Demystifying Climate Models A Users Guide to Earth System Models 1st Edition Andrew Gettelman

Demystifying Climate Models A Users Guide to
Earth System Models 1st Edition Andrew Gettelman
download
https://textbookfull.com/product/demystifying-climate-models-a-
users-guide-to-earth-system-models-1st-edition-andrew-gettelman/
Download full version ebook from https://textbookfull.com

We believe these products will be a great fit for you. Click
the link to download now, or visit textbookfull.com
to discover even more!
Marketing and management models a guide to
understanding and using business models First Edition
Yvonne Helen Strong
https://textbookfull.com/product/marketing-and-management-models-
a-guide-to-understanding-and-using-business-models-first-edition-
yvonne-helen-strong/
Entrepreneurship, Innovation, and Technology: A Guide
to Core Models and Tools, 2nd Edition Lorenzo
https://textbookfull.com/product/entrepreneurship-innovation-and-
technology-a-guide-to-core-models-and-tools-2nd-edition-lorenzo/
Models for Tropical Climate Dynamics Waves Clouds and
Precipitation Boualem Khouider
https://textbookfull.com/product/models-for-tropical-climate-
dynamics-waves-clouds-and-precipitation-boualem-khouider/
Interior Design Visual Presentation a Guide to Graphics
Models and Presentation Techniques 4th Edition Mitton
https://textbookfull.com/product/interior-design-visual-
presentation-a-guide-to-graphics-models-and-presentation-
techniques-4th-edition-mitton/

Beginner s Guide to Create Models With 3ds Max Ravi
Conor
https://textbookfull.com/product/beginner-s-guide-to-create-
models-with-3ds-max-ravi-conor/
Current Trends in the Representation of Physical
Processes in Weather and Climate Models David A.
Randall
https://textbookfull.com/product/current-trends-in-the-
representation-of-physical-processes-in-weather-and-climate-
models-david-a-randall/
Introduction to Statistical Methods for Financial
Models 1st Edition Thomas A Severini
https://textbookfull.com/product/introduction-to-statistical-
methods-for-financial-models-1st-edition-thomas-a-severini/
Developing Modular Oriented Simulation Models Using
System Dynamics Libraries 1st Edition Christian K. Karl
https://textbookfull.com/product/developing-modular-oriented-
simulation-models-using-system-dynamics-libraries-1st-edition-
christian-k-karl/
From Models to Simulations Franck Varenne
https://textbookfull.com/product/from-models-to-simulations-
franck-varenne/

Earth Systems Data and Models
Andrew Gettelman
Richard B. Rood
Demystifying
Climate
Models
A Users Guide to Earth System Models

Volume 2
Series editors
Bernd Blasius, Carl von Ossietzky University Oldenburg, Oldenburg, Germany
William Lahoz, NILU—Norwegian Institute for Air Research, Kjeller, Norway
Dimitri P. Solomatine, UNESCO—IHE Institute for Water Education, Delft,
The Netherlands

Aims and Scope
The book series Earth Systems Data and Models publishes state-of-the-art research
and technologies aimed at understanding processes and interactions in the earth
system. A special emphasis is given to theory, methods, and tools used in earth,
planetary and environmental sciences for: modeling, observation and analysis; data
generation, assimilation and visualization; forecasting and simulation; and
optimization. Topics in the series include but are not limited to: numerical, data-
driven and agent-based modeling of the earth system; uncertainty analysis of models;
geodynamic simulations, climate change, weather forecasting, hydroinformatics,
and complex ecological models; model evaluation for decision-making processes
and other earth science applications; and remote sensing and GIS technology.
The series publishes monographs, edited volumes and selected conference
proceedings addressing an interdisciplinary audience, which not only includes
geologists, hydrologists, meteorologists, chemists, biologists and ecologists but
also physicists, engineers and applied mathematicians, as well as policy makers
who use model outputs as the basis of decision-making processes.
More information about this series at http://www.springer.com/series/10525

Andrew Gettelman • Richard B. Rood
Demystifying Climate
Models
A Users Guide to Earth System Models

Andrew Gettelman
National Center for Atmospheric Research
Boulder
USA
Richard B. Rood
Climate and Space Sciences and Engineering
University of Michigan
Ann Arbor
USA
ISSN 2364-5830 ISSN 2364-5849 (electronic)
ISBN 978-3-662-48957-4 ISBN 978-3-662-48959-8 (eBook)
DOI 10.1007/978-3-662-48959-8
Library of Congress Control Number: 2015958748
© The Editor(s) (if applicable) and The Author(s) 2016. This book is published open access.
Open Access This book is distributed under the terms of the Creative Commons Attribution-
Noncommercial 2.5 License (http://creativecommons.org/licenses/by-nc/2.5/) which permits any
noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and
source are credited.
The images or other third party material in this chapter are included in the work’s Creative Commons
license, unless indicated otherwise in the credit line; if such material is not included in the work’s
Creative Commons license and the respective action is not permitted by statutory regulation, users will
need to obtain permission from the license holder to duplicate, adapt or reproduce the material.
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part
of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,
recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission
or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar
methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publi-
cation does not imply, even in the absence of a specific statement, that such names are exempt from the
relevant protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this
book are believed to be true and accurate at the date of publication. Neither the publisher nor the
authors or the editors give a warranty, express or implied, with respect to the material contained herein or
for any errors or omissions that may have been made.
Printed on acid-free paper
This Springer imprint is published by SpringerNature
The registered company is Springer-Verlag GmbH Berlin Heidelberg

Acknowledgments
Amy Marks provided very careful and thorough edit, as well as numerous helpful
suggestions. Cheryl Craig, Teresa Foster, Andrew Dolan, and Galia Guentchev
contributed their time to reading through drafts and providing a needed reality
check. Prof. Reto Knutti helped this book take shape while Andrew Gettelman was
on sabbatical at ETH in Zurich. David Lawrence shared critical insights and
PowerPoint figures on terrestrial systems. Thanks also to Markus Jochum for
straightening us out on explaining how the ocean works. Jan Sedlacek,
ETH-Zürich, helped with figures in Chap. 11 (especially Fig. 11.6).
Mike Moran and David Edwards of the National Center for Atmospheric
Research provided financial support. Lawrence Buja and the National Center for
Atmospheric Research hosted Richard Rood’s visitor status. The National Center
for Atmospheric Research is funded by the U.S. National Science Foundation.
We thank the staff and students of the University of Michigan’s Climate Center
for reviews of the manuscript: Samantha Basile, William Baule, Matt Bishop, Laura
Briley, Daniel Brown, Kimberly Channell, Omar Gates, and Elizabeth Gibbons.
Richard Rood thanks the students in his classes on climate change problem-solving
at the University of Michigan and acknowledges in particular the project work of:
James Arnott, Christopher Curtis, Kevin Kacan, Kazuki Ito, Benjamin Lowden,
Sabrina Shuman, Kelsey Stadnikia, Anthony Torres, Zifan Yang.
Richard Rood acknowledges the support of the University of Michigan and the
Graham Sustainability Institute, and grants from the National Oceanographic and
Atmospheric Administration (Great Lakes Sciences and Assessments Center
(GLISA)—NOAA Climate Program Office NA10OAR4310213) and the
Department of the Interior, National Park Service (Cooperative Agreement
P14AC00898).
Francesca Gettelman exhibited nearly unlimited patience with some late nights.
v

Contents
Part I Basic Principles and the Problem of Climate Forecasts
1 Key Concepts in Climate Modeling . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1 What Is Climate? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 What Is a Model? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3 Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.3.1 Model Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.3.2 Scenario Uncertainty. . . . . . . . . . . . . . . . . . . . . . . . . 10
1.3.3 Initial Condition Uncertainty . . . . . . . . . . . . . . . . . . . 11
1.3.4 Total Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2 Components of the Climate System . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1 Components of the Earth System . . . . . . . . . . . . . . . . . . . . . . 13
2.1.1 The Atmosphere. . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.1.2 The Ocean and Sea Ice . . . . . . . . . . . . . . . . . . . . . . . 17
2.1.3 Terrestrial Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2 Timescales and Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3 Climate Change and Global Warming . . . . . . . . . . . . . . . . . . . . . 23
3.1 Coupling of the Pieces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2 Forcing the Climate System. . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.3 Climate History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.4 Understanding Where the Energy Goes. . . . . . . . . . . . . . . . . . 30
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4 Essence of a Climate Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.1 Scientiﬁc Principles in Climate Models . . . . . . . . . . . . . . . . . . 38
4.2 Basic Formulation and Constraints . . . . . . . . . . . . . . . . . . . . . 41
4.2.1 Finite Pieces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.2.2 Processes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
vii

4.2.3 Marching Forward in Time . . . . . . . . . . . . . . . . . . . . 49
4.2.4 Examples of Finite Element Models . . . . . . . . . . . . . . 50
4.3 Coupled Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.4 A Brief History of Climate Models. . . . . . . . . . . . . . . . . . . . . 52
4.5 Computational Aspects of Climate Modeling . . . . . . . . . . . . . . 53
4.5.1 The Computer Program. . . . . . . . . . . . . . . . . . . . . . . 53
4.5.2 Running a Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Part II Model Mechanics
5 Simulating the Atmosphere. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.1 Role of the Atmosphere in Climate . . . . . . . . . . . . . . . . . . . . 62
5.2 Types of Atmospheric Models . . . . . . . . . . . . . . . . . . . . . . . . 66
5.3 General Circulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.4 Parts of an Atmosphere Model. . . . . . . . . . . . . . . . . . . . . . . . 71
5.4.1 Clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.4.2 Radiative Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
5.4.3 Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
5.5 Weather Models Versus Climate Models. . . . . . . . . . . . . . . . . 78
5.6 Challenges for Atmospheric Models . . . . . . . . . . . . . . . . . . . . 79
5.6.1 Uncertain and Unknown Processes . . . . . . . . . . . . . . . 79
5.6.2 Scales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.6.3 Feedbacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.6.4 Cloud Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.7 Applications: Impacts of Tropical Cyclones . . . . . . . . . . . . . . . 83
5.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6 Simulating the Ocean and Sea Ice. . . . . . . . . . . . . . . . . . . . . . . . . 87
6.1 Understanding the Ocean . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
6.1.1 Structure of the Ocean . . . . . . . . . . . . . . . . . . . . . . . 88
6.1.2 Forcing of the Ocean . . . . . . . . . . . . . . . . . . . . . . . . 89
6.2 “Limited” Ocean Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
6.3 Ocean General Circulation Models . . . . . . . . . . . . . . . . . . . . . 92
6.3.1 Topography and Grids . . . . . . . . . . . . . . . . . . . . . . . 92
6.3.2 Deep Ocean. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
6.3.3 Eddies in the Ocean . . . . . . . . . . . . . . . . . . . . . . . . . 96
6.3.4 Surface Ocean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
6.3.5 Structure of an Ocean Model . . . . . . . . . . . . . . . . . . . 100
6.3.6 Ocean Versus Atmosphere Models . . . . . . . . . . . . . . . 101
6.4 Sea-Ice Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
6.5 The Ocean Carbon Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
6.6 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
6.6.1 Challenges in Ocean Modeling. . . . . . . . . . . . . . . . . . 105
6.6.2 Challenges in Sea Ice Modeling . . . . . . . . . . . . . . . . . 105
viii Contents

6.7 Applications: Sea-Level Rise, Norfolk, Virginia . . . . . . . . . . . . 106
6.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
7 Simulating Terrestrial Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
7.1 Role of the Land Surface in Climate. . . . . . . . . . . . . . . . . . . . 109
7.1.1 Precipitation and the Water Cycle. . . . . . . . . . . . . . . . 110
7.1.2 Vegetation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
7.1.3 Ice and Snow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
7.1.4 Human Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
7.2 Building a Land Surface Simulation . . . . . . . . . . . . . . . . . . . . 113
7.2.1 Evolution of a Terrestrial System Model . . . . . . . . . . . 113
7.2.2 Biogeophysics: Surface Fluxes and Heat . . . . . . . . . . . 115
7.2.3 Biogeophysics: Hydrology. . . . . . . . . . . . . . . . . . . . . 116
7.2.4 Ecosystem Dynamics (Vegetation
and Land Cover/Use Change) . . . . . . . . . . . . . . . . . . 118
7.2.5 Summary: Structure of a Land Model . . . . . . . . . . . . . 120
7.3 Biogeochemistry: Carbon and Other Nutrient Cycles . . . . . . . . 121
7.4 Land-Atmosphere Interactions . . . . . . . . . . . . . . . . . . . . . . . . 125
7.5 Land Ice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
7.6 Humans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
7.7 Integrated Assessment Models . . . . . . . . . . . . . . . . . . . . . . . . 131
7.8 Challenges in Terrestrial System Modeling . . . . . . . . . . . . . . . 132
7.8.1 Ice Sheet Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . 132
7.8.2 Surface Albedo Feedback . . . . . . . . . . . . . . . . . . . . . 133
7.8.3 Carbon Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
7.9 Applications: Wolf and Moose Ecosystem, Isle Royale
National Park. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
7.10 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
8 Bringing the System Together: Coupling and Complexity . . . . . . . 139
8.1 Types of Coupled Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
8.1.1 Regional Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
8.1.2 Statistical Models and Downscaling . . . . . . . . . . . . . . 141
8.1.3 Integrated Assessment Models . . . . . . . . . . . . . . . . . . 143
8.2 Coupling Models Together: Common Threads . . . . . . . . . . . . . 144
8.3 Key Interactions in Climate Models . . . . . . . . . . . . . . . . . . . . 147
8.3.1 Intermixing of the Feedback Loops. . . . . . . . . . . . . . . 147
8.3.2 Water Feedbacks . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
8.3.3 Albedo Feedbacks . . . . . . . . . . . . . . . . . . . . . . . . . . 149
8.3.4 Ocean Feedbacks . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
8.3.5 Sea-Level Change . . . . . . . . . . . . . . . . . . . . . . . . . . 150
8.4 Coupled Modes of Climate Variability . . . . . . . . . . . . . . . . . . 151
8.4.1 Tropical Cyclones . . . . . . . . . . . . . . . . . . . . . . . . . . 151
8.4.2 Monsoons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
Contents ix

8.4.3 El Niño. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
8.4.4 Precipitation and the Land Surface . . . . . . . . . . . . . . . 153
8.4.5 Carbon Cycle and Climate. . . . . . . . . . . . . . . . . . . . . 153
8.5 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
8.6 Applications: Integrated Assessment of Water Resources. . . . . . 155
8.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
Part III Using Models
9 Model Evaluation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
9.1 Evaluation Versus Validation. . . . . . . . . . . . . . . . . . . . . . . . . 161
9.1.1 Evaluation and Missing Information . . . . . . . . . . . . . . 162
9.1.2 Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
9.1.3 Model Improvement . . . . . . . . . . . . . . . . . . . . . . . . . 168
9.2 Climate Model Evaluation. . . . . . . . . . . . . . . . . . . . . . . . . . . 169
9.2.1 Types of Comparisons . . . . . . . . . . . . . . . . . . . . . . . 169
9.2.2 Model Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . 170
9.2.3 Using Model Evaluation to Guide Further
Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
9.3 Predicting the Future: Forecasts Versus Projections. . . . . . . . . . 173
9.3.1 Forecasts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
9.3.2 Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
9.4 Applications of Climate Model Evaluation:
Ozone Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
9.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
10 Predictability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
10.1 Knowledge and Key Uncertainties . . . . . . . . . . . . . . . . . . . . . 178
10.1.1 Physics of the System. . . . . . . . . . . . . . . . . . . . . . . . 178
10.1.2 Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
10.1.3 Sensitivity to Changes. . . . . . . . . . . . . . . . . . . . . . . . 180
10.2 Types of Uncertainty and Timescales . . . . . . . . . . . . . . . . . . . 181
10.2.1 Predicting the Near Term: Initial Condition
Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
10.2.2 Predicting the Next 30–50 Years: Scenario
Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
10.2.3 Predicting the Long Term: Model Uncertainty
Versus Scenario Uncertainty . . . . . . . . . . . . . . . . . . . 189
10.3 Ensembles: Multiple Models and Simulations . . . . . . . . . . . . . 191
10.4 Applications: Developing and Using Scenarios. . . . . . . . . . . . . 195
10.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196
x Contents

11 Results of Current Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
11.1 Organization of Climate Model Results. . . . . . . . . . . . . . . . . . 199
11.2 Prediction and Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . 200
11.2.1 Goals of Prediction. . . . . . . . . . . . . . . . . . . . . . . . . . 201
11.2.2 Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
11.2.3 Why Models? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
11.3 What Is the Conﬁdence in Predictions?. . . . . . . . . . . . . . . . . . 204
11.3.1 Conﬁdent Predictions . . . . . . . . . . . . . . . . . . . . . . . . 205
11.3.2 Uncertain Predictions: Where to Be Cautious. . . . . . . . 210
11.3.3 Bad Predictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
11.3.4 How Do We Predict Extreme Events?. . . . . . . . . . . . . 214
11.4 Climate Impacts and Extremes . . . . . . . . . . . . . . . . . . . . . . . . 215
11.4.1 Tropical Cyclones . . . . . . . . . . . . . . . . . . . . . . . . . . 216
11.4.2 Stream Flow and Extreme Events. . . . . . . . . . . . . . . . 216
11.4.3 Electricity Demand and Extreme Events . . . . . . . . . . . 217
11.5 Application: Climate Model Impacts in Colorado . . . . . . . . . . . 217
11.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
12 Usability of Climate Model Projections by Practitioners. . . . . . . . . 221
12.1 Knowledge Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
12.2 Interpretation and Translation. . . . . . . . . . . . . . . . . . . . . . . . . 224
12.2.1 Barriers to the Use of Climate Model Projections . . . . . 225
12.2.2 Downscaled Datasets . . . . . . . . . . . . . . . . . . . . . . . . 226
12.2.3 Climate Assessments . . . . . . . . . . . . . . . . . . . . . . . . 227
12.2.4 Expert Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228
12.3 Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228
12.3.1 Ensembles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
12.3.2 Uncertainty in Assessment Reports . . . . . . . . . . . . . . . 231
12.4 Framing Uncertainty. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232
12.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235
13 Summary and Final Thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
13.1 What Is Climate? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
13.2 Key Features of a Climate Model. . . . . . . . . . . . . . . . . . . . . . 238
13.3 Components of the Climate System . . . . . . . . . . . . . . . . . . . . 239
13.3.1 The Atmosphere. . . . . . . . . . . . . . . . . . . . . . . . . . . . 240
13.3.2 The Ocean. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
13.3.3 Terrestrial Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 242
13.3.4 Coupled Components . . . . . . . . . . . . . . . . . . . . . . . . 243
13.4 Evaluation and Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . 244
13.4.1 Evaluation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244
13.4.2 Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245
13.5 What We Know (and Do not Know) . . . . . . . . . . . . . . . . . . . 246
Contents xi

13.6 The Future of Climate Modeling . . . . . . . . . . . . . . . . . . . . . . 248
13.6.1 Increasing Resolution . . . . . . . . . . . . . . . . . . . . . . . . 248
13.6.2 New and Improved Processes. . . . . . . . . . . . . . . . . . . 249
13.6.3 Challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250
13.7 Final Thoughts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251
Climate Modeling Text Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
xii Contents

About the Authors
Andrew Gettelman is a Scientist in the Climate and Global Dynamics and
Atmospheric Chemistry and Modeling Laboratories at the National Center for
Atmospheric Research (NCAR). He is actively involved in developing atmosphere
and chemistry components for global climate models at NCAR. Dr. Gettelman
specializes in understanding and simulating cloud processes and their impact on
climate, especially ice clouds. He has numerous publications on cloud physics
representations in global models, as well as research on climate forcing and feed-
backs. He has participated in several international assessments of climate models,
particularly for assessing atmospheric chemistry. Gettelman holds a doctorate in
Atmospheric Science from the University of Washington, Seattle. He is a recent
recipient of the American Geophysical Union Ascent Award, and is a
Thompson-Reuters Highly Cited Researcher.
Richard B. Rood is a Professor in the Department of Climate and Space Sciences
and Engineering (CLaSP) at the University of Michigan. He is also appointed in the
School of Natural Resources and Environment. Prior to joining the University of
Michigan, he worked in modeling and high performance computing at the National
Aeronautics and Space Administration (NASA). His recent research is focused on
the usability of climate knowledge and data in management planning and practice.
He has started classes in climate-change problem solving, climate change uncer-
tainty in decision making, climate-change informatics (with Paul Edwards). In
addition to publications on numerical models, his recent publications include
software engineering, informatics, political science, social science, forestry and
public health. Rood’s professional degree is in Meteorology from Florida State
University. He recently served on the National Academy of Sciences Committee on
A National Strategy for Advancing Climate Modeling. He writes expert blogs on
climate change science and problem solving for the Weather Underground Richard
Rood is a Fellow of American Meteorological Society and a winner of the World
Meteorological Organization’s Norbert Gerbier Award.
xiii

Introduction
Human-caused climate change is perhaps the defining environmental issue of the
early twenty-first century. We observe the earth’s climate in the present, but
observations of future climate are not available yet. So in order to predict the future,
we rely on simulation models to predict future climate.
This book is designed to be a guide to climate simulation and prediction for the
non-specialist and an entry point for understanding uncertainties in climate models.
The goal is not to be simply a popular guide to climate modeling and prediction, but
to help those using climate models to understand the results. This book provides
background on the earth’s climate system and how it might change, a detailed
qualitative analysis of how climate models are constructed, and a discussion of
model results and the uncertainty inherent in those results. Throughout the text,
terms in bold will be referenced in the glossary. References are provided as foot-
notes in each chapter.
Who uses climate models? Climate model users are practitioners in many fields
who desire to incorporate information about climate and climate change into
planning and management decisions. Users may be scientists and engineers in fields
such as ecosystems or water resources. These scientists are familiar with models
and the roles of models in natural science. In other cases, the practitioners are
engineers, urban planners, epidemiologists, or architects. Though not necessarily
familiar with models of natural science, experts in these fields use quantitative
information for decision-making. These experts are potential users of climate
models. We hope in the end that by understanding climate models and their
uncertainties, the reader will understand how climate models are constructed to
represent the earth’s climate system. The book is intended to help the reader
become a more competent interpreter or translator of climate model output.
Climate is best thought of as the distribution of weather states, or the probability
of finding a particular weather state (usually described by temperature and pre-
cipitation) at any place and time. Climate science seeks to be able to describe this
distribution. In contrast, the goal of predicting the weather is to figure out exactly
which weather state will occur for a specific place and time (e.g., what the high
temperature and total precipitation will be on Tuesday for a given city). Even in
xv

modern societies, we are still more dependent on the weather than we like to admit.
Think of a winter storm snarling traffic and closing schools. Windstorms and
hailstorms can cause significant damage. Or think of the impact of severe tropical
cyclones (also called hurricanes or typhoons, depending on their location), per-
sonified and immortalized with names like Sandy, Andrew, or Katrina. Persistence
(or absence) of weather events is also important. Too little rain (leading to drought
and its resulting effects on agriculture and even contributing to wildfires) and too
much rain (leading to flooding) are both damaging.
Although we are tempted to speak of a single “climate,” there are many climates.
Every place has its own. We build our societies to be comfortable during the
expected weather events (the climate) in each place. Naturally, different climates
mean different expected weather events, and our societies adapt. Buildings in
Minneapolis are built to standards different from buildings in Miami or San
Francisco. City planning is also different in different climates. Minneapolis has
connected buildings so that people do not need to walk outside in winter, for
example. Singapore has connected buildings so that people do not need to walk
outside in heat and humidity. Not just the built environment, but the fabric of
society may be different with local climates. In warmer climates, social life takes
place outdoors, for example, or the flow of a day includes a rest period (siesta in
Spanish) during the hottest period of the day.
We construct our lives for possible and sometimes rare weather events: putting
on snow tires for winter even though snow may not be around for over half a
winter. We build into our lives the ability to deal with variations in the weather.
A closet contains coats, gloves, hats, rain jackets, umbrellas, sun hats, and sun-
glasses: We are ready for a range and for a distribution of possible weather states.
Some events are rare: Snow occasionally has fallen in Los Angeles, for example.
But it is usually the rare or extreme weather events that are damaging. These
outliers of the distribution are typically damaging because they are unexpected and
therefore we do not adequately prepare for them. Or rather, the expectation
(probability) is so rare that it is not cost-effective for society to prepare for them.
This applies to the individual as well: if you live in Miami your closet contains
more warm weather gear, and less cold weather gear. It is unlikely you would be
able to dress for temperatures well below freezing. The impacts of extreme weather
are dependent on the climate of a place as well. For example, a few inches (cen-
timeters) of snow is typical for Denver, Minneapolis, or Oslo, but it will shut down
Rome or Atlanta. One inch (25 millimeters) of annual rainfall is typical for Cairo,
but a disaster in most other places.
Where the most damaging weather events occur are at the extremes of the cli-
mate distribution. One problem is that we often do not know the distribution very
well. Every time we hit a record (e.g., a high temperature, rainfall in 24 hours, days
without rain), we expand the range of observed events a little, and we learn more
about what might happen in a particular place. Because extreme events are rare, we
do not really know the true chance of their occurrence. Think about your knowledge
of the climate where you live or in a place you have visited several times. At first,
you might not have a good grasp of what the seasons are like. After a few years,
xvi Introduction

you think you know how the seasons evolve. But there are always events that will
surprise you. The record events are those that surprise everyone. What is the
probability of a hurricane flooding Houston as New Orleans was flooded by
Katrina? It has not happened since the city of Houston has been there, so we may
not know. The extremes of the distribution of possible weather states are not well
known.
This creates even more of a problem when these extremes change. Changing the
distribution of weather is what we mean by climate change. The cause of those
changes might be natural or they might be human caused (anthropogenic).
So how do we predict the future of weather and the distribution of weather that
represents climate in a location? To understand and predict the future, we need a
way to represent the system. In other words, we need a model. This book is about
how we attempt to use models to represent the complex climate system and predict
the future. Our goal is to explain and provide a better understanding of the models
we use to describe the past, present, and future of the earth system. These are
commonly known as climate models. Scientists often refer to these models formally
as earth system models, but we use the term climate model.
The purpose of this book is to demystify the models we use to simulate present
and future climate. We explain how the models are constructed, why they are
uncertain, and what level of conﬁdence we should place in those models.
Uncertainty is not a weakness. Understanding uncertainty is a strength, and a key
part of using any model, including climate models. One key message is that the
level of conﬁdence depends on the questions we ask. What are we certain about in
the future and why? What are we less certain of and why? For policy-makers, this is
a critical issue. Understanding how climate models work and how we get there is an
important step in making intelligent decisions using (or not using) these climate
models. Climate models are being used not just to understand the earth system but
also to provide input for policy decisions to address human-caused climate change.
The direction of our environment and economy is dependent on policy options
chosen based on results of these models.
The chapters in Part I serve as a basic primer on climate and climate change. We
hope to give readers an appreciation for the complexity and even beauty of the
complex earth system so that they can better understand how we simulate it. In
Part II, we discuss the mechanics of models of the earth system: How they are built
and what they are trying to represent. Models are built to simulate each region
of the climate system (e.g., atmosphere, ocean, and land), critical processes within
each region, as well as critical interactions between regions and processes. Finally,
in Part III, we focus on uncertainties and probabilities in prediction, with a focus on
understanding what is known, what is unknown, and the degree of certainty. We
also discuss how climate models are evaluated. In the concluding chapter, we
discuss what we know, what we may learn in the future, and why we should (or
should not) use models.
Introduction xvii

Part I
Basic Principles and the Problem
of Climate Forecasts

Chapter 1
Key Concepts in Climate Modeling
In order to describe climate modeling and the climate system, it is necessary to have
a common conception of exactly what we are trying to simulate, and what a model
actually is. What is climate? What is a model? How do we measure the uncertainty
in a model? This chapter introduces some key terms and concepts. We start with
some basic definitions of climate and weather. Everyone will come to this book
with a preconceived definition of what climate and weather are, but separating these
concepts is important for understanding how modeling of climate and weather are
similar and why they are different. It also makes sense to discuss what a model is.
Even if we do not realize it, we use models all the time. So we describe a few
different conceptual types of models and put climate models in context. Finally we
introduce the concept of uncertainty. As we discuss later in the book, models may
have errors and still be useful, but this requires understanding the errors (the
uncertainties) and understanding where they come from. Most of these concepts are
common to many types of modeling, and we provide examples throughout the text.
1.1 What Is Climate?
Climate is perhaps easiest to explain as the distribution of possible weather states.
On any given day and in any given place, the history of weather events can be
compiled into a distribution with probabilities of what the weather might be (see
Fig. 1.1). This figure is called a probability distribution function, representing a
probability distribution.1
The horizontal axis represents a value (e.g., tempera-
ture), and the vertical axis represents the probability (or frequency of occurrence) of
that event’s (i.e., a given temperature) occurring or having occurred. If based on
observations, then the frequency can be the number of times a given temperature
occurs. The higher the line, the more probable the event. The most frequent
1
There is quite a bit of statistics in climate, by definition. For a technical background, a good
reference is Devore, J. L. (2011). Probability and Statistics for Engineering and the Sciences, 8th
ed. Duxbury, MA: Duxbury Press. Any specific aspect of statistics (e.g., standard deviation,
probability distribution function) can be looked up on Wikipedia (www.wikipedia.com).
© The Author(s) 2016
A. Gettelman and R.B. Rood, Demystifying Climate Models,
Earth Systems Data and Models 2, DOI 10.1007/978-3-662-48959-8_1
3

occurrence is the highest probability (the mode). The total area under the line is the
probability. If the total area is given a value of 1, then the area under each part of
the curve is the fractional chance that an event exceeding some threshold will occur.
In Fig. 1.1, the area to the right of point T1 is the probability that the temperature
will be greater than T1, which might be about 20 % of the curve. The mean, or
expected value, is the weighted average of the points. It need not be the point with
the highest frequency. The mean value is the point at which half the probability
(50 %) is on one side of the mean, and half on the other. The median is the value at
which half the points are on one side and half on the other.
Here is an important and obvious question: How can we predict the climate (for
next season, next year, or 50 years from now) if we cannot predict the weather (in 5
or 10 days)? The answer is, we use probability: The climate is the distribution of
probable weather. The weather is a particular location in that distribution, and it is
conditional on the current state of the system. The chance of a hurricane hitting
Miami next week depends mostly on whether one has formed or is forming, and if
one has formed, whether it is heading in the direction of Miami. As another
example, the chance of having a rainy day in Seattle in January is high. But, given a
particular day in January, with a weather state that might be pushing storms well to
the north or south, the probability of rain the next day might be very low. In 50
Januaries, though, the probability of rain would be high. So climate is the distri-
bution of weather (sometimes unknown). Weather is a given state in that distri-
bution (often uncertain).
In a probability distribution of climate, the probabilities and the curve change
over time: In the middle latitudes, the chance of snow is higher in winter than in
summer. The curves will look different from place to place: Some climates have
narrow distributions (see Fig. 1.2a), which means the weather is often very close to
the average. Think about Hawaii, where the average of the daily highs and lows do
not change much over the course of the year or Alaska, where the daily highs and
lows may be the same as in Hawaii in summer, but not in winter. For Alaska the
annual distribution of temperature is a very broad distribution (more like Fig. 1.2b).
Frequency
of
Occurrence
Value (e.g., temperature)
Hot
Cold Mean T1
Fig. 1.1 A probability distribution function with the value on the horizontal axis, and the
frequency of occurrence on the vertical axis
4 1 Key Concepts in Climate Modeling

Of course, even in Hawaii, extreme events occur. For a distribution like precipi-
tation, which is bounded at one end by zero (no precipitation) the distribution might
be “skewed” (Fig. 1.2c) with a low frequency of high events marking the
‘extreme’. As events are more extreme (think about hurricanes like Katrina or
Sandy), there are fewer such events in the historical record. There may even be
possible extreme events that have not occurred. So our description of climate is
incomplete or uncertain. This is particularly true for rare (low-probability) events.
These events are also the events that cause the most damage.
One aspect of shifting distributions is that extremes can change a lot more than
the mean value (see Fig. 1.3). The mean is the value at which the area is equal on
each side of the distribution. The mean is the same as the median if the distribution
is symmetric. Simply moving the distribution to the right or left causes the area
(meaning, the probability) beyond some ﬁxed threshold to increase (or decrease). If
the curve represents temperature, then shifting it to warmer temperatures (Fig. 1.3a)
decreases the chance of cold events and really increases the chance of warm events.
But note that some cold events still occur. Also, you can change the distribution
without changing the mean by making the distribution wider (or broader). The
mathematical term for the width of a distribution is variance, a statistical term for
variability. This situation is illustrated in Fig. 1.3b. The mean is unchanged in
Narrow
(a)
(b)
(c)
Wide
Uneven (Skewed)
Fig. 1.2 Different probability
distribution functions:
a narrow, b wide and
c skewed distributions
1.1 What Is Climate? 5

Fig. 1.3b, but the chance of exceeding a given threshold for warm or cold tem-
peratures changes. In other words, the climate (particularly a climate extreme)
changes dramatically, even if the mean stays the same. The change need not be
symmetric: Hot may change more than cold (or vice versa). Figure 1.3c is not a
symmetric distribution. The key is to see climate as the distribution, not as a ﬁxed
number (often the mean).
This brings us to the fundamental difference between weather and climate
forecasting. In weather forecasting,2
we need to know the current state of the
system and have a model for projecting it forward. Often the model can be simple.
One “model” we all use is called persistence: What is the weather now? It may be
like that tomorrow. In many places (e.g., Hawaii), such a model is not bad, but
sometimes it is horribly wrong (e.g., when a hurricane hits Hawaii). So we try to
use more sophisticated models, now typically numerical ones. These models go by
the name numerical weather prediction (NWP) models, and they are used to
“forecast” the evolution of the earth system from its current state.
Increase in mean
Lots more ‘hot’ (some off scale: records)
(a)
(b)
(c)
Less cold
Increase in variance
More hot and cold: both off scale
Same mean
Increase in mean and variance
Lots more hot, off scale
Less cold
Fig. 1.3 Shifting probability distribution functions are illustrated in different ways going from the
blue to red distribution. The thick lines are the distribution, the thin dashed lines are the mean of
the distributions and the dotted lines are ﬁxed points to illustrate probability. Shown is a increase
in mean, b increase in variance (width), c increase in mean and variance
2
For an overview of the history of weather forecasting, see Edwards, P. N. (2013). A Vast
Machine: Computer Models, Climate Data, and the Politics of Global Warming. Cambridge, MA:
MIT Press.

Climate forecasting uses essentially the same type of model. But the goal of
climate forecasting is to characterize the distribution. This may mean running the
model for a long time to describe the distribution of all possible weather states
correctly. If you start up a weather model from two different states (two different
days), you hope to get a different answer each time. But for climate, you want to get
the same distribution (the same climate), regardless of when the model started. We
return to these examples again later.
1.2 What Is a Model?
A model, in essence, is a representation of a system. A model can be physical
(building blocks) or abstract (an image on paper like a plan, or in your head).
Abstract models can also be mathematical (monetary or physical totals in a
spreadsheet). Ordinary physics that describes how cars go (or, more importantly,
how they stop suddenly) is a model for how the physical world behaves. Numbers
themselves are abstract models. A financial statement is a model of the money and
resource flows of a household, corporation or country. Models are all around us,
and we use them to abstract, make tractable and understand our human and natural
environment.
As a concrete example, think about different “models” of a building. There can
be many types of models of a building. A physical model of the building would
usually be at a smaller scale that you can hold in your hand. There are several
different abstract models of a building, and they are used for different purposes.
Architects and engineers produce building plans: two-dimensional representations
of the building, used to construct and document the building. Some of these are
highly detailed drawings of specific parts of the building, such as the exterior, or the
electrical and plumbing systems. The engineer may have built not just a physical
model of the building, but perhaps even a more detailed structural model designed
to understand how the building will react to wind or ground motion (earthquakes).
Increasingly, these models are “virtual”: The structure is simulated on a computer.
We are familiar with all these sorts of model, but there are other more abstract
models that deal with flows and budgets of materials or money. The owner and
builder also probably have a spreadsheet model of the costs of construction of the
building. This financial model is not certain, because it is really an estimate (or
forecast, see below) of all the different costs of construction. And, finally, the owner
likely has another model of the financial operation of the building: the money
borrowed to finance the building, any income from a commercial building, and the
costs of maintenance and operations of the building. The operating plan is really a
projection into the future: It depends on a lot of uncertainties, like the cost of
electricity or the value of the income from a building. The projection depends on
these inputs, which the spreadsheet does not try to predict. The prediction is con-
ditional on the inputs.
1.1 What Is Climate? 7

Models All Around Us
Models are everywhere in our world. Many models are familiar and physical,
such as a small-scale model of a building or a bridge, or a mockup of a
satellite or an airplane. Some models we use every day are made up of
numbers. Many people use a model with numbers to manage income and
expenses, savings and debts; that is, a budget. When the model of a bridge is
placed in a computer-assisted design program, or a financial budget is put into
a spreadsheet on a personal computer, then one has a “numerical” model.
These models have a set of mathematical equations that behave with a
specific set of rules, principles or laws.
Climate models are numerical models that calculate budgets of mass,
momentum (velocity) and energy based on the physical laws of conservation.
For example, energy is conserved (neither created or destroyed) and can,
therefore, be counted. The physical laws on which climate models are based
are discussed in more detail in Chap. 4. Weather and climate are dynamical
systems; that is, they evolve over time. We rely on models of dynamical
systems for many aspects of modern life. Here we illustrate a few examples of
models that affect our everyday lives.
Climate models are closely related to weather forecast models; both
simulate how fluids (air or water) move and interact, and how they exchange
heat. An obvious example of a model that affects daily life is the weather
forecast model, which is used in planning by individuals, governments,
corporations and finance. The exact same physical principles of fluid flow are
used to simulate a process in a chemical plant that takes different substances
as liquids or gasses, reacts them together under controlled temperature and
pressure, and produces new substances. Water and sewage treatment plants
share similar principles and models. Internal combustion engines used for
cars, trucks, ships and power plants are developed using models to understand
how fuel enters the engine and produces heat, and that heat produces motion.
Airplanes are also developed using modeling of the airflow around an aircraft.
In this case, computational modeling has largely replaced design using wind
tunnels. All of these models involve fluid flow and share physical princi-
ples with climate models. The details of the problem, for example, flow in
pipes as contrasted to flow in the free atmosphere, define the specific
requirements for the model construction.
So do you trust a model? Intuitively, we trust models all the time. You are
using the results of a model every time you start your car, flush your toilet,
turn on a light switch or get in an airplane. You count on models when you
drive over a bridge. When NASA sends a satellite or rover to another planet,
the path and behavior of the space vehicle relies on a model of simple physics
describing complex systems.
Models do not just describe physical objects. Models of infectious diseases
played an important role in management of the 2014 outbreak of Ebola. One
function of models is to provide plausible representations of events to come,

and then to place people into those plausible futures. It is a way to anticipate
and manage complexity. Though most times these models do not give an
exact story of the future, the planning and decision making that comes from
these modeling exercises improves our ability to anticipate the unexpected
and to manage risk. Think of modeling as a virtual, computational world in
which to exercise the practice of trial and error, and therefore, a method for
reducing the “error” in trial and error. Reducing these errors saves lives and
property. Models reduce the chance of errors: Airplanes do not regularly fall
out of the sky, bridges do not normally collapse and chemical plants do not
typically leak.
Ultimately, trust of models is anchored in evaluation of models compared
to observations and experiences. Weather models are evaluated every day
with billions of observations as well as billions of individuals’ experiences.
Trust is often highly personal. By many objective measures, weather models
have remarkable accuracy, for example, letting a city know more than five
days in advance that a major tropical cyclone is likely to make landfall near
that city. Of course, if the tropical cyclone makes landfall just 60 miles
(100 km) away from the city, many people might conclude the models cannot
be trusted.
Objectively, however, a model that simulated the tropical cyclone and
represented its evolution with an error of 60 miles (100 km) on a globe that
spans many thousands of miles can, also, be construed as being quite accu-
rate. This represents the fact that models provide plausible futures that inform
decision making.
Like weather models, climate models are evaluated with billions of
observations and investigations of past events. The results of models have
been scrutinized by thousands of scientists and practitioners. With virtual
certainty, we know the Earth will warm, sea level will rise, ice will melt and
weather will change. They provide plausible futures, not prescribed futures.
There are uncertainties, and there will always be uncertainties. However, our
growing experiences and vigilant efforts to evaluate and improve will help us
to understand, manage and, sometimes, reduce uncertainty. As the models
improve, trust and usability increase. There remains some uncertainty in most
physical models, but that can be accounted for, and we discuss uncertainty,
and its value in modeling, at length.
Our world is completely dependent on physical models, and their success is
seen around us in the fact that much of the world “works” nearly all of the time.
Models are certain enough to use in dangerous contexts that are both mundane
and ubiquitous. We answer the question of whether we should trust models and
make changes in our lives based on their results every time we get in an
elevator or an airplane. There is no issue of should we trust models; we have
been doing it for centuries since the first bridge was constructed, the first train
left a station or the first time a building was built more than one story high.
1.2 What Is a Model? 9

1.3 Uncertainty
Forecasting involves projecting what we know, using a model, onto what we do
not know. The result is a prediction or forecast. Forecasts may be wrong, of course,
and the chance of them being wrong is known as uncertainty. Uncertainty can
come from several different sources, but this is particularly the case when we think
about climate and weather. One way to better characterize uncertainty is to divide it
into categories based on model, scenario and initial conditions.3
1.3.1 Model Uncertainty
Obviously, a model can be wrong or have structural errors (model uncertainty).
For example, if one were modeling how many tires a delivery company would need
for their trucks in a year and assumed that the tires last 10,000 miles, when they
actually only last 7,000 miles, the forecast of tire use is probably wrong. If you
assumed each truck would drive 20,000 miles per year and tires last 10,000 miles,
the trucks would need two sets of tires. However, what if the trucks drove only
14,000 miles per year and each set of tires lasted only 7,000 miles, (still needing
just two sets of tires)? Then the forecast might be right, but the tire forecast would
be right for the wrong reason: in this case, a cancellation of errors.
1.3.2 Scenario Uncertainty
The preceding example also illustrates another potential uncertainty faced in climate
modeling: scenario uncertainty. The scenario4
is the uncertainty in the future
model inputs. In the tire forecast, the scenario assumed 20,000 miles per truck each
year. But the scenario was wrong. If the tire forecast model was correct (or “per-
fect”) and tires lasted 10,000 miles, but the mileage was incorrect (14,000 vs.
20,000 assumed), then the forecast will still be incorrect, even if the model is
perfect. If the actual mileage continued to deviate from the assumption (14,000
miles), then the forecast over time will continue to be incorrect. If one is concerned
with the total purchase of tires and total cost, then the situation becomes even more
uncertain. Other factors (e.g., growth of the company, change in type of tires) may
make forecasting the scenario, or inputs to the model, even more uncertain, even if
the model as it stands is perfect. As the timescale of the model looks farther into the
3
This deﬁnition of uncertainty has been developed by Hawkins, E., & Sutton, R. (2009). “The
Potential to Narrow Uncertainty in Regional Climate Prediction.” Bulletin of the American
Meteorological Society, 90(8): 1095–1107.
4
For a discussion of climate scenarios, see Chap. 10.

future, more and more different “variables” become uncertain (e.g., new types of
tire, new trucks, the cost of tires). These variables that cannot be predicted, but have
to be assumed, are often called parameters. Scenario uncertainty logically domi-
nates uncertainty farther into the future (see Chap. 10 for more detail).
1.3.3 Initial Condition Uncertainty
Finally, there is uncertainty in the initial state of the system used in the model, or
initial condition uncertainty. In our tire forecast, to be speciﬁc about how many
tires we will need in the current or next year, we also need to know what the current
state of tires is on all the trucks. Changes in the current state of tires will have big
effects on the near-term forecast: If all trucks have 6,000 or 8,000 miles on their
tires, there will be more purchases of tires in a given year than if they are all brand
new. Initial condition uncertainty is a similar problem in weather forecasting. As we
will discuss, some aspects of the climate system, particularly related to the oceans,
for example, have very long timescales and “memory,” so that knowing the state of
the oceans affects climate over several decades. However, over long timescales
(longer than the timescale of a process), these uncertainties fade. If you want to
know how many tires will be needed over 5 or 10 years, the uncertainty about the
current state of tires (which affects only the ﬁrst set of replacements) on the total
number of tires needed is small.
1.3.4 Total Uncertainty
In climate prediction, we must address all three types of uncertainties—model
uncertainty, scenario uncertainty and initial condition uncertainty—to estimate the
total uncertainty in a forecast. They operate on different time periods: Initial con-
dition uncertainty matters most for the short term (i.e., weather scales, or even
seasonal to annual, in some cases), and scenario uncertainty matters most in the
longer term (decades to centuries). Model uncertainty operates at all timescales and
can be “masked” or hidden by other uncertainties.
The complicated nature of these uncertainties makes prediction both harder and
easier. It certainly makes it easier to understand and characterize the uncertainty in a
forecast. One of our goals is to set down ideas and a framework for understanding
how climate predictions can be used. Judging the quality of a prediction is based on
understanding what the uncertainty is and where it comes from. Some comes from
the model and some comes from how the experiment is set up (the initial conditions
and the scenario).
1.3 Uncertainty 11

1.4 Summary
Climate can best be thought of as a distribution of all possible weather states. What
matters to us is the shape of the distribution. Weather is where we are on the
distribution at any point in time. The extreme values in the distribution, that usually
have low probability, are hard to predict, but that is where most of the impacts lie.
Weather and climate models are similar, except weather models are designed to
predict the exact location on a distribution, while climate models describe the
distribution itself.
We use models all the time to predict the future. Some models are physical
objects, some are numerical models. Climate models are one type of a numerical
model: As we shall see, they can often be thought of as giant spreadsheets that keep
track of the physical properties of the earth system, the same way a budget keeps
track of money.
Uncertainty in climate models has several components. They are related to the
model itself, to the initial conditions for the model (the starting point) and to the
inputs that affect the model over time in a “scenario.” All three must be addressed
for a model to be useful. Uncertainty is not to be feared. Uncertainty is not a failure
of models. Uncertainty can be understood and used to assess conﬁdence in
predictions.
Key Points
• Climate is the distribution of possible weather states at any place and time.
• Extremes of climate are where the impacts are.
• We use models all the time to predict the future, some models are even
numerical.
• Weather and climate models are similar but have different goals.
• Uncertainty has several different parts (model, scenario, initial conditions).
Open Access This chapter is distributed under the terms of the Creative Commons Attribution-
Noncommercial 2.5 License (http://creativecommons.org/licenses/by-nc/2.5/) which permits any
noncommercial use, distribution, and reproduction in any medium, provided the original author(s)
and source are credited.
The images or other third party material in this chapter are included in the work’s Creative
Commons license, unless indicated otherwise in the credit line; if such material is not included in
the work’s Creative Commons license and the respective action is not permitted by statutory
regulation, users will need to obtain permission from the license holder to duplicate, adapt or
reproduce the material.

Chapter 2
Components of the Climate System
We experience climate generally at the surface of the earth. This is the intersection
of a number of different and distinct parts of the climate system. Understanding the
different components of the climate system is critical for being able to simulate the
system. As we will discuss, the climate system is typically simulated as a set of
building blocks, from each individual process (i.e., the condensation of water to
form clouds) collected into a model of one part or component of the system (i.e., the
atmosphere), and then coupled to other components of the system (i.e., ocean, land,
ice). Understanding and then representing in a model the different interactions
between processes and then between components is critical for being able to build a
representation of the system: a climate model.
In this chapter we describe the basic parts of the earth system that comprise the
climate system, some of the key scientiﬁc principles and critical processes neces-
sary to simulate each of these components. This forms the background to a dis-
cussion of how climate might change (see Chap. 3) and a more detailed discussion
of each component and how it is simulated (Sect. 2.2, Chaps. 5–8). We discuss the
key components of the earth system, as well as some of the critical interactions
(discussed in more detail in Chap. 8).
2.1 Components of the Earth System
Figure 2.1 represents a schematic of many of the important components of the earth
system that govern and regulate climate. Broadly, there are three different regions of
the planet: the atmosphere, the oceans and the land (or terrestrial) surface. In
addition to these general regions, we also speak of a cryosphere, the snow and ice
covered regions of the planet. This fourth “sphere” spans the ocean (as sea ice) and
the terrestrial surface (as glaciers, snow and ice sheets). We address the cryosphere
in discussions of the ocean and terrestrial surface. While modeling the surface of the
earth is commonly thought of as just modeling the land surface, it also includes the
cryosphere (ice and snow) that sits on land. The term terrestrial is used to
encompass all these spheres, though the common term land is also used.
© The Author(s) 2016
A. Gettelman and R.B. Rood, Demystifying Climate Models,
Earth Systems Data and Models 2, DOI 10.1007/978-3-662-48959-8_2
13

These are the traditional physical components of the earth’s climate system. We
also introduce two more “spheres.” An important fifth component of the system is
the biosphere: the living organisms on the planet, again, which span the terrestrial
surface (plants, organisms in the soil, and animals) as well as the ocean (fish and
plants in the ocean). We discuss the biosphere as part of both the ocean and
terrestrial surface. Finally, although humans are technically part of the biosphere,
our large “footprint” and impact on the global environment and the climate system
is large enough that we can define a separate sphere for human activity and impacts
called the anthroposphere (see Chap. 3).
2.1.1 The Atmosphere
The atmosphere is usually the first part of the climate system we naturally think of.
It is literally the air we breathe: mostly inert nitrogen (78 %) with oxygen (16 %)
and then other trace gases (argon, water vapor, carbon dioxide). The oxygen is a
by-product of the respiration (“breathing”) of plants and other organisms: It is
evidence of life on earth. The oxygen in the atmosphere did not exist before the
emergence of living organisms.1
Oxygen is emitted by plants as an outcome of
photosynthesis that removes carbon from carbon dioxide. Oxygen reacts with
materials (rock and ore) at the earth’s surface (oxidation) and disappears from the
atmosphere. One of the most common reactants is iron (iron oxide = rust), which is
responsible for the red color of many rocks. Unless organisms continue to produce
oxygen, it will disappear from the atmosphere. It would take a long time however:
hundreds of thousands to millions of years. But it is the trace species—water vapor,
carbon dioxide and methane—known as the greenhouse gases, that are most
important in understanding the climate system and how climate might change.
Ocean
Ice (cryosphere)
Sun
Biosphere
Atmosphere
Anthroposphere
Terrestrial (land)
Fig. 2.1 The Earth system. The climate system contains different spheres (components):
atmosphere, ocean, terrestrial, cryosphere, biosphere and anthroposphere
1
Kasting, J. F., & Siefert, J. L. (2002). “Life and the Evolution of Earth’s Atmosphere.” Science,
296(5570): 1066–1068.
14 2 Components of the Climate System

Exploring the Variety of Random
Documents with Different Content

PLEASE READ THIS BEFORE YOU DISTRIBUTE OR USE THIS WORK
To protect the Project Gutenberg™ mission of promoting the free
distribution of electronic works, by using or distributing this work (or
any other work associated in any way with the phrase “Project
Gutenberg”), you agree to comply with all the terms of the Full
Project Gutenberg™ License available with this file or online at
www.gutenberg.org/license.
Section 1. General Terms of Use and
Redistributing Project Gutenberg™
electronic works
1.A. By reading or using any part of this Project Gutenberg™
electronic work, you indicate that you have read, understand, agree
to and accept all the terms of this license and intellectual property
(trademark/copyright) agreement. If you do not agree to abide by all
the terms of this agreement, you must cease using and return or
destroy all copies of Project Gutenberg™ electronic works in your
possession. If you paid a fee for obtaining a copy of or access to a
Project Gutenberg™ electronic work and you do not agree to be
bound by the terms of this agreement, you may obtain a refund
from the person or entity to whom you paid the fee as set forth in
paragraph 1.E.8.
1.B. “Project Gutenberg” is a registered trademark. It may only be
used on or associated in any way with an electronic work by people
who agree to be bound by the terms of this agreement. There are a
few things that you can do with most Project Gutenberg™ electronic
works even without complying with the full terms of this agreement.
See paragraph 1.C below. There are a lot of things you can do with
Project Gutenberg™ electronic works if you follow the terms of this
agreement and help preserve free future access to Project
Gutenberg™ electronic works. See paragraph 1.E below.

1.C. The Project Gutenberg Literary Archive Foundation (“the
Foundation” or PGLAF), owns a compilation copyright in the
collection of Project Gutenberg™ electronic works. Nearly all the
individual works in the collection are in the public domain in the
United States. If an individual work is unprotected by copyright law
in the United States and you are located in the United States, we do
not claim a right to prevent you from copying, distributing,
performing, displaying or creating derivative works based on the
work as long as all references to Project Gutenberg are removed. Of
course, we hope that you will support the Project Gutenberg™
mission of promoting free access to electronic works by freely
sharing Project Gutenberg™ works in compliance with the terms of
this agreement for keeping the Project Gutenberg™ name associated
with the work. You can easily comply with the terms of this
agreement by keeping this work in the same format with its attached
full Project Gutenberg™ License when you share it without charge
with others.
1.D. The copyright laws of the place where you are located also
govern what you can do with this work. Copyright laws in most
countries are in a constant state of change. If you are outside the
United States, check the laws of your country in addition to the
terms of this agreement before downloading, copying, displaying,
performing, distributing or creating derivative works based on this
work or any other Project Gutenberg™ work. The Foundation makes
no representations concerning the copyright status of any work in
any country other than the United States.
1.E. Unless you have removed all references to Project Gutenberg:
1.E.1. The following sentence, with active links to, or other
immediate access to, the full Project Gutenberg™ License must
appear prominently whenever any copy of a Project Gutenberg™
work (any work on which the phrase “Project Gutenberg” appears,
or with which the phrase “Project Gutenberg” is associated) is
accessed, displayed, performed, viewed, copied or distributed:

This eBook is for the use of anyone anywhere in the
United States and most other parts of the world at no
cost and with almost no restrictions whatsoever. You
may copy it, give it away or re-use it under the terms
of the Project Gutenberg License included with this
eBook or online at www.gutenberg.org. If you are not
located in the United States, you will have to check the
laws of the country where you are located before using
this eBook.
1.E.2. If an individual Project Gutenberg™ electronic work is derived
from texts not protected by U.S. copyright law (does not contain a
notice indicating that it is posted with permission of the copyright
holder), the work can be copied and distributed to anyone in the
United States without paying any fees or charges. If you are
redistributing or providing access to a work with the phrase “Project
Gutenberg” associated with or appearing on the work, you must
comply either with the requirements of paragraphs 1.E.1 through
1.E.7 or obtain permission for the use of the work and the Project
Gutenberg™ trademark as set forth in paragraphs 1.E.8 or 1.E.9.
1.E.3. If an individual Project Gutenberg™ electronic work is posted
with the permission of the copyright holder, your use and distribution
must comply with both paragraphs 1.E.1 through 1.E.7 and any
additional terms imposed by the copyright holder. Additional terms
will be linked to the Project Gutenberg™ License for all works posted
with the permission of the copyright holder found at the beginning
of this work.
1.E.4. Do not unlink or detach or remove the full Project
Gutenberg™ License terms from this work, or any files containing a
part of this work or any other work associated with Project
Gutenberg™.
1.E.5. Do not copy, display, perform, distribute or redistribute this
electronic work, or any part of this electronic work, without

prominently displaying the sentence set forth in paragraph 1.E.1
with active links or immediate access to the full terms of the Project
Gutenberg™ License.
1.E.6. You may convert to and distribute this work in any binary,
compressed, marked up, nonproprietary or proprietary form,
including any word processing or hypertext form. However, if you
provide access to or distribute copies of a Project Gutenberg™ work
in a format other than “Plain Vanilla ASCII” or other format used in
the official version posted on the official Project Gutenberg™ website
(www.gutenberg.org), you must, at no additional cost, fee or
expense to the user, provide a copy, a means of exporting a copy, or
a means of obtaining a copy upon request, of the work in its original
“Plain Vanilla ASCII” or other form. Any alternate format must
include the full Project Gutenberg™ License as specified in
paragraph 1.E.1.
1.E.7. Do not charge a fee for access to, viewing, displaying,
performing, copying or distributing any Project Gutenberg™ works
unless you comply with paragraph 1.E.8 or 1.E.9.
1.E.8. You may charge a reasonable fee for copies of or providing
access to or distributing Project Gutenberg™ electronic works
provided that:
• You pay a royalty fee of 20% of the gross profits you derive
from the use of Project Gutenberg™ works calculated using the
method you already use to calculate your applicable taxes. The
fee is owed to the owner of the Project Gutenberg™ trademark,
but he has agreed to donate royalties under this paragraph to
the Project Gutenberg Literary Archive Foundation. Royalty
payments must be paid within 60 days following each date on
which you prepare (or are legally required to prepare) your
periodic tax returns. Royalty payments should be clearly marked
as such and sent to the Project Gutenberg Literary Archive
Foundation at the address specified in Section 4, “Information

about donations to the Project Gutenberg Literary Archive
Foundation.”
• You provide a full refund of any money paid by a user who
notifies you in writing (or by e-mail) within 30 days of receipt
that s/he does not agree to the terms of the full Project
Gutenberg™ License. You must require such a user to return or
destroy all copies of the works possessed in a physical medium
and discontinue all use of and all access to other copies of
Project Gutenberg™ works.
• You provide, in accordance with paragraph 1.F.3, a full refund of
any money paid for a work or a replacement copy, if a defect in
the electronic work is discovered and reported to you within 90
days of receipt of the work.
• You comply with all other terms of this agreement for free
distribution of Project Gutenberg™ works.
1.E.9. If you wish to charge a fee or distribute a Project Gutenberg™
electronic work or group of works on different terms than are set
forth in this agreement, you must obtain permission in writing from
the Project Gutenberg Literary Archive Foundation, the manager of
the Project Gutenberg™ trademark. Contact the Foundation as set
forth in Section 3 below.
1.F.
1.F.1. Project Gutenberg volunteers and employees expend
considerable effort to identify, do copyright research on, transcribe
and proofread works not protected by U.S. copyright law in creating
the Project Gutenberg™ collection. Despite these efforts, Project
Gutenberg™ electronic works, and the medium on which they may
be stored, may contain “Defects,” such as, but not limited to,
incomplete, inaccurate or corrupt data, transcription errors, a
copyright or other intellectual property infringement, a defective or

damaged disk or other medium, a computer virus, or computer
codes that damage or cannot be read by your equipment.
1.F.2. LIMITED WARRANTY, DISCLAIMER OF DAMAGES - Except for
the “Right of Replacement or Refund” described in paragraph 1.F.3,
the Project Gutenberg Literary Archive Foundation, the owner of the
Project Gutenberg™ trademark, and any other party distributing a
Project Gutenberg™ electronic work under this agreement, disclaim
all liability to you for damages, costs and expenses, including legal
fees. YOU AGREE THAT YOU HAVE NO REMEDIES FOR
NEGLIGENCE, STRICT LIABILITY, BREACH OF WARRANTY OR
BREACH OF CONTRACT EXCEPT THOSE PROVIDED IN PARAGRAPH
1.F.3. YOU AGREE THAT THE FOUNDATION, THE TRADEMARK
OWNER, AND ANY DISTRIBUTOR UNDER THIS AGREEMENT WILL
NOT BE LIABLE TO YOU FOR ACTUAL, DIRECT, INDIRECT,
CONSEQUENTIAL, PUNITIVE OR INCIDENTAL DAMAGES EVEN IF
YOU GIVE NOTICE OF THE POSSIBILITY OF SUCH DAMAGE.
1.F.3. LIMITED RIGHT OF REPLACEMENT OR REFUND - If you
discover a defect in this electronic work within 90 days of receiving
it, you can receive a refund of the money (if any) you paid for it by
sending a written explanation to the person you received the work
from. If you received the work on a physical medium, you must
return the medium with your written explanation. The person or
entity that provided you with the defective work may elect to provide
a replacement copy in lieu of a refund. If you received the work
electronically, the person or entity providing it to you may choose to
give you a second opportunity to receive the work electronically in
lieu of a refund. If the second copy is also defective, you may
demand a refund in writing without further opportunities to fix the
problem.
1.F.4. Except for the limited right of replacement or refund set forth
in paragraph 1.F.3, this work is provided to you ‘AS-IS’, WITH NO
OTHER WARRANTIES OF ANY KIND, EXPRESS OR IMPLIED,

INCLUDING BUT NOT LIMITED TO WARRANTIES OF
MERCHANTABILITY OR FITNESS FOR ANY PURPOSE.
1.F.5. Some states do not allow disclaimers of certain implied
warranties or the exclusion or limitation of certain types of damages.
If any disclaimer or limitation set forth in this agreement violates the
law of the state applicable to this agreement, the agreement shall be
interpreted to make the maximum disclaimer or limitation permitted
by the applicable state law. The invalidity or unenforceability of any
provision of this agreement shall not void the remaining provisions.
1.F.6. INDEMNITY - You agree to indemnify and hold the Foundation,
the trademark owner, any agent or employee of the Foundation,
anyone providing copies of Project Gutenberg™ electronic works in
accordance with this agreement, and any volunteers associated with
the production, promotion and distribution of Project Gutenberg™
electronic works, harmless from all liability, costs and expenses,
including legal fees, that arise directly or indirectly from any of the
following which you do or cause to occur: (a) distribution of this or
any Project Gutenberg™ work, (b) alteration, modification, or
additions or deletions to any Project Gutenberg™ work, and (c) any
Defect you cause.
Section 2. Information about the Mission
of Project Gutenberg™
Project Gutenberg™ is synonymous with the free distribution of
electronic works in formats readable by the widest variety of
computers including obsolete, old, middle-aged and new computers.
It exists because of the efforts of hundreds of volunteers and
donations from people in all walks of life.
Volunteers and financial support to provide volunteers with the
assistance they need are critical to reaching Project Gutenberg™’s
goals and ensuring that the Project Gutenberg™ collection will

remain freely available for generations to come. In 2001, the Project
Gutenberg Literary Archive Foundation was created to provide a
secure and permanent future for Project Gutenberg™ and future
generations. To learn more about the Project Gutenberg Literary
Archive Foundation and how your efforts and donations can help,
see Sections 3 and 4 and the Foundation information page at
www.gutenberg.org.
Section 3. Information about the Project
Gutenberg Literary Archive Foundation
The Project Gutenberg Literary Archive Foundation is a non-profit
501(c)(3) educational corporation organized under the laws of the
state of Mississippi and granted tax exempt status by the Internal
Revenue Service. The Foundation’s EIN or federal tax identification
number is 64-6221541. Contributions to the Project Gutenberg
Literary Archive Foundation are tax deductible to the full extent
permitted by U.S. federal laws and your state’s laws.
The Foundation’s business office is located at 809 North 1500 West,
Salt Lake City, UT 84116, (801) 596-1887. Email contact links and up
to date contact information can be found at the Foundation’s website
and official page at www.gutenberg.org/contact
Section 4. Information about Donations to
the Project Gutenberg Literary Archive
Foundation
Project Gutenberg™ depends upon and cannot survive without
widespread public support and donations to carry out its mission of
increasing the number of public domain and licensed works that can
be freely distributed in machine-readable form accessible by the
widest array of equipment including outdated equipment. Many

small donations ($1 to $5,000) are particularly important to
maintaining tax exempt status with the IRS.
The Foundation is committed to complying with the laws regulating
charities and charitable donations in all 50 states of the United
States. Compliance requirements are not uniform and it takes a
considerable effort, much paperwork and many fees to meet and
keep up with these requirements. We do not solicit donations in
locations where we have not received written confirmation of
compliance. To SEND DONATIONS or determine the status of
compliance for any particular state visit www.gutenberg.org/donate.
While we cannot and do not solicit contributions from states where
we have not met the solicitation requirements, we know of no
prohibition against accepting unsolicited donations from donors in
such states who approach us with offers to donate.
International donations are gratefully accepted, but we cannot make
any statements concerning tax treatment of donations received from
outside the United States. U.S. laws alone swamp our small staff.
Please check the Project Gutenberg web pages for current donation
methods and addresses. Donations are accepted in a number of
other ways including checks, online payments and credit card
donations. To donate, please visit: www.gutenberg.org/donate.
Section 5. General Information About
Project Gutenberg™ electronic works
Professor Michael S. Hart was the originator of the Project
Gutenberg™ concept of a library of electronic works that could be
freely shared with anyone. For forty years, he produced and
distributed Project Gutenberg™ eBooks with only a loose network of
volunteer support.

Project Gutenberg™ eBooks are often created from several printed
editions, all of which are confirmed as not protected by copyright in
the U.S. unless a copyright notice is included. Thus, we do not
necessarily keep eBooks in compliance with any particular paper
edition.
Most people start at our website which has the main PG search
facility: www.gutenberg.org.
This website includes information about Project Gutenberg™,
including how to make donations to the Project Gutenberg Literary
Archive Foundation, how to help produce our new eBooks, and how
to subscribe to our email newsletter to hear about new eBooks.

Demystifying Climate Models A Users Guide to Earth System Models 1st Edition Andrew Gettelman

Welcome to our website – the ideal destination for book lovers and
knowledge seekers. With a mission to inspire endlessly, we offer a
vast collection of books, ranging from classic literary works to
specialized publications, self-development books, and children's
literature. Each book is a new journey of discovery, expanding
knowledge and enriching the soul of the reade
Our website is not just a platform for buying books, but a bridge
connecting readers to the timeless values of culture and wisdom. With
an elegant, user-friendly interface and an intelligent search system,
we are committed to providing a quick and convenient shopping
experience. Additionally, our special promotions and home delivery
services ensure that you save time and fully enjoy the joy of reading.
Let us accompany you on the journey of exploring knowledge and
personal growth!
textbookfull.com

Demystifying Climate Models A Users Guide to Earth System Models 1st Edition Andrew Gettelman

More Related Content

Similar to Demystifying Climate Models A Users Guide to Earth System Models 1st Edition Andrew Gettelman (20)

Recently uploaded (20)

Demystifying Climate Models A Users Guide to Earth System Models 1st Edition Andrew Gettelman