Climate service driven adaptation may alleviate the impacts of climate change in agriculture.

Building a resilient and sustainable agricultural sector requires the development and implementation of tailored climate change adaptation strategies. By focusing on durum wheat (Triticum turgidum subsp. durum) in the Euro-Mediterranean region, we estimate the benefits of adapting through seasonal cultivar-selection supported by an idealised agro-climate service based on seasonal climate forecasts. The cost of inaction in terms of mean yield losses, in 2021-2040, ranges from -7.8% to -5.8% associated with a 7% to 12% increase in interannual variability. Supporting cultivar choices at local scale may alleviate these impacts and even turn them into gains, from 0.4% to 5.3%, as soon as the performance of the agro-climate service increases. However, adaptation advantages on mean yield may come with doubling the estimated increase in the interannual yield variability.

Estimating resilience of crop production systems: From theory to practice.

Agricultural production systems are sensitive to weather and climate anomalies and extremes as well as to other environmental and socio-economic adverse events. An adequate evaluation of the resilience of such systems helps to assess food security and the capacity of society to cope with the effects of global warming and the associated increase of climate extremes. Here, we propose and apply a simple indicator of resilience of annual crop production that can be estimated from crop production time series. First, we address the problem of quantifying resilience in a simplified theoretical framework, focusing on annual crops. This results in the proposal of an indicator, measured by the reciprocal of the squared coefficient of variance, which is proportional to the return period of the largest shocks that the crop production system can absorb, and which is consistent with the original ecological definition of resilience. Subsequently, we show the sensitivity of the crop resilience indicator to the level of management of the crop production system, to the frequency of extreme events as well as to simplified socio-economic impacts of the production losses. Finally, we demonstrate the practical applicability of the indicator using historical production data at national and sub-national levels for France. The results show that the value of the resilience indicator steeply increases with crop diversity until six crops are considered, and then levels off. The effect of diversity on production resilience is highest when crops are more diverse (i.e. as reflected in less well correlated production time series). In the case of France, the indicator reaches about 60% of the value that would be expected if all crop production time-series were uncorrelated.

Narrowing uncertainties in the effects of elevated CO2 on crops.

Plant responses to rising atmospheric carbon dioxide (CO2) concentrations, together with projected variations in temperature and precipitation will determine future agricultural production. Estimates of the impacts of climate change on agriculture provide essential information to design effective adaptation strategies, and develop sustainable food systems. Here, we review the current experimental evidence and crop models on the effects of elevated CO2 concentrations. Recent concerted efforts have narrowed the uncertainties in CO2-induced crop responses so that climate change impact simulations omitting CO2 can now be eliminated. To address remaining knowledge gaps and uncertainties in estimating the effects of elevated CO2 and climate change on crops, future research should expand experiments on more crop species under a wider range of growing conditions, improve the representation of responses to climate extremes in crop models, and simulate additional crop physiological processes related to nutritional quality.

Concurrent climate extremes in the key wheat producing regions of the world.

Climate extremes have profound impacts on key socio-economic sectors such as agriculture. In a changing climate context, characterised by an intensification of these extremes and where the population is expected to grow, exposure and vulnerability must be accurately assessed. However, most risk assessments analyse extremes independently, thus potentially being overconfident in the resilience of the socio-economic sectors. Here, we propose a novel approach to defining and characterising concurrent climate extremes (i.e. extremes occurring within a specific temporal lag), which is able to identify spatio-temporal dependences without making any strict assumptions. The method is applied to large-scale heat stress and drought events in the key wheat producing regions of the world, as these extremes can cause serious yield losses and thus trigger market shocks. Wheat regions likely to have concurrent extremes (heat stress and drought events) are identified, as well as regions independent of each other or inhibiting each other in terms of these extreme events. This tool may be integrated in all risk assessments but could also be used to explore global climate teleconnections.

Land-surface initialisation improves seasonal climate prediction skill for maize yield forecast.

Seasonal crop yield forecasting represents an important source of information to maintain market stability, minimise socio-economic impacts of crop losses and guarantee humanitarian food assistance, while it fosters the use of climate information favouring adaptation strategies. As climate variability and extremes have significant influence on agricultural production, the early prediction of severe weather events and unfavourable conditions can contribute to the mitigation of adverse effects. Seasonal climate forecasts provide additional value for agricultural applications in several regions of the world. However, they currently play a very limited role in supporting agricultural decisions in Europe, mainly due to the poor skill of relevant surface variables. Here we show how a combined stress index (CSI), considering both drought and heat stress in summer, can predict maize yield in Europe and how land-surface initialised seasonal climate forecasts can be used to predict it. The CSI explains on average nearly 53% of the inter-annual maize yield variability under observed climate conditions and shows how concurrent heat stress and drought events have influenced recent yield anomalies. Seasonal climate forecast initialised with realistic land-surface achieves better (and marginally useful) skill in predicting the CSI than with climatological land-surface initialisation in south-eastern Europe, part of central Europe, France and Italy.

Global assessment of heat wave magnitudes from 1901 to 2010 and implications for the river discharge of the Alps.

Heat waves represent one of the most significant climatic stressors for ecosystems, economies and societies. A main topic of debate is whether they have increased or not in intensity and/or their duration due to the observed climate change. Firstly, this is because of the lack of reliable long-term daily temperature data at the global scale; secondly, because of the intermittent nature of such phenomena. Long datasets are required to produce a reliable and meaningful assessment. In this study, we provide a global estimate of heat wave magnitudes based on the three most appropriate datasets currently available, derived from models and observations (i.e. the 20th Century Reanalyses from NOAA and ECMWF), spanning the last century and before. The magnitude of the heat waves is calculated by means of the Heat Wave Magnitude Index daily (HWMId), taking into account both duration and amplitude. We compare the magnitude of the most severe heat waves occurred across different regions of the world and we discuss the decadal variability of the larger events since the 1850s. We concentrate our analysis from 1901 onwards, where all datasets overlap. Our results agree with other studies focusing on heat waves that have occurred in the recent decades, but using different data. In addition, we found that the percentage of global area covered by heat wave exceeding a given magnitude has increased almost three times, in the last decades, with respect to that measured in the early 20th century. Finally, we discuss the specific implications of the heat waves on the river runoff generated in the Alps, for which comparatively long datasets exist, affecting the water quality and availability in a significant portion of the European region in summer.

Observed shift towards earlier spring discharge in the main Alpine rivers.

In this study, we analyse the observed long-term discharge time-series of the Rhine, the Danube, the Rhone and the Po rivers. These rivers are characterised by different seasonal cycles reflecting the diverse climates and morphologies of the Alpine basins. However, despite the intensive and varied water management adopted in the four basins, we found common features in the trend and low-frequency variability of the spring discharge timings. All the discharge time-series display a tendency towards earlier spring peaks of more than two weeks per century. These results can be explained in terms of snowmelt, total precipitation (i.e. the sum of snowfall and rainfall) and rainfall variability. The relative importance of these factors might be different in each basin. However, we show that the change of seasonality of total precipitation plays a major role in the earlier spring runoff over most of the Alps.