A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios

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.

Climate change impacts on aflatoxin B1 in maize and aflatoxin M1 in milk: A case study of maize grown in Eastern Europe and imported to the Netherlands

“…A database of future daily weather data is available for the whole of Europe, specifically designed to be used for crop modelling [22]. In short, these data are based on three downscaled and bias-corrected regional climate model implementations of the IPCC A1B emission scenario, which is a middle of road scenario describing a world with rapid economic growth and a mix of fossil and non-fossil energy sources, as created within the ENSEMBLES project [23–25].…”

“…These future weather data are bias-corrected using the same weather database as our forecasting model baseline [21]. Duveiller et al [22] term the three climate model implementations DMI-HIRHAM5-ECHAM5 [26], ETHZ-CLM-HadCM3Q0 [27] and METO-HC-HadRM3Q0-HadCM3Q0 [28], which are respectively referred to as DMI, ETHZ and METO for short in this paper. Duveiller et al chose these models because they are based on widely used global circulation models and show maximum diversity in output weather variables.…”

“…Duveiller et al chose these models because they are based on widely used global circulation models and show maximum diversity in output weather variables. In general, DMI and METO are the coldest and warmest regarding surface air temperature, respectively, ETHZ falls in between but shows different precipitation patterns [22]. We uses the 2030 time horizon for all three climate models for our scenario analysis.…”

Operational Simulation Environment for SCADA Integration of Renewable Resources

“…In this sense, the provided tool allows reading these values from external files, for example the Comma Separated Value (CSV) format. Moreover, it is important to consider that using external CSV does not reduce the capacities of the solution, due to it not being incompatible with the use of synthetic data, which can be obtained, for example, from tools such as ClimGen [70].…”

Evaluation of Multiple Approaches to Estimate Daily Solar Radiation for Input to Crop Process Models

“…Given the limited availability of radiation measurements, point-based radiation models (i.e., stochastic, empirical, and mechanistic models) have been developed to estimate daily solar radiation at a location. Of these, stochastic methods are the most popular, especially for assessments of climate impacts on crop production [12][13][14][15]. Stochastic methods employ weather generators (e.g., WGEN [16] and SIMMETEO [17]) to generate, conditioned on the occurrence of wet and dry days, multiple time series of daily radiation for a particular site [18,19].…”