Search

The MethaneSAT satellite instrument and its aircraft precursor, MethaneAIR, are imaging spectrometers designed to measure methane concentrations with wide spatial coverage, fine spatial resolution, and high precision compared to currently deployed remote sensing instruments. At 12 960 m cruise altitude above ground (13 850 m above sea level), MethaneAIR datasets have a 4.5 km swath gridded to 10 m × 10 m pixels with 17-20 ppb standard deviation on a flat scene. MethaneAIR was deployed in the summer of 2021 in the Permian Basin to test the accuracy of the retrieved methane concentrations and emission rates using the algorithms developed for MethaneSAT. We report here point source emissions obtained during a single-blind volume-controlled release experiment, using two methods. (1) The modified integrated mass enhancement (mIME) method estimates emission rates using the total mass enhancement of methane in an observed plume combined with winds obtained from Weather Research Forecast driven by High-Resolution Rapid Refresh meteorological data in Large Eddy Simulations mode (WRF-LES-HRRR). WRF-LES-HRRR simulates winds in stochastic eddy-scale (100-1000 m) variability, which is particularly important for low-wind conditions and informing the error budget. The mIME can estimate emission rates of plumes of any size that are detectable by MethaneAIR. (2) The divergence integral (DI) method applies Gauss's theorem to estimate the flux divergence fields through a series of closed surfaces enclosing the sources. The set of boxes grows from the upwind side of the plume through the core of each plume and downwind. No selection of inflow concentration, as used in the mIME, is required. The DI approach can efï¬ ciently determine ï¬ uxes from large sources and clusters of sources but cannot resolve small point emissions. These methods account for the effects of eddy-scale variation in different ways: The DI averages across many eddies, whereas the mIME re-samples many eddies from the LES simulation. The DI directly uses HRRR winds, while mIME uses WRF-LES-HRRR wind products. Emissions estimates from both the mIME and DI methods agreed closely with the single-blind volume-controlled experiments (N Combining double low line 21). The York regression between the estimated emissions and the released emissions has a slope of 0.96 [0.84, 1.08], R Combining double low line 0.83 and N Combining double low line 21, with 30 % mean percentage error for the whole dataset, which indicates that MethaneAIR can quantify point sources emitting more than 200 kg h-1 for the mIME and 500 kg h-1 for the DI method. The two methods also agreed on methane emission estimates from various uncontrolled sources in the Permian Basin. The experiment thus demonstrates the powerful potential of the MethaneAIR instrument and suggests that the quantification method should be transferable to MethaneSAT if it meets the design specifications.

Understanding the effects of intensification of Amazon basin hydrological cycling-manifest as increasingly frequent floods and droughts-on water and energy cycles of tropical forests is essential to meeting the challenge of predicting ecosystem responses to climate change, including forest "tipping points". Here, we investigated the impacts of hydrological extremes on forest function using 12+ years of observations (between 2001-2020) of water and energy fluxes from eddy covariance, along with associated ecological dynamics from biometry, at the Tapajós National Forest. Measurements encompass the strong 2015-2016 El Niño drought and La Niña 2008-2009 wet events. We found that the forest responded strongly to El Niño-Southern Oscillation (ENSO): Drought reduced water availability for evapotranspiration (ET) leading to large increases in sensible heat fluxes (H). Partitioning ET by an approach that assumes transpiration (T) is proportional to photosynthesis, we found that water stress-induced reductions in canopy conductance (Gs ) drove T declines partly compensated by higher evaporation (E). By contrast, the abnormally wet La Niña period gave higher T and lower E, with little change in seasonal ET. Both El Niño-Southern Oscillation (ENSO) events resulted in changes in forest structure, manifested as lower wet-season leaf area index. However, only during El Niño 2015-2016, we observed a breakdown in the strong meteorological control of transpiration fluxes (via energy availability and atmospheric demand) because of slowing vegetation functions (via shutdown of Gs and significant leaf shedding). Drought-reduced T and Gs , higher H and E, amplified by feedbacks with higher temperatures and vapor pressure deficits, signaled that forest function had crossed a threshold, from which it recovered slowly, with delay, post-drought. Identifying such tipping point onsets (beyond which future irreversible processes may occur) at local scale is crucial for predicting basin-scale threshold-crossing changes in forest energy and water cycling, leading to slow-down in forest function, potentially resulting in Amazon forests shifting into alternate degraded states.

The hydroxyl radical (OH) fuels atmospheric chemical cycling as the main sink for methane and a driver of the formation and loss of many air pollutants, but direct OH observations are sparse. We develop and evaluate an observation-based proxy for short-term, spatial variations in OH (ProxyOH) in the remote marine troposphere using comprehensive measurements from the NASA Atmospheric Tomography (ATom) airborne campaign. ProxyOH is a reduced form of the OH steady-state equation representing the dominant OH production and loss pathways in the remote marine troposphere, according to box model simulations of OH constrained with ATom observations. ProxyOH comprises only eight variables that are generally observed by routine ground- or satellite-based instruments. ProxyOH scales linearly with in situ [OH] spatial variations along the ATom flight tracks (median r2 = 0.90, interquartile range = 0.80 to 0.94 across 2-km altitude by 20° latitudinal regions). We deconstruct spatial variations in ProxyOH as a first-order approximation of the sensitivity of OH variations to individual terms. Two terms modulate within-region ProxyOH variations-water vapor (H2O) and, to a lesser extent, nitric oxide (NO). This implies that a limited set of observations could offer an avenue for observation-based mapping of OH spatial variations over much of the remote marine troposphere. Both H2O and NO are expected to change with climate, while NO also varies strongly with human activities. We also illustrate the utility of ProxyOH as a process-based approach for evaluating intermodel differences in remote marine tropospheric OH.

The NASA Atmospheric Tomography (ATom) mission built a photochemical climatology of air parcels based on in situ measurements with the NASA DC-8 aircraft along objectively planned profiling transects through the middle of the Pacific and Atlantic oceans. In this paper we present and analyze a data set of 10s (2km) merged and gap-filled observations of the key reactive species driving the chemical budgets of O3 and CH4 (O3, CH4, CO, H2O, HCHO, H2O2, CH3OOH, C2H6, higher alkanes, alkenes, aromatics, NOx, HNO3, HNO4, peroxyacetyl nitrate, and other organic nitrates), consisting of 146494 distinct air parcels from ATom deployments 1 through 4. Six models calculated the O3 and CH4 photochemical tendencies from this modeling data stream for ATom 1. We find that 80%-90% of the total reactivity lies in the top 50% of the parcels and 25%-35% in the top 10%, supporting previous model-only studies that tropospheric chemistry is driven by a fraction of all the air. Surprisingly, the probability densities of species and reactivities averaged on a model scale (100 km) differ only slightly from the 2km ATom 10s data, indicating that much of the heterogeneity in tropospheric chemistry can be captured with current global chemistry models. Comparing the ATom reactivities over the tropical oceans with climatological statistics from six global chemistry models, we find generally good agreement with the reactivity rates for O3 and CH4. Models distinctly underestimate O3 production below 2km relative to the mid-troposphere, and this can be traced to lower NOx levels than observed. Attaching photochemical reactivities to measurements of chemical species allows for a richer, yet more constrained-to-what-matters, set of metrics for model evaluation. This paper presents a corrected version of the paper published under the same authors and title (sans "corrected") as 10.5194/acp-21-13729-2021.

Urban regions emit a large fraction of anthropogenic emissions of greenhouse gases (GHG) such as carbon dioxide (CO2) and methane (CH4) that contribute to modern-day climate change. As such, a growing number of urban policymakers and stakeholders are adopting emission reduction targets and implementing policies to reach those targets. Over the past two decades research teams have established urban GHG monitoring networks to determine how much, where, and why a particular city emits GHGs, and to track changes in emissions over time. Coordination among these efforts has been limited, restricting the scope of analyses and insights. Here we present a harmonized data set synthesizing urban GHG observations from cities with monitoring networks across North America that will facilitate cross-city analyses and address scientific questions that are difficult to address in isolation.

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.

The NASA Atmospheric Tomography (ATom) mission built a photochemical climatology of air parcels based on in situ measurements with the NASA DC-8 aircraft along objectively planned profiling transects through the middle of the Pacific and Atlantic oceans. In this paper we present and analyze a data set of 10s (2km) merged and gap-filled observations of the key reactive species driving the chemical budgets of O3 and CH4 (O3, CH4, CO, H2O, HCHO, H2O2, CH3OOH, C2H6, higher alkanes, alkenes, aromatics, NOx, HNO3, HNO4, peroxyacetyl nitrate, other organic nitrates), consisting of 146494 distinct air parcels from ATom deployments 1 through 4. Six models calculated the O3 and CH4 photochemical tendencies from this modeling data stream for ATom 1. We find that 80%-90% of the total reactivity lies in the top 50% of the parcels and 25%-35% in the top 10%, supporting previous model-only studies that tropospheric chemistry is driven by a fraction of all the air. In other words, accurate simulation of the least reactive 50% of the troposphere is unimportant for global budgets. Surprisingly, the probability densities of species and reactivities averaged on a model scale (100km) differ only slightly from the 2km ATom data, indicating that much of the heterogeneity in tropospheric chemistry can be captured with current global chemistry models. Comparing the ATom reactivities over the tropical oceans with climatological statistics from six global chemistry models, we find excellent agreement with the loss of O3 and CH4 but sharp disagreement with production of O3. The models sharply underestimate O3 production below 4km in both Pacific and Atlantic basins, and this can be traced to lower NOx levels than observed. Attaching photochemical reactivities to measurements of chemical species allows for a richer, yet more constrained-to-what-matters, set of metrics for model evaluation.

How, where, and why carbon (C) moves into and out of an ecosystem through time are long-standing questions in biogeochemistry. Here, we bring together hundreds of thousands of C-cycle observations at the Harvard Forest in central Massachusetts, USA, a mid-latitude landscape dominated by 80–120-yr-old closed-canopy forests. These data answered four questions: (1) where and how much C is presently stored in dominant forest types; (2) what are current rates of C accrual and loss; (3) what biotic and abiotic factors contribute to variability in these rates; and (4) how has climate change affected the forest’s C cycle? Harvard Forest is an active C sink resulting from forest regrowth following land abandonment. Soil and tree biomass comprise nearly equal portions of existing C stocks. Net primary production (NPP) averaged 680–750 g C·m−2·yr−1; belowground NPP contributed 38–47% of the total, but with large uncertainty. Mineral soil C measured in the same inventory plots in 1992 and 2013 was too heterogeneous to detect change in soil-C pools; however, radiocarbon data suggest a small but persistent sink of 10–30 g C·m−2·yr−1. Net ecosystem production (NEP) in hardwood stands averaged ~300 g C·m−2·yr−1. NEP in hemlock-dominated forests averaged ~450 g C·m−2·yr−1 until infestation by the hemlock woolly adelgid turned these stands into a net C source. Since 2000, NPP has increased by 26%. For the period 1992–2015, NEP increased 93%. The increase in mean annual temperature and growing season length alone accounted for ~30% of the increase in productivity. Interannual variations in GPP and NEP were also correlated with increases in red oak biomass, forest leaf area, and canopy-scale light-use efficiency. Compared to long-term global change experiments at the Harvard Forest, the C sink in regrowing biomass equaled or exceeded C cycle modifications imposed by soil warming, N saturation, and hemlock removal. Results of this synthesis and comparison to simulation models suggest that forests across the region are likely to accrue C for decades to come but may be disrupted if the frequency or severity of biotic and abiotic disturbances increases.

Abstract. Accurate simulation of atmospheric circulation,particularly in the lower stratosphere, is challenging due to unresolvedwave–mean flow interactions and limited high-resolution observations for validation. Gravity-induced pressure gradients lead to a small butmeasurable separation of heavy and light gases by molecular diffusion in thestratosphere. Because the relative abundance of Ar to N2 is exclusivelycontrolled by physical transport, the argon-to-nitrogen ratio (Ar∕N2)provides an additional constraint on circulation and the age of air (AoA),i.e., the time elapsed since entry of an air parcel into the stratosphere. Here we use airborne measurements of N2O and Ar∕N2 from ninecampaigns with global coverage spanning 2008–2018 to calculate AoA and toquantify gravitational separation in the lowermost stratosphere. To thisend, we develop a new N2O–AoA relationship using a Markov chain Monte Carlo algorithm. We observe that gravitational separation increases systematically with increasing AoA for samples with AoA between 0 and 3 years. These observations are compared to a simulation of the TOMCAT/SLIMCAT 3-D chemical transport model, which has been updated to includegravitational fractionation of gases. We demonstrate that although AoA atold ages is slightly underestimated in the model, the relationship betweenAr∕N2 and AoA is robust and agrees with the observations. Thishighlights the potential of Ar∕N2 to become a new AoA tracer that is subject only to physical transport phenomena and can supplement the suite of available AoA indicators.

Abstract. Accurate simulation of atmospheric circulation, particularly in the lower stratosphere, is challenging due to unresolved wave-mean flow interactions and limited high-resolution observations for validation. Gravity-induced pressure gradients lead to a small but measurable separation of heavy and light gases by molecular diffusion in the stratosphere. Because the relative abundance of Ar to N2 is exclusively controlled by physical transport, the argon-to-nitrogen ratio (Ar/N2) provides an additional constraint on circulation and the age of air (AoA), i.e. the time elapsed since entry of an air parcel into the stratosphere. Here we use airborne measurements of N2O and Ar/N2 from nine campaigns with global coverage spanning 2008–2018 to calculate AoA and to quantify gravitational separation in the lowermost stratosphere. To this end, we develop a new N2O-AoA relationship using a Markov Chain Monte Carlo algorithm. We observe that gravitational separation increases systematically with increasing AoA for samples with AoA between 0 to 3 years. These observations are compared to a simulation of the TOMCAT/SLIMCAT 3-D chemical transport model, which has been updated to include gravitational fractionation of gases. We demonstrate that although AoA at old ages is slightly underestimated in the model, the relationship between Ar/N2 and AoA is robust and agrees with the observations. This highlights the potential of Ar/N2 to become a new AoA tracer that is subject only to physical transport phenomena and can supplement the suite of available AoA indicators.

Earth system models (ESMs) have been developed to represent the role of terrestrial ecosystems on the energy, water, and carbon cycles. However, many ESMs still lack representation of within-ecosystem heterogeneity and diversity. In this paper, we present the Ecosystem Demography model version 2.2 (ED-2.2). In ED-2.2, the biophysical and physiological processes account for the horizontal and vertical heterogeneity of the ecosystem: The energy, water, and carbon cycles are solved separately for a series of vegetation cohorts (groups of individual plants of similar size and plant functional type) distributed across a series of spatially implicit patches (representing collections of microenvironments that have a similar disturbance history).We define the equations that describe the energy, water, and carbon cycles in terms of total energy, water, and carbon, which simplifies the differential equations and guarantees excellent conservation of these quantities in long-term simulation (

The Ecosystem Demography model version 2.2 (ED-2.2) is a terrestrial biosphere model that simulates the biophysical, ecological, and biogeochemical dynamics of vertically and horizontally heterogeneous terrestrial ecosystems. In a companion paper (Longo et al., 2019a), we described how the model solves the energy, water, and carbon cycles, and verified the high degree of conservation of these properties in long-term simulations that include long-term (multi-decadal) vegetation dynamics. Here, we present a detailed assessment of the model's ability to represent multiple processes associated with the biophysical and biogeochemical cycles in Amazon forests. We use multiple measurements from eddy covariance towers, forest inventory plots, and regional remote-sensing products to assess the model's ability to represent biophysical, physiological, and ecological processes at multiple timescales, ranging from subdaily to century long. The ED-2.2 model accurately describes the vertical distribution of light, water fluxes, and the storage of water, energy, and carbon in the canopy air space, the regional distribution of biomass in tropical South America, and the variability of biomass as a function of environmental drivers. In addition, ED-2.2 qualitatively captures several emergent properties of the ecosystem found in observations, specifically observed relationships between aboveground biomass, mortality rates, and wood density; however, the slopes of these relationships were not accurately captured. We also identified several limitations, including the model's tendency to overestimate the magnitude and seasonality of heterotrophic respiration and to overestimate growth rates in a nutrient-poor tropical site. The evaluation presented here highlights the potential of incorporating structural and functional heterogeneity within biomes in Earth system models (ESMs) and to realistically represent their impacts on energy, water, and carbon cycles. We also identify several priorities for further model development.

We report airborne measurements of acetaldehyde (CH3CHO) during the first and second deployments of the National Aeronautics and Space Administration (NASA) Atmospheric Tomography Mission (ATom). The budget of CH3CHO is examined using the Community Atmospheric Model with chemistry (CAM-chem), with a newly-developed online air-sea exchange module. The upper limit of the global ocean net emission of CH3CHO is estimated to be 34 Tg a-1 (42 Tg a-1 if considering bubble-mediated transfer), and the ocean impacts on tropospheric CH3CHO are mostly confined to the marine boundary layer. Our analysis suggests that there is an unaccounted CH3CHO source in the remote troposphere and that organic aerosols can only provide a fraction of this missing source. We propose that peroxyacetic acid (PAA) is an ideal indicator of the rapid CH3CHO production in the remote troposphere. The higher-than-expected CH3CHO measurements represent a missing sink of hydroxyl radicals (and halogen radical) in current chemistry-climate models.

Abstract. The Ecosystem Demography Model version 2.2 (ED-2.2) is a terrestrial biosphere model that simulates the biophysical and biogeochemical cycles of dynamic ecosystems while considering the role of vertical structure of plant communities and the heterogeneity of such structures across the landscape. In a companion paper, we described in detail how the model solves the energy, water, and carbon cycles, and verified the excellent conservation of such properties in long-term simulation. Here, we present a thorough assessment of the model's ability to represent multiple processes associated with the biophysical and biogeochemical cycles, with focus on the Amazon forest. We used multiple measurements from eddy covariance towers, forest inventory plots and regional remote-sensing products to assess the model's ability to represent biophysical, physiological, and ecological processes at multiple time scales ranging from sub-daily to century-long. The ED-2.2 model accurately describes the vertical distribution of light, water fluxes and the storage of water, energy and carbon in the canopy air space, the regional distribution of biomass in tropical South America, and the variability of biomass as a function of environmental drivers. In addition, ED-2.2 also simulates emerging properties of the ecosystem found in observations, such as the relationship between biomass and mortality rates and wood density, although the relationships predicted by the model were biased. We also identified some of the model limitations, such as the model's tendency to overestimate the magnitude and seasonality of heterotrophic respiration, and to overestimate growth rates in a nutrient-poor tropical site. The evaluation presented here highlights the potential of incorporating structural and functional heterogeneity within biomes in ESMs, to realistically represent the role of forest structure and composition on energy, water, and carbon cycles, as well as the priority areas for further model development.

The age of air is an important transport diagnostic that can be derived from trace gas measurements and compared to global chemistry climate model output. We describe a new technique to calculate the age of air, measuring transport times from the Earth's surface to any location in the atmosphere based on simultaneous in situ measurements of multiple key long-lived trace gases. The primary benefits of this new technique include (1) optimized ages of air consistent with simultaneously measured SF6 and CO2 ; (2) age of air from the upper troposphere through the stratosphere; (3) estimates of the second moment of age spectra that have not been well constrained from measurements; and (4) flexibility to be used with measurements across multiple instruments, platforms, and decades. We demonstrate the technique on aircraft and balloon measurements from the 1990s, the last period of extensive stratospheric in situ sampling, and several recent missions from the 2020s, and compare the results with previously published and modeled values. [ABSTRACT FROM AUTHOR]

In Amazon forests, the relative contributions of climate, phenology, and disturbance to net ecosystem exchange of carbon (NEE) are not well understood. To partition influences across various timescales, we use a statistical model to represent eddy-covariance-derived NEE in an evergreen eastern Amazon forest as a constant response to changing meteorology and phenology throughout a decade. Our best fit model represented hourly NEE variations as changes due to sunlight, while seasonal variations arose from phenology influencing photosynthesis and from rainfall influencing ecosystem respiration, where phenology was asynchronous with dry-season onset. We compared annual model residuals with biometric forest surveys to estimate impacts of drought disturbance. We found that our simple model represented hourly and monthly variations in NEE well (R 2 Combining double low line 0.81 and 0.59, respectively). Modeled phenology explained 1 % of hourly and 26 % of monthly variations in observed NEE, whereas the remaining modeled variability was due to changes in meteorology. We did not find evidence to support the common assumption that the forest phenology was seasonally light- or water-triggered. Our model simulated annual NEE well, with the exception of 2002, the first year of our data record, which contained 1.2 MgC ha'1 of residual net emissions, because photosynthesis was anomalously low. Because a severe drought occurred in 1998, we hypothesized that this drought caused a persistent, multi-year depression of photosynthesis. Our results suggest drought can have lasting impacts on photosynthesis, possibly via partial damage to still-living trees.

The impact of increases in drought frequency on the Amazon forest's composition, structure and functioning remain uncertain. We used a process- and individual-based ecosystem model (ED2) to quantify the forest's vulnerability to increased drought recurrence. We generated meteorologically realistic, drier-than-observed rainfall scenarios for two Amazon forest sites, Paracou (wetter) and Tapajós (drier), to evaluate the impacts of more frequent droughts on forest biomass, structure and composition. The wet site was insensitive to the tested scenarios, whereas at the dry site biomass declined when average rainfall reduction exceeded 15%, due to high mortality of large-sized evergreen trees. Biomass losses persisted when year-long drought recurrence was shorter than 2-7 yr, depending upon soil texture and leaf phenology. From the site-level scenario results, we developed regionally applicable metrics to quantify the Amazon forest's climatological proximity to rainfall regimes likely to cause biomass loss > 20% in 50 yr according to ED2 predictions. Nearly 25% (1.8 million km2 ) of the Amazon forests could experience frequent droughts and biomass loss if mean annual rainfall or interannual variability changed by 2σ. At least 10% of the high-emission climate projections (CMIP5/RCP8.5 models) predict critically dry regimes over 25% of the Amazon forest area by 2100.

Systematic biases in eddy covariance measurements of net ecosystem-atmosphere carbon dioxide exchange (NEE) are ubiquitous in forests when turbulence is low at night. We propose an alternative to the conventional bias correction, the friction velocity (u*) filter, by hypothesizing that these biases have two separate, concurrent causes: (1) a subcanopy CO₂ storage pool that eludes typical storage measurements, creating a turbulence-dependent bias, and (2) advective divergence loss of CO₂, creating a turbulence-independent bias. We correct for (1) using a simple parametric model of missing storage (MS). Prior experiments have inferred (2) directly from atmospheric measurements (DRAINO). For sites at which DRAINO experiments have not been performed or are infeasible, we estimate (2) empirically using a PAR-extrapolated advective respiration loss (PEARL) approach. We compare u* filter estimates of advection and NEE to MS-PEARL estimates at one temperate forest and two tropical forest sites.We find that for tropical forests, u* filters can produce a range of extreme NEE estimates, from long-term forest carbon emission to sequestration, that diverge from independent assessments and are not physically sustainable. Our MS model eliminates the dependence of nighttime NEE on u*, consistent with findings from DRAINO studies that nighttime advective losses of CO₂ are often not dependent on the strength of turbulence. Our PEARL estimates of mean advective loss agree with available DRAINO measurements. The MS-PEARL correction to long-term NEE produces better agreement with forest inventories at all three sites. Moreover, the correction retains all nighttime eddy covariance data and is therefore more widely applicable than the u* filter approach, which rejects substantial nighttime data—up to 93% at one of the tropical sites. The full MS-PEARL NEE correction is therefore an equally defensible and more practical alternative to the u* filter, but leads to different conclusions about the resulting carbon balance. Our results therefore highlight the need to investigate which approach’s underlying hypotheses are more physically realistic.

The first phase of the Atmospheric Tomography Mission (ATom-1) took place in July-August 2016 and included flights above the remote Pacific and Atlantic oceans. Sampling of atmospheric constituents during these flights is designed to provide new insights into the chemical reactivity and processes of the remote atmosphere and how these processes are affected by anthropogenic emissions. Model simulations provide a valuable tool for interpreting these measurements and understanding the origin of the observed trace gases and aerosols, so it is important to quantify model performance. Goddard Earth Observing System Model version 5 (GEOS-5) forecasts and analyses show considerable skill in predicting and simulating the CO distribution and the timing of CO enhancements observed during the ATom-1 aircraft mission. We use GEOS-5's tagged tracers for CO to assess the contribution of different emission sources to the regions sampled by ATom-1 to elucidate the dominant anthropogenic influences on different parts of the remote atmosphere. We find a dominant contribution from non-biomass-burning sources along the ATom transects except over the tropical Atlantic, where African biomass burning makes a large contribution to the CO concentration. One of the goals of ATom is to provide a chemical climatology over the oceans, so it is important to consider whether August 2016 was representative of typical boreal summer conditions. Using satellite observations of 700 hPa and column CO from the Measurement of Pollution in the Troposphere (MOPITT) instrument, 215 hPa CO from the Microwave Limb Sounder (MLS), and aerosol optical thickness from the Moderate Resolution Imaging Spectroradiometer (MODIS), we find that CO concentrations and aerosol optical thickness in August 2016 were within the observed range of the satellite observations but below the decadal median for many of the regions sampled. This suggests that the ATom-1 measurements may represent relatively clean but not exceptional conditions for lower-tropospheric CO.

High-latitude ecosystems have the capacity to release large amounts of carbon dioxide (CO2) to the atmosphere in response to increasing temperatures, representing a potentially significant positive feedback within the climate system. Here, we combine aircraft and tower observations of atmospheric CO2 with remote sensing data and meteorological products to derive temporally and spatially resolved year-round CO2 fluxes across Alaska during 2012-2014. We find that tundra ecosystems were a net source of CO2 to the atmosphere annually, with especially high rates of respiration during early winter (October through December). Long-term records at Barrow, AK, suggest that CO2 emission rates from North Slope tundra have increased during the October through December period by 73% ± 11% since 1975, and are correlated with rising summer temperatures. Together, these results imply increasing early winter respiration and net annual emission of CO2 in Alaska, in response to climate warming. Our results provide evidence that the decadal-scale increase in the amplitude of the CO2 seasonal cycle may be linked with increasing biogenic emissions in the Arctic, following the growing season. Early winter respiration was not well simulated by the Earth System Models used to forecast future carbon fluxes in recent climate assessments. Therefore, these assessments may underestimate the carbon release from Arctic soils in response to a warming climate.

Molecular hydrogen (H2 ) is an atmospheric trace gas with a large microbe-mediated soil sink, yet cycling of this compound throughout ecosystems is poorly understood. Measurements of the sources and sinks of H2 in various ecosystems are sparse, resulting in large uncertainties in the global H2 budget. Constraining the H2 cycle is critical to understanding its role in atmospheric chemistry and climate. We measured H2 fluxes at high frequency in a temperate mixed deciduous forest for 15 months using a tower-based flux-gradient approach to determine both the soil-atmosphere and the net ecosystem flux of H2 . We found that Harvard Forest is a net H2 sink (-1.4 ± 1.1 kg H2  ha-1 ) with soils as the dominant H2 sink (-2.0 ± 1.0 kg H2  ha-1 ) and aboveground canopy emissions as the dominant H2 source (+0.6 ± 0.8 kg H2  ha-1 ). Aboveground emissions of H2 were an unexpected and substantial component of the ecosystem H2 flux, reducing net ecosystem uptake by 30% of that calculated from soil uptake alone. Soil uptake was highly seasonal (July maximum, February minimum), positively correlated with soil temperature and negatively correlated with environmental variables relevant to diffusion into soils (i.e., soil moisture, snow depth, snow density). Soil microbial H2 uptake was correlated with rhizosphere respiration rates (r = 0.8, P

Abstract. An approach for analysis and modeling of global atmospheric chemistry is developed for application to measurements that provide a tropospheric climatology of those heterogeneously distributed, reactive species that control the loss of methane and the production and loss of ozone. We identify key species (e.g., O3, NOx, HNO3, HNO4, C2H3NO5, H2O, HOOH, CH3OOH, HCHO, CO, CH4, C2H6, acetaldehyde, acetone), and presume that they can be measured simultaneously in air parcels on the scale of a few km horizontally and a few tenths vertically. Six global models have prepared such climatologies (at model resolution) for August with emphasis on the vast central Pacific and Atlantic Ocean basins. We show clear differences in model generated reactivities as well as species covariances that could readily be discriminated with an unbiased climatology. A primary tool is comparison of multi-dimensional probability densities of key species weighted by frequency of occurrence as well as by the reactivity of the parcels with respect to methane and ozone. The reactivity-weighted probabilities tell us which parcels matter in this case. Testing 100-km scale models with 2-km measurements using these tools also addresses a core question about model resolution and whether fine-scale atmospheric structures matter to the overall ozone and methane budget. A new method enabling these six global chemistry-climate models to ingest an externally-sourced climatology and then compute air parcel reactivity is demonstrated. Such an observed climatology is anticipated from the NASA Atmospheric Tomography (ATom) aircraft mission (2015–2020), measuring the key species, executing profiles over the Pacific and Atlantic Ocean basins. This work is a core part of the design of ATom.

Accurate and comprehensive quantification of oil and gas methane emissions is pivotal in informing effective methane mitigation policies while also supporting the assessment and tracking of progress towards emissions reduction targets set by governments and industry. While national bottom-up source-level inventories are useful for understanding the sources of methane emissions, they are often unrepresentative across spatial scales, and their reliance on generic emission factors produces underestimations when compared with measurement-based inventories. Here, we compile and analyze previously reported ground-based facility-level methane emissions measurements (n=1540) in the major US oil- and gas-producing basins and develop representative methane emission profiles for key facility categories in the US oil and gas supply chain, including well sites, natural-gas compressor stations, processing plants, crude-oil refineries, and pipelines. We then integrate these emissions data with comprehensive spatial data on national oil and gas activity to estimate each facility's mean total methane emissions and uncertainties for the year 2021, from which we develop a mean estimate of annual national methane emissions resolved at 0.1° × 0.1° spatial scales (∼ 10 km × 10 km). From this measurement-based methane emissions inventory (EI-ME), we estimate total US national oil and gas methane emissions of approximately 16 Tg (95 % confidence interval of 14–18 Tg) in 2021, which is ∼ 2 times greater than the EPA Greenhouse Gas Inventory. Our estimate represents a mean gas-production-normalized methane loss rate of 2.6 %, consistent with recent satellite-based estimates. We find significant variability in both the magnitude and spatial distribution of basin-level methane emissions, ranging from production-normalized methane loss rates of < 1 % in the gas-dominant Appalachian and Haynesville regions to > 3 %–6 % in oil-dominant basins, including the Permian, Bakken, and the Uinta. Additionally, we present and compare novel comprehensive wide-area airborne remote-sensing data and results for total area methane emissions and the relative contributions of diffuse and concentrated methane point sources as quantified using MethaneAIR in 2021. The MethaneAIR assessment showed reasonable agreement with independent regional methane quantification results in sub-regions of the Permian and Uinta basins and indicated that diffuse area sources accounted for the majority of the total oil and gas emissions in these two regions. Our assessment offers key insights into plausible underlying drivers of basin-to-basin variabilities in oil and gas methane emissions, emphasizing the importance of integrating measurement-based data when developing high-resolution spatially explicit methane inventories in support of accurate methane assessment, attribution, and mitigation. The high-resolution spatially explicit EI-ME inventory is publicly available at 10.5281/zenodo.10734299 (Omara, 2024). [ABSTRACT FROM AUTHOR]

Soils are a major source of nitrogen oxides, which in the atmosphere help govern its oxidative capacity. Thus the response of soil nitric oxide (NO) emissions to forcings such as warming or forest loss has a meaningful impact on global atmospheric chemistry. We find that the soil emission rate of NO in Amazonia from a common inventory is biased low by at least an order of magnitude in comparison to tower‐based observations. Accounting for this regional bias decreases the modeled global methane lifetime by 1.4%–2.6%. In comparison, a fully deforested Amazonia, representing a 37% decrease in global emissions of isoprene, decreases methane lifetime by at most 4.6%, highlighting the sensitive response of oxidation rates to changes in emissions of NO compared to those of terpenes. Our results demonstrate that improving our understanding of soil NO emissions will yield a more accurate representation of atmospheric oxidative capacity. Plain Language Summary: Soils emit a gas called nitric oxide (NO). The amount of NO emitted from soils in tropical forests is not well known, but has been assumed to be small. We simulated how different amounts of NO emissions from soils in the Amazon impacted atmospheric chemistry. By comparing our modeled results to observations we found that NO emissions from soils in the Amazon may be between 10 and 20 times larger than the current default assumption. This matters because NO has a strong influence on the rate at which methane and other gasses are removed from the atmosphere. Therefore soil NO emissions end up impacting how long methane can last in the atmosphere, and that would have additional climate impacts. Even though soil NO emissions are smaller than other sources of NO emissions globally, we find that the amount of NO emitted in the Amazon matters a lot for how long methane can last in the atmosphere. Our results demonstrate that improving estimates of soil NO emissions is going to be necessary for making accurate estimates of how long methane and other reactive species will stay in the atmosphere. Key Points: Soil emission rates of NO in common inventories may be between 10× and 20× too low over the Amazon basinHigher soil NO over the Amazon basin alters the oxidative capacity of the atmosphere and decreases global methane lifetime by 1.4%–2.6%Global methane lifetime is more sensitive to Amazon soil NO fluxes than to the loss of terpene fluxes from total deforestation of the Amazon [ABSTRACT FROM AUTHOR]

Tropopause-overshooting convection in the midlatitudes provides a rapid transport pathway for air from the lower troposphere to reach the upper troposphere and lower stratosphere (UTLS) and can result in the formation of above-anvil cirrus plumes (AACPs) that significantly hydrate the stratosphere. Such UTLS composition changes alter the radiation budget and impact climate. Novel in situ observations from the NASA Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) field campaign are used in this study to examine UTLS impacts from AACP-generating overshooting convection. Namely, a research flight on 31 May 2022 sampled active convection over the state of Oklahoma for more than 3 h with the NASA ER-2 high-altitude research aircraft. An AACP was bisected during this flight, providing the first such extensive in situ sampling of this phenomenon. The convective observations reveal pronounced changes in air mass composition and stratospheric hydration up to altitudes of 2.3 km above the tropopause and concentrations more than double background levels. Unique dynamic and trace gas signatures were found within the AACP, including enhanced vertical mixing near the AACP edge and a positive correlation between water vapor and ozone. Moreover, the water vapor enhancement within the AACP was found to be limited to the saturation mixing ratio of the low temperature overshoot and AACP air. Comparison with all remaining DCOTSS flights demonstrates that the 31 May 2022 flight had some of the largest tropospheric tracer and water vapor perturbations in the stratosphere and within the AACP. [ABSTRACT FROM AUTHOR]

With rapid changes in climate and the seasonal amplitude of carbon dioxide (CO2) in the Arctic, it is critical that we detect and quantify the underlying processes controlling the changing amplitude of CO2 to better predict carbon cycle feedbacks in the Arctic climate system. We use satellite and airborne observations of atmospheric CO2 with climatically forced CO2 flux simulations to assess the detectability of Alaskan carbon cycle signals as future warming evolves. We find that current satellite remote sensing technologies can detect changing uptake accurately during the growing season but lack sufficient cold season coverage and near-surface sensitivity to constrain annual carbon balance changes at regional scale. Airborne strategies that target regular vertical profile measurements within continental interiors are more sensitive to regional flux deeper into the cold season but currently lack sufficient spatial coverage throughout the entire cold season. Thus, the current CO2 observing network is unlikely to detect potentially large CO2 sources associated with deep permafrost thaw and cold season respiration expected over the next 50 y. Although continuity of current observations is vital, strategies and technologies focused on cold season measurements (active remote sensing, aircraft, and tall towers) and systematic sampling of vertical profiles across continental interiors over the full annual cycle are required to detect the onset of carbon release from thawing permafrost.

Arctic terrestrial ecosystems are major global sources of methane (CH4); hence, it is important to understand the seasonal and climatic controls on CH4 emissions from these systems. Here, we report year-round CH4 emissions from Alaskan Arctic tundra eddy flux sites and regional fluxes derived from aircraft data. We find that emissions during the cold season (September to May) account for ≥ 50% of the annual CH4 flux, with the highest emissions from noninundated upland tundra. A major fraction of cold season emissions occur during the zero curtain period, when subsurface soil temperatures are poised near 0 °C. The zero curtain may persist longer than the growing season, and CH4 emissions are enhanced when the duration is extended by a deep thawed layer as can occur with thick snow cover. Regional scale fluxes of CH4 derived from aircraft data demonstrate the large spatial extent of late season CH4 emissions. Scaled to the circumpolar Arctic, cold season fluxes from tundra total 12 ± 5 (95% confidence interval) Tg CH4 y(-1), ∼ 25% of global emissions from extratropical wetlands, or ∼ 6% of total global wetland methane emissions. The dominance of late-season emissions, sensitivity to soil environmental conditions, and importance of dry tundra are not currently simulated in most global climate models. Because Arctic warming disproportionally impacts the cold season, our results suggest that higher cold-season CH4 emissions will result from observed and predicted increases in snow thickness, active layer depth, and soil temperature, representing important positive feedbacks on climate warming.

Wetlands are the largest global natural methane (CH4/ source, and emissions between 50 and 70° N latitude contribute 10-30% to this source. Predictive capability of land models for northern wetland CH4 emissions is still low due to limited site measurements, strong spatial and temporal variability in emissions, and complex hydrological and biogeochemical dynamics. To explore this issue, we compare wetland CH4 emission predictions from the Community Land Model 4.5 (CLM4.5-BGC) with siteto regional-scale observations. A comparison of the CH4 fluxes with eddy flux data highlighted needed changes to the model's estimate of aerenchyma area, which we implemented and tested. The model modification substantially reduced biases in CH4 emissions when compared with CarbonTracker CH4 predictions. CLM4.5 CH4 emission predictions agree well with growing season (May-September) CarbonTracker Alaskan regional-level CH4 predictions and sitelevel observations. However, CLM4.5 underestimated CH4 emissions in the cold season (October-April). The monthly atmospheric CH4 mole fraction enhancements due to wetland emissions are also assessed using the Weather Research and Forecasting-Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model coupled with daily emissions from CLM4.5 and compared with aircraft CH4 mole fraction measurements from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) campaign. Both the tower and aircraft analyses confirm the underestimate of cold-season CH4 emissions by CLM4.5. The greatest uncertainties in predicting the seasonal CH4 cycle are from the wetland extent, coldseason CH4 production and CH4 transport processes. We recommend more cold-season experimental studies in highlatitude systems, which could improve the understanding and parameterization of ecosystem structure and function during this period. Predicted CH4 emissions remain uncertain, but we show here that benchmarking against observations across spatial scales can inform model structural and parameter improvements.

HCFC-22 (CHClF2) and HFC-134a (CH2FCF3) are two major gases currently used worldwide in domestic and commercial refrigeration and air conditioning. HCFC-22 contributes to stratospheric ozone depletion, and both species are potent greenhouse gases. In this work, we study in situ observations of HCFC-22 and HFC-134a taken from research aircraft over the Pacific Ocean in a 3-y span [HIaper-Pole-to-Pole Observations (HIPPO) 2009-2011] and combine these data with long-term ground observations from global surface sites [National Oceanic and Atmospheric Administration (NOAA) and Advanced Global Atmospheric Gases Experiment (AGAGE) networks]. We find the global annual emissions of HCFC-22 and HFC-134a have increased substantially over the past two decades. Emissions of HFC-134a are consistently higher compared with the United Nations Framework Convention on Climate Change (UNFCCC) inventory since 2000, by 60% more in recent years (2009-2012). Apart from these decadal emission constraints, we also quantify recent seasonal emission patterns showing that summertime emissions of HCFC-22 and HFC-134a are two to three times higher than wintertime emissions. This unforeseen large seasonal variation indicates that unaccounted mechanisms controlling refrigerant gas emissions are missing in the existing inventory estimates. Possible mechanisms enhancing refrigerant losses in summer are (i) higher vapor pressure in the sealed compartment of the system at summer high temperatures and (ii) more frequent use and service of refrigerators and air conditioners in summer months. Our results suggest that engineering (e.g., better temperature/vibration-resistant system sealing and new system design of more compact/efficient components) and regulatory (e.g., reinforcing system service regulations) steps to improve containment of these gases from working devices could effectively reduce their release to the atmosphere.

Reducing methane emissions from the oil and gas (oil/gas) sector has been identified as a critically important global strategy for reducing near-term climate warming. Recent measurements, especially by satellite and aerial remote sensing, underscore the importance of targeting the small number of facilities emitting methane at high rates (i.e., “super-emitters”) for measurement and mitigation. However, the contributions from individual oil/gas facilities emitting at low emission rates that are often undetected are poorly understood, especially in the context of total national- and regional-level estimates. In this work, we compile empirical measurements gathered using methods with low limits of detection to develop a facility-level model to quantify total methane emissions from the continental United States (CONUS) midstream and upstream oil/gas sector for 2021. We find that ~70% (95% confidence intervals: 63-82%) of total oil/gas methane emissions in the CONUS for the year 2021 (Total: 14.3 Tg/yr) originate from facilities emitting <100 kg/hr. While there is variability among the emission distribution curves for different oil/gas production basins, facilities with low emissions are consistently found to account for the majority of total basin emissions (i.e., range across basins 63% - 90% of total basin emissions from facilities emitting <100 kg/hr). Production well sites were responsible for 70% of total regional oil/gas methane emissions, with the highest contributions from a large population of low-producing well sites. Our results are also in broad agreement with several independent aerial remote sensing campaigns (e.g., MethaneAIR, Bridger Gas Mapping LiDAR, AVIRIS-NG, and Global Airborne Observatory). Our findings highlight the importance of accounting for the significant contribution of small emission sources to total oil/gas methane emissions. While reducing emissions from high-emitting facilities is important, it is not sufficient for the overall mitigation of methane emissions from the oil and gas sector which according to this study is dominated by small emission sources across the US. Tracking changes in emissions over time and designing effective mitigation policies should consider the large contribution of small methane sources to total emissions. [ABSTRACT FROM AUTHOR]

We investigated the seasonal patterns of Amazonian forest photosynthetic activity, and the effects thereon of variations in climate and land-use, by integrating data from a network of ground-based eddy flux towers in Brazil established as part of the 'Large-Scale Biosphere Atmosphere Experiment in Amazonia' project. We found that degree of water limitation, as indicated by the seasonality of the ratio of sensible to latent heat flux (Bowen ratio) predicts seasonal patterns of photosynthesis. In equatorial Amazonian forests (5° N-5° S), water limitation is absent, and photosynthetic fluxes (or gross ecosystem productivity, GEP) exhibit high or increasing levels of photosynthetic activity as the dry season progresses, likely a consequence of allocation to growth of new leaves. In contrast, forests along the southern flank of the Amazon, pastures converted from forest, and mixed forest-grass savanna, exhibit dry-season declines in GEP, consistent with increasing degrees of water limitation. Although previous work showed tropical ecosystem evapotranspiration (ET) is driven by incoming radiation, GEP observations reported here surprisingly show no or negative relationships with photosynthetically active radiation (PAR). Instead, GEP fluxes largely followed the phenology of canopy photosynthetic capacity (Pc), with only deviations from this primary pattern driven by variations in PAR. Estimates of leaf flush at three non-water limited equatorial forest sites peak in the dry season, in correlation with high dry season light levels. The higher photosynthetic capacity that follows persists into the wet season, driving high GEP that is out of phase with sunlight, explaining the negative observed relationship with sunlight. Overall, these patterns suggest that at sites where water is not limiting, light interacts with adaptive mechanisms to determine photosynthetic capacity indirectly through leaf flush and litterfall seasonality. These mechanisms are poorly represented in ecosystem models, and represent an important challenge to efforts to predict tropical forest responses to climatic variations. © 2013 Elsevier B.V.

Changes in atmospheric CO2 variability during the twenty-first century may provide insight about ecosystem responses to climate change and have implications for the design of carbon monitoring programs. This paper describes changes in the three-dimensional structure of atmospheric CO2 for several representative concentration pathways (RCPs 4.5 and 8.5) using the Community Earth System Model–Biogeochemistry (CESM1-BGC). CO2 simulated for the historical period was first compared to surface, aircraft, and column observations. In a second step, the evolution of spatial and temporal gradients during the twenty-first century was examined. The mean annual cycle in atmospheric CO2 was underestimated for the historical period throughout the Northern Hemisphere, suggesting that the growing season net flux in the Community Land Model (the land component of CESM) was too weak. Consistent with weak summer drawdown in Northern Hemisphere high latitudes, simulated CO2 showed correspondingly weak north–south and vertical gradients during the summer. In the simulations of the twenty-first century, CESM predicted increases in the mean annual cycle of atmospheric CO2 and larger horizontal gradients. Not only did the mean north–south gradient increase due to fossil fuel emissions, but east–west contrasts in CO2 also strengthened because of changing patterns in fossil fuel emissions and terrestrial carbon exchange. In the RCP8.5 simulation, where CO2 increased to 1150 ppm by 2100, the CESM predicted increases in interannual variability in the Northern Hemisphere midlatitudes of up to 60% relative to present variability for time series filtered with a 2–10-yr bandpass. Such an increase in variability may impact detection of changing surface fluxes from atmospheric observations.

Deforestation in mid- to high latitudes is hypothesized to have the potential to cool the Earth's surface by altering biophysical processes. In climate models of continental-scale land clearing, the cooling is triggered by increases in surface albedo and is reinforced by a land albedo-sea ice feedback. This feedback is crucial in the model predictions; without it other biophysical processes may overwhelm the albedo effect to generate warming instead. Ongoing land-use activities, such as land management for climate mitigation, are occurring at local scales (hectares) presumably too small to generate the feedback, and it is not known whether the intrinsic biophysical mechanism on its own can change the surface temperature in a consistent manner. Nor has the effect of deforestation on climate been demonstrated over large areas from direct observations. Here we show that surface air temperature is lower in open land than in nearby forested land. The effect is 0.85 ± 0.44 K (mean ± one standard deviation) northwards of 45° N and 0.21 ± 0.53 K southwards. Below 35° N there is weak evidence that deforestation leads to warming. Results are based on comparisons of temperature at forested eddy covariance towers in the USA and Canada and, as a proxy for small areas of cleared land, nearby surface weather stations. Night-time temperature changes unrelated to changes in surface albedo are an important contributor to the overall cooling effect. The observed latitudinal dependence is consistent with theoretical expectation of changes in energy loss from convection and radiation across latitudes in both the daytime and night-time phase of the diurnal cycle, the latter of which remains uncertain in climate models.