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Massimo Bonavita, Rossella Arcucci, Alberto Carrassi, Peter Dueben, Alan J. Geer, Bertrand Le Saux, Nicolas Longépé, Pierre-Philippe Mathieu, and Laure Raynaud
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Alexander P. Trishchenko and Calin Ungureanu

Abstract

A novel satellite image processing technique developed at the Canada Centre for Remote Sensing has been utilized to produce annual time series of the minimum snow/ice (MSI) extent over the northern circumpolar landmass area (9,000 km × 9,000 km) for 2000–19. The information has been derived from the Moderate Resolution Imaging Spectroradiometer 10-day clear-sky composites generated at 250-m spatial resolution over the April–September period. Derived interannual variations agree very well with the warm-season average surface air temperatures from the European reanalysis (ERA5). The region-average correlation coefficient is −0.78. The total MSI extent demonstrated a statistically significant declining trend equal to −1,477 km2 yr−1. Results have been compared with data from the Randolph Glacier Inventory (RGI 6.0). The comparison points to a significant contribution of minimum seasonal snow cover relative to RGI glacierized areas. Quantitative estimates obtained for the first time showed that the region-average snow extent that survives the summer melt and resides outside of RGI area can be as high as 15% (or 53 × 103 km2) while in the northern Canadian Arctic it can reach 41% (or 43 × 103 km2). The derived MSI time series data can be recommended to the glacier and land-cover scientific community as a source of validation data and annual updates of snow and ice maps over the northern circumpolar landmass.

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Barbara Brown, Tara Jensen, John Halley Gotway, Randy Bullock, Eric Gilleland, Tressa Fowler, Kathryn Newman, Dan Adriaansen, Lindsay Blank, Tatiana Burek, Michelle Harrold, Tracy Hertneky, Christina Kalb, Paul Kucera, Louisa Nance, John Opatz, Jonathan Vigh, and Jamie Wolff

Abstract

Forecast verification and evaluation is a critical aspect of forecast development and improvement, day-to-day forecasting, and the interpretation and application of forecasts. In recent decades, the verification field has rapidly matured, and many new approaches have been developed. However, until recently, a stable set of modern tools to undertake this important component of forecasting has not been available. The Model Evaluation Tools (MET) was conceived and implemented to fill this gap. MET (https://dtcenter.org/community-code/model-evaluation-tools-met) was developed by the National Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (NOAA), and the U.S. Air Force (USAF) and is supported via the Developmental Testbed Center (DTC) and collaborations with operational and research organizations. MET incorporates traditional verification methods, as well as modern verification capabilities developed over the last two decades. MET stands apart from other verification packages due to its inclusion of innovative spatial methods, statistical inference tools, and a wide range of approaches to address the needs of individual users, coupled with strong community engagement and support. In addition, MET is freely available, which ensures that consistent modern verification capabilities can be applied by researchers and operational forecasting practitioners, enabling the use of consistent and scientifically meaningful methods by all users. This article describes MET and the expansion of MET to an umbrella package (METplus) that includes a database and display system and Python wrappers to facilitate the wide use of MET. Examples of MET applications illustrate some of the many ways that the package can be used to evaluate forecasts in a meaningful way.

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Greg M. McFarquhar, Christopher S. Bretherton, Roger Marchand, Alain Protat, Paul J. DeMott, Simon P. Alexander, Greg C. Roberts, Cynthia H. Twohy, Darin Toohey, Steve Siems, Yi Huang, Robert Wood, Robert M. Rauber, Sonia Lasher-Trapp, Jorgen Jensen, Jeffrey L. Stith, Jay Mace, Junshik Um, Emma Järvinen, Martin Schnaiter, Andrew Gettelman, Kevin J. Sanchez, Christina S. McCluskey, Lynn M. Russell, Isabel L. McCoy, Rachel L. Atlas, Charles G. Bardeen, Kathryn A. Moore, Thomas C. J. Hill, Ruhi S. Humphries, Melita D. Keywood, Zoran Ristovski, Luke Cravigan, Robyn Schofield, Chris Fairall, Marc D. Mallet, Sonia M. Kreidenweis, Bryan Rainwater, John D’Alessandro, Yang Wang, Wei Wu, Georges Saliba, Ezra J. T. Levin, Saisai Ding, Francisco Lang, Son C. H. Truong, Cory Wolff, Julie Haggerty, Mike J. Harvey, Andrew R. Klekociuk, and Adrian McDonald

Abstract

Weather and climate models are challenged by uncertainties and biases in simulating Southern Ocean (SO) radiative fluxes that trace to a poor understanding of cloud, aerosol, precipitation, and radiative processes, and their interactions. Projects between 2016 and 2018 used in situ probes, radar, lidar, and other instruments to make comprehensive measurements of thermodynamics, surface radiation, cloud, precipitation, aerosol, cloud condensation nuclei (CCN), and ice nucleating particles over the SO cold waters, and in ubiquitous liquid and mixed-phase clouds common to this pristine environment. Data including soundings were collected from the NSF–NCAR G-V aircraft flying north–south gradients south of Tasmania, at Macquarie Island, and on the R/V Investigator and RSV Aurora Australis. Synergistically these data characterize boundary layer and free troposphere environmental properties, and represent the most comprehensive data of this type available south of the oceanic polar front, in the cold sector of SO cyclones, and across seasons. Results show largely pristine environments with numerous small and few large aerosols above cloud, suggesting new particle formation and limited long-range transport from continents, high variability in CCN and cloud droplet concentrations, and ubiquitous supercooled water in thin, multilayered clouds, often with small-scale generating cells near cloud top. These observations demonstrate how cloud properties depend on aerosols while highlighting the importance of dynamics and turbulence that likely drive heterogeneity of cloud phase. Satellite retrievals confirmed low clouds were responsible for radiation biases. The combination of models and observations is examining how aerosols and meteorology couple to control SO water and energy budgets.

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Adam J. Clark, Israel L. Jirak, Burkely T. Gallo, Brett Roberts, Andrew R. Dean, Kent H. Knopfmeier, Louis J. Wicker, Makenzie Krocak, Patrick S. Skinner, Pamela L. Heinselman, Katie A. Wilson, Jake Vancil, Kimberly A. Hoogewind, Nathan A. Dahl, Gerald J. Creager, Thomas A. Jones, Jidong Gao, Yunheng Wang, Eric D. Loken, Montgomery Flora, Christopher A. Kerr, Nusrat Yussouf, Scott R. Dembek, William Miller, Joshua Martin, Jorge Guerra, Brian Matilla, David Jahn, David Harrison, David Imy, and Michael C. Coniglio
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Dylan R. Card, Heather S. Sussman, and Ajay Raghavendra
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Daniel B. Wright, Constantine Samaras, and Tania Lopez-Cantu

Abstract

Intensification of extreme rainfall due to climate change means that federally published rainfall metrics such as the “100-yr storm” are outdated throughout much of the United States. Given their central role in a wide range of infrastructure designs and risk management decisions, updating these metrics to reflect recent and future changes is essential to protect communities. There have been considerable advances in recent years in data collection, statistical methods, and climate modeling that can now be brought to bear on the problem. Scientists must take a lead in this updating process, which should be open, inclusive, and leverage recent scientific advances.

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Weihong Qian, Jun Du, and Yang Ai

Abstract

Comparisons between anomaly and full-field methods have been carried out in weather analysis and forecasting over the last decade. Evidence from these studies has demonstrated the superiority of anomaly to full field in the following four aspects: depiction of weather systems, anomaly forecasts, diagnostic parameters, and model prediction. To promote the use and further discussion of the anomaly approach, this article summarizes those findings. After examining many types of weather events, anomaly weather maps show at least five advantages in weather system depiction: 1) less vagueness in visually connecting the location of an event with its associated meteorological conditions, 2) clearer and more complete depictions of vertical structures of a disturbance, 3) easier observation of time and spatial evolution of an event and its interaction or connection with other weather systems, 4) simplification of conceptual models by unifying different weather systems into one pattern, and 5) extension of model forecast length due to earlier detection of predictors. Anomaly verification is also mentioned. The anomaly forecast is useful for raising one’s awareness of potential societal impact. Combining the anomaly forecast with an ensemble is emphasized, where a societal impact index is discussed. For diagnostic parameters, two examples are given: an anomalous convective instability index for convection, and seven vorticity and divergence related parameters for heavy rain. Both showed positive contributions from the anomalous fields. For model prediction, the anomaly version of the beta-advection model consistently outperformed its full-field version in predicting typhoon tracks with clearer physical explanation. Application of anomaly global models to seasonal forecasts is also reviewed.

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Markus Rapp, Bernd Kaifler, Andreas Dörnbrack, Sonja Gisinger, Tyler Mixa, Robert Reichert, Natalie Kaifler, Stefanie Knobloch, Ramona Eckert, Norman Wildmann, Andreas Giez, Lukas Krasauskas, Peter Preusse, Markus Geldenhuys, Martin Riese, Wolfgang Woiwode, Felix Friedl-Vallon, Björn-Martin Sinnhuber, Alejandro de la Torre, Peter Alexander, Jose Luis Hormaechea, Diego Janches, Markus Garhammer, Jorge L. Chau, J. Federico Conte, Peter Hoor, and Andreas Engel

Abstract

The southern part of South America and the Antarctic peninsula are known as the world’s strongest hotspot region of stratospheric gravity wave (GW) activity. Large tropospheric winds are deflected by the Andes and the Antarctic Peninsula and excite GWs that might propagate into the upper mesosphere. Satellite observations show large stratospheric GW activity above the mountains, the Drake Passage, and in a belt centered along 60°S. This scientifically highly interesting region for studying GW dynamics was the focus of the Southern Hemisphere Transport, Dynamics, and Chemistry–Gravity Waves (SOUTHTRAC-GW) mission. The German High Altitude and Long Range Research Aircraft (HALO) was deployed to Rio Grande at the southern tip of Argentina in September 2019. Seven dedicated research flights with a typical length of 7,000 km were conducted to collect GW observations with the novel Airborne Lidar for Middle Atmosphere research (ALIMA) instrument and the Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) limb sounder. While ALIMA measures temperatures in the altitude range from 20 to 90 km, GLORIA observations allow characterization of temperatures and trace gas mixing ratios from 5 to 15 km. Wave perturbations are derived by subtracting suitable mean profiles. This paper summarizes the motivations and objectives of the SOUTHTRAC-GW mission. The evolution of the atmospheric conditions is documented including the effect of the extraordinary Southern Hemisphere sudden stratospheric warming (SSW) that occurred in early September 2019. Moreover, outstanding initial results of the GW observation and plans for future work are presented.

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Cheng Liu, Meng Gao, Qihou Hu, Guy P. Brasseur, and Gregory R. Carmichael

Abstract

Monitoring and modeling/predicting air pollution are crucial to understanding the links between emissions and air pollution levels, to supporting air quality management, and to reducing human exposure. Yet, current monitoring networks and modeling capabilities are unfortunately inadequate to understand the physical and chemical processes above ground and to support attribution of sources. We highlight the need for the development of an international stereoscopic monitoring strategy that can depict three-dimensional (3D) distribution of atmospheric composition to reduce the uncertainties and to advance diagnostic understanding and prediction of air pollution. There are three reasons for the implementation of stereoscopic monitoring: 1) current observation networks provide only partial view of air pollution, and this can lead to misleading air quality management actions; 2) satellite retrievals of air pollutants are widely used in air pollution studies, but too often users do not acknowledge that they have large uncertainties, which can be reduced with measurements of vertical profiles; and 3) air quality modeling and forecasting require 3D observational constraints. We call on researchers and policymakers to establish stereoscopic monitoring networks and share monitoring data to better characterize the formation of air pollution, optimize air quality management, and protect human health. Future directions for advancing monitoring and modeling/predicting air pollution are also discussed.

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