Potential applications of subseasonal‐to‐seasonal (S2S) predictions

Abstract

While seasonal outlooks have been operational for many years, until recently the extended‐range timescale referred to as subseasonal‐to‐seasonal (S2S) has received little attention. S2S prediction fills the gap between short‐range weather prediction and long‐range seasonal outlooks. Decisions in a range of sectors are made in this extended‐range lead time; therefore, there is a strong demand for this new generation of forecasts. International efforts are under way to identify key sources of predictability, improve forecast skill and operationalize aspects of S2S forecasts; however, challenges remain in advancing this new frontier. If S2S predictions are to be used effectively, it is important that, along with science advances, an effort is made to develop, communicate and apply these forecasts appropriately. In this study, the emerging operational S2S forecasts are presented to the wider weather and climate applications community by undertaking the first comprehensive review of sectoral applications of S2S predictions, including public health, disaster preparedness, water management, energy and agriculture. The value of applications‐relevant S2S predictions is explored, and the opportunities and challenges facing their uptake are highlighted. It is shown how social sciences can be integrated with S2S development, from communication to decision‐making and valuation of forecasts, to enhance the benefits of ‘climate services’ approaches for extended‐range forecasting. While S2S forecasting is at a relatively early stage of development, it is concluded that it presents a significant new window of opportunity that can be explored for application‐ready capabilities that could allow many sectors the opportunity to systematically plan on a new time horizon.

Skillful empirical subseasonal prediction of landfalling atmospheric river activity using the Madden–Julian oscillation and quasi-biennial oscillation

“…36 This MJO index is a combination of two component indices, RMM1 and RMM2, representing the two leading principal components from a multivariate (equatorially averaged tropical outgoing longwave radiation and 200-and 850-hPa zonal winds) empirical orthogonal function analysis. When combined and considered in terms of their two-dimensional phase space, these component indices provide daily phase (1)(2)(3)(4)(5)(6)(7)(8) and amplitude values (see ref. 36 ).…”

“…The QBO is not considered alone based on the presumption that the influence of the QBO on anomalous AR activity will primarily manifest via the modulation of the MJO's convection and its ability to elicit an extratropical response. Hence, the predictors investigated in this study are simply the daily MJO phase (1)(2)(3)(4)(5)(6)(7)(8) and the monthly QBO phase (EQBO or WQBO). …”

“…[1][2][3] Thus, opportunities abound to add far-reaching value to society with skillful predictions of extreme, and frequently hazardous, weather events that occur within this so-called subseasonal-to-seasonal gap. 3 Sectors such as agriculture, energy production, resource management, and insurance stand to benefit from advance notice of weather extremes in order to prepare for such events.…”

On climate prediction: how much can we expect from climate memory?

“…It is not only important for scientific researches, but also relevant for societal sectors including agriculture, water management, energy and health (Brunet et al 2010;Robertson and Wang 2012;Clements et al 2013;Raff et al 2013;White et al 2017). During the past years, many studies have been presented that target at seasonal climate predictions (van den Dool 2007;Fan 2010;Bengtsson et al 1993;Stockdale et al 1998), while studies focusing on subseasonal to seasonal (S2S), annual, and decadal scales are also growing in number promoted by international projects (Vitart et al 2012;Robertson et al 2014;García-Serrano and Doblas-Reyes 2012).…”

Incorporating the effect of weather in construction scheduling and management with sine wave curves: application in the United Kingdom

“…These types of projects normally have large budgets, and thus they are more likely to have resources for monitoring how and when on-site weather will cause changes to scheduled activities. Developments in sub-seasonal and seasonal weather forecasting have shown early promise too, but forecasts of acceptable quality still just span a maximum 10-d window (White et al 2017).…”