Application of real time S2S forecasts over Eastern Africa in the co-production of climate services

Gudoshava, Masilin; Wanzala, Maureen; Thompson, Elisabeth Morgan; Mwesigwa, Jasper Batureine; Endris, Hussen Seid; Segele, Zewdu T.; Hirons, Linda; Kipkogei, Oliver; Mumbua, Charity; Njoka, Wawira; Baraibar, Marta; Andrade, Felipe M. de; Woolnough, Steven J.; Atheru, Zachary; Artan, Guleid

doi:10.1016/j.cliser.2022.100319

Cited by 16 publications

(22 citation statements)

References 63 publications

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“…Such an approach shifts the user, in this context the conservation practitioner, from a recipient of forecast information to a participant in the knowledge and service generation process (Vincent et al, 2020), for instance, drawing on conservation practitioners' understanding of species' ecology to define relevant forecast thresholds and early actions, thereby increasing the likelihood that bespoke forecast information is ‘useful, usable and used’ (Boaz & Hayden, 2002; Hirons et al, 2021). For example, this could involve user‐directed iterations to the visualisation, communication or content of forecasts (Gudoshava et al, 2022; Hirons et al, 2023; Lawal et al, 2021) or applying user‐defined thresholds for specific decision‐making applications (Dione et al, 2022). However, it is also increasingly clear that the iterative co‐production process itself is extremely resource intensive (Hirons et al, 2021) and to be effective, stakeholder engagement, monitoring and evaluation need to be institutionalised as the operational norm (Visman et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

The potential value of seasonal drought forecasts in the context of climate change: A case study of the African elephant conservation sector

Black,

Boult,

Hirons

et al. 2024

Meteorological Applications

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This study investigates meteorological drought in sub‐Saharan Africa within the context of elephant conservation. Prolonged drought significantly impacts elephants, leading to increased mortality rates and heightened human–elephant conflicts. We assess both the anticipated 21st century changes in impact‐relevant meteorological drought metrics and the efficacy of existing forecasting systems in predicting such droughts on seasonal time scales. The climate change element of our study uses the 6th Coupled Model Intercomparison Project (CMIP6) ensemble to evaluate projected change in 3‐month Standardized Precipitation Index (SPI3). We then carry out a quantitative assessment of seasonal forecast skill, utilizing 110 years of precipitation hindcasts generated by the European Centre for Medium Range Forecasting (ECMWF) system. Our findings indicate that persistent drought is projected to become more frequent over the 21st century in southern Africa, where the majority of elephants reside. Analysis of seasonal hindcasts indicates that, while the forecasts have greater skill than climatology, they remain highly uncertain. Previous work suggests that it may be possible to reduce this uncertainty by contextualizing forecasts within specific climate regimes. However, even with improved forecast skill, effective action hinges on the alignment of forecasts with the practical needs of conservation practitioners. Over the next decades, a co‐production approach will be critical for leveraging seasonal forecasts for climate change adaptation within the conservation sector.

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Section: Discussionmentioning

confidence: 99%

The potential value of seasonal drought forecasts in the context of climate change: A case study of the African elephant conservation sector

Black,

Boult,

Hirons

et al. 2024

Meteorological Applications

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“…The Greater Horn of Africa (GHA) region is highly vulnerable to recurrent climate hazards such as droughts, heat waves, and floods (Kijazi and Reason, 2009; Hastenrath et al ., 2010; Mwangi et al ., 2014; Billi et al ., 2015; Russo et al ., 2016; Haile et al ., 2019; Garuma, 2023). A high percentage of inhabitants in this region are vulnerable to climate hazards, as most of the region's socioeconomic activities are rain‐dependent (Salami et al ., 2010; Coughlan de Perez et al ., 2019; Gudoshava et al ., 2022). Notably, climate risks are becoming increasingly complex as these hazards are compounded by local, remote, political, and economic instability (Pörtner et al ., 2022).…”

Section: Introductionmentioning

confidence: 99%

Subselection of seasonal ensemble precipitation predictions for East Africa

Heinrich-Mertsching

Sørland

Gudoshava

et al. 2023

Quart J Royal Meteoro Soc

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The Greater Horn of Africa (GHA) is highly vulnerable to climate and weather hazards such as drought, heat waves, and floods. There is a need for accurate seasonal forecasts to prepare for risks (such as crop failure and reduced grazing opportunities) and take advantage of favorable conditions (rains arrive on time and where they are needed) when they arise. As such, information at finer spatial scales than current state‐of‐the‐art global prediction models can provide is needed. Dynamical downscaling is one method employed to obtain information at finer scales. However, providers of seasonal forecasts over the GHA are hampered by limited computational resources and time constraints that restrict the number of global model ensemble members that can be downscaled. Some ensemble subselection criteria must be employed. Currently, providers take an uninformed (or random) approach. Specifically, forecasters simply take the first ensemble member of the global model seasonal forecast ensemble. However, recent work, focused on decadal prediction, has shown that subselecting global model ensemble members in an informed way, that is, according to their ability to reproduce key features of the climate system, results in improved predictions. This emerges from the fact that the climate system is likely more predictable than our models would have us believe. Seeing an opportunity for improvement, we apply the same thinking to the seasonal context and assess several procedures for subselecting ensemble members from seasonal predictions with exchangeable members. Such informed subselections have the potential to take advantage of information in an ensemble of global simulations that might be missed by random selection. Three subselection methods are investigated, with a focus on seasonal predictions for rainfall over GHA. We demonstrate that informed subselection leads to systematically higher skill than random subselection. We find that (1) for small subsample sizes, such as would be chosen for dynamical downscaling and/or downstream impact modeling, informed subselection nearly always outperforms random subselection, (2) subselecting based on well‐known teleconnections benefits those seasons in which such pathways are active, such as OND and JJAS, and (3) ‐means subselection outperforms random selection for small ensemble sizes throughout all seasons, including the notoriously difficult to predict MAM season. These techniques require only input that is available at the time of the forecast release and are easy to apply operationally.

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“…Therefore, the model-based RZSM predictions are physically interpretable, while the data-driven outputs are relatively difficult to interpret. However, the initial conditions, parameters and model structures suffer from substantial uncertainties, and the model-based forecasts are limited by these uncertainty sources [12,28,38,39].…”

Section: Introductionmentioning

confidence: 99%

“…At sub-seasonal timescales, the S2S project provides multiple model-based soil moisture predictions by several major climate centers worldwide [45]. The S2S soil moisture predictions are preliminarily evaluated over regional areas and demonstrate appealing predictive skill for a couple of days in advance [38,39,49]. For example, the European Centre for Medium-Range Weather Forecasts (ECMWF) S2S model exhibits good prediction skill 20 days in advance over the Tibetan Plateau and northwestern China [38].…”

Section: Introductionmentioning

confidence: 99%

“…For example, the European Centre for Medium-Range Weather Forecasts (ECMWF) S2S model exhibits good prediction skill 20 days in advance over the Tibetan Plateau and northwestern China [38]. The S2S predictions are also assessed in South America and utilized in eastern Africa to guide crop management services [39,49]. Although the model-based soil moisture predictions provide some forecasting skills over longer lead times, the uncertainties in the initial conditions, parameters and model structures lead to unexpected biases and errors in forecasting, of which the uncertainty of the initial conditions largely limits the forecasting skill for a specific model.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Deep Learning and S2S Model for Improved Sub-Seasonal Surface and Root-Zone Soil Moisture Forecasting

Chen

et al. 2023

Remote Sensing

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Surface soil moisture (SSM) and root-zone soil moisture (RZSM) are key hydrological variables for the agricultural water cycle and vegetation growth. Accurate SSM and RZSM forecasting at sub-seasonal scales would be valuable for agricultural water management and preparations. Currently, weather model-based soil moisture predictions are subject to large uncertainties due to inaccurate initial conditions and empirical parameterization schemes, while the data-driven machine learning methods have limitations in modeling long-term temporal dependences of SSM and RZSM because of the lack of considerations in the soil water process. Thus, here, we innovatively integrate the model-based soil moisture predictions from a sub-seasonal-to-seasonal (S2S) model into a data-driven stacked deep learning model to construct a hybrid SSM and RZSM forecasting framework. The hybrid forecasting model is evaluated over the Yangtze River Basin and parts of Europe from 1- to 46-day lead times and is compared with four baseline methods, including the support vector regression (SVR), random forest (RF), convolutional long short-term memory (ConvLSTM) and the S2S model. The results indicate substantial skill improvements in the hybrid model relative to baseline models over the two study areas spatiotemporally, in terms of the correlation coefficient, unbiased root mean square error (ubRMSE) and RMSE. The hybrid forecasting model benefits from the long-lead predictive skill from S2S and retains the advantages of data-driven soil moisture memory modeling at short-lead scales, which account for the superiority of hybrid forecasting. Overall, the developed hybrid model is promising for improved sub-seasonal SSM and RZSM forecasting over global and local areas.

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Application of real time S2S forecasts over Eastern Africa in the co-production of climate services

Cited by 16 publications

References 63 publications

The potential value of seasonal drought forecasts in the context of climate change: A case study of the African elephant conservation sector

The potential value of seasonal drought forecasts in the context of climate change: A case study of the African elephant conservation sector

Subselection of seasonal ensemble precipitation predictions for East Africa

Hybrid Deep Learning and S2S Model for Improved Sub-Seasonal Surface and Root-Zone Soil Moisture Forecasting

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