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.
A coarse resolution model is developed to study the thermohaline circulation of the North Atlantic. This model is driven by the annual mean Hellerman and Rosenstein wind stress field, Levitus sea surface restoring temperatures, and Schmitt, Bogden, and Dorman freshwater flux fields (mixed boundary conditions) together with various parameterizations of Arctic freshwater export into the North Atlantic. The model simulations indicate the existence of self-sustained, internal variability of the thermohaline circulation with a period of about 20 years. Associated with the variability is a large variation in the deep-water formation rate in the Labrador Sea and hence the poleward heat transport in the North Atlantic. It is shown that the variability is insensitive to the freshwater flux and wind forcing used and that the timescale for this thermally driven convective/advective oscillation is set by the cooling time of the Labrador Sea. The variability is robust to various parameterizations of Arctic freshwater export but may be suppressed if there is a strong freshwater flux through the Canadian Archipelago (or equivalently, large precipitation) into the Labrador Sea. The importance of topography, although poorly resolved in this coarse resolution study, is addressed and the results are compared with a coupled atmosphereocean simulation and observations taken over the North Atlantic. Associated with the decadal/interdecadal thermohaline 12,423 12,424 WEAVER ET AL.' INTERDECADAL NORTH ATLANTIC VARIABILITY variability found in the coarse resolution simulations of Weaver and Sarachik [1991a, b] were large changes in the poleward heat transport. The changes in heat transport corresponded directly to changes in the heat lost/gained to the overlying atmosphere at high latitudes, as the ocean stored little heat during the oscillation. Weaver et al. [1991]concluded that the existence of decadal/interdecadal variability was linked to the existence of a large area of negative precipitation-evaporation (PE) at middle to high latitudes, together with freshwater gain further north. The meridional gradients in the freshwater flux-forcing field also had to be sufficiently strong so that the system was in a "haline dominant" regime. Weaver et al. [1991] suggested that when the thermohaline circulation was weak, it slowly passed through the region with negative PE, and hence the surface waters become more saline. A warm, saline surface anomaly then developed through convection and this anomaly was advected to the eastern boundary by the mean flow where it was convected to the deeper ocean (as in Weaver and Sarachik [1991b]), leading to the subsequent generation of a reverse cell which in turn caused the thermohaline circula-
One major challenge facing farmers and other end users of weather and climate information (WCI) in Kenya is the linkage between their perceptions, needs, and engagements with producers of the information. This is highlighted by increased interest in understanding the constraints on appropriate use of weather information by farmers in decision-making. The choice between sub-seasonal and seasonal forecasts can enable better decisions by farmers if the forecast information is reliable and integrated through a coproduction process. This study analyzes user needs and perceptions of crop farmers, pastoralists, and agro-pastoralists in relation to sub-seasonal and seasonal forecasts for five counties in Kenya. A total of 258 peer-reviewed articles and gray literature were systematically analyzed using Search, Appraisal, Synthesis and Analysis (SALSA) to understand how the needs and perceptions of users of WCI shaped access and use in decision-making. The study also evaluated factors influencing use and uptake of sub-seasonal and seasonal forecasts as well as the barriers to use. Results show that farmers' perceptions shaped the choice of WCI that is used and also highlight how sub-seasonal and seasonal forecasts were used for diverse applications. Gender, availability of resources, access, and mode of communication were key factors influencing the use of seasonal forecasts. For example, access to seasonal forecasts of farmers in drier counties enabled them to manage floods and reduce risk. One lesson learned was that farmers combined WCI with other coping practices such as agronomic practices and water efficiency management. Despite a number of challenges by forecast users such as insufficient resources and lack of access to information, there is potential to improve forecasts according to user needs through a coproduction process. This study recommends stakeholder engagements with producers in the development and evaluation of forecast products and communication pathways to improve uptake and use of forecasts in decision-making.
This commentary discusses new advances in the predictability of east African rains and highlights the potential for improved early warning systems (EWS), humanitarian relief efforts, and agricultural decision‐making. Following an unprecedented sequence of five droughts, 23 million east Africans faced starvation in 2022, requiring >$2 billion in aid. Here, we update climate attribution studies showing that these droughts resulted from an interaction of climate change and La Niña. Then we describe, for the first time, how attribution‐based insights can be combined with the latest dynamical models to predict droughts at 8‐month lead‐times. We then discuss behavioral and social barriers to forecast use, and review literature examining how EWS might (or might not) enhance agro‐pastoral advisories and humanitarian interventions. Finally, in reference to the new World Meteorological Organization “Early Warning for All” Executive Action Plan, we conclude with a set of recommendations supporting actionable and authoritative climate services. Trust, urgency, and accuracy can help overcome barriers created by limited funding, uncertain tradeoffs, and inertia. Understanding how climate change is producing predictable climate extremes now, investing in African‐led EWS, and building better links between EWS and agricultural development efforts can support long‐term adaptation, reducing chronic needs for billions of dollars in reactive assistance. In Africa and beyond, climate change brings increasingly extreme sea surface temperature (SST) gradients. Using climate models, we can often see these extremes coming. Prediction, therefore, offers opportunities for proactive risk management and improved advisory services, if we can create effective societal linkages via cross‐silo collaborations.
This commentary discusses new advances in the predictability of east African rains and highlights the potential for improved early warning systems (EWS), humanitarian relief efforts, and agricultural decision-making. Following an unprecedented sequence of five droughts, in 2022 23 million east Africans faced starvation, requiring >$2 billion in aid. Here, we update climate attribution studies showing that these droughts resulted from an interaction of climate change and La Niña. Then we describe, for the first time, how attribution-based insights can be combined with the latest dynamic models to predict droughts at eight-month lead-times. We then discuss behavioral and social barriers to forecast use, and review literature examining how EWS might (or might not) enhance agro-pastoral advisories and humanitarian interventions. Finally, in reference to the new World Meteorological Organization (WMO) “Early Warning for All” plan, we conclude with a set of recommendations supporting actionable and authoritative climate services. Trust, urgency, and accuracy can help overcome barriers created by limited funding, uncertain tradeoffs, and inertia. Understanding how climate change is producing predictable climate extremes now, investing in African-led EWS, and building better links between EWS and agricultural development efforts can support long-term adaptation, reducing chronic needs for billions of dollars in reactive assistance. The main messages of this commentary will be widely. Climate change is interacting with La Niña to produce extreme, but extremely predictable, Pacific sea surface temperature gradients. These gradients will affect the climate in many countries creating opportunities for prediction. Effective use of such predictions, however, will demand cross-silo collaboration.
Meteorological data is useful for varied applications and sectors ranging from weather and climate forecasting, landscape planning to disaster management among others. However, the availability of these data requires a good network of manual meteorological stations and other support systems for its collection, recording, processing, archiving, communication and dissemination. In sub-Saharan Africa, such networks are limited due to low investment and capacity. To bridge this gap, the National Meteorological Services in Kenya and few others from African countries have moved to install a number of Automatic Weather Stations (AWSs) in the past decade including a few additions from private institutions and individuals. Although these AWSs have the potential to improve the existing observation network and the early warning systems in the region, the quality and capacity of the data collected from the stations are not well exploited. This is mainly due to low confidence, by data users, in electronically observed data. In this study, we set out to confirm that electronically observed data is of comparable quality to a human observer recorded data, and can thus be used to bridge data gaps at temporal and spatial scales. To assess this potential, we applied the simple Pearson correlation method and other statistical tests and approaches by conducting inter-comparison analysis of weather observations from the manual synoptic station and data from two Automatic Weather Stations
Coastal management is criƟ cal in view of the danger posed to coastal communiƟ es by fl ooding from the sea due to storm surges, sea-level rise and Tsunamis. The low-lying Kenyan coast is vulnerable to these hazards, therefore modeling their eff ects is necessary for understanding their socioeconomic impacts. A Decision Support Tool (DST) was developed to study the hydrodynamics along the Kenyan coast. The bathymetry grid for the DST was created using Arc View GIS from nauƟ cal charts. MIKE 21 Hydrodynamic Module (HD) Demo version was used to organize the bathymetry and enforce boundary condiƟ ons for ELCOM simulaƟ on. Tidal data was obtained from both the Kenya Meteorological Department's Ɵ dal gauges and the GLOSS staƟ on. The computed Ɵ de and currents from ELCOM were validated using graphical and staƟ sƟ cal comparison. Their predicƟ ve ability was analyzed. The ELCOM water levels and currents compared well to observed values, and their dominant signals were detectable. ELCOM could, therefore, simulate and forecast coastal hydrodynamics. This DST can assist the Government in operaƟ onal forecasƟ ng for marine environmental protecƟ on, resources management and disaster risk reducƟ on and miƟ gaƟ on as well as infrastructure mapping and development along the Kenyan coast.
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