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To what extent does bias correction and downscaling increase the value of GCM outputs for regional-scale applications? This chapter provides an overview of the concept of added value for downscaling studies and discusses the methods and metrics used for evaluating the value that bias correction and downscaling, using an RCM and/or an ESDM, adds to climate projections for impact assessments
Previous chapters have laid out the process of creating high-resolution climate projections at spatial and time scales appropriate for assessing the impacts of climate change at the local to regional scale, and described available assessments, global climate model archives, and both RCM- and ESDM-based high-resolution projections. This chapter builds on this information to discuss the process by which high-resolution climate projections can be selected, applied, and interpreted to quantify future impacts for a given location, system, or assessment.
Global models of the Earth’s climate system are the primary tool scientists use to understand the Earth’s climate system. They yield critical insights into the components of the earth’s climate system, including the atmosphere, land, oceans, and biosphere, the processes at work within and between them, and how natural factors and human activities affect climate at the regional to global scale. This chapter summarizes the evolution of climate modeling and describes current global climate models and how they are being used to study the changing climate.
Although climate change is a global issue, its impacts are experienced primarily at the local to regional scale. This chapter describes important aspects of regional climate and how climate projections can be used to assess climate impacts at the regional to local scale. It summarizes projections and sources of information on changes in continental-scale annual and seasonal temperature and precipitation, climate and weather extremes, and sea-level rise projections.
Dynamical downscaling uses high-resolution regional climate models (RCMs) to bias-correct and downscale global climate model output. This chapter discusses the models and methods used in dynamical downscaling. It provides an overview of the basic physics used in RCMs, and how this is similar to and differs from that used in global models. It also discusses the methods and metrics used to evaluate RCMs, and how projections from RCMs can be used to assess climate impacts at the regional scale
What lies in the future of regional downscaling, and how will future developments advance the information available for impacts and policymaking analyses? Models and methods are constantly under development; what might users expect to become available over the next decades as computing resources reach and extend beyond exascale (over 100 times faster than the current fastest computers), and global climate model resolutions reach regional scales? What uncertainties will remain to be resolved, even with these advances? By its nature, much of this discussion is somewhat philosophical and this chapter does not include real-world case studies. Instead, it highlights specific ways assessments may be affected by future advances and discusses key areas of future development.
Climate change is a broad-reaching, global challenge. It impacts most human and natural systems, from agriculture and ecosystems to energy and health, and exacerbates other preexisting issues, from poverty to political instability. Evaluating these impacts and our vulnerability to them increases awareness of the need for adaptation and resilience. This chapter provides a brief history of impact assessments, focusing on the models, tools, and information that is needed and is available to quantify future impacts across a wide range of systems and scales and to provide valuable input to adaptation and resilience planning
Empirical-statistical downscaling combines observations with global climate model outputs to generate high-resolution spatial and temporal projections. This chapter describes some of the common methods and models used in spatial and temporal disaggregation and bias correction, focusing on aspects of their design and performance that are relevant to their application for quantifying local to regional-scale impacts.
Future projections are uncertain, for multiple reasons. Limits to scientific understanding of natural variability, structural and parametric uncertainty in scientific modeling, climate sensitivity, bias correction and downscaling all play a role. The uncertainty due to human choices that will determine emissions of heat-trapping gases becomes increasingly important over time, to the point where it dominates the uncertainty in many aspects of global and regional change by the end of century. Quantifying how a given system will respond to a changing climate adds yet another layer of uncertainty that can be prohibitively large in systems that are complex and/or not well understood. Understanding the source of these uncertainties and how they can be addressed when applying downscaled climate projections to assess future impacts is essential to quantifying the range of future change and resulting impacts on human and natural systems
Downscaling is a widely used technique for translating information from large-scale climate models to the spatial and temporal scales needed to assess local and regional climate impacts, vulnerability, risk and resilience. This book is a comprehensive guide to the downscaling techniques used for climate data. A general introduction of the science of climate modeling is followed by a discussion of techniques, models and methodologies used for producing downscaled projections, and the advantages, disadvantages and uncertainties of each. The book provides detailed information on dynamic and statistical downscaling techniques in non-technical language, as well as recommendations for selecting suitable downscaled datasets for different applications. The use of downscaled climate data in national and international assessments is also discussed using global examples. This is a practical guide for graduate students and researchers working on climate impacts and adaptation, as well as for policy makers and practitioners interested in climate risk and resilience.
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