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Atmospheric cold pools are pockets of air cooler than their surrounding environment that form when rain evaporates underneath thunderstorms. These relatively dense air masses, ranging between 10 and 200 kilometers in diameter, lead to downdrafts that upon hitting the ocean surface, produce temperature fronts and strong winds that affect the surrounding environment. Cold pools over the tropical oceans produce large changes in air temperature and wind speed in the planetary boundary layer. But how they affect the larger atmospheric circulation is not clear. To understand the role of cold pools in tropical convection, scientists need detailed measurements of these events; however, observations in hard-to-reach ocean locations have been lacking.

Uncrewed sailing vehicles, or USVs, could be a solution. In a new study, Wills et al. describe the use of Saildrone USVs, wind-propelled sailing drones with a tall, hard wing and solar-powered scientific instruments. Over three 6-month missions, 10 USVs covered a distance of over 137,000 kilometers within regions of the central and eastern tropical Pacific Ocean and made measurements of over 300 cold pool events, defined as temperature drops of at least 1.5°C in 10 minutes. In one case, four USVs separated by several kilometers captured the minute-by-minute evolution of an event and revealed how the cold pool propagated across the region.

The Saildrone USVs measured variations in air temperature, wind speed, humidity, pressure, and sea surface temperature and salinity. Analysis of these variables revealed key features of cold pool events, including how much and how quickly air temperatures dropped, how long it took for wind speeds to reach their peaks, and the dynamics of sea surface temperature changes. The results could be used to evaluate mathematical models of tropical convection and explore more questions, such as how wind gusts at cold pool fronts affect air–sea heat fluxes. (Geophysical Research Letters, https://doi.org/10.1029/2021GL093373, 2021)

—Jack Lee, Science Writer

Glaciers have long been thought of as static, picturesque totems or as changeless coverings over permanently frozen landscapes, particularly among societies distant from mountains and the poles. However, as traditional mountain cultures with firsthand experience have long known and treasured—and as glaciologists, hydrologists, and climate scientists have deciphered and communicated—glaciers are by no means static. Rather, they are dynamic landscape agents and unmistakable indicators of rapid environmental transformation [Gagné et al., 2014]. With widespread media coverage of anthropogenic climate change and the realization that glaciers are endangered species [Carey, 2007], popular perceptions are gradually changing, and scientists, grassroots movements, and policymakers are increasingly committing to developing legal protections for glaciers.

With Chile facing significant challenges associated with a long drought affecting its most populated regions, environmentally focused legislation has become a main priority for many Chileans.In 2006, legislative efforts to enact a glacier protection law in Chile started as a result of increasing concerns about how mining activities were endangering small glaciers in the north of the country [Herrera Perez and Segovia, 2019]. Around the same time, other initiatives affecting glacierized basins, such as the HidroAysén hydroelectric project, helped to galvanize local and national activists, who demanded stronger environmental actions from the government. With the country facing significant challenges associated with a long drought affecting its most populated regions, environmentally focused legislation has become a main priority for many Chileans after the populace overwhelmingly requested a new constitution. As of early 2021, the latest initiative related to glaciers, called the “Ley sobre protección de glaciares” (law for glacier protection), is still in discussion in the Senate chamber. Despite the law’s admirable aims, in its current form it includes some flaws that, if passed, will undermine its effectiveness.

Chile’s Crucial Cryosphere

Stretching roughly 4,300 kilometers from Cape Horn in the south to its northern border while spanning only about 180 kilometers on average between the Andes and the Pacific Ocean, Chile contains most of the ice and snow cover in the Southern Hemisphere outside the polar regions. It also hosts a significant yet little-studied periglacial landscape characterized by permafrost features, including soils and rock glaciers, among others (Figure 1). Glaciers, snow, and permafrost are found along the Chilean Andes and across several climatic regimes, from nearly tropical to subantarctic, epitomizing the wide range of the environmental conditions where these water reservoirs can grow and wane.

Fig. 1. This world map shows the locations of glaciers around the world according to the Randolph Glacier Inventory. Chile’s borders are outlined in red. The inset map of Chile shows potential locations of permafrost as indicated by the Global Permafrost Zonation Index Map. Waterfalls pour from an outlet glacier in Queulat National Park in Chilean Patagonia. Credit: Alfonso Fernández

Chile’s cold environments are part of the essence of the country, and socioeconomic development here is ineradicably linked to cryosphere dynamics. Agriculture, mining, drinking water provision, hydroelectricity, tourism, and ecosystem services depend, in one way or another, upon the presence of snow and ice. In the south, for example, the majestic glacierized Patagonian landscape attracts visitors from all over the world. In the semiarid north and center of the country, large agricultural areas, including Chile’s world-renowned wine-producing regions, are watered largely by mountain streams nourished by ice and snow melt. In a sense, anyone enjoying a Chilean Carménère is likely tasting drops of the Chilean cryosphere.

A legal framework that considers the latest technical and theoretical understanding of Chile’s cold environments is essential for effective regulation and for maintaining the cultural and socioeconomic value these environments provide. Members—including ourselves—of the Sociedad Chilena de la Criósfera, the only scientific society in Chile dedicated to studying the country’s cryosphere, and other geoscientists have appealed to the National Congress of Chile and the public to provide support and advice to develop scientifically sound and accurate legislation. However, we are increasingly concerned about the effectiveness of the glacier protection law because current iterations under discussion in the congress include misleading concepts and criteria.

Some Limitations of the Proposed Law

At the most basic level, we are alarmed by how proposed legislation uses “cryosphere” and “glaciers” synonymously.At the most basic level, we are alarmed by how these proposals use “cryosphere” and “glaciers” synonymously. The proposed legislation covers glaciers and permafrost, so a more accurate framework would entail protection of the entire cryosphere. However, there are more profound concerns that may become hard to correct once the law is enacted. Among others, these concerns include unfeasible definitions of what a glacier is, poor understanding of the relationship between glacial and periglacial environments, and impacts of the proposed legislation on key infrastructure.

Fundamentally, a glacier is a body of ice massive enough to flow under its own weight, a characteristic that sets it apart from perennial snowfields or smaller patches of snow and ice. Combining understanding from Glen’s flow law, a fundamental glaciological tenet that relates ice flow velocity with slope and thickness [Cuffey and Paterson, 2010], with well-established relationships between glacier surface area and volume [Bahr et al., 1997] offers guidance on the minimum size of an ice patch that can be considered a glacier.

A panoramic view of Universidad Glacier in central Chile. Credit: Alfonso Fernández

During congressional discussions, overly simplistic definitions of glaciers based on flow properties and debris cover have been contrasted with definitions considering more operative yet technically contestable criteria, such as a minimum surface area threshold to be used for mapping and protection purposes under the law. Some proposals by members of congress have argued that this minimum limit should be as small as 0.1 hectare (1,000 square meters). This threshold is much stricter than what is normally applied in the scientific literature, which suggests instead that a surface area of 1 hectare (about the size of a soccer field) may be a more reasonable threshold to use in mapping glacial inventories [Paul et al., 2009; Leigh et al., 2019].

A 0.1-hectare threshold would make it possible to misinterpret ephemeral firn or snow patches as bodies of glacier ice. Under a wide range of realistic glacier surface slopes, the flow law predicts surface velocities well within the uncertainty range of modern measurement techniques like high-precision GPS for average ice thicknesses (about 4 meters) corresponding to the 0.1-hectare threshold. Also, energy and mass balance research shows that Chilean glaciers can melt at rates in excess of 15 meters per year [e.g., Kinnard et al., 2018]. Thus, plausible rates of 4 meters per year result in total melt out of a 0.1-hectare surficial frozen water body within a year or so, further supporting the idea that such small bodies should not be cataloged as glaciers in inventories.

The current proposal and ongoing debate are flawed because they do not consider the hydrological role of permafrost and periglacial areas.Within the law, permafrost and periglacial environments are key elements to be protected, and rightly so. There is plenty of science suggesting that many areas experiencing water stress are covered by sediments and soils that may contain either perennial or seasonal ice (Figure 1) [Ruiz Pereira et al., 2021]. These environments are demarcated on the basis of morphological features, such as the presence of frozen ground, as well as climatic thresholds, particularly with respect to the elevation of the zero-degree isotherm (above which the air temperature is always below 0°C). Although these criteria are in line with established understanding of the conditions that sustain permafrost and rock glaciers [Dobinski, 2011], the current proposal and ongoing debate are flawed nonetheless because they do not consider the hydrological role of permafrost and periglacial areas. In high-elevation regions, including large areas of the Chilean Andes, water storage and drainage are sensitive to permafrost, rock glacier, and glacial changes. Therefore, overlooking this role is inexplicable, especially considering that the first article in the law explicitly indicates that the main reason for preserving glaciers, permafrost, and periglacial areas is their critical value as strategic water reservoirs.

How Geoscientists Can Contribute

We understand some of the considerations and debates surrounding the glacier protection law in Chile—for example, over the minimum surface area threshold—on the grounds that lawmakers are hoping to forestall future legal battles over its interpretation and application. Such battles have occurred in Argentina following implementation of a law similarly intended to preserve that country’s cryosphere. There, conflicting civil and private judicial challenges associated with the use of a 1-hectare threshold in the official glacier inventory have been launched, with the mining sector contending the threshold was too restrictive and others saying it did not protect enough area. These challenges led to the indictment of the chief scientist in charge of compiling the inventory for allegedly failing to uphold the country’s glacier protection law when he adopted the 1-hectare threshold, ironically punishing one of the few people who fought to use the most reliable scientific evidence in cryosphere protections.

Glaciologists traverse a valley glacier in central Chile to deploy sensors. Credit: Alfonso Fernández

As scientists, we know that enacting a law will not end conflicts over how to govern Chile’s glaciers and cryospheric environments. We want to build bridges between citizens and the government to inform expectations of the law on all sides and to provide clear and accurate information for policymaking. As Isaac Asimov said, “The saddest aspect of life right now is that science gathers knowledge faster than society gathers wisdom.” We echo this and believe there is a unique opportunity to fine-tune Chile’s policymaking by implementing dynamic and updatable features in the country’s cryosphere protection law.

For example, the law should establish panels of academic experts, citizens, and public officers tasked with regularly updating operative definitions used in the legislation and with reviewing the latest technical developments (e.g., improvements in methods for glacier inventorying and monitoring). This approach could facilitate assessment of potential effects of activities, such as tourism and/or the development of water management infrastructure, within glacierized and periglacial areas.

This practice is not new: Expert panels often support policy—related to the ongoing COVID-19 pandemic and to fisheries management, for instance—by providing guidance about implications of the latest research and by proposing and evaluating metrics. In the case of glaciers, such an advisory group could, for instance, study criteria for mapping small glaciers [Leigh et al., 2019]. Considering the substantial seasonal and interannual dynamics and variation of glaciers, this kind of approach can help harmonize preservation and management.

The scale and preeminence of Chile’s glacial and periglacial landscapes argue for the nation’s responsibility and opportunity to lead the world in cryosphere protection.Today we see with great hope that Chile is finally awakening to the value and fragility of its grandiose glacierized Andean landscapes, rather than turning its back, as celebrated French glaciologist Louis Lliboutry lamented during his 20th-century journeys through the Andes [Lliboutry, 1956]. The scale and preeminence of Chile’s glacial and periglacial landscapes argue for the nation’s responsibility and opportunity to lead the world in cryosphere protection. Thus, governmental actions regarding protection should serve as frameworks for other nations facing impacts of climate change in mountainous areas.

In our view, the current version of the proposed law regrettably suffers from uncertainties and omissions that could sow further conflict instead of the solutions expected by Chile’s public. We assert that it can be improved significantly if the country’s well-trained scientific community is consulted. This community is eager to cooperate in developing accurate regulation that can serve as a milestone for the rest of the world. We hope that the congress heeds our offer before passing misguided legislation.

Volcanoes and earthquakes are intrinsically linked: both are outcomes of Earth’s dynamic plate tectonics. However, they are hard to study in unison. This is because they are often spatially separated by hundreds of kilometers, they are largely based in different types of science (earthquakes occur in the brittle crust; volcanoes are driven by melt), and approaches to monitoring them can be very different, since volcano science aims for predictions while earthquake science relies primarily on long-term forecasts.

When an earthquake occurs on a volcano, it is therefore often treated as a volcanic event – one not only triggered by volcano-related deformation, but also responding to the volcano and providing information about the volcano.

When a Mw 4.9 earthquake occurred on the eastern flank of Mount Etna, several papers promptly described how the earthquake indeed matched to a dyke intrusion. In contrast, Romagnoli et al. [2021] take a broader view. Their careful measurements of fault offset and interpretation of the tectonic setting led them to a different conclusion: while the event may have been triggered by the volcano, the deformation patterns are controlled by long-term tectonics. The geometry of the southeastern part of the rupture matches to a major tectonically active lineament that extends offshore, accommodating tectonic extension. To the northwest, approaching the volcano, the rupture splinters into a series of en echelon conjugate fractures; here, the slip patterns match the stress field of the broad Sicily collisional zone, which controls the deformation in this region.

Thus, long-term tectonics, rather than short-term volcanic deformation, seems to be responsible for both the geometry and the slip patterns in this event – although the volcano may have helped to trigger it.

This leaves room for further studies, as the authors point out: if earthquakes are telling us about regional tectonics rather than transient volcanic behavior, we may be able to use observations of past earthquake deformation to better understand the tectonics, and then leverage that understanding into a better forecast of future earthquakes.

Citation: Romagnoli, G., Pavano, F., Tortorici, G., & Catalano, S. [2021]. The 2018 Mount Etna earthquake (Mw 4.9): Depicting a natural model of a composite fault system from coseismic surface breaks. Tectonics, 40, e2020TC006286. https://doi.org/10.1029/2020TC006286

—Judith Hubbard, Associate Editor, Tectonics

When a volcano violently erupts, a plume of ash and gases spews skyward. The hot slurry quickly rises into the atmosphere, where various atmospheric dynamics interact to shape the volcanic cloud’s composition, height, and radiative properties. Volcanic clouds reflect solar radiation, cool Earth, cause weather extremes, and delay global warming, but scientists have long wondered exactly how volcanic material evolves and parses itself after eruption. To date, observations of the initial stage of strong eruptions have been sparse, and conventional climate models used to study the impact of volcanic eruptions cannot capture this initial stage in great detail.

Animation from the study’s simulations of the evolution of volcanic plumes from the Pinatubo volcano eruption in 1991 in the Philippines. The simulation includes 25-kilometer-grid spacing considering simultaneous injections of sulfur dioxide (SO2), ash, sulfate, and water vapor. Credit: Sergey Osipov

In a new study, Stenchikov et al. modified a regional atmospheric chemistry model, WRF-Chem, to better capture the initial stage of volcanic cloud development. The researchers modeled the 1991 Pinatubo volcanic eruption in the Philippines for their study, assuming that along with the eruptive jet, a significant amount of volcanic debris was delivered into the lower stratosphere. They conducted simulations with 25-kilometer-grid spacing considering simultaneous injections of sulfur dioxide (SO2), ash, sulfate, and water vapor. In addition, they accounted for radiative heating and cooling effects of all plume components including gaseous SO2.

The researchers found that differential heating played an essential role in the initial evolution of a volcanic cloud and its separation into layers, which then dispersed or fell to the ground. Their new model showed that during the first week after eruption, the volcanic cloud rose into the atmosphere 1 kilometer per day, driven initially by ash solar absorption and later by sulfate aerosol absorption of solar and terrestrial radiation.

The researchers note that their findings could be helpful in many applications, from aviation safety to understanding climate and geoengineering technologies. (Journal of Geophysical Research: Atmospheres, https://doi.org/10.1029/2020JD033829, 2021)

—Sarah Derouin, Science Writer

Raging wildfires pump tiny pollutants into the air, degrading air quality across vast areas. These pollutants, or aerosols, can soar high into the atmosphere at the tops of smoke plumes or creep close to the ground where they pose a health risk to humans. To accurately track these pollutants and their spread, scientists need accurate monitoring systems that can see the whole picture.

In the past, satellite monitoring, while providing a huge visual scope, fell short of on-the-ground measurements. In a new study, Loría-Salazar et al. evaluated improved algorithms using imagery from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments. They tracked the spread of aerosols outward from the fire source and upward into the atmosphere, focusing on August 2013 fires in the western United States.

The researchers found that the new algorithms are much more accurate, aligning well with on-the-ground measurements. Research into their plume heights also revealed that whether aerosols from wildfires will remain within the planetary boundary layer—the lowest layer of the atmosphere (where people live, breathe, and are exposed to smoke), which is heavily influenced by Earth’s surface—depends on local geography and daily weather conditions. In addition, the researchers argue that it is necessary to continue investigation into the role of aerosols in local and regional weather when smoke penetrates different layers of the atmosphere.

The new research suggests that satellites may fill in observation gaps in areas without ground-based monitoring or by providing additional data in areas with complex weather patterns. The accurate algorithms used to track aerosol distribution can also inform models of future aerosol conditions, especially to predict impacts to human health. (Journal of Geophysical Research: Atmospheres, https://doi.org/10.1029/2020JD034180, 2021)

—Elizabeth Thompson, Science Writer

Citation Jane K. Willenbring

It is an honor and a source of great pride to present Jane Willenbring as winner of the inaugural Marguerite T. Williams Award for midcareer scientists. Jane received this award in recognition of her contributions toward the use of cosmogenic radionuclides to advance foundational understanding of Earth surface processes and of her support of woman and minorities in science, technology, engineering, and mathematics (STEM) fields. Jane’s research has tackled a broad range of questions and technique developments that are pertinent to critical zone processes, geomorphic change, and erosion rates. Her publications are thought-provoking and dip into the heart of leading-edge questions related to the application of cosmogenic nuclides to geomorphic research. Importantly, Jane is recognized for her tireless mentorship and community outreach activities and her active voice in support of change regarding discrimination, equity, and harassment in the geosciences and other STEM fields. Jane’s combined research and outreach efforts exemplify some of the best characteristics of a scientific leader and embody the spirit of the Marguerite T. Williams Award for contributions to research and community-building.

—Tammy Rittenour, Utah State University, Logan

 

Response

Thank you, Tammy, for your generous citation, to my nominators for their support, and to the Earth and Planetary Surface Processes group that proposed this new award named after the first Black geologist to receive a Ph.D. in the United States. Thanks also to Nicole Gasparini, friend and collaborator, who led both the creation of the award and my nomination.

Although at heart I’m still just a kid making rivers in the mud with a garden hose, my worldview has been shaped by many people since then. As a high school student, then as a McNair Scholar at North Dakota State University, I worked with Prof. Allan Ashworth. Having his example of patient mentorship showing me how science can bring decades of joy and adventure to life was truly formative. I note that those student opportunities created possibilities for me that the award’s namesake and many others would have benefited from in the past.

I like to imagine that I have navigated my science life path using a constellation of scientist stars too numerous to mention—each shining in their own way. Some guided me toward ideas without even knowing me. Some showed me new ways of thinking about problems. Some provided examples of how to be a decent human—or not. I’m grateful to those who walk with me now. I’m so inspired by how far we’ve all come together and how far we can still go.

—Jane Willenbring, Stanford University, Stanford, Calif.

Citation James M. Russell

It is my great pleasure to introduce Jim Russell as the 2020 AGU Willi Dansgaard Award recipient. Jim began his research career with a Ph.D. from the University of Minnesota in 2004 and completed a postdoc at the Large Lakes Observatory in 2005. He joined Brown University’s Department of Earth, Environmental and Planetary Sciences in 2006 and is currently its chair.

Jim has long been a leader in marrying classical methods in paleolimnology with more novel tools from organic geochemistry to investigate the environmental history of the tropics. He applies this diverse skill set to investigate a wide array of phenomena, ranging from glacial geology to past atmospheric circulation, climate change, paleoecology, and human prehistory. Among his many accomplishments, Jim and his advisees have developed new molecular methods to reconstruct continental temperature and have produced some of the first continuous records of tropical continental temperature. They have also developed networks of long, isotope-based hydrological records from tropical Africa and Southeast Asia to better inform our understanding of the mechanisms of Quaternary rainfall change in these monsoonal regions. His research in these areas represents major breakthroughs.

In addition to his research accomplishments, Jim is a committed educator and mentor. He has trained over a dozen graduate students and postdocs, and the very high level of achievement of so many of his former mentees is strong testimony to his excellence as an adviser. He is also deeply committed to undergraduate education and currently holds a Royce Family Professor of Teaching Excellence chair at Brown. Jim is also recognized as an international service leader in paleoceanography and paleoclimatology through his efforts to promote continental scientific drilling, as associate editor of Paleoceanography and Paleoclimatology, and for his efforts to promote climate change literacy in the global south.

For all of these achievements, Jim merits recognition with the Dansgaard Award.

—Paul Baker, Duke University, Durham, N.C.

 

Response

I thank Paul Baker for his kind words and for the nomination. I also thank my other nominators and the Paleoceanography and Paleoclimatology award committee for selecting me. It’s truly a great feeling to be recognized by one’s colleagues, and I am honored to join the outstanding paleoceanographers and paleoclimatologists who have won this award before me.

As Paul described, much of my work seeks to understand tropical climate and environmental change. This can be frustrating work. We are limited by the available archives, but fieldwork is logistically difficult. We develop cutting-edge geochemical techniques that produce more and more robust estimates of past climate and environmental change, but we are left with uncertainty and new questions. At the same time, our pursuit to understand the climate system is critically important and fascinating work, and I always feel blessed that I found this profession. I have been extraordinarily lucky to work with a group of outstanding graduate students and postdocs, and with colleagues and collaborators at Brown and beyond who keep me energized and enthusiastic to learn. They have contributed greatly to my scientific accomplishments and career, and this award would have been impossible without all of their hard work and insight.

—Jim Russell, Brown University, Providence, R.I.

The past few years has seen a surge of papers applying machine learning and deep learning, a particular form of neural networks, to predicting hydrological variables. Although, predictions by deep learning methods are often more accurate than physically based models, they are usually restricted to single components of the hydrological cycle. Bennett and Nijssen [2021] use a component-based hydrological modeling framework to replace a physically based parameterization of turbulent heat fluxes with trained deep learning representations. Evaluation with observations shows that when more information is allowed to exchange between the physically based models and the deep learning methods, predictions are increasingly accurate.

Citation: Bennett, A., & Nijssen, B. [2021]. Deep learned process parameterizations provide better representations of turbulent heat fluxes in hydrologic models. Water Resources Research, 57, e2020WR029328. https://doi.org/10.1029/2020WR029328

—Marc F. P. Bierkens, Editor, Water Resources Research

Uganda is venturing into the field of space technology, aiming to launch its first satellite in 2022. The project, first announced in 2019, recently took a major step forward with the approval of funding for a ground station near Kampala.

The station, located at the Mpoma facility where Uganda already has two antennas, will serve as the operations and communications center for satellites launched by the government and universities. The existing antennas are associated with Intelsat’s Atlantic Ocean and Indian Ocean satellites.

“The site was chosen because it already had some infrastructure that the country has been using for international telecommunication satellites. This was decided on to minimize on [the] cost of developing new structure,” said Elioda Tumwesigye, Uganda’s minister for Science, Technology and Innovation.

Uganda has already invested significant resources to develop the technology. The country has committed $2 million for technology, research, and development and another $200,000 to improve infrastructure at Mpoma.

Tumwesigye said the satellite and facility will receive capacity-building funding support from Russia and will be launched from Asia. “The satellite will be launched from Japan, but it will be for Uganda,” he said.

In addition, Tumwesigye said, the country is working to establish an education network around space technology; it already sends Ugandan engineers to train at facilities in Japan. Kampala’s Makerere University has recently started a teaching program in space technology.

Security and Education

Judith Nabakooba, Uganda’s minister for Lands, Housing and Urban Development, said Uganda will join the growing list of African countries to have launched satellites: Algeria, Angola, Egypt, Ethiopia, Ghana, Kenya, Morocco, Nigeria, Rwanda, South Africa, and Sudan.

“We will not be gambling with technology.”Nabakooba said the satellite program will primarily address national security concerns. “We will not be gambling with technology,” she said. “We are sure that our defense and security will improve through improved capabilities for cross-border movement monitoring and surveillance for the country.”

In his 2021 state of the union speech, Ugandan president Yoweri Museveni also prioritized the security benefits of a satellite. He said Uganda is concerned with stabilizing security in East Africa.

The president also emphasized the educational benefits of a space program, pointing to the new space technology program at Makerere University’s College of Engineering, Design, Art and Technology and the possibility of establishing a space camp in Uganda.

“I have asked my officials to work closely with [the] European Organization for Nuclear Research (CERN) in Switzerland regarding this program. This will create an opportunity for having a space camp in Uganda,” Museveni said. Ideally, he explained, tutors from CERN would train Ugandan students at the camp.

Investing in Uganda

Nabakooba also stressed the possibility of increased private sector investment in space science, technology, and research and innovation, including foreign direct investment and collaborations.

“Space science is new in Uganda, and we will seek to [work with] foreign countries that implemented space science before so that we can exchange knowledge and use their research as [a] benchmark to improve on ours.”“Space science is new in Uganda, and we will seek to [work with] foreign countries, including Japan, Russian, and Israel among others that are already developed with high technology and have implemented space science before, so that we can exchange knowledge and use their research as [a] benchmark to improve on ours,” she said.

The satellite venture will also help improve weather forecasts used by the Uganda Civil Aviation Authority (UCAA), added Chris Nsamba, chief executive officer and founder of the African Space Research Program.

“With the change in climate, sometimes the unpredictable weather has been delaying some flights from Entebbe International Airport. But with the satellite, UCAA will have more accurate weather forecasts to allow flights to take off and land at the scheduled time,” he said.

—Hope Mafaranga (@Mafaranga), Science Writer

This is an authorized translation of an Eos article. 本文是Eos文章的授权翻译。

气象站可提供气温、降水和风暴事件的详细记录。然而，这些站点的间隔未必适当，可能分散在城市中，在有些偏远地区甚至可能没有。

在没有直接的天气测量数据时，研究人员有一个变通办法。他们使用现有的网格气候数据集(gridded climate data sets, GCDs)在不同的空间分辨率下对一个特定网格内的天气做平均处理。与监测站不同的是，这些网格单元的估计温度是基于模型预报和气候模型以及观测数据的结合，其中观测数据可能来自地面监测仪、飞机、海上浮标和卫星图像等。这些GCD在大尺度气候研究和生态研究中非常有用，尤其是在没有监测站的地区。

那么，GCD在流行病学研究中是否有效呢？例如，在观察不利温度可能如何影响人类健康和死亡率方面?

在一项新的研究中，de Schrijver等人测试了在气象站稀少地区使用GCD研究与温度有关的死亡率时是否有用。他们将网格化的温度数据与英格兰威尔士和瑞士两个地方的气象站温度数据进行了比较，以观察其中一组数据是否比另一组数据更为有效。这些地区的地形、温度范围各不相同，人口分布也各不相同，这些都导致了区域内温度的不规则分布。

为了解哪一种温度数据对预测社区的健康风险最有帮助，研究人员比较了GCD和气象站数据的高温和低温死亡人数。他们使用每个国家的气象站数据以及高分辨率和低分辨率的地方和区域尺度GCD数据，来看看哪一种数据能更好地预测因寒冷或炎热而死亡的风险。

研究团队发现，这两组数据对于温度暴露对健康的影响预测出了相似的结果。不过，在某些情形下，当人口分布不均时，高分辨率的GCD数据与气象站数据相比能够更好地捕捉到极端高温。这在人口密集的城市地区尤其如此，这些地区内部存在显著的温度差异。

研究人员得出结论称，在地势崎岖的城市和地区，当地的GCD数据可能比气象站数据更适合用于流行病学研究。(GeoHealth, https://doi.org/10.1029/2020GH000363, 2021)

—科学作家Sarah Derouin

This translation was made by Wiley. 本文翻译由Wiley提供。

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Citation Suzanne Anderson

Prof. Suzanne Anderson works on (and defines) the interface between geomorphology, hydrology, and geochemistry; her creative and novel scholarship and strong leadership in the critical zone community have vastly expanded and enriched surface processes research and inspired colleagues and students to tackle interdisciplinary topics.

As a graduate student, I fondly remember immersing myself in Suzanne’s papers; the margins of my hard copies were littered with comments, questions, and sketches…evidence of the inspiration she continues to impart.

Dr. Anderson cowrote the textbook through which our students learn geomorphology, and my students have been fortunate to stand atop hillslopes where Suzanne used fundamental observations, experiments, and models to characterize how rock is transformed in the near-surface environment. She deftly weaves together concepts and processes like rock fracturing, chemical weathering, soil production, and hydrologic response to make sense of the terrain.

Suzanne also takes time to write papers that synthesize our knowledge of critical zone processes. Her conceptual model of a chemical reactor on a hillslope provides an elegant and accessible framework, and her work on the geochemistry of glaciated landscapes has key implications for global solute loads and the carbon cycle.

As a central player in critical zone research, Suzanne established the Boulder Creek Critical Zone Observatory, where a limited footprint existed previously. Such work requires a ridiculous amount of time, patience, and vision, and Suzanne’s efforts led to a welcoming and inclusive platform for others to collaborate and forge discovery. With dedication and care, Suzanne’s outreach opened up the critical zone to a new and more diverse generation of scientists.

In the words of Kate Maher, “When we connect the dots back to the origins of critical zone science, Suzanne’s work is at the center.” Suzanne Anderson is supremely deserving of the G. K. Gilbert Award. Congratulations!

—Josh Roering, University of Oregon, Eugene

 

Response

I thank Josh Roering and the committee for their kind words and effort. I’m indebted to Bob and our daughters, Grace and Hannah, for their support and love. It’s doubly humbling to receive the G. K. Gilbert Award: Gilbert is legendary, and previous recipients are personal heroes.

Until the 2018 award to the incomparable Ellen Wohl, only white men had received the Gilbert Award. In 2019, awardee Kelin Whipple urged us to focus on increasing diversity. This year, explosion of the Black Lives Matter movement has heightened awareness of racism and of barriers to persons of color. Our commitment to inclusiveness must redouble.

Commitment is easy; it’s harder to find effective ways to build an inclusive community. From personal experience, some simple actions can make a difference.

Be an example. My mother attended community college when I was in junior high school. Botany class inspired her lifelong volunteer work documenting and cataloging plant specimens, using her homemade plant press and her microscope. She followed her interests, an example that freed me to beat my own drum.

Inspire, encourage, and validate. Exploring geology, I found inspiration in the geomorphology class cotaught by Tom Dunne and Bernard Hallet. As my M.S. adviser, Bernard listened to my nascent ideas and validated my timid steps. At a social event, Tom explicitly encouraged me to pursue a Ph.D. These actions matter.

Foster community. Myriad surface processes grad students formed a community that nurtured my sense of belonging. Bill Dietrich modeled inclusivity with a diverse and gender-balanced group of students and visitors.

We all have the power to set an example, to encourage, to listen, to validate, and to build community. I urge all to use your powers—your superpowers—to build our surface processes community into one that is diverse and welcoming.

—Suzanne Anderson, University of Colorado Boulder

Citation Keri Nicoll

Keri Nicoll is known internationally for her expertise in fair weather atmospheric electricity measurements and instrumentation development. Her pioneering research in instrumentation development has resulted in the creation of many new sensors (including space charge, conductivity, and energetic particle sensors) for balloon and small aircraft, some of which are now commercially available. This has enabled Keri to become a world leader in investigating fundamental questions related to charge and atmospheric electricity effects on cloud and aerosol microphysics, which are important for climate projections.

Keri is well recognized for her research achievements (54 journal papers in 10 years, which have been cited over 1,000 times) and her scientific leadership—leading (or coleading) 11 different projects. Her work on the Global Coordination of Atmospheric Electricity Measurements project (GloCAEM; https://glocaem.wordpress.com/) is particularly valuable, bringing global atmospheric electricity researchers together for the first time to create a new network and a publicly accessible data archive for atmospheric electricity measurements. The legacy of this project is likely to continue for many decades to come and since its completion has inspired many others to contribute data to the new archive. Further evidence of her high standing within the international community is her invitation to join three separate European Union cost actions (including leading one as a working group leader), International Space Science Institute (ISSI) teams, and her current role as training manager for the EU Marie Curie training network, SAINT.

Keri’s research activities have literally spanned the globe. She has led balloon and aircraft field campaigns in Antarctica, the Arctic, the Middle East, and Europe, and the instrumentation developed by her research group is highly sought after by researchers all over the world.

—Colin Price, Porter School of the Environment and Earth Sciences, Tel Aviv University, Tel Aviv, Israel

 

Response

I am very honored to receive this year’s AGU Atmospheric and Space Electricity Early Career Award, and I thank Colin Price for his nomination and the Atmospheric and Space Electricity committee for their consideration of all the applications. I am also particularly grateful to such colleagues as Giles Harrison, Yoav Yair, Michael Rycroft, Martin Fullekrug, Karen Aplin, and Alec Bennett for their advice and stimulating science discussions over the past 10 or so years.

Atmospheric electricity is a subject I have long been passionate about, and I feel very fortunate to work in a field that has afforded me many exciting opportunities to perform new and interesting research in some unique locations. At the core of my research has been the development of small, disposable atmospheric electricity sensors (especially for airborne use), and I am particularly thankful to Giles Harrison for being such a fantastic mentor in this. Among his many other useful insights, Giles helped me realize very early in my career the satisfaction of turning a theoretical concept of a sensor into a physical device, which we launch into the air, make some measurements with, and discover something new about the atmosphere. The sensors that we have developed have enabled me to work with many different research groups around the world and get involved with multidisciplinary projects that have taken me from the top of volcanoes to the frozen Antarctic.

With its range of different science subjects, AGU highlights the importance of multidisciplinary science and the new discoveries that can be made when we merge knowledge and techniques from different subject areas, and I look forward to working with many more colleagues in the future to better understand Earth’s atmospheric electrical environment.

—Keri Nicoll, Department of Meteorology, University of Reading, Reading, U.K.

Scale-dependent feedbacks in space, which couple short-distance positive feedback with long-distance negative feedback, are considered a prime reason or even a necessary condition for self-organization that results in regular patterns of many kinds.

Building on previous research in geomorphology, Dong et al. [2021] introduce a competition model that captures scale-dependent feedbacks in time, revealing a new form of self-organization that may explain regular patterns found in nature. They illustrate the new concept by focusing on the evenly spaced cypress depressions in the Big Cypress National Preserve, South Florida, USA.

Neighboring cypress depressions compete for catching precipitation, which promotes weathering and therefore leads to expansion of the depression volume. As a depression grows, it retains more precipitation, further increasing the duration of wet periods and the associated weathering. The growth rate of the depressions decreases with size, which causes a movement of the divides between depressions in favor of the smaller depression, until all depressions are similar in size and shape.

The authors devised a mathematical model describing this process of regular pattern formation, which may be applicable to other landscape pattern formation processes both in geomorphology and in ecology.

Citation: Dong, X., Murray, A. B., & Heffernan, J. B. [2021]. Competition among limestone depressions leads to self-organized regular patterning on a flat landscape. Journal of Geophysical Research: Earth Surface, 126, e2021JF006072. https://doi.org/10.1029/2021JF006072

—Ton A. J. F. Hoitink, Editor, JGR: Earth Surface

This is an authorized translation of an Eos article. Esta es una traducción al español autorizada de un artículo de Eos.

En diciembre de 2019, la península de Reykjanes, que se adentra en el Océano Atlántico al suroeste de Reykjavík, la capital de Islandia, comenzó a experimentar intensos enjambres sísmicos. Desde entonces, los científicos de la Oficina Meteorológica de Islandia han estado rastreando y monitoreando la deformación de la superficie de la Tierra a medida que el magma se empuja (se intruye) en la corteza superficial. Tres intrusiones iniciales ocurrieron cerca del monte Thorbjörn, en las afueras de la ciudad de Grindavík. Una cuarta intrusión infló ligeramente el extremo más occidental de la península, y una quinta intrusión dio un salto hacia el este, más allá de Grindavík, hacia Krýsuvík, según Sara Barsotti, vulcanóloga italiana y coordinadora de peligros volcánicos en la Oficina Meteorológica de Islandia.

La Península de Reykjanes al suroeste de Islandia experimentó miles de sismos asociados a intrusiones de magma subterráneas a principios del 2021. Los primeros sismos fueron identificados cerca del Monte Thorbjörn y Krýsuvík. El sismo más grande (M5.7) sacudió la península entre Keilir y Fagradalsfjall. (El aeropuerto internacional de Keflavik y la ciudad capital de Rreykjavík son mostradas como escala). Fagradalsfjall se convirtió pronto en el volcán activo de Islandia más nuevo. Crédito: Google Earth.

Más de un año después de que comenzara esta agitación, el 24 de febrero, un gran terremoto de magnitud 5.7 sacudió la península entre Keilir y Fagradalsfjall, “marcando un punto de inflexión”, dijo Barsotti.

Poco después, la red sísmica de la Oficina Meteorológica de Islandia registró más de 50.000 terremotos en la península. Usando las herramientas de monitoreo a su disposición, los científicos encontraron un corredor de magma entre Keilir y Fagradalsfjall, dijo Barsotti. Este magma fluyó bajo tierra durante aproximadamente 3 semanas, con terremotos definiendo los bordes de la cámara subterránea. Luego, tanto la sismicidad como la deformación se desplomaron.

Para ese momento, algunos científicos plantearon la hipótesis de que la intrusión se congelaría dentro de la corteza, dijo Kristín Jónsdóttir, sismóloga de la Oficina Meteorológica de Islandia. “Entonces”, dijo, “comenzó la erupción”.

En contra de un cielo gris, lava naranja sale del Fagradalsfjall en el segundo día de la erupción. Al frente, lava enfriándose brilla en contraste con el basalto negro que ya está solidificado. Crédito: Toby Elliott/Unsplash Mantener a las multitudes seguras

El 19 de marzo, la lava comenzó a salir desde el borde de la intrusión cerca de Fagradalsfjall, y los islandeses acudieron en masa a las montañas sobre la fisura para hacer un picnic, jugar al fútbol o simplemente observar el espectáculo de luces de lava de la naturaleza. “Los islandeses… sienten que esto es parte de su vida”, dijo Barsotti. “Realmente quieren disfrutar de lo que su país es capaz [de darles]”.

People casually playing volleyball at the #volcano in #Fagradalsfjall, #Iceland yesterday

Betelgeuse has the most name recognition of the known supergiant stars, due partly to its proximity to the Sun (a mere 724 light-years away) and partly to its prominence in a well-known constellation (it’s Orion’s right shoulder). But if you’re looking for a star about to go supernova, Miguel Montargès has a few more promising candidates he can recommend. “I always say VY Canis Majoris,” he suggested. “It’s only 5 times farther away than Betelgeuse, but it’s also brighter because it’s much older,” though it’s hard to see through its thick blanket of obscuring dust.

Betelgeuse is young among supergiant stars, which include some of the largest and most evolved stars that still undergo nuclear fusion. By “young,” astronomers mean that Betelgeuse is still tens of thousands of years away from a catastrophic meltdown. That doesn’t stop the world from watching with bated breath whenever it behaves peculiarly.

Betelgeuse, circled in red, is the second-closest red supergiant star to Earth and the brightest star in the constellation Orion. (Red supergiant Antares in the constellation Scorpius is about 175 light-years closer.) Credit: ESO/N. Risinger (skysurvey.org), CC BY 4.0

The intrigue stems from the fact that astronomers are still learning how some of the physical processes that lead to a supernova manifest visibly, said Montargès, an astrophysicist at Observatoire de Paris in France. Betelgeuse, however, is not likely to take us by surprise. Montargès is part of a subfield of astronomers who regularly observe supergiant stars to better understand the very last stages of a star’s life cycle. “There are thousands of red supergiants in the galaxy, and I would say 100 or 200 that are nicely visible from Earth,” he said. “And of those, there are about 10-ish that we are observing regularly. Betelgeuse is the most observed by far.”

Montargès has been observing Betelgeuse for years to understand the processes rapidly propelling it toward its explosive end. In January 2019, he led an observing campaign on the Very Large Telescope (VLT) in Chile to study the star at the faintest point of its regular 400-day cycle of brightness variation. Months later, when he was just beginning to analyze those data, the first reports started coming in that Betelgeuse was dimming rapidly and unexpectedly. The period, now called the Great Dimming, prompted astronomers around the world to swing their telescopes toward Betelgeuse to catch whatever happened next.

Colder or Dustier? Actually, It Was Both

Proposals to observe with major telescopes like VLT can take weeks or months to be approved during normal review cycles. But thanks to the small allotment of telescope time known as director’s discretionary time (set aside for sudden and unexpected astronomical events), Montargès and his team could jump to the front of the queue. They applied to study the Great Dimming with VLT and the VLT Interferometer late in December 2019, and days later their request was approved. “I was asked to send in the observing commands immediately, and [the star] was observed the same night,” he said.

He initially set out to prove that nothing at all atypical was going on with Betelgeuse. “And I was wrong,” he quipped. As the dimming continued, the team applied for more discretionary observing time so they could see the entire event from beginning to end. They were awarded two more chances to observe Betelgeuse with VLT instruments: one right when the star was at its faintest and one as it started to brighten again. It was just in the nick of time: Paranal Observatory, which hosts VLT, closed because of COVID-19 safety concerns just 3 days after the researchers collected their final set of observations.

Researchers observed Betelgeuse with the Very Large Telescope before the Great Dimming (leftmost image) and then three times during the event (three subsequent images). These high-resolution images show that Betelgeuse’s southwestern quadrant (lower right area of the star) cooled significantly and was obscured by a newly formed dust cloud. Credit: ESO/M. Montargès et al., CC BY 4.0

These new observations, combined with archival data from professional and amateur astronomers around the world, revealed that Betelgeuse’s Great Dimming was caused by a large mass loss event on the star’s surface. The mass loss event cooled the star’s southwestern quadrant by an estimated 500°C, which then triggered the expelled gas to condense into dust, “like dew forming on the outside of a glass of ice water, except dark,” science communicator and astronomer Phil Plait explained on Twitter. The combination of the quick cooling and the dust obscuration made Betelgeuse’s southern hemisphere appear about 10 times fainter than it should have been (see video below).

Large mass loss events like this one, during which Betelgeuse created a cloud about a tenth of the mass of Earth, are theorized to be a regular occurrence during the supergiant phase of a star’s life. VY Canis Majoris, for example, may have already expelled half its original mass and is obscured by a thick veil of dust. However, Betelgeuse’s Great Dimming is the first time a large-scale mass loss event has been monitored in real time for this type of star. The researchers published these results in Nature on 16 June.

Are Great Dimmings Normal?

“Did we see this event on Betelgeuse because there is something special about this star, or [did] we see it because that’s the one that we’re observing the most?”Even though astronomers have pinned down the likely cause of Betelgeuse’s Great Dimming, they can’t answer whether supergiants lose most of their mass by a continuous stellar wind or through large mass loss events like this. “Did we see this event on Betelgeuse because there is something special about this star, or [did] we see it because that’s the one that we’re observing the most?” Montargès asked. Astronomers have never seen this type of event on another supergiant star, “but perhaps it is because we are not looking.”

Pandemic-related observatory closures in 2020 and 2021 stymied some of the researchers’ hoped-for follow-up observations, but they are continuing to apply for telescope time with VLT and other telescopes. A forthcoming proposal to observe Betelgeuse with the Atacama Large Millimeter/submillimeter Array (ALMA) aims to pin down the chemical composition of the dust that condensed from the expelled stellar material.

Farther down the line, Montargès explained, the continuous monitoring capabilities of the Vera C. Rubin Observatory, scheduled to come online in 2023, and the more sensitive eye of the Extremely Large Telescope, scheduled to come online in 2025, will help fill the gaps in our understanding of not just Betelgeuse but also other supergiant stars that remain under-studied.

—Kimberly M. S. Cartier (@AstroKimCartier), Staff Writer

Today in Eos, American Geophysical Union (AGU) Publications again recognizes a number of outstanding reviewers for their work in 2020, as selected by the editors of each journal.

Every article decision relies on dedicated individuals who take time out from their own research to volunteer their expertise.Peer review is central to communicating and advancing science. While there have never been more ways to distribute ideas and research outputs, a robust peer review ensures that we maintain the highest integrity in our scientific discourse. The peer review process is organized by our journal editors, but every article decision relies on dedicated individuals who take time out from their own research to volunteer their time and expertise. The work of these reviewers ensures proper evaluation of thousands of articles each year. We are truly thankful for their efforts.

As the uses for scientific literature have grown, so has the complexity of papers, which now typically include more authors bringing more techniques, data, simulations, and results. This increase in complexity has increased the challenge and role of reviewing. The outstanding reviewers listed here have provided in-depth evaluations, often through multiple revisions, that greatly improved the final published papers. Their contributions helped raise the quality of submissions received from around the world, providing valuable feedback that elevates the prominence of our journals to the high standards aligned with the AGU tradition.

Many Reviewers: A Key Part of AGU Journals

While we note these few outstanding reviewers here, we also acknowledge the broad efforts by many AGU reviewers in helping ensure the quality, timeliness, and reputation of AGU journals. We also welcome new and first-time reviewers who have joined the family of integrity stewards and have been providing authors valuable evaluations. In 2020, AGU received more than 18,100 submissions, which is up from the 16,700 submissions received in 2019, and published more than 7,163, up from 7,000 articles in 2019. Many of these submissions were reviewed multiple times—in all, 19,227 reviewers completed 42,564 reviews in 2020 compared to the 39,368 reviews completed in 2019.

Our thanks are a small recognition of the large responsibility that reviewers bear in improving our science and its role in society.This increase has happened in the past year while each AGU journal worked to shorten the time from submission to first decision and publication or consistently maintained industry-leading standards. Several AGU journals regularly return first decisions within 1 month of submission, and most others do so now within 2 months. Reviewers represent a key part of this improvement. We look back at 2020 here, but we have already seen that in 2021, during the pandemic and unrest, members of our amazing community have continued to accept invitations to peer review article submissions.

Editorials in each journal (some already published, some upcoming) express our appreciation along with recognition lists. Our thanks are a small recognition of the large responsibility that reviewers bear in improving our science and its role in society.

Additional Thanks

We are working to highlight the valuable role of reviewers through events (though they may be virtual) at Fall Meeting and other meetings.

Each reviewer also receives a discount on AGU and Wiley books. We will continue to work with the Open Researcher and Contributor Identification network (ORCID) to provide official recognition of reviewers’ efforts, so that reviewers receive formal credit there. As of 5 May we have over 71,700 ORCIDs linked to GEMS user accounts as compared to 59,962 at this time last year.

Getting Your Feedback

We are working to improve the peer review process itself, using new online tools. We conducted a full survey in 2020, and we continue to provide a short questionnaire for feedback after each review is completed.

We value your feedback, including ideas about how we can recognize your efforts even more, improve your experience, and increase your input on the science.

We look forward to hearing from you. If you’d like to respond directly, feel free to take our survey.

Once again: Thanks!

—Matt Giampoala (mgiampoala@agu.org), Vice President, Publications, AGU; and Carol Frost, Chair, AGU Publications Committee

 

 

 

 

 

 

 

Sarah Aarons Scripps Institution of Oceanography, University of California, San Diego Rose Cory Geophysical Research Letters

 

 

 

 

 

Franciscus Aben University College London JGR-Solid Earth editors JGR-Solid Earth

 

 

 

 

 

Eldert L. Advokaat University of Birmingham Tectonics editors Tectonics

 

 

 

 

 

Hoori Ajami University of California, Riverside Water Resources Research editors Water Resources Research

 

 

 

 

 

Mark Allen University of Durham Tectonics editors Tectonics

 

 

 

 

 

Grace Andrews University of Southampton Rose Cory Geophysical Research Letters

 

 

 

 

 

Sylvain Barbot University of Southern California Germán Prieto Geophysical Research Letters

 

 

 

 

 

Roberto Basili Istituto Nazionale di Geofisica e Vulcanologia Tom Parsons AGU Advances

 

 

 

 

 

Cameron Batchelor University of Wisconsin–Madison Valerie Trouet Geophysical Research Letters

 

 

 

 

 

Timothy Bates University of Washington JGR-Atmospheres editors JGR-Atmospheres

 

 

 

 

 

Sarah N. Bentley Northumbria University Space Weather editors Space Weather

 

 

 

 

 

Tom G. Beucler University of Lausanne and University of California, Irvine Journal of Advances in Modeling Earth Systems editors Journal of Advances in Modeling Earth Systems (JAMES)

 

 

 

 

 

Kevin Bladon Oregon State University Water Resources Research editors Water Resources Research

 

 

 

 

 

Lina Boljka University of Bergen JGR-Atmospheres editors JGR-Atmospheres

 

 

 

 

 

Pierre Boué Université Grenoble Alpes JGR-Solid Earth editors JGR-Solid Earth

 

 

 

 

 

Ian Brooks University of Leeds Hui Su Geophysical Research Letters

 

 

 

 

 

Josephine Brown University of Melbourne Earth’s Future editors Earth’s Future

 

 

 

 

 

Manuela Brunner University of Freiburg Water Resources Research editors Water Resources Research

 

 

 

 

 

Ishi Buffam Swedish University of Agricultural Sciences JGR-Biogeosciences editors JGR-Biogeosciences

 

 

 

 

 

William Burt University of Alaska Fairbanks JGR-Oceans editors JGR-Oceans

 

 

 

 

 

Michael P. Byrne University of St. Andrews and University of Oxford Alessandra Giannini Geophysical Research Letters

 

 

 

 

 

Miguel Angel Cabrera Universidad de los Andes JGR-Oceans editors JGR-Oceans

 

 

 

 

 

Harish Chandra Physical Research Laboratory Sana Salous Radio Science

 

 

 

 

 

Daniel Chavas Purdue University Suzana Camargo Geophysical Research Letters

 

 

 

 

 

Marie-Luce Chevalier Institute of Geology, Chinese Academy of Geological Sciences Tectonics editors Tectonics

 

 

 

 

 

John Chiang University of California, Berkeley Hui Su Geophysical Research Letters

 

 

 

 

 

Gabriele Chiogna Technical University of Munich Water Resources Research editors Water Resources Research

 

 

 

 

 

Xinzhao Chu University of Colorado Boulder Earth and Space Science editors Earth and Space Science

 

 

 

 

 

Hye-Yeong Chun Yonsei University JGR-Atmospheres editors JGR-Atmospheres

 

 

 

 

 

Julia Cole University of Michigan Fabio Florindo Reviews of Geophysics

 

 

 

 

 

Brian D. Collins U.S. Geological Survey JGR-Earth Surface editors JGR-Earth Surface

 

 

 

 

 

Mathias Collins National Oceanic and Atmospheric Administration Water Resources Research editors Water Resources Research

 

 

 

 

 

Kristen Corbosiero The University at Albany Suzana Camargo Geophysical Research Letters

 

 

 

 

 

Kenneth Cummins University of Arizona and Florida Institute of Technology JGR-Atmospheres editors JGR-Atmospheres

 

 

 

 

 

Chimene Laure Daleu University of Reading Alessandra Giannini Geophysical Research Letters

 

 

 

 

 

Francisco Delgado Universidad de Chile Christian Huber Geophysical Research Letters

 

 

 

 

 

Meagan Eagle Woods Hole Coastal and Marine Science Center/U.S. Geological Survey JGR-Biogeosciences editors JGR-Biogeosciences

 

 

 

 

 

Jennifer Eccles University of Auckland JGR-Solid Earth editors JGR-Solid Earth

 

 

 

 

 

David Evans Goethe-Universität Frankfurt Paleoceanography and Paleoclimatology editors Paleoceanography and Paleoclimatology

 

 

 

 

 

Ian Faloona University of California, Davis Christopher Cappa Geophysical Research Letters

 

 

 

 

 

Michel Faure Institut des Sciences de la Terre d’Orléans/Université d’Orléans-CNRS Tectonics editors Tectonics

 

 

 

 

 

Grant Ferguson University of Saskatchewan Water Resources Research editors Water Resources Research

 

 

 

 

 

Christopher Fisher University of Western Australia Geochemistry, Geophysics, Geosystems editors Geochemistry, Geophysics, Geosystems

 

 

 

 

 

David D. Flagg Marine Meteorology Division, U.S. Naval Research Laboratory Yu Gu Geophysical Research Letters

 

 

 

 

 

Gwenn Flowers Simon Fraser University JGR-Earth Surface editors JGR-Earth Surface

 

 

 

 

 

Heather L. Ford Queen Mary University of London Paleoceanography and Paleoclimatology editors Paleoceanography and Paleoclimatology

 

 

 

 

 

Sven Frei University of Bayreuth Water Resources Research editors Water Resources Research

 

 

 

 

 

Melodie French Rice University JGR-Solid Earth editors JGR-Solid Earth

 

 

 

 

 

Martin Galis Comenius University in Bratislava and Slovak Academy of Sciences JGR-Solid Earth editors JGR-Solid Earth

 

 

 

 

 

Rolando Garcia National Center for Atmospheric Research JGR-Space Physics editors JGR-Space Physics

 

 

 

 

 

Sarah N. Giddings Scripps Institution of Oceanography, University of California, San Diego JGR-Oceans editors JGR-Oceans

 

 

 

 

 

Patricia M. Glibert University of Maryland Center for Environmental Science JGR-Biogeosciences editors JGR-Biogeosciences

 

 

 

 

 

Maxime Grandin University of Helsinki Mary Hudson AGU Advances

 

 

 

 

 

Carlo Gualtieri University of Naples Federico II Water Resources Research editors Water Resources Research

 

 

 

 

 

Mike Hapgood RAL Space, Science and Technology Facilities Council Andrew Yau Geophysical Research Letters

 

 

 

 

 

Elima Hassanzadeh Polytechnique Montréal Earth’s Future editors Earth’s Future

 

 

 

 

 

Xiaogang He National University of Singapore Valeriy Ivanov Geophysical Research Letters

 

 

 

 

 

Nicholas G. Heavens Imperial College London JGR-Planets editors JGR-Planets

 

 

 

 

 

Agnès Helmstetter Université Grenoble Alpes JGR-Earth Surface editors JGR-Earth Surface

 

 

 

 

 

Jonathan Herman University of California, Davis Water Resources Research editors Water Resources Research

 

 

 

 

Jennifer Hertzberg International Ocean Discovery Program, Texas A&M University Geochemistry, Geophysics, Geosystems editors Geochemistry, Geophysics, Geosystems

 

 

 

 

 

Takehiko Hiraga University of Tokyo JGR-Solid Earth editors JGR-Solid Earth

 

 

 

 

 

William Hockaday Baylor University JGR-Biogeosciences editors JGR-Biogeosciences

 

 

 

 

 

Cathy Hohenegger Max Planck Institute for Meteorology Journal of Advances in Modeling Earth Systems editors Journal of Advances in Modeling Earth Systems (JAMES)

 

 

 

 

 

Quan Hua Australian Nuclear Science and Technology Organisation Valerie Trouet Geophysical Research Letters

 

 

 

 

 

Wenxin Huai Wuhan University Water Resources Research editors Water Resources Research

 

 

 

 

 

Chengxin Jiang The Australian National University JGR-Solid Earth editors JGR-Solid Earth

 

 

 

 

 

Cathleen Jones NASA Jet Propulsion Laboratory, California Institute of Technology Water Resources Research editors Water Resources Research

 

 

 

 

 

McArthur Jones, Jr. Space Science Division, U.S. Naval Research Laboratory JGR-Space Physics editors JGR-Space Physics

 

 

 

 

 

Jason Kean U.S. Geological Survey Harihar Rajaram Geophysical Research Letters

 

 

 

 

 

Minseok Kim University of Arizona Water Resources Research editors Water Resources Research

 

 

 

 

Vassili Kitsios Commonwealth Scientific and Industrial Research Organisation and Monash University Journal of Advances in Modeling Earth Systems editors Journal of Advances in Modeling Earth Systems (JAMES)

 

 

 

 

 

 

Wouter J. M. Knoben University of Saskatchewan Centre for Hydrology Water Resources Research editors Water Resources Research

 

 

 

 

 

Julian Koch Geological Survey of Denmark and Greenland Water Resources Research editors Water Resources Research

 

 

 

 

 

Nicolas Kolodziejczyk University of Brest Kathleen Donohue Geophysical Research Letters

 

 

 

 

 

Alexandra Konings Stanford University Valeriy Ivanov Geophysical Research Letters

 

 

 

 

 

Tobias Kukulka University of Delaware JGR-Oceans editors JGR-Oceans

 

 

 

 

 

Evgeniy V. Kulakov University of Oslo JGR-Solid Earth editors JGR-Solid Earth

 

 

 

 

 

Zachary M. Labe Colorado State University Harihar Rajaram Geophysical Research Letters

 

 

 

 

 

Phoebe Lam University of California, Santa Cruz JGR-Oceans editors JGR-Oceans

 

 

 

 

 

Timothy James Lang Marshall Space Flight Center, NASA JGR-Atmospheres editors JGR-Atmospheres

 

 

 

 

 

Jason Leach Canadian Forest Service, Natural Resources Canada Water Resources Research editors Water Resources Research

 

 

 

 

 

Solène Lejosne University of California, Berkeley JGR-Space Physics editors JGR-Space Physics

 

 

 

 

 

Jing Li Peking University Kaicun Wang Geophysical Research Letters

 

 

 

 

 

Zhonghui Liu The University of Hong Kong Paleoceanography and Paleoclimatology editors Paleoceanography and Paleoclimatology

 

 

 

 

 

Lifeng Luo Michigan State University JGR-Atmospheres editors JGR-Atmospheres

 

 

 

 

 

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Farming practices of the Vikings and their ancestors could provide inspiration for resilient food systems today. That’s thanks to a study exploring how Scandinavian societies adapted their agricultural activities in a period of European history marked by stark climate fluctuations.

The Viking Age kicked off around 800 as societies in Scandinavia expanded, partly as a result of a rise in temperature that allowed agriculture to flourish. Historians believe that a growth in population and the pressure it placed on available farmland were reasons why Vikings began venturing beyond their homelands.

In popular culture today, a lot of focus is placed on Viking raids and attacks on religious sites, partly because many firsthand accounts were written by besieged Christian scholars. But archaeological evidence suggests that above all else Vikings were agriculturalists who cultivated crops and reared livestock, often on self-sufficient farms.

Less is known about farming practices in pre-Viking societies, those existing in an era known as the Dark Ages Cold Period. During this half millennium between 300 and 800, northern Europe experienced cold climates driven by volcanoes spewing gases and dust into the atmosphere, which reduced the amount of solar radiation reaching Earth’s surface.

Digging in the Mud Near Rakni’s Mound

The new research finds evidence that a community in Norway responded to this climate turbulence by regularly adapting its cereal production and animal husbandry practices. It is one of the first studies from a multidisciplinary project called Volcanic Eruptions and their Impacts on Climate, Environment, and Viking Society in 500–1250 CE (VIKINGS).

“Our findings demonstrate that climate already changed in the past—it is not something new—and societies had to adapt to it already 1,500 years ago.”“Our findings demonstrate that climate already changed in the past, it is not something new, and societies had to adapt to it already 1,500 years ago. This shows that we also have to adapt to the rapid climate change we observe today in order to maintain and improve our food production,” said Manon Bajard of the University of Oslo, who presented the research in April at the 2021 general assembly of the European Geosciences Union.

Bajard’s team analyzed sediments from Lake Ljøgottjern in southeastern Norway. Lake Ljøgottjern is located next to Rakni’s mound, one of the largest barrows in northern Europe. Previous archaeological studies have dated the mound’s construction to the mid-6th century and found extensive evidence of farming and food preparation activities in the area.

Constructed between the 5th and 6th centuries, Rakni’s mound is one of the largest barrows in northern Europe and near the site of new research into the farming practices of pre-Viking societies. Credit: Øyvind Holmstad/Wikimedia, CC BY-SA 3.0

Bajard and her colleagues steered a raft to Ljøgottjern’s deepest section, where lake bed sediments are least affected by lateral flows. By lowering a weighted tube, the team retrieved a 6-meter sediment core. Muds have been accumulating at Lake Ljøgottjern since the last glacial retreat more than 10,000 years ago, so the sediments contain clues about the area’s history.

Mud cores from Lake Ljøgottjern contain sediments dating back to the last glacial retreat. Credit: VIKINGS project/University of Oslo

To analyze the core, Bajard’s group used carbon-14 dating to identify the section corresponding to 300–800. Past temperature fluctuations were reconstructed from calcium deposits: During warmer periods, there was more biotic activity in the lake, which resulted in a greater accumulation of calcium carbonate deposits on the lake bed.

The key finding was that warmer phases were dominated by the cultivation of crops, whereas cooler phases were dominated by livestock farming. Manon’s team, as well as archaeologists working at Rakni’s mound, suggest that it is, perhaps, not surprising that farmers would rely more on animals during colder periods (when crop yields are reduced) and are reexamining archaeological evidence to support this theory.

Pollen grains in the core revealed the types and extents of staple crops, which included rye, wheat, and barley. Overall, cold periods corresponded to reduced crop yields, with barley being the most affected by climate shifts.

Animal grazing near the lake was inferred from the core’s quantity of Sordaria, fungi that thrive on animal feces. Small quantities of DNA recovered from the core also revealed the presence of cows, pigs, and sheep.

Strategic Farmers

Bajard said Viking ancestors may have strategically prioritized the best land close to the community for crops. During warmer periods when harvests were more robust, animals were relocated to areas less suitable for crops, perhaps land that was still forested.

“Later, during the Viking Age and Middle Ages, both activities were occurring at the same time, but it was much warmer then, so the cultivation area could have been extended,” Bajard said.

“Over generations, hard-won experience taught a farmer what works and [that] experiments could be fatal.”To build a more complete picture of how farming practices evolved, Bajard’s team will try to collect more DNA samples from near the lake to start quantifying how the mix of animal types varied over time.

Peter Hambro Mikkelsen, an environmental archaeologist at Aarhus University in Denmark not involved in the VIKINGS research, said food producers today might learn from this community’s ability to diversify. “Over generations, hard-won experience taught a farmer what works and [that] experiments could be fatal. As opposed to modern farming where specialization is the key to large-scale production, traditional agriculture KNOWS that when weather fails, livestock can perish—and the enemy can be at the gates of one’s village.”

—James Dacey (@JamesDacey), Science Writer

The Earth and space sciences present ideal use cases for machine learning (ML) applications because the problems being addressed are globally important and the data are often freely available, voluminous, and of high quality.Machine learning (ML), loosely defined as the “ability of computers to learn from data without being explicitly programmed,” has become tremendously popular in technical disciplines over the past decade or so, with applications including complex game playing and image recognition carried out with superhuman capabilities. The Earth and space sciences (ESS) community has also increasingly adopted ML approaches to help tackle pressing questions and unwieldy data sets. From 2009 to 2019, for example, the number of studies involving ML published in AGU journals approximately doubled.

In many ways, ESS present ideal use cases for ML applications because the problems being addressed—like climate change, weather forecasting, and natural hazards assessment—are globally important; the data are often freely available, voluminous, and of high quality; and computational resources required to develop ML models are steadily becoming more affordable. Free computational languages and ML code libraries are also now available (e.g., scikit-learn, PyTorch, and TensorFlow), contributing to making entry barriers lower than ever. Nevertheless, our experience has been that many young scientists and students interested in applying ML techniques to ESS data do not have a clear sense of how to do so.

The Tools of the Trade

An ML algorithm can be thought of broadly as a mathematical function containing many free parameters (thousands or even millions) that takes inputs (features) and maps those features into one or more outputs (targets). The process of “training” an ML algorithm involves optimizing the free parameters to map the features to the targets accurately.

There are two broad categories of ML algorithms relevant in most ESS applications: supervised and unsupervised learning (a third category, reinforcement learning, is used infrequently in ESS). Supervised learning, which involves presenting an ML algorithm with many examples of input-output pairs (called the “training set”), can be further divided, according to the type of target that is being learned, as either categorical (classification; e.g., does a given image show a star cluster or not?) or continuous (regression; e.g., what is the temperature at a given location on Earth?). In unsupervised learning, algorithms are not given a particular target to predict; rather, an algorithm’s task is to learn the natural structure in a data set without being told what that structure is.

Supervised learning is more commonly used in ESS, although it has the disadvantage that it requires labeled data sets (in which each training input sample must be tagged, or labeled, with a corresponding output target), which are not always available. Unsupervised learning, on the other hand, may find multiple structures in a data set, which can reveal unanticipated patterns and relationships, but it may not always be clear which structures or patterns are “correct” (i.e., which represent genuine physical phenomena).

Applications in Earth and Space Sciences

Books and classes about ML often present a range of algorithms but leave people to imagine specific applications of these algorithms on their own.Books and classes about ML often present a range of algorithms that fall into one of the above categories but leave people to imagine specific applications of these algorithms on their own. However, in practice, it is usually not obvious how such approaches (some seemingly simple) may be applied in a rich variety of ways, which can create an imposing obstacle for scientists new to ML. Below we briefly describe various themes and ways in which ML is currently applied to ESS data sets (Figure 1), with the hope that this list—necessarily incomplete and biased by our personal experience—inspires readers to apply ML in their research and catalyzes new and creative use cases.

Fig. 1. Ten ideas for applying machine learning (ML) in the Earth and space sciences, roughly organized by the degree of involvement of physics-based models (horizontal scale) and the degree to which ML codes are available and readily applicable versus being in development and requiring significant customization (vertical scale). Credit: Jacob Bortnik 1. Pattern Identification and Clustering

One of the simplest and most powerful applications of ML algorithms is pattern identification, which works particularly well with very large data sets that cannot be traversed manually and in which signals of interest are faint or highly dimensional. Researchers, for example, applied ML in this way to detect signatures of Earth-sized exoplanets in noisy data making up millions of light curves observed by the Kepler space telescope. Detected signals can be further split into groups through clustering, an unsupervised form of ML, to identify natural structure in a data set.

Conversely, atypical signals may be teased out of data by first identifying and excluding typical signals, a process called anomaly or outlier detection. This technique is useful, for example, in searching for signatures of new physics in particle collider experiments.

2. Time Series and Spatiotemporal Prediction

An important and widespread application of supervised ML is the prediction of time series data from instruments or from an index (or average value) that is intended to encapsulate the behavior of a large-scale system. Approaches to this application often involve using past data in the time series itself to predict future values; they also commonly involve additional inputs that act as drivers of the quantities measured in the time series. A typical example of ML applied to time series in ESS is its use in local weather prediction, with which trends in observed air temperature and pressure data, along with other quantities, can be predicted.

In many instances, however, predicting a single time series of data is insufficient, and knowledge of the temporal evolution of a physical system over regional (or global) spatial scales is required. This spatiotemporal approach is used, for example, in attempts to predict weather across the entire globe as a function of time and 3D space in high-capacity models such as deep neural networks.

3. Emulators and Surrogates

Physics-based simulations can take days or weeks to run on even the most powerful computers. An alternate solution is to train ML models to act as emulators for physics-based models.Traditional, physics-based simulations (e.g., global climate models) are often used to model complex systems, but such models can take days or weeks to run on even the most powerful computers, limiting their utility in practice. An alternate solution is to train ML models to act as emulators for physics-based models or to replicate computationally intensive portions within such models. For example, global climate models that run on a coarse grid (e.g., 50- to 100-kilometer resolution) can include subgrid processes, like convection, modeled using ML-based parameterizations. Results with these approaches are often indistinguishable from those produced by the original model alone but can run millions or billions of times faster.

4. Boundary or Driving Conditions

Many physics-based simulations proceed by integrating a set of partial differential equations (PDEs) that rely on time-varying boundary conditions and other conditions that drive interior parts of the simulation. The physics-based model then propagates information from these boundary and driver conditions into the simulation space—imagine, for example, a 3D cube being heated at its boundary faces with time-varying heating rates or with thermal conductivity that varies spatiotemporally within the cube. ML models can be trained to reflect the time-varying parameterizations both within and along the simulation boundaries of a physical model, which again may be computationally cheaper and faster.

5. Interpretability and Knowledge Discovery

If a spatiotemporal ML model of a physical system can be trained to produce accurate results under a variety of input conditions, then the implication is that the model implicitly accounts for all the physical processes that drive that system, and thus, it can be probed to gain insights into how the system works. Certain algorithms (e.g., random forests) can automatically provide a ranking of “feature importance,” giving the user a sense of which input parameters affect the output most and hence an intuition about how the system works.

More sophisticated techniques, such as layerwise relevance propagation, can provide deeper insights into how different features interact to produce a given output at a particular location and time. For example, a neural network trained to predict the evolution of the El Niño–Southern Oscillation (ENSO), which is predominantly associated with changes in sea surface temperature in the equatorial Pacific Ocean, revealed that precursor conditions for ENSO events occur in the South Pacific and Indian Oceans.

6. Accelerating Inversions

A ubiquitous challenge in ESS is to invert observations of a physical entity or process into fundamental information about the entity or the causes of the process (e.g., interpreting seismic data to determine rock properties). Historically, inverse problems are solved in a Bayesian framework requiring multiple runs of a forward model, which can be computationally expensive and often inaccurate. ML offers alternative methods to approach inverse problems, either by using emulators to speed up forward models or by using physics-informed machine learning to discover hidden physical quantities directly. ML models trained on prerun physics-based model outputs can be used for rapid inversion.

7. Creating High-Resolution Global Data Sets

Satellite observations often provide global, albeit low-resolution and sometimes indirect (i.e., proxy-based), measurements of quantities of interest, whereas local measurements provide more accurate and direct observations of those quantities at smaller scales. A popular and powerful use for ML models is to estimate the relationship between global proxy satellite observations and local accurate observations, which enables the creation of estimated global observations on the basis of localized measurements. This approach often includes the use of ML to create superresolution images and other data products.

8. Uncertainty Quantification

Typically, uncertainty in model outputs is quantified using a single metric such as the root-mean-square of the residual (the difference between model predictions and observations). ML models can be trained to explicitly predict the confidence interval, or inherent uncertainty, of this residual value, which not only serves to indicate conditions under which model predictions are trustworthy (or dubious) but can also be used to generate insights about model performance. For instance, if there is a large error at a certain location in a model output under specific conditions, it could suggest that a particular physical process is not being properly represented in the simulation.

9. Physics-Informed Neural Networks

Domain experts analyzing data from a given system, even in relatively small quantities, are often able to extrapolate the behavior of the system—at least conceptually—because of their understanding of and trained intuition about the system based on physical principles. In a similar way, laws and relationships that govern physical processes and conserved quantities can be explicitly encoded into neural network algorithms, resulting in more accurate and physically meaningful models that require less training data.

10. Finding and Solving Governing Equations

In certain applications, the values of terms or coefficients in PDEs that drive a system—and thus that should be represented in a model—are not known. Various ML algorithms were developed recently that automatically determine PDEs that are consistent with the available physical observations, affording a new and powerful discovery tool.

In still newer work, ML methods are being developed to directly solve PDEs. These methods offer accuracy comparable to traditional numerical integrators but can be dramatically faster, potentially allowing large-scale simulations of complex sets of PDEs that have otherwise been unattainable.

Addressing Urgent Challenges

The Earth and space sciences are poised for a revolution centered around the application of existing and rapidly emerging ML techniques to large and complex ESS data sets being collected. These techniques have great potential to help scientists address some of the most urgent challenges and questions about the natural world facing us today. We hope the above list sparks creative and valuable new applications of ML, particularly among students and young scientists, and that it becomes a community resource to which the ESS community can add more ideas.

Acknowledgments

We thank the AGU Nonlinear Geophysics section for promoting interdisciplinary, data-driven research, for supporting the idea of writing this article, and for suggesting Eos as the ideal venue for dissemination. The authors gratefully acknowledge the following sources of support: J.B. from subgrant 1559841 to the University of California, Los Angeles, from the University of Colorado Boulder under NASA Prime Grant agreement 80NSSC20K1580, the Defense Advanced Research Projects Agency under U.S. Department of the Interior award D19AC00009, and NASA/SWO2R grant 80NSSC19K0239 and E.C. from NASA grants 80NSSC20K1580 and 80NSSC20K1275. Some of the ideas discussed in this paper originated during the 2019 Machine Learning in Heliophysics conference.

Author Information

Jacob Bortnik (jbortnik@gmail.com), University of California, Los Angeles; and Enrico Camporeale, Space Weather Prediction Center, NOAA, Boulder, Colo.; also at Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder

Beneath Florida’s cities and swamps lies a complex network of karst conduits. The same chemical weathering that carves truck-sized tunnels through the calcium carbonate rock also leads to sinkholes at the surface. For Florida insurance agents, sinkholes are a headache. But for the state’s hydrogeologists, every sinkhole is an opportunity to understand the aquifer below.

Sinkholes allow surface water, as well as contaminants, to flood into an aquifer. By mapping the network of entry points and exit springs, hydrogeologists can better understand the underground system and better protect drinking water at the source. That understanding is important to populations outside Florida: Karst aquifers provide drinking water for 25% of people on Earth.

Isotope analysis helps hydrogeologists trace water origins, but the technique’s use has generally been limited to sinkhole lakes and springs no more than 4 kilometers apart. Recently, however, a team in Florida used isotope ratios to connect points 32 kilometers apart. It’s the farthest hydraulic connection between a sinkhole and a spring yet documented and the first connection involving a first-magnitude spring (those discharging an average of 100 cubic feet—2.8 cubic meters—of water per second).

“Dyeing” to Know Greenhalgh introduces tracer dye into the sinkhole at Lake Miccosukee. Credit: Ming Ye

“Normally, for hydrogeology, we only care about subsurface water flows,” explained Ming Ye, a hydrogeologist at Florida State University and a coauthor of the research, published in Groundwater in April. But when studying sinkholes, he said, scientists have to consider surface water flows, too.

In 2010, two sinkholes appeared at the edge of Lake Miccosukee in north central Florida, and in 2018 Ye and his colleagues used a technique called dye tracing to detect water flows from sinkhole to spring.

Dye tracing requires guesswork, Ye said. It’s “like hunting a treasure.”

Florida Geological Survey technicians poured lime-green fluorescein dye into the sinkholes, then placed monitors at likely outflow sites downslope. None detected the diluted dye. The researchers also placed cheaper charcoal packets at less likely locations. One of those sites, Natural Bridge Spring, 32 kilometers away, turned up evidence of the dye.

Heavy Signatures

Connecting the dots with dye was only step one. The team next explored whether isotopes could also establish the hydraulic connection.

Isotope signatures are a common method for assessing groundwater origins. A small percentage of oxygen molecules are 18O, “heavy isotopes” that evaporate less readily than common 16O isotopes, giving lake signatures a substantially higher proportion of 18O than groundwater or rainfall. Knowing the isotope signatures of the sinkhole, spring, and groundwater, the researchers determined that roughly 8.5% of Natural Bridge Spring water originated at Lake Miccosukee.

That mixing fraction was based on pairs of water samples. Using weekly water samples, the researchers compared isotopes from Natural Bridge Spring to isotopes collected earlier at Lake Miccosukee. They found that the dye reached the spring 18 days after application at Lake Miccosukee, and the presence of the dye at Natural Bridge Spring peaked at day 34. Removing the effects of rainfall, the isotope ratios at both sites were perfectly correlated 35 days apart, demonstrating a hydraulic connection and validating the expected transit time.

Connecting Dots Underground

The researchers now plan to reverse the process, tying a spring back to its source and using isotopes as a primary confirmation method.

The method would remove the guesswork of dumping dye into a sinkhole and expand the understanding of the karst aquifer at lower cost and effort.By collecting regular water samples from area springs and sinkhole lakes, researchers can look for isotope ratio trends over time. Possible connections can be confirmed with dye tracing. The method would remove the guesswork of dumping dye into a sinkhole and expand the understanding of the karst aquifer at lower cost and effort, researchers said.

“What they’re exposing here is a very sound method to backtrack type of infiltration,” said Joanna Doummar, an assistant professor of hydrogeology at American University of Beirut who was not involved in the research.

Using isotopes to connect the dots allows hydrogeologists a wider window of sampling and evidence. “[Dye] tracing is very important, but it’s very static,” said Doummar. “It doesn’t tell you how this is varying through time.”

Ultimately, knowledge of the subsurface system will help water managers protect spring water at its upslope entry point. Knowing the transit time and mixing fraction will also help managers gauge threats, as contaminants may decay or dilute while traveling through the aquifer.

“It’s really important, given the heterogeneity of this infiltration, to detect all these areas and identify all the transit times,” Doummar said. “With the assumptions that [Ye and his team] have taken, which are very legitimate, they have exposed a method to backtrack the percentage of water coming from the sinkhole.”

Until the system is developed, Ye and his collaborators will continue treasure hunting. A 1,618-hectare Florida lake completely drained into a sinkhole in early June, offering another chance to explore the aquifer with bags of organic dye.

—J. Besl (@J_Besl), Science Writer

Methane is a potent greenhouse gas, second to carbon dioxide in its overall influence on anthropogenic warming at Earth’s surface, so scientists are keen to keep a close eye on emissions of the gas, both natural and anthropogenic. Thorough records of methane emissions from the landscape allow researchers to better understand processes that contribute emissions, forecast changes in ecosystems, validate models of land–atmosphere exchanges, and estimate regional and continental methane budgets. But collecting such records from a variety of environments over broad spatial and temporal scales is a massive endeavor. Aiding in this task is the National Ecological Observatory Network (NEON).

The National Ecological Observatory Network’s capabilities and data can aid scientists in answering pressing questions about how our environment functions and how it is changing.NEON, which comprises numerous sites distributed across the United States and Puerto Rico, will have its newly implemented and community-championed methane concentration data online and openly available by summer 2022. The addition of methane concentration measurements at NEON’s 47 terrestrial sites—spanning the observatory’s 20 designated ecoclimatic domains—will support and facilitate new research investigating greenhouse gas emissions and budgets across the observatory.

Improving understanding of methane emissions across the country is just one example of how NEON’s capabilities and data can aid scientists in answering pressing questions about how our environment functions and how it is changing. Incorporating feedback from the research community is fundamental to NEON’s role in promoting successful science, as is keeping the community updated on developments and pathways for using the observatory. With this in mind, we describe here the available tools and ways to use NEON and invite you to leverage the observatory in your research.

What Is NEON?

NEON is a continental-scale observation facility designed to enable characterization and quantification of complex and rapidly changing ecological processes as well as forecasting of future conditions. The observatory network began construction in 2012 and entered its operational phase in 2019. Today the network includes 47 field sites distributed throughout the major terrestrial ecosystems of the United States and Puerto Rico, plus 34 freshwater aquatic sites located in streams, lakes, and rivers (Figure 1).

Fig. 1. NEON includes 47 field sites and 34 freshwater aquatic sites distributed throughout 20 designated ecoclimatic domains (numbered here) in the United States and Puerto Rico. Credit: NEON

Throughout the network, NEON instruments and staff collect environmental data to characterize trajectories of change in plants, animals, soils, nutrients, freshwater, and the atmosphere. This information composes NEON’s catalog of 181 open-access data products that can be freely downloaded via the NEON Data Portal. Identifying and implementing standardized infrastructure and instrumentation capable of collecting data across a variety of field environments—as well as methodologies that allow for direct comparisons of data among all sites—was a demanding undertaking. For example, installing an array of soil sensors in ecosystems ranging from deserts to tundra required significant engineering flexibility and site-specific expertise. Also challenging were designing and constructing a cyberinfrastructure framework capable of ingesting, processing, formatting, and serving all of NEON’s data and associated metadata in a scalable manner.

NEON also provides an expansive collection of physical specimens and samples that are available to the research community for analysis. Each year, NEON catalogs more than 100,000 individual specimens and samples of soils, vegetation, microbes, insects and other invertebrates, and tissue (e.g., blood, hair, DNA) from fish and other vertebrates into searchable and publicly available archives at the NEON Biorepository Data Portal.

Other archives to which NEON contributes include the Megapit Soil Archive (soils collected and characterized from each NEON site during their initial construction), the NEON Initial Characterization Soils Archive (soils collected during initial operations in partnership with the Natural Resources Conservation Service), and tick samples in the U.S. National Tick Collection at Georgia Southern University.

Leveraging Assignable Assets

The scientific community can use and supplement the observatory’s resources through the Assignable Assets (AA) program. This program allows researchers to leverage the combination of NEON’s robust and diverse standardized observations with the flexibility and creativity of community-led research to advance and innovate in their work (see sidebar).

Resources available via the AA program include specialized equipment and boots-on-the-ground research support at NEON sites. For example, the AA program maintains a fleet of five Mobile Deployment Platforms (MDPs), which are fully instrumented (they largely use the same sensors as NEON’s fixed sites) and can be deployed in environments across the network, provided there is sufficient road access.

A Mobile Deployment Platform (MDP) instrument hut (left) and an instrumented tower (right) are seen here during commissioning at the NEON Kings Creek site at the Konza Prairie Biological Station in Kansas (aquatic sensors not shown). NEON’s fleet of five MDPs use the same sensors, processing algorithms, and data quality assurance/quality control criteria as standard NEON sites, allowing researchers to generate standardized NEON data, but they can be rapidly deployed to recent disturbance events or ongoing research projects. Credit: Michael SanClements

Collaborating with NEON

The AA program serves as the entry point to collaborations with NEON, and projects are initiated by filing a formal request through NEON’s website. NEON evaluates not the scientific merit of projects but, rather, the feasibility of completing them while maintaining the efficacy of core NEON measurements.

The AA program is “cost-recoverable,” meaning NEON requires reimbursement for costs (e.g., labor) associated with approved projects. As such, we request that principal investigators (PIs) work with the NEON AA team to ensure the feasibility of their project and to fine-tune expected costs for inclusion in proposals to funding agencies.

NEON collaborators (left to right) Dave Bowling, Zoe Pierrat, and Troy Magney install a photosynthesis spectrometer on the NEON tower at Ordway-Swisher Biological Station in Melrose, Fla., as part of an AA project. The spectrometer, which measures solar-induced chlorophyll fluorescence and changes in plant pigments, will be used to develop better satellite-based methods for measuring photosynthesis from space. Credit: Troy Magney

The level of coordination and planning needed for proposed AA projects scales with the complexity of the request. A simple request, such as a researcher or educational group asking to arrange a site tour or an introduction to NEON staff at a domain support facility, is typically accommodated within 2 weeks. A moderately complex request could involve, for example, a PI asking to deploy simple temperature monitors (iButtons) and collect soil or sediment samples at a few sites over one or two field seasons. In this case, we would encourage the researcher to engage the AA program at least 6 weeks prior to any proposal deadlines to ensure feasibility, evaluate costs, and, if needed, secure a letter of support to include in funding proposals.

For even more substantial requests involving, for example, deployment of an MDP, addition of specialized sensors to multiple NEON towers, or an extensive multisite and multiyear field sampling campaign, PIs should begin coordination and planning with the AA program several months prior to any proposal submission deadline. This extended timeline is necessary, as PIs must work closely with the observatory to iterate deployment details (e.g., locations, dates, and timelines); facilitation of permits and permissions; schedules; and access to infrastructure, power, and communications.

An MDP allows scientists to incorporate standardized NEON measurement systems—atmospheric, terrestrial, and aquatic—into planned or ongoing research projects at stand-alone sites or sites within other research networks, such as the Long-Term Agroecosystem Research Network, Long Term Ecological Research (LTER) Network, AmeriFlux, or the Critical Zone Collaborative Network. MDPs use data processing algorithms and quality assurance/quality control criteria identical to the instruments at NEON field sites. MDPs thus help tie external research sites into the NEON network and can expand data synthesis, hypothesis testing, and the applicability of research findings to larger regional or even continental scales.

MDPs can also serve as test beds for new technologies. This summer, NEON will deploy an MDP to Ohio State University as part of a proof-of-concept artificial intelligence (AI) and cyberinfrastructure project that will serve as the basis for an adaptive AI system for in-the-field environmental sensing.

NEON also has three Airborne Observation Platforms (AOPs), each comprising a sophisticated remote sensing package deployed on a Twin Otter aircraft. These payloads include a high-resolution digital camera; lidar instrumentation to provide 3D structural information about the landscape; and an imaging spectrometer to allow identification of plant species and communities, map vegetation health, and provide data on canopy chemical constituents.

For example, in work supported by the National Science Foundation (NSF) in conjunction with the University of Colorado (CU) Boulder, an AOP was used to map postfire burns at several sites where fire emissions had been previously observed during the CU Boulder Biomass Burning Fluxes of Trace Gases and Aerosols campaign. This research aimed to relate wildfire emission characteristics to ecosystem parameters to improve the prediction of environmental and human health impacts from wildfires. AOP data provided information on total area burned and allowed partners from the U.S. Forest Service to calculate biomass lost.

Through the AA program, scientists can request an MDP or flight time with the AOP, request samples, coordinate visits to NEON field sites to conduct their own sampling campaigns, and deploy their own instruments to existing NEON infrastructure [e.g., Kramer and Chadwick, 2018; Chlus et al., 2020; Dangal and Sanderman, 2020; Hall et al., 2020; Zhang et al., 2021]. NEON’s field science staff and 18 domain support facilities are additional resources and can help principal investigators (PIs) with logistics, sample collection and processing, and sensor deployment [e.g., Blair et al., 2020; Chadwick et al., 2020; Chaudhary et al., 2020; Heckman et al., 2020; Seyednasrollah et al., 2021].

Targets of Opportunity

Bala Chaudhary (rear) and Paul Metzler pose while installing dust collectors at the Niwot Ridge NEON site in Colorado. The work is part of an Assignable Assets (AA) project to examine the role of wind in dispersing arbuscular mycorrhizal fungi [Chaudhary et al., 2020]. Credit: Paul MetzlerEvents like floods, fires, hurricanes, and earthquakes, or perturbations such as dramatic predator population shifts, can drive rapid ecological change and often present time-sensitive “targets of opportunity” for research [Whitman et al., 2021]. These disturbances often occur without warning and outside the boundaries of NEON sites. NEON’s mobile sensing systems offer the capacity to investigate targets of opportunity, and the AA program is equipped to expedite review of time-sensitive requests to help PIs take advantage of these fleeting research opportunities.In 2016, for example, the Chimney Tops 2 Fire burned 4,167 hectares of Great Smoky Mountains National Park, including the NEON site contained within the park’s boundaries. Leveraging rapid response funding from NSF, researchers worked with the NEON AA program to access the site immediately following the fire and collect samples for a study of pyrogenic carbon mobility [Matosziuk et al., 2020].

Evolving to Meet Community Needs

As NEON evolves over its planned 30-year life span, the observatory is working to increase the discoverability and accessibility of its core data products and infrastructure, while also building new inroads for assimilating and responding to community feedback. Sustaining core functionality while evaluating evolving scientific priorities, new methodologies, and advances in instrumentation and computing will be a continual challenge over the life span of the observatory. Creating channels for bidirectional communication with the community is, and will continue to be, critical to NEON’s ability to balance its resources with community priorities and shifting technologies, and we look forward to growing these conversations and collaborations over time.

To that end, NEON consults with a diverse set of 180 scientists and educators from across academia, government, and industry who make up more than 20 technical working groups (TWGs) and advise NEON on wide-ranging technical issues. TWG recommendations that involve changes to the scope or science requirements of NEON, or significant potential enhancements of it, are vetted with help from a community-based advisory body. These collaborative pathways allow for communication among NSF, NEON, and the scientific community and enable appropriate evolution of NEON to maintain its value in facilitating forefront science and education.

In addition, frequent workshops, presentations, facility tours, and other events—whether instigated within NEON or by members of the external research community—provide opportunities to optimize NEON. For example, over 2 days in 2019, the first NEON Science Summit, organized by CU Boulder, brought together 170 participants to explore major questions that can be addressed at continental scales using NEON data. NEON scientists and other staff provided guidance on data products and captured community feedback to enhance the delivery of NEON data, including, for example, through the creation of concise user guides for NEON data products.

With exciting developments on the horizon such as the NSF Center for Advancement and Synthesis of Open Environmental Data and Sciences, which will coordinate open data assets at NEON, LTER, and other programs to speed the research discovery process, we anticipate many new ways that NEON’s data and samples will be used across the community. We encourage researchers to explore opportunities to become involved with NEON, engage with our staff at meetings, and, most important, provide feedback to help us best meet the research community’s evolving needs.

Acknowledgments

NEON is sponsored by NSF and operated under cooperative agreement by Battelle. This material is based in part upon work supported by NSF through the NEON program. ORCID numbers for the authors are 0000-0002-1962-3561 (SanClements) and 0000-0002-8455-3213 (Mabee).