Seasonal Climate Forecasts Research Articles

Advances and challenges of operational seasonal prediction in Pacific Island Countries

Seasonal climate forecasts play a critical role in building a climate-resilient society in the Pacific Island Countries (PICs) that are highly exposed to high-impact climate events. To assist the PICs National Meteorological and Hydrological Services in generating reliable national climate outlooks, we developed a hybrid seasonal prediction system, the Pacific Island Countries Advanced Seasonal Outlook (PICASO), which has the strengths of both statistical and dynamical systems. PICASO is based on the APEC Climate Center Multi-Model Ensemble (APCC-MME), tailored to generate station-level rainfall forecasts for 49 stations in 13 countries by applying predictor optimization and the large-scale relationship-based Bayesian regression approaches. Overall, performance is improved and further stabilized temporally and spatially relative to not only APCC-MME but also other existing operational prediction systems in the Pacific. Gaps and challenges in operationalization of the PICASO system and its incorporation into operational climate services in the PICs are discussed.

Soybean Crops Penalize Subsequent Wheat Yield During Drought in the North China Plain.

Contemporary wisdom suggests that inclusion of legumes into crop rotations benefit subsequent cereal crop yields. To investigate whether this maxim was generically scalable, we contrast summer soybean–winter wheat (SW) with summer maize–winter wheat (MW) rotation systems in an extensive field campaign in the North China Plain (NCP). We identify heretofore unseen interactions between crop rotation, synthetic N fertilizer application, and stored soil water. In the year with typical rainfall, inclusion of soybean within rotation had no effect on wheat ear number and yield, while N fertilization penalized wheat yields by 6–8%, mainly due to lower dry matter accumulation after anthesis. In contrast, in dry years prior crops of soybean reduced the rate and number of effective ears in wheat by 5–27 and 14–17%, respectively, leading to 7–23% reduction in wheat yield. Although N fertilization increased the stem number before anthesis in dry years, there was no corresponding increase in ear number and yield of wheat in such years, indicating compensating reduction in yield components. We also showed that N fertilization increased wheat yield in MW rather than SW as the former better facilitated higher dry matter accumulation after flowering in dry years. Taken together, our results suggest that soybean inclusion reduced soil available water for subsequent wheat growth, causing yield penalty of subsequent wheat under drought conditions. We call for more research into factors influencing crop soil water, including initial state, crop water requirement, and seasonal climate forecasts, when considering legumes into rotation systems. Graphical Response of wheat population and yield to soybean inclusion under limited-irrigation.

Re-prioritizing climate services for agriculture: Insights from Bangladesh

Considerable progress has been made in establishing climate service capabilities over the last few decades, but the gap between the resulting services and national needs remains large. Using climate services for agriculture in Bangladesh as a case study example, we highlight mismatches between local needs on the one hand, and international initiatives that have focused largely on prediction on the other, and we make suggestions for addressing such mismatches in similar settings. To achieve greater benefit at the national level, there should be a stronger focus on addressing important preliminaries for building services. These preliminaries include the identification of priorities, the definition of responsibilities and expectations, the development of climate services skills, and the construction of a high-quality and easily usable national climate record. Once appropriate institutional, human resources and data infrastructure are in place, the implementation of a climate monitoring and watch system would form a more logical basis for initial climate service implementation than attempting to promote sub-seasonal to seasonal climate forecasting, especially when and where the inherent predictability is limited at best. When and where forecasting at these scales is viable, efforts should focus on defining and predicting high-impact events important for decision making, rather than on simple seasonal aggregates that often correlate poorly with outcomes. Some such forecasts may be more skillful than the 3- to 4-month seasonal aggregates that have become the internationally adopted standard. By establishing a firm foundation for climate services within National Meteorological Services, there is a greater chance that individual climate service development initiatives will be sustainable after their respective project lifetimes.

Factors Undermining the Use of Seasonal Climate Forecasts Among Farmers in South Africa and Zimbabwe: Implications for the 1st and 2nd Sustainable Development Goals

The adverse effects of climate change on food production coupled with growing inconsistencies in Indigenous knowledge systems have necessitated some farmers' willingness to rely on seasonal climate forecasts (SCFs) to make informed farming decisions. SCFs provide information regarding the likelihood that the rainfall in the forthcoming season will be higher, lower or normal. While SCFs have scaled up food production among some rural households in Sub-Saharan Africa (SSA), some farmers find it difficult to plug into this vital technology. Failure to utilize SCFs could have severe consequences for household food and nutrition security, especially in semi-arid countries like South Africa and Zimbabwe. By systematically unpacking the literature in South Africa and Zimbabwe from 2011 to 2021, this article seeks to demystify the factors that hamper the use of SCFs in the aforementioned countries. Results indicate that failure to comprehensively understand and interpret probabilistic forecasts as well as ill-timing of forecast dissemination, among others, are factors that undermine the use of SCFs. These issues are discussed both within the broader theoretical debates revolving around ways to dismantle the barriers undermining the use of SCFs in SSA, which could hamper the attainment of both the first and second sustainable development goals.

Seasonal prediction of the Caribbean rainfall cycle

Rainfall in the Caribbean is an important resource for numerous stakeholders in the region. Based on previous work, which identified several variables that could provide predictive skill of rainfall in the region, canonical correlation analysis is applied to assess forecast skill for station-averaged sub-regional frequency and intensity of the Early-Rainy Season (ERS) and Late-Rainy Season (LRS) wet days, and magnitude of the Mid-Summer Drought (MSD). Predictor fields are explored from the ERA-Interim and North American Multi-Model Ensemble (NMME). The use of sea-level pressure (SLP), 850 hPa zonal winds (u850), vertically integrated zonal (UQ), and meridional (VQ) moisture fluxes show comparable, if not better, forecast skill than sea-surface temperatures (SSTs), which is generally the commonly-used predictor in a given region’s seasonal climate forecasts. Generally, the highest predictive skill is found for the frequency of wet days. Rainfall characteristics in the Central and Eastern Caribbean have significant predictive skill. Forecast skill of rainfall characteristics in the Northwestern and Western Caribbean are lower and less consistent. The sub-regional differences and consistently significant skill across lead times up to at least two months can be attributed to persistent SST/SLP anomalies during the ERS that resemble the North Atlantic Oscillation, and that resembles the summer-time onset of the El Niño-Southern Oscillation during the LRS. The anomalous spatial patterns during the MSD bear resemblance to both the ERS and LRS signals. The results provide additional variables that can be used to forecast rainfall characteristics in the Caribbean and a way to tailor seasonal forecasts for each sub-region.

Seasonal climate predictions for German cities to strengthen urban resilience to climate variability

Seasonal climate predictions for German cities to strengthen urban resilience to climate variability

Uncovering the influence of hydrological and climate variables in chlorophyll-A concentration in tropical reservoirs with machine learning.

Climate variability and change, associated with increasing water demands, can have significant implications for water availability. In the Brazilian semi-arid, eutrophication in reservoirs raises the risk of water scarcity. The reservoirs have also a high seasonal and annual variability of water level and volume, which can have important effects on chlorophyll-a concentration (Chla). Assessing the influence of climate and hydrological variability on phytoplankton growth can be important to find strategies to achieve water security in tropical regions with similar problems. This study explores the potential of machine learning models to predict Chla in reservoirs and to understand their relationship with hydrological and climate variables. The model is based mainly on satellite data, which makes the methodology useful for data-scarce regions. Tree-based ensemble methods had the best performances among six machine learning methods and one parametric model. This performance can be considered satisfactory as classical empirical relationships between Chla and phosphorus may not hold for tropical reservoirs. Water volume and the mix-layer depth are inversely related to Chla, while mean surface temperature, water level, and surface solar radiation have direct relationships with Chla. These findings provide insights on how seasonal climate prediction and reservoir operation might influence water quality in regions supplied by superficial reservoirs.

A New Look into the South America Precipitation Regimes: Observation and Forecast

South America is a vast continent characterized by diverse atmospheric phenomena and climate regimes. In this context, seasonal climate predictions are helpful for decision-making in several relevant socioeconomic segments in this territory, such as agriculture and energy generation. Thus, the present work evaluates the performance of ECMWF-SEAS5 in simulating the South American precipitation regimes by applying a non-hierarchical clustering technique. In addition, the study describes the main atmospheric systems that cause precipitation in each cluster and updates a previous work performed in South America in 2010. As a result, ECMWF-SEAS5 simulates (with good correspondence) the eight climate regimes identified in the analysis of precipitation from the Climate Prediction Center (CPC). Moreover, ECMWF-SEAS5 has a satisfactory ability in representing the rainfall regime in low and medium climate predictability regions, such as central and southern South America. ECMWF-SEAS5 has good performance in the climate characterization of South America and it gives us confidence in using its seasonal climate predictions throughout the continent.

Exploring the landscape of seasonal forecast provision by Global Producing Centres

Despite the growing demand for seasonal climate forecasts, there is limited understanding of the landscape of organisations providing this critically important climate information. This study attempts to fill this gap by presenting results from an in-depth dialogue with the organisations entrusted with the provision of seasonal forecasts by the World Meteorological Organisation, known as the Global Producing Centres for Long-Range Forecasts (GPCs-LRF). The results provide an overview and detailed description of the organisational setup, mandate, target audience of GPCs-LRF and their interactions with other centres. Looking beyond the GPCs-LRF to other centres providing seasonal forecasts, some of which have been rapidly taking prominent places in this landscape, revealed a heterogeneous and still maturing community of practice, with an increasing number of players and emerging efforts to produce multi-model ensemble forecasts. The dialogues pointed at the need to not only improve climate models and produce more skilful climate forecasts, but also to improve the transformation of the forecasts into useful and usable products. Finally, using the lenses of credibility, salience and legitimacy, we explore ways to bridge the fragmentation of the information offered across the organisations considered and the people involved in the delivery and use of seasonal forecasts. The paper concludes by suggesting ways to address the boundary crossing between science, policy and society in the context of seasonal climate prediction.

Influence of the Quasi‐Biennial Oscillation on the Spatial Structure of the Wintertime Arctic Oscillation

AbstractThis study reveals that the Quasi‐Biennial Oscillation (QBO) has a marked impact on the spatial structure of the Arctic Oscillation (AO) in boreal winter. In particular, the North Pacific center (NPC) of the AO is stronger during the westerly phase of the QBO (WQBO) than the easterly phase of the QBO (EQBO). In other words, the variability associated with the AO over the North Pacific is stronger in WQBO years than EQBO years, even though the overall variability is insensitive to QBO phase. The QBO is suggested to influence the spatial pattern of the winter AO mainly via modulating the intensity of the stratospheric polar vortex, with a stronger stratospheric polar vortex during WQBO than EQBO years. A stronger stratospheric polar vortex can lead to more planetary wave refraction and facilitate the eastward propagation of wave activity flux from the mid‐latitude North Pacific to the North Atlantic. Thus an enhanced connection between North Pacific and North Atlantic atmospheric variability is established, and a more prominent NPC of the AO occurs in WQBO years. In addition, the modulation of the AO structure by the QBO and the associated physical process is supported by output from the CNRM‐CM6‐1 model. Our results indicate that, for seasonal climate prediction in North America based on the AO, WQBO leads to a larger predictable signal while EQBO leads to reduced skill.

Data fusion using climatology and seasonal climate forecasts improves estimates of Australian national wheat yields

National and regional yield forecasts can provide important insights into agribusiness beyond the farm gate. The incorporation of dynamical climate models into these forecasting systems strengthen their predictive performance in many cases but may contribute inherent biases to the final yield estimates. Downscaling the native climate model output so that is suitable for crop simulation modelling can also present challenges in representing realistic conditions for plant growth from a climate model. This study evaluated the performance of an operational national wheat yield forecast system for the Australian wheatbelt using climatology and seasonal climate model-based input data, and introduces an alternative approach using a data fusion method. The crop forecasting system uses the APSIM wheat model to estimate water-limited potential yield. The climatology-based forecast tended to over predict national yield (high yield bias; 1.5 to 7% across forecast months), while the model-based method (using ACCESS-S1 dynamical model) tended to under predict yield (low yield bias; -5.9 to -0.5% across forecast months) and had a lower spread than climatology (10 to 50% lower across forecast months). The model-based forecast had skill in terms of accuracy and reliability during the second half of the season. The newly developed data fusion method used a weighting method calibrated for separate forecast locations (stations) to remove bias in the mean forecast yield and reduce ensemble spread. This resulted in improvements in the Australia-wide yield forecasts across all forecast start dates. This study provides a demonstration of how a data-driven approach can be applied to a crop forecast to improve accuracy and resolution of crop yield forecasts without the need for more computationally intensive downscaling approaches.

Climate Information and Dengue Risk Reduction: Strategizing at the Barangay Level in Baybay, Leyte

This study is a descriptive analysis of barangay officials’ risk reduction strategies against dengue, an infectious disease transmitted by mosquitoes in their communities. The objectives behind the study are: (1) to determine respondents’ access and exposure to information on dengue, (2) to ascertain respondents’ understanding of dengue, (3) to find out the strategies respondents currently use to reduce risks of dengue, and (4) to determine respondents’ risk reduction strategies for dengue based on seasonal climate forecasts. The study’s respondents are officials of Barangay Kansungka and Barangay Gacat, Baybay, Leyte who participated in a focus group discussion to enable the researcher to extract data on the disease, knowledge of the disease, sources of information on the disease, and experiences on the disease. The study is anchored on the constructivist perspective of research, a view that argues that reality is a product of an individual’s construction, a product of his/her interaction with the environment, other people, and years of experience. The findings of the study are the following: (1) the respondent’s sources of information on the disease is through the media (especially the television and radio), and information drives and lectures by health workers in the city; (2) the officials are knowledgeable about the disease; (3) current strategies include cleanup operations, discarding standing water containers, and burning of garbage; and (4) their strategies based on climate information – normal, dry and wet climate forecast – are very much similar. This study concludes that the respondents do not recognize the relationship between rainfall patterns and risk of dengue. This suggests the need to heighten awareness of the relationship between rainfall patterns and the disease. Moreover, climate information should be disseminated or made available to local officials.

Climate Information Services Available to Farming Households in Northern Region, Ghana

Abstract Climate information services can build the resilience of African farmers to address the increasing threats associated with climate change. This study used household surveys with 200 farmers and focus group discussions to identify the types of climate information services available to farming households in two selected districts (Tolon and Nanton) in the Northern Region of Ghana. The study also identified the dissemination channels and the barriers faced by farmers in their access and use of climate information services for building climate resilience in Ghanaian farming systems. Multinomial logistic regression analysis was used to evaluate the determinants of farmers’ access to climate information services. Results show that 70% of the surveyed farmers had access to varied forms of climate information services. The most prevalent meteorological variables accessible to them were rainfall, temperature, and windstorms in the form of daily and weekly weather forecasts, with only very limited availability and use of seasonal climate forecasts. Radio, television, and advice from extension agents were reported as the major dissemination channels by study respondents. A majority of the farmers reported lack of communication devices, mistrust in weather and climate forecasts, and lack of visual representations in the forecasts as major barriers to access and use of climate information services. The results highlight the importance of timely and reliable access to climate information services in enhancing farmers’ decision-making capacities and the need for training and recruitment of more extension agents to work with farmers on linking climate information services to targeted actions on crop and land management.

Seasonal climate predictions for marine risk assessment in the Barents Sea

We investigate the potential for using seasonal climate predictions for marine risk assessment in the Barents Sea. Marine risk is based on the diagnostic of the probability of extreme climate conditions and their consequences to identify time and regions vulnerable to climate hazards. The information on marine risk is operationally provided by Det Norske Veritas (DNV) to support sustainable and safe marine activities. So far, the probability of climate conditions was based on historical observations. Here, we implement predicted probabilities from an ensemble of seasonal predictions provided by the German Meteorological Service. We analyze predicted probabilities for summer for three indicators from the DNV’s marine service: two of them represent meteorological properties such as wind speed and 2-meter temperature. The third indicator, the wind chill index (WCI), is a combination of the previous two. The prediction skill, the “trust level” for predictions, is assessed over 1990–2017 and suggests that 2-meter temperature has the highest skill followed by WCI and wind speed. In addition, we use a real-time prediction to represent an actual application example. The maps of the real-time predicted probabilities show that large areas of the Barents Sea represent favorable conditions for marine operations considering low likelihood for WCI above 1000 W/m2 and T2m below 0 °C in July and August. Wind speed is poorly predicted beyond the first forecast month. We describe the workflow for an application of seasonal predictions for the DNV’s marine service as well as lessons learned for similar applications involving risk assessment.

Opportunities for seasonal forecasting to support water management outside the tropics

Abstract. Advance warning of seasonal conditions has the potential to assist water management in planning and risk mitigation, with large potential social, economic, and ecological benefits. In this study, we explore the value of seasonal forecasting for decision-making at five case study sites located in extratropical regions. The forecasting tools used integrate seasonal climate model forecasts with freshwater impact models of catchment hydrology, lake conditions (temperature, water level, chemistry, and ecology), and fish migration timing and were co-developed together with water managers. To explore the decision-making value of forecasts, we carried out a qualitative assessment of (1) how useful forecasts would have been for a problematic past season and (2) the relevance of any windows of opportunity (seasons and variables where forecasts are thought to perform well) for management. Overall, water managers were optimistic about the potential for improved decision-making and identified actions that could be taken based on forecasts. However, there was often a mismatch between those variables that could best be predicted and those which would be most useful for management. Reductions in forecast uncertainty and a need to develop practical, hands-on experience were identified as key requirements before forecasts would be used in operational decision-making. Seasonal climate forecasts provided little added value to freshwater forecasts in these extratropical study sites, and we discuss the conditions under which seasonal climate forecasts with only limited skill are most likely to be worth incorporating into freshwater forecasting workflows.

A canonical model for seasonal climate prediction using Big Data

This article addresses the elaboration of a canonical model, involving methods, techniques, metrics, tools, and Big Data, applied to the knowledge of seasonal climate prediction, aiming at greater dynamics, speed, conciseness, and scalability. The proposed model was hosted in an environment capable of integrating different types of meteorological data and centralizing data stores. The seasonal climate prediction method called M-PRECLIS was designed and developed for practical application. The usability and efficiency of the proposed model was tested through a case study that made use of operational data generated by an atmospheric numerical model of the climate area found in the supercomputing environment of the Center for Weather Forecasting and Climate Studies linked to the Brazilian Institute for Space Research. The seasonal climate prediction uses ensemble members method to work and the main Big Data technologies used for data processing were: Python language, Apache Hadoop, Apache Hive, and the Optimized Row Columnar (ORC) file format. The main contributions of this research are the canonical model, its modules and internal components, the proposed method M-PRECLIS, and its use in a case study. After applying the model to a practical and real experiment, it was possible to analyze the results obtained and verify: the consistency of the model by the output images, the code complexity, the performance, and also to perform the comparison with related works. Thus, it was found that the proposed canonical model, based on the best practices of Big Data, is a viable alternative that can guide new paths to be followed.

Performance of State‐of‐the‐Art C3S European Seasonal Climate Forecast Models for Mean and Extreme Precipitation Over Africa

AbstractSeasonal hydrological forecasts at high spatial and temporal resolution can help manage water resources and mitigate impacts of extreme events but are dependent on skillful and operational seasonal forecasts from climate models. In this study, we evaluate precipitation forecasts from five operational climate models with a potential to drive hydrological forecasts: European Centre for Medium‐Range Weather Forecasts (ECMWF), UK Met Office (UK‐Met), Météo France, Deutscher Wetterdienst, and Centro Euro‐Mediterraneo sui Cambiamenti Climatici. The Multi‐Source Weighted‐Ensemble Precipitation is used as a reference data set to evaluate the model skill. The performance of individual models is evaluated on daily, weekly, monthly, seasonal, and climatological periods, and for selected target months, lead‐times and drought events, and compared to unweighted and skill‐weighted multi‐model ensemble mean forecast. For all models, the lead 1‐month forecast can replicate the climatological mean, monthly mean, and monthly anomaly precipitation, although much of this skill originates from the first week of the forecast. The skill drops rapidly for lead 2‐month and longer and is highest in drier regions and seasons. The forecast skill of monthly meteorological drought events at lead 1‐month is modest. All models represent the monthly variation in the length of wet and dry spell days at lead 1‐month, but the skill is weak for heavy and very heavy precipitation days. ECMWF is found to be the most skillful model, followed by the UK‐Met, although the multi‐model weighted average provides the highest performance compared to individual models and the un‐weighted multi‐model mean.

Subseasonal to Seasonal Climate Forecasts Provide the Backbone of a Near-Real-Time Event Explainer Service

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).CORRESPONDING AUTHOR: Pandora Hope, pandora.hope@bom.gov.au

A Decade of the North American Multimodel Ensemble (NMME): Research, Application, and Future Directions

Abstract Ten years, 16 fully coupled global models, and hundreds of research papers later, the North American Multimodel Ensemble (NMME) monthly-to-seasonal prediction system is looking ahead to its second decade. The NMME comprises both real-time, initialized predictions and a substantial research database; both retrospective and real-time forecasts are archived and freely available for research and development. Many U.S.-based and international entities, both private and public, use NMME data for regional or otherwise tailored forecasts. The system’s built-in evolution, with new models gradually replacing older ones, has been demonstrated to gradually improve the skill of 2-m temperature and sea surface temperature, although precipitation prediction remains a difficult problem. This paper reviews some of the NMME-based contributions to seasonal climate prediction research and applications, progress on scientific understanding of seasonal prediction and multimodel ensembles, and new techniques. Several prediction-oriented aspects are explored, including model representation of observed trends and the underprediction of below-average temperature. We discuss potential new directions, such as higher-resolution models, hybrid statistical–dynamical techniques, or prediction of environmental hazards such as coastal flooding and the risk of mosquito-borne diseases.

Seasonal climate forecasts (SCFs) based risk management strategies: A case study of rainfed rice cultivation in India

Seasonal climate forecasts (SCFs) have gained popularity in agriculture for climate risk management studies. The available forms of SCFs are not conducive to decision making because of a mismatch in scales over space and time. In this study, available SCFs were disaggregated using the FResampler1 technique to simulate rice yield (cultivar PR 114) under different nitrogen levels and planting dates using DSSAT (Decision Support System for Agrotechnology Transfer) for Sitamarhi district, Bihar, India. Results showed that the late planting of rice predicted the highest yield (3800 kg ha-1) with high variability under SCF (wet) and 200 kg ha-1 application of nitrogen fertilizer. Similarly, for SCF (dry), the late planting of rice simulated high yield (3100 kg ha-1) attributes with 200 kg ha-1 of nitrogen fertilizer. However, rice yield under climatology (3450 kg ha-1) was more than SCF (dry) (3100 kg ha-1). Planting of rice on 15th June 2019 under the SCF (normal) predicted low uncertainty with high mean yields as compared to the mid (05th July 2019), and late (25th July 2019) planting. The present study showed that by applying SCF, we can have a better understanding on “relative” changes in yield attributes with fertilizer doses and planting dates, which may be adopted by the climate adviser to offset the climate risk without compromising productivity.