Sustainable Cities in a Changing Climate E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Build and manage the sustainable cities of the future with this comprehensive guide Climate change is among the biggest challenges facing today's cities, which are in turn a major factor in driving or mitigating climate change. It is no surprise then that urban planning authorities are under mounting pressure to create cityscapes suited to the 21st century. Sustainable Cities in a Changing Climate offers a systematic overview of the environmental and sustainability challenges facing urban planners and policymakers, and how to meet those challenges. Beginning with an analysis of how climate change impacts built environments, it proceeds to offer quantitative analysis and practical solutions for strengthening urban resilience. Sustainable Cities in a Changing Climate readers will also find: * A future-oriented approach that accounts for both known and unknown threats * Detailed discussion of threats including environmental changes, global pandemics, natural disasters, and more * Case studies from around the globe, including biofuel generation in China and the 2022 World Cup in Qatar Sustainable Cities in a Changing Climate is indispensable for environmental engineers, urban planners and policymakers, and advanced students in environmental planning and architecture.
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Veröffentlichungsjahr: 2023
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
List of Contributors
About the Editor
Preface
Abbreviations
Part I: Climate Change and The Built Environment: Foundations and Implications
1 Understanding Climate Change Fundamentals: Exploring the Forces Shaping Our Planet's Future
Introduction
Modes of Climate Variability
Find, Read, and Process Climatic Data
Conclusion
References
Notes
2 Advancing Urban Resilience and Sustainability Through the WRF‐Urban Model: Bridging Numerical Modeling and Real‐World Applications
Introduction
Nexus Between Urbanization and Climate Change
Urban Modeling Through WRF‐Urban Model
Relevant Case Studies
Limitations of the WRF‐Urban Model
Ways Forward for Improvement
Conclusions
References
3 Assessing and Projecting Climatic Changes in the Middle East and North Africa (MENA) Region: Insights from Regional Climate Model (RCM) Simulations and Future Projections
Introduction
Methodology
Conclusion
References
4 Building for Climate Change: Examining the Environmental Impacts of the Built Environment
Introduction
Embodied Carbon Emission in Building Environment
Embodied Carbon Emission for Selected Building Materials
Embodied Carbon Mitigation Strategies
Operation Carbon Emissions in Building Environment
Operation Carbon Mitigation Strategies
Conclusion
References
5 Unveiling the Nexus: Human Developments and Their Influence on Climate Change
Introduction
Life Cycle Assessment for Environmental Impact
ReCiPe Impact Category: Climate Change
Energy Sector Impact on Climate Change
Emissions Savings from Energy Sector
Freshwater Sector Impact on Climate Change
Emission Savings from Water Sector
Concluding Remarks
References
Part II: Quantifying Resilience and Its Qualities
6 Assessing Resilience in Urban Critical Infrastructures: Interdependencies and Considerations
Introduction
Individual Network Resilience
Case Study About Individual System Resilience: Transportation Resilience During Mega Sport Events
Infrastructures Interdependencies and Resilience
Case Study About Interdependent Systems Resilience
Conclusion
References
7 Assessing Infrastructure Resilience: Approaches and Considerations
Introduction
Complex Networks
Simulation Approaches
Other Approaches
Conclusion
References
8 Enhancing Buildings Resilience: A Comprehensive Perspective on Earthquake Resilient Design
Introduction
Structural Resilience Representation
Performance‐Based Design (PBD)
Supporting Systems
Conclusion
References
9 Enhancing Built Environment Resilience: Exploring Themes and Dimensions
Introduction
Types of Resilience
Resilience Dimensions and Capitals
Resilience Measuring
Conclusion
References
10 Unveiling Urban Resilience: Exploring the Qualities and Interconnections of Urban Systems
Introduction
Urban Resilience to Climate Change
Resilience Qualities
Interrelation of Resilience Qualities
Conclusion
References
11 Quantifying Urban Resilience: Methods and Approaches for Comprehensive Assessment
Introduction
Urban Resilience
Resilience Assessment Approaches
Frameworks and Tools for Measuring Resilience
Conclusion
References
Part III: Resilient Urban Systems: Navigating Climate Change and Enhancing Sustainability
12 Building Climate Resilience Through Urban Planning: Strategies, Challenges, and Opportunities
Introduction
Understanding Climate Change Impacts on Urban Areas
Urban Planning Strategies for Mitigating Climate Change Impacts
Risk Assessment and Adaptation in Urban Planning
Case Studies of Successful Climate‐Responsive Urban Planning
Challenges and Opportunities
Major Key Points
Conclusion
References
13 Integrating Green–Blue–Gray Infrastructure for Sustainable Urban Flood Risk Management: Enhancing Resilience and Advantages
Introduction
Green–Blue–Gray Infrastructure Combination
Conclusion
References
14 Enhancing Energy System Resilience: Navigating Climate Change and Security Challenges
Introduction
Adapting the Theory of Resilience to Energy Systems
Why Incorporate Resilience into Energy Systems?
What are the Threats to the Energy System?
Domains of Resilience Approaches to Energy Systems
Resilience Enhancement Approaches for Energy Systems
Conclusion
References
15 Building Resilient Health Policies: Incorporating Climate Change Impacts for Sustainable Adaptation
Introduction
Climate Change Impacts on Public Health
Considerations in Health Policy Development
Conclusion
References
16 Enhancing Resilience: Surveillance Strategies for Monitoring the Spread of Vector‐Borne Diseases
Introduction
Vector‐Borne Diseases
Surveillance Strategies
Conclusion
References
Glossary
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Some of the reanalyses that have global coverage. All these reana...
Table 1.2 Freely available tools that are used to visualize and process cli...
Chapter 4
Table 4.1 CO
2
emissions per kilogram of cement produced for a range of fuel...
Chapter 5
Table 5.1 US electric power sector CO
2
emissions by fuel type in 2022.
Chapter 6
Table 6.1 Urban infrastructure flow types and their interdependencies.
Table 6.2 Doha road network assessment results and resilience index.
Table 6.3 Examples of network component.
Chapter 7
Table 7.1 Examples of graph theory and resilience in several research field...
Chapter 9
Table 9.1 Resilience concepts.
Table 9.2 Urban resilience key capacities to achieve resilience.
Table 9.3 Resilience components and theme areas.
Table 9.4 Community resilience against climate change.
Chapter 10
Table 10.1 Resilience‐specific definitions in different contexts.
Table 10.2 General definitions of resilience.
Table 10.3 Categorization of built environment systems and services showing...
Chapter 11
Table 11.1 Applied methods in the assessment of urban resilience, use, matr...
Table 11.2 Community resilience and example programs.
Table 11.3 Comparison of the key definitions and characteristics of urban s...
Table 11.4 Qualitative framework categories and indicators.
Table 11.5 Example of quantitative resilience assessment approaches.
Table 11.6 Example of community resilience to climate change frameworks.
Table 11.A.1 Resilience tools and measurement efforts made by different ins...
Chapter 12
Table 12.1 Approaches used in risk assessment and vulnerability analysis th...
Chapter 13
Table 13.1 Methods, demographic details, methodologies, and findings of the...
Table 13.2 Research development, methods, and demographic details of the mo...
Chapter 14
Table 14.1 Key elements in resiliency definitions for engineering systems t...
Table 14.2 Resilience characteristics and examples of mitigations.
Table 14.3 Climate change affects all parts of the energy system.
Table 14.4 Paradigms of resilience applicable to energy system.
Table 14.5 Resilience benefits of energy efficiency.
Table 14.6 Relationship between components of resilience and resilience‐enh...
List of Illustrations
Chapter 1
Figure 1.1 The changes in global mean surface air temperature in
°C
dur...
Figure 1.2 Surface warming in CMIP6 models for periods over which the global...
Figure 1.3 The differences in maximum surface air temperature for 2010 (a) b...
Chapter 3
Figure 3.1 The mean surface air temperature for July in RCMs (right column) ...
Figure 3.2 The mean annual precipitation (mm) in RCMs (right column) and the...
Figure 3.3 The difference between July mean surface air...
Figure 3.4 The near‐term (2031–2050) and long‐term (2081–2100) projected fut...
Chapter 4
Figure 4.1 Life cycle stages of buildings and associated embodied carbon emi...
Figure 4.2 The machines used, together with their energy requirements and CO
Figure 4.3 Category‐wise global CO
2
emissions.
Figure 4.4 Greenhouse gas emissions from cement production in the United Sta...
Figure 4.5 Evaluation system boundary for GHG emissions during asphalt pavem...
Figure 4.6 Five strategies to tackle embodied carbon emissions and contribut...
Figure 4.7 Potential energy savings from incorporating alternative approache...
Figure 4.8 Carbon dioxide emissions from US commercial and residential struc...
Chapter 5
Figure 5.1 Trends in global carbon emissions from fossil fuels and carbon em...
Figure 5.2 The link between greenhouse gas emissions and their impacts on hu...
Figure 5.3 Life cycle of current Turkish electricity options.
Figure 5.4 Climate change scenario comparison.
Figure 5.5 Concentrated solar power plant's system boundary. TES, thermal en...
Figure 5.6 GW20a indicator per kWh of net produced electricity (IPCC techniq...
Figure 5.7 Impact results in five categories for every MSF desalination faci...
Chapter 6
Figure 6.1 Multilevel resilience assessment framework developed for MSEs....
Figure 6.2 Results of Doha road resilience assessment.
Figure 6.3 Case study abstract. The percentage represents the performance in...
Figure 6.4 (a) Failure propagation from node 3E and (b) network functionalit...
Figure 6.5 (a) Interdependent network performance during (2E) failure scenar...
Chapter 7
Figure 7.1 Directed and undirected graphs. (a) An undirected graph, how trav...
Chapter 8
Figure 8.1 Building seismic/earthquake resilience representation. This repre...
Chapter 9
Figure 9.1 Key concepts of ecological resilience. Valleys, balls, and arrows...
Figure 9.2 Resilience capitals (dimensions) and their variables (indicators)...
Figure 9.3 Key concepts of the resilience triangle.
Chapter 10
Figure 10.1 Resilience evolution schematic diagram.
Figure 10.2 Characteristics of climate change‐resilient system and desired o...
Chapter 11
Figure 11.1 Classification of resilience assessment approaches.
Chapter 12
Figure 12.1 The various climate change impacts on urban areas, resulting in ...
Figure 12.2 Urban planning strategies for mitigating climate change impacts....
Chapter 13
Figure 13.1 Conceptual framework linking the domains of GBGI for flood manag...
Figure 13.2 The suitable methodological framework for analyzing the combinat...
Chapter 14
Figure 14.1 Components of an energy system.
Figure 14.2 Damages to energy systems by natural disasters.
Figure 14.3 Key themes in resiliency definitions available in academic liter...
Figure 14.4 Relationships between key elements in resiliency definitions and...
Figure 14.5 Five Rs of resilience energy system.
Figure 14.6 Different failure scenarios in energy systems.
Figure 14.7 Important climatic changes and their impacts on the energy syste...
Figure 14.8 Layout of a typical smart‐grid‐based energy system.
Chapter 15
Figure 15.1 Examination of impacts of climate change on various sectors.
Figure 15.2 An outline of how climate change affects the associated health r...
Chapter 16
Figure 16.1 Climate change factors affecting VBDs.
Figure 16.2 Surveillance strategies to control and prevent the spread of VBD...
Guide
Cover
Table of Contents
Title Page
Copyright
List of Contributors
About the Editor
Preface
Abbreviations
Begin Reading
Glossary
Index
End User License Agreement
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Sustainable Cities in a Changing Climate
Enhancing Urban Resilience
Edited by
Sami G. Al-Ghamdi
King Abdullah University of Science and Technology (KAUST)Saudi Arabia
This edition first published 2024© 2024 by John Wiley & Sons Ltd
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of Sami G. Al-Ghamdi to be identified as the author of the editorial material in this work has been asserted in accordance with law.
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Library of Congress Cataloging-in-Publication DataNames: Al-Ghamdi, Sami G, editor.
Title: Sustainable Cities in a Changing Climate: Enhancing
Urban Resilience / edited by Sami G. Al-Ghamdi.
Description: Hoboken, NJ : Wiley, 2024. | Includes index.
Identifiers: LCCN 2023035113 (print) | LCCN 2023035114 (ebook) | ISBN
9781394201549 (hardback) | ISBN 9781394201518 (adobe pdf) | ISBN
9781394201525 (epub)
Subjects: LCSH: Sustainable urban development. | Climate change mitigation.
| City planning.
Classification: LCC HT241 .C5955 2024 (print) | LCC HT241 (ebook) | DDC
307.76—dc23/eng/20231101
LC record available at https://lccn.loc.gov/2023035113
LC ebook record available at https://lccn.loc.gov/2023035114
Cover Design: WileyCover Image: © Photo of the KAUST campus
List of Contributors
Nisreen Abuwaer
King Abdullah University of Science and Technology (KAUST)
Thuwal
Saudi Arabia
Sami G. Al‐Ghamdi
King Abdullah University of Science and Technology (KAUST)
Thuwal
Saudi Arabia
Mohammed Al‐Humaiqani
Hamad Bin Khalifa University (HBKU)
Doha
Qatar
Salah Basem Ajjur
Hamad Bin Khalifa University (HBKU)
Doha
Qatar
Fama N. Dieng
Hamad Bin Khalifa University (HBKU)
Doha
Qatar
Muhammad Imran Khan
Hamad Bin Khalifa University (HBKU)
Doha
Qatar
Mohammed G
.
Madandola
Hamad Bin Khalifa University (HBKU)
Doha
Qatar
Mehzabeen Mannan
Hamad Bin Khalifa University (HBKU)
Doha
Qatar
Mohammad Zaher Serdar
Hamad Bin Khalifa University (HBKU)
Doha
Qatar
Furqan Tahir
King Abdullah University of Science and Technology (KAUST)
Thuwal
Saudi Arabia
Safi Ullah
King Abdullah University of Science and Technology (KAUST)
Thuwal
Saudi Arabia
About the Editor
Prof. Sami G. Al-Ghamdi is a distinguished professor specializing in sustainable built environment and climate change resilient infrastructure at King Abdullah University of Science and Technology (KAUST). He earned his PhD in civil and environmental engineering from the University of Pittsburgh in 2015, an MSc in civil and construction engineering from Western Michigan University in 2010, and a BSc in architecture and building science from King Saud University in 2005. Prof. Al-Ghamdi is also a LEED-accredited professional, specializing in green building design and construction, certified by the US Green Building Council.
As a founding faculty member of KAUST's Climate and Livability Initiative (CLI), Prof. Al-Ghamdi's passion lies in conducting multidisciplinary research to develop innovative solutions that address climate change mitigation, optimize energy, water, and material consumption, and enhance the overall quality of life. His research group has focused on seven core objectives: reducing the contribution to global climate change, promoting individual human health, advocating for local sustainable and regenerative material cycles, protecting and restoring water resources, fostering a domestic green economy through technology entrepreneurship, enhancing community quality of life, and preserving and enhancing biodiversity and ecosystem services.
Demonstrating academic excellence, Prof. Al-Ghamdi boasts an impressive track record, having supervised and mentored numerous postdoctoral scholars, PhD students, and MSc students. His efforts have been recognized and rewarded, securing significant external competitive funding for research projects centered on resiliency, climate change, and related fields. Notably, he has been honored with several prestigious awards, including the Qatar Sustainability Awards in various years (research category) and the 2018 National Program for Conservation and Energy Efficiency Award for the Best Green Sustainable Initiative. Moreover, his dedication to research excellence is evident through the publication of a substantial number of refereed, Scopus-indexed papers.
Prof. Al-Ghamdi's contributions to the field have been invaluable in promoting sustainable development and advancing the domain of sustainable built environment. His commitment to innovative research and holistic approaches in tackling the challenges of climate change has had a far-reaching impact, not only in academia but also in practical applications and the wider community. As the editor of “Sustainable Cities in a Changing Climate: Enhancing Urban Resilience,” Prof. Al-Ghamdi's expertise and leadership have brought together his group of experts, practitioners, and researchers to create a comprehensive and timely resource for shaping the future of resilient and sustainable cities.
Preface
Cities are the vibrant epicenters of human civilization, serving as hubs of innovation, economic growth, and cultural exchange. However, as we navigate the challenges of the twenty-first century, cities face unprecedented pressures, particularly in the face of climate change. Rising temperatures, extreme weather events, sea level rise, and other climate-related impacts pose significant threats to urban environments and the well-being of their inhabitants. To ensure a sustainable and prosperous future, it is imperative that we enhance the resilience of our cities.
“Sustainable Cities in a Changing Climate: Enhancing Urban Resilience” is a comprehensive and timely exploration of the vital intersection between urban development, climate change, and resilience. In this book, we bring together a diverse group of experts, practitioners, and researchers who share their insights, experiences, and innovative approaches to tackle the complex challenges of climate change in urban settings.
Divided into three main parts, this book takes readers on a journey through the foundations of climate change and its implications for the built environment, the quantification of resilience and its qualities, and the strategies for navigating climate change and enhancing sustainability in urban systems. It provides a holistic and multidisciplinary perspective that spans climate science, urban planning, infrastructure resilience, energy systems, healthcare, and more.
In Part I, “Climate Change and the Built Environment: Foundations and Implications,” we lay the groundwork for understanding climate change and its impacts. From the basics of climate science and greenhouse gases to advancements in climate modeling and observations, readers gain a solid foundation in the science of climate change. We delve into the role of urbanization in shaping local hydroclimate and explore regional climate modeling to assess future projections. Additionally, we examine the impacts of the built environment and human developments on climate change, emphasizing the need for sustainable practices and efficient resource management.
Part II, “Quantifying Resilience and Its Qualities,” delves into the assessment and enhancement of resilience in urban environments. We explore the interdependencies of critical infrastructures, ranging from transportation and water systems to buildings' structural resilience. The chapters provide insights into quantifying infrastructure and urban systems' resilience qualities, emphasizing the importance of holistic approaches and considering multiple dimensions of resilience. We also delve into the assessment methods and frameworks used to evaluate the resilience of physical infrastructures, providing valuable tools for resilience planning and decision-making.
In Part III, “Resilient Urban Systems: Navigating Climate Change and Enhancing Sustainability,” we turn our attention to the critical domains of energy and healthcare systems within urban environments. These chapters focus on developing and implementing resilience strategies to mitigate climate change impacts in these key sectors. From integrating green–blue–gray infrastructure for flood risk management to enhancing energy system resilience and incorporating climate change considerations into health policies, we explore innovative approaches to enhance sustainability and adapt to a changing climate.
Throughout this book, our contributors emphasize the importance of collaboration, interdisciplinary approaches, and community engagement in building resilient cities. They highlight the need for policymakers, urban planners, researchers, and practitioners to work together to develop evidence-based solutions and implement transformative changes.
As the editor, I am deeply grateful to all the authors who have contributed their knowledge and expertise to this book. Their dedication and passion for enhancing urban resilience have made this collaborative effort possible. I also extend my gratitude to the readers for their interest in this important topic and their commitment to building sustainable cities in the face of climate change.
I hope that “Sustainable Cities in a Changing Climate: Enhancing Urban Resilience” serves as a valuable resource for all stakeholders involved in shaping the future of our cities. May this book inspire and empower readers to take action, innovate, and create urban environments that are not only resilient but also sustainable, inclusive, and equitable.
Together, let us embark on this journey toward a future where our cities thrive, adapt, and flourish in the face of a changing climate.
Prof. Sami G. Al-Ghamdi, Editor
King Abdullah University of Science and Technology (KAUST)
Abbreviations
These abbreviations provide readers with a convenient reference to commonly used terms throughout the book, allowing for easier comprehension and efficient communication of ideas.
ABM
agent-based-modelling
AHP
analytic hierarchy process
AIACC
assessments of impacts and adaptation of climate change
ASPIRE
Atlas of social protection indicators of resilience and equity
BEdZED
Beddington zero energy development
BEM
building energy model
BEP
building effect parameterization
BI
blue infrastructure
BMPs
best management practices
BRACED
building resilience and adaptation to climate extremes and disasters
BREEAM
building research establishment environmental assessment method
BRT
bus rapid transit
CBA
cost–benefit analysis
CCS
carbon capture and storage
CHP
combined heat and power
CI
critical infrastructure
CN
complex networks
CO2
carbon dioxide
CoBRA
community-based resilience analysis
COP26
26th UN climate change Conference
COP
conference of the Parties
CP
collapse prevention
CSP
concentrated solar power
DUCT
digital urban climate twin
DRM
disaster risk management
DRR
disaster risk reduction
EAD
expected annual damage
EHEs
extreme heat events
EI
Aenvironmental impact assessment
EIP
extrinsic incubation period
EPA
Environmental Protection Agency
EWF
energy–water–food
EW
Searly warning system
FCM
fuzzy cognitive map
FMC
fifteen minutes city
FTOPSIS
fuzzy technique for order preference by similarity to ideal solution
FVI
Flood Vulnerability Index
GBGI
Green–Blue–Gray Infrastructure
GBI
Green–Blue Infrastructure
GCM
sglobal climate models
GDP
gross domestic product
GHG
greenhouse gas
GHGI
greenhouse gas inventory
GI
green infrastructure
GIS
Geographic Information System
GPS
Global Positioning System
GRAI
gray infrastructure
HGBGI
Hybrid Green–Blue–Gray Infrastructure
HVAC
Heating, Ventilation, And Air Conditioning
ICT
information and communication technology
IO
immediate occupancy
IPCC
Intergovernmental Panel on Climate Change
ISS
International space station
LCC
life cycle cost
LCCA
life cycle cost analysis
LCLUC
land cover and land use change
LEED
Leadership in Energy and Environmental Design
LID
low impact development
LS
life safety
MCA
multi-criteria analysis
MCDA
multi-criteria decision analysis
ME
Middle East
MENA
Middle East and North Africa
MEP
Mechanical, Electrical, and Plumping
MSE
Mega Sport Event
MSERRI
Mega Sport Event Road Resilience Index
NBS
nature-based solutions
NDCs
Nationally Determined Contributions
NGO
nongovernmental organization
PBD
Performance Based Design
PCA
Principal component analysis
PES
Payment for Ecosystem Services
PNW
Pacific Northwest
PPP
public–private partnership
PSP
participatory scenario planning
PTSD
post-traumatic stress disorder
PV
photovoltaic
R&D
research and development
RCP
representative concentration pathway
REDI
Resilience-Based Earthquake Design Initiative
RELI
Resilient Design Rating System
RRA
rapid risk assessment
RS
remote sensing
SCP
Sponge City Program
SDGs
sustainable development goals
SIA
social impact assessment
SUDS
sustainable urban drainage systems
SVI
social vulnerability index
SWMM
Storm Water Management Model
SWOT
strengths, weaknesses, opportunities, and threats
TDM
transportation demand management
TOD
transit-oriented development
TSS
total suspended solid
UCCR
urban climate change resilience
UCM
urban Canopy model
UHC
Universal health coverage
UHI
urban heat island
UN
United Nations
UNFCCC
United Nations Framework Convention on Climate Change
UNDP
United Nations Development Program
USAID
United States Agency for International Development
USGBC
U.S. Green Building Council
VBDs
vector-borne diseases
WASH
water, sanitation, and hygiene
WHO
World Health Organization
WRF
Weather Research and Forecasting
WSUD
water-sensitive urban design
WWC
Waterway Corridors
Part IClimate Change and The Built Environment: Foundations and Implications
Part 1 of the book lays the groundwork for understanding climate change and its implications for the built environment. It begins with Chapter 1, where readers are introduced to the basics of climate change, including the role of greenhouse gases, global warming potential, and the scientific evidence of human-induced global warming. The chapter also explores natural climate variability and provides an overview of cutting-edge improvements in climate models and observations.
Chapter 2 delves into the WRF-Urban model and its significance in promoting urban resilience and sustainability. It highlights the impact of urbanization on local hydroclimate, emphasizing the need for advanced modeling techniques to improve real-time weather prediction and understand urban land surface processes.
In Chapter 3, the focus shifts to the MENA-CORDEX domain, where climate change simulations from regional climate models (RCMs) are evaluated and projected. The chapter presents a step-by-step methodology for assessing the trends in surface air temperature and precipitation, enabling a high-resolution evaluation of temperature and precipitation projections for the MENA region through the twenty-first century.
Chapter 4 examines the impacts of the built environment on climate change, emphasizing the importance of considering both embodied and operational carbon emissions. It highlights the need for sustainable practices such as utilizing low-embodied carbon materials, improving construction efficiency, and exploring carbon sequestration techniques to mitigate building-related emissions.
Finally, Chapter 5 focuses on the impact of human developments on climate change, specifically the production and consumption of energy and water and our reliance on fossil fuels. It underscores the need to use energy and water more efficiently to successfully tackle climate change issues.
Together, these chapters provide a comprehensive understanding of climate change, regional climate modeling, the role of the built environment in contributing to climate change, and the importance of sustainable practices in mitigating its effects. This knowledge sets the stage for the subsequent sections of the book, which will delve into strategies and approaches for enhancing climate resilience in the built environment.
1Understanding Climate Change Fundamentals: Exploring the Forces Shaping Our Planet's Future
Salah Basem Ajjur1,2 and Sami G. Al‐Ghamdi1,3,4
1 Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
2 Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA
3 Environmental Science and Engineering Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
4 KAUST Climate and Livability Initiative, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Introduction
Climate change is a global problem with severe consequences for humans and the environment. During the last decades, climatic observations and records have provided unequivocal evidence of increasing hazardous risks and tipping points that could leave future generations with a less livable planet. The implications of these tipping points include many irreversible actions for all species on Earth. Previous literature has demonstrated that poor and marginalized countries are the most affected by climatic change, which increases global inequalities [1]. Therefore, it is necessary to understand the basics of climate change and the role everyone can play in adaptation and mitigation measures. This chapter aims to provide such knowledge to researchers from outside the climate change community.
The chapter begins by giving a brief, but comprehensive definition of some specific terms, which should be interpreted in the context of this book. The terms are atmosphere, greenhouse gases (GHGs), Global Warming Potential (GWP), CO2 equivalent (CO2‐eq) emission, aerosols, and carbon budget. Understanding these terms is essential in climate change studies. The next lines provide the scientific evidence that demonstrates the human cause of recent global warming and show the spatial distribution of global warming. The global modes of climate variability are defined after that. To include necessary knowledge in climate change studies, this chapter summarizes the cutting‐edge improvements in global climate models, observations, reanalysis datasets, and relevant programs needed to visualize and process climatic data. The chapter is concluded by highlighting the main points and outlooks.
The atmosphere is the gaseous envelope that surrounds the Earth. It has five layers: the inner to the outer, troposphere (contains 50% of the Earth's atmosphere), stratosphere, mesosphere, thermosphere, and exosphere. The atmosphere acts like a barrier to protect the Earth from harmful solar radiation and helps to keep the Earth's temperature stable. GHGs are necessary components in the atmosphere to trap heat from the sun. Without GHGs, the planet would be cold and deserted like Mars. On the other hand, with too much GHGs, the planet would be hot and lifeless like Venus. Hence, a proper amount of GHGs is needed to have an optimum climate on the Earth. Factories, vehicles, power plants, and some agricultural activities burn fossil fuels (coal, oil, natural gas, etc.) and emit substantial amounts of GHGs, making the Earth warm. The GHGs are typically emitted in smaller quantities. However, some, mainly water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and fluorinated gases,1 are effective in thickening the Earth’s blanket and changing its climate. For example, CO2 is responsible for 80% of global temperature rise, making up only 0.04% of our atmosphere. Thus, even a small variation in CO2 concentration can have significant consequences for the Earth.
Three questions determine the effectiveness of a GHG. These questions are (1) How much GHG is in the atmosphere? (2) How long does it remain? (3) How strongly does it affect the atmosphere? To address these questions, scientists calculate the GWP index for each GHG. The GWP index reflects the average time the gas remains in the atmosphere and causes “radiative forcing” (or heating effect) relative to CO2. The GWP of CO2 is 1, thus, a higher GWP means that the gas contributes more to warming the Earth. If the emission of GHG is multiplied by its GWP for a hundred‐year time horizon, then one can determine the CO2‐eq emission. CO2‐eq emission denotes the amount of CO2 emission that causes identical integrated radiative forcing (or global warming) as an emitted amount of another GHG. The metric CO2‐eq can be calculated for a mixture of GHGs by adding the CO2‐eq emissions of each GHG. CO2‐eq is commonly expressed as MMTCDE (million metric tons of carbon dioxide equivalents).
Aerosols are atmospheric particles suspended in a gas, like air, with tiny diameters (a few nanometers/micrometers). Aerosols can be suspended for several days in the troposphere and for years in the stratosphere. Aerosols in the troposphere might be natural or anthropogenic, whereas aerosols in the troposphere are generally natural (volcanic eruptions). Aerosols can scatter and absorb radiation, causing an effective radiative forcing. They also act as cloud condensation nuclei and ice nuclei, affecting the cloud’s droplets, radiation, and precipitation characteristics. There are different types of aerosols, including sulfate, organic and black carbon, sea salt, and dust.
Carbon budget is the global amount of GHG emitted for a given level of global warming above a reference period. The distribution of the carbon budget to the regional level is established according to equity, cost, or efficiency considerations. Knowing the carbon budget is helpful in determining whether climate change mitigation plans are sufficient to limit global warming below a specific temperature. To curb excessive emissions and drive investment of cleaner and more efficient alternatives, such as renewable energies, climate scientists suggest putting a price on carbon emissions (carbon tax). Carbon tax is a way to raise fossil fuel prices, motivating users to switch to cleaner (non‐carbon) and economically rewarding energy. A key drawback of the carbon tax is the increase in the cost of energy‐related services, such as electricity, heating, and cooling, which increases the economic strain on people who are already struggling. This might be solved by giving some of the carbon tax revenue back to households.
Recent Climate Change is Anthropogenic
Although the climate has permanently changed, the phenomenon of climate change is relatively new. Humans began noticing the warming of the Earth's land surface in the 1930s. However, it was confusing as to whether to attribute this warming to a long‐term trend in natural fluctuation or to recent human activity. After 1979, satellites provided a wealth of climatic data at finer resolutions that produced particular patterns of change, showing that the planet has been warming fast during the last few decades. The global air temperature has grown by at least 1 °C since the industrial revolution2 . Two‐thirds of the latter increase has happened since 1975 (see Figure 1.1). Projections show a warming trend of 20 times faster during the twenty‐first century. Moreover, the observation‐based datasets show that the troposphere and oceans have warmed while the stratosphere has cooled, less heat has escaped to space, ice and glacier masses have declined, sea levels have risen, extreme weather patterns have changed, and ocean salinity and acidity have increased.
Figure 1.1 The changes in global mean surface air temperature in °C during 1850 and 2020, relative to the industrial period in HadCRUT4 observations, CMIP6 simulations of the response to greenhouse gases only (yellow band), natural forcings only (green band), aerosols only (blue band), and combined human and natural forcings (gray band). Solid colored lines present the CMIP6 multi‐model mean.
Thus, climatic changes during the recent decades are a result of a surge in the concentration of GHGs. Additionally, other evidence from the past, such as fossils and sediments, bridges the gap in the evolution of Earth's climate [2]. Taken together, the evidence is overwhelming, and science confirms that recent climate change is mainly anthropogenic, i.e., caused by human activity.
Figure 1.1 depicts the changes in global near‐surface air temperature since 1850, compared with the industrial period, according to the sixth phase of the Coupled Model Intercomparison Project 6 (CMIP6). The CMIP6 models simulate changes due to natural forcings as well as human causes (GHG and aerosols). Natural forcings include internal climate variability related to global teleconnections, solar brightness variations, and volcanic emissions. The clearly observed global warming is simulated only when models include the human impact, particularly GHG emissions (orange band). The GHG emissions contribute substantially to such warming, partly offset by the cooling effect of increases in atmospheric aerosols (blue band). The models' simulations show that natural forcings cannot reproduce the observed global warming since these (natural) forcings simulate much smaller temperature trends (green band).
In short, only through the sustained reduction of GHG emissions can the world limit the globally averaged surface warming. This requires substantial efforts for reaching net‐zero or net‐negative CO2 emissions and decreasing the net non‐CO2 forcings. Nonetheless, the resulting slowdown in warming would be masked by natural year‐to‐year variability, which means that mitigation benefits will take a few decades to be detected globally and regionally.
Spatial Distribution of Global Warming
Long‐term global warming is not evenly distributed over the Earth [3]. Observational‐based and model‐based datasets substantiate this, and to prove this, we will depict the spatial pattern of near‐surface air temperature at three specific levels of global warming. Figure 1.2 illustrates the spatial changes of surface warming under 1.5, 2, and 3 °C global warming levels. Generally, land areas are warmer than oceans. Very strong warming is observed over the Arctic. There is stronger warming in the Northern Hemisphere than in the Southern Hemisphere. High northern latitudes exhibit greater heat than tropical regions. Figure 1.2 c shows regional changes between global land areas under 3 °C global warming. Global hotspots for surface warming are the Russian Arctic, Central Asia, the Tibetan Plateau, the Middle East and North Africa, and North America.
Figure 1.2 Surface warming in CMIP6 models for periods over which the global average near‐surface warming is (a) 1.5 °C, (b) 2 °C, and (c) 3 °C, relative to the industrial period (1850–1900).
The spatial patterns of other climatic parameters, e.g., precipitation, snow, and sea ice cover, may not be robust because changes in precipitation depend on several complex factors such as global mean temperature, aerosol emissions, and land use variations. Additionally, establishing a robust spatial pattern for snow and ice cover changes is difficult, as snow/ice vanishes completely if a certain temperature threshold is reached.
Modes of Climate Variability
They are also called “regimes” and “teleconnections.” Teleconnections refer to modes of natural climate variability that link weather changes in widely separated points of the globe. Teleconnections are triggered by large changes in air movement around the atmosphere and occur over months and longer timescales. For additional details about the global modes of climate variability, the reader is directed to the NOAA website.
The two dominant annular modes that describe the total variation in the extratropical atmospheric flow are the Northern Hemisphere Annular Mode (NAM) and the Southern Hemisphere Annular Mode (SAM). The NAM, also known as the Arctic Oscillation (AO), is a winter fluctuation in the amplitude of a pattern described by low surface pressure in the Arctic and mid‐latitude solid westerlies. Another mode of climate variability that has a strong correlation with the NAM is the North Atlantic Oscillation (NAO). The NAO is provided by the difference between sea‐level pressure in Iceland and the Azores. The NAM and NAO are available from 1950 to the present. The SAM, also called the Antarctic Oscillation (AAO) or the High Latitude Mode, is the primary teleconnection of the Southern Hemisphere sea‐level pressure and geopotential height and is related to changes in the latitude of the midlatitude jet. The SAM is a station‐based index on monthly, seasonal, and annual time steps. It has been available since 1957.
Two key teleconnections that describe the climate variability of the tropical Atlantic on a year‐to‐year timescale are the Atlantic equatorial (or zonal) mode and the Atlantic meridional mode. The Atlantic zonal mode is associated with sea surface temperature anomalies near the equator. It peaks in the eastern basin. It is also called Atlantic Niño. The Atlantic meridional mode is characterized by an inter‐hemispheric gradient of SST and wind anomalies.
The El Niño‐Southern Oscillation (ENSO) is a coupled atmosphere–ocean phenomenon with time scales of two to seven years. It is often measured by the Southern Oscillation Index (SOI): the surface pressure anomaly difference between Tahiti and Darwin, Australia. The SOI has been available since 1951. The ENSO can also be measured by the sea surface temperatures in the equatorial Pacific Ocean (Niño regions). The long‐lived El Niño‐like pattern of Pacific climate variability is known as the Pacific Decadal Oscillation (PDO) index [4]. It has been available since January 1854. A broadened PDO index that occurs in the whole Pacific Basin is the Inter‐decadal Pacific Oscillation (IPO). The PDO and IPO display temporal evolution. The PDO data go back to 1854, while the PDO data extend back to 1900.
Find, Read, and Process Climatic Data
Climatic data are available from several resources in different formats. The next lines show the primary sources of climate models, observations, and reanalysis data, respectively. The text then illustrates the development and characteristics of future scenarios and pathways and summarizes some essential tools that are necessary for handling and processing climatic data.
Climate Models (GCMs and RCMs)
General Circulation Models (GCMs) are time‐dependent models representing atmosphere, ocean, and land interactions with discrete grid points distributed over the Earth. The GCMs use physics laws such as thermodynamics, fluid mechanics, momentum, continuity, and radiation. During the last half‐century, humans have invented powerful computers that are able to run complex computer codes. These recent advances in technology have allowed GCM simulations to greatly improve. Scientists used advanced instruments to measure climatic parameters, e.g., temperature, precipitation, cloudiness, humidity, sea level, and wind. Climate scientists incorporated many more natural processes and provided much more data on the interaction between the atmosphere and land surfaces, ice, vegetation, and oceans. Thus, today we have evidence‐based simulations on most aspects of the climate system. We can determine when the gigantic ice sheets of Greenland and Antarctica experience high melt rates, leading to major changes such as sea‐level rise. Simulations are not restricted to oceans and land surfaces but include many other variables such as ice, snow, vegetation, and land use. Some climate models can simulate up to 50 km high in the atmosphere, enabling a better understanding of the processes between atmospheric layers. They can simulate eddies at a 100‐km scale, improving our experience of heat transport in oceans. They can also determine the amount of CO2 absorbed by plants and oceans under various climate and environmental changes.
The improvements in climate models become apparent when comparing most climatic variable simulations with observations. Consequently, climate models decrease spaces between grid points, producing higher (finer) resolutions. Nevertheless, the horizontal resolution in GCMs still cannot identify local weather conditions in many regions. For instance, urban development and the interactions between sea and land are main local forcings, but not easily captured through several GCMs [5, 6].
To overcome the issue of large horizontal resolution, two main downscaling types are used. The first is the statistical method, where empirical links are developed between atmospheric conditions at the grid points of the GCMs and the observed conditions of the specified domain. The second is the dynamic method, where outputs from GCMs force Regional Climate Models (RCMs) with higher resolutions to characterize the physical and dynamic features of a particular domain. Other types, like the weather regime method, where the outputs of the GCMs are categorized as a restricted number of weather regimes or analogs, are less common. Each method has its benefits and drawbacks [7–9]; however, the dynamic and weather regime methods consume time and money, as they require complex computations [8]. In contrast, the statistical approach mitigates systematic biases in the GCMs, improves spatial details, and generates variables that are not explicitly rendered by GCMs [8]. The results from the statistical method are reliable if an appropriate methodology with high‐quality observations is applied [6, 8].
RCMs are climatic models forced by specific conditions from GCMs or observation‐based reanalysis at finer resolutions over a specific area domain. The Coordinated Regional Climate Downscaling Experiment (CORDEX; www.cordex.org) involves the participation of 16 RCMs (according to the ESGF). These RCMs are COSMO‐CLM, WRF, ALADIN, ALARO‐0, CCAM, CRCM, ETa, HadGEM3‐RA, HadRM3P, HIRHAM5, MAR36, RACMO, RCA, RegCM4, REMO, and RRCM. The COSMO‐CLM and WRF models have become community models due to the interests, contributions, and long‐term development of an international user base. Several climatic models participating in the CMIP5 were used as boundary conditions for different domains in CORDEX regional simulations. For instance, the Africa domain has one simulation available for the historical experiment, one simulation for the RCP4.5 experiment, and one simulation for the RCP8.5 experiment. The CanESM2‐r0i0p0 was used as a boundary condition in the latter experiments. Similar simulations for these experiments are found under the boundary conditions of the CSIRO‐Mk3‐6‐0‐r0i0p0, EC‐EARTH‐r1i1p1, EC‐EARTH‐r3i1p1, GFDL‐ESM2M‐r0i0p0, IPSL‐CM5A‐MR‐r0i0p0, and NorESM1‐M‐r1i1p1 models. The CORDEX has 14 domains with a prioritized horizontal grid resolution of 0.44° (~50 km). These domains are listed and described on the CORDEX website, https://cordex.org/domains/. The MENA domain has a higher resolution of 0.22° (~25 km). Two other domains (NAM and EURO) have higher resolutions of 0.11° (~12 km) and 0.22°.
Pathways and Scenarios
To better find, understand, and document climatic data, the CMIP has coordinated the design and distribution of climate model experiments from multiple international institutions and modeling groups. The CMIP began in 1995 as an initiative of the World Climate Research Programme (WCRP). It started with 10 climate centers carrying out global climate simulations. The number has increased to 17 climate centers in CMIP5 and 26 registered for CMIP6 [10]. Over time, experiments have been developed, and models run at higher resolutions, including integrations using idealized forcings. The latest phase of CMIP, i.e., CMIP6, has historical simulations (from 1850 to near‐present), common experiments, the Diagnostic, Evaluation and Characterization of Klima (DECK), and an ensemble of CMIP‐Endorsed Model Intercomparison Projects (MIPs).
Future simulations are represented in climate models in many forms. The CMIP3 multi‐model dataset [11] includes future projections using the Special Report on Emissions Scenarios (SRES). The CMIP5 [12] uses the Representative Concentration Pathways (RCPs) to simulate the future climate [13]. Four RCPs are labeled by their projected radiative forcing values reached in the year 2100. These are RCP2.6, RCP4.5, RCP6.0, and RCP8.5. The radiative forcing in RCP2.6 peaks at ~3 Watts per square meter (W m−2) and then declines to 2.6 W m−2 at the end of the twenty‐first century. The RCP4.5 and RCP6.0 are intermediate pathways in which radiative forcing is limited at ~4.5 and ~6.0 W m−2 in 2100, respectively. The RCP8.5 is the high pathway that leads to more than 8.5 W m−2 radiative forcing in 2100. The RCPs in the CMIP5 models are purposefully separated from the socioeconomic drivers.
The latest CMIP simulations (CMIP6) draw on the Shared Socioeconomic Pathways (SSPs) [14] to complement the previous RCPs. Five SSPs establish a matrix of global forcing levels and socioeconomic storylines. Two abbreviations label these SSPs. The first abbreviation indicates five socioeconomic scenario families. These are SSP1 for sustainable pathways, SSP2 for middle‐of‐the‐road, SSP3 for regional rivalry, and SSP5 for fossil‐fuel‐rich development. The second label (SSP1‐1.9, SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5) denotes the approximate level of global radiative forcing resulting from the scenario and reached by 2100. For example, the SSP2‐4.5 comprises a radiative forcing level of 4.5 W m−2 in 2100, consistent with the SSP2, where socioeconomic and technical trends do not shift their historical patterns significantly. The SSP5‐8.5 comprises a radiative forcing level of 8.5 W m−2 in 2100, consistent with the SSP5, where mitigating the unconstrained economic growth and energy use is very difficult.
Observations and Reanalysis
Observations and reanalysis are prevalent sources for monitoring historical climatic changes [15, 16]. Observational platforms include measurements of the land and ocean surfaces, upper‐atmospheric observations such as aircraft, satellite‐based retrievals, and paleoclimatic records. The observational datasets may not completely cover the global land because these observations are generated by gridding records at station locations onto an international grid. On the other hand, the reanalysis uses frozen data assimilation models to reanalyze archived observations and create a global dataset that describes the history of the atmospheric temperature and wind, land surface, and oceanographic temperature and currents. A wide range of observations are available with different spatial and temporal coverage. Table 1.1 lists information on some reanalysis datasets.
Table 1.1 Some of the reanalyses that have global coverage. All these reanalyses have sub‐daily, daily, and monthly timesteps.
Name
Institution
Time coverage
Website
CERA‐20C: Coupled Ocean‐Atmosphere Reanalysis of the Twentieth Century
ECMWF
01/1901 to 12/2010
https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/cera-20c
Climate Forecast System Reanalysis (CFSR)
NOAA, NCEP
CFSR is available from 1979/01 to 2011/03
CFSv2 Operational Analysis is available from 2011/04 to the present
https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/climate-forecast-system-version2-cfsv2
ERA5 atmospheric reanalysis
ECMWF
1979/01 to the present
https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5
JRA‐55
Japanese Meteorological Agency
1957/12 to the present
https://jra.kishou.go.jp/JRA-55/index_en.html
NASA MERRA
National Aeronautics and Space Administration (NASA)
MERRA1 is available from 1979/01 to 2016/02
MERRA2 is available from 1980/01 to the present
https://gmao.gsfc.nasa.gov/reanalysis/
NCEP‐DOE Reanalysis 2
NOAA, NCEP
1979/01 to the present
https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html
NOAA‐CIRES Twentieth Century Reanalysis (V2c)
NOAA/ESRL/PSL,CIRES CDC
1851/01 to 2014/12
https://psl.noaa.gov/data/gridded/data.20thC_ReanV2c.html
The reanalysis had some limitations. They may include several derived fields such as soil moisture over land and heating without direct observations. Reanalyses yield data that are as temporally homogeneous as possible. Additionally, the varying mix of observations as well as biases in observations and models may lead to false reanalysis output. Observational constraints may substantially differ according to the location, time period, and variable considered. This variation affects the reanalysis' reliability. Therefore, global reanalysis still suffers from some biases. The reanalysis bias is apparent when simulating extreme climates.
Figure 1.3 compares maximum surface air temperature in 2010 in two reanalysis products (ERA5 [17] and JRA‐55 [18]) with HadEX3 observations [19]. The figure also depicts the variation between both reanalysis datasets when generating the maximum surface air temperature in 2010. The ERA5 reanalysis was generated by the European Centre for Medium‐Range Weather Forecasts (ECMWF). It is available on sub‐daily, daily, and monthly timesteps from 1 January 1950 to the present on TL639 (~31 km) spatial resolution. The JRA‐55 reanalysis was generated by the Japan Meteorological Agency. It is available on sub‐daily and monthly timesteps from 1958 to the present at TL319 (~55 km) resolution. The HadEX3 is an updated gridded observation generated by the Met Office Hadley Centre (UK). HadEX3 derived extreme indices from daily in situ records from 1901 to 2018 with a spatial resolution of 1.875°×1.25° longitude–latitude. The HadEX3 has no complete global land coverage.
Figure 1.3 The differences in maximum surface air temperature for 2010 (a) between ERA5 and HadEX3, (b) between JRA55 and hadEX3, and (c) between ERA5 and JRA55 datasets. Gray colors in (a) and (b) indicate missing values in the hadEX3 dataset.
Both reanalyses had similar simulations in 2010, as shown by Figure 1.3a,b. They tend to overestimate the maximum surface air temperature over the MENA, northern and eastern Australia, and South American Monsoon. At the same time, they underestimate the Tibetan Plateau and Iceland. The ERA5 and JRA55 reanalyses in other global regions are close to HadEX3 observations. Figure 1.3c depicts the difference between ERA5 and JRA55 datasets when generating the maximum surface air temperature in 2010. The ERA5 had larger values than JRA55 over West Siberia, central North America, northern South America, and East Australia. On the other hand, JRA55 had a larger reanalysis than ERA5 over other global land regions. According to Antarctica, the ERA5 clearly had a larger maximum near‐surface air temperature than the JRA55.
Visualizing and Processing Climatic Data
Climatic data such as temperature, humidity, pressure, and wind are mainly produced in two formats: GRIB (GRIdded Binary or General Regularly distributed Information in Binary form) and netCDF (network Common Data Form). The GRIB format has been developed by the World Meteorological Organization (WMO) in three editions: 0, 1, and 2. GRIB2 is the latest format with some improvements in file compression and the inclusion of the parameter table that is required to unpack the data. Usually, one step is required to decode the GRIB file. The data, then, can be visualized and used as inputs for further applications. Data records of the GRIB file start with a header, followed by packed binary data. The header comprises unsigned 8‐bit numbers and contains information about the field, level, time of production and forecasting, geographical location of the grid, and so on. In the netCDF format, data are stored in array form. A typical netCDF format should contain dimension names and sizes, the variables on the file (including the temporal/spatial coordinates), and the attributes (file contents). There are two versions of netCDF: netCDF‐3 (also called the classic model) and netCDF‐4. The netCDF‐4 format supports compression, string variables, and parallel processing of large datasets. In comparison, the use of the netCDF‐3 design is limited to smaller datasets with less complicated grids.
Several tools, freely available and commercially licensed, can visualize and process GRIB and netCDF files. Table 1.2 presents a list of free tools and provides helpful information on them. Some of these tools involve built‐in statistical and arithmetic functions and support, producing high‐quality figures. The ncl_convert2nc tool can convert GRIB and HDF files to netCDF format. Using the NASA tool (i.e., Panoply) is straightforward, but it requires installing a recent Java version. By implementing the CDO software, users can subsample data and spatially interpolate datasets. For further details on manipulating and displaying GRIB and netCDF, the University Corporation for Atmospheric Research (UCAR) summarized a list of software (Link: https://www.unidata.ucar.edu/software/netcdf/software.html).
Table 1.2 Freely available tools that are used to visualize and process climatic data.
Tool
File types supported
Institution name
Last version
Web page
NCAR Command Language (NCL)
GRIB, netCDF, HDF, HDF‐EOS, shapefile, ASCII, binary
NCAR, Boulder, Colorado
6.6.2
http://www.ncl.ucar.edu/
Grid Analysis and Display System (GrADS)
GRIB, netCDF, HDF, Binary, and BUFR
George Mason University, Virginia
2.2.1
http://cola.gmu.edu/grads/grads.php
Panoply
GRIB, netCDF, and HDF
NASA
4.12.8
https://www.giss.nasa.gov/tools/panoply/
wgrib
GRIB
NCEP, NOAA
1.8
https://www.cpc.ncep.noaa.gov/products/wesley/wgrib.html
Climate Data Operators (CDO)
GRIB, netCDF, SERVICE, EXTRA and IEG
Max‐Planck‐Institut für Meteorologie, Germany
1.9.10
https://code.mpimet.mpg.de/projects/cdo
The GCMs have different spatial resolutions, depending on the computational performance of each model and how it addresses pole singularities and physical constraints. Therefore, one may need to regrid (interpolate) these models to a typical resolution to evaluate models' data on different grids quantitively or get the multi‐model ensemble (MME). There are several types of regridding3: bilinear (most common), nearest neighbor, spline, inverse distance, binning, spectral, and triangulation. Selecting the appropriate regridding method is challenging as it depends on the intended task; however, it should be noted that regridding does not provide any additional information from that of the original grid. Instead, using an inappropriate regridding method can lead to misleading outcomes. Climatic data are mostly georeferenced on a sphere. Regridding, therefore, should address pole singularities and the convergence of the longitude meridians issues.
Conclusion
This chapter starts by defining some common terms in the climate change arena and concludes with the following points:
Human‐induced climate change has catapulted our planet into a risky state that humans and ecosystems have never experienced before. Every day, people lose their lives due to extreme weather events, their houses due to sea‐level rise and floods, and their crops due to drought. The implications of climate change include many irreversible actions of life. Therefore, the world should move toward a sustained reduction of GHG emissions. The benefits of such reductions will be evident in a few decades.
Climate change is not evenly distributed over the Earth. Land areas are warmed more than oceans, the Northern Hemisphere is heated more than the Southern Hemisphere, and extreme warming is observed over the Arctic.
Compared to previous generations, the latest advancements in climate models have improved their representations of physical and biogeochemical processes. Observations and reanalyses are powerful tools to monitor historical climatic changes; however, reanalyses have some limitations, especially when simulating extreme climates.
Although climate change data are readily available to the public, their explanation still needs to be more clearly communicated. To illustrate, previous studies documented a decrease of 0.1 in the pH of the surface ocean after the industrial era [1]. The reader may underestimate this decrease as it may seem like a slight change; however, it represents a 26% increase in ocean acidity.
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