Seasonal Climate Forecasts Research Articles

ProbFire: a probabilistic fire early warning system for Indonesia

Abstract. Recurrent extreme landscape fire episodes associated with drought events in Indonesia pose severe environmental, societal and economic threats. The ability to predict severe fire episodes months in advance would enable relevant agencies and communities to more effectively initiate fire-preventative measures and mitigate fire impacts. While dynamic seasonal climate predictions are increasingly skilful at predicting fire-favourable conditions months in advance in Indonesia, there is little evidence that such information is widely used yet by decision makers. In this study, we move beyond forecasting fire risk based on drought predictions at seasonal timescales and (i) develop a probabilistic early fire warning system for Indonesia (ProbFire) based on a multilayer perceptron model using ECMWF SEAS5 (fifth-generation seasonal forecasting system) dynamic climate forecasts together with forest cover, peatland extent and active-fire datasets that can be operated on a standard computer; (ii) benchmark the performance of this new system for the 2002–2019 period; and (iii) evaluate the potential economic benefit of such integrated forecasts for Indonesia. ProbFire's event probability predictions outperformed climatology-only based fire predictions at 2- to 4-month lead times in south Kalimantan, south Sumatra and south Papua. In central Sumatra, an improvement was observed only at a 0-month lead time, while in west Kalimantan seasonal predictions did not offer any additional benefit over climatology-only-based predictions. We (i) find that seasonal climate forecasts coupled with the fire probability prediction model confer substantial benefits to a wide range of stakeholders involved in fire management in Indonesia and (ii) provide a blueprint for future operational fire warning systems that integrate climate predictions with non-climate features.

Eddy-induced meridional transport variability at ocean western boundary

Meridional transport variability induced by westward-propagating eddies impinging on the western boundary is investigated both analytically and numerically. A simple theory is first developed in the framework of the reduced-gravity model which relates eddy-induced meridional transport to eddy thickness anomalies propagated into the western boundary by long Rossby waves, and this is followed by a suite of numerical model experiments. It is found that eddies impinging on the western boundary excite boundary waves that propagate equatorward along the western boundary, which leads to coherent meridional overturning circulation (MOC) anomalies equatorward of the incident eddy field. The magnitude and duration of eddy-induced MOC anomalies are variable and irregular, ranging from less than 1 Sv to over 5 Sv and from less than 10 days to over 100 days. Importantly, these eddy-induced MOC anomalies lead to considerable meridional heat transport variability across the latitudes, with implications for seasonal and interannual climate variability and prediction.

Framework for implementation of the Pitman-WR2012 model in seasonal hydrological forecasting: a case study of Kraai River, South Africa

Hydrological forecasting becomes an important tool in water resources management in forecasting the future state of the water resources in a catchment. The need for a reliable seasonal hydrologic forecast is significant and is becoming even more urgent under future climate conditions, as the assimilation of seasonal forecast information in decision making becomes part of the short and long-term climate change adaptation strategies in a range of contexts, such as energy supply, water supply and management, rural-urban, agriculture, infrastructure and disaster preparedness and relief. This work deals with the framework for implementation of the Pitman-WR2012 model in a hydrological forecasting mode. The Pitman-WR2012 model was forced with 10-member ensemble seasonal climate forecast from Climate Forecast Systems v.2 (CFSv2), which is downscaled using the principal components regression (PCR) approach. The generated seasonal hydrological forecast focused on the summer season, in particular on the Dec–Jan–Feb (DJF) period, which is the rainy season in the studied catchment (Kraai River catchment in the Eastern Cape Province of South Africa). The hydrological forecast issued at the end of November showed skill in December and February (assessed through Receiver Operating Characteristic (ROC) and Ranked Probability Skill Score (RPSS)), with poorer skill in January. Importantly, the skill of streamflow forecast was better than that of rainfall forecast, which likely results from the influence of the initial conditions of the hydrological model. In conclusion Pitman-WR2012 model performed realistically when implemented in seasonal hydrological forecasts mode, and it is important that in that mode the model is run with near-real-time rainfall data in order to maximize forecast skill arising from initial conditions.

Use and economic benefits of indigenous seasonal climate forecasts: evidence from Benin, West Africa

ABSTRACT Since immemorial times, farmers especially in Africa have built and transmitted orally from one generation to another a complex network of seasonal climate knowledge enabling them to lower climate variabilities and vagaries. Despite the prominent role of this knowledge system in smallholder farming, empirical studies relative to the production process, use and economic valuation of this knowledge to inform decision-making are scanty. Travel cost method, descriptive statistics and a two-step Heckman method are used to analyse the use and economic value of indigenous seasonal climate forecasts (ISCF) in Benin. ISCF were produced based on the observation of abiotic and biotic indicators in Kandi, Glazoué and Zè with the observations largely undertaken by local elders and professional traditional forecasters. Most farmers got ISCF either by travelling to visit a source of knowledge or by sacrificing their time. The use of ISCF increased a maize producer's net income by at least 3%, implying that ISCF are valuables goods. The main factors driving the use and value of ISCF were the use of fertilizer, large farm size, traditional African religions and access to market. Therefore, policy to promote the integration of indigenous forecasting knowledge with modern forecasting system should be taken.

Improving early-season wheat yield forecasts driven by probabilistic seasonal climate forecasts

Seasonal climate forecasts (SCF) are evolving rapidly alongside improvements in climate modelling and downscaling research, and have great potential for weather-sensitive sectors, especially agriculture, by reducing weather-related risks and increasing productivity. Skilful yield forecasts at the beginning of, or before, a cropping season can provide farmers and other stakeholders in agribusiness with the necessary information for early planning and actions. Only a few yield forecast studies have a forecast lead time of four months or longer due to the problem complexity. To enable SCFs from Global Climate Models (GCMs) to be used for early-season yield forecasts, this paper uses a statistical downscaling technique, Extended Copula Post-Processing (ECPP) and the Schaake shuffle, to downscale four climate variables to generate weather-like daily data that are suitable for agricultural applications. Climate forecasts drive a process-based crop model APSIM (Agricultural Production Systems sIMulator) to simulate crop forecasts on 50 stations, well-distributed across the Australian grain zone. To focus on yield forecast skills attributable to SCF, we propose best practice management rules to predict water-limited winter wheat yield. Yield forecasts from ECPP have a significant improvement over quantile mapping downscaling and raw SCF from the Australian recent seasonal forecast model ACCESS-S1 in terms of bias, accuracy, reliability, and overall forecast skill. In addition, even at the beginning of a cropping season with a forecast lead time of four or more months, yield forecasts driven by ECPP illustrate higher skill than climatology, a benchmark for yield forecast. Early-season yield forecasts driven by SCFs provides a promising alternative to regression/machine-learning-based forecasts. Performance sensitivity and issues, and gaps on using skilful SCFs to help growers with their farming decision-making are discussed.

The Predictability of Ocean Environments that Contributed to the 2020/21 Extreme Cold Events in China: 2020/21 La Niña and 2020 Arctic Sea Ice Loss

Several consecutive extreme cold events impacted China during the first half of winter 2020/21, breaking the low-temperature records in many cities. How to make accurate climate predictions of extreme cold events is still an urgent issue. The synergistic effect of the warm Arctic and cold tropical Pacific has been demonstrated to intensify the intrusions of cold air from polar regions into middle-high latitudes, further influencing the cold conditions in China. However, climate models failed to predict these two ocean environments at expected lead times. Most seasonal climate forecasts only predicted the 2020/21 La Niña after the signal had already become apparent and significantly underestimated the observed Arctic sea ice loss in autumn 2020 with a 1–2 month advancement. In this work, the corresponding physical factors that may help improve the accuracy of seasonal climate predictions are further explored. For the 2020/21 La Niña prediction, through sensitivity experiments involving different atmospheric–oceanic initial conditions, the predominant southeasterly wind anomalies over the equatorial Pacific in spring of 2020 are diagnosed to play an irreplaceable role in triggering this cold event. A reasonable inclusion of atmospheric surface winds into the initialization will help the model predict La Niña development from the early spring of 2020. For predicting the Arctic sea ice loss in autumn 2020, an anomalously cyclonic circulation from the central Arctic Ocean predicted by the model, which swept abnormally hot air over Siberia into the Arctic Ocean, is recognized as an important contributor to successfully predicting the minimum Arctic sea ice extent.

Development of Global Crop Forecasting System and Service Based on Seasonal Climate Forecasts

Development of Global Crop Forecasting System and Service Based on Seasonal Climate Forecasts

Integration of seasonal precipitation forecast information into local-level agricultural decision-making using an agent-based model to support community adaptation

Accessibility and variability of water resources can have profound impacts on social, political, and economic security. In regions with pronounced climate variability (e.g., seasonal and inter-annual variability in precipitation), seasonal climate forecasts issued in advance may enhance sectoral planning and management decisions to benefit vulnerable communities. Yet, as the development and communication of seasonal climate forecasts continue to advance, integration of forecasts into decision-making remains sparse. This work investigates the integration of a locally-tailored seasonal precipitation forecast into agricultural decision-making using a simple agent-based model designed to resemble a stylized local Ethiopian community, to understand factors that may influence adoption. We do not make claims on representativeness, yet results from our model indicate that forecasts improve gross benefit to farmer agents across different climate series, with potential for improved profit, yields, and nutritional outcomes. Accuracy of a seasonal forecast seems to correlate with increased adoption and therefore benefit; yet, the sequence of precipitation conditions, risk preference and heuristics for building trust nuance this relationship. Further, similar to well-established literature in economics and sociology, our stylized model suggests that community-level social dynamics (e.g., peer interaction, sensing others’ trust in the forecast, and the ability to learn from peers) seem to have a large impact on patterns of forecast adoption. Ultimately, if the motivation for seasonal forecast development is to enhance water and food security for adaptation to climate variability in vulnerable regions, then interdisciplinary collaborations that connect local-scale forecasts with public engagement and attention to community-level social dynamics are critical.

Post-processing sub-seasonal to seasonal climate forecasts under climate change

Post-processing sub-seasonal to seasonal climate forecasts under climate change

Skill Assessment of North American Multi-Models Ensemble (NMME) for June-September (JJAS) Seasonal Rainfall over Ethiopia

In recent years, there has been increasing demand for high-resolution seasonal climate forecasts at sufficient lead times to allow response planning from users in agriculture, hydrology, disaster risk management, and health, among others. This paper examines the forecasting skill of the North American Multi-model Ensemble (NMME) over Ethiopia during the June to September (JJAS) season. The NMME, one of the multi-model seasonal forecasting systems, regularly generates monthly seasonal rainfall forecasts over the globe with 0.5 - 11.5 months lead time. The skill and predictability of seasonal rainfall are assessed using 28 years of hindcast data from the NMME models. The forecast skill is quantified using canonical correlation analysis (CCA) and root mean square error. The results show that the NMME models capture the JJAS seasonal rainfall over central, northern, and northeastern parts of Ethiopia while exhibiting weak or limited skill across western and southwestern Ethiopia. The performance of each model in predicting the JJAS seasonal rainfall is variable, showing greater skill in predicting dry conditions. Overall, the performance of the multi-model ensemble was not consistently better than any single ensemble member. The correlation of observed and predicted seasonal rainfall for the better performing models—GFDL-CM2p5-FLOR-A06, CMC2-CanCM4, GFDL-CM2p5-FLOR-B01 and NASA-GMAO-062012—is 0.68, 0.58, 0.52, and 0.5, respectively. The COLA-RSMAS-CCSM4, CMC1- CanCM3 and NCEP-CFSv2 models exhibit less skill, with correlations less than 0.4. In general, the NMME offers promising skill to predict seasonal rainfall over Ethiopia during the June-September (JJAS) season, motivating further work to assess its performance at longer lead times.

Transformer for EI Niño-Southern Oscillation Prediction

Accurate prediction of EI Niño-southern oscillation (ENSO) is of great significance to seasonal climate forecast. Recently, a convolutional neural network (CNN) has shown an optimal skill for ENSO prediction. However, it is difficult for the convolutional kernel to capture long-range precursors of ENSO due to its build-in local property. The transformer model has long been used in natural language processing (NLP) for its ability to focus on global features. Here, we introduce it to the ENSO research community and propose the ENSO transformer (ENSOTR). We show that using the ENSOTR model, the monthly average Niño3.4 index can be skillfully predicted up to one and a half years ahead. The model can also predict strong EI Niño cases more than a year ahead, such as 1997–1998. Experimental results show that our model achieves better skill than CNN for ENSO prediction.

Upgrade of a climate service tailored to water reservoirs management

We present the upgrade of a web tool designed to help in the decision making process for water reservoirs management in Spain. The tool, called S-ClimWaRe (Seasonal Climate predictions in support of Water Reservoirs management), covers the extended winter season (from November to March), when the North Atlantic Oscillation (NAO) pattern strongly influences the hydrological interannual variability in South-Western Europe. This climate service is fully user driven, and aims at meeting their requirements incorporating recent scientific progress.The latest S-ClimWaRe version includes some technical enhancements requested by customers and new seasonal predictions obtained through application of two postprocessing steps to ECMWF System-5 forecasts: a downscaling statistical procedure, and a new methodology that improves the forecast skill of hydrological variables through an enhanced prediction of the winter NAO. The new version enriches the forecasting panel of the web tool with precipitation forecast skill, and provides additional forecasts for accumulated snowfall and temperature. Water reservoir inflow forecasts are generated by a hybrid statistical system and are also skilful in areas influenced by the NAO pattern. In regions not directly affected by this climate driver, a revision of this simple impact model seems to be needed. A prototype based on two different hydrological models has been tested over a pilot reservoir. The assessment of this downscaling procedure shows promising results with respect to the existing seasonal forecasts based on the statistical approach relying on the predicted NAO index.

Seasonal Rainfall Prediction for Tamil Nadu Using Climate Predictability Tool

Seasonal rainfall prediction is potentially valuable input for agriculture and allied sectors. Climate Predictability Tool (CPT) is a computational statistical software and intended to facilitate the widespread development and dissemination of quality-controlled seasonal climate forecasts. In the present study, CPT was used to predict South West Monsoon (SWM) rainfall by utilizing the Canonical Correlation Analysis (CCA) technique. Results indicate that observed sea surface temperature (SST) from the ERSSTv5 model of March-April-May (MAM) months for the Nino 3.4 region exhibited a strong association with rainfall (the CCA score of 0.71). Thus, MAM SST was used to predict the SWM (2021) rainfall for 32 districts of Tamil Nadu. The prediction accuracy was tested with ROC score, Pearson correlation and 2AFC score. The good predictive skill of cross-validated hindcast in the below and the above-normal category of rainfall (1981-2020) was observed in 17 districts and poor discrimination was observed in 15 districts of Tamil Nadu.

Monitoring Climate Impacts on Annual Forage Production across U.S. Semi-Arid Grasslands

The ecosystem performance approach, used in a previously published case study focusing on the Nebraska Sandhills, proved to minimize impacts of non-climatic factors (e.g., overgrazing, fire, pests) on the remotely-sensed signal of seasonal vegetation greenness resulting in a better attribution of its changes to climate variability. The current study validates the applicability of this approach for assessment of seasonal and interannual climate impacts on forage production in the western United States semi-arid grasslands. Using a piecewise regression tree model, we developed the Expected Ecosystem Performance (EEP), a proxy for annual forage production that reflects climatic influences while minimizing impacts of management and disturbances. The EEP model establishes relations between seasonal climate, site-specific growth potential, and long-term growth variability to capture changes in the growing season greenness measured via a time-integrated Normalized Difference Vegetation Index (NDVI) observed using a Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting 19 years of EEP were converted to expected biomass (EB, kg ha−1 year−1) using a newly-developed relation with the Soil Survey Geographic Database range production data (R2 = 0.7). Results were compared to ground-observed biomass datasets collected by the U.S. Department of Agriculture and University of Nebraska-Lincoln (R2 = 0.67). This study illustrated that this approach is transferable to other semi-arid and arid grasslands and can be used for creating timely, post-season forage production assessments. When combined with seasonal climate predictions, it can provide within-season estimates of annual forage production that can serve as a basis for more informed adaptive decision making by livestock producers and land managers.

Contextualising seasonal climate forecasts by integrating local knowledge on drought in Malawi

Droughts and changing rainfall patterns due to natural climate variability and climate change, threaten the livelihoods of Malawi’s smallholder farmers, who constitute 80% of the population. Provision of seasonal climate forecasts (SCFs) is one means to potentially increase the resilience of rainfed farming to drought by informing farmers in their agricultural decisions. Local knowledge can play an important role in improving the value of SCFs, by making the forecast better-suited to the local environment and decision-making. This study explores whether the contextual relevance of the information provided in SCFs can be improved through the integration of farmers’ local knowledge in three districts in central and southern Malawi. A forecast threshold model is established that uses meteorological indicators before the rainy season as predictors of dry conditions during that season. Local knowledge informs our selection of the meteorological indicators as potential predictors. Verification of forecasts made with this model shows that meteorological indicators based on local knowledge have a predictive value for forecasting dry conditions in the rainy season. The forecast skill differs per location, with increased skill in the Southern Highlands climate zone. In addition, the local knowledge indicators show increased predictive value in forecasting locally relevant dry conditions, in comparison to the currently-used El Niño-Southern Oscillation (ENSO) indicators. We argue that the inclusion of local knowledge in the current drought information system of Malawi may improve the SCFs for farmers. We show that it is possible to capture local knowledge using observed station and climate reanalysis data. Our approach could benefit National Meteorological and Hydrological Services in the development of relevant climate services and support drought-risk reduction by humanitarian actors.

Potential Seasonal Predictability of the Risk of Local Rainfall Extremes Estimated Using High‐Resolution Large Ensemble Simulations

AbstractSignificant progress has been made in seasonal climate predictions based on general circulation models (GCMs). However, seasonal prediction of local heavy rain remains challenging due to limited resolution. A small signal‐to‐noise ratio also prevents meaningful predictions. In this study, 100‐member large‐ensemble sets of GCM simulations and high‐resolution downscaled products with a regional climate model were used to identify the unknown source of seasonal predictability for the risk of local heavy rainfall around Japan and Taiwan. The detected predictable signals depended on the location and the season. Highly predictable signals were found for events caused by typhoons and the stationary front. Further analyses of the large‐scale backgrounds simulated by the parent‐GCM revealed that each source of predictability comes from various flavors of El Niño/Southern Oscillation and anomalies in the Indian Ocean. The approach of this study potentially provides an opportunity to identify unknown predictable regional signals all over the world.

Seasonal discharge forecasting for the Upper Danube

Study RegionThe authors contributed with a seasonal forecast system for discharge in the case study area of the Upper Danube basin upstream of Vienna. The basin, with a drainage area of approx. 100.000 km2, is characterized by complex topography dominated by the Alps. Study FocusNew advances in the mean term (seasonal) climate forecasting increase the potential of seasonal hydrological forecasting. In the frame of an European project (Clim2Power, 2020) a climate service for the assessment of seasonal energy production of renewable resources was developed. Seasonal discharge forecasts are generated using a dynamical approach running the hydrological model COSERO with forecasted climate inputs provided by Germany’s National Meteorological Service (Deutscher Wetterdienst – DWD). The seasonal climate forecasts are based on a large ensemble of daily predictions available up to 6 months in the future. These forecasts are downscaled to a spatial resolution of 6 km. The following analysis is based on aggregation of these daily values to monthly and seasonal values. New Hydrological Insights for the RegionDownscaled historical forecasts (hindcasts) and historical discharge observations over the last 15 years are used to assess the skill of the seasonal forecast in several Alpine basins. In general, the results show a limited skill. Therefore, the seasonal discharge forecasting system in this region is still not sufficient to incorporate the results into water resources decision support systems.

Drought in Northeast Brazil: A review of agricultural and policy adaptation options for food security

AbstractThe semiarid lands of Northeast Brazil represent one of the most densely populated regions of the country. Rainfall variability together with land degradation and large‐scale poverty in rural areas makes this region vulnerable to droughts. Most of the agriculture in this region is rainfed and deficient rainfall leads to severe drought impacts. In this review, we examine different short‐ and long‐term strategies directed to cope with possible impacts of droughts proposed by the government, farmers, civil society, and the private sector. These are approaches to adaptation to drought in the Northeast of Brazil, and among them, we have agricultural management and soil conservation and better management of water resources. Other actions include seasonal climate forecasts and funds transfer and credits to affected small‐scale farmers. Although some of these actions are for the short term and may help to survive the drought situation, they may be only postdisaster mitigation options that do not improve adaptive capacity. They favor maladaptation and create dependency of farmers to government actions. Some experiences such as AdaptaSertão show potential benefits for small‐scale farmers. We identify key challenges for moving toward a more holistic risk management approach and highlight the need to integrate actions and tools for adaptation, combining technology‐based solutions with in‐depth knowledge of local and regional social, economic, and cultural aspects, among them seasonal climate forecasts and drought impacts studies, among some other proactive predisaster ways, rather than reactive postdisaster actions. Adaptation strategies must increase long‐term resilience of food production in the Brazilian Northeast, going beyond an individual drought event.

Development of Tailored Early Warning Agromet Advisories for Farmers in Zambia, Indonesia, and South Africa

Farmers do not often use climate and weather information on a regular basis, as the specific influence of weather parameters on farm-level decision making is not well-known. Agromet advisories are needed for local farming systems. Effective agrometeorological advisory systems, need tailored agricultural weather forecasts, and two-way communication. Transdisciplinary teams together with farmers can co-develop early warning Agromet advisory systems to address farmers' needs. Three examples of Agromet advisories are discussed- CAPES in Zambia, Science Field Shops in Indonesia, and the AgriCloud mobile App in South Africa. Community Agrometeorological Participatory Extension Service (CAPES) began in Monze, Zambia to communicate seasonal climate forecasts to farmers through researchers and extension interactions. Participatory groups collected spatial and temporal data about local farming systems to highlight opportunities. Communication methods used were local radio, farmers' days, trials, with farm visits. CAPES resulted in lifelong learning about climate and co-development of tailored Agromet advisories to improve climate resilience. In Science Field Shops (SFS) groups of Indonesian farmers meet experts regularly to exchange information about climate and farming activities. Farmers measure rainfall and observe their agroecological systems each day. At monthly meetings, the seasonal forecasts are discussed using dialogue-discussion methods. Agrometeorological learning is trans-disciplinary through interaction between anthropologists, agrometeorologist, and extension personnel. SFS includes eight climate services that empower farmers to address challenges and sustain their productivity. AgriCloud is an online weather-based agricultural advisory system enriching weather forecasts with agricultural information and local knowledge. Real-time overviews and warnings are tailored to farmer's needs. AgriCloud provides farmers, extension staff, and advisors daily updated weather-related farm-specific advice in 11 South African official languages. AgriCloud is available as an android mobile App, or API to use via a platform. These examples illustrate the use of weather forecasting together with tailored forecasts and communication systems to deliver Agromet advisories, showing different aspects of the incorporation of local knowledge in co-developing advisories for the farmers. In the future, various combinations can be used around the world when co-developing with the farmers.

Toward usable predictive climate information at decadal timescales

Toward usable predictive climate information at decadal timescales