Challenges to link climate change data provision and user needs: Perspective from the COST‐action VALUE

The application of climate change impact assessment (CCIA) studies in general and especially the linkages between different actor groups typically involved is often not trivial and subject to many limitations and uncertainties. Disciplinary issues like competing downscaling approaches, imperfect climate and impact model data and uncertainty propagation as well as the selection of appropriate data sets are only one part of the story. Interdisciplinary and transdisciplinary challenges add to these, as climate data and impact model data provision and their usage require at least a minimum of common work and shared understanding among actors. Here, we provide the VALUE perspective on current disciplinary challenges and limitations at the downscaling interface and elaborate transdisciplinary issues that hamper a proper working downscaling interface. The perspective is partly based on a survey on user needs of downscaled data that was distributed among 62 participants across Europe involving 22 sectors. Partly, it is based on the exchanges and experiences gained during the lifetime of VALUE that brought together different actor groups of different disciplines: climate modellers, impact modellers, statisticians and stakeholders. We outline a sketch that summarizes the linkages between the main identified actor groups: climate model data providers, impact modellers and societal users. We summarize review and structure current actors groups, needs and issues. We argue that this structuring enables involved actors to tackle these issues in a more organized and hence effective way. A key solution to several difficulties at the downscaling interface is to our understanding the development of guidelines based on benchmark tests like the VALUE framework. In addition, fostering communication between actor groups—and financing this communication—is essential to obtain the best possible CCIA as a prerequisite for robust adaptation.

[1]  M. Liniger,et al.  Climate change signals of CMIP5 general circulation models over the Alps – impact of model selection , 2016 .

[2]  H. Storch,et al.  Reconsidering the Quality and Utility of Downscaling , 2016 .

[3]  Vladimir A. Semenov,et al.  Evidence for added value of convection‐permitting models for studying changes in extreme precipitation , 2015 .

[4]  J. Evans,et al.  Resolution Sensitivity of Cyclone Climatology over Eastern Australia Using Six Reanalysis Products , 2015 .

[5]  P. Soares,et al.  Thermal stratification of Portuguese reservoirs: potential impact of extreme climate scenarios , 2015 .

[6]  R. Leung,et al.  A review on regional convection‐permitting climate modeling: Demonstrations, prospects, and challenges , 2015, Reviews of geophysics.

[7]  R. Vautard,et al.  Bias Correction Intercomparison Project (BCIP): an introduction and the first results , 2015 .

[8]  C. Schär,et al.  Heavy precipitation in a changing climate: Does short‐term summer precipitation increase faster? , 2015 .

[9]  David B. Lobell,et al.  Incorporating Climate Uncertainty into Estimates of Climate Change Impacts , 2015, Review of Economics and Statistics.

[10]  Déborah Idier,et al.  Le climat de la France au XXIe siècle. Vol 5 : Changement climatique et niveau de la mer : de la planète aux côtes françaises , 2015 .

[11]  S. Kotlarski,et al.  VALUE: A framework to validate downscaling approaches for climate change studies , 2015 .

[12]  A. Hall.  Projecting regional change , 2014, Science.

[13]  Rasmus E. Benestad,et al.  Warmer and wetter winters: characteristics and implications of an extreme weather event in the High Arctic , 2014 .

[14]  Rolf Weingartner,et al.  Robust changes and sources of uncertainty in the projected hydrological regimes of Swiss catchments , 2014 .

[15]  U. Strasser,et al.  Coupled component modelling for inter- and transdisciplinary climate change impact research: Dimensions of integration and examples of interface design , 2014, Environ. Model. Softw..

[16]  Emma K. Austin,et al.  Bridging the gap between end user needs and science capability: decision making under uncertainty , 2014 .

[17]  H. Fowler,et al.  Heavier summer downpours with climate change revealed by weather forecast resolution model , 2014 .

[18]  Maarten Plieger,et al.  Towards a dedicated impact portal to bridge the gap between the impact and climate communities : Lessons from use cases , 2014, Climatic Change.

[19]  C. Mechoso,et al.  A global perspective on CMIP5 climate model biases , 2014 .

[20]  C. Field.  Climate change 2014 : impacts, adaptation and vulnerability : Working Group II contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change , 2014 .

[21]  Alice Owen,et al.  Unseen influence-The role of low carbon retrofit advisers and installers in the adoption and use of domestic energy technology , 2014 .

[22]  Joseph Daron,et al.  Interrogating empirical-statistical downscaling , 2014, Climatic Change.

[23]  Linda O. Mearns,et al.  The Practitioner's Dilemma: How to Assess the Credibility of Downscaled Climate Projections , 2013 .

[24]  W. Collins,et al.  Evaluation of climate models , 2013 .

[25]  J. Bedia,et al.  Dangers of using global bioclimatic datasets for ecological niche modeling. Limitations for future climate projections , 2013 .

[26]  Using Climate Predictions to Better Serve Society's Needs , 2013 .

[27]  Matthias Bergmann,et al.  Methods for Transdisciplinary Research: A Primer for Practice , 2013 .

[28]  L. Mearns,et al.  The Practitioner's Dilemma: How to Assess the Credibility of Downscaled Climate Projections , 2013 .

[29]  Victor Brovkin,et al.  Determining robust impacts of land-use induced land-cover changes on surface climate over North America and Eurasia; Results from the first set of LUCID experiments , 2012 .

[30]  H. Storch,et al.  Regional climate models add value to global model data, A Review and Selected Examples , 2011 .

[31]  R. Benestad,et al.  Modeling the temperature evolution of Svalbard permafrost during the 20th and 21st century , 2011 .

[32]  R. J. Swart,et al.  Bridging Climate Research Data and the Needs of the Impact Community - Proceedings of IS-ENES/EEA/CIRCLE-2, 11-12 January 2011 , 2011 .

[33]  Malin Mobjörk.  Consulting versus participatory transdisciplinarity: A refined classification of transdisciplinary research , 2010 .

[34]  D. Maraun,et al.  Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user , 2010 .

[35]  W. Landman.  Climate change 2007: the physical science basis , 2010 .

[36]  M. Rummukainen.  State‐of‐the‐art with regional climate models , 2010 .

[37]  L. Binder,et al.  Assessing Pacific Northwest water resources stakeholder data needs , 2010 .

[38]  D. Rodríguez,et al.  Adaptation science for agriculture and natural resource management - urgency and theoretical basis , 2009 .

[39]  Martijn Gough.  Climate change , 2009, Canadian Medical Association Journal.

[40]  J. Christensen,et al.  Improved confidence in climate change projections of precipitation evaluated using daily statistics from the PRUDENCE ensemble , 2009 .

[41]  N. Stenseth,et al.  Expected future plague levels in a wildlife host under different scenarios of climate change , 2009 .

[42]  F. Giorgi,et al.  Addressing climate information needs at the regional level: the CORDEX framework , 2009 .

[43]  Deliang L. Chen,et al.  Empirical-Statistical Downscaling , 2008 .

[44]  D. Lüthi,et al.  Aspects of the diurnal cycle in a regional climate model , 2008 .

[45]  R. Laprise,et al.  Challenging some tenets of Regional Climate Modelling , 2008 .

[46]  Christopher A. Pohl,et al.  Methodological challenges of transdisciplinary research , 2008 .

[47]  J. Hinkel,et al.  Transdisciplinary knowledge integration : cases from integrated assessment and vulnerability assessment , 2008 .

[48]  Martin J. Eppler.  Knowledge Communication Problems between Experts and Decision Makers: an Overview and Classification , 2007 .

[49]  J. Christensen,et al.  A summary of the PRUDENCE model projections of changes in European climate by the end of this century , 2007 .

[50]  E. McNie.  Reconciling the supply of scientific information with user demands: an analysis of the problem and review of the literature , 2007 .

[51]  D. Randall,et al.  Climate models and their evaluation , 2007 .

[52]  H. L. Miller,et al.  Climate Change 2007: The Physical Science Basis , 2007 .

[53]  J. Räisänen,et al.  How reliable are climate models? , 2007 .

[54]  S. Schiavon,et al.  Climate Change 2007: Impacts, Adaptation and Vulnerability. , 2007 .

[55]  C. Frei,et al.  Future change of precipitation extremes in Europe: Intercomparison of scenarios from regional climate models , 2006 .

[56]  Martin J. Eppler.  Knowledge Communication Problems between Experts and Managers An Analysis of Knowledge Transfer in Decision Processes , 2004 .

[57]  K. Cuhls.  From forecasting to foresight processes—new participative foresight activities in Germany , 2003 .

[58]  William C. Clark,et al.  From Science to Policy: Assessing the Assessment Process , 2001 .

[59]  Roger Jones,et al.  Managing Uncertainty in Climate Change Projections – Issues for Impact Assessment , 2000 .

[60]  Edward L. Miles,et al.  Policy implications of climate forecasts for water resources management in the Pacific Northwest , 1999 .

[61]  Gary T. Bates,et al.  The climatological skill of a regional model over complex terrain , 1989 .

[62]  J. Jackson.  A practitioner's dilemma. , 1970, The Journal of reproductive medicine.