Atmospheric ammonia emissions are harmful to ecosystems and human health. These emissions have traditionally been monitored using thermal infrared spectrometers, though such techniques are limited by thermal contrast requirements, the coarse spatial resolution of existing satellite sensors, and low measurement frequency of higher-resolution aerial surveys. Here, we show that ammonia emissions can be quantified using shortwave infrared imaging spectroscopy, circumventing these challenges by using reflected sunlight instead of thermal emission for signal and by enabling a large class of existing and future imaging spectrometers to enter the ammonia observing system. As a proof of concept for this capability, we use Tanager-1 satellite data to quantify emissions from industrial point sources of ammonia in Pakistan and Uzbekistan.

Abstract Arctic and boreal regions are experiencing rapid environmental changes that include thawing permafrost and increasing disturbances. The NASA Arctic-Boreal Vulnerability Experiment (ABoVE) sought to better understand these changes through field, airborne, and remote sensing measurements. One key airborne instrument was the Land, Vegetation, and Ice Sensor (LVIS), a wide-swath imaging laser altimeter system. LVIS conducted 32 flights during June-August periods of 2017 and 2019, capturing data across more than 91,000 km² of diverse Arctic and boreal ecosystems. The surface topography and vegetation structure data collected throughout Alaska and Northwestern Canada spans boreal forests to Arctic tundra, crossing 12 distinct ecoregions. This airborne collection enables direct comparison with coincident NASA Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data, extends research beyond the ~52° N limit of NASA’s Global Ecosystem Dynamics Investigation (GEDI) sensor, and provides precursor data for future satellite missions, such as NASA’s recently selected Earth Dynamics Geodetic Explorer (EDGE). We summarize detailed information on LVIS data records from ABoVE deployments, including access and visualization using custom open source tools.

Abstract This study reviews the current state of spaceborne atmospheric tomography and assesses its scientific potential to address key science questions related to the carbon cycle, planetary boundary layer (PBL), and clouds and aerosols. We explore a range of hypothetical mission architectures to characterize the multidimensional trade space of tomographic performance, which depends on observation geometry, instrument characteristics, and the target field. We identified the effective vertical and horizontal reconstruction resolutions as a key parameter linking tomographic capabilities to scientific return. Our evaluation uses the newly developed JPL Tomography Simulator, which efficiently simulates tomographic reconstruction of realistic cloud and water vapor fields under various observational configurations. Simulations show that a system with 9 viewing angles within ±70° of nadir could achieve effective resolutions on the order of tens of meters for clouds and hundreds of meters for water vapor in both the horizontal and vertical. These fine-resolution volumetric observations would support detailed studies of PBL thermodynamics, entrainment processes, and three-dimensional radiative effects in clouds. The findings may extend to other atmospheric constituents, such as trace gases and aerosols, which share similar spatial structures. We further identify tomographic technology maturation targets that would improve mission formulation rigor with research program support. In particular, we highlight the promise of machine learning approaches to overcome the significant computational demands of current tomographic reconstruction techniques. These advancements have the potential to improve future observing system design and scientific return across a broad range of atmospheric disciplines. Significance Statement This study assesses the potential of atmospheric tomography for addressing key questions related to the carbon cycle, the planetary boundary layer (PBL), and clouds and aerosols. No current single technology provides the required combination of fine horizontal and vertical resolution for many science questions identified in the NASA PBL Incubation Study Team Report. Compared to proposed observing systems consisting of joint orbital and suborbital components, tomographic remote sensing could provide the necessary observations at finer resolution or at reduced cost. The main area for advancement is reducing the computational demands of current tomography algorithms, thereby maturing the method toward operational use. If successful, tomography could unlock atmospheric measurements with unprecedented resolution and spatial coverage.

Accurately quantifying carbon fluxes is critical for understanding carbon cycle dynamics and predicting carbon-climate feedbacks in rapidly-warming Arctic and boreal ecosystems. Here we evaluate net ecosystem exchange (NEE) estimates derived from three distinct approaches: atmospheric inversions, upscaled flux measurements, and terrestrial biosphere models. We leverage atmospheric CO2 observations collected during airborne campaigns organized as part of the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) and the Arctic Carbon Atmospheric Profiles (Arctic-CAP) campaigns during 2012-2014 and 2017, respectively. We find that the ability to reproduce observed atmospheric CO2 variability varies substantially across the three modeling approaches. Model-data consistency is strongly associated with estimated NEE seasonality; models showing less consistency with atmospheric observations often exhibit earlier spring onset, earlier carbon uptake peak, or delayed autumn senescence. Furthermore, we find that these NEE seasonality biases result largely from the seasonality of gross primary production and, to a lesser extent, that of heterotrophic respiration. These findings underscore the need for integrated frameworks that incorporate diverse approaches to generate carbon flux estimates that are constrained at large scales while providing detailed process-level insights. Our results provide insights to guide model refinement and highlight the need to expand in situ measurements in Arctic and boreal regions and derive region-specific constraints on the relationships among vegetation phenology, carbon fluxes and environmental drivers.

Abstract Arctic ecosystems are warming at an accelerated rate, with heatwaves becoming more frequent and intense. We examined productivity responses of Arctic ecosystems to major warming events from 2000 to 2022. Results indicated that early‐season warming generally enhances productivity, whereas prolonged high temperatures suppress productivity later in the growing season. Responses varied by region and event timing. For instance, the 2004 Northwestern North American hot year increased productivity through summer, while the 2010 Arctic warming boosted early‐season productivity that declined as heat persisted. Similar early gains followed by moisture‐driven declines during the 2015 Northwestern North American and 2020 Eastern Siberian events. Overall, early warmth stimulates growth, but sustained heat and low moisture constrain productivity, particularly in taiga and tundra ecosystems. These patterns highlight the vulnerability of Arctic carbon sinks to warming and underscore the need for improved observations and modeling to assess ecosystem resilience under increasing climate extremes.

Complex non-linear relationships exist between the permafrost thermal state, active layer thickness, and terrestrial carbon cycle dynamics In Arctic and boreal Alaska. The rate, magnitude, and extent of permafrost degradation remain uncertain, with an increasing recognition of the importance of abrupt thaw mechanisms. Similarly, large uncertainties in the rate, magnitude, timing, location, and composition of the permafrost carbon feedback complicate this issue. The challenge of monitoring sub-surface phenomena, such as the soil temperature and soil moisture profiles, with remote sensing technology further complicates the situation. There is an urgent need to understand how and to what extent permafrost degradation is destabilizing the Alaskan carbon balance and to characterize the feedbacks involved. We employ our artificial intelligence (AI)-driven model GeoCryoAI to quantify permafrost thaw dynamics and greenhouse gas emissions in Alaska. GeoCryoAI uses a hybridized multimodal deep learning architecture of stacked convolutionally layered memory-encoded bidirectional recurrent neural networks and 12.4 million parameters to simultaneously ingest and analyze 13.1 million in situ measurements (i.e., CALM, GTNP, ABoVE ReSALT, FLUXNET, NEON), 8.06 billion remote sensing airborne observations (i.e., UAVSAR, AVIRIS-NG), and 7.48 billion process-based modeling outputs (i.e., SIBBORK-TTE, TCFM-Arctic) with disparate spatiotemporal sampling and data densities. This framework introduces ecological memory components and effectively learns subtle spatiotemporal covariate complexities in high-latitude ecosystems by emulating permafrost degradation and carbon flux dynamics across Alaska with high precision and minimal loss (RMSE: 1.007cm, 0.694nmolCH4m-2s-1, 0.213µmolCO2m-2s-1). GeoCryoAI captures abrupt and persistent changes while providing a novel methodology for assimilating contemporaneous information on scales from individual sites to the pan-Arctic. Our approach overcomes traditional model inefficiencies and seamlessly resolves spatiotemporal disparities.

Given a world increasingly dominated by climate extremes, modifying the Earth’s climate with large-scale geoengineering intervention is inevitable. However, geoengineering faces a conundrum: forecasting the consequences of climate intervention accurately in a system for which we have incomplete observations and an imperfect understanding. We evaluate the global response and potential implications of mitigation and intervention deployment by utilizing CRU TS4.08 observations, ERA5 reanalysis data, and CMIP6 scenario-based UKESM0-1-LL simulations. From 1950 to 2022, global weighted mean surface temperature (Tsurf) and total precipitation (P) rose by 1.37 $$\:\pm\:$$ 0.48 °C and 0.05 $$\:\pm\:$$ 0.57 mm day-1. Significant regional Tsurf anomalies and erratic interannual variability of P were revealed, with ranges from 7.63 °C in Greenland and northern Siberia to -2.38 °C in central Africa and 1.17 mm day-1 in southern Alaska to -1.20 mm day-1 in Colombia and east Africa. Collectively, mitigation and intervention simulations tended to overestimate the variability and magnitude of Tsurf and P, exhibiting substantial regional discrepancies and scenario-specific heterogeneity when estimating atmospheric methane concentration ([CH4]). Despite capturing significant departures in Tsurf, P, and [CH₄], replicating historical P teleconnections and spatial patterns of warming remained a challenge. These results underscore regional disparities with global implications, harkening the necessity to refine existing architectures while developing novel methods to evaluate the risks and feasibility of geoengineering intervention.

The global climate is changing rapidly, with cascading impacts across the world. Even though the modern instrument-based record of Earth observations reflects decades of critical work, multi-century time series may be required to understand and forecast key elements of Earth system dynamics. Here, we review the potential uses of non-traditional climate data records—observations reported without using modern instruments or standardized measurement protocols—to identify climate and ecosystem dynamics that predate modern methodologies and tools. We compile a list of diverse datasets collected over more than 500 years, including landscape paintings, sea lore, and animal migration data. This initial review presents opportunities for further investigation to reconstruct past climate or to use non-traditional records to complement modern instrument methods.

The Western Antarctic Peninsula is undergoing rapid environmental change. Regional warming is causing increased glacial meltwater discharge, but the ecological impact of this meltwater over large spatiotemporal scales is not well understood. Here, we leverage 20 years of remote sensing data, reanalysis products, and field observations to assess the effects of sea surface glacial meltwater on phytoplankton biomass and highlight its importance as a key environmental driver for this region's productive ecosystem. We find a strong correlation between meltwater and phytoplankton chlorophyll-a across multiple time scales and datasets. We attribute this relationship to nutrient fertilization by glacial meltwater, with potential additional contribution from surface ocean stabilization associated with sea-ice presence. While high phytoplankton biomass typically follows prolonged winter sea-ice seasons and depends on the interplay between light and nutrient limitation, our results indicate that the positive effects of increased glacial meltwater on phytoplankton communities likely mitigate the negative impact of sea-ice loss in this region in recent years. Our findings underscore the critical need to consider glacial meltwater as a key ecological driver in polar coastal ecosystems.

Processes affecting the transformation of riverine dissolved organic carbon (DOC) across the land-to-ocean aquatic continuum are still poorly constrained in Arctic models, leading to large uncertainties in simulated air-sea CO2 fluxes of the coastal periphery. Here we use the ECCO-Darwin regional configuration of the Southeastern Beaufort Sea to analyze the sensitivity of simulated carbon cycling to (1) the model vertical discretization and (2) different parameterizations of Mackenzie River carbon discharge. We show that riverine DOC lifetime rather than its volume largely modulates Mackenzie River plume air-sea CO2 fluxes, leading to the Southeastern Beaufort Sea (SBS) being either a source (0.03 Tg C year(-1)) or sink (-0.20 Tg C year(-1)) of atmospheric carbon. We show that estuarine processes, such as flocculation, also play an important role and can dampen CO2 outgassing by up to 0.07 Tg C year(-1). In terms of model physics, by increasing the vertical grid resolution, we better fit observed plume structure, without altering the simulated concentrations of DOC. However, the decrease in river forcing cell volume increases local pCO(2) and promotes elevated outgassing in the vicinity of the Delta. Our work demonstrates that future Arctic land-ocean models must consider the intricate details of river plume systems to realistically simulate coastal-ocean physics and biogeochemistry.

Current biodiversity metrics derived from remote sensing data are typically applied to small local areas, require significant training data, and are not easily extensible globally. Here we propose the mathematical concept of intrinsic dimensionality (ID) as a method to quantify terrestrial vegetation variability without a need for in situ training data. We apply this technique to airborne imaging spectroscopy data from the Surface Biology and Geology High Frequency Time series (SHIFT) airborne campaign, with weekly overflights from February to May 2022 over a region in California stretching from Figueroa Mountain in the Los Padres National Forest to Point Conception and adjacent coastal areas. ID is considered in both spatial and temporal context—spatial ID represents spectral variability across a geographical region at a single time step, and temporal ID represents spectral variability over time for a single geographical location. Results show an encouraging and significant correlation between spatially calculated ID and in situ vegetation species richness data despite different spatial scales between the two ( p = 0.01). Spatial ID remained largely unchanged at each time step over the course of three months during the spring green‐up period when vegetation characteristics and spectral responses were changing rapidly (number of species remains unchanged even though spectra reflect phenological change over time). The temporal ID remained constant for pseudo‐invariant surfaces such as parking lots, roofs, and rock, but showed increased ID with time for trees, shrubs, and grasses. This robustness of spatial ID to seasonal change is desirable in any measure of species richness because it is insulated from changes in vegetation condition that are unrelated to plant species richness. Even though the spatial ID is consistent across acquisition dates, when considering the full time series (temporal ID), we find that subweekly sampling may be necessary to spectrally capture the full phenological cycle of certain vegetation types.

Permafrost degradation in the Arctic is accelerating and is forecast to enhance greenhouse gas (GHG) emissions from the large permafrost carbon pool. Earth observation has a key role in determining GHG sources and sinks, and multiple current and future missions are useful to track baseline parameters for determining GHG fluxes. NASA and ESA have initialized the Arctic Methane and Permafrost Challenge (AMPAC) as a transatlantic networking action that strives to promote related scientific work and improve observation capabilities. Key variables observable from space include methane concentrations as well as landcover properties to inform process-based models as proxy for sources as well as temperature-related constraints for microbial activity. Upcoming missions are expected to advance these capabilities significantly with increased sampling intervals through future synthetic aperture radar missions and constellations of multiple multispectral sensors. This will allow better representation of seasonality and advance methane source attribution in general. In addition, continuity of current missions, which provide GHG observations, including methane, is crucial. Hyperspectral and superspectral sensors targeting primarily landsurface observation are expected to complement methane retrievals through the identification of emission hotspots. Arctic monitoring also requires active optical instruments for concentration retrieval, a type of instrumentation that is still novel for satellite-based observations. A comprehensive portfolio of hyperspectral, passive microwave, synthetic aperture radar, altimeter and landsurface temperature, and lidar measurements in addition to imaging spectrometers will be available by 2032/2033, at the time of the International Polar Year. This will allow for advanced experiments when also accompanying in situ observations become available.

Since 2015, NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) has investigated how climate change impacts the vulnerability and/or resilience of the permafrost-affected ecosystems of Alaska and northwestern Canada. ABoVE conducted extensive surveys with the Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) during 2017, 2018, 2019, and 2022 and with AVIRIS-3 in 2023 to characterize tundra, taiga, peatlands, and wetlands in unprecedented detail. The ABoVE AVIRIS dataset comprises ~1700 individual flight lines covering ~120,000 km2 with nominal 5 m × 5 m spatial resolution. Data include individual transects to capture important gradients like the tundra-taiga ecotone and maps of up to 10,000 km2 for key study areas like the Mackenzie Delta. The ABoVE AVIRIS surveys enable diverse ecosystem science, provide crucial benchmark data for validating retrievals from the PACE, PRISMA, and EnMAP satellite sensors and help prepare for the SBG and CHIME missions. This paper guides interested researchers to fully explore the ABoVE AVIRIS spectral imagery and complements our guide to the ABoVE airborne synthetic aperture radar surveys.

Climate change in the northern circumpolar regions is rapidly thawing organic-rich permafrost soils, leading to the substantial release of dissolved CO2 and CH4 into river systems. This mobilization impacts local ecosystems and regional climate feedback loops, playing a crucial role in the Arctic carbon cycle. Here, we analyze the stable carbon (delta 13C) and radiocarbon (F14C) isotopic compositions of dissolved CO2 and CH4 in the Sagavanirktok and Kuparuk River watersheds on the North Slope, Alaska. By examining spatial and seasonal variations in these isotopic signatures, we identify patterns of carbon release and transport across the river continuum. We find consistent CO2 isotopic values along the geomorphological gradient, reflecting a mixture of geogenic and biogenic sources integrated throughout the watershed. Bayesian mixing models further demonstrate a systematic depletion in 13C and 14C signatures of dissolved CO2 sources from spring to fall, indicating increasing contributions of aged carbon as the active layer deepens. This seasonal deepening allows percolating groundwater to access deeper, older soil horizons, transporting CO2 produced by aerobic and anaerobic soil respiration to streams and rivers. In contrast, we observe no clear relationships between the 13C and 14C compositions of dissolved CH4 and landscape properties. Given the reduced solubility of CH4, which facilitates outgassing and limits its transport in aquatic systems, the isotopic signatures are likely indicative of localized contributions from streambeds, adjacent water saturated soils, and lake outflows. Our study illustrates that dissolved greenhouse gases are sensitive indicators of old carbon release from thawing permafrost and serve as early warning signals for permafrost carbon feedbacks. It establishes a crucial baseline for understanding the role of CO2 and CH4 in regional carbon cycling and Arctic environmental change.

Permafrost-affected ecosystems of the Arctic–boreal zone in northwestern North America are undergoing profound transformation due to rapid climate change. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) is investigating characteristics that make these ecosystems vulnerable or resilient to this change. ABoVE employs airborne synthetic aperture radar (SAR) as a powerful tool to characterize tundra, taiga, peatlands, and fens. Here, we present an annotated guide to the L-band and P-band airborne SAR data acquired during the 2017, 2018, 2019, and 2022 ABoVE airborne campaigns. We summarize the ∼80 SAR flight lines and how they fit into the ABoVE experimental design (Miller et al., 2023; https://doi.org/10.3334/ORNLDAAC/2150). The Supplement provides hyperlinks to extensive maps, tables, and every flight plan as well as individual flight lines. We illustrate the interdisciplinary nature of airborne SAR data with examples of preliminary results from ABoVE studies including boreal forest canopy structure from TomoSAR data over Delta Junction, AK, and the Boreal Ecosystem Research and Monitoring Sites (BERMS) area in northern Saskatchewan and active layer thickness and soil moisture data product validation. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band airborne SAR data (https://uavsar.jpl.nasa.gov/cgi-bin/data.pl).

Abstract The modern climate is changing faster and on larger spatial scales than ever in human history. Though the modern instrument-based record of Earth observations reflects decades of critical work, multi-century time series may be required to understand and forecast key elements of Earth system dynamics. Here, we explore the utility of non-traditional climate data records – observations reported without using modern instruments or standardized measurement protocols – to illuminate important patterns of climate change that predate modern methodologies and tools. We compile a list of diverse datasets collected during the past 500 years including landscape paintings, sea lore, and fish haul data. This initial review and analysis present novel possibilities for scientists across regions and disciplines to reconstruct past climate in ways that complement more traditional methods.

Phytoplankton primary production is a crucial component of Arctic Ocean (AO) biogeochemistry, playing a pivotal role in the carbon cycling by supporting higher trophic levels and removing atmospheric carbon dioxide. The advent of satellite observations measuring chlorophyll a concentration (Chl_ a) has yielded unprecedented insights into the distribution of AO phytoplankton, enhancing our ability to assess oceanic productivity. However, the optical properties of AO waters differ significantly from those of lower‐latitude waters, and standard Chl_a algorithms perform poorly in the AO. In particular, Chl_a retrievals are challenged by interferences from other marine constituents including higher pigment packaging and higher proportion of light absorption by colored dissolved organic matter. To derive phytoplankton-originating signature as well as mitigate those effects, solar-induced chlorophyll fluorescence (SIF) emerges as a valuable tool for acquiring physiological insights into the direct photosynthetic processes in the AO. In this study, we leverage satellite-based SIF measurements to assess their correlation with a set of predictive factors influencing phytoplankton photosynthesis. We extend the temporal coverage of AO SIF data to cover the period 2004 - 2020. This novel dataset offers a pathway to monitor the physiological interactions of phytoplankton with changes in climate, promising to significantly improve our understanding of the Arctic water’s productivity. The application of this data is expected to provide insights into how phytoplankton respond to shifts in environmental changes, contributing to a more nuanced understanding of their role in High-Latitude Northern Oceans ecosystems.

Since 2007, the National Academy for Sciences Engineering and Medicine (NASEM) has recommended Earth Science research and investment priorities every 10 years. The Decadal Survey balances the continuation of essential climate variable time series against unmet measurement needs and new Earth Observations made possible by technological breakthroughs. The next survey (2027–2028, DS28) will be framed by a rapidly changing world, and it will be critical to anticipate the observational needs of the 2030s–2040s, a world increasingly dominated by climate extremes and a rapidly changing Earth system. Here, we highlight some of the changes that factor into a framework for the DS28.

The Arctic is warming four times faster than the global average, resulting in widespread ground thaw and state changes. Due to the rapid rate and large scale of ecosystem shifts, identifying and understanding Arctic boreal zone changes and feedbacks requires frequent observations across multiple scales. The last decade has witnessed significant increases in the number and coverage of in-situ, airborne, and satellite observations. However, additional resolution, coverage, and sustained, long-term time series data records are urgently required to characterize and understand the considerable heterogeneity of Northern permafrost environments. Here, we review the physical and technical gaps that limit the ability to detect rapid state changes and tipping points in permafrost ecosystems. Understanding and accurately forecasting changes to the Arctic is an essential component of managing climate change in this rapidly transforming system.