Introduction to Special Collection: “Bridging Weather and Climate: Subseasonal‐to‐Seasonal (S2S) Prediction”

Lang, Alex H.; Pegion, Kathy; Barnes, Elizabeth A.

doi:10.1029/2019jd031833

Cited by 32 publications

(23 citation statements)

References 50 publications

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“…There has been continued development and production of climate forecasts from national and international agencies at ever higher resolution and with incremental improvements in skill (Kirtman et al., 2013; Wanders et al., 2019). Of note is the push for extended skill at sub‐seasonal time scales (beyond 2 weeks but less than seasonal) (Lang et al., 2020) that has important implications for a range of applications, including disaster risk reduction and for different economic sectors (Morss et al., 2008). Criteria for choosing a set of seasonal climate models are based on their resolution (space and time), availability of hindcasts (historic forecasts) to evaluate their skill, the skill of the models for the particular application, and availability of forecasts operationally.…”

Section: Introductionmentioning

confidence: 99%

Performance of State‐of‐the‐Art C3S European Seasonal Climate Forecast Models for Mean and Extreme Precipitation Over Africa

Gebrechorkos

Pan

Beck

et al. 2022

Water Resources Research

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Seasonal hydrological forecasts at high spatial and temporal resolution can help manage water resources and mitigate impacts of extreme events but are dependent on skillful and operational seasonal forecasts from climate models. In this study, we evaluate precipitation forecasts from five operational climate models with a potential to drive hydrological forecasts: European Centre for Medium‐Range Weather Forecasts (ECMWF), UK Met Office (UK‐Met), Météo France, Deutscher Wetterdienst, and Centro Euro‐Mediterraneo sui Cambiamenti Climatici. The Multi‐Source Weighted‐Ensemble Precipitation is used as a reference data set to evaluate the model skill. The performance of individual models is evaluated on daily, weekly, monthly, seasonal, and climatological periods, and for selected target months, lead‐times and drought events, and compared to unweighted and skill‐weighted multi‐model ensemble mean forecast. For all models, the lead 1‐month forecast can replicate the climatological mean, monthly mean, and monthly anomaly precipitation, although much of this skill originates from the first week of the forecast. The skill drops rapidly for lead 2‐month and longer and is highest in drier regions and seasons. The forecast skill of monthly meteorological drought events at lead 1‐month is modest. All models represent the monthly variation in the length of wet and dry spell days at lead 1‐month, but the skill is weak for heavy and very heavy precipitation days. ECMWF is found to be the most skillful model, followed by the UK‐Met, although the multi‐model weighted average provides the highest performance compared to individual models and the un‐weighted multi‐model mean.

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Section: Introductionmentioning

confidence: 99%

Performance of State‐of‐the‐Art C3S European Seasonal Climate Forecast Models for Mean and Extreme Precipitation Over Africa

Gebrechorkos

Pan

Beck

et al. 2022

Water Resources Research

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“…Knowing the temporal correlations of the daily NAO index beyond the synoptic time scale could contribute to exploring the possibilities for subseasonal‐to‐seasonal (S2S) predictions in the North Atlantic area and Europe. There are ongoing international efforts to advance the capability of S2S predictions (Lang et al., 2020; Mariotti et al., 2018, 2020). Present S2S predictions of the NAO are generally not without problems (Albers & Newman, 2021; Smith et al., 2016), most likely related to the signal‐to‐noise paradox (Dunstone et al., 2019).…”

Section: Discussionmentioning

confidence: 99%

Asymmetries in Circulation Anomalies Related to the Phases of the North Atlantic Oscillation on Synoptic Time Scales

Schmith

Olsen

Yang

et al. 2022

Geophysical Research Letters

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The North Atlantic Oscillation (NAO) index is often characterized by independent positive and negative NAO events with a characteristic spatial pattern and a typical lifetime of around 1 week. These events are separated by periods of near‐neutral NAO conditions. Here, we challenge this view by showing in reanalysis and observed data that the strength and spatial shape of NAO events depends on the NAO index prior to the window of 1 week and this dependency is most pronounced for negative NAO events. The influence is seen in the mean sea level pressure, and in other important features, including blocking frequency and jet stream characteristics, and also in air surface temperature and precipitation in parts of Europe. This new appreciation is important for efforts to improve methods for subseasonal‐to‐seasonal predictions of NAO.

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“…Indeed, subseasonal forecasting has long been considered a "predictability desert" due to its complex dependence on both local weather and global climate variables (Vitart et al, 2012). Nevertheless, recent large-scale research efforts have advanced the subseasonal capabilities of operational physics-based models (Vitart et al, 2017;Pegion et al, 2019;Lang et al, 2020), while parallel efforts have demonstrated the value of machine learning and deep learning methods in improving subseasonal forecasting (Li et al, 2016;Cohen et al, 2018;Hwang et al, 2019;Arcomano et al, 2020;He et al, 2020;Yamagami & Matsueda, 2020;Wang et al, 2021;Watson-Parris, 2021;Weyn et al, 2021;Srinivasan et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Learned Benchmarks for Subseasonal Forecasting

Mouatadid¹,

Orenstein²,

Flaspohler³

et al. 2021

Preprint

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We develop a subseasonal forecasting toolkit of 3 simple learned models that enhance the skill of dynamical models and statistical baselines • The toolkit is more skillful than the US and European operational dynamical models and 7 state-of-the-art machine and deep learning methods • To facilitate future development, we release our dataset and Python code for the toolkit, machine learning models, and statistical baselines

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Introduction to Special Collection: “Bridging Weather and Climate: Subseasonal‐to‐Seasonal (S2S) Prediction”

Cited by 32 publications

References 50 publications

Performance of State‐of‐the‐Art C3S European Seasonal Climate Forecast Models for Mean and Extreme Precipitation Over Africa

Performance of State‐of‐the‐Art C3S European Seasonal Climate Forecast Models for Mean and Extreme Precipitation Over Africa

Asymmetries in Circulation Anomalies Related to the Phases of the North Atlantic Oscillation on Synoptic Time Scales

Learned Benchmarks for Subseasonal Forecasting

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