Center for Climate Sciences

We integrate advanced artificial intelligence (AI) tools and detailed field-scale survey data from Alaska’s tundra and boreal regions over a period of 53 years in this study. Early tests of demonstrate effectiveness in accurately monitoring and forecasting changes in permafrost thaw and carbon release across Alaska with high precision and minimal error. Read more >

Across the Pan-Arctic, there exists a significant interplay between thawing permafrost and increasing carbon emissions, a feedback that disrupts the global carbon cycle and accelerates climate change. Paradoxically, few earth system models fully consider how thawing permafrost affects carbon release currently. Therefore, we integrate advanced artificial intelligence (AI) tools and detailed field-scale survey data from Alaska’s tundra and boreal regions over a period of 53 years (1969-2022) in this study.

Using a sophisticated AI model (GeoCryoAI), which combines different types of deep learning techniques, this approach captures and simulates the patterns of the permafrost carbon feedback (PCF) by integrating in situ and flux tower measurements for model training and improved learning. Early tests of the refined model demonstrate its effectiveness in accurately monitoring and forecasting changes in permafrost thaw (i.e., active layer thickness) and carbon release (i.e., carbon dioxide, methane) across Alaska with high precision and minimal error.

Our model is the first of its type to characterize these dynamics across space and time in this context while demonstrating the importance of monitoring permafrost thaw variability as a sensitive harbinger of change and a critical indicator of PCF variability and global climate change. Understanding, interpreting, and identifying the most vulnerable regions undergoing accelerated permafrost thaw is important to inform, engage, and promote high-impact cross-disciplinary research across the northern latitudes while providing evidence and support for mitigation strategies, infrastructure security, and informed policymaking.

For more, see the full paper.

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Improving the accuracy of Earth system models in projecting the impacts of climate change on land carbon dynamics is paramount for informing effective carbon management and climate mitigation strategies. Recent findings highlight the importance of incorporating hyperspectral reflectance data into Earth system models, which traditionally rely on simplified broadband albedo values. Read more >

Improving the accuracy of Earth system models in projecting the impacts of climate change on land carbon dynamics is paramount for informing effective carbon management and climate mitigation strategies. Recent findings highlight the importance of incorporating hyperspectral reflectance data into Earth system models, which traditionally rely on simplified broadband albedo values. This simplification, averaging values in the photosynthetically active radiation (PAR, 400–700 nm) and the near infrared (NIR, 700–2,500 nm) spectral bands, overlooks the detailed spectral characteristics crucial for accurate climate modeling.

This study reveals that the conventional approach, which only considers broad spectral bands, introduces significant biases in modeling the Earth's climate. Specifically, it underestimates soil albedo in desert areas by ± 0.2, leading to radiative forcing discrepancies up to 30 W m−2. Such inaccuracies have significant effects on climate simulations, impacting global energy fluxes, temperature, rainfall, and photosynthesis processes.

The adoption of hyperspectral data for soil reflectance in Earth system models promises a substantial improvement in the accuracy of climate predictions. By addressing these biases, the research underscores the potential for making direct use of hyperspectral data to achieve more precise forecasts of future climate conditions, aiding in the development of effective climate change mitigation and adaptation strategies. This paves the way for a deeper understanding of the Earth's climate system, emphasizing the critical role of detailed spectral and environmental data in enhancing model projections.

For more, see the full article.

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Evergreen needleleaf forests play a sizable role in the global carbon cycle and are highly susceptible to environmental change. This work outlines a measurement strategy that can be applied in ecosystems beyond ENF. Read more >

Evergreen needleleaf forests play a sizable role in the global carbon cycle and are highly susceptible to environmental change. However, it is difficult to fully understand the impact of these forests on the carbon cycle due to limited direct observations of photosynthesis in these forests. To overcome these limitations, new optical techniques detect light emitted and reflected by evergreen needleleaf forests which is closely linked to evergreen needleleaf biology and photosynthesis. By measuring these signals from space, we can better understand evergreen needleleaf forest’s role in the global carbon cycle. Here we summarize how fundamental plant biological and physical processes control the fate of photons from leaf to globe, and how to remotely sense evergreen needleleaf biology.

We use a suite of data across scales (from the leaf to satellite) collected across four ENF sites spanning a broad range of environmental conditions. We show that coordinated measurement campaigns across multiple spatial scales can enable a more mechanistic understanding of the biologic processes underpinning remote sensing observations. With the rapid proliferation of remote sensing observations from space, a need remains for multidisciplinary efforts to reconcile when, where, and to what extent remote sensing tracks changes in the carbon cycle. Our work outlines a measurement strategy that can be applied in ecosystems beyond ENF.

Pigment analyses provides direct insight into light absorption and partitioning but has limited opportunities for repeated measurements over time (samples must be collected and processed manually). Continuous pulse amplitude modulated fluorometry provides leaf-level information on the photosynthetic activity of plant photosystems, including information on the partitioning of light energy among different pathways. Tower-based remote sensing provides proxies for many variables related to photosynthetic carbon uptake, including forest chlorophyll and xanthophyll pigment content and net carbon uptake at a high spatiotemporal resolution (half-hourly, tree-canopy scale). Eddy-covariance-derived carbon-dioxide fluxes are the best available measure for carbon uptake via photosynthesis (gross primary production) at the canopy scale and can be derived at a half-hourly resolution. Satellite-based remote sensing is the only way we can detect photosynthesis at large spatial scales but is limited in its spatial and temporal resolutions.

For more, see the full article.

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Scientists gathered at the Linking Optical and Energy Fluxes Workshop to start building an international community connecting remote sensing and ecosystem flux science. Read more >

Our climate is changing, but to understand exactly how the climate will change in the future, we need to understand the movement (fluxes) of water, energy, and carbon. We can study these fluxes using tower-mounted instruments, but they only represent small spatial areas. Coupling these observations with remote sensing data from satellites or aircraft represents a significant advance toward overcoming this limitation. This past July, 40 scientists representing a wide range of disciplines, geographies, and career stages gathered at the Linking Optical and Energy Fluxes Workshop to start building an international community connecting remote sensing and ecosystem flux science. The group discussed opportunities, challenges, and next steps with respect to using remote sensing to estimate terrestrial water, energy, and carbon fluxes.

Major outcomes from this workshop, which are summarized in the full article, included identifying opportunities for proximal remote sensing data (using remote sensing instruments in close proximity to the field site, e.g. mounted on a tower) to be used in conjunction with flux data, highlighting major barriers limiting the use of these data, and issuing a call to action for community collaboration to develop a coordinated network of proximal remote sensing instruments.

For more, see the full article.

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When microscopic algae receive their favorable amount of light and nutrients, they can create expansive blooms along coastlines. Certain types of algae release toxins that are harmful to both marine life and human health. In this study, we tested a new method to retrieve HAB information in variable atmospheric conditions. Read more >

When microscopic algae receive their favorable amount of light and nutrients, they can create expansive blooms along coastlines. Certain types of algae release toxins that are harmful to both marine life and human health. Tracking harmful algal blooms (HABs) allows environmental decision-makers to respond to distressed wildlife and to close beaches and shellfish beds. However, current satellite tracking methods cannot track these blooms on cloudy days.

In this study, we tested a new method to retrieve HAB information in variable atmospheric conditions. We compared Sentinel 5 Precursor TROPOspheric Monitoring Instrument (TROPOMI), an instrument that can track HABs on cloudy days, with MODIS-Aqua, an instrument used for HAB monitoring. During massive harmful algal blooms along the West Florida Shelf, we found both instruments were consistent in detecting blooms. We also found TROPOMI was able to gather almost double the amount HAB information. This work presents the first application of TROPOMI for HAB monitoring and illuminates an approach for bolstering HAB early warning systems beyond clear sky days.

For more, see the full article.

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Traditionally, researchers focused on the role of a strong El Niño amplitude preceding La Niña for generating these long-lasting ‘multi-year’ La Niña events. However, the recent multi-year La Niña event during 2020-2022 puzzled researchers because it followed only a very weak El Niño, challenging the traditional view. This study found that 64% of observed multi-year La Niña events during 1900‒2022 didn't actually require a preceding strong El Niño. Read more >

The La Niña event of the El Niño/Southern Oscillation (ENSO) phenomenon, known for its colder conditions (opposite to the warmer conditions of El Niño), often lasts two years or more, causing severe global weather and climate impacts. Traditionally, researchers focused on the role of a strong El Niño amplitude preceding La Niña for generating these long-lasting ‘multi-year’ La Niña events. However, the recent multi-year La Niña event during 2020-2022 puzzled researchers because it followed only a very weak El Niño, challenging the traditional view. This led us to ask: Is a strong El Niño necessary for multi-year La Niña events?

In revisiting the traditional physical mechanism linking multi-year La Niña events with preceding strong El Niño events, our study found that 64% of observed multi-year La Niña events during 1900‒2022 didn't actually require a preceding strong El Niño. This result leads to the conclusion that the role of a preceding strong El Niño has been overly emphasized. Instead of the traditional mechanism, we noticed that ‘multi-year’ La Niña events were associated with colder sea surface temperatures extending southwestward from the Baja California coast towards the equator. This mechanism offers a new explanation for the generation of multi-year La Niña events irrespective of El Niño conditions in the preceding years. These findings significantly advance our understanding of tropical Pacific climate dynamics and have substantial socio-economic implications, particularly considering the intense and prolonged climate impacts of multi-year La Niña events.

For more, see the full article.

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Understanding the exact conditions that cause thunderstorm formation helps us predict where and when thunderstorms will occur, which is of great value to weather forecasters. This work developed technology to extend fixed-time satellite snapshots to predict atmospheric development by up to 6 hours. Read more >

On a warm day, air close to the surface starts to rise. On a clear, hot, and humid day, this rising air can lead to the development of thunderstorms. Understanding the exact conditions that cause thunderstorm formation helps us predict where and when thunderstorms will occur, which is of great value to weather forecasters. Some satellites observe the atmosphere at fixed times of day (e.g. 1:30 pm local time), but we need to be able to predict rapidly changing conditions that lead to thunderstorms within the space of a few hours. This work developed technology to extend satellite snapshots to predict atmospheric development by up to 6 hours, and the product has been tested by the National Weather Service.

Convective available potential energy (CAPE) is the amount of work that would be needed to move a parcel of air vertically through the entire atmosphere, and convective inhibition (CIN) is the “capping” effect of atmospheric layers that are warmer than the parcel, which prevents the air from rising further. These can be calculated from satellite observations (soundings) of temperature and humidity at different levels of the atmosphere, and can be compared to precipitation records. Areas that show increases in CAPE (air is forced upwards quickly) or reductions in CIN (less obstruction to the rising air) are more likely to be associated with areas of precipitation. These increases in CAPE (or reductions in CIN) are even larger when the precipitation increases in intensity. These results demonstrate that there is a wealth of time information contained within single time snapshots if one can extract future patterns.

For more, see the full article.

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The shifts in Arctic temperatures are exerting a profound impact on the way Arctic ecosystems function. A study revealed that the timing of spring photosynthesis onset and the magnitude of peak greenness in the tundra and taiga ecosystems have undergone significant changes in the last two decades, potentially impacting Arctic ecosystem processes and carbon cycling. Read more >

The shifts in Arctic temperatures are exerting a profound impact on the way Arctic ecosystems function. These changes encompass a number of interconnected environmental dynamics, including, but not limited to, shifts in species distribution, changes in resource availability, and alterations in seasonal phenological patterns. These consequences extend beyond local ecosystems, contributing to broader ecological and climatic changes on a global scale. Consequently, understanding and monitoring the intricate relationships between Arctic temperature fluctuations and ecosystem processes are crucial for advancing our comprehension of climate change and its far-reaching implications.

A study by Madani et al. revealed that the timing of spring photosynthesis onset and the magnitude of peak greenness in the tundra and taiga ecosystems have undergone significant changes in the last two decades, potentially impacting Arctic ecosystem processes and carbon cycling. The observed trends in phenology align with distinct ecoregions, which can be explained by species and community adaptations to climate and responses to climate changes. These responses include universal growth in regions with suitable environmental conditions and later growth in regions with temperature and moisture constraints.

For more, see the full article.

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Coastal flooding is worsening as climate change causes our global oceans to rise, increasingly threatening human and nonhuman ecosystems. Meanwhile, land is also in motion; subsidence (sinking) will worsen flooding, while uplift can alleviate it. Here we map vertical land motion across the New York City Metropolitan Area from May 2016 to March 2023. Read more >

Coastal flooding is worsening as climate change causes our global oceans to rise, increasingly threatening human and nonhuman ecosystems. Meanwhile, land is also in motion; subsidence (sinking) will worsen flooding, while uplift can alleviate it. The processes that cause this vertical land motion (comprising both uplift and subsidence) are varied, and can include both natural processes like the crustal response to the demise of the great ice sheets, and human induced effects, such as the settling of the land surface underneath construction. This diversity can lead to differences in vertical land motion from one neighborhood to the next, which can be important information for building sea-level rise resilience. Here we apply a technique called Interferometric Synthetic Aperture Radar to radar images captured by the Sentinel-1 satellite to map vertical land motion at ~30 m resolution across the New York City Metropolitan Area from May 2016 to March 2023.

Our results reveal both a broad pattern of subsidence throughout the region and many isolated hotspots of more rapid subsidence and uplift. The widespread sinking (-1.6 ±0.8 mm/yr) is in agreement with predictions from glacial isostatic adjustment models. Notable hotspots of subsidence include runway 13/31 at LaGuardia Airport (-3.7 ± 0.8 mm/yr), built on reclaimed land and actively being upgraded, and Arthur Ashe Tennis Stadium (-4.6 ± 0.8 mm/yr) which is built on a landfill and required special construction of a light-weight roof during renovation in order to reduce subsidence. Newtown Creek and Woodside in Queens are particularly vibrant hotspots of uplift that display nonlinearity indicative of anthropogenic effects. Rather than a single explanation for the uplift, it is most likely due to the combined influence of the many remediation and infrastructure projects underway in Brooklyn and Queens. Regardless of specific causes, the satellite observations alone reveal both spatial variability in vertical land motion essential for targeted sea-level rise adaptation measures, and hitherto undocumented hotspots of uplift. The upcoming launch of NISAR and land motion products from the OPERA project will continue to enhance our ability to monitor and manage a wide variety of natural and anthropogenic changes to the Earth's surface.

For more, see the full article.

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The Arctic region is heating up twice as fast as the global average. Despite relatively few plant per area compared to the temperate and tropic regions, Arctic regions are key stores of soil organic carbon (SOC) which play a major role in the greenhouse gas balance of high-latitude ecosystems. Read more >

The Arctic region is heating up twice as fast as the global average. Despite relatively few plant per area compared to the temperate and tropic regions, Arctic regions are key stores of soil organic carbon (SOC) which play a major role in the greenhouse gas balance of high-latitude ecosystems. Cold and sometimes moist environment conditions slow down the dead plant matter decomposition. This process emits the two most powerful greenhouse gases, methane (CH₄) and carbon dioxide (CO₂). While CH₄ emission accounts for a smaller component of the C balance, the climatic impact of CH₄ outweighs CO₂ (28-34 times larger Global Warming Potential on a 100-year scale), highlighting the need to jointly resolve the climatic sensitivities of CO₂ and CH₄.

Here we use a terrestrial biosphere model and field observations to understand what drives CO₂ and CH₄ exchanges in Alaska. Based on the combined CO₂ and CH₄ flux responses to climate variables, we find that 1970-present climate trends will induce positive C-climate feedback at all tundra sites (net warming effect), and negative C-climate feedback at the boreal and shrub fen sites (net cooling effect). The positive C-climate feedback at the tundra sites is predominantly driven by increased CH₄ emissions while the negative C-climate feedback at the boreal site is predominantly driven by increased CO₂ uptake (80% from slowed decomposition, and 20% from increased photosynthesis). This study demonstrates the need to jointly consider CO₂ and CH₄ biogeochemical processes – and their associated climatic sensitivities – in order to resolve the sign and magnitude of high-latitude ecosystem C-climate feedback in the coming decades.

For more, see the full article.

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Heatwaves in California occur in both dry and humid forms, but recent studies indicate a shift towards more humid events. This research focuses on two contrasting heatwaves that affected southern California in the summer of 2020. Read more >

Heatwaves in California occur in both dry and humid forms, but recent studies indicate a shift towards more humid events. This research focuses on two contrasting heatwaves that affected southern California in the summer of 2020, providing an excellent opportunity to understand the impacts of humid versus dry heatwaves on the urban environment, specifically in the greater Los Angeles metropolitan region (as shown in figure 1). The results reveal that, despite the September heatwave having higher air temperatures, the humid heat stress – quantified using wet bulb temperature linked to the risk of heat stroke – was greater in August. While dry and humid heat display different spatial patterns, three distinct spatial clusters emerge based on non-heatwave local climates. Both types of heatwaves reduced the gradient of heat stress within the city, with valley areas experiencing the most significant increase in heat stress. Notably, valley regions such as San Bernardino and Riverside faced the most severe impacts, with up to a 6±0.5℃ additional heat stress during heatwave nights.

While Southern California is no stranger to extreme heat, these findings highlight the importance of including moist heat in extreme heat warning frameworks and considering the diverse impacts of heatwaves at a fine scale within urban areas. Such insights play a crucial role in designing policies that facilitate fair and effective mitigation strategies for extreme heat, ensuring equitable protection for all communities.

For more, see the full article.

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In the quest to better understand Earth's ecosystems, scientists at Jet Propulsion Laboratory, California Institute of Technology and Oak Ridge National Laboratory, leveraging remote sensing data and Earth system modeling, have made significant strides in uncovering the global carbon costs associated with plant nutrient acquisition. Read more >

In the quest to better understand Earth's ecosystems, scientists at Jet Propulsion Laboratory, California Institute of Technology and Oak Ridge National Laboratory, leveraging remote sensing data and Earth system modeling, have made significant strides in uncovering the global carbon costs associated with plant nutrient acquisition. Plants rely on nutrients in the soil to grow, including Nitrogen (for leaf growth) and Phosphorus (for root development and growing seeds, flowers, and fruit). However, declining levels of these nutrients are detrimental to plant health. This study used cutting-edge models to understand global plant nitrogen and phosphorus uptake. The results revealed that both nutrients co-limit 80% of the global land area, with plants investing a substantial amount of energy to acquire these nutrients from multiple uptake pathways, including symbiotic relationships with fungi - mycorrhiza.

The study's findings have significant implications for our understanding of land productivity worldwide and climate change predictions. By considering the carbon costs of nutrient acquisition, the model and observations agreement increased, reducing uncertainty in predictions. The use of remote sensing data in this study has enabled a more comprehensive and accurate representation of plant nutrient acquisition in Earth system models, paving the way for improved modeling and better insights into the global carbon cycle. This research highlights the importance of leveraging remote sensing techniques to gain valuable insights into complex Earth system processes and improve our understanding of global biogeochemical cycles.

For more, see the full article:

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Land surface models are vital for predicting how our planet will respond to climate change. By combining these models with satellite-based observations – historically and in real time – we can make more accurate predictions. Read more >

Land surface models are vital for predicting how our planet will respond to climate change. By combining these models with satellite-based observations – historically and in real time – we can make more accurate predictions. Scientists at the Jet Propulsion Laboratory and California Institute of Technology have developed the Climate Modeling Alliance (CliMA) Earth System Model, representing a significant advancement in climate prediction. By successfully integrating satellite data, CliMA Land predicts the behavior of carbon and water, as well as vegetation response.

This advancement has the potential to enhance our understanding of global vegetation dynamics and contribute to more accurate climate and ecosystem models, aiding in the development of effective strategies for mitigating and adapting to climate change impacts. CliMA Land represents a promising revolution in land surface modeling, leveraging remote sensing data to improve predictions and advance our understanding of the Earth's ecosystems.

For more, see the full article:

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As concerns about climate change continue to mount, a recent study conducted by a team of scientists at Jet Propulsion Laboratory and California Institute of Technology has shed light on the effects of climate change on the Arctic-Boreal carbon balance. Read more >

As concerns about climate change continue to mount, a recent study conducted by a team of scientists at Jet Propulsion Laboratory and California Institute of Technology has shed light on the effects of climate change on the Arctic-Boreal carbon balance. In recent history, some of the excess carbon dioxide we produce is taken up by forests (called a “carbon sink”), but there is concern that this benefit may not continue as the climate changes. This study used two groups of Earth system models to predict the future of vegetation for the North American Arctic-Boreal region.

Both model group ensembles suggested a tipping point in the Net Biome Production (NBP) curve, indicating that the Arctic-Boreal ecosystems may become less productive by 2050-2080 and will stop absorbing excess carbon dioxide in the next century. This information is significant for policymakers as it highlights the urgent need for climate change mitigation and adaptation strategies to address the potential shift in the carbon balance of this vulnerable region. Further research and action are required to better understand and mitigate the impacts of climate change in the Arctic-Boreal region and protect this critical carbon sink.

For more, see the full article:

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In early 2020, as the COVID-19 pandemic spread around the globe, many countries imposed strict lockdowns to slow the spread of the coronavirus. As people stayed home, ground and air travel decreased, allowing us to study the impact on air pollution.

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In early 2020, as the COVID-19 pandemic spread around the globe, many countries imposed strict lockdowns to slow the spread of the coronavirus. As people stayed home, ground and air travel decreased, allowing us to study the impact on air pollution. Scientists at the NASA Jet Propulsion Laboratory (JPL), the Royal Netherlands Meteorological Institute (KNMI), the Japan Agency for Marine-Earth Science and Technology (JAMSTEC), and Nagoya University have collaborated to estimate the detailed changes in emissions and ozone across the world.

Of particular interest are global emissions of nitrogen oxides (NOx), which create ozone and are a danger to human health as well as a driver of climate change. The global COVID-19 lockdowns resulted in a decrease in NOx by 15% globally, with local reductions as high as 50%. This resulted in a 2% reduction of ozone in the troposphere (the lower portion of the atmosphere) by June 2020. The Intergovernmental Panel on Climate Change (IPCC), the authoritative body of international experts on climate, have proposed significant NOx emission controls to reduce global warming, but even the most aggressive of these were expected to decrease NOx by 4% over a 30-year period. The COVID-19 lockdowns temporarily achieved half of that target in just a few months.

For more, see the full article.

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The influenza, or flu, virus infects millions of people in the United States each year, mostly during winter. To help limit the impacts of seasonal flu outbreaks, scientists have been trying to anticipate when the outbreaks occur using a variety of information.

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The influenza, or flu, virus infects millions of people in the United States each year, mostly during winter. To help limit the impacts of seasonal flu outbreaks, scientists have been trying to anticipate when the outbreaks occur using a variety of information. Laboratory studies have pointed to humidity playing an important role in the transmission of flu. The average amount of water vapor in the air we breathe varies widely across the U.S., but even in the most humid areas, it begins to drop as winter approaches.

Researchers at NASA’s Jet Propulsion Laboratory and the University of Southern California have used NASA satellite data from the Atmospheric Infrared Sounder (AIRS) to illuminate the relationship between low humidity and the outbreak of flu in the U.S. They compared AIRS measurements of water vapor in the lower atmosphere with flu case estimates over a twelve year period. For each state, they identified specific levels of low humidity that signal the start of a flu outbreak. These threshold levels of low humidity vary between the states but follow regional patterns and are tied to each state’s average climate. States with humid climates, such as those in the Southeast, have higher threshold values than arid states, including those in the West and Southwest.

For more, see the full article.

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When designing a space mission, there are many parameters to take into account. There are often trade-offs between different design parameters.

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When designing a space mission, there are many parameters to take into account. For instance, a space-borne instrument will observe a swath of the Earth’s surface as it orbits, and the width of that swath depends on the instrument itself, as well as high above the Earth it flies. There are also often trade-offs between different design parameters. For example, the same instrument can be used in a mission that acquires very fine features on the surface infrequently, or it can focus on larger features but visit them more often. It can be difficult to optimize these design decisions for missions that produce many products in different science focus areas (e.g. the design best for studying water is probably not the same as the design best for land).

NASA’s Earth System Observatory will include a mission to map Earth’s Surface Biology and Geology. The mission will monitor volcanic eruptions, snow-melt, water quality, vegetation health, and more. Each of these areas prioritize different instrument parameters. This study proposed a new metric by which to optimize mission design parameters: intrinsic dimensionality. This is a mathematical metric independent of application area, and can be used to design a mission that provides the best compromise between the needs of different areas.

For more, read the full article.

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Heat stress is a rapidly increasing threat to human health and energy demand across the US and the globe. Both high temperatures and high moisture levels are important contributors to heat stress, and it is well-known that future temperature increases will generally be larger in mountains due to there being much less snow and drier soils.

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Heat stress is a rapidly increasing threat to human health and energy demand across the US and the globe. Both high temperatures and high moisture levels are important contributors to heat stress, and it is well-known that future temperature increases will generally be larger in mountains due to there being much less snow and drier soils. But with moisture increasing rapidly at low elevations, which factor will dominate the total response?

Scientists at JPL and several other institutions looked at all available high-resolution climate-model data and found that the temperature effect best explains the total change in heat stress. However, the more humid a climate zone, the more moisture matters in explaining the magnitude of the changes. These findings help understand where and why heat stress is likely to increase most dramatically, and motivate efforts to make investments and other preparations in the areas of greatest change, such as the southern Appalachians and southern Rocky Mountains.

For more, see the full article.

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Sequences of extreme climate events, or multiple ones occurring simultaneously, can have impacts that are ‘larger than the sum of their parts’.

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Sequences of extreme climate events, or multiple ones occurring simultaneously, can have impacts that are ‘larger than the sum of their parts’. For example, bad harvests in several major crop-growing areas can lead to shortages, price shocks, and malnutrition, while oscillations between wet and dry years create larger wildfire risks than dry years alone, by increasing the quantity of vegetation available for burning. Using many versions of the same climate model to represent these events increases the confidence with which scientists can draw conclusions about them.

JPL scientists and colleagues looked at 100 end-of-century climate-model outputs for specific sets of conditions that have been linked to impacts on, for example, food security and wildfire risk. The heat-and-drought levels that historically cause bad harvests will occur simultaneously in the six major corn-growing areas about every 4 years, versus every 18 years now. The chance of a very wet year followed by a very dry year will increase by about 25% in California and many other wildfire-prone areas. Such multi-part interactions are critical to take into account for societal resilience in a warming climate.

For more, see the full article.

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In the first year of the COVID-19 pandemic, measures enacted by local, state, and national governments to slow the spread of the virus also caused reductions in some human activities, such as traffic. This, in turn, led to lower emissions of greenhouse gases and air pollutants in a very short time period.

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In the first year of the COVID-19 pandemic, measures enacted by local, state, and national governments to slow the spread of the virus also caused reductions in some human activities, such as traffic. This, in turn, led to lower emissions of greenhouse gases and air pollutants in a very short time period. Changes in emissions that would normally take years or decades occurred over a few weeks, which gave a novel view into the relationship between human activities and atmospheric composition.

The Keck Institute for Space Studies launched a workshop to study these pandemic-induced changes. The results highlighted important ways climate and air quality are linked, such as reduced air pollutant emissions increasing the lifetime of methane in the atmosphere, or wildfires in the western US, which are worsening in response to climate change, causing poor air quality despite the reduction in traffic emissions. A key message was that it is becoming increasingly important to treat air quality and greenhouse gases as truly intertwined challenges.

For more, see the full article.

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As our climate changes, droughts are becoming more frequent, with more than 20% of land in the Western US currently experiencing extreme or exceptional drought.

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As our climate changes, droughts are becoming more frequent, with more than 20% of land in the Western US currently experiencing extreme or exceptional drought. Monitoring these droughts is important for farmers, resource managers, and other leaders to allocate resources to the hardest-hit regions. It is also important for scientists to understand the way that different plants respond to heat and drought, in order to predict how ecosystems may behave in the future.

Scientists at the NASA Jet Propulsion Laboratory (JPL) and the United States Department of Agriculture (USDA) have collaborated to produce a data product derived from ECOSTRESS, a thermal instrument on the International Space Station (ISS). This measure of plant stress is called evapotranspiration and is now available to the general public. The remote sensing product has been shown to be highly representative of ground conditions.

For more, see the full article.

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NOx is a family of air pollutants that are a key factor controlling amounts of both ozone and particulate matter (PM) in the air. Reducing NOx emissions has been an important strategy for improving air quality in the US for decades.

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NOx is a family of air pollutants that are a key factor controlling amounts of both ozone and particulate matter (PM) in the air. Reducing NOx emissions has been an important strategy for improving air quality in the US for decades. But emissions are only one half of the equation setting the amount of NOx in our cities’ air – its lifetime, or how long it stays in the air, is equally important.

Observation of NOx from space provide a unique insight into NOx lifetime. How quickly NOx concentrations decrease downwind of a source (such as a city) can be seen from space, and that rate of decrease gives a good measure of NOx lifetime. Scientists from UC Berkeley used NASA data to track how NOx lifetime changed in almost 50 cities across North America. They found that changes in lifetime help explain why NOx concentrations in the US haven’t been decreasing after 2010, despite evidence that emissions have continued to decrease.

For more, read the full article.

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