Hidden Potential in Predicting Wintertime Temperature Anomalies in the Northern Hemisphere

Dobrynin, Mikhail; Düsterhus, André; Fröhlich, Kristina; Athanasiadis, Panos; Ruggieri, Paolo; Müller, Wolfgang A.; Baehr, Johanna

doi:10.1029/2021gl095063

Cited by 5 publications

(1 citation statement)

References 36 publications

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“…New approaches have been proposed to improve AOGCM temperature forecast skill in Europe at seasonal to decadal scales, by capitalizing on process differences within AOGCM ensembles (e.g., Dobrynin et al., 2018 , 2022 ; Dusterhus, 2020 ; Smith et al., 2020 ). Each model is comprised of multiple members, which when initialized, represent a range of “guesses” of current day states to account for observational uncertainty (Balmaseda et al., 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Process‐Informed Subsampling Improves Subseasonal Rainfall Forecasts in Central America

Kowal,

Slater,

et al. 2024

Geophysical Research Letters

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Subseasonal rainfall forecast skill is critical to support preparedness for hydrometeorological extremes. We assess how a process‐informed evaluation, which subsamples forecasting model members based on their ability to represent potential predictors of rainfall, can improve monthly rainfall forecasts within Central America in the following month, using Costa Rica and Guatemala as test cases. We generate a constrained ensemble mean by subsampling 130 members from five dynamic forecasting models in the C3S multimodel ensemble based on their representation of both (a) zonal wind direction and (b) Pacific and Atlantic sea surface temperatures (SSTs), at the time of initialization. Our results show in multiple months and locations increased mean squared error skill by 0.4 and improved detection rates of rainfall extremes. This method is transferrable to other regions driven by slowly‐changing processes. Process‐informed subsampling is successful because it identifies members that fail to represent the entire rainfall distribution when wind/SST error increases.

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Section: Introductionmentioning

confidence: 99%

Process‐Informed Subsampling Improves Subseasonal Rainfall Forecasts in Central America

Kowal,

Slater,

et al. 2024

Geophysical Research Letters

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Skillful seasonal prediction of the boreal summer Pacific–Japan teleconnection pattern

Yi,

Wang,

Zhang

et al. 2024

Atmospheric Science Letters

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The Pacific–Japan (PJ) teleconnection pattern, a dominant mode of atmospheric variability over the western North Pacific during boreal summer, is pivotal in shaping regional climate dynamics. Despite its important implications, accurately predicting the PJ pattern remains challenging due to inherent model biases and uncertainties. This study delves into the impact of model biases on the prediction skill of the PJ pattern and evaluates its predictability using outputs from three operational seasonal forecast models. Our findings elucidate that the spatial structure of the PJ pattern simulated by models introduces substantial diversities in prediction skills. By discerning the variance in PJ teleconnection simulation among models, we unveil the high predictability of the PJ pattern, showcasing its capability for accurate forecasts up to 3 months in advance within the current seasonal forecast models. The predictability of the PJ pattern stems from concurrent El Niño–Southern Oscillation‐related sea surface temperature anomalies and its corresponding atmospheric teleconnection processes. Our research underscores the necessity of accounting for model biases in predicting the PJ pattern, and the potential for bolstering seasonal prediction skill through targeted mitigation of these biases.

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Hybrid statistical–dynamical seasonal prediction of summer extreme temperatures in Europe

Famooss Paolini,

Ruggieri,

Pascale

et al. 2024

Quart J Royal Meteoro Soc

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Although summer extreme temperatures over Europe are potentially predictable on seasonal time‐scales, state‐of‐the‐art dynamical seasonal prediction systems (SPSs) exhibit low skills in predicting such events in central and northern Europe. This limitation arises from the underestimation of predictable components of climate variability in the model ensemble. However, recent studies suggest that the skills in predicting extratropical climate can be largely improved through statistical postprocessing techniques, which increase the signal‐to‐noise ratio in the model ensemble. In this study, we evaluate the potential for improving the seasonal prediction skills of European summer extreme temperatures in a multimodel ensemble (MME) of SPSs by applying a teleconnection‐based subsampling technique in the hindcast period 1993–2016. This technique is applied to the North Atlantic Oscillation (NAO) and East Atlantic (EA) modes, which are key drivers of summer extreme temperatures in Europe. Results show that the subsampling substantially improves the MME prediction skills of both the summer NAO and EA. Specifically, correlations between the observed and subsampled MME NAO indices improve from to 0.77, and for the EA they improve from −0.11 to 0.84. Similarly, the root‐mean‐square error of the subsampled MME NAO (EA) index improves from 1.06 (1.02) to 0.65 (0.56). Moreover, retaining those ensemble members that accurately represent the NAO teleconnections enhances the MME prediction skills for the summer European climate, including the occurrence of summer extreme temperatures. This improvement is particularly pronounced in central and northern Europe; that is, the regions where current SPSs show the lowest skills in predicting European heat extremes. In contrast, selecting ensemble members that accurately represent the EA teleconnections does not improve the predictions of summer extreme temperatures. This is likely associated with the model deficiencies in realistically representing the spatial pattern of the summer EA and, thus, the physical processes driving summer extreme temperatures.

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Hidden Potential in Predicting Wintertime Temperature Anomalies in the Northern Hemisphere

Cited by 5 publications

References 36 publications

Process‐Informed Subsampling Improves Subseasonal Rainfall Forecasts in Central America

Process‐Informed Subsampling Improves Subseasonal Rainfall Forecasts in Central America

Skillful seasonal prediction of the boreal summer Pacific–Japan teleconnection pattern

Hybrid statistical–dynamical seasonal prediction of summer extreme temperatures in Europe

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