-of-sample prediction skills (R2 = 0.58) for the monthly mean snowfall followed by the Random Forest model. The BART also demonstrate strong predictive skills for seasonal and the extreme

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Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities

Cited by 245 publications

References 80 publications

Land–atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges

Land–atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges

Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

Modeling monthly and seasonal Michigan snowfall based on machine learning: A multiscale approach

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