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Faisal Hossain, Margaret Srinivasan, Nicolas Picot, Santiago Pena-Luque, and Bradley Doorn
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Ralf Merz, Arianna Miniussi, Stefano Basso, Karl-Jonas Petersen, and Larisa Tarasova

Abstract

Conceptual hydrological models are irreplaceable tools for large scale (i.e., from regional to global) hydrological predictions. Large scale modeling studies typically strive to employ one single model structure regardless of the diversity of catchments under study. However, little is known on the optimal model complexity for large scale applications. In a modelling experiment across 700 catchments in the contiguous US, we analyze the performance of a conceptual (bucket style) distributed hydrological model with varying complexity (5 model versions with 11 to 45 parameters) but with exactly the same inputs, spatial and temporal resolution, and implementing the same regional parameterization approach. The performance of all model versions compares well with those of contemporary large scale models tested in the US, suggesting that the applied model structures reasonably account for the dominant hydrological processes. Remarkably, our results favor a simpler model structure where the main hydrological processes of runoff generation and routing through soil, groundwater and the river network are conceptualized in distinct but parsimonious ways. As long as only observed runoff is used for model validation, including additional soil layers in the model structure to better represent vertical soil heterogeneity seems not to improve model performance. More complex models tend to have lower model performance and may result in rather large uncertainties in simulating states and fluxes (soil moisture and groundwater recharge) in model ensemble applications. Overall, our results indicate that simpler model structures tend to be a more reliable choice, given the limited validation data available at large scale.

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Harald Sodemann, Franziska Aemisegger, and Camille Risi
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Robert A Weller, Roger Lukas, James Potemra, Albert. J. Plueddemann, Chris Fairall, and Sebastien Bigorre

Abstract

There is great interest in improving our understanding of the respective roles of the ocean and atmosphere in variability and change in weather and climate. Due to the sparsity of sustained observing sites in the open ocean, information about the air-sea exchanges of heat, freshwater, and momentum is often drawn from models. In this paper observations from three long-term surface moorings deployed in the tradewind regions of the Pacific and Atlantic Oceans are used to compare observed means and low-passed air-sea fluxes from the moorings with coincident records from three atmospheric reanalyses (ERA5, NCEP2, and MERRA2) and from CMIP6 coupled models. To set the stage for the comparison, the methodologies of maintaining the long-term surface moorings, known as Ocean Reference Stations (ORS), and assessing the accuracies of their air-sea fluxes are described first. Biases in the reanalyses’ means and low-passed wind stresses and net air-sea heat fluxes are significantly larger than the observational uncertainties and in some case show variability in time. These reanalyses and most CMIP6 models fail to provide as much heat into the ocean as observed. In the discussion and conclusion section, long-term observing sites in the open ocean are seen as essential, independent benchmarks not only to document the coupling between the atmosphere and ocean but also to promote collaborative efforts to assess and improve the ability of models to simulate air-sea fluxes.

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Hanna Lee, Nadine Johnston, Lars Nieradzik, Andrew Orr, Ruth H. Mottram, Willem Jan van de Berg, and Priscilla A. Mooney
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Neil A. Stuart, Gail Hartfield, David M. Schultz, Katie Wilson, Gregory West, Robert Hoffman, Gary Lackmann, Harold Brooks, Paul Roebber, Teresa Bals-Elsholz, Holly Obermeier, Falko Judt, Patrick Market, Daniel Nietfeld, Bruce Telfeyan, Dan Depodwin, Jeffrey Fries, Elliot Abrams, and Jerry Shields

ABSTRACT

A series of webinars and panel discussions were conducted on the topic of the evolving role of humans in weather prediction and communication, in recognition of the 100th anniversary of the founding of the AMS. One main theme that arose was the inevitability that new tools using artificial intelligence will improve data analysis, forecasting, and communication. We discussed what tools are being created, how they are being created, and how the tools will potentially affect various duties for operational meteorologists in multiple sectors of the profession. Even as artificial intelligence increases automation, humans will remain a vital part of the forecast process as that process changes over time. Additionally, both university training and professional development must be revised to accommodate the evolving forecasting process, including addressing the need for computing and data skills (including artificial intelligence and visualization), probabilistic and ensemble forecasting, decision support, and communication skills. These changing skill sets necessitate that both the U.S. government’s Meteorologist General Schedule-1340 requirements and the AMS standards for a bachelor’s degree need to be revised. Seven recommendations are presented for student and forecaster preparation and career planning, highlighting the need for students and operational meteorologists to be flexible life-long learners, acquire new skills, and be engaged in the changes to forecast technology in order to best serve the user community throughout their careers. The article closes with our vision for the ways that humans can maintain an essential role in weather prediction and communication, highlighting the interdependent relationship between computers and humans.

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Satya Kalluri
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Christopher J. White, Daniela I. V. Domeisen, Nachiketa Acharya, Elijah A. Adefisan, Michael L. Anderson, Stella Aura, Ahmed A. Balogun, Douglas Bertram, Sonia Bluhm, David J. Brayshaw, Jethro Browell, Dominik Büeler, Andrew Charlton-Perez, Xandre Chourio, Isadora Christel, Caio A. S. Coelho, Michael J. DeFlorio, Luca Delle Monache, Francesca Di Giuseppe, Ana María García-Solórzano, Peter B. Gibson, Lisa Goddard, Carmen González Romero, Richard J. Graham, Robert M. Graham, Christian M. Grams, Alan Halford, W. T. Katty Huang, Kjeld Jensen, Mary Kilavi, Kamoru A. Lawal, Robert W. Lee, David MacLeod, Andrea Manrique-Suñén, Eduardo S. P. R. Martins, Carolyn J. Maxwell, William J. Merryfield, Ángel G. Muñoz, Eniola Olaniyan, George Otieno, John A. Oyedepo, Lluís Palma, Ilias G. Pechlivanidis, Diego Pons, F. Martin Ralph, Dirceu S. Reis Jr., Tomas A. Remenyi, James S. Risbey, Donald J. C. Robertson, Andrew W. Robertson, Stefan Smith, Albert Soret, Ting Sun, Martin C. Todd, Carly R. Tozer, Francisco C. Vasconcelos Jr., Ilaria Vigo, Duane E. Waliser, Fredrik Wetterhall, and Robert G. Wilson

Abstract

The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.

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Cristiana Stan, Cheng Zheng, Edmund Kar-Man Chang, Daniela I. V. Domeisen, Chaim I. Garfinkel, Andrea M. Jenney, Hyemi Kim, Young-Kwon Lim, Hai Lin, Andrew Robertson, Chen Schwartz, Frederic Vitart, Jiabao Wang, and Priyanka Yadav

Abstract

This study evaluates the ability of state-of-the-art subseasonal-to-seasonal (S2S) forecasting systems to represent and predict the teleconnections of the Madden–Julian oscillation and their effects on weather in terms of midlatitude weather patterns and North Atlantic tropical cyclones. This evaluation of forecast systems applies novel diagnostics developed to track teleconnections along their preferred pathways in the troposphere and stratosphere, and to measure the global and regional responses induced by teleconnections across both the Northern and Southern Hemispheres. Results of this study will help the modeling community understand to what extent the potential to predict the weather on S2S time scales is achieved by the current generation of forecasting systems, while informing where to focus further development efforts. The findings of this study will also provide impact modelers and decision-makers with a better understanding of the potential of S2S predictions related to MJO teleconnections.

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Daniela I. V. Domeisen, Christopher J. White, Hilla Afargan-Gerstman, Ángel G. Muñoz, Matthew A. Janiga, Frédéric Vitart, C. Ole Wulff, Salomé Antoine, Constantin Ardilouze, Lauriane Batté, Hannah C. Bloomfield, David J. Brayshaw, Suzana J. Camargo, Andrew Charlton-Pérez, Dan Collins, Tim Cowan, Maria del Mar Chaves, Laura Ferranti, Rosario Gómez, Paula L. M. González, Carmen González Romero, Johnna M. Infanti, Stelios Karozis, Hera Kim, Erik W. Kolstad, Emerson LaJoie, Llorenç Lledó, Linus Magnusson, Piero Malguzzi, Andrea Manrique-Suñén, Daniele Mastrangelo, Stefano Materia, Hanoi Medina, Lluís Palma, Luis E. Pineda, Athanasios Sfetsos, Seok-Woo Son, Albert Soret, Sarah Strazzo, and Di Tian

Abstract

Extreme weather events have devastating impacts on human health, economic activities, ecosystems, and infrastructure. It is therefore crucial to anticipate extremes and their impacts to allow for preparedness and emergency measures. There is indeed potential for probabilistic subseasonal prediction on time scales of several weeks for many extreme events. Here we provide an overview of subseasonal predictability for case studies of some of the most prominent extreme events across the globe using the ECMWF S2S prediction system: heatwaves, cold spells, heavy precipitation events, and tropical and extratropical cyclones. The considered heatwaves exhibit predictability on time scales of 3–4 weeks, while this time scale is 2–3 weeks for cold spells. Precipitation extremes are the least predictable among the considered case studies. ­Tropical cyclones, on the other hand, can exhibit probabilistic predictability on time scales of up to 3 weeks, which in the presented cases was aided by remote precursors such as the Madden–Julian oscillation. For extratropical cyclones, lead times are found to be shorter. These case studies clearly illustrate the potential for event-dependent advance warnings for a wide range of extreme events. The subseasonal predictability of extreme events demonstrated here allows for an extension of warning horizons, provides advance information to impact modelers, and informs communities and stakeholders affected by the impacts of extreme weather events.

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