From climate model ensembles to climate change impacts and adaptation: A case study of water resource management in the southwest of England

[1] The majority of climate change impacts and adaptation studies so far have been based on at most a few deterministic realizations of future climate, usually representing different emissions scenarios. Large ensembles of climate models are increasingly available either as ensembles of opportunity or perturbed physics ensembles, providing a wealth of additional data that is potentially useful for improving adaptation strategies to climate change. Because of the novelty of this ensemble information, there is little previous experience of practical applications or of the added value of this information for impacts and adaptation decision making. This paper evaluates the value of perturbed physics ensembles of climate models for understanding and planning public water supply under climate change. We deliberately select water resource models that are already used by water supply companies and regulators on the assumption that uptake of information from large ensembles of climate models will be more likely if it does not involve significant investment in new modeling tools and methods. We illustrate the methods with a case study on the Wimbleball water resource zone in the southwest of England. This zone is sufficiently simple to demonstrate the utility of the approach but with enough complexity to allow a variety of different decisions to be made. Our research shows that the additional information contained in the climate model ensemble provides a better understanding of the possible ranges of future conditions, compared to the use of single-model scenarios. Furthermore, with careful presentation, decision makers will find the results from large ensembles of models more accessible and be able to more easily compare the merits of different management options and the timing of different adaptation. The overhead in additional time and expertise for carrying out the impacts analysis will be justified by the increased quality of the decision-making process. We remark that even though we have focused our study on a water resource system in the United Kingdom, our conclusions about the added value of climate model ensembles in guiding adaptation decisions can be generalized to other sectors and geographical regions.

[1]  Arun Kumar,et al.  Long‐range experimental hydrologic forecasting for the eastern United States , 2002 .

[2]  C. Simmer,et al.  Statistical characteristics of surrogate data based on geophysical measurements , 2006 .

[3]  Suraje Dessai,et al.  Challenges in using probabilistic climate change information for impact assessments: an example from the water sector , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[4]  R. Wilby,et al.  A framework for assessing uncertainties in climate change impacts: Low‐flow scenarios for the River Thames, UK , 2006 .

[5]  M. Webb,et al.  Quantification of modelling uncertainties in a large ensemble of climate change simulations , 2004, Nature.

[6]  Jean-Philippe Vidal,et al.  A framework for developing high‐resolution multi‐model climate projections: 21st century scenarios for the UK , 2008 .

[7]  G. Brier,et al.  Some applications of statistics to meteorology , 1958 .

[8]  M. Clark,et al.  Hydrological responses to dynamically and statistically downscaled climate model output , 2000 .

[9]  J. Murphy,et al.  A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[10]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[11]  H. L. Penman Natural evaporation from open water, bare soil and grass , 1948, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[12]  P. Mote,et al.  Review of scenario selection and downscaling methods for the assessment of climate change impacts on hydrology in the United States pacific northwest , 2007 .

[13]  A R Young,et al.  Low Flows 2000: a national water resources assessment and decision support tool. , 2003, Water science and technology : a journal of the International Association on Water Pollution Research.

[14]  D A Stainforth,et al.  Confidence, uncertainty and decision-support relevance in climate predictions , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[15]  Hayley J. Fowler,et al.  Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling , 2007 .

[16]  K. Ebi,et al.  Climate Change-related Health Impacts in the Hindu Kush–Himalayas , 2007, EcoHealth.

[17]  M. Clark,et al.  Use of Regional Climate Model Output for Hydrologic Simulations , 2001 .

[18]  B. Bates,et al.  Climate change and water. , 2008 .

[19]  Chris E Forest,et al.  Ensemble climate predictions using climate models and observational constraints , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[20]  Richard L. Smith,et al.  Quantifying Uncertainty in Projections of Regional Climate Change: A Bayesian Approach to the Analysis of Multimodel Ensembles , 2005 .

[21]  E. Maurer,et al.  Fine‐resolution climate projections enhance regional climate change impact studies , 2007 .

[22]  Vincent R. Gray Climate Change 2007: The Physical Science Basis Summary for Policymakers , 2007 .

[23]  D. Lettenmaier,et al.  Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs , 2004 .

[24]  C. Tebaldi,et al.  Two Approaches to Quantifying Uncertainty in Global Temperature Changes , 2006 .

[25]  Philip B. Duffy,et al.  Detection, attribution, and sensitivity of trends toward earlier streamflow in the Sierra Nevada , 2007 .

[26]  Robert L. Wilby,et al.  Integrated modelling of climate change impacts on water resources and quality in a lowland catchment: River Kennet, UK , 2006 .

[27]  E. Maurer,et al.  Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods , 2007 .

[28]  Leonard A. Smith,et al.  Uncertainty in predictions of the climate response to rising levels of greenhouse gases , 2005, Nature.

[29]  Raquel V. Francisco,et al.  Evaluating uncertainties in the prediction of regional climate change , 2000 .

[30]  Robert L. Wilby,et al.  A coupled synoptic-hydrological model for climate change impact assessment , 1994 .

[31]  Reto Knutti,et al.  The use of the multi-model ensemble in probabilistic climate projections , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.