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Coupled atmosphere-ocean general circulation models (GCMs) simulate different realizations of possible future climates at global scale under contrasting scenarios of land-use and greenhouse gas emissions. Such data require several additional processing steps before it can be used to drive impact models. Spatial downscaling, typically by regional climate models (RCM), and bias-correction are two such steps that have already been addressed for Europe. Yet, the errors in resulting daily meteorological variables may be too large for specific model applications. Crop simulation models are particularly sensitive to these inconsistencies and thus require further processing of GCM-RCM outputs. Moreover, crop models are often run in a stochastic manner by using various plausible weather time series (often generated using stochastic weather generators) to represent climate time scale for a period of interest (e.g. 2000 ± 15 years), while GCM simulations typically provide a single time series for a given emission scenario. To inform agricultural policy-making, data on near- and medium-term decadal time scale is mostly requested, e.g. 2020 or 2030. Taking a sample of multiple years from these unique time series to represent time horizons in the near future is particularly problematic because selecting overlapping years may lead to spurious trends, creating artefacts in the results of the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data for Europe that addresses these problems. Input data consist of daily temperature and precipitation from three dynamically downscaled and bias-corrected regional climate simulations of the IPCC A1B emission scenario created within the ENSEMBLES project. Solar radiation is estimated from temperature based on an auto-calibration procedure. Wind speed and relative air humidity are collected from historical series. From these variables, reference evapotranspiration and vapour pressure deficit are estimated ensuring consistency within daily records. The weather generator ClimGen is then used to create 30 synthetic years of all variables to characterize the time horizons of 2000, 2020 and 2030, which can readily be used for crop modelling studies. [ABSTRACT FROM AUTHOR]

Precipitation variability imposes significant pressure in areas where agricultural practice is dominated by smallholder farmers who are dependent on subsistence farming. Advances in the understanding of this variability, in both time and space, have an important role to play in increasing the resilience of agricultural systems. The need is particularly pressing in regions of the world such as the African continent, which is already affected by multiple stresses including poverty and economic and political instability. In this paper, we explore the use of generalised linear models (GLMs) for this purpose, via a case study from north-east South Africa. A GLM is used to link the local rainfall variability to large-scale climate drivers identified from previous subcontinental-scale analyses, and the ability of the resulting model to simulate precipitation features that are relevant in agricultural applications is evaluated. We focus in particular on a set of growing season indices, proposed for the investigation of intraseasonal characteristics relevant for maize production in the region. Seven indices were computed from spatially averaged daily rainfall series from nine stations in the study area. As a first attempt to use GLMs for this type of application, the results are encouraging and suggest that the models are able to reproduce a range of agriculture-relevant indices. However, further research into spatial correlation structure is recommended to improve the multisite generation of the rainfall-derived characteristics. [ABSTRACT FROM AUTHOR]

As the global climate changes, different degrees of variation in climatic factors will have various impacts on agricultural production, leading to appropriate or stressed conditions. The impact threshold of climate change (ITCC) for agriculture refers to the value of a climatic factor at which the agricultural status mutates. Due to the urgency of addressing the negative impacts of climate change on agriculture, research on the ITCC of agriculture is crucial. Here, we built models to reflect the relationship between agricultural climate yield and climatic factors based on climate change characteristics and objective agricultural status. Using the models, we obtained the appropriate and stressed thresholds of different climatic factors. We calculated the ITCC of spring wheat in Wuchuan County as an example and identified warming and drying trends in Wuchuan County from 1961 to 2013, especially during the period from 1991 to 2013 (i.e., the period after mutation or the change period). Over the past 53 years, spring wheat yield increased with an average rate of 81.3 kg ha−1 dec−1. Over the change period, however, yield decreased with an average rate of 13.8 kg ha−1 dec−1 and the fluctuation range increased. The appropriate threshold for average temperature during the growth period of spring wheat was 11.4 °C, and the stressed thresholds were 8.2 and 14.6 °C. The appropriate threshold for precipitation during the growth period was 391.1 mm, and the stressed thresholds were 247.4 and 534.9 mm. During the period before mutation (i.e., the basic period), the average temperature was below the upper temperature threshold, and precipitation was 26.9 mm above the lower precipitation threshold. During the change period, the average temperature was 0.3 °C above the upper temperature threshold, and precipitation was 9.8 mm above the lower precipitation threshold. [ABSTRACT FROM AUTHOR]

Over the past decades, drought has become a major concern in Morocco due to the importance of agriculture in the economy of the country. In the present work, the standardized precipitation index (SPI) is used to monitor the evolution, frequency, and severity of droughts in the High Atlas basins (N'Fis, Ourika, Rhéraya, Zat, and R'dat), located south of Marrakech city. The spatiotemporal characterization of drought in these basins is performed by computing the SPI with precipitation spatially interpolated over the catchments. The Haouz plain, located downstream of these basins, is strongly dependent on water provided by the mountain ranges, as shown by the positive correlations between the normalized difference vegetation index (NDVI) in the plain and the 3, 6, and 12-month SPI in the High Atlas catchments. On the opposite, no significant correlations are found with piezometric levels of the Haouz groundwater due to intensified pumping for irrigation in the recent decades. A relative SPI index was computed to evaluate the climate change impacts on drought occurrence, based on the projected precipitation (2006-2100) from five high-resolution CORDEX regional climate simulations, under two emission scenarios (RCP 4.5 and RCP 8.5). These models show a decrease in precipitation towards the future up to − 65% compared to the historical period. In terms of drought events, the future projections indicate a strong increase in the frequency of SPI events below − 2, considered as severe drought condition. [ABSTRACT FROM AUTHOR]