Understanding Seasonal Climate Forecast (SCF)
Purpose
Farmers face several risks related to climate such as droughts and heat stress, flooding, wildfires, sea level rise, invasive species and extreme events; this climate variability puts pressure on agriculture, as it is highly sensitive to climate.
In this context, Seasonal Climate Forecast (SCF), which refers to the use of climate models and observation data to predict climate conditions over a period of months to a year, serves as a valuable tool to guide decision-making. By presenting climate information in a more understandable and tailored way, seasonal forecasts encourage an adaptive mindset among farmers, enabling them to take proactive measures in managing climate-related risks (Klemm & McPherson, 2017).
Components of SCF
Seasonal forecasting is generated using Global Climate Models (GCMs) and Earth System Models (ESMs), which simulate the Earth’s climate system to provide predictions. The forecast begins with establishing the initial state of the global atmosphere, land surface, and ocean using reanalysis datasets such as ERA5. ERA5 aims to provide a detailed and consistent record of the global atmosphere, land surface, and ocean waves. Reanalyses such as ERA5 are critical for climate monitoring applications and contribute to international climate assessment, such as those by the WMO and IPCC (Hersbach et al., 2020). Climate models are evaluated and refined using reanalysis datasets, which combine climate observations and models to provide consistent maps. Since GCMs operate at a coarse spatial resolution of (50-200km grid spacing), forecasts experience scale limitations especially local level application. To address the limitations of the model, post-processing techniques such as bias correction, statistical downscaling and ensemble calibration are applied to improve forecasting skill and tailored outputs (Cannon, 2016). These techniques help translate global model resolution into finer spatial scales that are required by climate services around the world.
In climate modeling there has been a clear progression towards higher spatial resolution as seen in Coupled Model Intercomparison Projects (CMIP). While CMIP6 models operate at a 100m resolution, CMIP7 is expected to achieve kilometer scale ultra high resolution models that represent convection (Nilsen et al., 2017). These advances may reduce the reliance on statistical downscaling and improve regional climate dynamics. Although post-processing will likely remain essential for tailored forecasts for specific user needs.
Output of SCF
Forecast can be presented in three ways: a probabilistic forecast, a deterministic forecast, and an emerging new approach which is impact based forecast.
Probabilistic forecasting involves providing a predictive probability distribution over future events of interest rather than a single valued prediction. This approach is essential for quantifying uncertainty in a prediction (Gneiting & Katzfuss, 2014). For example, the forecast might say there’s a 70% chance of above average rainfall. It does not say exactly what will happen but it tells you how likely each possibility is.
On the other hand, a deterministic forecast provides a single-valued expectation series of future outcomes. This approach assumes that, given the initial state, the future evolution of a system can be uniquely predicted (Garcia-Moya et al., 2016). For example, the forecast might say the temperature will be 25°C or rainfall will be 50mm. A typical deterministic output is presented through a “best guess” scenario.
Impact Based Forecasting (IbF) is an emerging approach in early warning services that focus on predicting and communicating the potential impacts and consequences of weather and climate events. This bridges the gap between science and action by providing actionable information to decision makers and the public (Shyrokaya et al., 2024). For example, the forecast may not specify the wind speeds and rainfall amounts but instead gives a visual presentation of what extreme events can have over an area.
Using SCF in Agricultural Decision-Making
For agriculture decision - making, SCF can be used in different ways depending on the stage of agricultural production.
Before the season starts, during the planning phase, SCF can help to determine which type or variety of crop might be best suited for the season in terms of water necessity, considering the projected ENSO events or the projected rainfall; it can also help to allocate resources such as fertilisers and chemical application to improve soil moisture if drought conditions are forecasted and the variety needs more moisture (Klemm & McPherson, 2017).
During the planting stage, SCF can help to adjust sowing dates. For example, in the case of rice, a crop that is sensitive to the availability of water, temperature and sunlight, time adjustments allow farmers to plant with favourable conditions, avoiding early–season water shortages, reducing heat stress during flowering and ensuring enough amount of sunlight during their growth (An Vo et al., 2021).
Through the cropping stage, it can support reactive agrotechnical management by optimising irrigation during growth due to water scarcity, by mitigating stress caused by extreme events such as floodings, heatwaves, and providing information about potential seasonal pests that might develop due to humid and hot weather, allowing farmers to integrate pest management in advance (Neta et al., 2023).
During harvest, like the planting stage, SCF can support timing planning. Heavy rainfall can cause complications in transportation from the field to the storage sites, as well as in the use of mechanical harvesting, which usually requires dry conditions for safe operation (Everingham et al., 2002). On the other hand, difficult climate conditions, such as extreme events like hail, can cause physical damage to the crops during harvest (Klemm & McPherson, 2018).
And finally, after harvest, SCF can help to strategically manage stocks, if it is better to sell quickly to avoid losses due to insects, pests and heatwaves; SCF can also improve transportation planning, helping to anticipate disruptions caused by flooding, and improve storage management by providing early warnings of high moisture levels or heatwaves that could increase the risk of spoilage or fire (Stathers et al., 2013).
Case Studies
Across the globe, SCF is being used to adapt to the forecast climate; because climate is different in each region, countries normally develop their own system using global and local information. In this section, two examples are given, which use the same ESM, which is the Australian Community Climate and Earth System Simulator - Seasonal (ACCESS-S), but using different local climate data tailored to different contexts.
SCF in Australia
Australia is recognised as a notable example of the successful application of SCF, particularly in large-scale wheat and livestock production systems. Interest in SCF emerged in the early 1980s, driven by the need to improve agricultural risk management (Nicholls, 1980). This interest has been reinforced by Australia’s highly variable climate, where rainfall variability is greater than in other regions with comparable climatic conditions (Parton et al., 2019).
SCFs began with statistical techniques based on the Southern Oscillation Index (SOI) and sea surface temperature (SST) phase systems (Luo et al., 2024). Then, it was replaced by the Predictive Ocean Atmosphere Model for Australia (POAMA) in the 2000s as a dynamic forecasting model (Charles et al., 2015). These days, ACCESS-S is the current operational model replacing POAMA, offering improved spatial resolution (from 250 km to 60km) and ensemble forecasting of 99 members. And upgrade to ACCESS-S2 to demonstrate varying skill in predicting extreme climate indices, contributing to measurable yield gains in agriculture (Luo et al., 2024).
Applications and Benefits in Agriculture
SCF shows varying skill in predicting extreme climate indices, offering potential benefits for agricultural production. For example, according to the study made by Parton et al. (2019), in which they took data from 86 farms across Australia, the results showed that the cotton industry had estimated benefits of over $100/ha, while wheat gained an exceeding $80/ha. By providing advance information, farmers can enhance farm profitability either by maximising returns in favourable seasons or by minimising losses during dry periods (Parton et al., 2019).
Optimal management strategies also varied by ___location (Parton et al., 2019); for example, earlier sowing was particularly effective in drier regions, enabling substantial yield gains in the Australian wheat industry through contingent, forecast-informed decision-making (Luo et al., 2024). Government as the Bureau of Meteorology (BoM) and academic partner, evaluations indicate increased awareness, improved knowledge, and greater adoption of SCF tools in agricultural decision-making. These improvements in on-farm decision-making contribute to enhanced farm resilience, with the potential for lasting impact. Over time, this process fosters a network of knowledgeable and trusted climate information resources within farming communities (Cobon et al., 2021).
SCF in Samoa
In Samoa, Seasonal Climate Forecasting (SCF) has been in use since the late 1990s, starting with simple statistical models linked to the ENSO index. These early forecasts gave basic rainfall predictions for the Pacific region, but they were not very suitable for the varied microclimates on the islands. Over time, it changed to use ESM downscaled for Samoa’s conditions (Pacific Island Countries Advanced Seasonal Outlook [PICASO] model), improving reliability and making them more useful for farmers.
The National Adaptation Programme of Action (NAPA) submitted to the United Nations Framework Convention on Climate Change (UNFCCC) in 2005 identified agriculture and water security as priority sectors vulnerable to climate change and gave a framework to guide action (Ministry of Natural Resources and Environment, 2005). Smallholders, who grow maize, sorghum, and taro, now use SCF to plan planting times, choose drought or flood tolerant crops, and manage soils better.
Technical help from the Secretariat of the Pacific Regional Environment Programme (SPREP) supports the Samoa Meteorology Division (SMD) with quality climate data and training, which strengthen the science behind these measures. A big challenge noted in the Climate Risk Profile for Samoa is that coarse global models often fail to reflect the local microclimates shaped by the mountainous terrain (Australian Bureau of Meteorology, CSIRO, & SPREP, 2011).
This problem in producing local forecasts has encouraged combining science with Samoan Traditional Knowledge (TK) known as “matai”, built on generations of observing weather and nature. Signs like the flowering of the Pua tree and the movement of the Tuli or Pacific golden plover have long been used to mark seasonal change (Lefale, 2010). The Samoa Meteorology Division, with SPREP, worked with community elders to record and check these signs, then combined them with SCF in official bulletins. This work was in collaboration with the Ministry of Natural Resources and Environment, the Samoa Red Cross Society, and the International Federation of Red Cross and Red Crescent Societies (IFRC), creating climate services that are scientific and culturally relevant (Taise et al., 2017). Combining both systems helps overcome communication problems and build trust, so farmers more likely follow the advice in the forecasts.
Applications and benefits in Agriculture
In Lepa, a cyclone prone district, the Samoa Red Cross Society used a farmer field school to train farmers in climate resilient methods. Three months before planting, SCF showed a forecast of above average rainfall and high flood risk. Traditional signs confirmed this, which gave more confidence in the advice. Farmers delayed maize planting by two weeks, and as a result, yields rose by 18% compared to villages that did not change planting times. The program also set up rainwater harvesting, gave out water tanks, and trained households in resilient vegetable gardening (International Federation of Red Cross and Red Crescent Societies, 2013). The Ministry of Natural Resources and Environment made sure the work matched NAPA priorities, the SMD gave localised forecasts, and SPREP trained extension officers to explain and share the info. This link from national policy to science to community action shows how SCF with government and NGO support can lead to real, measurable gains in resilience for small farmers.
Challenges and Gaps in Seasonal Climate Forecasting Implementation
The availability of information from sophisticated Seasonal Climate Forecasting (SCF) does not guarantee successful adaptation. In fact, the implementation of SCF still experiences many limitations in field application and model capacity. Several factors influence the potential of SCF, including but not limited to:
Scientific and Model Limitation
Accuracy limitations and biases remain among the most significant constraints to the model’s effectiveness in supporting adaptation. Although the reliability of the model has been improved, for example, through the development of Multi Model Ensemble (MME) where forecasts are generated from several models, many users still complain about the model’s reliability. In a report by Meat & Livestock Australia, Kuehne (2024) stated that many farmers want the model to have a 90% confidence level. Nevertheless, probabilistic models find it difficult to reach this degree of accuracy.
Furthermore, the PICASO model of Pacific Islands, which uses Global Climate Models (GCMs) in atmospheric and oceanic simulations for seasonal forecasting, faces additional challenges during La Niña. During such conditions, models often reduce their performance and increase uncertainty, resulting in spatial biases in rainfall forecasts (Lee et al., 2022). Missing a rainfall prediction, particularly in a strong La Niña year, can make producers hesitant to make decisions and reduce trust in the system.
Communication and Capacity Gap
SCF implementation still faces challenges in that its values are considered difficult to understand and interpret. Hartmann et al. ( 2002) pointed out that forecast information is often presented in complex formats such as probability maps and skill score models, which are generally only understood by climatologists and not by users such as farmers or fishermen. Moreover, the overabundance of information makes users confused to make decisions. For instance, Kuehne (2024) highlights that the concepts presented in SCF, such as deciles and shifts in probability mean, make it difficult to sort out which information is helpful and what the possible outcomes might be.
On the other hand, the culture of users such as graziers in Queensland, whose occupational identities tend to be formed through experience and who are highly independent in their decision-making, is a barrier to their acceptance of SCF as part of their adaptation. Their financial or emotional dependence on natural resources makes them doubt whether it is worth taking risks for external science information (Hartmann et al., 2002).
Consequences of Error
The impact of inaccurate forecasts also serves as a barrier to adaptation strategies. This was evident in a report by Kuehne (2024), which highlighted that during severe drought in Australia in 2017, the seasonal forecast issued by the Bureau of Meteorology (BoM) indicated a 50/50 rain probability for March. This neutral forecast suggested no tendency towards either wet or dry conditions, leading graziers to increase the number of livestock. However, it turned out that there was no significant rain until January 2020. The risk experienced by users in this case contributes to skepticism that, over time, may lead to a decline in adoption even if the quality of the forecast improves. According to Marshall et al., (2011) interest in SCF as a form of adaptation will increase if science-based information is more integrated with business strategy because users view these tools as part of investment planning.
Future Directions and Innovations in SCF for Agriculture
Future efforts should prioritize technical advancements, capacity building, and policy reforms to ensure effective use of SCF.
Forecast Technology and Application
Accurate forecasts alone are no longer sufficient; the focus must shift to making forecasts usable and relevant for decision-making. In Australia, SCF applied forecasting systems at a more localized level, such as The Break newsletter and ClimateDogs animations, which illustrate the more intricate key climate drivers like SAM (Southern Annular Mode), ENSO, and IOD (Indian Ocean Dipole). These tools demonstrate the need to move from statistical to actionable forecasts. Darbyshire et al. (2020) emphasize that the gap between climatologists and farmers lies in translating forecasts into farm-level decisions, underscoring the integration of SCF into decision systems. They also note that the forecast value is highly context-dependent, varying with farming system, ___location, and lead time. Simulation studies using VIC-CropSyst depict that even minimal improvements in forecast accuracy can enhance the precision of fertilization, irrigation, sowing, and timing (Malek et al., 2017). In addition, to improve communication and usability, SCF should improve rainfall prediction accuracy, especially onset, cessation, and dry spell patterns, as these are more valuable to farmers than the total seasonal amount (Klemm, McPherson, 2017).
Capacity Building and Training
Effective adaptation requires forecasts to be understood, trusted, and seen as useful. Farmers are most likely to adopt practices when they believe the forecasts are credible (evidence-based), when they see personal or financial benefits, and when they have the knowledge, resources, and institutional support to act (Arunrat et al., 2017). Extension workshops, podcasts, interactive tools, and the Learning Management System (LMS) are crucial for building trust and capacity that can make forecasts understandable and actionable. Interest in learning is often triggered by climate extremes such as droughts or floods. Tailored communication and the involvement of local influencers can boost credibility and adoption, especially in risk-averse communities.
A strong example is Samoa’s community-based programs, which train farmers in sustainable, climate-smart agriculture. Through workshops and local adaptation leaders, these initiatives have increased trust in forecasts and strengthened resilience. Combining scientific tools with local knowledge enables farmers to make informed decisions on weather-sensitive practices like irrigation and crop rotation (Ministry of Agriculture and Fisheries; UNDP).
Strengthening Policy
Building on advances in technology and capacity building, the next step is to embed SCF within agricultural and climate adaptation policies. Strong frameworks ensure innovations and training translate into sustained use of SCF for farm planning, risk reduction, and market resilience. In Australia, integrating SCF into decision-support tools, targeted extension, and cross-sectoral collaboration has improved farmer profitability and adaptive capacity (Parton et al., 2019; Hochman et al., 2009). In Samoa, ICCRAHS (Integrating climate change risk in Agriculture and Health) and Participatory Rural Assessment-based engagement have expanded access to tailored forecasts, training, and local capacity building (MNRE & UNDP, 2013; LRD, 2012). Future policies must guarantee sustained funding, inter-agency data sharing, and farmer-led co-design to make SCF a routine part of agricultural decision-making.
Conclusion
Coping with the challenges of climate change will require continuous innovation and technological advancement to support successful adaptation. Achieving higher accuracy in climate models remains a major challenge for forecasters, but it is critical for building trust and ensuring that the information provided is both valuable and actionable. Reliable and trusted forecasts will empower farmers to make informed decisions, thereby supporting adaptation strategies that contribute to long-term sustainability and resilient crop production.
Future innovation in SCF must focus on three areas: improving technical skill and relevance, strengthening farmer engagement through literacy and learning, and embedding forecasts in institutional, market, and policy frameworks. Australia’s experience offers a roadmap for globally scaling SCFs as a core tool for climate-resilient agriculture. On the other hand, in Samoa, community-based training and integration of local knowledge illustrate how SCF can be successfully adapted to strengthen resilience in small island farming systems.
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