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Dust particles from high latitudes have a potentially large local, regional, and global significance to climate and the environment as short-lived climate forcers, air pollutants, and nutrient sources. Identifying the locations of local dust sources and their emission, transport, and deposition processes is important for understanding the multiple impacts of high-latitude dust (HLD) on the Earth’s systems. Here, we identify, describe, and quantify the source intensity (SI) values, which show the potential of soil surfaces for dust emission scaled to values 0 to 1 concerning globally best productive sources, using the Global Sand and Dust Storms Source Base Map (G-SDS-SBM). This includes 64 HLD sources in our collection for the northern (Alaska, Canada, Denmark, Greenland, Iceland, Svalbard, Sweden, and Russia) and southern (Antarctica and Patagonia) high latitudes. Activity from most of these HLD sources shows seasonal character. It is estimated that high-latitude land areas with higher (SI ≥ 0.5), very high (SI ≥ 0.7), and the highest potential (SI ≥ 0.9) for dust emission cover > 1 670 000 km2 , > 560 000 km2 , and > 240 000 km2 , respectively. In the Arctic HLD region (≥ 60◦ N), land area with SI ≥ 0.5 is 5.5 % (1 035 059 km2), area with SI ≥ 0.7 is 2.3 % (440 804 km2), and area with SI ≥ 0.9 is 1.1 % (208 701 km2). Minimum SI values in the northern HLD region are about 3 orders of magnitude smaller, indicating that the dust sources of this region greatly depend on weather conditions. Our spatial dust source distribution analysis modeling results showed evidence supporting a northern HLD belt, defined as the area north of 50◦ N, with a “transitional HLD-source area” extending at latitudes 50–58◦ N in Eurasia and 50–55◦ N in Canada and a “cold HLD-source area” including areas north of 60◦ N in Eurasia and north of 58◦ N in Canada, with currently “no dust source” area between the HLD and low-latitude dust (LLD) dust belt, except for British Columbia. Using the global atmospheric transport model SILAM, we estimated that 1.0 % of the global dust emission originated from the high-latitude regions. About 57 % of the dust deposition in snow- and ice-covered Arctic regions was from HLD sources. In the southern HLD region, soil surface conditions are favorable for dust emission during the whole year. Climate change can cause a decrease in the duration of snow cover, retreat of glaciers, and an increase in drought, heatwave intensity, and frequency, leading to the increasing frequency of topsoil conditions favorable for dust emission, which increases the probability of dust storms. Our study provides a step forward to improve the representation of HLD in models and to monitor, quantify, and assess the environmental and climate significance of HLD.

Atmospheric CO 2 concentration measurements at Barrow, Alaska, together with coupled atmospheric transport and terrestrial ecosystem models show a declining spring net primary productivity response to temperature at high latitudes. Ongoing spring warming allows the growing season to begin earlier, enhancing carbon uptake in northern ecosystems 1 , 2 , 3 . Here we use 34 years of atmospheric CO 2 concentration measurements at Barrow, Alaska (BRW, 71° N) to show that the interannual relationship between spring temperature and carbon uptake has recently shifted. We use two indicators: the spring zero-crossing date of atmospheric CO 2 (SZC) and the magnitude of CO 2 drawdown between May and June (SCC). The previously reported strong correlation between SZC, SCC and spring land temperature (ST) was found in the first 17 years of measurements, but disappeared in the last 17 years. As a result, the sensitivity of both SZC and SCC to warming decreased. Simulations with an atmospheric transport model 4 coupled to a terrestrial ecosystem model 5 suggest that the weakened interannual correlation of SZC and SCC with ST in the last 17 years is attributable to the declining temperature response of spring net primary productivity (NPP) rather than to changes in heterotrophic respiration or in atmospheric transport patterns. Reduced chilling during dormancy and emerging light limitation are possible mechanisms that may have contributed to the loss of NPP response to ST. Our results thus challenge the ‘warmer spring–bigger sink’ mechanism.

With the majority of the world population residing in urban areas, attempts to monitor and mitigate greenhouse gas emissions must necessarily center on cities. However, existing carbon dioxide observation networks are ill-equipped to resolve the specific intra-city emission phenomena targeted by regulation. Here we describe the design and implementation of the BErkeley Atmospheric CO2 Observation Network (BEACO2N), a distributed CO2 monitoring instrument that utilizes low-cost technology to achieve unprecedented spatial density throughout and around the city of Oakland, California. We characterize the network in terms of four performance parameters - cost, reliability, precision, and systematic uncertainty - and find the BEACO2N approach to be sufficiently cost-effective and reliable while nonetheless providing high-quality atmospheric observations. First results from the initial installation successfully capture hourly, daily, and seasonal CO2 signals relevant to urban environments on spatial scales that cannot be accurately represented by atmospheric transport models alone, demonstrating the utility of high-resolution surface networks in urban greenhouse gas monitoring efforts.

Landscape fires produce smoke containing a very wide variety of chemical species, both gases and aerosols. For larger, more intense fires that produce the greatest amounts of emissions per unit time, the smoke tends initially to be transported vertically or semi-vertically close by the source region, driven by the intense heat and convective energy released by the burning vegetation. The column of hot smoke rapidly entrains cooler ambient air, forming a rising plume within which the fire emissions are transported. The characteristics of this plume, and in particular the height to which it rises before releasing the majority of the smoke burden into the wider atmosphere, are important in terms of how the fire emissions are ultimately transported, since for example winds at different altitudes may be quite different. This difference in atmospheric transport then may also affect the longevity, chemical conversion, and fate of the plumes chemical constituents, with for example very high plume injection heights being associated with extreme long-range atmospheric transport. Here we review how such landscape-scale fire smoke plume injection heights are represented in larger-scale atmospheric transport models aiming to represent the impacts of wildfire emissions on component of the Earth system. In particular we detail (i) satellite Earth observation data sets capable of being used to remotely assess wildfire plume height distributions and (ii) the driving characteristics of the causal fires. We also discuss both the physical mechanisms and dynamics taking place in fire plumes and investigate the efficiency and limitations of currently available injection height parameterizations. Finally, we conclude by suggesting some future parameterization developments and ideas on Earth observation data selection that may be relevant to the instigation of enhanced methodologies aimed at injection height representation.

Power plants and large industrial facilities contribute more than half of global anthropogenic CO2 emissions. Quantifying the emissions of these point sources is therefore one of the main goals of the planned constellation of anthropogenic CO2 monitoring satellites (CO2M) of the European Copernicus program. Atmospheric transport models may be used to study the capabilities of such satellites through observing system simulation experiments and to quantify emissions in an inverse modeling framework. How realistically the CO2 plumes of power plants can be simulated and how strongly the results may depend on model type and resolution, however, is not well known due to a lack of observations available for benchmarking. Here, we use the unique data set of aircraft in situ and remote sensing observations collected during the CoMet (Carbon Dioxide and Methane Mission) measurement campaign downwind of the coal-fired power plants at Bełchatów in Poland and Jänschwalde in Germany in 2018 to evaluate the simulations of six different atmospheric transport models. The models include three large-eddy simulation (LES) models, two mesoscale numerical weather prediction (NWP) models extended for atmospheric tracer transport, and one Lagrangian particle dispersion model (LPDM) and cover a wide range of model resolutions from 200 m to 2 km horizontal grid spacing. At the time of the aircraft measurements between late morning and early afternoon, the simulated plumes were slightly (at Jänschwalde) to highly (at Bełchatów) turbulent, consistent with the observations, and extended over the whole depth of the atmospheric boundary layer (ABL; up to 1800 m a.s.l. (above sea level) in the case of Bełchatów). The stochastic nature of turbulent plumes puts fundamental limitations on a point-by-point comparison between simulations and observations. Therefore, the evaluation focused on statistical properties such as plume amplitude and width as a function of distance from the source. LES and NWP models showed similar performance and sometimes remarkable agreement with the observations when operated at a comparable resolution. The Lagrangian model, which was the only model driven by winds observed from the aircraft, quite accurately captured the location of the plumes but generally underestimated their width. A resolution of 1 km or better appears to be necessary to realistically capture turbulent plume structures. At a coarser resolution, the plumes disperse too quickly, especially in the near-field range (0–8 km from the source), and turbulent structures are increasingly smoothed out. Total vertical columns are easier to simulate accurately than the vertical distribution of CO2, since the latter is critically affected by profiles of vertical stability, especially near the top of the ABL. Cross-sectional flux and integrated mass enhancement methods applied to synthetic CO2M data generated from the model simulations with a random noise of 0.5–1.0 ppm (parts per million) suggest that emissions from a power plant like Bełchatów can be estimated with an accuracy of about 20 % from single overpasses. Estimates of the effective wind speed are a critical input for these methods. Wind speeds in the middle of the ABL appear to be a good approximation for plumes in a well-mixed ABL, as encountered during CoMet.

Quantification of greenhouse gas emissions is receiving a lot of attention because of its relevance for climate mitigation. Complementary to official reported bottom-up emission inventories, quantification can be done with an inverse modelling framework, combining atmospheric transport models, prior gridded emission inventories and a network of atmospheric observations to optimize the emission inventories. An important aspect of such a method is a correct quantification of the uncertainties in all aspects of the modelling framework. The uncertainties in gridded emission inventories are, however, not systematically analysed. In this work, a statistically coherent method is used to quantify the uncertainties in a high-resolution gridded emission inventory of CO2 and CO for Europe. We perform a range of Monte Carlo simulations to determine the effect of uncertainties in different inventory components, including the spatial and temporal distribution, on the uncertainty in total emissions and the resulting atmospheric mixing ratios. We find that the uncertainties in the total emissions for the selected domain are 1 % forCO2 and 6 % for CO. Introducing spatial disaggregation causes a significant increase in the uncertainty of up to 40 % for CO2 and 70 % for CO for specific grid cells. Using gridded uncertainties, specific regions can be defined that have the largest uncertainty in emissions and are thus an interesting target for inverse modellers. However, the largest sectors are usually the best-constrained ones (low relative uncertainty), so the absolute uncertainty is the best indicator for this. With this knowledge, areas can be identified that are most sensitive to the largest emission uncertainties, which supports network design.

The production of chlorofluorocarbons (CFCs) that would ultimately be released to the atmosphere was banned globally in 2010 under the Montreal Protocol. Here we use measurements combined with an atmospheric transport model to show how atmospheric abundances and emissions of five CFCs increased between 2010 and 2020, contrary to the goals of the phase-out. The Montreal Protocol allows CFC production for use as a feedstock to produce other chemicals. Emissions of CFC-113a, CFC-114a and CFC-115 probably arise during the production of hydrofluorocarbons, which have replaced CFCs for many applications. The drivers behind increasing emissions of CFC-13 and CFC-112a are more uncertain. The combined emissions of CFC-13, CFC-112a, CFC-113a, CFC-114a and CFC-115 increased from 1.6 ± 0.2 to 4.2 ± 0.4 ODP-Gg yr-1 (CFC-11-equivalent ozone-depleting potential) between 2010 and 2020. The anticipated impact of these emissions on stratospheric ozone recovery is small. However, ongoing emissions of the five CFCs of focus may negate some of the benefits gained under the Montreal Protocol if they continue to rise. In addition, the climate impact of the emissions of these CFCs needs to be considered, as their 2020 emissions are equivalent to 47 ± 5 TgCO2.Levels of five chlorofluorocarbons rose in the atmosphere from 2010 to 2020 despite their production being banned by the Montreal Protocol, probably arising as by-products of hydrofluorocarbon production, according to analysis of abundance and emissions data.

The COVID-19 pandemic created an extreme natural experiment in which sudden changes in human behavior and economic activity resulted in significant declines in nitrogen oxide (NOx) emissions, immediately after strict lockdowns were imposed. Here we examined the impact of multiple waves and response phases of the pandemic on nitrogen dioxide (NO2) dynamics and the role of meteorology in shaping relative contributions from different emission sectors to NO2 pollution in post-pandemic New York City. Long term (> 3.5 years), high frequency measurements from a network of ground-based Pandora spectrometers were combined with TROPOMI satellite retrievals, meteorological data, mobility trends, and atmospheric transport model simulations to quantify changes in NO2 across the New York metropolitan area. The stringent lockdown measures after the first pandemic wave resulted in a decline in top-down NOx emissions by approx. 30 % on top of long-term trends, in agreement with sector-specific changes in NOx emissions. Ground-based measurements showed a sudden drop in total column NO2 in spring 2020, by up to 36 % in Manhattan and 19 %–29 % in Queens, New Jersey (NJ), and Connecticut (CT), and a clear weakening (by 16 %) of the typical weekly NO2 cycle. Extending our analysis to more than a year after the initial lockdown captured a gradual recovery in NO2 across the NY/NJ/CT tri-state area in summer and fall 2020, as social restrictions eased, followed by a second decline in NO2 coincident with the second wave of the pandemic and resurgence of lockdown measures in winter 2021. Meteorology was not found to have a strong NO2 biassing effect in New York City after the first pandemic wave. Winds, however, were favorable for low NO2 conditions in Manhattan during the second wave of the pandemic, resulting in larger column NO2 declines than expected based on changes in transportation emissions alone. Meteorology played a key role in shaping the relative contributions from different emission sectors to NO2 pollution in the city, with low-speed (< 5 m s-1) SW-SE winds enhancing contributions from the high-emitting power-generation sector in NJ and Queens and driving particularly high NO2 pollution episodes in Manhattan, even during – and despite – the stringent early lockdowns. These results have important implications for air quality management in New York City, and highlight the value of high resolution NO2 measurements in assessing the effects of rapid meteorological changes on air quality conditions and the effectiveness of sector-specific NOx emission control strategies.

According to the United States Environmental Protection Agency (US EPA), emissions from oil and gas infrastructure contribute 30 % of all anthropogenic methane (CH4) emissions in the US. Studies in the last decade have shown emissions from this sector to be substantially larger than bottom-up assessments, including the EPA inventory, highlighting both the increased importance of methane emissions from the oil and gas sector in terms of their overall climatological impact and the need for independent monitoring of these emissions. In this study we present continuous monitoring of regional methane emissions from two oil and gas basins using tower-based observing networks. Continuous methane measurements were taken at four tower sites in the northeastern Marcellus basin from May 2015 through December 2016 and five tower sites in the Delaware basin in the western Permian from March 2020 through April 2022. These measurements, an atmospheric transport model, and prior emission fields are combined using an atmospheric inversion to estimate monthly methane emissions in the two regions. This study finds the mean overall emission rate from the Delaware basin during the measurement period to be 146–210 Mg CH4 h−1 (energy-normalized loss rate of 1.1 %–1.5 %, gas-normalized rate of 2.5 %–3.5 %). Strong temporal variability in the emissions was present, with the lowest emission rates occurring during the onset of the COVID-19 pandemic. Additionally, a synthetic model–data experiment performed using the Delaware tower network shows that the presence of intermittent sources is not a significant source of uncertainty in monthly quantification of the mean emission rate. In the Marcellus, this study finds the overall mean emission rate to be 19–28 Mg CH4 h−1 (gas-normalized loss rate of 0.30 %–0.45 %), with relative consistency in the emission rate over time. These totals align with aircraft top-down estimates from the same time periods. In both basins, the tower network was able to constrain monthly flux estimates within ±20 % uncertainty in the Delaware and ±24 % uncertainty in the Marcellus. The results from this study demonstrate the ability to monitor emissions continuously and detect changes in the emissions field, even in a basin with relatively low emissions and complex background conditions.

Atmospheric measurements can be used as a tool to evaluate national greenhouse gas inventories through inverse modelling. Using 8 years of continuous methane (CH4) concentration data, this work assesses the United Kingdom's (UK) CH4 emissions over the period 2013–2020. Using two different inversion methods, we find mean emissions of 2.10 ± 0.09 and 2.12 ± 0.26 Tg yr−1 between 2013 and 2020, an overall trend of −0.05 ± 0.01 and −0.06 ± 0.04 Tg yr−2 and a 2 %–3 % decrease each year. This compares with the mean emissions of 2.23 Tg yr−1 and the trend of −0.03 Tg yr−2 (1 % annual decrease) reported in the UK's 2021 inventory between 2013 and 2019. We examine how sensitive these estimates are to various components of the inversion set-up, such as the measurement network configuration, the prior emissions estimate, the inversion method and the atmospheric transport model used. We find the decreasing trend to be due, primarily, to a reduction in emissions from England, which accounts for 70 % of the UK CH4 emissions. Comparisons during 2015 demonstrate consistency when different atmospheric transport models are used to map the relationship between sources and atmospheric observations at the aggregation level of the UK. The posterior annual national means and negative trend are found to be consistent across changes in network configuration. We show, using only two monitoring sites, that the same conclusions on mean UK emissions and negative trend would be reached as using the full six-site network, albeit with larger posterior uncertainties. However, emissions estimates from Scotland fail to converge on the same posterior under different inversion set-ups, highlighting a shortcoming of the current observation network in monitoring all of the UK. Although CH4 emissions in 2020 are estimated to have declined relative to previous years, this decrease is in line with the longer-term emissions trend and is not necessarily a response to national lockdowns.