A large set of potential past, present and future hydro-meteorological time series for the UK

Abstract

Abstract. Hydro-meteorological extremes such as drought and heavy precipitation can have large impacts on society and the economy. With potentially increasing risks associated with such events due to climate change, properly assessing the associated impacts and uncertainties is critical for adequate adaptation. However, the application of riskbased approaches often requires large sets of extreme events, which are not commonly available. Here, we present such a large set of hydro-meteorological time series for recent past and future conditions for the United Kingdom based on weather@home 2, a modelling framework consisting of a global climate model (GCM) driven by observed or projected sea surface temperature (SST) and sea ice which is downscaled to 25 km over the European domain by a regional climate model (RCM). Sets of 100 time series are generated for each of (i) a historical baseline , (ii) five nearfuture scenarios (2020-2049) and (iii) five far-future scenarios . The five scenarios in each future time slice all follow the Representative Concentration Pathway 8.5 (RCP8.5) and sample the range of sea surface temperature and sea ice changes from CMIP5 (Coupled Model Intercomparison Project Phase 5) models. Validation of the historical baseline highlights good performance for temperature and potential evaporation, but substantial seasonal biases in mean precipitation, which are corrected using a linear approach. For extremes in low precipitation over a long accumulation period (> 3 months) and shorter-duration high precipitation (1-30 days), the time series generally represents past statistics well. Future projections show small precipitation increases in winter but large decreases in summer on average, leading to an overall drying, consistently with the most recent UK Climate Projections (UKCP09) but larger in magnitude than the latter. Both drought and high-precipitation events are projected to increase in frequency and intensity in most regions, highlighting the need for appropriate adaptation measures. Overall, the presented dataset is a useful tool for assessing the risk associated with drought and more generally with hydro-meteorological extremes in the UK.

A Probabilistic Risk Assessment of the National Economic Impacts of Regulatory Drought Management on Irrigated Agriculture

“…Uncertainty in projections of future climate change arises from multiple sources—in particular, internal climate variability, climate model uncertainty, and emissions uncertainty. Our study has used the future meteorological data of Guillod et al () who used a single global climate model‐regional climate model (with sampling from the CMIP5 uncertainty range in sea surface temperature and sea ice extent) and “worst‐case” RCP (RCP8.5). However, Kirtman et al () demonstrate that the uncertainty due to emissions for the near future is minimal, given the limited divergence in global greenhouse gas emissions associated with each RCP and ocean‐atmosphere system lags.…”

“…The hydrometeorological data set used in this study was produced within the MaRIUS project (http://www.mariusdroughtproject.org/; Managing the Risks, Impacts and Uncertainties of droughts and water Scarcity), using the weather@home2 modeling framework (Guillod et al, ; Massey et al, ), in which the HadAM3P atmosphere‐global climate model is dynamically downscaled by the HadRM3P regional climate model. Accounting for uncertainty through application of small perturbations to the temperature field in the initial conditions (Massey et al, ) and sampling from the CMIP5 uncertainty range in sea surface temperature and sea ice extent allowed a large number (100) of spatial‐temporally consistent ensemble members to be generated for the recent past and for the future (Guillod et al, ). The data set by Guillod et al () provides synthetic daily weather time series for the United Kingdom at a 25‐km × 25‐km resolution.…”

“…Accounting for uncertainty through application of small perturbations to the temperature field in the initial conditions (Massey et al, ) and sampling from the CMIP5 uncertainty range in sea surface temperature and sea ice extent allowed a large number (100) of spatial‐temporally consistent ensemble members to be generated for the recent past and for the future (Guillod et al, ). The data set by Guillod et al () provides synthetic daily weather time series for the United Kingdom at a 25‐km × 25‐km resolution. The baseline (1977–2004) data set provides 100 simulated climate ensemble members that could have occurred in the past, while the near future (2022–2049) and far future (2072–2099) represent climate time series that might occur in the future under the Representative Concentration Pathway 8.5 (RCP8.5) emissions scenario.…”

“…The weather@home2 climate ensemble (Guillod et al, ) has been used to drive the national Grid‐to‐Grid (G2G) hydrological model (Bell et al, ) to provide simulated daily river flows for the baseline, near future, and far future periods (Bell, Kay, et al, ; Bell, Rudd, et al, ). For each period, 100 simulations of daily mean river flow for each of 129 catchments across England and Wales have been used in this study.…”

“…Two methodological innovations for a probabilistic risk‐based economic assessment in irrigated agriculture are presented. First, it applies current environmental regulatory triggers to daily catchment‐scale rainfall from dynamically downscaled and bias‐corrected climate projections (Guillod et al, ; Guillod et al, ) and simulated river flow from a national‐scale hydrological model (Bell et al, ), to obtain the current and future timing, frequency, severity, and duration of abstraction restrictions. Second, a probabilistic risk‐based analysis of economic losses in irrigated agriculture from catchment to national scale is performed through applying crop‐specific loss functions to a national spatial data set of irrigated cropping (Rey et al, ) according to the timing, duration, and severity of the restrictions.…”