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Clara Deser and Catherine A. Smith

Abstract

The climatological large-scale patterns of diurnal and semidiurnal near-surface wind variations over the tropical Pacific Ocean are documented using 3 yr of hourly measurements from the Tropical Atmosphere–Ocean moored buoy array. Semidiurnal variations account for 68% of the mean daily variance of the zonal wind component, while diurnal variations account for 82% of the mean daily variance of the meridional wind component. The spatially uniform amplitude (0.15 m s−1) and phase (0300 LT) of the semidiurnal zonal wind variations are shown to be consistent with atmospheric thermal tidal theory.

The diurnal meridional wind variations on either side of the equator are approximately out of phase. This pattern results in a diurnal variation of wind divergence along the equator, with maximum divergence in the early morning (∼0800 LT). The average amplitude of the diurnal cycle in zonal mean divergence is 0.45 × 10−6 s−1, which corresponds to a day–night change of 45% relative to the daily mean. The relative day–night changes in near-surface equatorial wind divergence are larger in the western Pacific (78%) than in the eastern Pacific (31%) due mainly to differences in the daily mean divergence. The diurnal amplitude of equatorial wind divergence changes seasonally and interannually in proportion to the strength of the mean divergence.

It is suggested that diurnal heating of the sea surface may contribute to the zonally symmetric diurnal cycle of equatorial wind divergence.

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John R. Taylor, Katherine M. Smith, and Catherine A. Vreugdenhil

Abstract

We use idealized large-eddy simulations (LES) and a simple analytical theory to study the influence of submesoscales on the concentration and export of sinking particles from the mixed layer. We find that restratification of the mixed layer following the development of submesoscales reduces the rate of vertical mixing which, in turn, enhances the export rate associated with gravitational settling. For a neutral tracer initially confined to the mixed layer, subinertial (submesoscale) motions enhance the downward tracer flux, consistent with previous studies. However, the sign of the advective flux associated with the concentration of sinking particles reverses, indicating reentrainment into the mixed layer. A new theory is developed to model the gravitational settling flux when the particle concentration is nonuniform. The theory broadly agrees with the LES results and allows us to extend the analysis to a wider range of parameters.

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Klaus Wolter, Randall M. Dole, and Catherine A. Smith

Abstract

This study documents statistical relationships between the El Niño–Southern Oscillation (ENSO) phenomenon and extreme seasonal temperature anomalies over the continental United States. Relationships are examined for El Niño and La Niña conditions for each of the four standard seasons. Two complementary approaches are used. In the first approach, seasonal temperature anomalies are ranked from coldest to warmest over a 100-yr climate division dataset. Mean Southern Oscillation index (SOI) values are then computed for times preceding or concurrent with extreme seasonal temperature anomalies to define regions where relationships between the SOI and seasonal temperature extremes are statistically significant. In the second approach, seasonal extremes in the SOI, which are generally related to El Niño or La Niña, are first identified, and then the numbers of extreme temperature seasons occurring in association with these events are determined. Comparison of the observed number of extreme seasons with the climatologically expected values provides quantitative estimates of how ENSO alters the conditional probability, or risk, of large seasonal temperature anomalies in a given region.

The results show that the greatest geographical coverage of statistically significant relationships between ENSO and seasonal temperature extremes occurs in winter and spring, especially with the SOI leading by one season. Certain well-recognized relationships for seasonal temperature anomalies are also confirmed for extreme seasons, such as the association of El Niño conditions with very warm winters over the Pacific Northwest and very cold winters along the Gulf Coast. Other less-discussed relationships also appear, including possible nonlinearities in relationships between El Niño and La Niña events and extremes in autumn temperatures. Some relationships show evidence of secular changes, especially in summer.

In some regions and times of year, El Niño and La Niña conditions substantially alter the probabilities of very warm or very cold seasons. For example, over Texas, El Niño conditions in winter almost triple the risk that the subsequent spring will be very cold, while significantly reducing the risk of a very warm spring. In the same region, wintertime La Niña conditions double the risk that the following spring will be very warm, while significantly reducing the likelihood of a very cold spring. Therefore, given the proper ENSO phase, skillful forecasts of regional risks of seasonal temperature extremes appear feasible.

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Catherine A. Smith, Gilbert P. Compo, and Don K. Hooper

While atmospheric reanalysis datasets are widely used in climate science, many technical issues hinder comparing them to each other and to observations. The reanalysis fields are stored in diverse file architectures, data formats, and resolutions. Their metadata, such as variable name and units, can also differ. Individual users have to download the fields, convert them to a common format, store them locally, change variable names, regrid if needed, and convert units. Even if a dataset can be read via the Open-Source Project for a Network Data Access Protocol (commonly known as OPeNDAP) or a similar protocol, most of this work is still needed. All of these tasks take time, effort, and money. Our group at the Cooperative Institute for Research in the Environmental Sciences at the University of Colorado and affiliated colleagues at the NOAA's Earth System Research Laboratory Physical Sciences Division have expertise both in making reanalysis datasets available and in creating web-based climate analysis tools that have been widely used throughout the meteorological community. To overcome some of the obstacles in reanalysis intercomparison, we have created a set of web-based Reanalysis Intercomparison Tools (WRIT) at www.esrl.noaa.gov/psd/data/writ/. WRIT allows users to easily plot and compare reanalysis datasets, and to test hypotheses. For standard pressure-level and surface variables there are tools to plot trajectories, monthly mean maps and vertical cross sections, and monthly mean time series. Some observational datasets are also included. Users can refine date, statistics, and plotting options. WRIT also facilitates the mission of the Reanalyses.org website as a convenient toolkit for studying the reanalysis datasets.

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Michael A. Alexander, James D. Scott, Dustin Swales, Mimi Hughes, Kelly Mahoney, and Catherine A. Smith

Abstract

Two methods were used to identify the paths of moisture transport that reach the U.S. Intermountain West (IMW) during heavy precipitation events in winter. In the first, the top 150 precipitation events at stations located within six regions in the IMW were identified, and then back trajectories were initiated at 6-h intervals on those days at the four Climate Forecast System Reanalysis grid points nearest the stations. The second method identified the leading patterns of integrated water vapor transport (IVT) using the three leading empirical orthogonal functions of IVT over land that were first normalized by the local standard deviation. The top 1% of the associated 6-hourly time series was used to construct composites of IVT, atmospheric circulation, and precipitation. The results from both methods indicate that moisture originating from the Pacific that leads to extreme precipitation in the IMW during winter takes distinct pathways and is influenced by gaps in the Cascades (Oregon–Washington), the Sierra Nevada (California), and Peninsular Ranges (from Southern California through Baja California). The moisture transported along these routes appears to be the primary source for heavy precipitation for the mountain ranges in the IMW. The synoptic conditions associated with the dominant IVT patterns include a trough–ridge couplet at 500 hPa, with the trough located northwest of the ridge where the associated circulation funnels moisture from the west-southwest through the mountain gaps and into the IMW.

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Matthew Newman, Andrew T. Wittenberg, Linyin Cheng, Gilbert P. Compo, and Catherine A. Smith
Open access
Matthew Newman, Michael A. Alexander, Toby R. Ault, Kim M. Cobb, Clara Deser, Emanuele Di Lorenzo, Nathan J. Mantua, Arthur J. Miller, Shoshiro Minobe, Hisashi Nakamura, Niklas Schneider, Daniel J. Vimont, Adam S. Phillips, James D. Scott, and Catherine A. Smith

Abstract

The Pacific decadal oscillation (PDO), the dominant year-round pattern of monthly North Pacific sea surface temperature (SST) variability, is an important target of ongoing research within the meteorological and climate dynamics communities and is central to the work of many geologists, ecologists, natural resource managers, and social scientists. Research over the last 15 years has led to an emerging consensus: the PDO is not a single phenomenon, but is instead the result of a combination of different physical processes, including both remote tropical forcing and local North Pacific atmosphere–ocean interactions, which operate on different time scales to drive similar PDO-like SST anomaly patterns. How these processes combine to generate the observed PDO evolution, including apparent regime shifts, is shown using simple autoregressive models of increasing spatial complexity. Simulations of recent climate in coupled GCMs are able to capture many aspects of the PDO, but do so based on a balance of processes often more independent of the tropics than is observed. Finally, it is suggested that the assessment of PDO-related regional climate impacts, reconstruction of PDO-related variability into the past with proxy records, and diagnosis of Pacific variability within coupled GCMs should all account for the effects of these different processes, which only partly represent the direct forcing of the atmosphere by North Pacific Ocean SSTs.

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Donald Murray, Andrew Hoell, Martin Hoerling, Judith Perlwitz, Xiao-Wei Quan, Dave Allured, Tao Zhang, Jon Eischeid, Catherine A. Smith, Joseph Barsugli, Jeff McWhirter, Chris Kreutzer, and Robert S. Webb

Abstract

The Facility for Weather and Climate Assessments (FACTS) developed at the NOAA Physical Sciences Laboratory is a freely available resource that provides the science community with analysis tools; multimodel, multiforcing climate model ensembles; and observational/reanalysis datasets for addressing a wide class of problems on weather and climate variability and its causes. In this paper, an overview of the datasets, the visualization capabilities, and data dissemination techniques of FACTS is presented. In addition, two examples are given that show the use of the interactive analysis and visualization feature of FACTS to explore questions related to climate variability and trends. Furthermore, we provide examples from published studies that have used data downloaded from FACTS to illustrate the types of research that can be pursued with its unique collection of datasets.

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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.

Open access
Peter Bissolli, Catherine Ganter, Tim Li, Ademe Mekonnen, Ahira Sánchez-Lugo, Eric J. Alfaro, Lincoln M. Alves, Jorge A. Amador, B. Andrade, Francisco Argeñalso, P. Asgarzadeh, Julian Baez, Reuben Barakiza, M. Yu. Bardin, Mikhail Bardin, Oliver Bochníček, Brandon Bukunt, Blanca Calderón, Jayaka D. Campbell, Elise Chandler, Ladislaus Chang’a, Vincent Y. S. Cheng, Leonardo A. Clarke, Kris Correa, Catalina Cortés, Felipe Costa, A.P.M.A. Cunha, Mesut Demircan, K. R. Dhurmea, A. Diawara, Sarah Diouf, Dashkhuu Dulamsuren, M. ElKharrim, Jhan-Carlo Espinoza, A. Fazl-Kazem, Chris Fenimore, Steven Fuhrman, Karin Gleason, Charles “Chip” P. Guard, Samson Hagos, Mizuki Hanafusa, H. R. Hasannezhad, Richard R. Heim Jr., Hugo G. Hidalgo, J. A. Ijampy, Gyo Soon Im, Annie C. Joseph, G. Jumaux, K. R. Kabidi, P-H. Kamsu-Tamo, John Kennedy, Valentina Khan, Mai Van Khiem, Philemon King’uza, Natalia N. Korshunova, A. C. Kruger, Hoang Phuc Lam, Mark A. Lander, Waldo Lavado-Casimiro, Tsz-Cheung Lee, Kinson H. Y. Leung, Gregor Macara, Jostein Mamen, José A. Marengo, Charlotte McBride, Noelia Misevicius, Aurel Moise, Jorge Molina-Carpio, Natali Mora, Awatif E. Mostafa, Habiba Mtongori, Charles Mutai, O. Ndiaye, Juan José Nieto, Latifa Nyembo, Patricia Nying’uro, Xiao Pan, Reynaldo Pascual Ramírez, David Phillips, Brad Pugh, Madhavan Rajeevan, M. L. Rakotonirina, Andrea M. Ramos, M. Robjhon, Camino Rodriguez, Guisado Rodriguez, Josyane Ronchail, Benjamin Rösner, Roberto Salinas, Hirotaka Sato, Hitoshi Sato, Amal Sayouri, Joseph Sebaziga, Serhat Sensoy, Sandra Spillane, Katja Trachte, Gerard van der Schrier, F. Sima, Adam Smith, Jacqueline M. Spence, O. P. Sreejith, A. K. Srivastava, José L. Stella, Kimberly A. Stephenson, Tannecia S. Stephenson, S. Supari, Sahar Tajbakhsh-Mosalman, Gerard Tamar, Michael A. Taylor, Asaminew Teshome, Wassila M. Thiaw, Skie Tobin, Adrian R. Trotman, Cedric J. Van Meerbeeck, A. Vazifeh, Shunya Wakamatsu, Wei Wang, Fei Xin, F. Zeng, Peiqun Zhang, and Zhiwei Zhu
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