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Amy McGovern
,
David John Gagne II
,
Christopher D. Wirz
,
Imme Ebert-Uphoff
,
Ann Bostrom
,
Yuhan Rao
,
Andrea Schumacher
,
Montgomery Flora
,
Randy Chase
,
Antonios Mamalakis
,
Marie McGraw
,
Ryan Lagerquist
,
Robert J. Redmon
, and
Taysia Peterson

Abstract

Many of our generation’s most pressing environmental science problems are wicked problems, which means they cannot be cleanly isolated and solved with a single “correct” answer. The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) seeks to address such problems by developing synergistic approaches with a team of scientists from three disciplines: environmental science (including atmospheric, ocean, and other physical sciences), artificial intelligence (AI), and social science including risk communication. As part of our work, we developed a novel approach to summer school, held from 27 to 30 June 2022. The goal of this summer school was to teach a new generation of environmental scientists how to cross disciplines and develop approaches that integrate all three disciplinary perspectives and approaches in order to solve environmental science problems. In addition to a lecture series that focused on the synthesis of AI, environmental science, and risk communication, this year’s summer school included a unique “trust-a-thon” component where participants gained hands-on experience applying both risk communication and explainable AI techniques to pretrained machine learning models. We had 677 participants from 63 countries register and attend online. Lecture topics included trust and trustworthiness (day 1), explainability and interpretability (day 2), data and workflows (day 3), and uncertainty quantification (day 4). For the trust-a-thon, we developed challenge problems for three different application domains: 1) severe storms, 2) tropical cyclones, and 3) space weather. Each domain had associated user persona to guide user-centered development.

Open access
Xueying Zhang
,
Yetang Wang
,
Shugui Hou
, and
Petra Heil

Abstract

During the second half of the twentieth century, the West Antarctic Ice Sheet (WAIS) has undergone significant warming at more than twice the global mean and thus is regarded as one of the most rapidly warming regions on Earth. However, a reversal of this trend was observed in the 1990s, resulting in regional cooling. In particular, during 1999–2018, the observed annual average surface air temperature had decreased at a statistically significant rate, with the strongest cooling in austral spring. The spring cooling correlates significantly with the second leading modes (EOF2) derived from empirical orthogonal function (EOF) analysis on the sea level pressure over Antarctica during 1999–2018, associated with the negative phase of the interdecadal Pacific oscillation with an average of cooling of central and eastern tropical Pacific surface sea temperature (SST) anomalies. The EOF2 results in the enhanced cold southerly winds on the continental WAIS through the cyclonic conditions over the Amundsen Sea region and a blocking high in the Drake Passage and northern Antarctic Peninsula, causing the WAIS cooling trend.

Open access
Emile Elias
,
Brian Fuchs
,
Joel Lisonbee
,
Tonya Bernadt
,
Viktorya Martinez
, and
Tonya Haigh

Abstract

The 2018 exceptional drought over the Colorado Plateau motivated unprecedented responses by individuals and organizations. Some of these responses made clear that proactive adaptive measures were fundamental to drought resilience. Climate service organizations (CSOs) supporting and observing these responses realized the utility of a network to share and document successful drought responses. In February 2020, a small group of CSOs and resource managers (RMs) met to envision the Southwest Drought Learning Network (DLN) to align with other existing efforts, but with the specific goal of enabling peer-to-peer learning to build resilience to future droughts. Since then, the network has grown into five organized teams focused on specific aspects of building drought resilience. Team activities include sharing case studies to help others learn from past experiences, hosting monthly drought briefings that introduce drought data and management tools, identifying information needed to support critical management decisions, innovating and sharing new and traditional drought monitoring technologies, and building drought resilience with indigenous communities. The network allows for collaboration and leveraging partner resources and strengths. The DLN website (https://dln.swclimatehub.info/) hosts more information about network teams and activities. This innovative network continues to grow in response to management needs and water scarcity in the region. For the benefit of others who may be considering a similar network and supporting peer-to-peer learning, we document the history, process, and lessons learned regarding the Southwest DLN.

Open access
Gregory R. Carmichael
,
Oksana Tarasova
,
Øystein Hov
,
Leonard Barrie
, and
James H. Butler

Abstract

Further long-term investments in high-quality, research-driven, fit-for-purpose observations of atmospheric composition are needed globally to meet urgent societal needs related to weather, climate, air quality, and other environmental issues. Challenges include maintaining current observing systems in the face of eroding budgets for long-term monitoring and filling the geographical gaps for key constituents needed for sound services and policies. The observing systems can be bolstered through science-for-services applications, by embracing interoperable observation systems and standardized metadata, and ensuring that the data are findable, accessible, interoperable, and reusable. There is an urgent need to move from opportunities-driven one-component observations to more systematic, planned multifunctional infrastructure, where the observational data flow is coupled with Earth system models to serve both operational and research purposes. This approach fosters a community where user experience feeds back into the research components and where mature research results are translated into operational applications. This will lead to faster exploration and exploitation of atmospheric composition information and more impactful applications for science and society. We discuss here the urgent need to (i) achieve global coverage, (ii) harmonize infrastructure operations, (iii) establish focused policies, and (iv) strengthen coordination of atmospheric composition infrastructure.

Open access
Erik Crosman
,
Aaron M. Ward
,
Stephen W. Bieda III
,
T. Todd Lindley
,
Mike Gittinger
,
Sandip Pal
, and
Hemanth Vepuri

Abstract

While numerous collaborations exist between the atmospheric sciences research community and the U.S. National Weather Service (NWS), collaborative research field studies between undergraduate (UG) students at universities and the NWS are less common. The Summertime Canyon Observations and Research to Characterize Heat Extreme Regimes (SCORCHER) study was an UG student-driven research field campaign conducted in Palo Duro Canyon State Park, Texas, United States, during the summer of 2021. The SCORCHER campaign was mainly aimed at improving our basic scientific understanding of extreme heat, public safety, and forecasting applications, and creating an empowering UG educational field research experience. This “In Box” article highlights the collaborative study design, execution, and lessons learned.

Open access
Zack Guido
,
Ben McMahan
,
Dharma Hoy
,
Calvin Larsen
,
Benni Delgado
,
Rey L. Granillo III
, and
Michael Crimmins

ABSTRACT

The North American monsoon generates heavy rainfall across the southwestern United States between July and September, delivering beneficial moisture to the region and creating hazards that affect public and personal safety. The monsoon thus has the rapt attention of the public and science community, providing an opportunity to improve weather and climate literacy and public engagement in science. Engaging the public to forecast weather and climate phenomenon through contests offers an innovative way to reach diverse audiences and increase weather and climate literacy. We describe a “Monsoon Fantasy Forecasting” game conducted in 2021 with approximately 300 participants. The game that engaged the public in the forecasting of monthly rainfall at cities in Arizona, New Mexico, and Texas. We report on the game’s interactive design, results, and feedback. We show that the game attracted a diverse audience who was not the typical weather and climate enthusiast, and we provide suggestive results that the game may have influenced the players information-seeking behaviors. We argue that activities that provoke people to observe and think routinely about climate can help educate and build awareness about weather and climate issues.

Open access
F. Vitart
,
A. W. Robertson
,
A. Spring
,
F. Pinault
,
R. Roškar
,
W. Cao
,
S. Bech
,
A. Bienkowski
,
N. Caltabiano
,
E. De Coning
,
B. Denis
,
A. Dirkson
,
J. Dramsch
,
P. Dueben
,
J. Gierschendorf
,
H. S. Kim
,
K. Nowak
,
D. Landry
,
L. Lledó
,
L. Palma
,
S. Rasp
, and
S. Zhou

Abstract

There is a high demand and expectation for subseasonal to seasonal (S2S) prediction, which provides forecasts beyond 2 weeks, but less than 3 months ahead. To assess the potential benefit of artificial intelligence (AI) methods for S2S prediction through better postprocessing of ensemble prediction system outputs, the World Meteorological Organization (WMO) coordinated a prize challenge in 2021 to improve subseasonal prediction. The goal of this competition was to produce the most skillful forecasts of precipitation and 2-m temperature globally averaged over forecast weeks 3 and 4 and over weeks 5 and 6 for the year 2020 using artificial intelligence techniques. The top three submissions, described in this article, succeeded in producing S2S forecasts significantly more skillful than the bias-corrected ECMWF operational reference forecasts, particularly for precipitation, through improved calibration of the ECMWF raw forecast outputs or multimodel combination. These forecast improvements should benefit the use of S2S forecasts in applications.

Free access
Natalie Teale
and
Steven M. Quiring

Abstract

As the private sector becomes increasingly aware of the risks associated with climate change, climatologists have been engaging with companies to assess climate change risks and opportunities. Here, we outline how we have collaborated with a Fortune 500 company to assess the physical risks of climate change to their facilities. We provide a template for a climate change report card that we generated for >100 facilities globally. This report card is designed to communicate risk to company leadership and local facility managers. We believe that by sharing our experiences, climate scientists will be able to quantify and communicate climate risk more effectively to the private sector.

Free access
Tom Matthews
,
Baker Perry
,
Arbindra Khadka
,
Tenzing Gyalzen Sherpa
,
Dibas Shrestha
,
Deepak Aryal
,
Subash Tuldahar
,
Nirakar Thapa
,
Niraj Pradhananga
,
Peter Athans
,
Dawa Yangzum Sherpa
,
Heather Guy
,
Anton Seimon
,
Aurora Elmore
,
Kristina Li
, and
Nicole Alexiev

Abstract

The predictability of the weather on Mount Everest’s upper slopes can be a matter of life or death for those trying to climb the world’s highest mountain, yet the performance of forecasts has been almost unknown due to a lack of surface observations. The extent to which climate change may be affecting this iconic location is also uncertain for the same reason. To address this data limitation, the National Geographic and Rolex Perpetual Planet Expedition installed the world’s highest weather station network (reaching within 420 m of the summit) on the Nepal side of Mount Everest in 2019. Its observations have already generated considerable advances in understanding the meteorological environment on the mountain’s upper slopes, but the network was compromised by damage to the highest stations in recent years. Here, we describe the expedition that upgraded the network and took it to new heights, focusing on the installation at the Bishop Rock (8,810 m MSL), just below the summit. Almost 70 years after Everest was first climbed successfully, we can now provide open access data to illuminate conditions at Earth’s highest climate frontier.

Free access
Yongkang Xue
,
Ismaila Diallo
,
Aaron A. Boone
,
Tandong Yao
,
Yang Zhang
,
Xubin Zeng
,
J. David Neelin
,
William K. M. Lau
,
Yan Pan
,
Ye Liu
,
Xiaoduo Pan
,
Qi Tang
,
Peter J. van Oevelen
,
Tomonori Sato
,
Myung-Seo Koo
,
Stefano Materia
,
Chunxiang Shi
,
Jing Yang
,
Constantin Ardilouze
,
Zhaohui Lin
,
Xin Qi
,
Tetsu Nakamura
,
Subodh K. Saha
,
Retish Senan
,
Yuhei Takaya
,
Hailan Wang
,
Hongliang Zhang
,
Mei Zhao
,
Hara Prasad Nayak
,
Qiuyu Chen
,
Jinming Feng
,
Michael A. Brunke
,
Tianyi Fan
,
Songyou Hong
,
Paulo Nobre
,
Daniele Peano
,
Yi Qin
,
Frederic Vitart
,
Shaocheng Xie
,
Yanling Zhan
,
Daniel Klocke
,
Ruby Leung
,
Xin Li
,
Michael Ek
,
Weidong Guo
,
Gianpaolo Balsamo
,
Qing Bao
,
Sin Chan Chou
,
Patricia de Rosnay
,
Yanluan Lin
,
Yuejian Zhu
,
Yun Qian
,
Ping Zhao
,
Jianping Tang
,
Xin-Zhong Liang
,
Jinkyu Hong
,
Duoying Ji
,
Zhenming Ji
,
Yuan Qiu
,
Shiori Sugimoto
,
Weicai Wang
,
Kun Yang
, and
Miao Yu

Abstract

Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface ­temperatures show a lag correlation with summer precipitation in several remote regions, but current global land–atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hotspot” regions identified here; the ensemble means in some “hotspots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.

Free access