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1. Subseasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models

   https://doi.org/10.1175/AIES-D-22-0038.1  

   Weirich-Benet, Elizabeth ; Pyrina, Maria ; Jiménez-Esteve, Bernat ; Fraenkel, Ernest ; Cohen, Judah ; Domeisen, Daniela I. V. ( April 2023 , Artificial Intelligence for the Earth Systems)

   Heatwaves are extreme near-surface temperature events that can have substantial impacts on ecosystems and society. Early warning systems help to reduce these impacts by helping communities prepare for hazardous climate-related events. However, state-of-the-art prediction systems can often not make accurate forecasts of heatwaves more than two weeks in advance, which are required for advance warnings. We therefore investigate the potential of statistical and machine learning methods to understand and predict central European summer heatwaves on time scales of several weeks. As a first step, we identify the most important regional atmospheric and surface predictors based on previous studies and supported by a correlation analysis: 2-m air temperature, 500-hPa geopotential, precipitation, and soil moisture in central Europe, as well as Mediterranean and North Atlantic sea surface temperatures, and the North Atlantic jet stream. Based on these predictors, we apply machine learning methods to forecast two targets: summer temperature anomalies and the probability of heatwaves for 1–6 weeks lead time at weekly resolution. For each of these two target variables, we use both a linear and a random forest model. The performance of these statistical models decays with lead time, as expected, but outperforms persistence and climatology at all lead times. For lead times longer than two weeks, our machine learning models compete with the ensemble mean of the European Centre for Medium-Range Weather Forecast’s hindcast system. We thus show that machine learning can help improve subseasonal forecasts of summer temperature anomalies and heatwaves.

   Heatwaves (prolonged extremely warm temperatures) cause thousands of fatalities worldwide each year. These damaging events are becoming even more severe with climate change. This study aims to improve advance predictions of summer heatwaves in central Europe by using statistical and machine learning methods. Machine learning models are shown to compete with conventional physics-based models for forecasting heatwaves more than two weeks in advance. These early warnings can be used to activate effective and timely response plans targeting vulnerable communities and regions, thereby reducing the damage caused by heatwaves.

    

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2. Extreme stratospheric wave activity as harbingers of cold events over North America

   https://doi.org/10.1038/s43247-023-00845-y  

   Ding, Xiuyuan ; Chen, Gang ; Zhang, Pengfei ; Domeisen, Daniela I. V. ; Orbe, Clara ( May 2023 , Communications Earth & Environment)

   Extreme cold events over North America such as the February 2021 cold wave have been suggested to be linked to stratospheric polar vortex stretching. However, it is not resolved how robustly and on which timescales the stratosphere contributes to the surface anomalies. Here we introduce a simple measure of stratospheric wave activity for reanalyses and model outputs. In contrast to the well-known surface influences of sudden stratospheric warmings (SSWs) that increase the intraseasonal persistence of weather regimes, we show that extreme stratospheric wave events are accompanied by intraseasonal fluctuations between warm and cold spells over North America in observations and climate models. Particularly, strong stratospheric wave events are followed by an increased risk of cold extremes over North America 5–25 days later. Idealized simulations in an atmospheric model with a well-resolved stratosphere corroborate that strong stratospheric wave activity precedes North American cold spells through vertical wave coupling. These findings potentially benefit the predictability of high-impact winter cold extremes over North America.

    

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3. Mapping Large-Scale Climate Variability to Hydrological Extremes: An Application of the Linear Inverse Model to Subseasonal Prediction

   https://doi.org/10.1175/JCLI-D-20-0502.1  

   Tseng, Kai-Chih ; Johnson, Nathaniel C. ; Maloney, Eric D. ; Barnes, Elizabeth A. ; Kapnick, Sarah B. ( June 2021 , Journal of Climate)

   null (Ed.)

   Abstract The excitation of the Pacific–North American (PNA) teleconnection pattern by the Madden–Julian oscillation (MJO) has been considered one of the most important predictability sources on subseasonal time scales over the extratropical Pacific and North America. However, until recently, the interactions between tropical heating and other extratropical modes and their relationships to subseasonal prediction have received comparatively little attention. In this study, a linear inverse model (LIM) is applied to examine the tropical–extratropical interactions. The LIM provides a means of calculating the response of a dynamical system to a small forcing by constructing a linear operator from the observed covariability statistics of the system. Given the linear assumptions, it is shown that the PNA is one of a few leading modes over the extratropical Pacific that can be strongly driven by tropical convection while other extratropical modes present at most a weak interaction with tropical convection. In the second part of this study, a two-step linear regression is introduced that leverages a LIM and large-scale climate variability to the prediction of hydrological extremes (e.g., atmospheric rivers) on subseasonal time scales. Consistent with the findings of the first part, most of the predictable signals on subseasonal time scales are determined by the dynamics of the MJO–PNA teleconnection while other extratropical modes are important only at the shortest forecast leads. 

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4. Listening to Stakeholders: Initiating Research on Subseasonal-to-Seasonal Heavy Precipitation Events in the Contiguous United States by First Understanding What Stakeholders Need

   https://doi.org/10.1175/BAMS-D-20-0313.1  

   VanBuskirk, Olivia ; Ćwik, Paulina ; McPherson, Renee A. ; Lazrus, Heather ; Martin, Elinor ; Kuster, Charles ; Mullens, Esther ( October 2021 , Bulletin of the American Meteorological Society)

   Abstract Heavy precipitation events and their associated flooding can have major impacts on communities and stakeholders. There is a lack of knowledge, however, about how stakeholders make decisions at the subseasonal-to-seasonal (S2S) time scales (i.e., 2 weeks to 3 months). To understand how decisions are made and S2S predictions are or can be used, the project team for “Prediction of Rainfall Extremes at Subseasonal to Seasonal Periods” (PRES 2 iP) conducted a 2-day workshop in Norman, Oklahoma, during July 2018. The workshop engaged 21 professionals from environmental management and public safety communities across the contiguous United States in activities to understand their needs for S2S predictions of potential extended heavy precipitation events. Discussions and role-playing activities aimed to identify how workshop participants manage uncertainty and define extreme precipitation, the time scales over which they make key decisions, and the types of products they use currently. This collaboration with stakeholders has been an integral part of PRES 2 iP research and has aimed to foster actionable science. The PRES 2 iP team is using the information produced from this workshop to inform the development of predictive models for extended heavy precipitation events and to collaboratively design new forecast products with our stakeholders, empowering them to make more-informed decisions about potential extreme precipitation events. 

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5. The Role of the Stratosphere in Subseasonal to Seasonal Prediction: 2. Predictability Arising From Stratosphere‐Troposphere Coupling

   https://doi.org/10.1029/2019JD030923  

   Domeisen, Daniela I. V. ; Butler, Amy H. ; Charlton‐Perez, Andrew J. ; Ayarzagüena, Blanca ; Baldwin, Mark P. ; Dunn‐Sigouin, Etienne ; Furtado, Jason C. ; Garfinkel, Chaim I. ; Hitchcock, Peter ; Karpechko, Alexey Yu. ; et al ( January 2020 , Journal of Geophysical Research: Atmospheres)

   The stratosphere can have a significant impact on winter surface weather on subseasonal to seasonal (S2S) timescales. This study evaluates the ability of current operational S2S prediction systems to capture two important links between the stratosphere and troposphere: (1) changes in probabilistic prediction skill in the extratropical stratosphere by precursors in the tropics and the extratropical troposphere and (2) changes in surface predictability in the extratropics after stratospheric weak and strong vortex events. Probabilistic skill exists for stratospheric events when including extratropical tropospheric precursors over the North Pacific and Eurasia, though only a limited set of models captures the Eurasian precursors. Tropical teleconnections such as the Madden‐Julian Oscillation, the Quasi‐Biennial Oscillation, and El Niño–Southern Oscillation increase the probabilistic skill of the polar vortex strength, though these are only captured by a limited set of models. At the surface, predictability is increased over the United States, Russia, and the Middle East for weak vortex events, but not for Europe, and the change in predictability is smaller for strong vortex events for all prediction systems. Prediction systems with poorly resolved stratospheric processes represent this skill to a lesser degree. Altogether, the analyses indicate that correctly simulating stratospheric variability and stratosphere‐troposphere dynamical coupling are critical elements for skillful S2S wintertime predictions.

    

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