Advances in the Application and Utility of Subseasonal-to-Seasonal Predictions

Abstract

The subseasonal-to-seasonal (S2S) predictive timescale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this timescale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a ‘knowledge-value’ gap, where a lack of evidence and awareness of the potential socio-economic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development – demonstrating both skill and utility across sectors – this dialogue can be used to help promote and accelerate the awareness, value and co-generation of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting timescale.

Quantifying the Effect of Climate Change on Midlatitude Subseasonal Prediction Skill Provided by the Tropics

“…While accurate subseasonal predictions are important for society (White et al., 2022), this timescale is known to exhibit limited predictability (Vitart et al., 2017). One method to improve prediction skill on subseasonal timescales is to utilize Earth system states which are known to provide enhanced subseasonal predictability when they are present (forecasts of opportunity; Mariotti et al., 2020).…”

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Accurate predictions on subseasonal timescales (2 weeks-2 months) are important for many public and private sectors such as water management and agriculture (White et al, 2022). This is because prediction on these timescales provides pivotal lead times for saving lives and property in these sectors (White et al, 2022). The tropics is of particular importance for this timescale because of intraseasonal phenomena like the Madden-Julian Oscillation (MJO; Madden & Julian, 1971, 1972.

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Accurate predictions on subseasonal timescales (2 weeks-2 months) are important for many public and private sectors such as water management and agriculture (White et al, 2022). This is because prediction on these timescales provides pivotal lead times for saving lives and property in these sectors (White et al, 2022).

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Predictive Skill of Teleconnection Patterns in Twentieth Century Seasonal Hindcasts and Their Relationship to Extreme Winter Temperatures in Europe

“…Many applications across a multitude of sectors benefit from skillful sub‐seasonal to seasonal climate predictions, for example, disaster preparedness for extreme events, long‐term flood and drought risks or potential threats to food security (White et al., 2021). Here, we focused on large‐scale circulation patterns and their impact on temperature extremes during winter, which are particularly relevant for the energy industry (Orlov et al., 2020).…”

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Skillful seasonal forecasts provide valuable contextual information to various decision makers across a number of sectors, such as energy, public health, water management, disaster risk management, and agriculture (Bruno Soares & Dessai, 2016;White et al, 2021). In particular, the energy sector is strongly impacted by climate variability, as present-day power system demand is strongly correlated with near-surface temperatures (Bessec & Fouquau, 2008;Bloomfield et al, 2021b;Taylor & Buizza, 2003) and conventional (e.g., coal, gas, and nuclear power), as well as renewable energies (e.g., wind, solar, and hydro power) all have various degrees of sensitivity to weather and climate (Orlov et al, 2020).

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