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Matthew Newman
,
Prashant D. Sardeshmukh
,
Christopher R. Winkler
, and
Jeffrey S. Whitaker

Abstract

The predictability of weekly averaged circulation anomalies in the Northern Hemisphere, and diabatic heating anomalies in the Tropics, is investigated in a linear inverse model (LIM) derived from their observed simultaneous and time-lag correlation statistics. In both winter and summer, the model's forecast skill at week 2 (days 8–14) and week 3 (days 15–21) is comparable to that of a comprehensive global medium-range forecast (MRF) model developed at the National Centers for Environmental Prediction (NCEP). Its skill at week 3 is actually higher on average, partly due to its better ability to forecast tropical heating variations and their influence on the extratropical circulation. The geographical and temporal variations of forecast skill are also similar in the two models. This makes the much simpler LIM an attractive tool for assessing and diagnosing atmospheric predictability at these forecast ranges.

The LIM assumes that the dynamics of weekly averages are linear, asymptotically stable, and stochastically forced. In a forecasting context, the predictable signal is associated with the deterministic linear dynamics, and the forecast error with the unpredictable stochastic noise. In a low-order linear model of a high-order chaotic system, this stochastic noise represents the effects of both chaotic nonlinear interactions and unresolved initial components on the evolution of the resolved components. Its statistics are assumed here to be state independent.

An average signal-to-noise ratio is estimated at each grid point on the hemisphere and is then used to estimate the potential predictability of weekly variations at the point. In general, this predictability is about 50% higher in winter than summer over the Pacific and North America sectors; the situation is reversed over Eurasia and North Africa. Skill in predicting tropical heating variations is important for realizing this potential skill. The actual LIM forecast skill has a similar geographical structure but weaker magnitude than the potential skill.

In this framework, the predictable variations of forecast skill from case to case are associated with predictable variations of signal rather than of noise. This contrasts with the traditional emphasis in studies of shorter-term predictability on flow-dependent instabilities, that is, on the predictable variations of noise. In the LIM, the predictable variations of signal are associated with variations of the initial state projection on the growing singular vectors of the LIM's propagator, which have relatively large amplitude in the Tropics. At times of strong projection on such structures, the signal-to-noise ratio is relatively high, and the Northern Hemispheric circulation is not only potentially but also actually more predictable than at other times.

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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
,
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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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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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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