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Bertrand Denis
,
Jean Côté
, and
René Laprise

Abstract

For most atmospheric fields, the larger part of the spatial variance is contained in the planetary scales. When examined over a limited area, these atmospheric fields exhibit an aperiodic structure, with large trends across the domain. Trying to use a standard (periodic) Fourier transform on regional domains results in the aliasing of large-scale variance into shorter scales, thus destroying all usefulness of spectra at large wavenumbers. With the objective of solving this particular problem, the authors have evaluated and adopted a spectral transform called the discrete cosine transform (DCT). The DCT is a widely used transform for compression of digital images such as MPEG and JPEG, but its use for atmospheric spectral analysis has not yet received widespread attention.

First, it is shown how the DCT can be employed for producing power spectra from two-dimensional atmospheric fields and how this technique compares favorably with the more conventional technique that consists of detrending the data before applying a periodic Fourier transform. Second, it is shown that the DCT can be used advantageously for extracting information at specific spatial scales by spectrally filtering the atmospheric fields. Examples of applications using data produced by a regional climate model are displayed. In particular, it is demonstrated how the 2D-DCT spectral decomposition is successfully used for calculating kinetic energy spectra and for separating mesoscale features from large scales.

Full access
Ramón de Elía
,
René Laprise
, and
Bertrand Denis

Abstract

The fundamental hypothesis underlying the use of limited-area models (LAMs) is their ability to generate meaningful small-scale features from low-resolution information, provided as initial conditions and at their lateral boundaries by a model or by objective analyses. This hypothesis has never been seriously challenged in spite of some reservations expressed by the scientific community. In order to study this hypothesis, a perfect-model approach is followed. A high-resolution large-domain LAM driven by global analyses is used to generate a “reference run.” These fields are filtered afterward to remove small scales in order to mimic a low-resolution run. The same high-resolution LAM, but in a small-domain grid, is nested within these filtered fields and run for several days. Comparison of both runs over the same region allows for the estimation of the ability of the LAM to regenerate the removed small scales.

Results show that the small-domain LAM recreates the right amount of small-scale variability but is incapable of reproducing it with the precision required by a root-mean-square (rms) measure of error. Some success is attained, however, during the first hours of integration. This suggests that LAMs are not very efficient in accurately downscaling information, even in a perfect-model context. On the other hand, when the initial conditions used in the small-domain LAM include the small-scale features that are still absent in the lateral boundary conditions, results improve dramatically. This suggests that lack of high-resolution information in the boundary conditions has a small impact on the performance.

Results of this study also show that predictability timescales of different wavelengths exhibit a behavior similar to those of a global autonomous model.

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René Laprise
,
Mundakkara Ravi Varma
,
Bertrand Denis
,
Daniel Caya
, and
Isztar Zawadzki

Abstract

This note investigates the nature of the extended predictability commonly attributed to high-resolution limited-area models (LAM) nested with low-resolution data at their lateral boundaries. LAM simulations are performed with two different sets of initial, nesting, and verification data: one is a set of regional objective analyses and the other is a synthetic high-resolution model-generated dataset. The simulation differences (equivalent to forecast errors in an operational framework) are studied in terms of their horizontal scale distribution normalized by the natural variability in each scale, as a measure of predictability, which constitutes an original contribution of this note. The results suggest that the extended predictability in LAM is confined to those scales that are present both in the initial condition and lateral boundary conditions (LBCs). No evidence is found for extended predictability of scales that are not forced through the LBCs. Instead, these smaller scales exhibit predictive timescales in direct relation to their spatial scales, similar to the behavior in autonomous global models.

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Arlan Dirkson
,
Bertrand Denis
,
Michael Sigmond
, and
William J. Merryfield

Abstract

Dynamical forecasting systems are being used to skillfully predict deterministic ice-free and freeze-up date events in the Arctic. This paper extends such forecasts to a probabilistic framework and tests two calibration models to correct systematic biases and improve the statistical reliability of the event dates: trend-adjusted quantile mapping (TAQM) and nonhomogeneous censored Gaussian regression (NCGR). TAQM is a probability distribution mapping method that corrects the forecast for climatological biases, whereas NCGR relates the calibrated parametric forecast distribution to the raw ensemble forecast through a regression model framework. For NCGR, the observed event trend and ensemble-mean event date are used to predict the central tendency of the predictive distribution. For modeling forecast uncertainty, we find that the ensemble-mean event date, which is related to forecast lead time, performs better than the ensemble variance itself. Using a multidecadal hindcast record from the Canadian Seasonal to Interannual Prediction System (CanSIPS), TAQM and NCGR are applied to produce categorical forecasts quantifying the probabilities for early, normal, and late ice retreat and advance. While TAQM performs better than adjusting the raw forecast for mean and linear trend bias, NCGR is shown to outperform TAQM in terms of reliability, skill, and an improved tendency for forecast probabilities to be no worse than climatology. Testing various cross-validation setups, we find that NCGR remains useful when shorter hindcast records (~20 years) are available. By applying NCGR to operational forecasts, stakeholders can be more confident in using seasonal forecasts of sea ice event timing for planning purposes.

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Hai Lin
,
Normand Gagnon
,
Stephane Beauregard
,
Ryan Muncaster
,
Marko Markovic
,
Bertrand Denis
, and
Martin Charron

Abstract

Dynamical monthly prediction at the Canadian Meteorological Centre (CMC) was produced as part of the seasonal forecasting system over the past two decades. A new monthly forecasting system, which has been in operation since July 2015, is set up based on the operational Global Ensemble Prediction System (GEPS). This monthly forecasting system is composed of two components: 1) the real-time forecast, where the GEPS is extended to 32 days every Thursday; and 2) a 4-member hindcast over the past 20 years, which is used to obtain the model climatology to calibrate the monthly forecast. Compared to the seasonal prediction system, the GEPS-based monthly forecasting system takes advantage of the increased model resolution and improved initialization.

Forecasts of the past 2-yr period (2014 and 2015) are verified. Analysis is performed separately for the winter half-year (November–April), and the summer half-year (May–October). Weekly averages of 2-m air temperature (T2m) and 500-hPa geopotential height (Z500) are assessed. For Z500 in the Northern Hemisphere, limited skill can be found beyond week 2 (days 12–18) in summer, while in winter some skill exists over the Pacific and North American region beyond week 2. For T2m in North America, significant skill is found over a large part of the continent all the way to week 4 (days 26–32). The distribution of the wintertime T2m skill in North America is consistent with the influence of the Madden–Julian oscillation, indicating that a significant part of predictability likely comes from the tropics.

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Luc Fillion
,
Monique Tanguay
,
Ervig Lapalme
,
Bertrand Denis
,
Michel Desgagne
,
Vivian Lee
,
Nils Ek
,
Zhuo Liu
,
Manon Lajoie
,
Jean-François Caron
, and
Christian Pagé

Abstract

This paper describes the recent changes to the regional data assimilation and forecasting system at the Canadian Meteorological Center. A major aspect is the replacement of the currently operational global variable resolution forecasting approach by a limited-area nested approach. In addition, the variational analysis code has been upgraded to allow limited-area three- and four-dimensional variational data assimilation (3D- and 4DVAR) analysis approaches. As a first implementation step, the constraints were to impose similar background error correlation modeling assumptions, equal computer resources, and the use of the same assimilated data. Both bi-Fourier and spherical-harmonics spectral representations of background error correlations were extensively tested for the large horizontal domain considered for the Canadian regional system. Under such conditions, it is shown that the new regional data assimilation and forecasting system performs as well as the current operational system and it produces slightly better 24-h accumulated precipitation scores as judged from an ensemble of winter and summer cases. Because of the large horizontal extent of the regional domain considered, a spherical-harmonics spectral representation of background error correlations was shown to perform better than the bi-Fourier representation, considering all evaluation scores examined in this study. The latter is more suitable for smaller domains and will be kept for the upcoming use in the kilometric-scale local analysis domains in order to support the Canadian Meteorological Center’s (CMC’s) operations using multiple domains over Canada. The CMC’s new regional system [i.e., a regional limited-area 3DVAR data assimilation system coupled to a limited-area model (REG-LAM3D)] is now undergoing its final evaluations before operational transfer. Important model and data assimilation upgrades are currently under development to fully exploit this new system and are briefly presented.

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Julie M. Thériault
,
Roy Rasmussen
,
Trevor Smith
,
Ruping Mo
,
Jason A. Milbrandt
,
Melinda M. Brugman
,
Paul Joe
,
George A. Isaac
,
Jocelyn Mailhot
, and
Bertrand Denis

Abstract

Accurate forecasting of precipitation phase and intensity was critical information for many of the Olympic venue managers during the Vancouver 2010 Olympic and Paralympic Winter Games. Precipitation forecasting was complicated because of the complex terrain and warm coastal weather conditions in the Whistler area of British Columbia, Canada. The goal of this study is to analyze the processes impacting precipitation phase and intensity during a winter weather storm associated with rain and snow over complex terrain. The storm occurred during the second day of the Olympics when the downhill ski event was scheduled. At 0000 UTC 14 February, 2 h after the onset of precipitation, a rapid cooling was observed at the surface instrumentation sites. Precipitation was reported for 8 h, which coincided with the creation of a nearly 0°C isothermal layer, as well as a shift of the valley flow from up valley to down valley. Widespread snow was reported on Whistler Mountain with periods of rain at the mountain base despite the expectation derived from synoptic-scale models (15-km grid spacing) that the strong warm advection would maintain temperatures above freezing. Various model predictions are compared with observations, and the processes influencing the temperature, wind, and precipitation types are discussed. Overall, this case study provided a well-observed scenario of winter storms associated with rain and snow over complex terrain.

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Paul Joe
,
Chris Doyle
,
Al Wallace
,
Stewart G. Cober
,
Bill Scott
,
George A. Isaac
,
Trevor Smith
,
Jocelyn Mailhot
,
Brad Snyder
,
Stephane Belair
,
Quinton Jansen
, and
Bertrand Denis
Full access
Ben P. Kirtman
,
Dughong Min
,
Johnna M. Infanti
,
James L. Kinter III
,
Daniel A. Paolino
,
Qin Zhang
,
Huug van den Dool
,
Suranjana Saha
,
Malaquias Pena Mendez
,
Emily Becker
,
Peitao Peng
,
Patrick Tripp
,
Jin Huang
,
David G. DeWitt
,
Michael K. Tippett
,
Anthony G. Barnston
,
Shuhua Li
,
Anthony Rosati
,
Siegfried D. Schubert
,
Michele Rienecker
,
Max Suarez
,
Zhao E. Li
,
Jelena Marshak
,
Young-Kwon Lim
,
Joseph Tribbia
,
Kathleen Pegion
,
William J. Merryfield
,
Bertrand Denis
, and
Eric F. Wood

The recent U.S. National Academies report, Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users.

The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model.

Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2011), a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data are readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (www.cpc.ncep.noaa.gov/products/NMME/). Moreover, the NMME forecast is already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, and presents an overview of the multimodel forecast quality and the complementary skill associated with individual models.

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Tim Boyer
,
Ellen Bartow-Gillies
,
A. Abida
,
Melanie Ades
,
Robert Adler
,
Susheel Adusumilli
,
W. Agyakwah
,
Brandon Ahmasuk
,
Laura S. Aldeco
,
Mihai Alexe
,
Eric J. Alfaro
,
Richard P. Allan
,
Adam Allgood
,
Lincoln. M. Alves
,
Jorge A. Amador
,
John Anderson
,
B. Andrade
,
Orlane Anneville
,
Yasuyuki Aono
,
Anthony Arguez
,
Carlo Arosio
,
C. Atkinson
,
John A. Augustine
,
Grinia Avalos
,
Cesar Azorin-Molina
,
Stacia A. Backensto
,
Stephan Bader
,
Julian Baez
,
Rebecca Baiman
,
Thomas J. Ballinger
,
Alison F. Banwell
,
M. Yu Bardin
,
Jonathan Barichivich
,
John E. Barnes
,
Sandra Barreira
,
Rebecca L. Beadling
,
Hylke E. Beck
,
Emily J. Becker
,
E. Bekele
,
Guillem Martín Bellido
,
Nicolas Bellouin
,
Angela Benedetti
,
Rasmus Benestad
,
Christine Berne
,
Logan. T. Berner
,
Germar H. Bernhard
,
Uma S. Bhatt
,
A. E. Bhuiyan
,
Siiri Bigalke
,
Tiago Biló
,
Peter Bissolli
,
W. Bjerke Jarle
,
Kevin Blagrave
,
Eric S. Blake
,
Stephen Blenkinsop
,
Jessica Blunden
,
Oliver Bochníček
,
Olivier Bock
,
Xavier Bodin
,
Michael Bosilovich
,
Olivier Boucher
,
Deniz Bozkurt
,
Brian Brettschneider
,
Francis G. Bringas
,
Francis Bringas
,
Dennis Buechler
,
Stefan A. Buehler
,
Brandon Bukunt
,
Blanca Calderón
,
Suzana J. Camargo
,
Jayaka Campbell
,
Diego Campos
,
Laura Carrea
,
Brendan R. Carter
,
Ivona Cetinić
,
Don P. Chambers
,
Duo Chan
,
Elise Chandler
,
Kai-Lan Chang
,
Hua Chen
,
Lin Chen
,
Lijing Cheng
,
Vincent Y. S. Cheng
,
Leah Chomiak
,
Hanne H. Christiansen
,
John R. Christy
,
Eui-Seok Chung
,
Laura M. Ciasto
,
Leonardo Clarke
,
Kyle R. Clem
,
Scott Clingan
,
Caio A.S. Coelho
,
Judah L. Cohen
,
Melanie Coldewey-Egbers
,
Steve Colwell
,
Owen R. Cooper
,
Richard C. Cornes
,
Kris Correa
,
Felipe Costa
,
Curt Covey
,
Lawrence Coy
,
Jean-François Créatux
,
Lenka Crhova
,
Theresa Crimmins
,
Meghan F. Cronin
,
Thomas Cropper
,
Molly Crotwell
,
Joshua Culpepper
,
Ana P. Cunha
,
Diego Cusicanqui
,
Rajashree T. Datta
,
Sean M. Davis
,
Veerle De Bock
,
Richard A. M. de Jeu
,
Jos De Laat
,
Bertrand Decharme
,
Doug Degenstein
,
Reynald Delaloye
,
Mesut Demircan
,
Chris Derksen
,
Ricardo Deus
,
K. R. Dhurmea
,
Howard J. Diamond
,
S. Dirkse
,
Dmitry Divine
,
Martin T. Dokulil
,
Markus G. Donat
,
Shenfu Dong
,
Wouter A. Dorigo
,
Caroline Drost Jensen
,
Matthew L. Druckenmiller
,
Paula Drumond
,
Marcel du Plessis
,
Hilary A. Dugan
,
Dashkhuu Dulamsuren
,
Devon Dunmire
,
Robert J. H. Dunn
,
Imke Durre
,
Geoff Dutton
,
Gregory Duveiller
,
Mithat Ekici
,
Alesksandra Elias Chereque
,
M. ElKharrim
,
Howard E. Epstein
,
Jhan-Carlo Espinoza
,
Thomas W. Estilow
,
Nicole Estrella
,
Nicolas Fauchereau
,
Robert S. Fausto
,
Richard A. Feely
,
Chris Fenimore
,
David Fereday
,
Xavier Fettweis
,
vitali E. Fioletov
,
Johannes Flemming
,
Chris Fogarty
,
Ryan L. Fogt
,
Bruce C. Forbes
,
Michael J. Foster
,
Bryan A. Franz
,
Natalie M. Freeman
,
Helen A. Fricker
,
Stacey M. Frith
,
Lucien Froidevaux
,
Gerald V. Frost
,
Steven Fuhrman
,
Martin Füllekrug
,
Catherine Ganter
,
Meng Gao
,
Alex S. Gardner
,
Judith Garforth
,
Jay Garg
,
Sebastian Gerland
,
Badin Gibbes
,
Sarah T. Gille
,
John Gilson
,
Karin Gleason
,
Nadine Gobron
,
Scott J. Goetz
,
Stanley B. Goldenberg
,
Gustavo Goni
,
Steven Goodman
,
Atsushi Goto
,
Jens-Uwe Grooß
,
Alexander Gruber
,
Guojun Gu
,
Charles “Chip” P. Guard
,
S. Hagos
,
Sebastian Hahn
,
Leopold Haimberger
,
Bradley D. Hall
,
Benjamin D. Hamlington
,
Edward Hanna
,
Inger Hanssen-Bauer
,
Daniel S. Harnos
,
Ian Harris
,
Qiong He
,
Richard R. Heim Jr.
,
Sverker Hellström
,
Deborah L. Hemming
,
Stefan Hendricks
,
J. Hicks
,
Hugo G. Hidalgo
,
Martin Hirschi
,
Shu-peng Ho
,
W. Hobbs
,
Robert M. Holmes
,
Robert Holzworth
,
Filip Hrbáček
,
Guojie Hu
,
Zeng-Zhen Hu
,
Boyin Huang
,
Hongjie Huang
,
Dale F. Hurst
,
Iolanda Ialongo
,
Antje Inness
,
Ketil Isaksen
,
Masayoshi Ishii
,
Gerardo Jadra
,
Svetlana Jevrejeva
,
Viju O. John
,
W. Johns
,
Bjørn Johnsen
,
Bryan Johnson
,
Gregory C. Johnson
,
Philip D. Jones
,
Timothy Jones
,
Simon A. Josey
,
G. Jumaux
,
Robert Junod
,
Andreas Kääb
,
K. Kabidi
,
Johannes W. Kaiser
,
Robb S.A. Kaler
,
Lars Kaleschke
,
Viktor Kaufmann
,
Amin Fazl Kazemi
,
Linda M. Keller
,
Andreas Kellerer-Pirklbauer
,
Mike Kendon
,
John Kennedy
,
Elizabeth C. Kent
,
Kenneth Kerr
,
Valentina Khan
,
Mai Van Khiem
,
Richard Kidd
,
Mi Ju Kim
,
Seong-Joong Kim
,
Zak Kipling
,
Philip J. Klotzbach
,
John A. Knaff
,
Akash Koppa
,
Natalia N. Korshunova
,
Benjamin M. Kraemer
,
Natalya A. Kramarova
,
A. C. Kruger
,
Andries Kruger
,
Arun Kumar
,
Michelle L’Heureux
,
Sofia La Fuente
,
Alo Laas
,
Zachary M. Labe
,
Rick Lader
,
Mónika Lakatos
,
Kaisa Lakkala
,
Hoang Phuc Lam
,
Xin Lan
,
Peter Landschützer
,
Chris W. Landsea
,
Timothy Lang
,
Matthias Lankhorst
,
Kathleen O. Lantz
,
Mark J. Lara
,
Waldo Lavado-Casimiro
,
David A. Lavers
,
Matthew A. Lazzara
,
Thierry Leblanc
,
Tsz-Cheung Lee
,
Eric M. Leibensperger
,
Chris Lennard
,
Eric Leuliette
,
Kinson H. Y. Leung
,
Jan L. Lieser
,
Tanja Likso
,
I-I. Lin
,
Jackie Lindsey
,
Yakun Liu
,
Ricardo Locarnini
,
Norman G. Loeb
,
Bryant D. Loomis
,
Andrew M. Lorrey
,
Diego Loyola
,
Rui Lu
,
Rick Lumpkin
,
Jing-Jia Luo
,
Kari Luojus
,
John M. Lyman
,
Stephen C. Maberly
,
Matthew J. Macander
,
Michael MacFerrin
,
Graeme A. MacGilchrist
,
Michelle L. MacLennan
,
Remi Madelon
,
Andrew D. Magee
,
Florence Magnin
,
Jostein Mamen
,
Ken D. Mankoff
,
Gloria L. Manney
,
Izolda Marcinonienė
,
Jose A. Marengo
,
Mohammadi Marjan
,
Ana E. Martínez
,
Robert A. Massom
,
Shin-Ichiro Matsuzaki
,
Linda May
,
Michael Mayer
,
Matthew R. Mazloff
,
Stephanie A. McAfee
,
C. McBride
,
Matthew F. McCabe
,
James W. McClelland
,
Michael J. McPhaden
,
Tim R. Mcvicar
,
Carl A. Mears
,
Walter N. Meier
,
A. Mekonnen
,
Annette Menzel
,
Christopher J. Merchant
,
Mark A. Merrifield
,
Michael F. Meyer
,
Tristan Meyers
,
David E. Mikolajczyk
,
John B. Miller
,
Diego G. Miralles
,
Noelia Misevicius
,
Alexey Mishonov
,
Gary T. Mitchum
,
Ben I. Moat
,
Leander Moesinger
,
Aurel Moise
,
Jorge Molina-Carpio
,
Ghislaine Monet
,
Stephan A. Montzka
,
Twila A. Moon
,
G. W. K. Moore
,
Natali Mora
,
Johnny Morán
,
Claire Morehen
,
Colin Morice
,
A. E. Mostafa
,
Thomas L. Mote
,
Ivan Mrekaj
,
Lawrence Mudryk
,
Jens Mühle
,
Rolf Müller
,
David Nance
,
Eric R. Nash
,
R. Steven Nerem
,
Paul A. Newman
,
Julien P. Nicolas
,
Juan J. Nieto
,
Jeannette Noetzli
,
Ben Noll
,
Taylor Norton
,
Kelsey E. Nyland
,
John O’Keefe
,
Naomi Ochwat
,
Yoshinori Oikawa
,
Yuka Okunaka
,
Timothy J. Osborn
,
James E. Overland
,
Taejin Park
,
Mark Parrington
,
Julia K. Parrish
,
Richard J. Pasch
,
Reynaldo Pascual Ramírez
,
Cécile Pellet
,
Mauri S. Pelto
,
Melita Perčec Tadić
,
Donald K. Perovich
,
Guðrún Nína Petersen
,
Kyle Petersen
,
Irina Petropavlovskikh
,
Alek Petty
,
Alexandre B. Pezza
,
Luciano P. Pezzi
,
Coda Phillips
,
Gareth K. Phoenix
,
Don Pierson
,
Izidine Pinto
,
Vanda Pires
,
Michael Pitts
,
Stephen Po-Chedley
,
Paolo Pogliotti
,
Kristin Poinar
,
Lorenzo Polvani
,
Wolfgang Preimesberger
,
Colin Price
,
Merja Pulkkanen
,
Sarah G. Purkey
,
Bo Qiu
,
Kenny Quisbert
,
Willy R. Quispe
,
M. Rajeevan
,
Andrea M. Ramos
,
William J. Randel
,
Mika Rantanen
,
Marilyn N. Raphael
,
James Reagan
,
Cristina Recalde
,
Phillip Reid
,
Samuel Rémy
,
Alejandra J. Reyes Kohler
,
Lucrezia Ricciardulli
,
Andrew D. Richardson
,
Robert Ricker
,
David A. Robinson
,
M. Robjhon
,
Willy Rocha
,
Matthew Rodell
,
Esteban Rodriguez Guisado
,
Nemesio Rodriguez-Fernandez
,
Vladimir E. Romanovsky
,
Josyane Ronchail
,
Matthew Rosencrans
,
Karen H. Rosenlof
,
Benjamin Rösner
,
Henrieke Rösner
,
Alexei Rozanov
,
Jozef Rozkošný
,
Frans Rubek
,
Olga O. Rusanovskaya
,
This Rutishauser
,
C. T. Sabeerali
,
Roberto Salinas
,
Ahira Sánchez-Lugo
,
Michelle L. Santee
,
Marcelo Santini
,
Katsunari Sato
,
Parnchai Sawaengphokhai
,
A. Sayouri
,
Theodore Scambos
,
Verena Schenzinger
,
Semjon Schimanke
,
Robert W. Schlegel
,
Claudia Schmid
,
Martin Schmid
,
Udo Schneider
,
Carl J. Schreck
,
Cristina Schultz
,
Science Systems and Applications Inc. Science Systems and Applications Inc.
,
Z. T. Segele
,
Serhat Sensoy
,
Shawn P. Serbin
,
Mark C. Serreze
,
Amsari Mudzakir Setiawan
,
Fumi Sezaki
,
Sapna Sharma
,
Jonathan D. Sharp
,
Gay Sheffield
,
Jia-Rui Shi
,
Lei Shi
,
Alexander I. Shiklomanov
,
Nikolay I. Shiklomanov
,
Svetlana V. Shimaraeva
,
R. Shukla
,
David A. Siegel
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Eugene A. Silow
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F. Sima
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Adrian J. Simmons
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David A. Smeed
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Adam Smith
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Sharon L. Smith
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Andrea K. Steiner
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,
Pietro Stradiotti
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Piet Verburg
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Jean-Paul Vernier
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Sebastián Vivero
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Denis L. Volkov
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Holger Vömel
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Laura Ohlmann
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Lukas Noguchi
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Deborah B. Riddle
, and
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Abstract

—J. BLUNDEN, T. BOYER, AND E. BARTOW-GILLIES

Earth’s global climate system is vast, complex, and intricately interrelated. Many areas are influenced by global-scale phenomena, including the “triple dip” La Niña conditions that prevailed in the eastern Pacific Ocean nearly continuously from mid-2020 through all of 2022; by regional phenomena such as the positive winter and summer North Atlantic Oscillation that impacted weather in parts the Northern Hemisphere and the negative Indian Ocean dipole that impacted weather in parts of the Southern Hemisphere; and by more localized systems such as high-pressure heat domes that caused extreme heat in different areas of the world. Underlying all these natural short-term variabilities are long-term climate trends due to continuous increases since the beginning of the Industrial Revolution in the atmospheric concentrations of Earth’s major greenhouse gases.

In 2022, the annual global average carbon dioxide concentration in the atmosphere rose to 417.1±0.1 ppm, which is 50% greater than the pre-industrial level. Global mean tropospheric methane abundance was 165% higher than its pre-industrial level, and nitrous oxide was 24% higher. All three gases set new record-high atmospheric concentration levels in 2022.

Sea-surface temperature patterns in the tropical Pacific characteristic of La Niña and attendant atmospheric patterns tend to mitigate atmospheric heat gain at the global scale, but the annual global surface temperature across land and oceans was still among the six highest in records dating as far back as the mid-1800s. It was the warmest La Niña year on record. Many areas observed record or near-record heat. Europe as a whole observed its second-warmest year on record, with sixteen individual countries observing record warmth at the national scale. Records were shattered across the continent during the summer months as heatwaves plagued the region. On 18 July, 104 stations in France broke their all-time records. One day later, England recorded a temperature of 40°C for the first time ever. China experienced its second-warmest year and warmest summer on record. In the Southern Hemisphere, the average temperature across New Zealand reached a record high for the second year in a row. While Australia’s annual temperature was slightly below the 1991–2020 average, Onslow Airport in Western Australia reached 50.7°C on 13 January, equaling Australia's highest temperature on record.

While fewer in number and locations than record-high temperatures, record cold was also observed during the year. Southern Africa had its coldest August on record, with minimum temperatures as much as 5°C below normal over Angola, western Zambia, and northern Namibia. Cold outbreaks in the first half of December led to many record-low daily minimum temperature records in eastern Australia.

The effects of rising temperatures and extreme heat were apparent across the Northern Hemisphere, where snow-cover extent by June 2022 was the third smallest in the 56-year record, and the seasonal duration of lake ice cover was the fourth shortest since 1980. More frequent and intense heatwaves contributed to the second-greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Glaciers in the Swiss Alps lost a record 6% of their volume. In South America, the combination of drought and heat left many central Andean glaciers snow free by mid-summer in early 2022; glacial ice has a much lower albedo than snow, leading to accelerated heating of the glacier. Across the global cryosphere, permafrost temperatures continued to reach record highs at many high-latitude and mountain locations.

In the high northern latitudes, the annual surface-air temperature across the Arctic was the fifth highest in the 123-year record. The seasonal Arctic minimum sea-ice extent, typically reached in September, was the 11th-smallest in the 43-year record; however, the amount of multiyear ice—ice that survives at least one summer melt season—remaining in the Arctic continued to decline. Since 2012, the Arctic has been nearly devoid of ice more than four years old.

In Antarctica, an unusually large amount of snow and ice fell over the continent in 2022 due to several landfalling atmospheric rivers, which contributed to the highest annual surface mass balance, 15% to 16% above the 1991–2020 normal, since the start of two reanalyses records dating to 1980. It was the second-warmest year on record for all five of the long-term staffed weather stations on the Antarctic Peninsula. In East Antarctica, a heatwave event led to a new all-time record-high temperature of −9.4°C—44°C above the March average—on 18 March at Dome C. This was followed by the collapse of the critically unstable Conger Ice Shelf. More than 100 daily low sea-ice extent and sea-ice area records were set in 2022, including two new all-time annual record lows in net sea-ice extent and area in February.

Across the world’s oceans, global mean sea level was record high for the 11th consecutive year, reaching 101.2 mm above the 1993 average when satellite altimetry measurements began, an increase of 3.3±0.7 over 2021. Globally-averaged ocean heat content was also record high in 2022, while the global sea-surface temperature was the sixth highest on record, equal with 2018. Approximately 58% of the ocean surface experienced at least one marine heatwave in 2022. In the Bay of Plenty, New Zealand’s longest continuous marine heatwave was recorded.

A total of 85 named tropical storms were observed during the Northern and Southern Hemisphere storm seasons, close to the 1991–2020 average of 87. There were three Category 5 tropical cyclones across the globe—two in the western North Pacific and one in the North Atlantic. This was the fewest Category 5 storms globally since 2017. Globally, the accumulated cyclone energy was the lowest since reliable records began in 1981. Regardless, some storms caused massive damage. In the North Atlantic, Hurricane Fiona became the most intense and most destructive tropical or post-tropical cyclone in Atlantic Canada’s history, while major Hurricane Ian killed more than 100 people and became the third costliest disaster in the United States, causing damage estimated at $113 billion U.S. dollars. In the South Indian Ocean, Tropical Cyclone Batsirai dropped 2044 mm of rain at Commerson Crater in Réunion. The storm also impacted Madagascar, where 121 fatalities were reported.

As is typical, some areas around the world were notably dry in 2022 and some were notably wet. In August, record high areas of land across the globe (6.2%) were experiencing extreme drought. Overall, 29% of land experienced moderate or worse categories of drought during the year. The largest drought footprint in the contiguous United States since 2012 (63%) was observed in late October. The record-breaking megadrought of central Chile continued in its 13th consecutive year, and 80-year record-low river levels in northern Argentina and Paraguay disrupted fluvial transport. In China, the Yangtze River reached record-low values. Much of equatorial eastern Africa had five consecutive below-normal rainy seasons by the end of 2022, with some areas receiving record-low precipitation totals for the year. This ongoing 2.5-year drought is the most extensive and persistent drought event in decades, and led to crop failure, millions of livestock deaths, water scarcity, and inflated prices for staple food items.

In South Asia, Pakistan received around three times its normal volume of monsoon precipitation in August, with some regions receiving up to eight times their expected monthly totals. Resulting floods affected over 30 million people, caused over 1700 fatalities, led to major crop and property losses, and was recorded as one of the world’s costliest natural disasters of all time. Near Rio de Janeiro, Brazil, Petrópolis received 530 mm in 24 hours on 15 February, about 2.5 times the monthly February average, leading to the worst disaster in the city since 1931 with over 230 fatalities.

On 14–15 January, the Hunga Tonga-Hunga Ha'apai submarine volcano in the South Pacific erupted multiple times. The injection of water into the atmosphere was unprecedented in both magnitude—far exceeding any previous values in the 17-year satellite record—and altitude as it penetrated into the mesosphere. The amount of water injected into the stratosphere is estimated to be 146±5 Terragrams, or ∼10% of the total amount in the stratosphere. It may take several years for the water plume to dissipate, and it is currently unknown whether this eruption will have any long-term climate effect.

Open access