The CSTools (v4.0) Toolbox: from Climate Forecasts to Climate Forecast Information

Despite the wealth of existing climate forecast data, only a small part is effectively exploited for sectoral applications. A major cause of this is the lack of integrated tools that allow the translation of data into useful and skilful climate information. This barrier is addressed through the development of an R package. CSTools is an easy-to-use toolbox designed and built to assess and improve the quality of climate forecasts for seasonal to multi–annual scales. The package contains process-based state-of-the-art methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products. Due to the design of the toolbox in individual functions, the users can develop their own post-processing chain of functions as shown in the use cases presented in this manuscript: the analysis of an extreme wind speed event, the generation of seasonal forecasts of snow depth based on the SNOWPACK model and the post-processing of data to be used as input for the SCHEME hydrological model.

Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting

Evapotranspiration plays an important role in the terrestrial water cycle. Reference crop evapotranspiration (ETo) has been widely used to estimate water transfer from vegetation surface to the atmosphere. Seasonal ETo forecasting provides valuable information for effective water resource management and planning. Climate forecasts from General Circulation Models (GCMs) have been increasingly used to produce seasonal ETo forecasts. Statistical calibration plays a critical role in correcting bias and dispersion errors in ETo forecasts. However, time-dependent errors, resulting from GCM’s misrepresentations of climate trends, have not been explicitly corrected in ETo forecast calibrations. We hypothesize that reconstructing climate trends through statistical calibration will add extra skills to seasonal ETo forecasts. To test this hypothesis, we calibrate raw seasonal ETo forecasts constructed with climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model across Australia, using the recently developed Bayesian Joint Probability trend-aware (BJP-ti) model. Raw ETo forecasts demonstrate significant inconsistencies with observations in both magnitudes and spatial patterns of temporal trends, particularly at long lead times. The BJP-ti model effectively corrects misrepresented trends and reconstructs the observed trends in calibrated forecasts. Improving trends through statistical calibration increases the correlation coefficient between calibrated forecasts and observations (r) by up to 0.25 and improves the continuous ranked probability score (CRPS) skill score by up to 15 % in regions where climate trends are misrepresented by raw forecasts. Skillful ETo forecasts produced in this study could be used for streamflow forecasting, modelling of soil moisture dynamics, and irrigation water management. This investigation confirms the necessity of reconstructing climate trends in GCM-based seasonal ETo forecasts, and provides an effective tool for addressing this need. We anticipate that future GCM-based seasonal ETo forecasting will benefit from correcting time-dependent errors through trend reconstruction.

CultivData: Application of IoT to the Cultivation of Agricultural Data

CultivData proposes the convergence of technologies, such as IoT, big data, HPC, open data and artificial intelligence, to apply HPDA (High Performance Data Analytics) to the cultivation of agricultural data and improve the efficiency and effectiveness of farms. An information system has been developed as an IT platform for the cultivation of open data to extract knowledge and to support the decision making of stakeholders in the agricultural sector, so that it is possible to improve product quality and farm productivity. The resulting system integrates access to data provided by IoT devices that sensorize farms and public and open data sources (Open Data). The platform was designed to make precision agriculture a reality and to be useful not only to farmers, but also to agricultural decision-makers who plan species and crops based on data such as available water; expected weather; prices and market demands, and so forth. In addition, the platform provides to agricultural producers access to historical climate data; climate forecasts to anticipate times of drought or disasters; pest situations or monitoring of their plantations with sensorization and orthophotographs.

ProbFire: a probabilistic fire early warning system for Indonesia

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 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 multilayer perceptron model using ECMWF SEAS5 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 such integrated forecasts for Indonesia. ProbFire's event probability predictions outperformed climatology-only based fire predictions at three to five-month lead times in south Kalimantan, south Sumatra and south Papua. In central Sumatra, an improvement was observed only at one 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.

Opportunities for seasonal forecasting to support water management outside the tropics

Advance warning of seasonal conditions has 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, level, chemistry and ecology) and fish migration timing, and were co-developed together with stakeholders. 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, stakeholders 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 the 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.

Progress and Challenges of Demand-Led Co-Produced Sub-Seasonal-to-Seasonal (S2S) Climate Forecasts in Nigeria

This paper identifies fundamental issues which prevent the effective uptake of climate information services in Nigeria. We propose solutions which involve the extension of short-range (1 to 5 days) forecasts beyond that of medium-range (7 to 15 days) timescales through the operational use of current forecast data as well as improve collaboration and communication with forecast users. Using newly available data to provide seamless operational forecasts from short-term to sub-seasonal timescales, we examine evidence to determine if effective demand-led sub-seasonal-to-seasonal (S2S) climate forecasts can be co-produced. This evidence involves: itemization of forecast products delivered to stakeholders, with their development methodology; enumeration of inferences of forecast products and their influences on decisions taken by stakeholders; user-focused discussions of improvements on co-produced products; and the methods of evaluating the performance of the forecast products.We find that extending the production pipeline of short-range forecast timescales beyond the medium-range, such that the medium-range forecast timescales can be fed into existing tools for applying short-range forecasts, assisted in mitigating the risks of sub-seasonal climate variability on socio-economic activities in Nigeria. We also find that enhancing of collaboration and communication channels between the producers and the forecast product users helps to: enhance the development of user-tailored impact-based forecasts; increases users' trusts in the forecasts; and, seamlessly improves forecast evaluations. In general, these measures lead to more smooth delivery and increase in uptake of climate information services in Nigeria.