Regional climate downscaling has arrived at an important juncture. Some in the research community favour continued refinement and evaluation of downscaling techniques within a broader framework of uncertainty characterisation and reduction. Others are calling for smarter use of downscaling tools, accepting that conventional, scenario-led strategies for adaptation planning have limited utility in practice. This paper sets out the rationale and new functionality of the Decision Centric (DC) version of the Statistical DownScaling Model (SDSM-DC). This tool enables synthesis of plausible daily weather series, exotic variables (such as tidal surge), and climate change scenarios guided, not determined, by climate model output. Two worked examples are presented. The first shows how SDSM-DC can be used to reconstruct and in-fill missing records based on calibrated predictor-predictand relationships. Daily temperature and precipitation series from sites in Africa, Asia and North America are deliberately degraded to show that SDSM-DC can reconstitute lost data. The second demonstrates the application of the new scenario generator for stress testing a specific adaptation decision. SDSM-DC is used to generate daily precipitation scenarios to simulate winter flooding in the Boyne catchment, Ireland. This sensitivity analysis reveals the conditions under which existing precautionary allowances for climate change might be insufficient. We conclude by discussing the wider implications of the proposed approach and research opportunities presented by the new tool.

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The Statistical DownScaling Model - Decision Centric (SDSM-DC): conceptual basis and applications

Adverse weather conditions for UK wheat production under climate change

This study describes integration of climate change projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble with the LARS-WG weather generator, which delivers an attractive option for the downscaling of large-scale climate projections from global climate models (GCMs) to local-scale climate scenarios for impact assess- ments. A subset of 18 GCMs from the CMIP5 ensemble and 2 Representative Concentration Path- ways (RCPs), RCP4.5 and RCP8.5, were integrated with LARS-WG. For computationally de - manding im pact assessments, where it is not practical to explore all possible combinations of GCM ×R CP, a climate sensitivity index could be used to select a subset of GCMs which preserves the range of uncertainty found in CMIP5. This would allow us to quantify uncertainty in predictions of impacts resulting from the CMIP5 ensemble by conducting fewer simulation experiments. In a case study, we describe the use of the Sirius wheat simulation model to design in silico wheat ideotypes that are optimised for future climates in Europe, sampling uncertainty in GCMs, emission scenar- ios, time periods and European locations with contrasting climates. Two contrasting GCMs were selected for the analysis, 'hot' HadGEM2-ES and 'cool' GISS-E2-R-CC. Despite large uncertainty in future climate projections, we were able to identify target traits for wheat improvement which may assist breeding for high-yielding wheat cultivars with increased yield stability.

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Adapting wheat ideotypes for climate change: accounting for uncertainties in CMIP5 climate projections

Climate change has placed considerable pressure on the residential environment in different areas of the world. These issues have increased the motivation of researchers to analyse and forecast the changes in critical climatic factors, such as temperature, in order to offer valuable reference outcomes for management and planning in the future. This study set out to determine to what extent global warming would affect Columbia City, Missouri, USA. The Long Ashton Research Station Weather Generator (LARS-WG) model is used for downscaling daily maximum temperatures based on the SRA1B scenario. Seven General Circulation Models (GCMs) outputs are employed for three selected periods, 2011–2030, 2046–2065 and 2080-2099. The findings show that (1) statistical analysis confirmed the skill and reliability of the LARS-WG model to downscale maximum temperature time series; (2) the ensemble mean of seven GCMs exhibited an increasing based on yearly and monthly data for all periods compared with baseline period 1980-1999. The findings can contribute to a better understanding of the impacts of climate change on the urban environment and encourage planners and stakeholders to find the best solution for mitigation of these impacts.

Using LARS ─WG model for prediction of temperature in Columbia City, USA

Extreme weather events, such as heatwaves, droughts, and excess rainfall, are a major cause of crop yield losses and food insecurity worldwide. Statistical or process-based crop models can be used to quantify how yields will respond to extreme weather and future climate change. However, the accuracy of weather-yield relationships derived from crop models, whether statistical or process-based, is dependent on the quality of the underlying input data used to run these models. In this context, a major challenge in many developing countries is the lack of accessible and reliable meteorological datasets. Gridded weather datasets, derived from combinations of in-situ gauges, remote sensing, and climate models, provide a solution to fill this gap, and have been widely used to evaluate climate impacts on agriculture in data-scarce regions worldwide. However, these reference datasets are also known to contain important biases and uncertainties. To date, there has been little research to assess how the choice of reference datasets in influences projected sensitivity of crop yields to weather. We compare multiple freely available gridded datasets that provide daily weather data over the Indian sub-continent over the period 1983-2005, and explore their implications for estimates of yield responses to weather variability for key crops grown in the region (wheat and rice). Our results show that individual gridded weather datasets vary in their representation of historic spatial and temporal temperature and precipitation patterns across India. We show that these differences create large uncertainties in estimated crop yield responses and exposure to extreme weather events, which highlight the need for improved consideration of input data uncertainty in statistical studies that explore impacts of climate variability and change on agriculture.

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Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change

Local-scale daily climate scenarios are required for assessment of climate change impacts. ELPIS is a repository of local-scale climate scenarios for Europe, which are based on the LARS-WG weather generator and future projections from 2 multi-model ensembles, CMIP3 and EU-ENSEMBLES. In ELPIS, the site parameters for the 1980�2010 baseline scenarios were esti- mated by LARS-WG using daily weather from the European Crop Growth Monitoring System (CGMS) used in many European agricultural assessment studies. The objective of this paper was to compare ELPIS baseline scenarios with observed daily weather obtained independently from the European Climate Assessment (ECA) data set. Several statistical tests were used to com - pare distributions of climatic variables derived from ECA-observed daily weather and ELPIS- generated baseline scenarios. About 30% of selected sites have a difference in altitude of >50 m compared with the CGMS grid-cell altitude that was selected to represent agricultural land within a grid-cell. Differences in altitude can explain significant Kolmogorov-Smirnov test (KS-test) results for distribution of daily temperature and in t-tests for temperature monthly means, because of the well-known negative correlation between temperature and elevation. For daily precipita- tion, the KS-test showed little difference between generated and observed data; however, the more sensitive t-test showed significant results for the sites where altitude differences were large. Approximately 11% of sites showed small positive or negative bias in monthly solar radiation, although 86% sites showed >3 significant t-test results for monthly means. These results can be explained by differences in conversion of sunshine hours to solar radiation used in CGMS and LARS-WG. We conclude that, considering the limitations above, ELPIS baseline scenarios are suitable for agricultural impact assessments in Europe.

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Scott Pilkington-Bennett

Papers

Validation of ELPIS 1980-2010 baseline scenarios using the observed European Climate Assessment data set