Search Results

You are looking at 21 - 30 of 31 items for

  • Author or Editor: William J. Merryfield x
  • Refine by Access: All Content x
Clear All Modify Search
Reinel Sospedra-Alfonso
,
William J. Merryfield
,
Viatsheslav V. Kharin
,
Woo-Sung Lee
,
Hai Lin
,
Gulilat T. Diro
, and
Ryan Muncaster

Abstract

We evaluate the soil moisture hindcasts and the reconstruction runs giving the hindcasts initial conditions in version 2.1 of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2.1). Different strategies are used to initialize the hindcasts for the two CanSIPSv2.1 models, CanCM4i and the coupled Global Environmental Multiscale, version 5.1, (GEM5)–NEMO model (GEM5-NEMO), with contrasting impacts on the soil moisture initial conditions and forecast performance. Forecast correlation skill is decomposed into contributions from persistence of the initial anomalies and contributions not linked to persistence, with performance largely driven by the accuracy of the initial conditions in regions of strong persistence. Seasonal soil moisture correlation skill is significant for several months into the hindcasts depending on initial and target months, with contributions not linked to persistence becoming more notable at longer lead times. For the first 2–4 months, the quality of CanSIPSv2.1 ensemble mean forecasts tends to be higher on average during summer and fall and is comparable to that of the best performing model, whereas CanSIPSv2.1 outperforms the single models during spring and winter. For longer lead times, remote climate influences from the Pacific Ocean are notable and contribute to predictable soil moisture variability in teleconnected regions.

Open access
William J. Merryfield
,
Woo-Sung Lee
,
George J. Boer
,
Viatcheslav V. Kharin
,
John F. Scinocca
,
Gregory M. Flato
,
R. S. Ajayamohan
,
John C. Fyfe
,
Youmin Tang
, and
Saroja Polavarapu

Abstract

The Canadian Seasonal to Interannual Prediction System (CanSIPS) became operational at Environment Canada's Canadian Meteorological Centre (CMC) in December 2011, replacing CMC's previous two-tier system. CanSIPS is a two-model forecasting system that combines ensemble forecasts from the Canadian Centre for Climate Modeling and Analysis (CCCma) Coupled Climate Model, versions 3 and 4 (CanCM3 and CanCM4, respectively). Mean climate as well as climate trends and variability in these models are evaluated in freely running historical simulations. Initial conditions for CanSIPS forecasts are obtained from an ensemble of coupled assimilation runs. These runs assimilate gridded atmospheric analyses by means of a procedure that resembles the incremental analysis update technique, but introduces only a fraction of the analysis increment in order that differences between ensemble members reflect the magnitude of observational uncertainties. The land surface is initialized through its response to the assimilative meteorology, whereas sea ice concentration and sea surface temperature are relaxed toward gridded observational values. The subsurface ocean is initialized through surface forcing provided by the assimilation run, together with an offline variational assimilation of gridded observational temperatures followed by an adjustment of the salinity field to preserve static stability. The performance of CanSIPS historical forecasts initialized every month over the period 1981–2010 is documented in a companion paper. The CanCM4 model and the initialization procedures developed for CanSIPS have been employed as well for decadal forecasts, including those contributing to phase 5 of the Coupled Model Intercomparison Project.

Full access
Hai Lin
,
William J. Merryfield
,
Ryan Muncaster
,
Gregory C. Smith
,
Marko Markovic
,
Frédéric Dupont
,
François Roy
,
Jean-François Lemieux
,
Arlan Dirkson
,
Viatcheslav V. Kharin
,
Woo-Sung Lee
,
Martin Charron
, and
Amin Erfani

Abstract

The second version of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2) was implemented operationally at Environment and Climate Change Canada (ECCC) in July 2019. Like its predecessors, CanSIPSv2 applies a multimodel ensemble approach with two coupled atmosphere–ocean models, CanCM4i and GEM-NEMO. While CanCM4i is a climate model, which is upgraded from CanCM4 of the previous CanSIPSv1 with improved sea ice initialization, GEM-NEMO is a newly developed numerical weather prediction (NWP)-based global atmosphere–ocean coupled model. In this paper, CanSIPSv2 is introduced, and its performance is assessed based on the reforecast of 30 years from 1981 to 2010, with 10 ensemble members of 12-month integrations for each model. Ensemble seasonal forecast skill of 2-m air temperature, 500-hPa geopotential height, precipitation rate, sea surface temperature, and sea ice concentration is assessed. Verification is also performed for the Niño-3.4, the Pacific–North American pattern (PNA), the North Atlantic Oscillation (NAO), and the Madden–Julian oscillation (MJO) indices. It is found that CanSIPSv2 outperforms the previous CanSIPSv1 system in many aspects. Atmospheric teleconnections associated with the El Niño–Southern Oscillation (ENSO) are reasonably well captured by the two CanSIPSv2 models, and a large part of the seasonal forecast skill in boreal winter can be attributed to the ENSO impact. The two models are also able to simulate the Northern Hemisphere teleconnection associated with the tropical MJO, which likely provides another source of skill on the subseasonal to seasonal time scale.

Open access
Hsi-Yen Ma
,
A. Cheska Siongco
,
Stephen A. Klein
,
Shaocheng Xie
,
Alicia R. Karspeck
,
Kevin Raeder
,
Jeffrey L. Anderson
,
Jiwoo Lee
,
Ben P. Kirtman
,
William J. Merryfield
,
Hiroyuki Murakami
, and
Joseph J. Tribbia

Abstract

The correspondence between mean sea surface temperature (SST) biases in retrospective seasonal forecasts (hindcasts) and long-term climate simulations from five global climate models is examined to diagnose the degree to which systematic SST biases develop on seasonal time scales. The hindcasts are from the North American Multimodel Ensemble, and the climate simulations are from the Coupled Model Intercomparison Project. The analysis suggests that most robust climatological SST biases begin to form within 6 months of a realistically initialized integration, although the growth rate varies with location, time, and model. In regions with large biases, interannual variability and ensemble spread is much smaller than the climatological bias. Additional ensemble hindcasts of the Community Earth System Model with a different initialization method suggest that initial conditions do matter for the initial bias growth, but the overall global bias patterns are similar after 6 months. A hindcast approach is more suitable to study biases over the tropics and subtropics than over the extratropics because of smaller initial biases and faster bias growth. The rapid emergence of SST biases makes it likely that fast processes with time scales shorter than the seasonal time scales in the atmosphere and upper ocean are responsible for a substantial part of the climatological SST biases. Studying the growth of biases may provide important clues to the causes and ultimately the amelioration of these biases. Further, initialized seasonal hindcasts can profitably be used in the development of high-resolution coupled ocean–atmosphere models.

Full access
Adrian M. Tompkins
,
María Inés Ortiz De Zárate
,
Ramiro I. Saurral
,
Carolina Vera
,
Celeste Saulo
,
William J. Merryfield
,
Michael Sigmond
,
Woo-Sung Lee
,
Johanna Baehr
,
Alain Braun
,
Amy Butler
,
Michel Déqué
,
Francisco J. Doblas-Reyes
,
Margaret Gordon
,
Adam A. Scaife
,
Yukiko Imada
,
Masayoshi Ishii
,
Tomoaki Ose
,
Ben Kirtman
,
Arun Kumar
,
Wolfgang A. Müller
,
Anna Pirani
,
Tim Stockdale
,
Michel Rixen
, and
Tamaki Yasuda
Open access
Leon Hermanson
,
Doug Smith
,
Melissa Seabrook
,
Roberto Bilbao
,
Francisco Doblas-Reyes
,
Etienne Tourigny
,
Vladimir Lapin
,
Viatcheslav V. Kharin
,
William J. Merryfield
,
Reinel Sospedra-Alfonso
,
Panos Athanasiadis
,
Dario Nicoli
,
Silvio Gualdi
,
Nick Dunstone
,
Rosie Eade
,
Adam Scaife
,
Mark Collier
,
Terence O’Kane
,
Vassili Kitsios
,
Paul Sandery
,
Klaus Pankatz
,
Barbara Früh
,
Holger Pohlmann
,
Wolfgang Müller
,
Takahito Kataoka
,
Hiroaki Tatebe
,
Masayoshi Ishii
,
Yukiko Imada
,
Tim Kruschke
,
Torben Koenigk
,
Mehdi Pasha Karami
,
Shuting Yang
,
Tian Tian
,
Liping Zhang
,
Tom Delworth
,
Xiaosong Yang
,
Fanrong Zeng
,
Yiguo Wang
,
François Counillon
,
Noel Keenlyside
,
Ingo Bethke
,
Judith Lean
,
Jürg Luterbacher
,
Rupa Kumar Kolli
, and
Arun Kumar

Abstract

As climate change accelerates, societies and climate-sensitive socioeconomic sectors cannot continue to rely on the past as a guide to possible future climate hazards. Operational decadal predictions offer the potential to inform current adaptation and increase resilience by filling the important gap between seasonal forecasts and climate projections. The World Meteorological Organization (WMO) has recognized this and in 2017 established the WMO Lead Centre for Annual to Decadal Climate Predictions (shortened to “Lead Centre” below), which annually provides a large multimodel ensemble of predictions covering the next 5 years. This international collaboration produces a prediction that is more skillful and useful than any single center can achieve. One of the main outputs of the Lead Centre is the Global Annual to Decadal Climate Update (GADCU), a consensus forecast based on these predictions. This update includes maps showing key variables, discussion on forecast skill, and predictions of climate indices such as the global mean near-surface temperature and Atlantic multidecadal variability. it also estimates the probability of the global mean temperature exceeding 1.5°C above preindustrial levels for at least 1 year in the next 5 years, which helps policy-makers understand how closely the world is approaching this goal of the Paris Agreement. This paper, written by the authors of the GADCU, introduces the GADCU, presents its key outputs, and briefly discusses its role in providing vital climate information for society now and in the future.

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.

Full access
Christopher J. White
,
Daniela I. V. Domeisen
,
Nachiketa Acharya
,
Elijah A. Adefisan
,
Michael L. Anderson
,
Stella Aura
,
Ahmed A. Balogun
,
Douglas Bertram
,
Sonia Bluhm
,
David J. Brayshaw
,
Jethro Browell
,
Dominik Büeler
,
Andrew Charlton-Perez
,
Xandre Chourio
,
Isadora Christel
,
Caio A. S. Coelho
,
Michael J. DeFlorio
,
Luca Delle Monache
,
Francesca Di Giuseppe
,
Ana María García-Solórzano
,
Peter B. Gibson
,
Lisa Goddard
,
Carmen González Romero
,
Richard J. Graham
,
Robert M. Graham
,
Christian M. Grams
,
Alan Halford
,
W. T. Katty Huang
,
Kjeld Jensen
,
Mary Kilavi
,
Kamoru A. Lawal
,
Robert W. Lee
,
David MacLeod
,
Andrea Manrique-Suñén
,
Eduardo S. P. R. Martins
,
Carolyn J. Maxwell
,
William J. Merryfield
,
Ángel G. Muñoz
,
Eniola Olaniyan
,
George Otieno
,
John A. Oyedepo
,
Lluís Palma
,
Ilias G. Pechlivanidis
,
Diego Pons
,
F. Martin Ralph
,
Dirceu S. Reis Jr.
,
Tomas A. Remenyi
,
James S. Risbey
,
Donald J. C. Robertson
,
Andrew W. Robertson
,
Stefan Smith
,
Albert Soret
,
Ting Sun
,
Martin C. Todd
,
Carly R. Tozer
,
Francisco C. Vasconcelos Jr.
,
Ilaria Vigo
,
Duane E. Waliser
,
Fredrik Wetterhall
, and
Robert G. Wilson

Abstract

The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.

Full access
William J. Merryfield
,
Johanna Baehr
,
Lauriane Batté
,
Emily J. Becker
,
Amy H. Butler
,
Caio A. S. Coelho
,
Gokhan Danabasoglu
,
Paul A. Dirmeyer
,
Francisco J. Doblas-Reyes
,
Daniela I. V. Domeisen
,
Laura Ferranti
,
Tatiana Ilynia
,
Arun Kumar
,
Wolfgang A. Müller
,
Michel Rixen
,
Andrew W. Robertson
,
Doug M. Smith
,
Yuhei Takaya
,
Matthias Tuma
,
Frederic Vitart
,
Christopher J. White
,
Mariano S. Alvarez
,
Constantin Ardilouze
,
Hannah Attard
,
Cory Baggett
,
Magdalena A. Balmaseda
,
Asmerom F. Beraki
,
Partha S. Bhattacharjee
,
Roberto Bilbao
,
Felipe M. de Andrade
,
Michael J. DeFlorio
,
Leandro B. Díaz
,
Muhammad Azhar Ehsan
,
Georgios Fragkoulidis
,
Sam Grainger
,
Benjamin W. Green
,
Momme C. Hell
,
Johnna M. Infanti
,
Katharina Isensee
,
Takahito Kataoka
,
Ben P. Kirtman
,
Nicholas P. Klingaman
,
June-Yi Lee
,
Kirsten Mayer
,
Roseanna McKay
,
Jennifer V. Mecking
,
Douglas E. Miller
,
Nele Neddermann
,
Ching Ho Justin Ng
,
Albert Ossó
,
Klaus Pankatz
,
Simon Peatman
,
Kathy Pegion
,
Judith Perlwitz
,
G. Cristina Recalde-Coronel
,
Annika Reintges
,
Christoph Renkl
,
Balakrishnan Solaraju-Murali
,
Aaron Spring
,
Cristiana Stan
,
Y. Qiang Sun
,
Carly R. Tozer
,
Nicolas Vigaud
,
Steven Woolnough
, and
Stephen Yeager
Full access
William J. Merryfield
,
Johanna Baehr
,
Lauriane Batté
,
Emily J. Becker
,
Amy H. Butler
,
Caio A. S. Coelho
,
Gokhan Danabasoglu
,
Paul A. Dirmeyer
,
Francisco J. Doblas-Reyes
,
Daniela I. V. Domeisen
,
Laura Ferranti
,
Tatiana Ilynia
,
Arun Kumar
,
Wolfgang A. Müller
,
Michel Rixen
,
Andrew W. Robertson
,
Doug M. Smith
,
Yuhei Takaya
,
Matthias Tuma
,
Frederic Vitart
,
Christopher J. White
,
Mariano S. Alvarez
,
Constantin Ardilouze
,
Hannah Attard
,
Cory Baggett
,
Magdalena A. Balmaseda
,
Asmerom F. Beraki
,
Partha S. Bhattacharjee
,
Roberto Bilbao
,
Felipe M. de Andrade
,
Michael J. DeFlorio
,
Leandro B. Díaz
,
Muhammad Azhar Ehsan
,
Georgios Fragkoulidis
,
Alex O. Gonzalez
,
Sam Grainger
,
Benjamin W. Green
,
Momme C. Hell
,
Johnna M. Infanti
,
Katharina Isensee
,
Takahito Kataoka
,
Ben P. Kirtman
,
Nicholas P. Klingaman
,
June-Yi Lee
,
Kirsten Mayer
,
Roseanna McKay
,
Jennifer V. Mecking
,
Douglas E. Miller
,
Nele Neddermann
,
Ching Ho Justin Ng
,
Albert Ossó
,
Klaus Pankatz
,
Simon Peatman
,
Kathy Pegion
,
Judith Perlwitz
,
G. Cristina Recalde-Coronel
,
Annika Reintges
,
Christoph Renkl
,
Balakrishnan Solaraju-Murali
,
Aaron Spring
,
Cristiana Stan
,
Y. Qiang Sun
,
Carly R. Tozer
,
Nicolas Vigaud
,
Steven Woolnough
, and
Stephen Yeager

Abstract

Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.

Free access