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M. Susan Lozier
,
Sheldon Bacon
,
Amy S. Bower
,
Stuart A. Cunningham
,
M. Femke de Jong
,
Laura de Steur
,
Brad deYoung
,
Jürgen Fischer
,
Stefan F. Gary
,
Blair J. W. Greenan
,
Patrick Heimbach
,
Naomi P. Holliday
,
Loïc Houpert
,
Mark E. Inall
,
William E. Johns
,
Helen L. Johnson
,
Johannes Karstensen
,
Feili Li
,
Xiaopei Lin
,
Neill Mackay
,
David P. Marshall
,
Herlé Mercier
,
Paul G. Myers
,
Robert S. Pickart
,
Helen R. Pillar
,
Fiammetta Straneo
,
Virginie Thierry
,
Robert A. Weller
,
Richard G. Williams
,
Chris Wilson
,
Jiayan Yang
,
Jian Zhao
, and
Jan D. Zika

Abstract

For decades oceanographers have understood the Atlantic meridional overturning circulation (AMOC) to be primarily driven by changes in the production of deep-water formation in the subpolar and subarctic North Atlantic. Indeed, current Intergovernmental Panel on Climate Change (IPCC) projections of an AMOC slowdown in the twenty-first century based on climate models are attributed to the inhibition of deep convection in the North Atlantic. However, observational evidence for this linkage has been elusive: there has been no clear demonstration of AMOC variability in response to changes in deep-water formation. The motivation for understanding this linkage is compelling, since the overturning circulation has been shown to sequester heat and anthropogenic carbon in the deep ocean. Furthermore, AMOC variability is expected to impact this sequestration as well as have consequences for regional and global climates through its effect on the poleward transport of warm water. Motivated by the need for a mechanistic understanding of the AMOC, an international community has assembled an observing system, Overturning in the Subpolar North Atlantic Program (OSNAP), to provide a continuous record of the transbasin fluxes of heat, mass, and freshwater, and to link that record to convective activity and water mass transformation at high latitudes. OSNAP, in conjunction with the Rapid Climate Change–Meridional Overturning Circulation and Heatflux Array (RAPID–MOCHA) at 26°N and other observational elements, will provide a comprehensive measure of the three-dimensional AMOC and an understanding of what drives its variability. The OSNAP observing system was fully deployed in the summer of 2014, and the first OSNAP data products are expected in the fall of 2017.

Full access
Gregory C. Johnson
,
Rick Lumpkin
,
Simone R. Alin
,
Dillon J. Amaya
,
Molly O. Baringer
,
Tim Boyer
,
Peter Brandt
,
Brendan R. Carter
,
Ivona Cetinić
,
Don P. Chambers
,
Lijing Cheng
,
Andrew U. Collins
,
Cathy Cosca
,
Ricardo Domingues
,
Shenfu Dong
,
Richard A. Feely
,
Eleanor Frajka-Williams
,
Bryan A. Franz
,
John Gilson
,
Gustavo Goni
,
Benjamin D. Hamlington
,
Josefine Herrford
,
Zeng-Zhen Hu
,
Boyin Huang
,
Masayoshi Ishii
,
Svetlana Jevrejeva
,
John J. Kennedy
,
Marion Kersalé
,
Rachel E. Killick
,
Peter Landschützer
,
Matthias Lankhorst
,
Eric Leuliette
,
Ricardo Locarnini
,
John M. Lyman
,
John J. Marra
,
Christopher S. Meinen
,
Mark A. Merrifield
,
Gary T. Mitchum
,
Ben I. Moat
,
R. Steven Nerem
,
Renellys C. Perez
,
Sarah G. Purkey
,
James Reagan
,
Alejandra Sanchez-Franks
,
Hillary A. Scannell
,
Claudia Schmid
,
Joel P. Scott
,
David A. Siegel
,
David A. Smeed
,
Paul W. Stackhouse
,
William Sweet
,
Philip R. Thompson
,
Joaquin A. Triñanes
,
Denis L. Volkov
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Rik Wanninkhof
,
Robert A. Weller
,
Caihong Wen
,
Toby K. Westberry
,
Matthew J. Widlansky
,
Anne C. Wilber
,
Lisan Yu
, and
Huai-Min Zhang
Free access
Travis A. O’Brien
,
Ashley E. Payne
,
Christine A. Shields
,
Jonathan Rutz
,
Swen Brands
,
Christopher Castellano
,
Jiayi Chen
,
William Cleveland
,
Michael J. DeFlorio
,
Naomi Goldenson
,
Irina V. Gorodetskaya
,
Héctor Inda Díaz
,
Karthik Kashinath
,
Brian Kawzenuk
,
Sol Kim
,
Mikhail Krinitskiy
,
Juan M. Lora
,
Beth McClenny
,
Allison Michaelis
,
John P. O’Brien
,
Christina M. Patricola
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Alexandre M. Ramos
,
Eric J. Shearer
,
Wen-Wen Tung
,
Paul A. Ullrich
,
Michael F. Wehner
,
Kevin Yang
,
Rudong Zhang
,
Zhenhai Zhang
, and
Yang Zhou
Free access
Armin Sorooshian
,
Bruce Anderson
,
Susanne E. Bauer
,
Rachel A. Braun
,
Brian Cairns
,
Ewan Crosbie
,
Hossein Dadashazar
,
Glenn Diskin
,
Richard Ferrare
,
Richard C. Flagan
,
Johnathan Hair
,
Chris Hostetler
,
Haflidi H. Jonsson
,
Mary M. Kleb
,
Hongyu Liu
,
Alexander B. MacDonald
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Allison McComiskey
,
Richard Moore
,
David Painemal
,
Lynn M. Russell
,
John H. Seinfeld
,
Michael Shook
,
William L. Smith Jr
,
Kenneth Thornhill
,
George Tselioudis
,
Hailong Wang
,
Xubin Zeng
,
Bo Zhang
,
Luke Ziemba
, and
Paquita Zuidema

Abstract

We report on a multiyear set of airborne field campaigns (2005–16) off the California coast to examine aerosols, clouds, and meteorology, and how lessons learned tie into the upcoming NASA Earth Venture Suborbital (EVS-3) campaign: Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE; 2019–23). The largest uncertainty in estimating global anthropogenic radiative forcing is associated with the interactions of aerosol particles with clouds, which stems from the variability of cloud systems and the multiple feedbacks that affect and hamper efforts to ascribe changes in cloud properties to aerosol perturbations. While past campaigns have been limited in flight hours and the ability to fly in and around clouds, efforts sponsored by the Office of Naval Research have resulted in 113 single aircraft flights (>500 flight hours) in a fixed region with warm marine boundary layer clouds. All flights used nearly the same payload of instruments on a Twin Otter to fly below, in, and above clouds, producing an unprecedented dataset. We provide here i) an overview of statistics of aerosol, cloud, and meteorological conditions encountered in those campaigns and ii) quantification of model-relevant metrics associated with aerosol–cloud interactions leveraging the high data volume and statistics. Based on lessons learned from those flights, we describe the pragmatic innovation in sampling strategy (dual-aircraft approach with combined in situ and remote sensing) that will be used in ACTIVATE to generate a dataset that can advance scientific understanding and improve physical parameterizations for Earth system and weather forecasting models, and for assessing next-generation remote sensing retrieval algorithms.

Full access
Jordan G. Powers
,
Joseph B. Klemp
,
William C. Skamarock
,
Christopher A. Davis
,
Jimy Dudhia
,
David O. Gill
,
Janice L. Coen
,
David J. Gochis
,
Ravan Ahmadov
,
Steven E. Peckham
,
Georg A. Grell
,
John Michalakes
,
Samuel Trahan
,
Stanley G. Benjamin
,
Curtis R. Alexander
,
Geoffrey J. Dimego
,
Wei Wang
,
Craig S. Schwartz
,
Glen S. Romine
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Zhiquan Liu
,
Chris Snyder
,
Fei Chen
,
Michael J. Barlage
,
Wei Yu
, and
Michael G. Duda

Abstract

Since its initial release in 2000, the Weather Research and Forecasting (WRF) Model has become one of the world’s most widely used numerical weather prediction models. Designed to serve both research and operational needs, it has grown to offer a spectrum of options and capabilities for a wide range of applications. In addition, it underlies a number of tailored systems that address Earth system modeling beyond weather. While the WRF Model has a centralized support effort, it has become a truly community model, driven by the developments and contributions of an active worldwide user base. The WRF Model sees significant use for operational forecasting, and its research implementations are pushing the boundaries of finescale atmospheric simulation. Future model directions include developments in physics, exploiting emerging compute technologies, and ever-innovative applications. From its contributions to research, forecasting, educational, and commercial efforts worldwide, the WRF Model has made a significant mark on numerical weather prediction and atmospheric science.

Full access
Clark Evans
,
Kimberly M. Wood
,
Sim D. Aberson
,
Heather M. Archambault
,
Shawn M. Milrad
,
Lance F. Bosart
,
Kristen L. Corbosiero
,
Christopher A. Davis
,
João R. Dias Pinto
,
James Doyle
,
Chris Fogarty
,
Thomas J. Galarneau Jr.
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Christian M. Grams
,
Kyle S. Griffin
,
John Gyakum
,
Robert E. Hart
,
Naoko Kitabatake
,
Hilke S. Lentink
,
Ron McTaggart-Cowan
,
William Perrie
,
Julian F. D. Quinting
,
Carolyn A. Reynolds
,
Michael Riemer
,
Elizabeth A. Ritchie
,
Yujuan Sun
, and
Fuqing Zhang

Abstract

Extratropical transition (ET) is the process by which a tropical cyclone, upon encountering a baroclinic environment and reduced sea surface temperature at higher latitudes, transforms into an extratropical cyclone. This process is influenced by, and influences, phenomena from the tropics to the midlatitudes and from the meso- to the planetary scales to extents that vary between individual events. Motivated in part by recent high-impact and/or extensively observed events such as North Atlantic Hurricane Sandy in 2012 and western North Pacific Typhoon Sinlaku in 2008, this review details advances in understanding and predicting ET since the publication of an earlier review in 2003. Methods for diagnosing ET in reanalysis, observational, and model-forecast datasets are discussed. New climatologies for the eastern North Pacific and southwest Indian Oceans are presented alongside updates to western North Pacific and North Atlantic Ocean climatologies. Advances in understanding and, in some cases, modeling the direct impacts of ET-related wind, waves, and precipitation are noted. Improved understanding of structural evolution throughout the transformation stage of ET fostered in large part by novel aircraft observations collected in several recent ET events is highlighted. Predictive skill for operational and numerical model ET-related forecasts is discussed along with environmental factors influencing posttransition cyclone structure and evolution. Operational ET forecast and analysis practices and challenges are detailed. In particular, some challenges of effective hazard communication for the evolving threats posed by a tropical cyclone during and after transition are introduced. This review concludes with recommendations for future work to further improve understanding, forecasts, and hazard communication.

Open access
Pamela L. Heinselman
,
Patrick C. Burke
,
Louis J. Wicker
,
Adam J. Clark
,
John S. Kain
,
Jidong Gao
,
Nusrat Yussouf
,
Thomas A. Jones
,
Patrick S. Skinner
,
Corey K. Potvin
,
Katie A. Wilson
,
Burkely T. Gallo
,
Montgomery L. Flora
,
Joshua Martin
,
Gerry Creager
,
Kent H. Knopfmeier
,
Yunheng Wang
,
Brian C. Matilla
,
David C. Dowell
,
Edward R. Mansell
,
Brett Roberts
,
Kimberly A. Hoogewind
,
Derek R. Stratman
,
Jorge Guerra
,
Anthony E. Reinhart
,
Christopher A. Kerr
, and
William Miller

Abstract

In 2009, advancements in NWP and computing power inspired a vision to advance hazardous weather warnings from a warn-on-detection to a warn-on-forecast paradigm. This vision would require not only the prediction of individual thunderstorms and their attributes but the likelihood of their occurrence in time and space. During the last decade, the warn-on-forecast research team at the NOAA National Severe Storms Laboratory met this challenge through the research and development of 1) an ensemble of high-resolution convection-allowing models; 2) ensemble- and variational-based assimilation of weather radar, satellite, and conventional observations; and 3) unique postprocessing and verification techniques, culminating in the experimental Warn-on-Forecast System (WoFS). Since 2017, we have directly engaged users in the testing, evaluation, and visualization of this system to ensure that WoFS guidance is usable and useful to operational forecasters at NOAA national centers and local offices responsible for forecasting severe weather, tornadoes, and flash floods across the watch-to-warning continuum. Although an experimental WoFS is now a reality, we close by discussing many of the exciting opportunities remaining, including folding this system into the Unified Forecast System, transitioning WoFS into NWS operations, and pursuing next-decade science goals for further advancing storm-scale prediction.

Significance Statement

The purpose of this research is to develop an experimental prediction system that forecasts the probability for severe weather hazards associated with individual thunderstorms up to 6 h in advance. This capability is important because some people and organizations, like those living in mobile homes, caring for patients in hospitals, or managing large outdoor events, require extended lead time to protect themselves and others from potential severe weather hazards. Our results demonstrate a prediction system that enables forecasters, for the first time, to message probabilistic hazard information associated with individual severe storms between the watch-to-warning time frame within the United States.

Restricted access
A. Gannet Hallar
,
Steven S. Brown
,
Erik Crosman
,
Kelley C. Barsanti
,
Christopher D. Cappa
,
Ian Faloona
,
Jerome Fast
,
Heather A. Holmes
,
John Horel
,
John Lin
,
Ann Middlebrook
,
Logan Mitchell
,
Jennifer Murphy
,
Caroline C. Womack
,
Viney Aneja
,
Munkhbayar Baasandorj
,
Roya Bahreini
,
Robert Banta
,
Casey Bray
,
Alan Brewer
,
Dana Caulton
,
Joost de Gouw
,
Stephan F.J. De Wekker
,
Delphine K. Farmer
,
Cassandra J. Gaston
,
Sebastian Hoch
,
Francesca Hopkins
,
Nakul N. Karle
,
James T. Kelly
,
Kerry Kelly
,
Neil Lareau
,
Keding Lu
,
Roy L. Mauldin III
,
Derek V. Mallia
,
Randal Martin
,
Daniel L. Mendoza
,
Holly J. Oldroyd
,
Yelena Pichugina
,
Kerri A. Pratt
,
Pablo E. Saide
,
Philip J. Silva
,
William Simpson
,
Britton B. Stephens
,
Jochen Stutz
, and
Amy Sullivan

Abstract

Wintertime episodes of high aerosol concentrations occur frequently in urban and agricultural basins and valleys worldwide. These episodes often arise following development of persistent cold-air pools (PCAPs) that limit mixing and modify chemistry. While field campaigns targeting either basin meteorology or wintertime pollution chemistry have been conducted, coupling between interconnected chemical and meteorological processes remains an insufficiently studied research area. Gaps in understanding the coupled chemical–meteorological interactions that drive high-pollution events make identification of the most effective air-basin specific emission control strategies challenging. To address this, a September 2019 workshop occurred with the goal of planning a future research campaign to investigate air quality in western U.S. basins. Approximately 120 people participated, representing 50 institutions and five countries. Workshop participants outlined the rationale and design for a comprehensive wintertime study that would couple atmospheric chemistry and boundary layer and complex-terrain meteorology within western U.S. basins. Participants concluded the study should focus on two regions with contrasting aerosol chemistry: three populated valleys within Utah (Salt Lake, Utah, and Cache Valleys) and the San Joaquin Valley in California. This paper describes the scientific rationale for a campaign that will acquire chemical and meteorological datasets using airborne platforms with extensive range, coupled to surface-based measurements focusing on sampling within the near-surface boundary layer, and transport and mixing processes within this layer, with high vertical resolution at a number of representative sites. No prior wintertime basin-focused campaign has provided the breadth of observations necessary to characterize the meteorological–chemical linkages outlined here, nor to validate complex processes within coupled atmosphere–chemistry models.

Full access
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.

Full access
Mary C. Barth
,
Christopher A. Cantrell
,
William H. Brune
,
Steven A. Rutledge
,
James H. Crawford
,
Heidi Huntrieser
,
Lawrence D. Carey
,
Donald MacGorman
,
Morris Weisman
,
Kenneth E. Pickering
,
Eric Bruning
,
Bruce Anderson
,
Eric Apel
,
Michael Biggerstaff
,
Teresa Campos
,
Pedro Campuzano-Jost
,
Ronald Cohen
,
John Crounse
,
Douglas A. Day
,
Glenn Diskin
,
Frank Flocke
,
Alan Fried
,
Charity Garland
,
Brian Heikes
,
Shawn Honomichl
,
Rebecca Hornbrook
,
L. Gregory Huey
,
Jose L. Jimenez
,
Timothy Lang
,
Michael Lichtenstern
,
Tomas Mikoviny
,
Benjamin Nault
,
Daniel O’Sullivan
,
Laura L. Pan
,
Jeff Peischl
,
Ilana Pollack
,
Dirk Richter
,
Daniel Riemer
,
Thomas Ryerson
,
Hans Schlager
,
Jason St. Clair
,
James Walega
,
Petter Weibring
,
Andrew Weinheimer
,
Paul Wennberg
,
Armin Wisthaler
,
Paul J. Wooldridge
, and
Conrad Ziegler

Abstract

The Deep Convective Clouds and Chemistry (DC3) field experiment produced an exceptional dataset on thunderstorms, including their dynamical, physical, and electrical structures and their impact on the chemical composition of the troposphere. The field experiment gathered detailed information on the chemical composition of the inflow and outflow regions of midlatitude thunderstorms in northeast Colorado, west Texas to central Oklahoma, and northern Alabama. A unique aspect of the DC3 strategy was to locate and sample the convective outflow a day after active convection in order to measure the chemical transformations within the upper-tropospheric convective plume. These data are being analyzed to investigate transport and dynamics of the storms, scavenging of soluble trace gases and aerosols, production of nitrogen oxides by lightning, relationships between lightning flash rates and storm parameters, chemistry in the upper troposphere that is affected by the convection, and related source characterization of the three sampling regions. DC3 also documented biomass-burning plumes and the interactions of these plumes with deep convection.

Full access