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Multiple metrics have been proposed and utilized to assess the performance of linear Bayesian and geostatistical inverse problems. These metrics are mostly related to assessing reduction in prior uncertainties, comparing modeled observations to true observations, and checking distributional assumptions. These metrics though important should be augmented with sensitivity analysis to obtain a comprehensive understanding of the performance of inversions and critically improve confidence in the estimated fluxes. With this motivation, we derive analytical forms of the local sensitivities with respect to the number of inputs such as measurements, covariance parameters, covariates, and forward operator or jacobian. In addition to local sensitivity, we develop a framework for global sensitivity analysis that shows the apportionment of the uncertainty of different inputs to an inverse problem. The proposed framework is applicable to any other domain that employs linear Bayesian and geostatistical inverse methods. We show the application of our methodology in the context of an atmospheric inverse problem for estimating urban GHG emissions in Los Angeles. Within its context, we also propose a mathematical framework to construct correlation functions and components of uncertainty matrices from a pre-computed jacobian that encompasses non-stationary structures.

The contribution of soil heterotrophic respiration to the boreal–Arctic carbon (CO2) cycle and its potential feedback to climate change remains poorly quantified. We developed a remote-sensing-driven permafrost carbon model at intermediate scale (∼1 km) to investigate how environmental factors affect the magnitude and seasonality of soil heterotrophic respiration in Alaska. The permafrost carbon model simulates snow and soil thermal dynamics and accounts for vertical soil carbon transport and decomposition at depths up to 3 m below the surface. Model outputs include soil temperature profiles and carbon fluxes at 1 km resolution spanning the recent satellite era (2001–2017) across Alaska. Comparisons with eddy covariance tower measurements show that the model captures the seasonality of carbon fluxes, with favorable accuracy in simulating net ecosystem CO2 exchange (NEE) for both tundra (R>0.8, root mean square error (RMSE – 0.34 g C m−2 d−1), and boreal forest (R>0.73; RMSE – 0.51 g C m−2 d−1). Benchmark assessments using two regional in situ data sets indicate that the model captures the complex influence of snow insulation on soil temperature and the temperature sensitivity of cold-season soil heterotrophic respiration. Across Alaska, we find that seasonal snow cover imposes strong controls on the contribution from different soil depths to total soil heterotrophic respiration. Earlier snowmelt in spring promotes deeper soil warming and enhances the contribution of deeper soils to total soil heterotrophic respiration during the later growing season, thereby reducing net ecosystem carbon uptake. Early cold-season soil heterotrophic respiration is closely linked to the number of snow-free days after the land surface freezes (R=-0.48, p), i.e., the delay in snow onset relative to surface freeze onset. Recent trends toward earlier autumn snow onset in northern Alaska promote a longer zero-curtain period and enhanced cold-season respiration. In contrast, southwestern Alaska shows a strong reduction in the number of snow-free days after land surface freeze onset, leading to earlier soil freezing and a large reduction in cold-season soil heterotrophic respiration. Our results also show nonnegligible influences of subgrid variability in surface conditions on the model-simulated CO2 seasonal cycle, especially during the early cold season at 10 km scale. Our results demonstrate the critical role of snow cover affecting the seasonality of soil temperature and respiration and highlight the challenges of incorporating these complex processes into future projections of the boreal–Arctic carbon cycle.

Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO2 later in the season.

Postoperative adhesions represent a frequent complication of abdominal surgery. Adhesions can result from infection, ischemia, and foreign body reaction, but commonly develop after any surgical procedure. The morbidity caused by adhesions affects quality of life and, therefore, it is paramount to continue to raise awareness and scientific recognition of the burden of adhesions in healthcare and clinical research. This 2021 Global Expert Consensus Group worked together to produce consented statements to guide future clinical research trials and advise regulatory authorities. It is critical to harmonize the expectations of research, to both develop and bring to market improved anti-adhesion therapies, with the ultimate, shared goal of improved patient outcomes.

Estimating the impacts of climate change on the global carbon cycle relies on projections from Earth system models (ESMs). While ESMs currently project large warming in the high northern latitudes, the magnitude and sign of the future carbon balance of Arctic-Boreal ecosystems are highly uncertain. The new generation of increased complexity ESMs in the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC AR6) is intended to improve future climate projections. Here, we benchmark the Coupled Model Intercomparison Project (CMIP) 5 and 6 (8 CMIP5 members and 12 CMIP6 members) with the International Land Model Benchmarking (ILAMB) tool over the region of NASA’s Arctic-Boreal vulnerability experiment (ABoVE) in North America. We show that the projected average net biome production (NBP) in 2100 from CMIP6 is higher than that from CMIP5 in the ABoVE domain, despite the model spread being slightly narrower. Overall, CMIP6 shows better agreement with contemporary observed carbon cycle variables (photosynthesis, respiration, biomass) than CMIP5, except for soil carbon and turnover time. Although both CMIP ensemble members project the ABoVE domain will remain a carbon sink by the end of the 21st century, the sink strength in CMIP6 increases with CO2 emissions. CMIP5 and CMIP6 ensembles indicate a tipping point defined here as a negative inflection point in the NBP curve by 2050–2080 independently of the shared socioeconomic pathway (SSP) for CMIP6 or representative concentration pathway (RCP) for CMIP5. The model ensembles therefore suggest that, if the carbon sink strength keeps declining throughout the 21st century, the Arctic-Boreal ecosystems in North America may become a carbon source over the next century.

Intrinsic dimensionality (ID) provides an objective metric with which to quantify the number of detectable signal components in a spectroscopic image. Here, we use ID to illustrate the information gained by fusing spectroscopic data acquired over different wavelength ranges. For Cuprite, a mineral-rich site in the Nevada desert, the signal content from visible to short-wave infrared (VSWIR) describes almost entirely different signal content from the thermal infrared (TIR). Due to the extremely limited number of coincident VSWIR and TIR acquisitions previously acquired, this article provides a unique opportunity to quantify the information content gained by adding TIR acquisitions to the more commonly acquired VSWIR data. We highlight the importance of combined VSWIR/TIR imaging for the complete characterization and mapping of mineral and other sites.

The northern high latitude (NHL, 40°N to 90°N) is where the second peak region of gross primary productivity (GPP) other than the tropics. The summer NHL GPP is about 80% of the tropical peak, but both regions are still highly uncertain (Norton et al. 2019, https://doi.org/10.5194/bg-16-3069-2019). Carbonyl sulfide (OCS) provides an important proxy for photosynthetic carbon uptake. Here we optimize the OCS plant uptake fluxes across the NHL by fitting atmospheric concentration simulation with the GEOS-CHEM global transport model to the aircraft profiles acquired over Alaska during NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (2012-2015). We use the empirical biome-specific linear relationship between OCS plant uptake flux and GPP to derive the six plant uptake OCS fluxes from different GPP data. Such GPP-based fluxes are used to drive the concentration simulations. We evaluate the simulations against the independent observations at two ground sites of Alaska. The optimized OCS fluxes suggest the NHL plant uptake OCS flux of -247 Gg S year

Wetland methane (CH₄) emissions comprise about one-third of the global CH₄ source. The latitudinal distribution and climate sensitivity of wetland CH₄ fluxes are the key determinants of the global CH₄-climate feedback. However, large differences exist between bottom-up estimates, informed by ground-based flux measurements, and top-down estimates derived from spaceborne total column CH₄. Despite the extensive coverage of satellite CH₄ concentration observations, challenges remain with using top-down estimates to test bottom-up models, mainly because of the uncertainties in the satellite retrievals, the model representation errors, the variable prior emissions, and the confounding role of the posterior error covariance structures. Here, we use satellite-based top-down CH₄ flux estimates (2010–2012) to test and refine 42 bottom-up estimates of wetland emissions that use a range of hypothesized wetland extents and process controls. Our comparison between bottom-up models and satellite-based fluxes innovatively accounts for cross-correlations and spatial uncertainties typically found in top-down inverse estimates, such that only the information from satellite observations and the atmospheric transport model is kept as a constraint. We present a satellite-constrained wetland CH₄ ensemble product derived from assembling the highest-performance bottom-up models, which estimates global wetland CH₄ emissions of 148 (117–189, 5th–95th percentile) Tg CH₄ yr⁻¹. We find that tropical wetland emissions contribute 72% (63%–85%) to the global wetland total. We also find that a lower-than-expected temperature sensitivity agrees better with atmospheric CH₄ measurements. Overall, our approach demonstrates the potential for using satellites to quantitatively refine bottom-up wetland CH₄ emission estimates, their latitudinal distributions, and their sensitivity to climate.

The inventory and variability of oceanic dissolved inorganic carbon (DIC) is driven by the interplay of physical, chemical, and biological processes. Quantifying the spatiotemporal variability of these drivers is crucial for a mechanistic understanding of the ocean carbon sink and its future trajectory. Here, we use the Estimating the Circulation and Climate of the Ocean-Darwin ocean biogeochemistry state estimate to generate a global-ocean, data-constrained DIC budget and investigate how spatial and seasonal-to-interannual variability in three-dimensional circulation, air-sea CO2 flux, and biological processes have modulated the ocean sink for 1995–2018. Our results demonstrate substantial compensation between budget terms, resulting in distinct upper-ocean carbon regimes. For example, boundary current regions have strong contributions from vertical diffusion while equatorial regions exhibit compensation between upwelling and biological processes. When integrated across the full ocean depth, the 24-year DIC mass increase of 64 Pg C (2.7 Pg C year−1) primarily tracks the anthropogenic CO2 growth rate, with biological processes providing a small contribution of 2 (1.4 Pg C). In the upper 100 m, which stores roughly 13 (8.1 Pg C) of the global increase, we find that circulation provides the largest DIC gain (6.3 Pg C year−1) and biological processes are the largest loss (8.6 Pg C year−1). Interannual variability is dominated by vertical advection in equatorial regions, with the 1997–1998 El Niño-Southern Oscillation causing the largest year-to-year change in upper-ocean DIC (2.1 Pg C). Our results provide a novel, data-constrained framework for an improved mechanistic understanding of natural and anthropogenic perturbations to the ocean sink. © 2022. The Authors.

On Jan. 5, 2018, at the request of NASA, the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Geological Survey (USGS), the Committee on the Decadal Survey for Earth Science and Applications from Space (ESAS) of the National Academies of Sciences, Engineering and Medicine (NASEM) Space Studies Board, Division on Engineering and Physical Sciences released the 2017 Decadal Survey, “Thriving on Our Changing Planet: A Decadal Strategy for Earth Observations from Space” [1]. The 700-page document is the second such Earth sciences survey produced by NASEM. The first, “Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond,” was released in 2007. The 2018 study designated a global “Surface Biology and Geology” (SBG) investigation that would include both imaging spectroscopy and thermal infrared observations [1]. This suite of measurements would address a wide range of global science questions. Its themes include: flows of energy, carbon, water, and nutrients sustaining terrestrial and marine ecosystems; the variability of the land surface and the fluxes of water and energy; inventory of the world's volcanoes, and the composition and temperature of volcanic products immediately following eruptions; other natural hazards including wildfires; snow accumulation and melt; water balance from the headwaters to the continent; land and water use effects on evapotranspiration; functional traits and diversity of terrestrial and aquatic ecosystems and vegetation; and more. Figure 1 shows example spectra from these surfaces, illustrating the enormous diversity of scene content that would be observed. Tables 1 and 2 show examples of the core and higher-level products that the SBG mission would produce.

We used the full polarimetric L-band and P-band SAR data collected from recent NASA Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign to understand the sensitivity of longwave radar backscatter intensity and phase to the post-fire recovery process of Arctic tundra. The 2007 Anaktuvuk River fire was used as a case study. At 10-years post-fire, we observed a strong increase (>∼4 dB) in both the P- and L-band radar backscatter in the severely burned areas, in contrast to limited backscatter differences (VV, VH) between burned and unburned areas at C-band. The polarimetric target decomposition analysis indicated a general trend towards more random surface scattering, and strong increases of the double-bounce and volumetric scattering power at both P- and L-band in the burned areas. Large differences were also observed in the Pauli phase angle and the dominant-scattering-type Touzi phase angle between burned and adjacent unburned areas. The above changes are likely caused by increasing surface roughness and microtopography due to thermokarst development and ice degradation, and increasing subsurface scattering due to an overall drier and deeper active layer in the burned areas.

Responses to COVID‐19 have resulted in unintended reductions of city‐scale carbon dioxide (CO2) emissions. Here, we detect and estimate decreases in CO2 emissions in Los Angeles and Washington DC/Baltimore during March and April 2020. We present three lines of evidence using methods that have increasing model dependency, including an inverse model to estimate relative emissions changes in 2020 compared to 2018 and 2019. The March decrease (25%) in Washington DC/Baltimore is largely supported by a drop in natural gas consumption associated with a warm spring whereas the decrease in April (33%) correlates with changes in gasoline fuel sales. In contrast, only a fraction of the March (17%) and April (34%) reduction in Los Angeles is explained by traffic declines. Methods and measurements used herein highlight the advantages of atmospheric CO2 observations for providing timely insights into rapidly changing emissions patterns that can empower cities to course‐correct CO2 reduction activities efficiently., Key Points Atmospheric CO2 observations can be used to detect the onset of the COVID‐19 response in Los Angeles and Washington DC/BaltimoreRelative reductions in April 2020 associated with COVID‐19 are ∼30% when compared to emissions in 2018 and 2019Decreases in vehicular traffic do not completely explain observed emissions reductions in both Los Angeles and Washington DC/Baltimore

The increase in wildfire occurrence and severity seen over the past decades in the boreal and Arctic biomes is expected to continue in the future in response to rapid climate change in this region. Recent studies documented positive trends in gross primary productivity (GPP) for Arctic boreal biomes driven by warming, but it is unclear how GPP trends are affected by wildfires. Here, we used satellite vegetation observations and environmental data with a diagnostic GPP model to analyze recovery from large fires in Alaska over the period 2000–2019. We confirmed earlier findings that warmer-than-average years provide favorable climate conditions for vegetation growth, leading to a GPP increase of 1 Tg C yr−1, contributed mainly from enhanced productivity in the early growing season. However, higher temperatures increase the risk of wildfire occurrence leading to direct carbon loss over a period of 1–3 years. While mortality related to severe wildfires reduce ecosystem productivity, post-fire productivity in moderately burned areas shows a significant positive trend. The rapid GPP recovery following fires reported here might be favorable for maintaining the region's net carbon sink, but wildfires can indirectly promote the release of long-term stored carbon in the permafrost. With the projected increase in severity and frequency of wildfires in the future, we expect a reduction of GPP and therefore amplification of climate warming in this region.

Anthropogenic carbon dioxide (CO₂) emissions dominate uncertainties in the global carbon budget. Global inventories, such as the National Greenhouse Gas Inventories, have latencies of 12–24 months and may not keep pace with rapidly changing infrastructure, particularly in the developing world. Our work reveals that airborne and satellite imaging spectrometers provide 3–30 m spatial resolution and accurate quantification of CO₂ emissions at the facility scale. Examples from 17 coal and gas fired power plants across the United States demonstrate robust correlation and 21% agreement on average between our remotely sensed estimates and simultaneous in situ measured emissions. We highlight four examples of coal-fired power plants in India, Poland, and South Korea, where we quantify significant carbon dioxide emissions from power plants where limited public emissions data exist. Leveraging previous work on methane (CH₄) plume detection, we present a strategy to exploit joint CO₂ and CH₄ plume imaging to quantify carbon emissions across widely distributed industrial infrastructure, including facilities that co-emit CO₂ and CH₄. We show an example of a coal operation, where we attribute 25% of greenhouse gas emissions to coal extraction (CH₄) and the remaining 75% to energy generation (CO₂). Satellite spectrometers could track high emitting coal-fired power plants that collectively contribute to 60% or more of global coal CO₂ emissions. Multiple revisits and coordinated targeting of these high emitting facilities by multiple spaceborne instruments will be key to reducing uncertainties in global anthropogenic CO₂ emissions and supporting emissions mitigation strategies.

High-resolution bottom-up estimation provides a detailed guide for city greenhouse gas mitigation options, offering details that can increase the economic efficiency of emissions reduction options and synergize with other urban policy priorities at the human scale. As a critical constraint to urban atmospheric CO2 inversion studies, bottom-up spatiotemporally explicit emissions data products are also necessary to construct comprehensive urban CO2 emission information systems useful for trend detection and emissions verification. The “Hestia Project” is an effort to provide bottom-up granular fossil fuel (FFCO2) emissions for the urban domain with building/street and hourly space–time resolution. Here, we report on the latest urban area for which a Hestia estimate has been completed – the Los Angeles megacity, encompassing five counties: Los Angeles County, Orange County, Riverside County, San Bernardino County and Ventura County. We provide a complete description of the methods used to build the Hestia FFCO2 emissions data product for the years 2010–2015. We find that the LA Basin emits 48.06 (±5.3) MtC yr−1, dominated by the on-road sector. Because of the uneven spatial distribution of emissions, 10 % of the largest-emitting grid cells account for 93.6 %, 73.4 %, 66.2 %, and 45.3 % of the industrial, commercial, on-road, and residential sector emissions, respectively. Hestia FFCO2 emissions are 10.7 % larger than the inventory estimate generated by the local metropolitan planning agency, a difference that is driven by the industrial and electricity production sectors. The detail of the Hestia-LA FFCO2 emissions data product offers the potential for highly targeted, efficient urban greenhouse gas emissions mitigation policy. The Hestia-LA v2.5 emissions data product can be downloaded from the National Institute of Standards and Technology repository (https://doi.org/10.18434/T4/1502503, Gurney et al., 2019).

Legislation in the State of California mandates reductions in emissions of short‐lived climate pollutants of 40% from 2013 levels by 2030 for CH_4. Identification of the sector(s) responsible for these emissions and their temporal and spatial variability is a key step in achieving these goals. Here, we determine the emissions of CH_4 in Los Angeles from 2011–2017 using a mountaintop remote sensing mapping spectrometer. We show that the pattern of CH_4 emissions contains both seasonal and nonseasonal contributions. We find that the seasonal component peaks in the winter and is correlated (R^2 = 0.58) with utility natural gas consumption from the residential and commercial sectors and not from the industrial and gas‐fired power plant sectors. The nonseasonal component is (22.9 ± 1.4) Gg CH_4/month. If the seasonal correlation is causal, about (1.4 ± 0.1)% of the commercial and residential natural gas consumption in Los Angeles is released into the atmosphere.

AIM-North is a proposed satellite mission that would provide observations of unprecedented frequency and density for monitoring northern greenhouse gases (GHGs), air quality (AQ) and vegeta...

Preprocessing has long been a standard practice in NIR spectroscopy, likely because it was a key component to the earliest applications of NIR technology in the food and agricultural industry in the 1960s. As applied NIR technology and enabling technologies have matured over the past several decades, so have the scope and complexity of preprocessing options that are at the method developer’s disposal. Much like a customer shopping for a solution to a specific problem, this expansion is a double-edged sword for the NIR method developer: The presence of more options improves the chances of finding a more effective solution to one’s specific problem, but it also challenges the method developer to better understand and differentiate the attributes of these options. With the above in mind, the objective of this chapter is the following: to allow NIR method developers to better understand, and thus to better assess, the value of the ever-expanding repertoire of preprocessing solutions.

Responses to COVID-19 have resulted in unintended reductions of city-scale carbon dioxide (CO2) emissions. Here we detect and estimate decreases in CO2 emissions in Los Angeles and Washington DC/Baltimore during March and April 2020. Our analysis uses three lines of evidence with increasing model dependency. The first detects the timing of emissions declines using the variability in atmospheric CO2 observations, the second assesses the continuation of reduced emissions using CO2 enhancements, and the third employs an inverse model to estimate the relative emissions changes in 2020 compared to 2018 and 2019. Emissions declines began in mid-March in both cities. The March decrease (25%) in Washington DC/Baltimore is largely supported by a drop in natural gas consumption associated with a warm spring whereas the decrease in April (33%) correlates with changes in gasoline fuel sales, a proxy for vehicular emissions. In contrast, only a fraction of the March (17%) and April (34%) reduction in Los Angeles is explained by traffic declines, while the remainder of the emissions reduction remains unexplained. To help diagnose such observed changes in emissions, more reliable, publicly available emission information from all significant sectors needs to be made available. Methods and measurements used herein highlight the advantages of atmospheric CO2 observations for providing timely insights into rapidly changing urban emissions patterns that can empower cities to course-correct mitigation activities more efficiently.

Thermokarst lakes accelerate deep permafrost thaw and the mobilization of previously frozen soil organic carbon. This leads to microbial decomposition and large releases of carbon dioxide (CO2) and methane (CH4) that enhance climate warming. However, the time scale of permafrost-carbon emissions following thaw is not well known but is important for understanding how abrupt permafrost thaw impacts climate feedback. We combined field measurements and radiocarbon dating of CH4 ebullition with (a) an assessment of lake area changes delineated from high-resolution (1–2.5 m) optical imagery and (b) geophysical measurements of thaw bulbs (taliks) to determine the spatiotemporal dynamics of hotspot-seep CH4 ebullition in interior Alaska thermokarst lakes. Hotspot seeps are characterized as point-sources of high ebullition that release 14C-depleted CH4 from deep (up to tens of meters) within lake thaw bulbs year-round. Thermokarst lakes, initiated by a variety of factors, doubled in number and increased 37.5% in area from 1949 to 2009 as climate warmed. Approximately 80% of contemporary CH4 hotspot seeps were associated with this recent thermokarst activity, occurring where 60 years of abrupt thaw took place as a result of new and expanded lake areas. Hotspot occurrence diminished with distance from thermokarst lake margins. We attribute older 14C ages of CH4 released from hotspot seeps in older, expanding thermokarst lakes (14CCH4 20 079 ± 1227 years BP, mean ± standard error (s.e.m.) years) to deeper taliks (thaw bulbs) compared to younger 14CCH4 in new lakes (14CCH4 8526 ± 741 years BP) with shallower taliks. We find that smaller, non-hotspot ebullition seeps have younger 14C ages (expanding lakes 7473 ± 1762 years; new lakes 4742 ± 803 years) and that their emissions span a larger historic range. These observations provide a first-order constraint on the magnitude and decadal-scale duration of CH4-hotspot seep emissions following formation of thermokarst lakes as climate warms.

Measurements of Δ(14)C and CO(2) can cleanly separate biogenic and fossil contributions to CO(2) enhancements above background. Our measurements of these tracers in air around Los Angeles in 2015 reveal high values of fossil CO(2) and a significant and seasonally varying contribution of CO(2) from the urban biosphere. The biogenic CO(2) is composed of sources such as biofuel combustion and human metabolism and an urban biospheric component likely originating from urban vegetation, including turf and trees. The urban biospheric component is a source in winter and a sink in summer, with an estimated amplitude of 4.3 parts per million (ppm), equivalent to 33% of the observed annual mean fossil fuel contribution of 13 ppm. While the timing of the net carbon sink is out of phase with wintertime rainfall and the sink seasonality of Southern California Mediterranean ecosystems (which show maximum uptake in spring), it is in phase with the seasonal cycle of urban water usage, suggesting that irrigated urban vegetation drives the biospheric signal we observe. Although 2015 was very dry, the biospheric seasonality we observe is similar to the 2006–2015 mean derived from an independent Δ(14)C record in the Los Angeles area, indicating that 2015 biospheric exchange was not highly anomalous. The presence of a large and seasonally varying biospheric signal even in the relatively dry climate of Los Angeles implies that atmospheric estimates of fossil fuel–CO(2) emissions in other, potentially wetter, urban areas will be biased in the absence of reliable methods to separate fossil and biogenic CO(2).

The last five years have seen dramatic growth in the use of Visible Shortwave Infrared (VSWIR) and Thermal Infrared (TIR) imaging spectrometers to detect and characterize greenhouse methane sources. Targets include: dairy and animal husbandry emissions; landfills; fossil fuel extraction, storage, and transport infrastructure; geologic sources; natural emissions associated with sensitive arctic ecosystems; and more. These campaigns have resulted in significant new discoveries and advances in our understanding of the North American CH4 budget. Recent algorithm improvements have been critical for these campaigns, enabling robust statistical CH 4 measurement, fully-automated image-space source identification, and quantification of flux. Here we survey recent campaigns by NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) and NASA's Hyperspectral Thermal Emission Spectrometer (HyTES). We describe their algorithmic advances and major findings.

The Arctic Carbon Atmospheric Profiles (Arctic-CAP) project conducted six airborne surveys of Alaska and northwestern Canada between April and November 2017 to capture the spatial and temporal gradients of northern high-latitude carbon dioxide (CO2), methane (CH4) and carbon monoxide (CO) as part of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE). The Arctic-CAP sampling strategy involved acquiring vertical profiles of CO2, CH4 and CO from the surface to 5 km altitude at 25 sites around the ABoVE domain on a 4- to 6-week time interval. We observed vertical gradients of CO2, CH4 and CO that vary by eco-region and duration of the sampling period, which spanned the majority of the seasonal cycle. All Arctic-CAP measurements were compared to a global simulation using the Goddard Earth Observing System (GEOS) modeling system. Comparisons with GEOS simulations of atmospheric CO2, CH4 and CO highlight the potential of these multi-species observations to inform improvements in surface flux estimates and the representation of atmospheric transport. GEOS simulations provide estimates of the near surface average CO2 and CH4 enhancements that are well correlated with aircraft observations (R=0.74 and R=0.60 respectively), suggesting that GEOS has reasonable fidelity over this complex and heterogeneous region. This model-data comparison over the ABoVE domain reveals that while current state-of-the-art models and flux estimates are able to capture broadscale spatial and temporal patterns in near-surface CO2 and CH4 concentrations, more work is needed to resolve fine-scale flux features that are observed. The study also provides a framework for benchmarking a global model at regional scales, which is needed to use climate models as tools to investigate high-latitude carbon-climate feedbacks.

Objective To establish an endometrioma treatment paradigm (decision tree) in the treatment of an ovarian endometrioma through the review of current literature. Data Sources A thorough literature search, including PubMed, Google Scholar, and the Cochrane Library, was performed from April 2020 to July 2020. The review was completed by using the following keywords: • Endometriosis and Pain (pre and post-surgery) • Endometriosis and Infertility (pre and post-surgery) • Endometriosis and ART (with and without surgery) • Endometriomas and IVF (with and without surgery) • Effect of endometriomas on implantation • Effect of endometriomas on antral follicle count (pre and post-surgery) • Evaluating the complex ovarian cyst (to rule out malignancy) • Resistive index in evaluation of the ovarian cyst • Endometriosis and BCL6 All relevant articles were assessed. The references of the assessed articles were then reviewed, and if pertinent, evaluated. Methods of Study Selection Articles published in English that addressed the endometrioma in regard to the following were included: (1) diagnosis, (2) treatment of pain on the basis of size and/or surgical intervention, (3) treatment of fertility on the basis of size and/or surgical intervention, (4) surgical technique, (5) in vitro fertilization success on the basis of size and/or surgical intervention, (6) risk of rupture at the time of egg retrieval, (7) impact on the antimullerian hormone and antral follicle count postsurgery, and (8) impact on implantation. Tabulation, Integration, and Results Fifty-six articles were included in this systematic review. While conducting this literature review, several themes were noted. In general, the literature on the ovarian endometrioma seems to be homogeneous in regard to imaging the endometrioma, excision rather than desiccation for an endometrioma ≥3-cm causing pain and/or infertility, minimal use of bipolar energy at the time of ovarian surgery, and risk of severe infection secondary to inadvertent rupture of cysts during egg retrieval. Conversely, studies on the ovarian endometrioma are much more heterogeneous in terms of surgery and assisted reproductive technology, that is, whether surgery should be performed. Certainly, an endometrioma ≥5-cm should be excised before assisted reproductive technology. Moreover, it seems that the antral follicle count and implantation may be enhanced with surgery. Conclusion By completing an extensive literature review, an easy-to-use algorithm for the diagnosis, evaluation, and treatment of endometriomas was developed to help clinicians in their treatment of patients with endometriosis in the short and long terms.

Methane is the second most important anthropogenic greenhouse gas in the Earth's atmosphere. Reducing methane emissions is consequently an important element in limiting the global temperature increase below 2 °C compared to preindustrial times. Therefore, a good knowledge of source strengths and source locations is required. Anthropogenic methane emissions often originate from point sources or small areal sources, such as fugitive emissions at oil and gas production sites or landfills. Airborne remote sensing instruments such as the Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) with meter scale imaging capabilities are able to yield information about the locations and magnitudes of methane sources, especially in areas with many potential emission sources. To extract methane column enhancement information from spectra recorded with the AVIRIS-NG instrument, different retrieval algorithms have been used, e.g. the matched filter (MF) or the Iterative Maximum A Posteriori DOAS (IMAP-DOAS) retrieval. The WFM-DOAS algorithm, successfully applied to AVIRIS-NG data in this study, fills a gap between those retrieval approaches by being a fast, non-iterative algorithm based on a first order approximation of the Lambert-Beer law, which calculates the change in gas concentrations from deviations from one background radiative transfer calculation using precalculated weighting functions specific to the state of the atmosphere during the overflight. This allows the fast quantitative processing of large data sets. Although developed for high spectral resolution measurements from satellite instruments such as SCIAMACHY, TROPOMI and the MAMAP airborne sensor, the algorithm can be applied well to lower spectral resolution AVIRIS-NG measurements. The data set examined here was recorded in Canada over different gas and coal extraction sites as part of the larger Arctic Boreal Vulnerability Experiment (ABoVE) Airborne Campaign in 2017. The noise of the retrieved CH4 imagery over bright surfaces (> 1 μW cm−2 nm−1 sr−1 at 2140 nm) was typically ±2.3 % of the background total column of CH4 when fitting strong absorption lines around 2300 nm, but could reach over ±5 % for darker surfaces (

Top-down estimates of CO2 fluxes are typically constrained by either surface-based or space-based CO2 observations. Both of these measurement types have spatial and temporal gaps in observational coverage that can lead to differences in inferred fluxes. Assimilating both surface-based and space-based measurements concurrently in a flux inversion framework improves observational coverage and reduces sampling related artifacts. This study examines the consistency of flux constraints provided by these different observations and the potential to combine them by performing a series of 6-year (2010?2015) CO2 flux inversions. Flux inversions are performed assimilating surface-based measurements from the in situ and flask network, measurements from the Total Carbon Column Observing Network (TCCON), and space-based measurements from the Greenhouse Gases Observing Satellite (GOSAT), or all three data sets combined. Combining the data sets results in more precise flux estimates for subcontinental regions relative to any of the data sets alone. Combining the data sets also improves the accuracy of the posterior fluxes, based on reduced root-mean-square differences between posterior flux-simulated CO2 and aircraft-based CO2 over midlatitude regions (0.33?0.56?ppm) in comparison to GOSAT (0.37?0.61?ppm), TCCON (0.50?0.68?ppm), or in situ and flask measurements (0.46?0.56?ppm) alone. These results suggest that surface-based and GOSAT measurements give complementary constraints on CO2 fluxes in the northern extratropics and can be combined in flux inversions to improve constraints on regional fluxes. This stands in contrast with many earlier attempts to combine these data sets and suggests that improvements in the NASA Atmospheric CO2 Observations from Space (ACOS) retrieval algorithm have significantly improved the consistency of space-based and surface-based flux constraints.

The contribution of soil heterotrophic respiration to the boreal-Arctic carbon (CO2) cycle and its potential feedback to climate change remain poorly quantified. We developed a remote sensing driven permafrost carbon model at intermediate scale (~ 1 km) to investigate how environmental factors affect the magnitude and seasonality of soil heterotrophic respiration in Alaska. The permafrost carbon model simulates snow and soil thermal dynamics, and accounts for vertical soil carbon transport and decomposition at depths up to 3 m below surface. Model outputs include soil temperature profiles and carbon fluxes at 1-km resolution spanning the recent satellite era (2001–2017) across Alaska. Comparisons with eddy covariance tower measurements show that the model captures the seasonality of carbon fluxes, with favorable accuracy in predicting net ecosystem CO2 exchange (NEE) in both tundra (R > 0.8, RMSE = 0.34 g C m−2 d−1) and boreal forest (R > 0.73, RMSE = 0.51 g C m−2 d−1). Benchmark assessments using two regional in-situ datasets indicate that the model captures the complex influence of snow insulation on soil temperature, and the temperature sensitivity of cold-season soil respiration. Across Alaska, we find that seasonal snow cover imposes strong controls on the contribution from different soil depths to total soil carbon emissions. Earlier snow melt in spring promotes deeper soil warming and enhances the contribution of deeper soils to total soil respiration during the later growing season, thereby reducing net ecosystem carbon uptake. Early cold-season soil respiration is closely linked to the number of snow-free days after land surface freezes (R = −0.48, p 2 seasonal cycle, especially during the early cold season at 10-km scale. Our results demonstrate the critical role of snow cover affecting the seasonality of soil temperature and respiration and highlight the challenges of incorporating these complex processes into future projections of boreal-Arctic carbon cycle.

Accurately characterizing isoprene ozonolysis continues to challenge atmospheric chemists. The reaction is believed to be a spontaneous, concerted cycloaddition. However, little information is available about the entrance channel and isoprene-ozone complexes thought to define the long-range portion of the reaction coordinate. Our coupled cluster and auxiliary field quantum Monte Carlo calculations predict multiple stable isoprene-ozone van der Waals complexes for the trans-isoprene in the gas-phase with moderate association energies. These results indicate that long-range dynamics in the isoprene-ozone entrance channel can impact the overall reaction in the troposphere and provide the spectroscopic information necessary to extend microwave characterization of isoprene ozonolysis to pre-reactive complexes. At the air-water interface, Born-Oppenheimer Molecular Dynamics simulations indicate that the cycloaddition reaction between ozone and trans-isoprene follows a stepwise mechanism, which is quite distinct from our proposed gas-phase mechanism and occurs on a femtosecond time scale. The stepwise nature of isoprene ozonolysis on the aqueous surface is more consistent with the DeMore mechanism than with the Criegee mechanism suggested by the gas-phase calculations, suggesting that the reaction media may play an important role in the reaction. Overall, these predictions aim to provide a missing fundamental piece of molecular insight into isoprene ozonolysis, which has broad tropospheric implications due to its critical role as a nighttime source of hydroxyl radical.

Accurate and timely detection, quantification, and attribution of methane emissions from Underground Gas Storage (UGS) facilities is essential for improving confidence in greenhouse gas inventories, enabling emission mitigation by facility operators, and supporting efforts to assess facility integrity and safety. We conducted multiple airborne surveys of the 12 active UGS facilities in California between January 2016 and November 2017 using advanced remote sensing and in situ observations of near-surface atmospheric methane (CH4). These measurements where combined with wind data to derive spatially and temporally resolved methane emission estimates for California UGS facilities and key components with spatial resolutions as small as 1–3 m and revisit intervals ranging from minutes to months. The study spanned normal operations, malfunctions, and maintenance activity from multiple facilities including the active phase of the Aliso Canyon blowout incident in 2016 and subsequent return to injection operations in summer 2017. We estimate that the net annual methane emissions from the UGS sector in California averaged between 11.0 ± 3.8 GgCH4 yr−1 (remote sensing) and 12.3 ± 3.8 GgCH4 yr−1 (in situ). Net annual methane emissions for the 7 facilities that reported emissions in 2016 were estimated between 9.0 ± 3.2 GgCH4 yr−1 (remote sensing) and 9.5 ± 3.2 GgCH4 yr−1 (in situ), in both cases around 5 times higher than reported. The majority of methane emissions from UGS facilities in this study are likely dominated by anomalous activity: higher than expected compressor loss and leaking bypass isolation valves. Significant variability was observed at different time-scales: daily compressor duty-cycles and infrequent but large emissions from compressor station blow-downs. This observed variability made comparison of remote sensing and in situ observations challenging given measurements were derived largely at different times, however, improved agreement occurred when comparing simultaneous measurements. Temporal variability in emissions remains one of the most challenging aspects of UGS emissions quantification, underscoring the need for more systematic and persistent methane monitoring.