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Matthew A. Janiga, Carl J. Schreck III, James A. Ridout, Maria Flatau, Neil P. Barton, E. Joseph Metzger, and Carolyn A. Reynolds

Abstract

In this study, the contribution of low-frequency (>100 days), Madden–Julian oscillation (MJO), and convectively coupled equatorial wave (CCEW) variability to the skill in predicting convection and winds in the tropics at weeks 1–3 is examined. We use subseasonal forecasts from the Navy Earth System Model (NESM); NCEP Climate Forecast System, version 2 (CFSv2); and ECMWF initialized in boreal summer 1999–2015. A technique for performing wavenumber–frequency filtering on subseasonal forecasts is introduced and applied to these datasets. This approach is better able to isolate regional variations in MJO forecast skill than traditional global MJO indices. Biases in the mean state and in the activity of the MJO and CCEWs are smallest in the ECMWF model. The NESM overestimates cloud cover as well as MJO, equatorial Rossby, and mixed Rossby–gravity/tropical depression activity over the west Pacific. The CFSv2 underestimates convectively coupled Kelvin wave activity. The predictive skill of the models at weeks 1–3 is examined by decomposing the forecasts into wavenumber–frequency signals to determine the modes of variability that contribute to forecast skill. All three models have a similar ability to simulate low-frequency variability but large differences in MJO skill are observed. The skill of the NESM and ECMWF model in simulating MJO-related OLR signals at week 2 is greatest over two regions of high MJO activity, the equatorial Indian Ocean and Maritime Continent, and the east Pacific. The MJO in the CFSv2 is too slow and too weak, which results in lower MJO skill in these regions.

Open access
Philip J. Klotzbach, Kimberly M. Wood, Michael M. Bell, Eric S. Blake, Steven G. Bowen, Louis-Philippe Caron, Jennifer M. Collins, Ethan J. Gibney, Carl J. Schreck III, and Ryan E. Truchelut

Abstract

The active 2020 Atlantic hurricane season produced 30 named storms, 14 hurricanes, and 7 major hurricanes (Category 3+ on the Saffir-Simpson Hurricane Wind Scale). Though the season was active overall, the final two months (October–November) raised 2020 into the upper echelon of Atlantic hurricane activity for integrated metrics such as Accumulated Cyclone Energy (ACE). This study focuses on October–November 2020, when 7 named storms, 6 hurricanes, and 5 major hurricanes formed and produced ACE of 74 * 104 kt2. Since 1950, October–November 2020 ranks tied for 3rd for named storms, 1st for hurricanes and major hurricanes, and 2nd for ACE. Six named storms also underwent rapid intensification (≥30 kt intensification in ≤24 hr) in October–November 2020—the most on record.

This manuscript includes a climatological analysis of October–November tropical cyclones (TCs) and their primary formation regions. In 2020, anomalously low wind shear in the western Caribbean and Gulf of Mexico, likely driven by a moderate intensity La Niña event and anomalously high sea surface temperatures (SSTs) in the Caribbean provided dynamic and thermodynamic conditions that were much more conducive than normal for late-season TC formation and rapid intensification. This study also highlights October–November 2020 landfalls, including Hurricanes Delta and Zeta in Louisiana and in Mexico and Hurricanes Eta and Iota in Nicaragua. The active late season in the Caribbean would have been anticipated by a statistical model using the July–September-averaged ENSO Longitude Index and Atlantic warm pool SSTs as predictors.

Full access
Boyin Huang, Matthew J. Menne, Tim Boyer, Eric Freeman, Byron E. Gleason, Jay H. Lawrimore, Chunying Liu, J. Jared Rennie, Carl J. Schreck III, Fengying Sun, Russell Vose, Claude N. Williams, Xungang Yin, and Huai-Min Zhang

Abstract

This analysis estimates uncertainty in the NOAA global surface temperature (GST) version 5 (NOAAGlobalTemp v5) product, which consists of sea surface temperature (SST) from the Extended Reconstructed SST version 5 (ERSSTv5) and land surface air temperature (LSAT) from the Global Historical Climatology Network monthly version 4 (GHCNm v4). Total uncertainty in SST and LSAT consists of parametric and reconstruction uncertainties. The parametric uncertainty represents the dependence of SST/LSAT reconstructions on selecting 28 (6) internal parameters of SST (LSAT), and is estimated by a 1000-member ensemble from 1854 to 2016. The reconstruction uncertainty represents the residual error of using a limited number of 140 (65) modes for SST (LSAT). Uncertainty is quantified at the global scale as well as the local grid scale. Uncertainties in SST and LSAT at the local grid scale are larger in the earlier period (1880s–1910s) and during the two world wars due to sparse observations, then decrease in the modern period (1950s–2010s) due to increased data coverage. Uncertainties in SST and LSAT at the global scale are much smaller than those at the local grid scale due to error cancellations by averaging. Uncertainties are smaller in SST than in LSAT due to smaller SST variabilities. Comparisons show that GST and its uncertainty in NOAAGlobalTemp v5 are comparable to those in other internationally recognized GST products. The differences between NOAAGlobalTemp v5 and other GST products are within their uncertainties at the 95% confidence level.

Open access
Christopher C. Hennon, Kenneth R. Knapp, Carl J. Schreck III, Scott E. Stevens, James P. Kossin, Peter W. Thorne, Paula A. Hennon, Michael C. Kruk, Jared Rennie, Jean-Maurice Gadéa, Maximilian Striegl, and Ian Carley

Abstract

The global tropical cyclone (TC) intensity record, even in modern times, is uncertain because the vast majority of storms are only observed remotely. Forecasters determine the maximum wind speed using a patchwork of sporadic observations and remotely sensed data. A popular tool that aids forecasters is the Dvorak technique—a procedural system that estimates the maximum wind based on cloud features in IR and/or visible satellite imagery. Inherently, the application of the Dvorak procedure is open to subjectivity. Heterogeneities are also introduced into the historical record with the evolution of operational procedures, personnel, and observing platforms. These uncertainties impede our ability to identify the relationship between tropical cyclone intensities and, for example, recent climate change.

A global reanalysis of TC intensity using experts is difficult because of the large number of storms. We will show that it is possible to effectively reanalyze the global record using crowdsourcing. Through modifying the Dvorak technique into a series of simple questions that amateurs (“citizen scientists”) can answer on a website, we are working toward developing a new TC dataset that resolves intensity discrepancies in several recent TCs. Preliminary results suggest that the performance of human classifiers in some cases exceeds that of an automated Dvorak technique applied to the same data for times when the storm is transitioning into a hurricane.

Full access
Carl J. Schreck III, Stephen Bennett, Jason M. Cordeira, Jake Crouch, Jenny Dissen, Andrea L. Lang, David Margolin, Adam O’Shay, Jared Rennie, Thomas Ian Schneider, and Michael J. Ventrice

Abstract

Day-to-day volatility in natural gas markets is driven largely by variability in heating demand, which is in turn dominated by cool-season temperature anomalies over the northeastern quadrant of the United States (“Midwest–East”). Energy traders rely on temperature forecasts at horizons of 2–4 weeks to anticipate those fluctuations in demand. Forecasts from dynamical models are widely available, so the markets react quickly to changes in the model predictions. Traders often work with meteorologists who leverage teleconnections from the tropics and the Arctic to improve upon the model forecasts. This study demonstrates how natural gas prices react to Midwest–East temperatures using the anomalous winters of 2011/12 and 2013/14. These examples also illustrate how energy meteorologists use teleconnections from the Arctic and the tropics to forecast heating demand.

Winter 2011/12 was exceptionally warm, consistent with the positive Arctic Oscillation (AO). March 2012 was a fitting exclamation point on the winter as it featured the largest warm anomaly for the United States above the twentieth-century climatology of any month since 1895. The resulting lack of heating demand led to record surpluses of natural gas storage and spurred prices downward to an 11-yr low in April 2012. In sharp contrast, winter 2013/14 was unusually cold. An anomalous Alaskan ridge led to cold air being transported from Siberia into the United States, despite the AO generally being positive. The ensuing swell in heating demand exhausted the surplus natural gas inventory, and prices rose to their highest levels since the beginning of the global recession in 2008.

Full access
Stephen Baxter, Gerald D Bell, Eric S Blake, Francis G Bringas, Suzana J Camargo, Lin Chen, Caio A. S Coelho, Ricardo Domingues, Stanley B Goldenberg, Gustavo Goni, Nicolas Fauchereau, Michael S Halpert, Qiong He, Philip J Klotzbach, John A Knaff, Michelle L'Heureux, Chris W Landsea, I.-I Lin, Andrew M Lorrey, Jing-Jia Luo, Andrew D Magee, Richard J Pasch, Petra R Pearce, Alexandre B Pezza, Matthew Rosencrans, Blair C Trewin, Ryan E Truchelut, Bin Wang, H Wang, Kimberly M Wood, and John-Mark Woolley
Free access
Howard J. Diamond, Carl J. Schreck III, Emily J. Becker, Gerald D. Bell, Eric S. Blake, Stephanie Bond, Francis G. Bringas, Suzana J. Camargo, Lin Chen, Caio A. S. Coelho, Ricardo Domingues, Stanley B. Goldenberg, Gustavo Goni, Nicolas Fauchereau, Michael S. Halpert, Qiong He, Philip J. Klotzbach, John A. Knaff, Michelle L'Heureux, Chris W. Landsea, I.-I. Lin, Andrew M. Lorrey, Jing-Jia Luo, Kyle MacRitchie, Andrew D. Magee, Ben Noll, Richard J. Pasch, Alexandre B. Pezza, Matthew Rosencrans, Michael K. Tippet, Blair C. Trewin, Ryan E. Truchelut, Bin Wang, Hui Wang, Kimberly M. Wood, John-Mark Woolley, and Steven H. Young
Full access
Tim Li, Abdallah Abida, Laura S. Aldeco, Eric J. Alfaro, Lincoln M. Alves, Jorge A. Amador, B. Andrade, Julian Baez, M. Yu. Bardin, Endalkachew Bekele, Eric Broedel, Brandon Bukunt, Blanca Calderón, Jayaka D. Campbell, Diego A. Campos Diaz, Gilma Carvajal, Elise Chandler, Vincent. Y. S. Cheng, Chulwoon Choi, Leonardo A. Clarke, Kris Correa, Felipe Costa, A. P. Cunha, Mesut Demircan, R. Dhurmea, Eliecer A. Díaz, M. ElKharrim, Bantwale D. Enyew, Jhan C. Espinoza, Amin Fazl-Kazem, Nava Fedaeff, Z. Feng, Chris Fenimore, S. D. Francis, Karin Gleason, Charles “Chip” P. Guard, Indra Gustari, S. Hagos, Richard R. Heim Jr., Rafael Hernández, Hugo G. Hidalgo, J. A. Ijampy, Annie C. Joseph, Guillaume Jumaux, Khadija Kabidi, Johannes W. Kaiser, Pierre-Honore Kamsu-Tamo, John Kennedy, Valentina Khan, Mai Van Khiem, Khatuna Kokosadze, Natalia N. Korshunova, Andries C. Kruger, Nato Kutaladze, L. Labbé, Mónika Lakatos, Hoang Phuc Lam, Mark A. Lander, Waldo Lavado-Casimiro, T. C. Lee, Kinson H. Y. Leung, Andrew D. Magee, Jostein Mamen, José A. Marengo, Dora Marín, Charlotte McBride, Lia Megrelidze, Noelia Misevicius, Y. Mochizuki, Aurel Moise, Jorge Molina-Carpio, Natali Mora, Awatif E. Mostafa, uan José Nieto, Lamjav Oyunjargal, Reynaldo Pascual Ramírez, Maria Asuncion Pastor Saavedra, Uwe Pfeifroth, David Phillips, Madhavan Rajeevan, Andrea M. Ramos, Jayashree V. Revadekar, Miliaritiana Robjhon, Ernesto Rodriguez Camino, Esteban Rodriguez Guisado, Josyane Ronchail, Benjamin Rösner, Roberto Salinas, Amal Sayouri, Carl J. Schreck III, Serhat Sensoy, A. Shimpo, Fatou Sima, Adam Smith, Jacqueline Spence, Sandra Spillane, Arne Spitzer, A. K. Srivastava, José L. Stella, Kimberly A. Stephenson, Tannecia S. Stephenson, Michael A. Taylor, Wassila Thiaw, Skie Tobin, Dennis Todey, Katja Trachte, Adrian R. Trotman, Gerard van der Schrier, Cedric J. Van Meerbeeck, Ahad Vazifeh, José Vicencio Veloso, Wei Wang, Fei Xin, Peiqun Zhang, Zhiwei Zhu, and Jonas Zucule
Free access