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  • Author or Editor: Gilbert P. Compo x
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Peter Stucki
,
Stefan Brönnimann
,
Olivia Martius
,
Christoph Welker
,
Ralph Rickli
,
Silke Dierer
,
David N. Bresch
,
Gilbert P. Compo
, and
Prashant D. Sardeshmukh
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Benjamin S. Giese
,
Gilbert P. Compo
,
Niall C. Slowey
,
Prashant D. Sardeshmukh
,
James A. Carton
,
Sulagna Ray
, and
Jeffrey S. Whitaker

Abstract

El Niño is widely recognized as a source of global climate variability. However, because of limited ocean observations during the early part of the twentieth century, little is known about El Niño events prior to the 1950s. An ocean model, driven with surface boundary conditions from a recently completed atmospheric reanalysis of the first half of the twentieth century, is used to provide the first comprehensive description of the structure and evolution of the 1918/19 El Niño. In contrast with previous descriptions, the modeled El Niño is one of the strongest of the twentieth century, comparable in intensity to the prominent events of 1982/83 and 1997/98.

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Laura C. Slivinski
,
Donald E. Lippi
,
Jeffrey S. Whitaker
,
Guoqing Ge
,
Jacob R. Carley
,
Curtis R. Alexander
, and
Gilbert P. Compo

Abstract

The U.S. operational global data assimilation system provides updated analysis and forecast fields every 6 h, which is not frequent enough to handle the rapid error growth associated with hurricanes or other storms. This motivates development of an hourly updating global data assimilation system, but observational data latency can be a barrier. Two methods are presented to overcome this challenge: “catch-up cycles,” in which a 1-hourly system is reinitialized from a 6-hourly system that has assimilated high-latency observations; and “overlapping assimilation windows,” in which the system is updated hourly with new observations valid in the past 3 h. The performance of these methods is assessed in a near-operational setup using the Global Forecast System by comparing forecasts with in situ observations. At short forecast leads, the overlapping windows method performs comparably to the 6-hourly control in a simplified configuration and outperforms the control in a full-input configuration. In the full-input experiment, the catch-up cycle method performs similarly to the 6-hourly control; reinitializing from the 6-hourly control does not appear to provide a significant benefit. Results suggest that the overlapping windows method performs well in part because of the hourly update cadence, but also because hourly cycling systems can make better use of available observations. The impact of the hourly update relative to the 6-hourly update is most significant during the first forecast day, while impacts on longer-range forecasts were found to be mixed and mostly insignificant. Further effort toward an operational global hourly updating system should be pursued.

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Jih-Wang A. Wang
,
Prashant D. Sardeshmukh
,
Gilbert P. Compo
,
Jeffrey S. Whitaker
,
Laura C. Slivinski
,
Chesley M. McColl
, and
Philip J. Pegion

Abstract

An important issue in developing a forecast system is its sensitivity to additional observations for improving initial conditions, to the data assimilation (DA) method used, and to improvements in the forecast model. These sensitivities are investigated here for the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP). Four parallel sets of 7-day ensemble forecasts were generated for 100 forecast cases in mid-January to mid-March 2016. The sets differed in their 1) inclusion or exclusion of additional observations collected over the eastern Pacific during the El Niño Rapid Response (ENRR) field campaign, 2) use of a hybrid 4D–EnVar versus a pure EnKF DA method to prepare the initial conditions, and 3) inclusion or exclusion of stochastic parameterizations in the forecast model. The Control forecast set used the ENRR observations, hybrid DA, and stochastic parameterizations. Errors of the ensemble-mean forecasts in this Control set were compared with those in the other sets, with emphasis on the upper-tropospheric geopotential heights and vorticity, midtropospheric vertical velocity, column-integrated precipitable water, near-surface air temperature, and surface precipitation. In general, the forecast errors were found to be only slightly sensitive to the additional ENRR observations, more sensitive to the DA methods, and most sensitive to the inclusion of stochastic parameterizations in the model, which reduced errors globally in all the variables considered except geopotential heights in the tropical upper troposphere. The reduction in precipitation errors, determined with respect to two independent observational datasets, was particularly striking.

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Laura C. Slivinski
,
Gilbert P. Compo
,
Jeffrey S. Whitaker
,
Prashant D. Sardeshmukh
,
Jih-Wang A. Wang
,
Kate Friedman
, and
Chesley McColl

Abstract

Given the network of satellite and aircraft observations around the globe, do additional in situ observations impact analyses within a global forecast system? Despite the dense observational network at many levels in the tropical troposphere, assimilating additional sounding observations taken in the eastern tropical Pacific Ocean during the 2016 El Niño Rapid Response (ENRR) locally improves wind, temperature, and humidity 6-h forecasts using a modern assimilation system. Fields from a 50-km reanalysis that assimilates all available observations, including those taken during the ENRR, are compared with those from an otherwise-identical reanalysis that denies all ENRR observations. These observations reveal a bias in the 200-hPa divergence of the assimilating model during a strong El Niño. While the existing observational network partially corrects this bias, the ENRR observations provide a stronger mean correction in the analysis. Significant improvements in the mean-square fit of the first-guess fields to the assimilated ENRR observations demonstrate that they are valuable within the existing network. The effects of the ENRR observations are pronounced in levels of the troposphere that are sparsely observed, particularly 500–800 hPa. Assimilating ENRR observations has mixed effects on the mean-square difference with nearby non-ENRR observations. Using a similar system but with a higher-resolution forecast model yields comparable results to the lower-resolution system. These findings imply a limited improvement in large-scale forecast variability from additional in situ observations, but significant improvements in local 6-h forecasts.

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Franklin R. Robertson
,
Jason B. Roberts
,
Michael G. Bosilovich
,
Abderrahim Bentamy
,
Carol Anne Clayson
,
Karsten Fennig
,
Marc Schröder
,
Hiroyuki Tomita
,
Gilbert P. Compo
,
Marloes Gutenstein
,
Hans Hersbach
,
Chiaki Kobayashi
,
Lucrezia Ricciardulli
,
Prashant Sardeshmukh
, and
Laura C. Slivinski

Abstract

Four state-of-the-art satellite-based estimates of ocean surface latent heat fluxes (LHFs) extending over three decades are analyzed, focusing on the interannual variability and trends of near-global averages and regional patterns. Detailed intercomparisons are made with other datasets including 1) reduced observation reanalyses (RedObs) whose exclusion of satellite data renders them an important independent diagnostic tool; 2) a moisture budget residual LHF estimate using reanalysis moisture transport, atmospheric storage, and satellite precipitation; 3) the ECMWF Reanalysis 5 (ERA5); 4) Remote Sensing Systems (RSS) single-sensor passive microwave and scatterometer wind speed retrievals; and 5) several sea surface temperature (SST) datasets. Large disparities remain in near-global satellite LHF trends and their regional expression over the 1990–2010 period, during which time the interdecadal Pacific oscillation changed sign. The budget residual diagnostics support the smaller RedObs LHF trends. The satellites, ERA5, and RedObs are reasonably consistent in identifying contributions by the 10-m wind speed variations to the LHF trend patterns. However, contributions by the near-surface vertical humidity gradient from satellites and ERA5 trend upward in time with respect to the RedObs ensemble and show less agreement in trend patterns. Problems with wind speed retrievals from Special Sensor Microwave Imager/Sounder satellite sensors, excessive upward trends in trends in Optimal Interpolation Sea Surface Temperature (OISST AVHRR-Only) data used in most satellite LHF estimates, and uncertainties associated with poor satellite coverage before the mid-1990s are noted. Possibly erroneous trends are also identified in ERA5 LHF associated with the onset of scatterometer wind data assimilation in the early 1990s.

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Roberto Buizza
,
Paul Poli
,
Michel Rixen
,
Magdalena Alonso-Balmaseda
,
Michael G. Bosilovich
,
Stefan Brönnimann
,
Gilbert P. Compo
,
Dick P. Dee
,
Franco Desiato
,
Marie Doutriaux-Boucher
,
Masatomo Fujiwara
,
Andrea K. Kaiser-Weiss
,
Shinya Kobayashi
,
Zhiquan Liu
,
Simona Masina
,
Pierre-Philippe Mathieu
,
Nick Rayner
,
Carolin Richter
,
Sonia I. Seneviratne
,
Adrian J. Simmons
,
Jean-Noel Thépaut
,
Jeffrey D. Auger
,
Michel Bechtold
,
Ellen Berntell
,
Bo Dong
,
Michal Kozubek
,
Khaled Sharif
,
Christopher Thomas
,
Semjon Schimanke
,
Andrea Storto
,
Matthias Tuma
,
Ilona Välisuo
, and
Alireza Vaselali
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Randall M. Dole
,
J. Ryan Spackman
,
Matthew Newman
,
Gilbert P. Compo
,
Catherine A. Smith
,
Leslie M. Hartten
,
Joseph J. Barsugli
,
Robert S. Webb
,
Martin P. Hoerling
,
Robert Cifelli
,
Klaus Wolter
,
Christopher D. Barnet
,
Maria Gehne
,
Ronald Gelaro
,
George N. Kiladis
,
Scott Abbott
,
Elena Akish
,
John Albers
,
John M. Brown
,
Christopher J. Cox
,
Lisa Darby
,
Gijs de Boer
,
Barbara DeLuisi
,
Juliana Dias
,
Jason Dunion
,
Jon Eischeid
,
Christopher Fairall
,
Antonia Gambacorta
,
Brian K. Gorton
,
Andrew Hoell
,
Janet Intrieri
,
Darren Jackson
,
Paul E. Johnston
,
Richard Lataitis
,
Kelly M. Mahoney
,
Katherine McCaffrey
,
H. Alex McColl
,
Michael J. Mueller
,
Donald Murray
,
Paul J. Neiman
,
William Otto
,
Ola Persson
,
Xiao-Wei Quan
,
Imtiaz Rangwala
,
Andrea J. Ray
,
David Reynolds
,
Emily Riley Dellaripa
,
Karen Rosenlof
,
Naoko Sakaeda
,
Prashant D. Sardeshmukh
,
Laura C. Slivinski
,
Lesley Smith
,
Amy Solomon
,
Dustin Swales
,
Stefan Tulich
,
Allen White
,
Gary Wick
,
Matthew G. Winterkorn
,
Daniel E. Wolfe
, and
Robert Zamora

Abstract

Forecasts by mid-2015 for a strong El Niño during winter 2015/16 presented an exceptional scientific opportunity to accelerate advances in understanding and predictions of an extreme climate event and its impacts while the event was ongoing. Seizing this opportunity, the National Oceanic and Atmospheric Administration (NOAA) initiated an El Niño Rapid Response (ENRR), conducting the first field campaign to obtain intensive atmospheric observations over the tropical Pacific during El Niño.

The overarching ENRR goal was to determine the atmospheric response to El Niño and the implications for predicting extratropical storms and U.S. West Coast rainfall. The field campaign observations extended from the central tropical Pacific to the West Coast, with a primary focus on the initial tropical atmospheric response that links El Niño to its global impacts. NOAA deployed its Gulfstream-IV (G-IV) aircraft to obtain observations around organized tropical convection and poleward convective outflow near the heart of El Niño. Additional tropical Pacific observations were obtained by radiosondes launched from Kiritimati , Kiribati, and the NOAA ship Ronald H. Brown, and in the eastern North Pacific by the National Aeronautics and Space Administration (NASA) Global Hawk unmanned aerial system. These observations were all transmitted in real time for use in operational prediction models. An X-band radar installed in Santa Clara, California, helped characterize precipitation distributions. This suite supported an end-to-end capability extending from tropical Pacific processes to West Coast impacts. The ENRR observations were used during the event in operational predictions. They now provide an unprecedented dataset for further research to improve understanding and predictions of El Niño and its impacts.

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