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- Author or Editor: David MacLeod x
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Abstract
El Niño–Southern Oscillation (ENSO) has large socioeconomic impacts worldwide. The positive phase of ENSO, El Niño, has been linked to intense rainfall over East Africa during the short rains season (October–December). However, we show here that during the extremely strong 2015 El Niño the precipitation anomaly over most of East Africa during the short rains season was less intense than experienced during previous El Niños, linked to less intense easterlies over the Indian Ocean. This moderate impact was not indicated by reforecasts from the ECMWF operational seasonal forecasting system, SEAS5, which instead forecast large probabilities of an extreme wet signal, with stronger easterly anomalies over the surface of the Indian Ocean and a colder eastern Indian Ocean/western Pacific than was observed. To confirm the relationship of the eastern Indian Ocean to East African rainfall in the forecast for 2015, atmospheric relaxation experiments are carried out that constrain the east Indian Ocean lower troposphere to reanalysis. By doing so the strong wet forecast signal is reduced. These results raise the possibility that link between ENSO and Indian Ocean dipole events is too strong in the ECMWF dynamical seasonal forecast system and that model predictions for the East African short rains rainfall during strong El Niño events may have a bias toward high probabilities of wet conditions.
Abstract
El Niño–Southern Oscillation (ENSO) has large socioeconomic impacts worldwide. The positive phase of ENSO, El Niño, has been linked to intense rainfall over East Africa during the short rains season (October–December). However, we show here that during the extremely strong 2015 El Niño the precipitation anomaly over most of East Africa during the short rains season was less intense than experienced during previous El Niños, linked to less intense easterlies over the Indian Ocean. This moderate impact was not indicated by reforecasts from the ECMWF operational seasonal forecasting system, SEAS5, which instead forecast large probabilities of an extreme wet signal, with stronger easterly anomalies over the surface of the Indian Ocean and a colder eastern Indian Ocean/western Pacific than was observed. To confirm the relationship of the eastern Indian Ocean to East African rainfall in the forecast for 2015, atmospheric relaxation experiments are carried out that constrain the east Indian Ocean lower troposphere to reanalysis. By doing so the strong wet forecast signal is reduced. These results raise the possibility that link between ENSO and Indian Ocean dipole events is too strong in the ECMWF dynamical seasonal forecast system and that model predictions for the East African short rains rainfall during strong El Niño events may have a bias toward high probabilities of wet conditions.
Abstract
Equatorial East Africa (EEA) suffers from significant flood risks. These can be mitigated with preemptive action; however, currently available early warnings are limited to a few days’ lead time. Extending warnings using subseasonal climate forecasts could open a window for more extensive preparedness activity. However, before these forecasts can be used, the basis of their skill and relevance for flood risk must be established. Here we demonstrate that subseasonal forecasts are particularly skillful over EEA. Forecasts can skillfully anticipate weekly upper-quintile rainfall within a season, at lead times of 2 weeks and beyond. We demonstrate the link between the Madden–Julian oscillation (MJO) and extreme rainfall events in the region, and confirm that leading forecast models accurately represent the EEA teleconnection to the MJO. The relevance of weekly rainfall totals for fluvial flood risk in the region is investigated using a long record of streamflow from the Nzoia River in western Kenya. Both heavy rainfall and high antecedent rainfall conditions are identified as key drivers of flood risk, with upper-quintile weekly rainfall shown to skillfully discriminate flood events. We additionally evaluate GloFAS global flood forecasts for the Nzoia basin. Though these are able to anticipate some flooding events with several weeks lead time, analysis suggests action based on these would result in a false alarm more than 50% of the time. Overall, these results build on the scientific evidence base that supports the use of subseasonal forecasts in EEA, and activities to advance their use are discussed.
Abstract
Equatorial East Africa (EEA) suffers from significant flood risks. These can be mitigated with preemptive action; however, currently available early warnings are limited to a few days’ lead time. Extending warnings using subseasonal climate forecasts could open a window for more extensive preparedness activity. However, before these forecasts can be used, the basis of their skill and relevance for flood risk must be established. Here we demonstrate that subseasonal forecasts are particularly skillful over EEA. Forecasts can skillfully anticipate weekly upper-quintile rainfall within a season, at lead times of 2 weeks and beyond. We demonstrate the link between the Madden–Julian oscillation (MJO) and extreme rainfall events in the region, and confirm that leading forecast models accurately represent the EEA teleconnection to the MJO. The relevance of weekly rainfall totals for fluvial flood risk in the region is investigated using a long record of streamflow from the Nzoia River in western Kenya. Both heavy rainfall and high antecedent rainfall conditions are identified as key drivers of flood risk, with upper-quintile weekly rainfall shown to skillfully discriminate flood events. We additionally evaluate GloFAS global flood forecasts for the Nzoia basin. Though these are able to anticipate some flooding events with several weeks lead time, analysis suggests action based on these would result in a false alarm more than 50% of the time. Overall, these results build on the scientific evidence base that supports the use of subseasonal forecasts in EEA, and activities to advance their use are discussed.
Abstract
This paper evaluates subseasonal precipitation forecasts for Africa using hindcasts from three models (ECMWF, UKMO, and NCEP) participating in the Subseasonal to Seasonal (S2S) prediction project. A variety of verification metrics are employed to assess weekly precipitation forecast quality at lead times of one to four weeks ahead (weeks 1–4) during different seasons. Overall, forecast evaluation indicates more skillful predictions for ECMWF over other models and for East Africa over other regions. Deterministic forecasts show substantial skill reduction in weeks 3–4 linked to lower association and larger underestimation of predicted variance compared to weeks 1–2. Tercile-based probabilistic forecasts reveal similar characteristics for extreme categories and low quality in the near-normal category. Although discrimination is low in weeks 3–4, probabilistic forecasts still have reasonable skill, especially in wet regions during particular rainy seasons. Forecasts are found to be overconfident for all weeks, indicating the need to apply calibration for more reliable predictions. Forecast quality within the ECMWF model is also linked to the strength of climate drivers’ teleconnections, namely, El Niño–Southern Oscillation, Indian Ocean dipole, and the Madden–Julian oscillation. The impact of removing all driver-related precipitation regression patterns from observations and hindcasts shows reduction of forecast quality compared to including all drivers’ signals, with more robust effects in regions where the driver strongly relates to precipitation variability. Calibrating forecasts by adding observed regression patterns to hindcasts provides improved forecast associations particularly linked to the Madden–Julian oscillation. Results from this study can be used to guide decision-makers and forecasters in disseminating valuable forecasting information for different societal activities in Africa.
Abstract
This paper evaluates subseasonal precipitation forecasts for Africa using hindcasts from three models (ECMWF, UKMO, and NCEP) participating in the Subseasonal to Seasonal (S2S) prediction project. A variety of verification metrics are employed to assess weekly precipitation forecast quality at lead times of one to four weeks ahead (weeks 1–4) during different seasons. Overall, forecast evaluation indicates more skillful predictions for ECMWF over other models and for East Africa over other regions. Deterministic forecasts show substantial skill reduction in weeks 3–4 linked to lower association and larger underestimation of predicted variance compared to weeks 1–2. Tercile-based probabilistic forecasts reveal similar characteristics for extreme categories and low quality in the near-normal category. Although discrimination is low in weeks 3–4, probabilistic forecasts still have reasonable skill, especially in wet regions during particular rainy seasons. Forecasts are found to be overconfident for all weeks, indicating the need to apply calibration for more reliable predictions. Forecast quality within the ECMWF model is also linked to the strength of climate drivers’ teleconnections, namely, El Niño–Southern Oscillation, Indian Ocean dipole, and the Madden–Julian oscillation. The impact of removing all driver-related precipitation regression patterns from observations and hindcasts shows reduction of forecast quality compared to including all drivers’ signals, with more robust effects in regions where the driver strongly relates to precipitation variability. Calibrating forecasts by adding observed regression patterns to hindcasts provides improved forecast associations particularly linked to the Madden–Julian oscillation. Results from this study can be used to guide decision-makers and forecasters in disseminating valuable forecasting information for different societal activities in Africa.
Abstract
The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale 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 socioeconomic 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 cogeneration 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 time scale.
Abstract
The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale 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 socioeconomic 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 cogeneration 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 time scale.
