Abstract
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.
Highlights
There is a general consensus in the scientific community that the change in the climatic system of the Earth is unequivocal (IPCC 2013)
The objective of this paper is to present a dataset of future weather data covering Europe that (1) is suitable to use as input data for crop growth simulation models and (2) can be used for impact studies targeting the short-term time horizons that are highly relevant for policy-making
The capacity of the generated weather to represent the climate in the baseline time horizon of 2000 is summarized by the QQ plots for maximum temperature, minimum temperature and daily precipitation in Figs. 4, 5 and 6, respectively
Summary
There is a general consensus in the scientific community that the change in the climatic system of the Earth is unequivocal (IPCC 2013). It is foreseen that there will be an increase in extreme events, such as the heat waves over Europe of 2003 and 2010 (Schär et al 2004; Barriopedro et al 2011; Russo et al 2014). These shifts and changes will offer opportunities and challenges requiring adaptation of European agriculture to the changing environment. In order to implement appropriate policies, appropriate tools are required to characterize spatially the vulnerability of its agriculture based on future climate predictions
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