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Chelsea R. Thompson, Steven C. Wofsy, Michael J. Prather, Paul A. Newman, Thomas F. Hanisco, Thomas B. Ryerson, David W. Fahey, Eric C. Apel, Charles A. Brock, William H. Brune, Karl Froyd, Joseph M. Katich, Julie M. Nicely, Jeff Peischl, Eric Ray, Patrick R. Veres, Siyuan Wang, Hannah M. Allen, Elizabeth Asher, Huisheng Bian, Donald Blake, Ilann Bourgeois, John Budney, T. Paul Bui, Amy Butler, Pedro Campuzano-Jost, Cecilia Chang, Mian Chin, RóISíN Commane, Gus Correa, John D. Crounse, Bruce Daube, Jack E. Dibb, Joshua P. Digangi, Glenn S. Diskin, Maximilian Dollner, James W. Elkins, Arlene M. Fiore, Clare M. Flynn, Hao Guo, Samuel R. Hall, Reem A. Hannun, Alan Hills, Eric J. Hintsa, Alma Hodzic, Rebecca S. Hornbrook, L. Greg Huey, Jose L. Jimenez, Ralph F. Keeling, Michelle J. Kim, Agnieszka Kupc, Forrest Lacey, Leslie R. Lait, Jean-Francois Lamarque, Junhua Liu, Kathryn Mckain, Simone Meinardi, David O. Miller, Stephen A. Montzka, Fred L. Moore, Eric J. Morgan, Daniel M. Murphy, Lee T. Murray, Benjamin A. Nault, J. Andrew Neuman, Louis Nguyen, Yenny Gonzalez, Andrew Rollins, Karen Rosenlof, Maryann Sargent, Gregory Schill, Joshua P. Schwarz, Jason M. St. Clair, Stephen D. Steenrod, Britton B. Stephens, Susan E. Strahan, Sarah A. Strode, Colm Sweeney, Alexander B. Thames, Kirk Ullmann, Nicholas Wagner, Rodney Weber, Bernadett Weinzierl, Paul O. Wennberg, Christina J. Williamson, Glenn M. Wolfe, and Linghan Zeng

Abstract

This article provides an overview of the NASA Atmospheric Tomography (ATom) mission and a summary of selected scientific findings to date. ATom was an airborne measurements and modeling campaign aimed at characterizing the composition and chemistry of the troposphere over the most remote regions of the Pacific, Southern, Atlantic, and Arctic Oceans, and examining the impact of anthropogenic and natural emissions on a global scale. These remote regions dominate global chemical reactivity and are exceptionally important for global air quality and climate. ATom data provide the in situ measurements needed to understand the range of chemical species and their reactions, and to test satellite remote sensing observations and global models over large regions of the remote atmosphere. Lack of data in these regions, particularly over the oceans, has limited our understanding of how atmospheric composition is changing in response to shifting anthropogenic emissions and physical climate change. ATom was designed as a global-scale tomographic sampling mission with extensive geographic and seasonal coverage, tropospheric vertical profiling, and detailed speciation of reactive compounds and pollution tracers. ATom flew the NASA DC-8 research aircraft over four seasons to collect a comprehensive suite of measurements of gases, aerosols, and radical species from the remote troposphere and lower stratosphere on four global circuits from 2016 to 2018. Flights maintained near-continuous vertical profiling of 0.15 – 13 km altitudes on long meridional transects of the Pacific and Atlantic Ocean basins. Analysis and modeling of ATom data have led to the significant early findings highlighted here.

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Elizabeth A. DiGangi, Michael Stock, and Jeff Lapierre

Abstract

Lightning data are often used to measure the location and intensity of thunderstorms. This study presents 5 years of data from the Earth Networks Global Lightning Detection Network (ENGLN) in the form of thunder hours. A thunder hour is defined as an hour during which thunder can be heard from a given location, and thunder hours can be calculated for the entire globe. Thunder hours are an intuitive measure of thunderstorm frequency where the one-hour interval corresponds to the life span of most thunderstorms, and the hourly temporal resolution of the data also represents long-lived systems well. Flash density-observing systems are incredibly useful, but they have some drawbacks which limit how they can be used to quantify global thunderstorm activity on a climatological scale: flash density distributions derived from satellite observations must sacrifice a great deal of their spatial resolution in order to capture the diurnal convective cycle, and the detection efficiencies of ground-based lightning detection systems are not uniform in space or constant in time. Examining convective patterns in the context of thunder hours lends insight into thunderstorm activity without being heavily influenced by network performance, making thunder hours particularly useful for studying thunderstorm climatology. The ENGLN thunder hour dataset offers powerful utility to climatological studies involving lightning and thunderstorms. This study first shows that the ENGLN thunder hours dataset is very consistent with past measurements of global thunderstorm activity and the global electric circuit using only 5 years of data. Then, this study showcases thunder anomaly fields, designed to be analogous to temperature anomalies, which can be used to diagnose changes in thunderstorm frequency relative to the long-term mean in both time and space.

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Ute Weber, Sabine Attinger, Burkard Baschek, Julia Boike, Dietrich Borchardt, Holger Brix, Nicolas Brüggemann, Ingeborg Bussmann, Peter Dietrich, Philipp Fischer, Jens Greinert, Irena Hajnsek, Norbert Kamjunke, Dorit Kerschke, Astrid Kiendler-Scharr, Arne Körtzinger, Christoph Kottmeier, Bruno Merz, Ralf Merz, Martin Riese, Michael Schloter, HaPe Schmid, Jörg-Peter Schnitzler, Torsten Sachs, Claudia Schütze, Ralf Tillmann, Harry Vereecken, Andreas Wieser, and Georg Teutsch

Abstract

MOSES (Modular Observation Solutions for Earth Systems) is a novel observation system that is specifically designed to unravel the impact of distinct, dynamic events on the long-term development of environmental systems. Hydro-meteorological extremes such as the recent European droughts or the floods of 2013 caused severe and lasting environmental damage. Modelling studies suggest that abrupt permafrost thaw events accelerate Arctic greenhouse gas emissions. Short-lived ocean eddies seem to comprise a significant share of the marine carbon uptake or release. Although there is increasing evidence that such dynamic events bear the potential for major environmental impacts, our knowledge on the processes they trigger is still very limited. MOSES aims at capturing such events, from their formation to their end, with high spatial and temporal resolution. As such, the observation system extends and complements existing national and international observation networks, which are mostly designed for long-term monitoring.

Several German Helmholtz Association centers have developed this research facility as a mobile and modular “system of systems” to record energy, water, greenhouse gas and nutrient cycles on the land surface, in coastal regions, in the ocean, in polar regions, and in the atmosphere – but especially the interactions between the Earth compartments. During the implementation period (2017-2021), the measuring systems were put into operation and test campaigns were performed to establish event-driven campaign routines. With MOSES’ regular operation starting in 2022, the observation system will then be ready for cross-compartment and cross-discipline research on the environmental impacts of dynamic events.

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Jouni Heiskanen, Christian Brümmer, Nina Buchmann, Carlo Calfapietra, Huilin Chen, Bert Gielen, Thanos Gkritzalis, Samuel Hammer, Susan Hartman, Mathias Herbst, Ivan A. Janssens, Armin Jordan, Eija Juurola, Ute Karstens, Ville Kasurinen, Bart Kruijt, Harry Lankreijer, Ingeborg Levin, Maj-Lena Linderson, Denis Loustau, Lutz Merbold, Cathrine Lund Myhre, Dario Papale, Marian Pavelka, Kim Pilegaard, Michel Ramonet, Corinna Rebmann, Janne Rinne, Léonard Rivier, Elena Saltikoff, Richard Sanders, Martin Steinbacher, Tobias Steinhoff, Andrew Watson, Alex T. Vermeulen, Timo Vesala, Gabriela Vítková, and Werner Kutsch

Abstract

Since 1750, land use change and fossil fuel combustion has led to a 46 % increase in the atmospheric carbon dioxide (CO2) concentrations, causing global warming with substantial societal consequences. The Paris Agreement aims to limiting global temperature increases to well below 2°C above pre-industrial levels. Increasing levels of CO2 and other greenhouse gases (GHGs), such as methane (CH4) and nitrous oxide (N2O), in the atmosphere are the primary cause of climate change. Approximately half of the carbon emissions to the atmosphere is sequestered by ocean and land sinks, leading to ocean acidification but also slowing the rate of global warming. However, there are significant uncertainties in the future global warming scenarios due to uncertainties in the size, nature and stability of these sinks. Quantifying and monitoring the size and timing of natural sinks and the impact of climate change on ecosystems are important information to guide policy-makers’ decisions and strategies on reductions in emissions. Continuous, long-term observations are required to quantify GHG emissions, sinks, and their impacts on Earth systems. The Integrated Carbon Observation System (ICOS) was designed as the European in situ observation and information system to support science and society in their efforts to mitigate climate change. It provides standardized and open data currently from over 140 measurement stations across 12 European countries. The stations observe GHG concentrations in the atmosphere and carbon and GHG fluxes between the atmosphere, land surface and the oceans. This article describes how ICOS fulfills its mission to harmonize these observations, ensure the related long-term financial commitments, provide easy access to well-documented and reproducible high-quality data and related protocols and tools for scientific studies, and deliver information and GHG-related products to stakeholders in society and policy.

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Toby N. Carlson, Arthur A. Person, and Thomas J. Canich

Abstract

Simsphere, a soil/vegetation/atmosphere/transfer (SVAT) model developed at Penn State, can be downloaded from the web for use by students and researchers. In existence for several decades, Simsphere has figured in both the classroom and in research at several universities. As such, Simsphere has been supported by a knowledgeable group of academic users and has been applied in a variety of applications, such as in remote sensing of surface soil water content, and in the assessment of water and ozone stresses on plants. This paper describes the model and how it can be downloaded and run.

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Hui Yu, Guomin Chen, Cong Zhou, Wai Kin Wong, Mengqi Yang, Yinglong Xu, Peiyan Chen, Rijin Wan, and Xinrong Hu

Abstract

The annual-mean position errors (PE) of tropical cyclone (TC) track forecasts from three forecast agencies (RSMC-Tokyo, CMA, and JTWC) are analyzed to document the past improvements and project future tendency in track forecast accuracy for TCs in the western North Pacific. An improvement of 48 h (2-day) in lead time has been achieved in the past thirty years, but with noticeable stepwise periods of improvements with superposed short-term fluctuations. The stepwise improvement features differ among the three forecast agencies, but are highly related to the development of objective forecast guidance and the application strategy. As demonstrated by an exponential model for the growth of PEs with lead time for TCs of tropical storm category and above, the improvements in the past ten years have mainly been due to the reduction in analysis errors rather than the reduction in the error growth rate. If the current trend continues, a further 2-day improvement in TC track forecast lead times may be projected for the coming fifteen years up to 2035, and we certainly have not reached yet the limit of TC track predictability in the western North Pacific.

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Kelly Mahoney, Chesley McColl, Douglas M. Hultstrand, William D. Kappel, Bill McCormick, and Gilbert P. Compo

Abstract

Accurate estimation of the potential “upper limit” for extreme precipitation is critical for dam safety and water resources management, as dam failures pose significant risks to life and property. Methods used to estimate the theoretical “upper limit” of precipitation are often outdated and in need of updating. The rarity of extreme events means that old storms with limited observational data are often used to define the upper bound of precipitation.

Observations of many important old storms are limited in spatial and temporal coverage, and sometimes of dubious quality. This reduces confidence in flood hazard assessments used in dam safety evaluations and leads to unknown or uncertain societal risk.

This paper describes a method for generating and applying ensembles of high-resolution, state-of-the-art numerical model simulations of historical past extreme precipitation events to meet contemporary stakeholder needs. The method was designed as part of a research-to-application-focused partnership project to update state dam safety rules in Colorado and New Mexico. The results demonstrated multiple stakeholder and user benefits which were applied directly into storm analyses utilized for extreme rainfall estimation, and diagnostics were developed and ultimately used to update Colorado state dam safety rules, officially passed in January 2020. We discuss how what started as a prototype research foray to meet a specific user need may ultimately inform wider adoption of numerical simulations for water resources risk assessment, and how the historical event downscaling method performed offers near-term, implementable improvements to current dam safety flood risk estimates that can better serve society today.

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Filippo Giorgi, Erika Coppola, Daniela Jacob, Claas Teichmann, Sabina Abba Omar, Moetasim Ashfaq, Nikolina Ban, Katharina Bülow, Melissa Bukovsky, Lars Buntemeyer, Tereza Cavazos, James Ciarlo', Rosmeri Porfirio Da Rocha, Sushant Das, Fabio di Sante, Jason P. Evans, Xuejie Gao, Graziano Giuliani, Russell H. Glazer, Peter Hoffmann, Eun-Soon Im, Gaby Langendijk, Ludwig Lierhammer, Marta Llopart, Sebastial Mueller, Rosa Luna-Nino, Rita Nogherotto, Emanuela Pichelli, Francesca Raffaele, Michelle Reboita, Diana Rechid, Armelle Remedio, Thomas Remke, Windmanagda Sawadogo, Kevin Sieck, Jose' Abraham Torres-Alavez, and Torsten Weber

Abstract

We describe the first effort within the Coordinated Regional Climate Downscaling Experiment - Coordinated Output for Regional Evaluation, or CORDEX-CORE EXP-I. It consists of a set of 21st century projections with two regional climate models (RCMs) downscaling three global climate model (GCM) simulations from the CMIP5 program, for two greenhouse gas concentration pathways (RCP8.5 and RCP2.6), over 9 CORDEX domains at ~25 km grid spacing. Illustrative examples from the initial analysis of this ensemble are presented, covering a wide range of topics, such as added value of RCM nesting, extreme indices, tropical and extratropical storms, monsoons, ENSO, severe storm environments, emergence of change signals, energy production. They show that the CORDEX-CORE EXP-I ensemble can provide downscaled information of unprecedented comprehensiveness to increase understanding of processes relevant for regional climate change and impacts, and to assess the added value of RCMs. The CORDEX-CORE EXP-I dataset, which will be incrementally augmented with new simulations, is intended to be a public resource available to the scientific and end-user communities for application to process studies, impacts on different socioeconomic sectors and climate service activities. The future of the CORDEX-CORE initiative is also discussed.

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D. A. Knopf, K. R. Barry, T. A. Brubaker, L. G. Jahl, K. A. Jankowski, J. Li, Y. Lu, L. W. Monroe, K. A. Moore, F. A. Rivera-Adorno, K. A. Sauceda, Y. Shi, J. M. Tomlin, H. S. K. Vepuri, P. Wang, N. N. Lata, E. J. T. Levin, J. M. Creamean, T. C. J. Hill, S. China, P. A. Alpert, R. C. Moffet, N. Hiranuma, R. C. Sullivan, A. M. Fridlind, M. West, N. Riemer, A. Laskin, P. J. DeMott, and X. Liu

Abstract

Prediction of ice formation in clouds presents one of the grand challenges in the atmospheric sciences. Immersion freezing initiated by ice-nucleating particles (INPs) is the dominant pathway of primary ice crystal formation in mixed-phase clouds, where supercooled water droplets and ice crystals coexist, with important implications for the hydrological cycle and climate. However, derivation of INP number concentrations from an ambient aerosol population in cloud-resolving and climate models remains highly uncertain. We conducted an aerosol–ice formation closure pilot study using a field-observational approach to evaluate the predictive capability of immersion freezing INPs. The closure study relies on collocated measurements of the ambient size-resolved and single-particle composition and INP number concentrations. The acquired particle data serve as input in several immersion freezing parameterizations, which are employed in cloud-resolving and climate models, for prediction of INP number concentrations. We discuss in detail one closure case study in which a front passed through the measurement site, resulting in a change of ambient particle and INP populations. We achieved closure in some circumstances within uncertainties, but we emphasize the need for freezing parameterization of potentially missing INP types and evaluation of the choice of parameterization to be employed. Overall, this closure pilot study aims to assess the level of parameter details and measurement strategies needed to achieve aerosol–ice formation closure. The closure approach is designed to accurately guide immersion freezing schemes in models, and ultimately identify the leading causes for climate model bias in INP predictions.

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