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E-raamat: Downscaling Techniques for High-Resolution Climate Projections: From Global Change to Local Impacts

(Texas Tech University), (National Center for Atmospheric Research, Boulder, Colorado), (University of Illinois, Urbana-Champaign), , (University of New Hampshire), (Argonne National Laboratory, Illinois)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 11-Feb-2021
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781108587068
  • Formaat - EPUB+DRM
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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 11-Feb-2021
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781108587068

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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.

This book equips the reader with a basic understanding of different downscaling methods and the advantages, disadvantages and uncertainties of each, making it a valuable resource for graduate students, researchers, policymakers and practitioners working on climate impacts and adaptation.

Muu info

A practical guide to understanding, using and producing downscaled climate data, for researchers, graduate students, policy makers and practitioners.
Preface ix
1 Impacts, Adaptation, Vulnerability, and Decision-Making
1(18)
1.1 Assessing Climate-Change Impacts on Human Systems
1(6)
1.1.1 A Changing Climate and the Need for Assessment
1(2)
1.1.2 A Brief History of Major Impact Assessments
3(3)
1.1.3 Generating Climate Information for Impact Assessments
6(1)
1.2 Adaptation Strategies for Coping with a Changing Climate
7(8)
1.2.1 Adapting to Changes in Average Climate
9(2)
1.2.2 Adapting to Changes in Weather Extremes
11(2)
1.2.3 Adapting to Rising Seas
13(2)
1.3 Decision Making for Adaptation and Climate Information Needs
15(4)
2 Global Climate Models
19(21)
2.1 The Need for Climate Models
19(1)
2.2 The Evolution of Climate Modeling
20(4)
2.3 Physical Processes in Global Climate Models
24(4)
2.4 Advances in Climate Modeling and Model Resolution
28(3)
2.5 Evaluating Climate Models Using Past Climate
31(3)
2.6 Archives of GCM Simulations
34(6)
3 Assessing Climate-Change Impacts at the Regional Scale
40(24)
3.1 Climate Projections for Regional Assessments
40(5)
3.2 Climate Projections by Region
45(16)
3.2.1 Projected Changes for North America
45(2)
3.2.2 Projected Changes for Central and South America
47(3)
3.2.3 Projected Changes for Europe
50(1)
3.2.4 Projected Changes for East Asia
51(3)
3.2.5 Projected Changes for South Asia
54(2)
3.2.6 Projected Changes for Africa
56(3)
3.2.7 Projected Changes for Australia
59(2)
3.3 Regional Projections of Sea Level Change and Marine Temperature
61(3)
4 Dynamical Downscaling
64(18)
4.1 Regional vs. Global Climate Models
64(3)
4.2 The Physics of Regional Climate Models
67(3)
4.3 Outputs from Dynamical Downscaling Models
70(3)
4.4 Workflow for Performing Dynamically Downscaled Simulations
73(3)
4.5 Evaluation of Dynamical Downscaled Model Simulations
76(3)
4.6 Availability and Use of Climate Projections from RCMs
79(3)
5 Empirical-Statistical Downscaling
82(20)
5.1 The Origin of Empirical-Statistical Bias Correction and Downscaling
82(1)
5.2 Statistical Methods and Models for Bias Correction and Spatial Disaggregation in ESDMs
83(9)
5.3 Statistical Methods and Models for Temporal Disaggregation in ESDMs
92(4)
5.4 Evaluation of Output from ESDMs
96(2)
5.5 Comparison between ESDMs
98(2)
5.6 Availability and Use of Climate Projections from ESDMs
100(2)
6 Added Value of Downscaling
102(19)
6.1 The Concept of Added Value in Downscaling
102(2)
6.2 Added Value from the Perspective of Scientists and Decision Makers
104(1)
6.3 Added Value in the Context of Dynamical Downscaling
105(4)
6.4 Added Value in the Context of ESDM
109(5)
6.5 Comparing Statistical and Dynamical Downscaling
114(3)
6.6 Research Needs to Further Determine Appropriate Use of Different Methods
117(4)
7 Uncertainty in Future Projections, and Approaches for Representing Uncertainty
121(18)
7.1 Identifying the Need for Quantitative Future Projections
121(2)
7.2 Uncertainty due to Natural Variability
123(2)
7.3 Scientific Uncertainty
125(5)
7.3.1 Climate Sensitivity
125(2)
7.3.2 Structural Uncertainty
127(1)
7.3.3 Parametric Uncertainty
128(1)
7.3.4 Accounting for Scientific Uncertainty
128(2)
7.4 Uncertainty due to Human Choices
130(5)
7.4.1 Scenarios Used for GCM Simulations
130(2)
7.4.2 Addressing Scenario Uncertainty in Impact Assessments
132(3)
7.5 The Relative Importance of Different Sources of Uncertainty
135(1)
7.6 The Importance of Quantifying Uncertainty
136(3)
8 Guidance and Recommendations for Use of (Downscaled) Climate Information
139(18)
8.1 Introduction
139(3)
8.2 Global Climate Model Selection
142(1)
8.3 Emission Scenarios
142(1)
8.4 Natural Variability
143(1)
8.5 Selecting Downscaling Approaches
143(2)
8.6 Use of the Different Downscaling Methods
145(7)
8.6.1 Comparing Statistical and Dynamical Approaches to Generating High-Resolution Climate Projections
146(6)
8.7 Recommendations Based on Particular Variables, Questions Asked, and Physical Characteristics of the Region
152(3)
8.7.1 Useful vs. Usable
152(1)
8.7.2 Climate Services and Web Portals
153(2)
8.8 Conclusion
155(2)
9 The Future of Regional Downscaling
157(9)
9.1 A Look at the Future
157(1)
9.2 Future Directions for Global Modeling
158(3)
9.3 Future Directions for Regional Modeling
161(1)
9.4 Future Directions for Empirical-Statistical Downscaling
162(2)
9.5 Will Downscaling Become Obsolete?
164(1)
9.6 Coupling with GIS and Other Tools
164(2)
References 166(22)
Index 188
Rao Kotamarthi is a Chief Scientist of the Environmental Science Division and Department Head for the Atmospheric Science and Climate research group at the Argonne National Laboratory. He applies numerical models to the assessment of climate change impacts and uses high performance computing and physics-based models for projecting changes at regional and local scales. His other research interests include the role of absorbing aerosols on radiative forcing and developing models for resource characterization of wind energy. Katharine Hayhoe is a Professor in the Public Administration program at Texas Tech University, where she is also Director of the Climate Center. Her research focuses on developing and applying high-resolution climate projections to evaluate the future impacts of climate change on human society and the natural environment. She has served as lead author on key reports for the U.S. Global Change Research Program and the National Academy of Sciences, including the Second, Third and Fourth U.S. National Climate Assessments and has been named the UN Champion of the Environment. Linda O. Mearns is Director of the Regional Climate Uncertainty Program and Head of the Regional Integrated Sciences Collective at the National Centre for Atmospheric Research (NCAR). She has authored chapters in many of the IPCC Assessment Reports, including the 2007 report that was awarded the Nobel Peace Prize. She is a fellow of the American Meteorological Society. Donald Wuebbles is the Harry E. Preble Professor of Atmospheric Sciences at the University of Illinois. He is an expert in numerical modeling of atmospheric physics and chemistry, and has received the AMS Cleveland Abbe Award, the U.S. EPA Stratospheric Ozone Protection Award, and the AGU Bert Bolin Global Environmental Change Award. He is a Fellow of the American Association for the Advancement of Science, the American Geophysical Union, and the American Meteorological Society. Jennifer Jacobs is a Professor in the Department of Civil and Environmental Engineering at the University of New Hampshire. She has over 25 years of experience using novel weather and climate information to enhance infrastructure design. She directs the National Science Foundation funded Infrastructure and Climate Networks (ICNet and ICNet Global); and was the Lead Author for the Transportation Sector Chapter of the 4th National Climate Assessment. Jennifer Jurado is Broward County's Chief Resilience Officer and Director of the Environmental Planning and Community Resilience Division. In 2013 she was recognized by the White House as a Champion of Change for her work on climate resilience. She serves on the Board of Directors for the American Society of Adaptation Professionals and the American Geophysical Union's Thriving Earth Exchange.