Assessing the spatial impact of climate on wheat productivity and the potential value of climate forecasts at a regional level

Quantification of the spatial impact of climate on crop productivity and the potential value of seasonal climate forecasts can effectively assist the strategic planning of crop layout and help to understand to what extent climate risk can be managed through responsive management strategies at a regional level. A simulation study was carried out to assess the climate impact on the performance of a dryland wheat-fallow system and the potential value of seasonal climate forecasts in nitrogen management in the Murray-Darling Basin (MDB) of Australia. Daily climate data (1889–2002) from 57 stations were used with the agricultural systems simulator (APSIM) to simulate wheat productivity and nitrogen requirement as affected by climate. On a good soil, simulated grain yield ranged from <2 t/ha in west inland to >7 t/ha in the east border regions. Optimal nitrogen rates ranged from <60 kgN/ha/yr to >200 kgN/ha/yr. Simulated gross margin was in the range of –$20/ha to $700/ha, increasing eastwards. Wheat yield was closely related to rainfall in the growing season and the stored soil moisture at sowing time. The impact of stored soil moisture increased from southwest to northeast. Simulated annual deep drainage ranged from zero in western inland to >200 mm in the east. Nitrogen management, optimised based on ‘perfect’ knowledge of daily weather in the coming season, could add value of $26～$79/ha compared to management optimised based on historical climate, with the maximum occurring in central to western part of MDB. It would also reduce the nitrogen application by 5～25 kgN/ha in the main cropping areas. Comparison of simulation results with the current land use mapping in MDB revealed that the western boundary of the current cropping zone approximated the isolines of 160 mm of growing season rainfall, 2.5t/ha of wheat grain yield, and $150/ha of gross margin in QLD and NSW. In VIC and SA, the 160-mm isohyets corresponded relatively lower simulated yield due to less stored soil water. Impacts of other factors like soil types were also discussed.     

Real-time multi-model decadal climate predictions

We present the first climate prediction of the coming decade made with multiple models, initialized with prior observations. This prediction accrues from an international activity to exchange decadal predictions in near real-time, in order to assess differences and similarities, provide a consensus view to prevent over-confidence in forecasts from any single model, and establish current collective capability. We stress that the forecast is experimental, since the skill of the multi-model system is as yet unknown. Nevertheless, the forecast systems used here are based on models that have undergone rigorous evaluation and individually have been evaluated for forecast skill. Moreover, it is important to publish forecasts to enable open evaluation, and to provide a focus on climate change in the coming decade. Initialized forecasts of the year 2011 agree well with observations, with a pattern correlation of 0.62 compared to 0.31 for uninitialized projections. In particular, the forecast correctly predicted La Niña in the Pacific, and warm conditions in the north Atlantic and USA. A similar pattern is predicted for 2012 but with a weaker La Niña. Indices of Atlantic multi-decadal variability and Pacific decadal variability show no signal beyond climatology after 2015, while temperature in the Niño3 region is predicted to warm slightly by about 0.5 °C over the coming decade. However, uncertainties are large for individual years and initialization has little impact beyond the first 4 years in most regions. Relative to uninitialized forecasts, initialized forecasts are significantly warmer in the north Atlantic sub-polar gyre and cooler in the north Pacific throughout the decade. They are also significantly cooler in the global average and over most land and ocean regions out to several years ahead. However, in the absence of volcanic eruptions, global temperature is predicted to continue to rise, with each year from 2013 onwards having a 50 % chance of exceeding the current observed record. Verification of these forecasts will provide an important opportunity to test the performance of models and our understanding and knowledge of the drivers of climate change.     

The Uncertain Value of Perfect ENSO Phase Forecasts: Stochastic Agricultural Prices and Intra-Phase Climatic Variations

Climate and crop yield variability associated with El Niño—Southern Oscillation (ENSO) are now predictable within limits. This predictability suggests a potential to tailor agricultural management to mitigate impacts of adverse conditions and to take advantage of favorable conditions. However, improved climate predictions may benefit society only with parallel advances in our ability to use this knowledge. We show that the value that will accrue to any given actor from an ENSO phase forecast should be viewed not as a known number but instead as a random draw from a distribution, even when the forecast is always correct. Forecast value depends on the highly variable contexts in which forecasts are used. Randomness in forecast value has significant implications for choices made by forecasters, forecast users and policy makers. To show randomness, we estimate potential economic values of ENSO forecasts for agricultural producers based on two realistic assumptions: the crop prices farmers receive are uncertain; and within an ENSO phase, the actual climate is variable in ways that affect profits. The use of synthetic weather and crop price series, with crop simulation models, helps show the range and likelihood of climate forecast value.     

Sensitivity of seasonal climate forecasts to persisted SST anomalies

Most estimates of the skill of atmospheric general circulation models (AGCMs) for forecasting seasonal climate anomalies have been based on simulations with actual observed sea surface temperatures (SSTs) as lower boundary forcing. Similarly estimates of the climatological response characteristics of AGCMs used for seasonal-to-interannual climate prediction generally rest on historical simulations using "perfect" SST forecasts. This work examines the errors and biases introduced into the seasonal precipitation response of an AGCM forced with persisted SST anomalies, which are generally considered to constitute a good prediction of SST in the first three-month season. The added uncertainty introduced by the persisted SST anomalies weakens, and in some cases nullifies, the skill of atmospheric predictions that is possible given perfect SST forcing. The use of persisted SST anomalies also leads to changes in local signal-to-noise characteristics. Thus, it is argued that seasonal-to-interannual forecasts using AGCMs should be interpreted relative to historical runs that were subject to the same strategy of boundary forcing used in the current forecast in order to properly account for errors and biases introduced by the particular SST prediction strategy. Two case studies are examined to illustrate how the sensitivity of the climate response to predicted SSTs may be used as a diagnostic to suggest improvements to the predicted SSTs.     

The impacts of climate change on agricultural production systems in China

Climate change can bring positive and negative effects on Chinese agriculture, but negative impacts tend to dominate. The annual mean surface temperature has risen about 0.5–0.8 °C. The precipitation trends have not been identified during the past 100 years in China, although the frequency and intensity of extreme weather/climate events have increased, especially of drought. Water scarcity, more frequent and serious outbreaks of insects and diseases, and soil degradation caused by climate change have impacted agro-environmental conditions. However, temperature rise prolonged the crop growth seasons and cold damages have reduced in Northeast China. The projection of climate change indicates that the surface temperature will continue to increase with about 3.9 to 6.0 °C and precipitation is expected to increase by 9 to 11 % at the end of 21st century in China. Climate warming will provide more heat and as a consequence, the boundary of the triple-cropping system (TCS) will extend northwards by as much as 200 to 300 km, from the Yangtze River Valley to the Yellow River Basin, and the current double-cropping system (DCS) will move to the central part of China, into the current single cropping system (SCS) area which will decrease in SCS surface area of 23.1 % by 2050. Climate warming will also affect the optimum location for the cultivation of China’s main crop varieties. If no measures are taken to adapt to climate changes, compared with the potential yield in 1961–1990, yields of irrigated wheat, corn and rice are projected to decrease by 2.2–6.7 %, 0.4 %–11.9 % and 4.3–12.4 % respectively in the 2050s. Climate warming will enhance potential evaporation and reduce the availability of soil moisture, thus causing a greater need for agricultural irrigation, intensifying the conflict between water supply and demand, especially in arid and semi-arid areas of China. With adequate irrigation, the extent of the reduction in yield of China’s corn and wheat can be improved by 5 % to 15 %, and rice by 5 % or so than the potential yield in 1961–1990. Adaptive measures can reduce the agricultural loss under climate change. If effective measures are taken in a timely way, then climate change in the next 30–50 years will not have a significant influence on China’s food security.     

Seasonal Climate Forecasts – Potential Agricultural-Risk Management Tools?

The importance of anchoring seasonal climate forecasts to user needs is examined in this paper. Although it is generally accepted that seasonal climate forecasts have potential value, many constraints preclude the optimal use of these forecasts, including the way forecasts are produced, interpreted and applied in a variety of decision-making processes. In South Africa, a variety of agricultural users exists, ranging from the small-scale farmer to larger commercial farming entities. Useful seasonal are those produced and disseminated with the end user in mind. A retroactive test period during the 1990s, evaluates the perceived impact of incorporating seasonal rainfall forecasts into decisions made by commercial crop farmers in the central parts of South Africa. Although a small sample of commercial farmers was interviewed, the results show some benefits to commercial agriculture if seasonal climate forecast information is continuously and effectively applied over the long-term.     

Calibration and combination of dynamical seasonal forecasts to enhance the value of predicted probabilities for managing risk

Seasonal probability forecasts produced with numerical dynamics on supercomputers offer great potential value in managing risk and opportunity created by seasonal variability. The skill and reliability of contemporary forecast systems can be increased by calibration methods that use the historical performance of the forecast system to improve the ongoing real-time forecasts. Two calibration methods are applied to seasonal surface temperature forecasts of the US National Weather Service, the European Centre for Medium Range Weather Forecasts, and to a World Climate Service multi-model ensemble created by combining those two forecasts with Bayesian methods. As expected, the multi-model is somewhat more skillful and more reliable than the original models taken alone. The potential value of the multimodel in decision making is illustrated with the profits achieved in simulated trading of a weather derivative. In addition to examining the seasonal models, the article demonstrates that calibrated probability forecasts of weekly average temperatures for leads of 2–4 weeks are also skillful and reliable. The conversion of ensemble forecasts into probability distributions of impact variables is illustrated with degree days derived from the temperature forecasts. Some issues related to loss of stationarity owing to long-term warming are considered. The main conclusion of the article is that properly calibrated probabilistic forecasts possess sufficient skill and reliability to contribute to effective decisions in government and business activities that are sensitive to intraseasonal and seasonal climate variability.     

The role of carbon plantations in mitigating climate change: potentials and costs

A methodology is presented to construct supply curves and cost–supply curves for carbon plantations based on land-use scenarios from the Integrated Model to Assess the Global Environment (IMAGE 2). A sensitivity analysis for assessing which factors are most important in shaping these curves is also presented. In the IPCC SRES B2 Scenario, the carbon sequestration potential on abandoned agricultural land increases from 60 MtC/year in 2010 to 2,700 MtC/year in 2100 for prices up to 1,000 $/tC, assuming harvest when the mean annual increment decreases and assuming no environmental, economical or political barriers in the implementation-phase. Taking these barriers into consideration would reduce the potential by at least 60%. On the other hand, the potential will increase 55 to 75% if plantations on harvested timberland are considered. Taking into account land and establishment costs, the largest part of the potential up to 2025 can be supplied below 100 $/tC (In this article all dollar values are in US dollars of 1995, unless indicated otherwise.). Beyond 2050, more than 50% of the costs come to over 200 $/tC. Compared to other mitigation options, this is relative cheap. So a large part of the potential will likely be used in an overall mitigation strategy. However, since huge emission reductions are probably needed, the relative contribution of plantations will be low (around 3%). The largest source of uncertainty with respect to both potentials and costs is the growth rate of plantations compared to the natural vegetation.     

Potential impacts of climate change on storage conditions for commercial agriculture: an example for potato production in Michigan

Climate change is anticipated to influence all parts of agricultural production systems. However, the potential impacts on crop storage have rarely been assessed, even though storage is an important component of a grower’s marketing strategy and is essential for the continuous supply of a commodity for processors, exporters, and consumers. The Michigan chip-processing potato industry provides an example of the importance of crop storage. Michigan is the largest producer of chip-processing potatoes in the USA, and potatoes are stored on farms from September to June. We use an ensemble of climate projections developed for three future time slices (early, mid, and late century) from 16 climate models forced by three greenhouse gas concentration pathways to assess future changes in potato storage conditions. Our findings indicate an increased future demand for ventilation and/or refrigeration immediately after harvest and again in spring and early summer, even for the early-century time slice. Furthermore, the period of reliably cold storage temperatures during winter is anticipated, when averaged across all models, to shorten by 11–17 days in Michigan’s primary production area and 14–20 days in the more southern secondary area by mid-century, and by 15–29 days and 31–35 days, respectively, for the northern and southern production areas by late century. The level of uncertainty, as indicated by the ensemble range, is large, although the sign of the projected changes in storage parameters is consistent. This case study provides an example of the potentially large effects of climate change on the storage conditions for agricultural commodities.     

A decision‐analytic study of the joint value of seasonal precipitation and temperature forecasts in a choice‐of‐crop problem

Abstract

A static decision‐analytic method is used to investigate the economic value of bivariate ‐ precipitation and temperature ‐ seasonal forecasts of the form currently issued by the U.S. National Weather Service. This method is applied to a corn versus spring wheat choice‐of‐crop decision‐making problem by considering a transect of four counties across the northwestern margin of the North American corn belt. Numerical results indicate that seasonal forecasts of current quality can be of appreciable value (≥$1/ha) for some locations when the optimal action chosen on the basis of climatological information is only marginally preferred to another action. Increases in forecast value follow from hypothetical increases in the quality of both the precipitation and temperature components of the forecasts in the spring wheat region, whereas forecast value increases primarily as a function of the quality of the precipitation forecasts alone in the corn belt region. The results are very sensitive to absolute and relative crop prices.     

A spatial and temporal drought risk assessment of three major tree species in Britain using probabilistic climate change projections

Probabilistic climate data have become available for the first time through the UK Climate Projections 2009, so that the risk of change in tree growth can be quantified. We assessed the drought risk spatially and temporally using drought probabilities calculated from the weather generator data and tree species vulnerabilities using Ecological Site Classification model across Britain. We evaluated the drought impact on the potential yield class of three major tree species (Picea sitchensis, Pinus sylvestris, and Quercus robur), which cover around 59 % (400,700 ha) of state-managed forests, across the lowlands and uplands. We show that drought impacts result mostly in reduced tree growth over the next 80 years when using B1, A1B, and A1FI IPCC emissions scenarios, but varied spatially. We found a maximum reduction of 94 % but also a maximum increase of 56 % in potential stand yield class in the 2080s from the baseline climate (1961–1990). Furthermore, potential production over the state-managed forests for all three species in the 2080s is estimated to decrease due to drought by 42 % in the lowlands and by 32 % in the uplands in comparison to the baseline climate. Our results reveal that potential tree growth and forest production on the state-managed forests in Britain is likely to reduce, and indicate where and when adaptation measures are required. Moreover, this paper demonstrates the value of probabilistic climate projections for an important economic and environmental sector.     

Extreme wave climate changes in Central-South America

The spatial and temporal variability of extreme wave climate along the Central-South American continent is analyzed. The study evaluates changes in the intensity of extreme significant wave height (SWH) throughout the year over the 1980–2008 period, using a calibrated long-term wave reanalysis database forced with NCEP/NCAR reanalysis. A non-stationary extreme value model, based on monthly maxima with a new approach for long-term trends, has been applied. Results show a common positive trend in the Pacific basin throughout all seasons and a significant decreasing trend pattern in the area of Surinam and north of Brazil, on the Atlantic border (up to ?1.5 cm/year in March–April–May). A higher increase of the extreme wave heights is found in the austral summer (December–January–February) at Tierra de Fuego and the Falkland Islands, reaching 6.5 cm/year (which means 1.82 m for the 28 years studied). Furthermore, the complete reanalysis period (1948–2008) is analyzed in order to compare results with the assimilation data period (1980–2008), resulting in some discrepancies, especially in the Atlantic basin.     

数值预报误差订正技术中相似-动力方法的发展

Due to the increasing requirement for high-level weather and climate forecasting accuracy, it is necessary to exploit a strategy for model error correction while developing numerical modeling and data assimilation techniques. This study classifies the correction strategies according to the types of forecast errors, and reviews recent studies on these correction strategies. Among others, the analogue-dynamical method has been developed in China, which combines statistical methods with the dynamical model, corrects model errors based on analogue information, and effectively utilizes historical data in dynamical forecasts. In this study, the fundamental principles and technical solutions of the analogue-dynamical method and associated development history for forecasts on different timescales are introduced. It is shown that this method can effectively improve medium- and extended-range forecasts, monthly-average circulation forecast, and short-term climate prediction. As an innovative technique independently developed in China, the analogue- dynamical method plays an important role in both weather forecast and climate prediction, and has potential applications in wider fields.     

Winter crop sensitivity to inter-annual climate variability in central India

India is predicted to be one of the most vulnerable agricultural regions to future climate changes. Here, we examined the sensitivity of winter cropping systems to inter-annual climate variability in a local market and subsistence-based agricultural system in central India, a data-rich validation site, in order to identify the climate parameters to which winter crops – mainly wheat and pulses in this region – might be sensitive in the future. We used satellite time-series data to quantify inter-annual variability in multiple climate parameters and in winter crop cover, agricultural census data to quantify irrigation, and field observations to identify locations for specific crop types. We developed three mixed-effect models (250 m to 1 km scale) to identify correlations between crop cover (wheat and pulses) and twenty-two climate and environmental parameters for 2001-2013. We find that winter daytime mean temperature (November–January) is the most significant factor affecting winter crops, irrespective of crop type, and is negatively associated with winter crop cover. With pronounced winter warming projected in the coming decades, effective adaptation by smallholder farmers in similar landscapes would require additional strategies, such as access to fine-scale temperature forecasts and heat-tolerant winter crop varieties.     

Decadal predictability and forecast skill

The “potential predictability” of the climate system is the upper limit of available forecast skill and can be characterized by the ratio p of the predictable variance to the total variance. While the potential predictability of the actual climate system is unknown its analog q may be obtained for a model of the climate system. The usual correlation skill score r and the mean square skill score M are functions of p in the case of actual forecasts and potential correlation ρ and potential mean square skill score $\mathcal{M}$ are the same functions of q in the idealized model context. In the large ensemble limit the connection between model-based potential predictability and skill scores is particularly straightforward with $q=\rho^{2}=\mathcal{M}.$ Decadal predictions of annual mean temperature produced with the Canadian Centre for Climate Modelling and Analysis coupled climate model are analyzed for information on decadal climate predictability and actual forecast skill. Initialized forecast results are compared with the results of uninitialized climate simulations. Model-based values of potential predictability q and potential correlation skill ρ are obtained and ρ is compared with the actual forecast correlation skill r. The skill of externally forced and internally generated components of the variability are separately estimated. As expected, ρ > r and both decline with forecast range τ, at least for the first five years. The decline of skill is associated mainly with the decline of the skill of the internally generated component. The potential and actual skill of a forecast of time-averaged temperature depends on the averaging period. The skill of uninitialized simulations is low for short averaging times and increases as averaging time increases. By contrast, skill is high at short averaging times for forecasts initialized from observations and declines as averaging times increase to about three years, then increases somewhat at longer averaging times. The skills of the initialized forecasts and uninitialized simulations begin to converge for longer averaging times. The potential correlation skill ρ of the externally forced component of temperature is largest at tropical latitudes and the skill of the internally generated component is largest over the North Atlantic, parts of the Southern Ocean and to some extent the North Pacific. Potential skill over extratropical land is somewhat weaker than over oceans. The distribution of actual correlation skill r is broadly similar to that of potential skill for the externally forced component but less so for the internally generated component. Differences in potential and actual skill suggest where improvements in the forecast system might be found.     

Modeling climate change and biophysical impacts of crop production in the Austrian Marchfeld Region

Climate change affects major biophysical processes in agricultural crop production (e.g. evaporation of plants and soils, nutrient cycles, and growth of plants). This analysis aims to assess some of these effects by simulating regional climate projections that are integrated in the biophysical process model EPIC (Environmental Policy Integrated Climate). Statistical climate models have been developed for six weather parameters based on daily weather records of a weather station in the Austrian Marchfeld region from 1975 to 2006. These models have been used to estimate daily weather parameters for the period 2007–2038. The resulting projections have been compared to climate scenarios provided from the TYNDALL Centre for Climate Change Research, which are based on General Circulation Models (GCMs). The comparison indicates some differences, namely a smaller temperature increase and a higher precipitation amount in the TYNDALL data. Both climate datasets have been used to simulate impacts of climate change on crop yields, topsoil organic carbon content, and nitrate leaching with EPIC and thus to perform a sensitivity analysis of EPIC. Yield impacts have been assessed for four simulated crops, i.e. 6.2?t/ha for winter wheat for statistical climate projections compared to 5.7?t/ha for TYNDALL scenarios, 10.6?t/ha for corn compared to 10.5?t/ha, 3.9?t/ha for sunflower compared to 3.7?t/ha, and 4.5?t/ha for spring barley compared to 4.3?t/ha—all values as an average over the period 2007–2038. Smaller differences have been simulated for topsoil organic carbon content i.e. 55.1?t/ha for the statistical climate projections compared to 55.3?t/ha for the TYNDALL scenarios and nitrate leaching i.e. 7.1?kg/ha compared to 11.1?kg/ha. All crop yields as well as topsoil organic carbon content and nitrate leaching show highest sensitivity to temperature and solar radiation.     

基于气象要素的逐日玉米产量气象影响指数

利用1981—2020年5—9月气象数据与玉米产量数据,通过改进逐日降水适宜度并构建逐日气候适宜度模型,建立基于相似年逐日气象要素的作物生育期气候适宜度序列,利用气象产量与气候适宜指数建立模型,设计逐日作物产量气象影响指数以表征气象条件对作物的影响程度,基于该指数构建东北地区玉米逐日产量预报模型并分析其逐日预报准确率,用以表明该指数的准确性。结果表明：利用3个相似年预报结果加权集成综合相似年逐日作物产量气象影响指数可提高逐日预报准确率,黑龙江年尺度逐日预报准确率年际间波动小于东北其他地区。综合相似年月尺度下,随着玉米发育期的推进和实时气象数据的引入,月尺度平均预报准确率逐渐提高。东北地区玉米产量8月31日的日尺度预报准确率普遍高于7月31日;辽宁日尺度预报差异较大,但随着玉米发育期推进逐日预报产量和实际产量接近,准确率也提高。基于气象要素构建的逐日作物产量影响指数和同期气象影响指数可以定量评估不同时段气象条件对作物产量的影响程度,在一定程度上可提高农业气象业务定量化评价水平。     

What determines perceived value of seasonal climate forecasts? A theoretical analysis

Seasonal forecasts have potential value as tools for the management of risks due to inter-annual climate variability and iterative adaptation to climate change. Despite their potential, forecasts are not widely used, in part due to poor performance and lack of relevance to specific users’ decision problems, and in part due to a variety of economic and behavioural factors. In this paper a theoretical model of perceived forecast value is proposed and applied to a stylized portfolio-type decision problem with wide applicability to actual forecast users, with a view to obtaining a more complete picture of the determinants of perceived value. The effects of user wealth, risk aversion, and perceived forecast trustworthiness, and presentational parameters, such as the position of forecast parameter categories, and the size of probability categories, on perceived value is investigated. Analysis of the model provides several strong qualitative predictions of how perceived forecast value depends on these factors. These predictions may be used to generate empirical hypotheses which offer the chance of evaluating the model's assumptions, and suggest several means of improving understanding of perceived value based on qualitative features of the results.     

Seasonal Forecasts of the Summer 2016 Yangtze River Basin Rainfall

The Yangtze River has been subject to heavy flooding throughout history,and in recent times severe floods such as those in 1998 have resulted in heavy loss of life and livelihoods.Dams along the river help to manage flood waters,and are important sources of electricity for the region.Being able to forecast high-impact events at long lead times therefore has enormous potential benefit.Recent improvements in seasonal forecasting mean that dynamical climate models can start to be used directly for operational services.The teleconnection from El Ni ?no to Yangtze River basin rainfall meant that the strong El Ni ?no in winter 2015/16 provided a valuable opportunity to test the application of a dynamical forecast system.This paper therefore presents a case study of a real-time seasonal forecast for the Yangtze River basin,building on previous work demonstrating the retrospective skill of such a forecast.A simple forecasting methodology is presented,in which the forecast probabilities are derived from the historical relationship between hindcast and observations.Its performance for2016 is discussed.The heavy rainfall in the May–June–July period was correctly forecast well in advance.August saw anomalously low rainfall,and the forecasts for the June–July–August period correctly showed closer to average levels.The forecasts contributed to the confidence of decision-makers across the Yangtze River basin.Trials of climate services such as this help to promote appropriate use of seasonal forecasts,and highlight areas for future improvements.     

Identifying climate services needs for national planning: insights from Malawi

The importance of climate services, i.e. providing targeted, tailored, and timely weather and climate information, has gained momentum, but requires improved understanding of user needs. This article identifies the opportunities and barriers to the use of climate services for planning in Malawi, to identify the types of information that can better inform future adaptation decisions in sub-Saharan Africa. From policy analysis, stakeholder interviews, and a national workshop utilizing serious games, it is determined that only 5–10 day and seasonal forecasts are currently being used in government decision making. Impediments to greater integration of climate services include spatial and temporal scale, accessibility, timing, credibility and the mismatch in timeframes between planning cycles (1–5 years) and climate projections (over 20 years). Information that could more usefully inform planning decisions includes rainfall distribution within a season, forecasts with 2–3 week lead times, likely timing and location of extreme events in the short term (1–5 years), and projections (e.g. rainfall and temperature change) in the medium term (6–20 years). Development of a national set of scenarios would also make climate information more accessible to decision makers, and capacity building around such scenarios would enable its improved use in short- to medium-term planning. Improved climate science and its integration with impact models offer exciting opportunities for integrated climate-resilient planning across sub-Saharan Africa. Accrual of positive impacts requires enhanced national capacity to interpret climate information and implement communication strategies across sectors.

Policy relevance

For climate services to achieve their goal of improving adaptation decision making, it is necessary to understand the decision making process and how and when various types of weather and climate information can be incorporated. Through a case study of public sector planning in Malawi, this article highlights relevant planning and policy-making processes. The current use of weather and climate information and needs, over various timescales – sub-annual to short term (1–5 years) to medium term (6–20 years) – is outlined. If climate scientists working with boundary organizations are able to address these issues in a more targeted, sector-facing manner they will improve the uptake of climate services and the likelihood of climate-resilient decisions across sub-Saharan Africa.