Department of Biological Sciences, University of Wisconsin-Milwaukee, USA
Anders Pape Møller
Ecologie Systematique Evolution, Université Paris-Sud, France
Great Clarendon Street, Oxford, OX2 6DP, United Kingdom
Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries
The moral rights of the authors have been asserted
First Edition published in 2010
Second Edition published in 2019
Impression: 1
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, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, by licence or under terms agreed with the appropriate reprographics rights organization. Enquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above
You must not circulate this work in any other form and you must impose this same condition on any acquirer
Published in the United States of America by Oxford University Press 198 Madison Avenue, New York, NY 10016, United States of America
British Library Cataloguing in Publication Data
Data available
Library of Congress Control Number: 2019930335
ISBN 978–0–19–882426–8 (hbk.)
ISBN 978–0–19–882427–5 (pbk.)
DOI: 10.1093/oso/9780198824268.001.0001
Printed and bound by CPI Group (UK) Ltd, Croydon, CR0 4YY
Links to third party websites are provided by Oxford in good faith and for information only. Oxford disclaims any responsibility for the materials contained in any third party website referenced in this work.
Acknowledgements
We would like to thank all authors for their efforts and timeliness. Ian Stewart was enthusiastic from the very beginning about bringing this project to fruition. Bethany Kershaw always helped resolve any problems. Julian Thomas promptly did the copy-editing bringing this to an end in hardly any
time. Finally, Anna Pape Møller had the idea for the cover that reveals humans and birds in the environment. Independent of what we do, there is always the carbon footprint left behind as illustrated so aptly by our shadow in a pristine Arctic environment.
List of contributors
Ambrosini, Roberto Department of Environmental Science and Policy, University of Milano, Via Celoria 26, I 20133 Milano, Italy. roberto.amobrosini@unimi.it
Bailey, Liam D Leibniz Institute for Zoo and Wildlife Research (IZW), Berlin, Germany. liam.bailey@liamdbailey.com
Both, Christiaan Faculty of Science and Engineering, Groningen Institute for Evolutionary Life Sciences, 9747 AG Groningen, Netherlands. c.both@rug.nl
Bretagnolle, Vincent Centre d’Etudes Biologiques de Chizé, UMR 7372, CNRS and Université de La Rochelle, Beauvoir sur Niort, 79360 France.
Charmantier, Anne Centre d’Ecologie Fonctionnelle et Evolutive, CEFE UMR 5175, Campus CNRS, 1919 route de Mende, F-34293 Montpellier Cedex 5, France. anne.charmantier@cefe.cnrs.fr
Dunn, Peter O Department of Biological Sciences, University of Wisconsin-Milwaukee, P.O. Box 413, Milwaukee, WI 53201, USA. pdunn@uwm.edu
Engler, Jan Terrestrial Ecology Unit, Department of Biology, Ghent University, K.L. Ledeganckstraat 35, B-9000 Ghent, Belgium. JanOliver.Engler@ugent.be
Engen, Steinar Centre for Conservation Biology, Department of Biology, Norwegian University
of Science and Technology, NO-7491 Trondheim, Norway.
steinar.engen@ntnu.no
Gamelon, Marlène Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Realfagsbygget, NO-7491 Trondheim, Norway.
marlene.gamelon@ntnu.no
Grøtan, Vidar Centre for Conservation Biology, Department of Biology, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway.
vidar.grotan@ntnu.no
Herrando, Sergi Institut Català d’Ornitologia, Nat-Museu de Ciències Naturals de Barcelona, Plaça Leonardo da Vinci 4–5, 08019 Barcelona, Spain.
ornitologia@ornitologia.org
Hochachka, Wesley M Cornell Laboratory of Ornithology, 159 Sapsucker Woods Road, Ithaca, New York, 14850 USA. wmh6@cornell.edu
Huntley, Brian Department of Biosciences, Durham University, Stockton Road, Durham, DH1 3LE, UK. brian.huntley@durham.ac.uk
Hurrell, James W Department of Atmospheric Science, Colorado State University, 200 West Lake Street, 371 Campus Delivery, Fort Collins, CO 80523-1371, USA.
jhurrell@rams.colostate.edu
Iles, David Woods Hole Oceanographic Institution. Woods Hole, MA 02543-1050 U.S.A. david.thomas.iles@gmail.com
Inouye, David W Department of Biology, University of Maryland, College Park, Maryland 207424415 USA.
Jiguet, Frédéric Museum National d’Histoire Naturel, CNRS-SU UMR 7204, CESCO 43 Rue Buffon, CP135 75005 Paris, France.
fjiguet@mnhn.fr
Lehikoinen, Aleksi Finnish Museum of National History, University of Helsinki, PO Box 17, 00014, Helsinki, Finland. aleksi.lehikoinen@helsinki.
Liang, Liang Department of Geography, University of Kentucky, Lexington, Kentucky 40506-0027 USA.
liang.liang@uky.edu
Marra, Peter P Migratory Bird Center, Smithsonian Conservation Biology Institute, National Zoological Park, PO Box 37012 MRC 5503, Washington, DC 20013 USA. marrap@si.edu
McKechnie, Andrew E South African Research Chair in Conservation Physiology, National Zoological Garden, South African National Biodiversity Institute, Pretoria, South Africa.
DST-NRF Centre of Excellence at the FitzPatrick Institute, Department of Zoology and Entomology, University of Pretoria, Private Bag X20, Hat eld 0028, South Africa.
aemckechnie@zoology.up.ac.za
Møller, Anders Pape Ecologie Systematique Evolution, UMR 8079 CNRS-Université ParisSud XI-AgroParisTech, Batiment 362 Université Paris-Sud XI, F-91405 Orsay Cedex, France. anders.moller@u-psud.fr
Merino, Santiago Museo Nacional de Ciencias Naturales, Consejo Superior de Investigaciones Cientí cas, José Gutiérrez Abascal 2, E-28006 Madrid, Spain. mcnsm508@mncn.csic.es
Romano, Andrea Department of Ecology and Evolution, University of Lausanne, Building Biophore, 1015, Lausanne, Switzerland. andrea.romano@unil.ch
Sæther, Bernt-Erik Center for Conservation Biology, Department of Biology, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway. bernt-erik.sather@bio.ntnu.no
Saino, Nicola Department of Environmental Science and Policy, University of Milan, Via Celoria 26, I-20133 Milano, Italy. nicola.saino@unimi.it
Schwartz, Mark D Department of Geography, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA. mds@uwm.edu
Teplitsky, Céline Centre d’Ecologie Fonctionnelle et Evolutive, CEFE UMR 5175, Campus CNRS, 1919 route de Mende, F-34293 Montpellier Cedex 5, France.
teplitsky@mnhn.fr
Terraube, Julien Faculty of Environmental and Biological Sciences, University of Helsinki, Helsinki, Finland.
julien.terraube-monich@helsinki.
Trenberth, Kevin E National Center for Atmospheric Research, Climate Analysis Section, P.O. Box 3000, Boulder, CO 80307-3000, USA.
trenbert@ucar.edu
van de Pol, Martijn Netherlands Institute of Ecology (NIOO-KNAW), Department of Animal Ecology, Wageningen, the Netherlands. M.vandePol@nioo.knaw.nl
Zuckerberg, Benjamin Department of Forest and Wildlife Ecology, University of WisconsinMadison, Madison, WI 53706-1598, USA. bzuckerberg@wisc.edu
Zurell, Damaris Swiss Federal Research Institute WSL, Zuercherstrasse 111, CH-8903 Birmensdorf, Switzerland. damaris@zurell.de
CHAPTER 1 Introduction
Peter O. Dunn and Anders Pape Møller
Climate change is considered the largest environmental problem of this century, and it is likely to have severe consequences for our environment (IPCC 2014). The latest special report from the Intergovernmental Panel on Climate Change (IPCC) predicts that global warming will likely surpass 1.5°C above pre-industrial levels by 2040, and it is increasing at 0.2°C per decade (IPCC 2018). Birds have been a bellwether of the impacts of climate change on animals because their behaviour and population changes have been documented for decades and even centuries in some cases. The increase in studies of the effects of climate change on birds has been exponential, and it has continued since the rst edition of this book was published in 2010. One of the principal reasons for updating the previous volume is that there has been a tremendous increase in the number (7574) and complexity of studies on climate change and birds since 2010, and it is dif cult for most researchers to keep track of the expanding literature. The number of papers speci cally dealing with climate change and birds is now more than 11 400 and the total number of papers in the eld of climate change exceeds 364 400. In the face of this complexity, we have opted for an edited volume that brings together a group of world experts to review the current state of knowledge, while simultaneously addressing alternative hypotheses and weak points in current research. Another justication for a new edition is that some new topics have become more prominent since the rst edition, such as the increasing use of citizen science data,
particularly eBird (over half a billion sightings from around the world), as well as new advances in theory, genomics, and ecological and demographic modelling that are providing new insights into the causes of climate change and its consequences on birds.
The book is aimed at a wide audience including undergraduate, graduate, and postgraduate students, scientists, administrators, and conservationists. Climate change issues are attracting rapidly increasing interest from many different kinds of biologists because of the widespread effects of climate change on animals and plants throughout the world. There is an enormous interest among students and post-docs for studies on this subject, and many universities are launching programmes on climate change. Furthermore, there is increasing demand for biologists trained in assessing and managing the impact of climate change on wildlife. To address this latter point, we have added a number of short methodological chapters that address speci c issues of analysis with examples and key references as an entryway for students and new researchers in these areas. To facilitate researchers entering the eld, several of the chapters have online supplements with R code and examples to provide help in getting started. (www.oup.co.uk/ companion/dunn&moller)
The book consists of four sections. In the rst section, Kevin Trenberth and James Hurrell provide a general introduction to climate and climate change (Chapter 2). In the second section, ve chapters provide an introduction to methods and
data sources for studying climate change and its effects. In Chapter 3 Mark Schwartz and Liang Liang outline sources available for long-term climate data and some of the analytical issues that need to be addressed. In Chapter 4 Anders Møller and Wesley Hochachka review the databases on birds that are available for study, including longterm surveys like the Christmas Bird Count, Atlases, and citizen science projects like eBird. In Chapter 5 Martijn van de Pol and Liam Bailey discuss methods of indentifying the climate variables that best predict the responses of individuals and populations, and how we can compare these predictors when they differ (e.g., temperature versus precipitation). In Chapter 6 Damaris Zurell and Jan Engler explain the concepts and assumptions of ecological niche modelling, and they provide a real-world example with R code in the online supplement. In Chapter 7 Bernt-Erik Sæther, Steinar Engen, Marlène Gamelon, and Vidar Grøtan outline the steps for predicting the effects of climate change on populations using life history variables and stochastic population models. In the third section, we have chapters that focus on the individual and population consequences of climate change, ranging from changes in physiology and behaviour to shifts in distribution and abundance and long-term evolutionary changes. The section begins with changes in migration patterns and their carry over effects by Roberto Ambrosini, Andrea Romano, and Nicola Saino in Chapter 8. Peter Dunn follows with a review of changes in the timing of breeding and its links to population trends in Chapter 9. In Chapter 10 Andrew McKechnie reviews the physiological and body size effects of climate change. The evolutionary consequences of climate change on birds are assessed by Céline Teplitsky and Anne Charmantier in Chapter 11. David Iles and Stephanie Jenouvrier expand the discussion of population models in Chapter 12 by reviewing models that incorporate changes in climate predicted by global circulation models and addressing the uncertainties in the model projections. In Chapter 13 Brian Huntley reviews changes in distributions in response to climate, starting with the Quaternary period and moving to the present.
In the fourth and last section, the chapters focus on interspeci c effects of climate change, some of the conservation challenges we face, and a review of how the effects on birds are linked to other taxa. We start this section with a review by Santiago Merino of how climate changes the interactions between birds and their parasites in Chapter 14. Predator and prey interactions are reviewed by Vincent Bretagnolle in Chapter 15. Lluis Brotons, Sergi Herando, Frederic Jiguet, and Aleksi Lehikoinen review the effects of climate change, as well as the contributing role of other anthropogenic changes, on bird communities in Chapter 16. In Chapter 17 Pete Marra, Ben Zuckerberg, and Christiaan Both review the conservation challenges and some of the mitigation strategies we can use to minimize the negative impacts of climate change. Finally, in Chapter 18 David Inouye provides a broader ecological perspective in a review of climate change effects on entire food webs, including birds. In the concluding Chapter 19, Anders Møller and Peter Dunn provide an overview of advances since the previous edition of the book, as well as a summary of remaining major questions and research recommendations. This new edition attempts to synthesize what is known about the effects of climate change on birds, as well as point out new methods and areas for future research. Each chapter attempts to provide a comprehensive review of the topic. Although some of the previous gaps in our knowledge have been lled since the rst edition, there are still some notable gaps, which we discuss in the concluding chapter. We hope that readers will nd some new perspectives and questions in this book that will inspire them to better understand and conserve bird populations.
References
IPCC (2014) Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Core Writing Team, R.K. Pachauri, and L.A. Meyer (eds). IPCC, Geneva, Switzerland. http://www.ipcc. ch/report/ar5/syr/
IPCC (2018) Global warming of 1.5˚C. IPCC Special Report 15. http://www.ipcc.ch/report/sr15/
CHAPTER 2
Climate change
Kevin E. Trenberth* and James W. Hurrell
2.1 Introduction
Global climate change is altering many ecosystems, leading to changes in the abundance and distribution of many populations (e.g., Stenseth et al. 2005; Rosenzweig et al. 2008). Advances in the scienti c understanding of climate make it clear that there has been a change in climate that goes well beyond the range of natural variability. As stated by the Intergovernmental Panel on Climate Change (IPCC) (2007a, 2013), the warming of the climate system, which includes the atmosphere, ocean, land, and cryosphere (regions of ice and frozen ground), is ‘unequivocal’ and is ‘very likely due to human activities’. The culprit is the astonishing rate at which heat-trapping carbon dioxide and other greenhouse gas concentrations are increasing in the atmosphere, mostly through the burning of fossil fuels and changes in land use, such as those associated with agriculture and deforestation. Greenhouse gases are relatively transparent to incoming solar radiation while they absorb and re-emit outgoing infrared radiation. The result is that more energy stays in the global climate system, most of which (over 90 per cent) goes into the oceans as heat. The ocean heat content is increasing along with sea level rise, through both expansion of the ocean and melting of land ice, and these provide a memory of the past climate change. The extra energy also further raises
surface temperature and produces many other direct and indirect changes in the climate system.
The indisputable evidence of anthropogenic climate change, and the knowledge that it will continue well into the future under any plausible emission scenario, is now a factor in the planning of many organizations and governments, encapsulated by the Paris Agreement of December 2015, rati ed in October 2016. Global warming does not imply, however, that future changes in weather and climate will be uniform around the globe. The land, for instance, is warming faster than the oceans, consistent with its smaller heat capacity. Moreover, uncertainties remain regarding how climate will change at regional and local scales where the signal of natural variability is large, especially over the next several decades (Hawkins and Sutton 2009). Regional differences in land and ocean temperatures arise, for instance, from natural variability such as El Niño-Southern Oscillation (ENSO) events. Natural variability can result from purely internal atmospheric processes, as well as from interactions among the different components of the climate system, such as those between the atmosphere and ocean or the atmosphere and land.
El Niño events, such as the major event in 2015–16, produce very strong warming of the central and eastern tropical Paci c Ocean while the ocean cools over portions of the subtropics and the tropical
* The National Center for Atmospheric Research is sponsored by the National Science Foundation. Any opinions, ndings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily re ect the views of the National Science Foundation.
western Paci c. Over the Atlantic, average basinwide warming is imposed on top of strong, natural variability on multi-decadal timescales, called the Atlantic Multi-decadal Oscillation (AMO). The level of natural variability, in contrast, is relatively small over the tropical Indian Ocean, where surface warming has been steady and large over recent decades.
Importantly, these differences in regional rates of sea surface temperature (SST) change perturb the atmospheric circulation and shift storm tracks, so that some land regions become warmer and drier, while other regions cool as they become wetter. On the regional scales on which most planning decisions are made and impacts felt, therefore, future warming will not be smooth. Instead, it will be strongly modulated by natural climate variations, and especially those driven by the slowly varying oceans on a timescale of decades. Moreover, regions that warm from both natural variability and global warming are likely to experience ampli ed impacts, and can endure broken records as well as sometimes disastrous outcomes for societies and ecosystems. This non-uniformity of change highlights the challenges of regional climate change that has considerable spatial structure and temporal variability.
It is the purpose of this chapter to review observed changes in climate, with a focus on changes in surface climate including variations in major patterns (modes) of climate variability. The next section describes how natural and anthropogenic drivers of climate change are assessed using climate models. The chapter concludes with a brief summary of future projected changes in climate. The physical evidence and the impacts on the environment and society, as documented by IPCC (2007a, b; 2013) and updated in the annual State of the Climate reports (such as for 2016, Blunden and Arndt 2017), provide
Table 2.2 Indices of circulation variability.
Southern Oscillation Index (SOI). The Tahiti minus Darwin sea level pressure anomalies, normalized by the long-term mean and standard deviation of the mean sea level pressure difference, or alternatively by the negative of the Darwin sea level pressure record (http://www.cgd. ucar.edu/cas/catalog/climind/soi.html).
Paci c-North American pattern (PNA) Index. The mean of normalized 500 hPa height anomalies at 20°N, 160°W and 55°N,115°W minus those at 45°N, 165°W and 30°N, 85°W (http://www.cpc.noaa.gov/ products/precip/CWlink/pna/month_pna_index2.shtml).
North Paci c Index (NPI). The average sea level pressure anomaly over the Gulf of Alaska (30°N–65°N, 160°E–140°W; https:// climatedataguide.ucar.edu/climate-data/north-paci c-np-indextrenberth-and-hurrell-monthly-and-winter).
AMO: Atlantic Multi-decadal Oscillation
RCP: Representative Concentration Pathway
ENSO: El Niño-Southern Oscillation
EOF: Empirical Orthogonal Function
GMST: Global Mean Surface Temperature
IPCC: Intergovernmental Panel on Climate Change
IPO: Inter-decadal Paci c Oscillation
NPI: North Paci c Index
NAM: Northern Annular Mode
NAO: North Atlantic Oscillation
PDO: Paci c Decadal Oscillation
PNA: Paci c-North American pattern
ppb: parts per billion
ppm: parts per million by volume
SAM: Southern Annular Mode
SOI: Southern Oscillation Index
SST: Sea Surface Temperature
Paci c Decadal Oscillation (PDO) index. The amplitude of the pattern de ned by the leading EOF of annual mean SST in the Paci c basin north of 20°N (http://jisao.washington.edu/pdo/PDO.latest).
Atlantic Multi-decadal Oscillation (AMO) Index. The time series of annual mean SST anomalies averaged over the North Atlantic (0–60°N, 0–80°W) as departures from the global mean SST; (http://www.cgd.ucar.edu/cas/catalog/climind/AMO.html).
North Atlantic Oscillation (NAO) Index. The difference of normalized winter (December–March) sea level pressure anomalies between Lisbon, Portugal and Stykkisholmur, Iceland, or alternatively the amplitude of the leading EOF of mean sea level pressure over the North Atlantic (20º–80ºN, 90ºW–40ºE; https://climatedataguide.ucar. edu/climate-data/hurrell-north-atlantic-oscillation-nao-index-pc-based).
Northern Annular Mode (NAM) Index. The amplitude of the pattern de ned by the leading EOF of winter monthly mean Northern Hemisphere sea level pressure anomalies poleward of 20°N (https:// climatedataguide.ucar.edu/climate-data/hurrell-wintertime-slp-basednorthern-annular-mode-nam-index).
Southern Annular Mode (SAM) Index. The difference in average sea level pressure between southern middle and high latitudes (usually 45°S and 65°S) from gridded or station data (http://www.antarctica. ac.uk/met/gjma/sam.html), or alternatively the amplitude of the leading EOF of monthly mean Southern Hemisphere 850 hPa height poleward of 20°S.
Table 2.1 Acronyms used in the text.
the main basis and references for the chapter. There are numerous acronyms used in climate science, especially for names of patterns or modes of variability, and these are listed in Tables 2.1 and 2.2.
2.2 Human and natural drivers of climate change
The IPCC (2007a, 2013) concluded that most of the observed global mean surface temperature (GMST) increase of the past 50 years (Figure 2.1) is ‘very likely’1 due to human activity, while anthropogenic in uences have ‘likely’ contributed to changes in wind patterns, affecting extratropical storm tracks and regional temperature patterns in both the Northern and Southern Hemispheres. These conclusions are based on studies that assess the causes of climate change, taking into account all possible agents of climate change (forcings), both natural and from human activities.
Forcings are factors external to the climate system and may arise naturally, such as from changes in the
1 The IPCC de nes the term ‘very likely’ as the likelihood of a result exceeding 90%, and the term ‘likely’ as exceeding 66%.
Sun or from changes in atmospheric composition associated with explosive volcanic eruptions, or from human activities that generate heat or which change the atmospheric composition. Feedbacks occur through interactions among the components of the climate system: the atmosphere, ocean, land, and cryosphere. Some amplify the original changes producing a positive feedback (such as warming melting snow and ice and reducing the re ection of the Sun’s rays), while others diminish them: a negative feedback (such as warming causing higher temperature that radiates more heat to space). The physical processes involved are depicted in climate models. Radiative forcing is a measure of the in uence that a factor has in altering the balance of incoming and outgoing energy in the Earth–atmosphere system and is an index of the importance of the factor as a potential climate change mechanism. Positive forcing tends to warm the surface while negative forcing tends to cool it.
The capability of climate models to simulate the past climate is comprehensively assessed by IPCC. Given good replications of the past, the forcings can be inserted one by one to disassemble their effects and allow attribution of the observed climate
Figure 2.1 Estimated changes in annual global mean surface temperatures (°C, bars) and CO2 concentrations (thick black line) over the past 149 years relative to 1901–2000 average values. Carbon dioxide concentrations since 1957 are from direct measurements at Mauna Loa, Hawaii, while earlier estimates are derived from ice core records. The scale for CO2 concentrations is in parts per million (ppm) by volume, relative to the twentieth century mean of 333.7 ppm, while the temperature anomalies are relative to a mean of 14°C. Also given as dashed values are the preindustrial estimated values, with the scale at right for carbon dioxide, where the value is 280 ppm. Updated from Trenberth (1997) and from Trenberth (2016) “The hottest year on record signals that global warming is alive and well”, The Conversation, January 20, 2016. https://theconversation.com/the-hottest-year-on-record-signals-that-global-warming-is-alive-and-well-53480
change. Therefore, climate models are a key tool to evaluate the role of various forcings in producing the observed changes in temperature and other climate variables.
The best climate models encapsulate the current understanding of the physical processes involved in the climate system, the interactions, and the performance of the system as a whole. Uncertainties arise, however, from shortcomings in the understanding and how to best represent complex processes in models. Yet, in spite of these uncertainties, today’s best climate models are able to reproduce the climate of the past century, and simulations of the evolution of GMST over the past millennium are consistent with paleoclimate reconstructions.
Human activities increase greenhouse gases, such as carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and other trace gases. They also increase aerosol concentrations in the atmosphere, mainly through the injection of sulfur dioxide (SO2) from power stations and industry, and through biomass burning. A direct effect of sulfate aerosols is the re ection of a fraction of solar radiation back to space, which tends to cool the Earth’s surface. Other aerosols (like soot) directly absorb solar radiation leading to local heating of the atmosphere, and some absorb and emit infrared radiation. A further in uence of aerosols is that many act as nuclei on which cloud droplets condense, affecting the number and size of droplets in a cloud and hence altering the re ection and the absorption of solar radiation by the cloud and the lifetime of the cloud (Stevens and Feingold 2009). The precise nature of aerosol/cloud interactions and how they interact with the water cycle remains a major uncertainty in our understanding of climate processes. Because human-made aerosols are mostly introduced near the Earth’s surface, they are washed out of the atmosphere by rain in, typically, a few days. Thus, they remain mostly concentrated near their sources and affect climate with a very strong regional pattern, usually producing cooling.
In contrast, greenhouse gases such as CO2 and CH4 have lifetimes much longer; of the order of a decade for CH4 but centuries for CO2. Both are globally mixed and concentrations build up over time. Greenhouse gas concentrations in the atmosphere
have increased markedly as a result of human activities since 1750, and they are now higher than at any time in at least the last 650 000 years. It took at least 10 000 years from the end of the last ice age (18 000 years ago) for levels of CO2 to increase 100 parts per million (ppm) by volume to 280 ppm, but a greater increase has occurred over only the past 150 years to current values in excess of 405 ppm (Figure 2.1). About half of that increase has occurred over the last 35 years, owing mainly to combustion of fossil fuels and changes in land use. The CO2 concentration growth-rate has been larger during the last decade than since the beginning of continuous direct measurements in the late 1950s. In the absence of controls, future projections are that the rate of increase in CO2 amount may accelerate, and concentrations could double from pre-industrial values within the next 40 to 100 years.
Methane is the second most important anthropogenic greenhouse gas. Owing predominantly to agriculture, wetlands, and fossil fuel use, the global atmospheric concentration of CH4 has increased 250 per cent from a pre-industrial value of 715 parts per billion (ppb) by volume to 1843 ppb in 2016. Global N2O concentrations have increased signi cantly from pre-industrial values as well. The total net anthropogenic forcing includes contributions from aerosols (a negative forcing) and several other sources, such as tropospheric ozone and halocarbons.
The latest IPCC report (2013), rather than estimating future human behaviour and the resulting forcings, developed a number of ‘Representative Concentration Pathways’ (RCPs) tied to possible radiative forcings in 2100. The RCPs are to be used for policy planning purposes and they enable projections of possible future climates. The values chosen for the RCPs range over 2.6, 4.5, 6.0, and 8.5 Watts per square metre in 2100, relative to 1750. These are transient, not equilibrium values, but may be compared with the 3.7 W m−2 that corresponds to the equilibrium forcing for a doubling of preindustrial carbon dioxide concentrations (from 280 to 560 parts per million by volume (ppm)). Including also the prescribed concentrations of methane and nitrous oxide, the combined CO2equivalent concentrations in 2100 are 475 ppm (RCP2.6), 630 ppm (RCP4.5), 800 ppm (RCP6.0),
and 1313 ppm (RCP8.5). Current CO2-equivalent forcing from greenhouse gases is about 3.0 W m−2, although accounting also for aerosols, the net is about 2.3 W m−2 (IPCC 2013). Hence RCP2.6 is for very low emissions and very unlikely to be realized; while RCP8.5 is closer to business as usual.
Climate model simulations that account for such changes in forcings have now reliably shown that global surface warming of recent decades is a response to the increased concentrations of greenhouse gases and sulfate aerosols in the atmosphere. When the models are run without these forcing changes, the remaining natural forcings and intrinsic natural variability fail to capture the almost linear increase in GMSTs over the past 40 years or so.
But when the anthropogenic forcings are included, the models simulate the observed GMST record with impressive delity (Figure 2.2). Changes in solar irradiance since 1750 are estimated to have caused a radiative forcing of +0.1 W m−2, mainly in the rst part of the twentieth century. Prior to 1979, when direct observations of the Sun from space began, changes in solar irradiance are more uncertain, but direct measurements show that the Sun has not caused warming since 1979. Moreover, the models indicate that volcanic and anthropogenic aerosols have offset some of the additional warming that would have resulted from observed increases in greenhouse gas concentrations alone. For instance, from 2000 to 2010 the sunspot cycle went from a
maximum to a minimum and a very quiet Sun, decreasing total solar irradiance by 0.1 per cent. This perhaps offset about 10 to 15 per cent of the warming, but the solar irradiance has gone through another somewhat weak cycle since then.
The patterns of warming over each continent except Antarctica and each ocean basin over the past 50 years are only simulated by models that include anthropogenic forcing (Figure 2.2). Attribution studies have also demonstrated that many of the observed changes in indicators of climate extremes consistent with warming, including the annual number of frost days, warm and cold days, and warm and cold nights, have likely occurred as a result of increased anthropogenic forcing. In other words, many of the recently observed changes in climate are now being simulated in models.
The ability of coupled climate models to simulate the temperature evolution on continental scales, and the detection of anthropogenic effects on each continent except Antarctica, has also increased. No climate model that has used natural forcing only has reproduced either the observed global mean warming trend or the continental mean warming trends. Attribution of temperature change on smaller than continental scales and over timescales of less than 50 years or so is more difcult because of the much larger natural variability on smaller space and timescales (Hawkins and Sutton 2009).
Observations Models using only natural forcings Models using
Figure 2.2 Comparison of observed global-scale changes in surface temperature with results simulated by climate models using natural and anthropogenic forcings. Decadal averages of observations are shown for 1906–2005 (black line) plotted against the centre of the decade and relative to the corresponding average for 1901–1950. Dark grey shaded bands show the 5–95% range for simulations from climate models using only the natural forcings due to solar activity and volcanoes. Light grey dotted shaded bands show the 5–95% range for simulations from climate models using both natural and anthropogenic forcings. The gure is adapted from the IPCC (2013).
2.3 Observed changes in surface climate
2.3.1
Temperature
The globe is warming dramatically compared with natural historical rates of change. GMSTs today are more than 0.9°C warmer than at the beginning of the twentieth century, and rates of GMST rise are greatest in recent decades (Figure 2.1). The average rate of increase in the GMST since 1901 is 0.78°–0.90°C century−1. The warmest 16 years are the most recent, except for 1998, and the three consecutive years (2014, 2015, and 2016) each set a new GMST record, which is extremely unusual. Global land regions have warmed the most (about 0.5°C more than the oceans), with the greatest warming in the northern winter and spring months over the Northern Hemisphere continents.
There is a very high degree of con dence in the GMST estimates and their changes (Figure 2.1).
The maximum difference, for instance, among three independent estimates of GMST change since 1979 is 0.01°C decade−1. Spatial coverage has improved,
and daily temperature data for an increasing number of land stations have also become available, allowing more detailed assessments of extremes, as well as potential urban in uences on both largescale temperature averages and microclimate. It is well documented, for instance, that urban heat island effects are real, but very local, and they have been accounted for in the analyses: the urban heat island in uence on continental, hemispheric, and global average trends is at least an order of magnitude smaller than decadal and longer timescale trends, as cities make up less than 0.5 per cent of global land areas (Schneider et al. 2009).
There is no urban heat bias in the SST record and the warming is strongly evident at all latitudes over each of the ocean basins. Moreover, the warming is manifest at depth as well, indicating that the ocean is absorbing most of the heat being added to the climate system (Figure 2.3) (Cheng et al. 2017a). The upper ocean has warmed, especially since 1970, but the penetration of heat into the ocean takes time, and the deeper ocean has mainly warmed only after about 1990. Indeed,
Figure 2.3 Time series of the vertically integrated ocean heat content for various layers since 1960. The total also has 95% error bars indicated as dashed grey curves to show how the uncertainty increases further into the past. From Cheng et al. (2017a).
most of the energy imbalance created by the increasing greenhouse gases (over 90 per cent) goes into the oceans, which therefore serve as the memory of past climate change and further provide an unequivocal view of the warming planet.
The largest short-term uctuations in GMSTs come from El Niño and La Niña events. Some heat stored in the ocean is released during an El Niño, and this contributes to increases in GMSTs. From late 2007 to the rst part of 2009, lower temperatures occurred in association with the large 2007–2008 La Niña event, followed by a weaker La Niña in 2008–2009. The major El Niño of 2015–16 led to these being the warmest years on record as some heat came out of the ocean (see the levelling off or slight decline at the end of Figure 2.3). The reason 1998 stands out as the warmest year last century is because of the major 1997–98 El Niño event.
2.3.2 Sea level
The ocean warming causes seawater to expand and, thus contributes to sea level rise (Figure 2.4). Melting of glaciers on land as well as ice caps and ice sheets also contribute. Instrumental measurements of sea level indicate that the global average increased approximately 17 cm over the last century. The rate
has been even faster recently (about 0.31 cm per year from 1993 through 2017, see Figure 2.4, Nerem et al. 2010), when truly global values have been measured from altimeters in space. Prior to 2004, about 60 per cent of global sea level rise was from ocean warming and expansion, while 40 per cent was from melting land ice adding to the ocean volume. Since 2004 melting ice sheets have contributed more than half. The observations of consistent global sea level rise over several decades, and also an increasing rate of sea level rise in the last decade or so, along with the increasing ocean heat content, are probably the single best metrics of the cumulative global warming experienced to date (Cheng et al. 2017b). Consequences include increasing risk of coral bleaching and coastal storm surge ooding.
2.3.3 Snow cover, sea, and land ice
The observed increases in GMST are consistent with nearly worldwide reductions in glacier and small ice cap mass and extent in the twentieth century. In addition, ow speed has increased for some Greenland and Antarctic outlet glaciers, which drain ice from the interior, and melting of Greenland and West Antarctica has increased after about 2000. Critical changes (not well measured) are occurring
Figure 2.4 Global mean sea level with the mean annual cycle removed. The 60-day mean is indicated along with the linear trend. Updated from Nerem et al. (2010).
in the ocean and ice shelves that buttress the ow of glaciers into the ocean. Glaciers and ice caps respond not only to temperature but also to changes in precipitation, and both winter accumulation and summer melting have increased over the last half century in association with temperature increases. In some regions, moderately increased accumulation observed in recent decades is consistent with changes in atmospheric circulation and associated increases in winter precipitation (e.g., southwestern Norway, parts of coastal Alaska, Patagonia, and the South Island of New Zealand) even though increased ablation has led to marked declines in mass balances in Alaska and Patagonia. Tropical glacier changes are synchronous with those at higher latitudes and all have shown declines in recent decades. Decreases in glaciers and ice caps contributed to sea level rise by 0.05 cm per year from 1961 to 2003, and 0.08 cm per year from 1993 to 2003. Taken together, shrinkage of the ice sheets of Greenland and Antarctica contributed 0.04 cm per year to sea level rise over 1993 to 2003 and about 0.1 cm per year to sea level rise since then.
Snow cover has decreased in many regions of the Northern Hemisphere, particularly in spring, consistent with greater increases in spring than autumn surface temperatures in middle latitudes. Sea-ice extents have decreased in the Arctic, particularly in the spring and summer seasons (13.3 per cent decade−1 decrease from 1978 through 2016 in September), and this is consistent with the fact that the average annual Arctic temperature has increased at twice the global average rate, although changes in winds are also a major factor. The lowest sea ice cover to date was in 2012. There have also been decreases in sea-ice thickness and an unprecedented increase in amount of rst year ice in the Arctic that is very vulnerable to melting. Temperatures at the top of the permafrost layer in the Arctic have increased since the 1980s (up to 3°C locally), and the maximum area covered by seasonally frozen ground has decreased by about 7 per cent in the Northern Hemisphere since 1900, with an even greater decrease (15 per cent) in the northern spring. There has been a reduction of about two weeks in the annual duration of northern lake and river ice cover.
In contrast to the Arctic, Antarctic sea ice did not exhibit any signi cant trend from the end of the
1970s through 2015, which is consistent with the lack of trend in surface temperature averaged south of 65°S over that period. However, in spring 2016 into autumn 2017, an exceptional drop in sea ice extent occurred in all sectors (Turner et al. 2017) in association with a strongly negative Southern Annular Mode (SAM) (see section 2.5.5). Moreover, along the Antarctic Peninsula, where signi cant warming has been observed, progressive break-up of ice shelves occurred beginning in the late 1980s, culminating in the break-up of the Larsen-B ice shelf in 2002 and Larsen-C in 2017. The latter created a huge iceberg the size of Delaware. Antarctic conditions are uniquely in uenced greatly by the ozone hole, which alters the atmospheric circulation over the southern regions.
2.3.4 Extremes
For changes in temperature, there is likely to be an ampli ed change in extremes. Extreme events, such as heat waves, are exceedingly important to both natural systems and human systems and infrastructure. People and ecosystems are adapted to a range of natural weather variations, but it is the extremes of weather and climate that exceed tolerances. Widespread changes in temperature extremes have been observed over the last 50 years. In particular, the number of heat waves globally has increased, and there have been widespread increases in the numbers of warm nights. Cold days, cold nights, and days with frost have become rarer. Such changes greatly affect the range of animals, including birds.
Satellite records suggest a global trend towards more intense and longer lasting tropical cyclones (including hurricanes and typhoons) since about 1970, correlated with observed warming of tropical SSTs, and consistent with expectations of more activity with global warming. There is no clear trend in the annual number of tropical cyclones globally although a substantial increase has occurred in the North Atlantic after 1994 and the most active month (in terms of hurricane days) ever on record is September 2017. There are concerns about the quality of tropical cyclone data, particularly before the satellite era. Further, strong multi-decadal variability is observed and complicates detection of long-term
trends in tropical cyclone activity. It has been estimated that heavy rains in tropical storms and hurricanes have increased by 10 to 15 per cent as a result of higher SSTs and more water vapour in the atmosphere (Trenberth 2007).
2.3.5 Precipitation and drought
Changes are occurring in the amount, intensity, frequency, and type of precipitation in ways that are also consistent with a warming planet. These aspects of precipitation generally exhibit large natural variability compared to temperature, making it harder to detect trends in the observational record. A key ingredient in changes in character of precipitation is the observed increase in water vapour and thus the supply of atmospheric moisture to all storms, increasing the intensity of precipitation events. This is consistent with the expectation that the water-holding capacity of the atmosphere increases by about 7 per cent per degree Celsius. Widespread increases in heavy precipitation events and risk of ooding have been observed, even in places where total amounts have decreased. Hence the frequency of heavy rain events has increased in most places and so too have episodic heavy snowfall events which are therefore associated with warming.
Long-term (1900–2015) trends have been observed in total precipitation amounts over some large regions. Signi cantly increased precipitation has been observed in eastern parts of North and South America, northern Europe, and northern Asia. Drying has been observed in the Sahel, the Mediterranean, southern Africa, and parts of eastern Asia. Precipitation is highly variable spatially and temporally. Robust long-term trends have not been observed for other large regions. The pattern of precipitation change is one of increases generally at higher northern latitudes (because as the atmosphere warms it holds more moisture) and drying in parts of the tropics and subtropics over land. Basin-scale changes in ocean salinity provide further evidence of changes in Earth’s water cycle, with freshening at high latitudes and increased salinity in the subtropics.
More intense and longer droughts have been observed over wider areas since the 1970s, particularly in the tropics and subtropics. Increased drying
due to higher temperatures and decreased precipitation have contributed to these changes, with the latter the dominant factor. The regions where droughts have occurred are determined largely by changes in SST, especially in the tropics (such as during El Niño), through changes in the atmospheric circulation and precipitation. In the western United States, diminishing snow pack and subsequent summer soil moisture reductions have also been a factor. In Australia and Europe, direct links to warming have been inferred through the extreme nature of high temperatures and heat waves accompanying drought.
In summary, there are an increasing number of many independent surface observations that give a consistent picture of a warming world.
2.4 Observed changes in atmospheric circulation
2.4.1 Sea level pressure
Much of the warming that has contributed to the GMST increases of recent decades (Figure 2.1) has occurred during northern winter and spring over the continents of the Northern Hemisphere. This pattern of warming is strongly related to decade-long changes in natural patterns of the atmospheric and oceanic circulation. The changes in northern winter circulation are re ected by lower-than-average sea level pressure over the middle and high latitudes of the North Paci c and North Atlantic Oceans, as well as over much of the Arctic, and higher-than-average sea level pressure over the subtropical Atlantic (Figure 2.5).
Over the North Paci c, the changes in sea level pressure correspond to an intensi cation of the Aleutian low-pressure system, while over the North Atlantic the changes correspond to intensi ed lowand high-pressure centres near Iceland and the Azores, respectively. These northern oceanic pressure systems are semi-permanent features of the winter atmospheric circulation (e.g., Hurrell and Deser 2009). Over the Southern Hemisphere, similar changes have been observed during the austral summer, with surface pressures lowering over the Antarctic and rising over middle latitudes since the late 1970s. The longterm signi cance of the southern sea level pressure
Figure 2.5 Northern winter (December–March) average Northern Hemisphere sea level pressure anomalies (hPa) 1981–2009 expressed as departures from the 1951–1980 values. Positive values are hatched. The sea level pressure data are from Trenberth and Paolino (1980).
change is more dif cult to establish, however, given the greater paucity of historical data over the Southern Ocean and Antarctica.
2.4.2 Winds and storm tracks
Changes in winds naturally accompany changes in sea level pressure because of the geostrophic relationship whereby the pressure gradients are largely balanced by the Coriolis force associated with the rotation of the Earth. Accordingly, winds rotate counterclockwise around a low-pressure system in the Northern Hemisphere and clockwise in the Southern Hemisphere. Cyclones are low pressure systems or depressions associated with unsettled stormy weather, as opposed to anticyclones which are high pressure systems and are dominated by ne, settled weather. In low latitudes, ‘tropical cyclone’ usually refers to a low-pressure system of a certain intensity (e.g., winds above gale force) and above another threshold they become hurricanes in the Western Hemisphere, typhoons in the northwest Paci c, or ‘cyclones’ in the Indian Ocean. Extratropical cyclones typically have cold and warm fronts attached to them.
Westerly ow across the middle latitudes of the Atlantic and Paci c sectors occurs throughout the year. As the vigour of the ow is related to the north-south (meridional) pressure gradient, the surface winds are strongest during winter when they average more than 5 m s−1 from the eastern United States across the Atlantic onto northern Europe as well as across the entire Paci c. These middle latitude westerly winds extend throughout the troposphere and reach their maximum (up to more than 40 m s−1 in the mean) at a height of about 12 km. This ‘jet stream’ roughly coincides with the path of storms travelling across the northern oceans onto the continents. These storm tracks play a critical role in both weather and climate, as they are associated with much of the precipitation and severe weather in middle latitudes, and they transport large amounts of heat, moisture, and momentum toward the poles.
Several studies indicate that there has been a poleward shift in the mean latitude of extratropical cyclones, and that cyclones have become fewer and more intense over the last fty years. For instance, the change towards a deeper polar vortex and Icelandic Low in northern winter (Figure 2.5) has been accompanied by intensi cation and poleward displacement of the Atlantic jet and associated enhancement of Atlantic storm track activity. Analogous changes have also been found over the North Paci c and in the Southern Hemisphere.
There are, however, signi cant uncertainties, with some studies suggesting that storm track activity during the last part of the twentieth century may not be more intense than the activity prior to the 1950s. Station pressure data over the Atlantic-European sector (where records are long and consistent) show a decline of storminess from high levels during the late nineteenth century to a minimum around 1960 and then a quite rapid increase to a maximum around 1990, followed again by a slight decline. Changes in storm tracks, however, are complex and are related to spatial shifts and strength changes in leading patterns of climate variability (next section).
There are a few studies of changes in surface winds themselves, but they are confounded by the nature of instrumentation, which has moving parts (that can rust or clog up) and which can be easily sheltered by growth of nearby trees (Vautard et al.
2010). Over oceans, the growing size of ships gives an apparent increase in wind that is likely spurious (Cardone et al. 1990). To the extent that they reveal real changes, they are associated mostly with decadal variations in teleconnections (next section).
2.5 Observed changes in patterns of circulation variability
2.5.1
Teleconnections
A consequence of the transient behaviour of atmospheric planetary-scale waves is that anomalies in climate on seasonal timescales typically occur over large geographic regions. Some regions may be cooler than average, while at the same time, thousands of kilometres away, warmer conditions prevail. These simultaneous variations in climate, often of opposite sign, over distant parts of the globe are commonly referred to as ‘teleconnections’ in the meteorological literature. Though their precise nature and shape vary to some extent according to the statistical methodology and the dataset employed in the analysis, consistent regional characteristics that identify the most conspicuous patterns emerge. Understanding the nature of teleconnections and changes in their behaviour is central to understanding regional climate variability and change, as well as impacts on humans and ecosystems.
The analysis of teleconnections has typically employed a linear perspective, which assumes a basic spatial pattern with varying amplitude and mirror image positive and negative polarities. In contrast, nonlinear interpretations identify preferred climate anomalies as recurrent states of a speci c polarity. Climate change may result as a preference for one polarity of a pattern, or through a change in the nature or number of states.
Arguably the most prominent teleconnections over the Northern Hemisphere extra-tropics are the North Atlantic Oscillation (NAO) and the Paci cNorth American (PNA) patterns, and their spatial structures are revealed most simply through onepoint correlation maps (Figure 2.6). A positive PNA teleconnection pattern in the middle troposphere coincides with the warm-phase ENSO pattern, and is typically associated with higher-than-normal pressure near Hawaii and over the northwestern
Figure 2.6 One-point correlation maps of 500 hPa geopotential heights for northern winter (December–February) over 1958–2006. In the top panel, the reference point is 45°N, 165°W, corresponding to the primary centre of action of the PNA pattern. In the lower panel, the NAO pattern is illustrated based on a reference point of 65°N, 30°W. Negative correlation coef cients are dashed, the contour increment is 0.2, and the zero contour has been excluded. Adapted from Hurrell and Deser (2009).
United States and western Canada, while pressures are typically lower-than-normal over the central North Paci c and the southeast United States. The difference of normalized height anomalies from these four centres forms the most commonly used time-varying index of the PNA (Table 2.2). Variations in the PNA pattern represent changes in the northsouth migration of the large-scale Paci c and North
American air masses, storm tracks and their associated weather, affecting precipitation in western North America and the frequency of Alaskan blocking events and associated cold air outbreaks over the western United States in winter.
In the Southern Hemisphere wave structures do not emerge as readily owing to the dominance of more zonally symmetric variability (the so-called SAM). Although teleconnections are best de ned over a grid, simple indices based on a few key station locations remain attractive, as the series can often be carried back in time long before complete gridded elds were available. The disadvantage of such station-based indices is increased noise from the reduced spatial sampling (Hurrell et al. 2003).
Many teleconnections have been identi ed, but combinations of only a small number of patterns can account for much of the interannual variability in the circulation and surface climate (Quadrelli and Wallace 2004). Trenberth et al. (2005) analysed global atmospheric mass and found four key patterns: the two annular modes (SAM and the Northern Annular Mode, or NAM), a global ENSO-related pattern, and a fourth closely related to the North Paci c Index (NPI) and the Paci c Decadal Oscillation (PDO), which in turn is closely related to ENSO and the PNA pattern.
Teleconnection patterns tend to be most prominent in winter (especially in the Northern Hemisphere), when the mean circulation is strongest. The strength of teleconnections and the way they in uence surface climate also vary over long timescales, and these aspects are exceedingly important for understanding regional climate change. In the following only a few predominant teleconnection patterns are documented.
2.5.2 ENSO and the PDO
Fluctuations in tropical Paci c SSTs are related to the occurrence of El Niño, during which the equatorial surface waters warm considerably from the International Date Line to the west coast of South America. The atmospheric phenomenon tied to El Niño is termed the Southern Oscillation, which is a global-scale standing wave in atmospheric mass (thus evident in sea level pressure), involving exchanges of air between Eastern and Western
Hemispheres centred in tropical and subtropical latitudes (Figure 2.7), and changes in the west-east overturning Walker Circulation near the Equator. The oscillation is characterized by the inverse variations in sea level pressure at Darwin (12.4°S, 130.9°E) in northern Australia and Tahiti (17.5°S, 149.6°W) in the south Paci c: annual mean pressures at these two stations are correlated at 0.8. A simple index of the SO is, therefore, often de ned by the Tahiti minus Darwin sea level pressure anomalies, normalized by the long-term mean and standard deviation of the mean sea level pressure difference, or simply by the negative of the Darwin record (Figure 2.7 and Table 2.2). During an El Niño event, the sea level pressure tends to be higher than usual at Darwin and lower than usual at Tahiti. Negative values of the SO index (SOI), therefore, are typically associated with warmer-than-average SSTs in the near equatorial Paci c, while positive values of the index are typically associated with colder-than-average SSTs. While changes in near equatorial Paci c SSTs can occur without a swing in the SO, El Niño (EN) and the SO are linked so closely that the term ENSO is used to describe the atmosphere–ocean interactions over the tropical Paci c. Warm ENSO events, therefore, are those in which both a negative SO extreme and an El Niño occur together.
During the warm phase of ENSO, the warming of the waters in the central and eastern tropical Paci c shifts the location of the heaviest tropical rainfall eastward toward or beyond the Date Line from its climatological position centered over Indonesia and the far western Paci c, weakening the Walker Circulation. This shift in rainfall also alters the heating patterns that force large-scale waves in the atmosphere. The waves in the air ow determine the preferred location of the extratropical storm tracks. Consequently, changes from one phase of the SO to another have a profound impact on regional temperatures (Figure 2.7). Most warm phase ENSO winters, for example, are mild over the western United States, although the regional details vary considerably from one event to another.
Although the SO has a typical period of 2–7 years, the strength of the oscillation has varied considerably. There were strong variations from the 1880s to the 1920s and after about 1950, but weaker variations
period 1866-1965
Figure 2.7 Correlations with the SOI (Table 1) for annual (May to April) means for sea level pressure (top left) and surface temperature (top right) for 1958 to 2004, and estimates of global precipitation for 1979 to 2003 (bottom left), updated from Trenberth and Caron (2000) and IPCC (2007a). The Darwin-based SOI, in normalized units of standard deviation, from 1866 to 2009 (lower right) features monthly values with an 11-point low-pass lter, which effectively removes uctuations with periods of less than eight months. The smooth black curve shows decadal variations. Light grey values indicate positive sea level pressure anomalies at Darwin and thus El Niño conditions.
in between (with the exception of the major 1939–41 event). A remarkable feature of the SOI is the decadal and longer-term variations in recent years, which is lacking from earlier periods.
Decadal to inter-decadal variability in the atmospheric circulation is especially prominent in the North Paci c (e.g., Trenberth and Hurrell 1994) where uctuations in the strength of the wintertime Aleutian Low pressure system, indicated by the North Paci c index (NPI; Table 2.2), co-vary with North Paci c SST in what has been termed the ‘Paci c Decadal Oscillation’ (PDO) or, its close cousin, the Inter-decadal Paci c Oscillation (the IPO) (Figure 2.8). The PDO/IPO has been described as a long-lived El Niño-like pattern of Indo-Paci c climate variability or as a low-frequency residual of ENSO variability on multi-decadal timescales. Phase changes of the PDO/IPO are associated with pronounced changes in temperature and rainfall patterns across North and South America, Asia, and Australia. Furthermore, ENSO teleconnections on
interannual timescales around the Paci c basin are signi cantly modi ed by the PDO/IPO.
Both the PDO (Figure 2.8) and the NPI (not shown) reveal extended periods of persistently anomalous values. Low PDO goes with high NPI values, indicative of a weakened circulation over the North Paci c (1900–1924, 1945–1976, 1999–2013) and predominantly high PDO values indicate a strengthened circulation (low NPI) (1925–1944, 1977–1998, and since 2014). The well-known decrease in Aleutian Low pressure from 1976 to 1977 is analogous to transitions that occurred from 1946 to 1947 and from 1924 to 1925, and these earlier changes were also associated with SST uctuations in the tropical Indian and Paci c Oceans (e.g., Deser et al. 2004).
The high PDO values relate to times of increases in the GMST (Figure 2.8) while the GMST no longer increases much for negative PDO values. From 1999 to 2013 this pause in the rise of GMST has also become known as a ‘hiatus’ in warming (Trenberth and Fasullo 2013; Trenberth et al. 2014; Trenberth
Darwin: Southern Oscillation index
Figure 2.8 Seasonal (December–January–February; etc.) global mean surface temperatures since 1920 (relative to the twentieth-century mean) vary considerably on interannual and decadal time scales. (B) Seasonal mean PDO anomalies show decadal regimes (positive in light grey; negative in dotted) as well as short-term variability. A 20-term Gaussian lter is used in both to show decadal variations, with anomalies re ected about the end point of March to May 2015 (heavy black curves). Adapted from Trenberth (2015).
2015; Fyfe et al. 2016). Although increases in GMST stall, the ocean heat content and sea level continue to rise, showing that the heat from global warming is being redistributed within the ocean, both with depth and regionally in the West Paci c. The main pacemaker of variability in rates of GMST increase appears to be the PDO, with aerosols likely playing a role in the earlier big hiatus in 1947–76. There have been three ‘super’ El Niños, where the main index has made it into the ‘very strong’ category: 1982–83, 1997–98 and 2015–16. The 1997–1998 event was the largest on record in terms of SST anomalies, and the GMST in 1998 was the highest on record last century. There are no ‘very strong’ La Niña events, which also highlights some aspects of the asymmetry between the two phases: La Niña events tend to last longer or be double-phased more often. Worldwide climate anomalies lasting several seasons have been identi ed with all of these events. The effects of the 2015–16 event were
not as great in coastal South America where the term ‘El Niño’ originated. Nevertheless, a very unusual coastal El Niño occurred in the rst few months of 2017, causing devastating stormy weather over northern Chile, Peru, and Colombia. Because of the enhanced activity in the Paci c and the changes in atmospheric circulation throughout the tropics, there is a decrease in the number of tropical storms and hurricanes in the tropical Atlantic during El Niño. Good examples are 1997 and 2015 which are the most active years globally; yet 1997 was one of the quietest Atlantic hurricane seasons on record. In contrast, the El Niño events of 1990–95, 1997–98, and 2015–16 terminated before the 1995, 1998, and 2017 hurricane seasons, which unleashed storms and placed those seasons among the most active on record in the Atlantic. In 2015, super typhoon Pam ripped through Vanuatu in March causing enormous damage, enabled by warm waters from the El Niño. Less than a year later, the
strongest hurricane on record in the Southern Hemisphere (Winston) severely damaged Fiji. The 2015 northern hurricane season featured by far the greatest number of category 4 and 5 hurricanes/ typhoons on record (25 vs previous record 18). Strong drought and wild res occurred in Indonesia, affecting air quality.
ENSO events involve large exchanges of heat between the ocean and atmosphere and affect GMST. Extremes of the hydrological cycle such as oods and droughts are common with ENSO and are apt to be enhanced with global warming. For example, the modest 2002–2003 El Niño was associated with a drought in Australia, made much worse by recordbreaking heat. A strong La Niña event took place 2007–08, contributing to 2008 being the coolest year since the turn of the twenty- rst century. 2016 is by far the warmest year on record, followed by 2015, in part because of the El Niño event, and 2014 is third (Figure 2.1). All of the impacts of El Niño are exacerbated by global warming.
2.5.3 Atlantic Multi-decadal Oscillation
Over the Atlantic sector decadal variability has large amplitude relative to interannual variability, especially over the North Atlantic. The Atlantic decadal variability has been termed the ‘Atlantic Multi-decadal Oscillation’ or AMO (Figure 2.9; Table 2.2) (Trenberth and Shea 2006). North Atlantic SSTs show a 65- to 75-year variation (±0.2°C range), with a warm phase 1930 to 1960, and after 1995, and cool phases during 1905 to 1925 and 1970 to 1995. Instrumental records are not long enough to determine whether AMO variability has a well-de ned period rather than a simpler character, such as ‘red
noise’. The robustness of the signal has therefore been addressed using paleoclimate records, and similar uctuations have been documented through the last four centuries (e.g., Delworth and Mann 2000). The slow changes in Atlantic SSTs have affected regional climate trends over parts of North America and Europe, hemispheric temperature anomalies, sea ice concentration in the Greenland Sea, and hurricane activity in the tropical Atlantic and Caribbean (e.g., Webster et al. 2005; Trenberth and Shea 2006). In addition, tropical Atlantic SST anomalies have contributed to rainfall anomalies over the Caribbean and the Nordeste region of Brazil, and severe multiyear droughts over parts of Africa including the Sahel (e.g., Hoerling et al. 2006). Tropical Atlantic SST variations are also a factor in producing drought conditions over portions of North America, although tropical Paci c SST variations appear to play a more dominant role (e.g., Seager et al. 2008). The tropical Atlantic SSTs were at record high levels in 2005, fuelling the very active hurricane season, but have also been exceptionally high in 2010 and 2017.
2.5.4 North Atlantic Oscillation
One of the most prominent teleconnection patterns is the NAO (Hurrell 1995), which refers to changes in the atmospheric sea level pressure difference between the Arctic and the subtropical Atlantic (Figures 2.9 and 2.10). Although it is the only teleconnection pattern evident throughout the year in the Northern Hemisphere, the climate anomalies associated with the NAO are largest during the northern winter months when the atmosphere is dynamically the most active.
A time series since 1900 of wintertime NAO variability, the spatial pattern of the oscillation, and NAO impacts on winter surface temperature and precipitation are shown in Figure 2.10. Most modern NAO indices are derived either from the simple difference in surface pressure anomalies between various northern and southern locations, or from the principal component time series of the leading (usually regional) mode2 of sea level pressure
2 The analysis to determine the dominant modes of variability is a principal component analysis which produces Empirical Orthogonal Functions (EOFs) as the eigenvectors of the covariance matrix.
Figure 2.9 Monthly Atlantic Multidecadal Oscillation (AMO) as de ned in Table 1, along with a low-pass ltered version to show decadal variability. Updated from Trenberth and Shea (2006).
Figure 2.10 Changes in northern winter (December–March) surface pressure, temperature, and precipitation corresponding to a unit deviation of the NAO index over 1900 to 2009. (Top left) Mean sea level pressure (0.1 hPa). (Top right) Land-surface air and SST (0.1 °C; contour increment 0.2 °C): regions of insuf cient data (e.g., over much of the Arctic) are not contoured. (Bottom left) Precipitation for 1979 to 2009 based on global estimates (0.1 mm day−1; contour interval 0.6 mm day−1). (Bottom right) Station-based index of winter NAO (Table 1). The heavy solid line represents the index smoothed to remove uctuations with periods less than four years. The indicated year corresponds to the January of the winter season (e.g., 1990 is the winter of 1989/1990). Adapted and updated from Hurrell et al. (2003) and IPCC (2007a).
(Hurrell and Deser 2009). A commonly used index (Figure 2.10; Table 2.2) is based on the differences in normalized sea level pressure anomalies between Lisbon, Portugal and Stykkisholmur, Iceland. This NAO index correlates with the NAM index (Table 2.2) at 0.85, which emphasizes the NAO and NAM re ect essentially the same mode of tropospheric variability.
The NAO exerts a dominant in uence on winter surface temperatures across much of the Northern
Hemisphere, and on storminess and precipitation over Europe and North Africa (Figure 2.10) (Hurrell et al. 2003). When the NAO index is positive, enhanced westerly ow across the North Atlantic in winter moves warm moist maritime air over much of Europe and far downstream, while stronger northerly winds over Greenland and northeastern Canada carry cold air southward and decrease land temperatures and SST over the northwest Atlantic. Temperature variations over North Africa and the
