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The paper reviews and evaluates a 30-year-long atmospheric CO2 data series measured at the Hegyhátsál tall-tower greenhouse gas monitoring site, a member of the World Meteorological Organization (WMO) Global Atmosphere Watch (GAW), US National Oceanic and Atmospheric Administration (NOAA), and pan-European Integrated Carbon Observation System (ICOS) networks. The paper also gives the technical description of the monitoring system and its changes over time and introduces the environment of the station. This low-elevation (248 m above mean sea level – m a.m.s.l.), mid-continental central European site shows a 3.90 ± 0.83 µmolmol-1 offset relative to the latitudinally representative marine boundary layer reference concentration, presumably due to European net anthropogenic emissions. The long-term trend (2.20 µmolmol-1yr-1) closely follows the global tendencies. In the concentration growth rate, the ENSO effect is clearly detectable with a 6–7-month lag time. The summer diurnal concentration amplitude is slightly decreasing due to the faster-than-average increase in the nighttime concentrations, which is related to the warming climate. The warming climate also caused a 0.96 ± 0.41 dyr-1 advance at the beginning of the summer CO2 -deficit season in the first half of the measurement period, which did not continue later. The summer CO2 -deficit season was extended by 9.0 ± 6.1 d during the measurement period. [ABSTRACT FROM AUTHOR]

Atmospheric emissions from anthropogenic hotspots, i.e., cities, power plants and industrial facilities, can be determined from remote sensing images obtained from airborne and space-based imaging spectrometers. In this paper, we present a Python library for data-driven emission quantification (ddeq) that implements various computationally light methods such as the Gaussian plume inversion, cross-sectional flux method, integrated mass enhancement method and divergence method. The library provides a shared interface for data input and output and tools for pre- and post-processing of data. The shared interface makes it possible to easily compare and benchmark the different methods. The paper describes the theoretical basis of the different emission quantification methods and their implementation in the ddeq library. The application of the methods is demonstrated using Jupyter notebooks included in the library, for example, for NO 2 images from the Sentinel-5P/TROPOMI satellite and for synthetic CO 2 and NO 2 images from the Copernicus CO 2 Monitoring (CO2M) satellite constellation. The library can be easily extended for new datasets and methods, providing a powerful community tool for users and developers interested in emission monitoring using remote sensing images. [ABSTRACT FROM AUTHOR]

Climatologies, which depict mean fields of oceanographic variables on a regular geographic grid, and atlases, which provide graphical depictions of specific areas, play pivotal roles in comprehending the societal vulnerabilities linked to ocean acidification (OA). This significance is particularly pronounced in coastal regions where most economic activities, such as commercial and recreational fisheries and aquaculture industries, occur. In this paper, we unveil a comprehensive data product featuring coastal ocean acidification climatologies and atlases, encompassing the fugacity of carbon dioxide, pH on the total scale, total hydrogen ion content, free hydrogen ion content, carbonate ion content, aragonite saturation state, calcite saturation state, Revelle factor, total dissolved inorganic carbon content, and total alkalinity content. These variables are provided on 1° × 1° spatial grids at 14 standardized depth levels, ranging from the surface to a depth of 500 m, along the North American ocean margins, defined as the region between the coastline and a distance of 200 nautical miles (∼370 km) offshore. The climatologies and atlases were developed using the World Ocean Atlas (WOA) gridding methods of the NOAA National Centers for Environmental Information (NCEI) based on the recently released Coastal Ocean Data Analysis Product in North America (CODAP-NA), along with the 2021 update to the Global Ocean Data Analysis Project version 2 (GLODAPv2.2021) data product. The relevant variables were adjusted to the index year of 2010. The data product is available in NetCDF (https://doi.org/10.25921/g8pb-zy76 , Jiang et al., 2022b) on the NOAA Ocean Carbon and Acidification Data System: https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0270962.html (last access: 15 July 2024). It is recommended to use the objectively analyzed mean fields (with "_an" suffix) for each variable. The atlases can be accessed at https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/synthesis/nacoastal.html (last access: 15 July 2024). [ABSTRACT FROM AUTHOR]

Previous research has shown that stratospheric aerosol causes only a small temperature change per unit forcing because they produce stronger cooling in the tropical Indian Ocean and the western Pacific Ocean than in the global mean. The enhanced temperature change in this so-called "warm-pool" region activates strongly negative local and remote feedbacks, which dampen the global mean temperature response. This paper addresses the question of why stratospheric aerosol forcing affects warm-pool temperatures more strongly than CO 2 forcing, using idealized MPI-ESM simulations. We show that the aerosol's enhanced effective forcing at the top of the atmosphere (TOA) over the warm pool contributes to the warm-pool-intensified temperature change but is not sufficient to explain the effect. Instead, the pattern of surface effective forcing, which is substantially different from the effective forcing at the TOA, is more closely linked to the temperature pattern. Independent of surface temperature changes, the aerosol heats the tropical stratosphere, accelerating the Brewer–Dobson circulation. The intensified Brewer–Dobson circulation exports additional energy from the tropics to the extratropics, which leads to a particularly strong negative forcing at the tropical surface. These results show how forced circulation changes can affect the climate response by altering the surface forcing pattern. Furthermore, they indicate that the established approach of diagnosing effective forcing at the TOA is useful for global means, but a surface perspective on the forcing must be adopted to understand the evolution of temperature patterns. [ABSTRACT FROM AUTHOR]

Quantifying water vapor and carbon dioxide (CO 2) exchange dynamics between the land and the atmosphere through observations and modeling is necessary in order to reproduce and project near-surface climate in coupled land–atmosphere models. The exchange of water and CO 2 occurs at the leaf surface (leaf level) and in a net manner through exchanges at all the leaf surfaces composing the vegetation canopy and at the soil surface (canopy level). These exchanges depend on the meteorological forcings imposed by the overlying atmosphere (atmospheric boundary layer level). In this paper, we investigate the effect of four canopy environmental variables (photosynthetic active radiation (PAR), water vapor pressure deficit (VPD), air temperature (T), and atmospheric CO 2 concentration (Ca)) on the local individual leaf exchange and canopy exchange of water and CO 2 at hourly timescales. Additionally, we investigate the effect of atmospheric boundary layer (ABL) processes on the local exchange. To that end, we simultaneously investigated the exchanges of water and CO 2 at leaf level and canopy level for an alfalfa field in northern Spain over 1 day in summer 2021. We used comprehensive observations ranging from stomatal conductance to ABL measurements collected during the Land Surface Interactions with the Atmosphere in the Iberian Semi-Arid Environment (LIAISE) experiment. To support the observational analysis, we used a coupled land–atmosphere model (CLASS model) that has representations at all levels considered. To relate how temporal changes of the four environmental variables modify the fluxes of water and CO 2 , we studied tendency equations of the leaf gas exchange. These mathematical expressions quantify the temporal evolution of the leaf gas exchange as a function of the temporal evolution of PAR, VPD, T , and Ca. To investigate the effects of ABL processes on the local exchange, we developed three modeling experiments that impose surface radiative perturbations by a cloud passage (which perturbed PAR, T , and VPD), entrainment of dry air from the free troposphere (which perturbed VPD), and advection of cold air (which perturbed T and VPD). The model results and observations matched the leaf gas exchange (r2 between 0.23 and 0.67) and canopy gas exchange (r2 between 0.90 and 0.95). The tendency equations of the modeled leaf gas exchange during the study day revealed that the temporal dynamics of PAR were the main contributor to the temporal dynamics of the leaf gas exchange, with atmospheric CO 2 temporal dynamics being the least important contributor. From the three modeling experiments with ABL perturbations, the surface radiative changes induced by a cloud perturbed the CO 2 exchange the most, whereas all of them perturbed the water exchange to a similar extent. Second-order effects on the dynamics of the leaf gas exchange were also identified using the tendency equations. For instance, the decrease in the net CO 2 assimilation rate during the cloud passage caused by a decrease in surface radiation was further enhanced due to the decrease in air temperature also associated with the cloud. With this research we showcase that the proposed tendency equations can disentangle the effect of environmental variables on the leaf exchange of water and CO 2 with the atmosphere, as represented in land–surface parameterization schemes. As such, this framework can become a useful tool with which to analyze these schemes in weather and climate models. [ABSTRACT FROM AUTHOR]

The Atmospheric Environment Monitoring Satellite (DQ-1) was successfully launched in April 2022, with the capability of providing continuous multi-sensor spatial and optical simultaneous observations of carbon dioxide, aerosols and clouds. The primary payload carried on DQ-1 is an Aerosol and Carbon dioxide Detection Lidar (ACDL). The instrument comprises a high-spectral-resolution channel at 532 nm, elastic channels at 532 nm and 1064 nm, and integrated-path differential absorption (IPDA) channel at 1572 nm. The optical properties of aerosols and clouds measured by the ACDL promote a quantitative characterization of the uncertainties in the global climate system, hence the precise calibrations for the ACDL are necessary. This paper outlines the algorithms employed for calibrating the nighttime 532 nm measurements for the first spaceborne high-spectral-resolution lidar with an iodine vapor absorption filter. The nighttime calibrations of the 532 nm data are fundamental to the ACDL measurement procedure, as they are utilized to derive the calibrations over daytime orbits and the calibrations of the 1064 nm channel relative to the 532 nm channel. This paper provides a review of the theoretical foundations for molecular normalization techniques as applied to spaceborne lidar measurements, includes a detailed discussion of auxiliary data and theoretical parameters used in ACDL calibrations, as well as a comprehensive description of the calibration algorithm procedure. To mitigate large errors stemming from high-energy events during calibration, a data filter is designed to obtain valid calibration signals. The paper also assesses the results of the calibration procedure, by analysing the errors of calibration coefficients and validating the attenuated backscatter coefficient results. The results indicate that the relative error of the calibrated attenuated backscatter coefficients is lower than 2 % in the calibration area, and the uncertainty of the pure-molecule attenuated scattering ratio was within anticipated range of 5 %. [ABSTRACT FROM AUTHOR]

The quantification of emissions of greenhouse gases and air pollutants through the inversion of plumes in satellite images remains a complex problem that current methods can only assess with significant uncertainties. The anticipated launch of the CO2M (Copernicus Anthropogenic Carbon Dioxide Monitoring) satellite constellation in 2026 is expected to provide high-resolution images of CO2 (carbon dioxide) column-averaged mole fractions (XCO2), opening up new possibilities. However, the inversion of future CO2 plumes from CO2M will encounter various obstacles. A challenge is the low CO2 plume signal-to-noise ratio due to the variability in the background and instrumental errors in satellite measurements. Moreover, uncertainties in the transport and dispersion processes further complicate the inversion task. To address these challenges, deep learning techniques, such as neural networks, offer promising solutions for retrieving emissions from plumes in XCO2 images. Deep learning models can be trained to identify emissions from plume dynamics simulated using a transport model. It then becomes possible to extract relevant information from new plumes and predict their emissions. In this paper, we develop a strategy employing convolutional neural networks (CNNs) to estimate the emission fluxes from a plume in a pseudo- XCO2 image. Our dataset used to train and test such methods includes pseudo-images based on simulations of hourly XCO2 , NO2 (nitrogen dioxide), and wind fields near various power plants in eastern Germany, tracing plumes from anthropogenic and biogenic sources. CNN models are trained to predict emissions from three power plants that exhibit diverse characteristics. The power plants used to assess the deep learning model's performance are not used to train the model. We find that the CNN model outperforms state-of-the-art plume inversion approaches, achieving highly accurate results with an absolute error about half of that of the cross-sectional flux method and an absolute relative error of ∼ 20 % when only the XCO2 and wind fields are used as inputs. Furthermore, we show that our estimations are only slightly affected by the absence of NO2 fields or a detection mechanism as additional information. Finally, interpretability techniques applied to our models confirm that the CNN automatically learns to identify the XCO2 plume and to assess emissions from the plume concentrations. These promising results suggest a high potential of CNNs in estimating local CO2 emissions from satellite images. [ABSTRACT FROM AUTHOR]

As the vegetation in the Arctic changes, tundra ecosystems along the southern border of the Arctic are becoming greener and gradually giving way to boreal ecosystems. This change is affecting local populations, wildlife, energy exchange processes between environmental compartments, and the carbon cycle. To understand the progression and the implications of this change in vegetation, satellite measurements and surface models can be employed. However, in situ observational data are required to validate these measurements and models. This paper presents observational data from two nearby sites in the forest–tundra ecotone in the Tasiapik Valley near Umiujaq in Northern Quebec, Canada. One site is on a mixture of lichen and shrub tundra. The associated data set comprises 9 years of meteorological, soil and snow data as well as 3 years of eddy covariance data. The other site, 850 m away, features vegetation consisting mostly of tall shrubs and black spruce. For that location, 6 years of meteorological, soil and snow data are available. In addition to the data from the automated stations, profiles of snow density and specific surface area were collected during field campaigns. The data are available at 10.1594/PANGAEA.964743 (Domine et al., 2024). [ABSTRACT FROM AUTHOR]

Predicting the responses of terrestrial ecosystem carbon to future global change strongly relies on our ability to model accurately the underlying processes at a global scale. However, terrestrial biosphere models representing the carbon and nitrogen cycles and their interactions remain subject to large uncertainties, partly because of unknown or poorly constrained parameters. Parameter estimation is a powerful tool that can be used to optimise these parameters by confronting the model with observations. In this paper, we identify sensitive model parameters from a recent version of the ORgainzing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) land surface model that includes the nitrogen cycle. These sensitive parameters include ones involved in parameterisations controlling the impact of the nitrogen cycle on the carbon cycle and, in particular, the limitation of photosynthesis due to leaf nitrogen availability. We optimise these ORCHIDEE parameters against carbon flux data collected on sites from the FLUXNET network. However, optimising against present-day observations does not automatically give us confidence in future projections of the model, given that environmental conditions are likely to shift compared to the present day. Manipulation experiments give us a unique look into how the ecosystem may respond to future environmental changes. One such type of manipulation experiment, the Free Air CO 2 Enrichment (FACE) experiment, provides a unique opportunity to assess vegetation response to increasing CO 2 by providing data under ambient and elevated CO 2 conditions. Therefore, to better capture the ecosystem response to increased CO 2 , we add the data from two FACE sites to our optimisations, in addition to the FLUXNET data. We use data from both CO 2 conditions of FACE, which allows us to gain extra confidence in the model simulations using this set of parameters. We find that we are able to improve the magnitude of modelled productivity. Although we are unable to correct the interannual variability fully, we start to simulate possible progressive nitrogen limitation at one of the sites. Using an idealised simulation experiment based on increasing atmospheric CO 2 by 1 % yr -1 over 100 years, we find that optimising against only FLUXNET data tends to imply a large fertilisation effect, whereas optimising against FLUXNET and FACE data (with information about nutrient limitation and acclimation of plants) decreases it significantly. [ABSTRACT FROM AUTHOR]

The Geostationary Carbon Cycle Observatory (GeoCarb) was selected as NASA's second Earth Venture Mission (EVM-2). The scientific objectives of GeoCarb were to advance our knowledge of the carbon cycle, in particular, land–atmosphere fluxes of the greenhouse gases carbon dioxide (CO 2) and methane (CH 4) and the effects of these fluxes on the Earth's radiation budget. GeoCarb would retrieve column-integrated dry-air mole fractions of CO 2 (XCO2), CH 4 (XCH4) and CO (XCO), important for understanding tropospheric chemistry), in addition to solar-induced fluorescence (SIF), from hyperspectral resolution measurements in the O 2 A-band at 0.76 µ m, the weak CO 2 band at 1.6 µ m, the strong CO 2 band at 2.06 µ m, and a CH 4 /CO band at 2.32 µ m. Unlike its predecessors (OCO-2/3, GOSAT-1/2, TROPOMI), GeoCarb would be in a geostationary orbit with a sub-satellite point centered over the Americas. This orbital configuration combined with its high-spatial-resolution imaging capabilities would provide an unprecedented view of these quantities on spatial and temporal scales accurate enough to resolve sources and sinks to improve land–atmosphere CO 2 and CH 4 flux calculations and reduce the uncertainty of these fluxes. This paper will present a description of the GeoCarb instrument and the L2 retrieval algorithms which will be followed by simulation experiments to determine an error budget for each target gas. Several sources of uncertainty will be explored, including that from the instrument calibration parameters for radiometric gain, the instrument line shape (ILS), the polarization, and the geolocation pointing, in addition to forward model parameters including meteorology and spectroscopy, although there are some other instrument-related sources of uncertainty that are left out for this study, including that from "smile", the keystone effect, stray light, detector persistence, and scene inhomogeneity. The results indicate that the errors (1 σ) are less than the instrument's multi-sounding precision requirements of 1.2 ppm, 10 ppb, and 12 ppb (10 %), for XCO2 , XCH4 , and XCO , respectively. In particular, when considering the sources of uncertainty separately and in combination (all sources included), we find overall RMSEs of 1.06 ppm for XCO2 , 8.2 ppb for XCH4 , and 2.5 ppb for XCO , respectively. Additionally, we find that, as expected, errors in XCO2 and XCH4 are dominated by forward model and other systematic errors, while errors in XCO are dominated by measurement noise. It is important to note that the GeoCarb mission was canceled by NASA; however, the instrument is still in development and will be delivered to NASA, in full, with the hope that it will eventually be adopted in a future mission proposal. [ABSTRACT FROM AUTHOR]

A key goal of the Paris Agreement (PA) is to reach net-zero greenhouse gas (GHG) emissions by 2050 globally, which requires mitigation efforts from all countries. Africa's rapidly growing population and gross domestic product (GDP) make this continent important for GHG emission trends. In this paper, we study the emissions of carbon dioxide (CO 2), methane (CH 4) and nitrous oxide (N 2 O) in Africa over 3 decades (1990–2018). We compare bottom-up (BU) approaches, including United Nations Convention Framework on Climate Change (UNFCCC) national inventories, FAO, PRIMAP-hist, process-based ecosystem models for CO 2 fluxes in the land use, land use change and forestry (LULUCF) sector and global atmospheric inversions. For inversions, we applied different methods to separate anthropogenic CH 4 emissions. The BU inventories show that, over the decade 2010–2018, fewer than 10 countries represented more than 75 % of African fossil CO 2 emissions. With a mean of 1373 Mt CO 2 yr -1 , total African fossil CO 2 emissions over 2010–2018 represent only 4 % of global fossil emissions. However, these emissions grew by +34% from 1990–1999 to 2000–2009 and by +31% from 2000–2009 to 2010–2018, which represents more than a doubling in 30 years. This growth rate is more than 2 times faster than the global growth rate of fossil CO 2 emissions. The anthropogenic emissions of CH 4 grew by 5 % from 1990–1999 to 2000–2009 and by 14.8 % from 2000–2009 to 2010–2018. The N 2 O emissions grew by 19.5 % from 1990–1999 to 2000–2009 and by 20.8 % from 2000–2009 to 2010–2018. When using the mean of the estimates from UNFCCC reports (including the land use sector) with corrections from outliers, Africa was a mean source of greenhouse gases of 262221863239 Mt CO 2 eq. yr -1 from all BU estimates (the subscript and superscript indicate min–max range uncertainties) and of +263717615873 Mt CO 2 eq. yr -1 from top-down (TD) methods during their overlap period from 2001 to 2017. Although the mean values are consistent, the range of TD estimates is larger than the one of the BU estimates, indicating that sparse atmospheric observations and transport model errors do not allow us to use inversions to reduce the uncertainty in BU estimates. The main source of uncertainty comes from CO 2 fluxes in the LULUCF sector, for which the spread across inversions is larger than 50 %, especially in central Africa. Moreover, estimates from national UNFCCC communications differ widely depending on whether the large sinks in a few countries are corrected to more plausible values using more recent national sources following the methodology of Grassi et al. (2022). The medians of CH 4 emissions from inversions based on satellite retrievals and surface station networks are consistent with each other within 2 % at the continental scale. The inversion ensemble also provides consistent estimates of anthropogenic CH 4 emissions with BU inventories such as PRIMAP-hist. For N 2 O, inversions systematically show higher emissions than inventories, on average about 4.5 times more than PRIMAP-hist, either because natural N 2 O sources cannot be separated accurately from anthropogenic ones in inversions or because BU estimates ignore indirect emissions and underestimate emission factors. Future improvements can be expected thanks to a denser network of monitoring atmospheric concentrations. This study helps to introduce methods to enhance the scope of use of various published datasets and allows us to compute budgets thanks to recombinations of those data products. Our results allow us to understand uncertainty and trends in emissions and removals in a region of the world where few observations exist and where most inventories are based on default IPCC guideline values. The results can therefore serve as a support tool for the Global Stocktake (GST) of the Paris Agreement. The referenced datasets related to the figures are available at 10.5281/zenodo.7347077 (Mostefaoui et al., 2022). [ABSTRACT FROM AUTHOR]

This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, http://www.isimip.org , last access: 2 November 2023) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human, and managed systems to climate change, rising CH 4 and CO 2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6. [ABSTRACT FROM AUTHOR]

Aviation emissions that are dispersed into the Earth's atmosphere affect the climate and air pollution, with significant spatiotemporal variation owing to heterogeneous aircraft activity. In this paper, we use historical flight trajectories derived from Automatic Dependent Surveillance–Broadcast (ADS-B) telemetry and reanalysis weather data for 2019–2021 to develop the Global Aviation emissions Inventory based on ADS-B (GAIA). In 2019, 40.2 million flights collectively travelled 61 billion kilometres using 283 Tg of fuel, leading to CO 2 , NO X and non-volatile particulate matter (nvPM) mass and number emissions of 893 Tg, 4.49 Tg, 21.4 Gg and 2.8 × 10 26 respectively. Global responses to COVID-19 led to reductions in the annual flight distance flown and CO 2 and NO X emissions in 2020 (- 43 %, - 48 % and - 50 % respectively relative to 2019) and 2021 (- 31 %, - 41 % and - 43 % respectively), with significant regional variability. Short-haul flights with durations < 3 h accounted for 83 % of all flights but only for 35 % of the 2019 CO 2 emissions, while long-haul flights with durations > 6 h (5 % of all flights) were responsible for 43 % of CO 2 and 49 % of NO X emissions. Globally, the actual flight trajectories flown are, on average, ∼ 5 % greater than the great circle path between the origin and destination airports, but this varies by region and flight distance. An evaluation of 8705 unique flights between London and Singapore showed large variabilities in the flight trajectory profile, fuel consumption and emission indices. GAIA captures the spatiotemporal distribution of aviation activity and emissions and is provided for use in future studies to evaluate the negative externalities arising from global aviation. [ABSTRACT FROM AUTHOR]

Since 1999, Environment and Climate Change Canada (ECCC) has been coordinating a multi-laboratory comparison of measurements of long-lived greenhouse gases in whole air samples collected at the Global Atmosphere Watch (GAW) Alert Observatory located in the Canadian High Arctic (82 ∘ 28 ′ N, 62 ∘ 30 ′ W). In this paper, we evaluate the measurement agreement of atmospheric CO 2 , CH 4 , N 2 O, SF 6 , and stable isotopes of CO 2 (δ13 C, δ18 O) between leading laboratories from seven independent international institutions. The measure of success is linked to target goals for network compatibility outlined by the World Meteorological Organization's (WMO) GAW greenhouse gas measurement community. Overall, based on ∼ 8000 discrete flask samples, we find that the co-located atmospheric CO 2 and CH 4 measurement records from Alert by CSIRO, MPI-BGC, SIO, UHEI-IUP, and ECCC versus NOAA (the designated reference laboratory) are generally consistent with the WMO compatibility goals of ± 0.1 ppm CO 2 and ± 2 ppb CH 4 over the 17-year period (1999–2016), although there are periods where differences exceed target levels and persist as systematic bias for months or years. Consistency with the WMO goals for N 2 O, SF 6 , and stable isotopes of CO 2 (δ13 C, δ18 O) has not been demonstrated. Additional analysis of co-located comparison measurements between CSIRO and SIO versus NOAA or INSTAAR (for the isotopes of CO 2) at other geographical sites suggests that the findings at Alert for CO 2 , CH 4 , N 2 O, and δ13 C–CO 2 could be extended across the CSIRO, SIO, and NOAA observing networks. The primary approach to estimate an overall measurement agreement level was carried out by pooling the differences of all individual laboratories versus the designated reference laboratory and determining the 95th percentile range of these data points. Using this approach over the entire data record, our best estimate of the measurement agreement range is -0.51 to + 0.53 ppm for CO 2 , -0.09 ‰ to + 0.07 ‰ for δ13 C, -0.50 ‰ to + 0.58 ‰ for δ18 O, -4.86 to + 6.16 ppb for CH 4 , -0.75 to + 1.20 ppb for N 2 O, and -0.14 to + 0.09 ppt for SF 6. A secondary approach of using the average of 2 standard deviations of the means for all flask samples taken in each individual sampling episode provided similar results. These upper and lower limits represent our best estimate of the measurement agreement at the 95 % confidence level for these individual laboratories, providing more confidence for using these datasets in various scientific applications (e.g., long-term trend analysis). [ABSTRACT FROM AUTHOR]

The Thermal and Near-Infrared Sensor for Carbon Observation Fourier-Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse Gases Observing SATellite-2 (GOSAT-2) observes a wide spectral region of the atmosphere, from the ShortWave-InfraRed (SWIR) to the longwave Thermal InfraRed radiation (TIR) with 0.2 cm -1 spectral sampling, and the corresponding spectral resolution (full width at half maximum, FWHM) of TIR region is less than 0.27 cm -1. TANSO-FTS-2 has operated nominally since February 2019, and the atmospheric radiance spectra it has acquired have been released to the public. This paper describes an updated model for spectral radiance calibration and its validation. The model applies to the version v210210 TIR products of TANSO-FTS-2 and integrates polarization sensitivity correction for the internal optics and the pointing mirror thermal emission. These correction parameters are characterized by an optimization that depends on the difference between the spectral radiance of TANSO-FTS-2 and coincident nadir observation data from the Infrared Atmospheric Sounding Interferometer (IASI) on METOP-B. To validate the updated spectral radiance product against other satellite products, temporally and spatially coincident observation points were considered for the simultaneous nadir overpass (SNO) from February 2019 to March 2021 from the Atmospheric Infrared Sounder (AIRS) on Aqua, IASI on METOP-B, and TANSO-FTS on GOSAT. The agreement of brightness temperatures between TANSO-FTS-2 and AIRS and IASI was better than 0.3 K (1 σ) from 180 to 330 K for the 680 cm -1 CO 2 spectral range. The brightness temperatures between TANSO-FTS-2 and TANSO-FTS of version v230231, which implemented a new polarization reflectivity of the pointing mirror and was released in June 2021, generally agree from 220 to 320 K. However, there is a discrepancy at lower brightness temperatures, pronounced for CO 2 spectral ranges at high latitudes. To characterize the spectral radiance bias for along-track and cross-track angles, a 2-orthogonal simultaneous off-nadir overpass (2O-SONO) is now done for TANSO-FTS-2 and IASI, TANSO-FTS-2 and AIRS, and TANSO-FTS-2 and TANSO-FTS. The 2O-SONO comparison results indicate that the TIR product for TANSO-FTS-2 has a bias that exceeds 0.5 K in the CO 2 spectral range for scenes with forward and backward viewing angles greater than 20 ∘. These multi-satellite sensor and multi-angle comparison results suggest that the calibration of spectral radiance for TANSO-FTS-2 TIR, version v210210, is superior to that of the previous version in its consistency of multi-satellite sensor data. In addition, the paper identifies the remaining challenging issues in current TIR products. [ABSTRACT FROM AUTHOR]

Aviation emission accounting is the key to establishing market measures to control aviation pollutant emissions. Based on the fuel percentage method (FPM), this paper applies the improved BFFM2-FOA-FPM (Boeing Fuel Flow Method 2–First Order Approximation FPM) to calculate the emissions of six pollutants (CO 2 , CO, HC, NO x , SO 2 , and PM 2.5) between Chinese and foreign cities from 2014 to 2019, including CCD (climbing, cruising, and descending) emissions and LTO (landing and take-off) emissions. The error rate between the calculated results and the official data is about 2.75 %. The results show that the emissions of six pollutants changed before and after the proposal of the "Carbon Neutral Growth 2020" strategy (CNG2020 strategy). Although the total amount has increased, the average emission per tonne-kilometer of CO 2 , CO, HC, NO x , SO 2 , and PM 2.5 has decreased by 17.77 %, 17.26 %, 25.15 %, 14.32 %, 17.77 %, and 16.35 %, respectively. The results of this paper can provide a data basis and method reference for implementing the CNG2020 strategy and realizing global carbon emission reduction goals. The dataset is available from https://doi.org/10.6084/m9.figshare.20071751.v1 (Cui., 2022). [ABSTRACT FROM AUTHOR]

Most anthropogenic carbon dioxide (CO2) emissions originate from urban areas. To improve understandings of urban and regional emissions, we design and construct a low-cost UAV coordinated carbon observation network (LUCCN) which uses mid-accuracy (±1 ppm) CO2 sensors. In this paper, we introduce our multi-variable non-linear regression method for calibrating the non-dispersive infrared (NDIR) CO2 sensors for LUCCN's ground stations. We tested our calibration method with concentration data collected at the Xinglong Atmospheric Background Observatory. With comparison against data simultaneously collected by a high-accuracy cavity ring-down spectrometer, we found the maximum standard deviation of LUCCN's sensors to be 0.782 ppm in a controlled laboratory environment with a 1-second window size and 0.53 ppm in an outdoor environment with a 1-hour running average window size. As validation of LUCCN's ground measurements, we identify and present consistent trends between local CO2 concentration variations and aerosol pollution events captured by the space-based moderate resolution imaging spectrometer (MODIS). [ABSTRACT FROM AUTHOR]

Ocean Alkalinity Enhancement (OAE) aims to transfer carbon dioxide (CO2) from the atmosphere to the ocean by increasing the capacity of seawater to store CO2. The potential effects of OAE-induced changes in seawater chemistry on marine biology must be assessed to understand if OAE, operated at a climate relevant scale, would be environmentally sustainable. Here, we describe the design of the Ocean Alkalinity Enhancement Pelagic Impact Intercomparison Project (OAEPIIP) – a standardised OAE microcosm experiment with plankton communities to be conducted worldwide. OAEPIIP provides funding for participating laboratories to conduct OAE experiments in their local environments. This paper constitutes a detailed manual on the standardised methodology that shall be adopted by all OAEPIIP participants. The individual studies will provide new insights into how plankton communities respond to OAE. The synthesis of these standardized studies, without publication bias, will reveal common OAE-responses that occur across geographic and environmental gradients and are therefore particularly important to determine. The funding available to OAEPIIP and resulting data will be shared to maximise its value and the accessibility. The globally coordinated effort has potential to promote scientific consensus about the potential effects of OAE on diverse plankton communities. Such consensus, through inclusion of the global community, will provide a sounder base to facilitate political decision making whether OAE should be upscaled or not. [ABSTRACT FROM AUTHOR]

Climatologies, which depict mean fields of oceanographic variables on a regular geographic grid, and atlases, which provide graphical depictions of specific areas, play pivotal roles in comprehending the societal vulnerabilities linked to ocean acidification (OA). This significance is particularly pronounced in coastal regions where most economic activities related to commercial and recreational fisheries as well as aquaculture industries occur. In this paper, we unveil a comprehensive data product featuring coastal climatologies and atlases for ten OA indicators, including fugacity of carbon dioxide, pH on the total scale, total hydrogen ion content, free hydrogen ion content, carbonate ion content, aragonite saturation state, calcite saturation state, Revelle Factor, total dissolved inorganic carbon content, and total alkalinity content. These indicators are provided on 1°×1° degree spatial grids at 14 standardized depth levels, ranging from the surface to a depth of 500 meters, along the North American ocean margins – defined as the region between the coastline and a distance of 200 nautical miles (∼370 km) offshore. The climatologies and atlases were developed using the World Ocean Atlas (WOA) gridding methods of the NOAA National Centers for Environmental Information (NCEI), based on the recently released Coastal Ocean Data Analysis Product in North America (CODAP-NA), along with the 2021 update to the Global Ocean Data Analysis Project version 2 (GLODAPv2.2021) data product. The relevant variables were adjusted to the index year of 2010. The data product is available in NetCDF (DOI: 10.25921/g8pb-zy76) at the NOAA Ocean Carbon and Acidification Data System: https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0270962.html. It is recommended to use the objectively analyzed mean fields (with '_an' suffix) for each variable. The atlases can be accessed at: https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/synthesis/nacoastal.html. [ABSTRACT FROM AUTHOR]

This paper introduces GCAM-CDR 1.0, an integrated assessment model for climate policy based on the open-source Global Change Analysis Model (GCAM). GCAM-CDR extends GCAM v5.4 by enabling users to model additional carbon dioxide removal (CDR) technologies and additional policies and controls related to CDR. New CDR technologies include terrestrial enhanced weathering with basalt, ocean liming, and additional versions of direct air capture. New CDR policies and controls include integration of bioenergy with carbon capture and storage (BECCS) into the CDR market, interregional trade in CDR, exogenous control over the rate of growth of CDR, the ability to set independent targets for emissions abatement and CDR, and a variety of mechanisms for setting demand for CDR at the regional and/or global level. These extensions enhance users' ability to study the potential roles of CDR in climate policy. [ABSTRACT FROM AUTHOR]

Ocean alkalinity enhancement (OAE) has recently gained attention as a potential method for carbon dioxide removal (CDR) at gigatonne (Gt) scale, with near-coast OAE operations being economically favorable due to proximity to mineral and energy sources. In this paper we study critical questions which determine the scale and viability of OAE. Which coastal locations are able to sustain a large flux of alkalinity at minimal pH and ΩArag (aragonite saturation) changes? What is the interference distance between adjacent OAE projects? How much CO2 is absorbed per unit of alkalinity added? How quickly does the induced CO2 deficiency equilibrate with the atmosphere? Choosing relatively conservative constraints on Δ pH or Δ Omega, we examine the limits of OAE using the ECCO LLC270 (0.3 ∘) global circulation model. We find that the sustainable OAE rate varies over 1–2 orders of magnitude between different coasts and exhibits complex patterns and non-local dependencies which vary from region to region. In general, OAE in areas of strong coastal currents enables the largest fluxes and depending on the direction of these currents, neighboring OAE sites can exhibit dependencies as far as 400 km or more. At these steady state fluxes most regional stretches of coastline are able to accommodate on the order of 10s to 100s of megatonnes of negative emissions within 300 km of the coast. We conclude that near-coastal OAE has the potential to scale globally to several Gt CO2 yr -1 of drawdown with conservative pH constraints, if the effort is spread over the majority of available coastlines. Depending on the location, we find a diverse set of equilibration kinetics, determined by the interplay of gas exchange and surface residence time. Most locations reach an uptake efficiency plateau of 0.6–0.8 mol CO2 per mol of alkalinity after 3–4 years, after which there is only slow additional CO2 uptake. Regions of significant downwelling (e.g., around Iceland) should be avoided by OAE deployments, as in such locations up to half of the CDR potential of OAE can be lost to bottom waters. The most ideal locations, reaching a molar uptake ratio of around 0.8, include North Madagascar, California, Brazil, Peru and locations close to the Southern Ocean such as Tasmania, Kerguelen and Patagonia, where the gas exchange appears to occur faster than the surface residence time. However, some locations (e.g., Hawaii) take significantly longer to equilibrate (up to 8–10 years) but can still eventually achieve high uptake ratios. [ABSTRACT FROM AUTHOR]

Although natural CO2 -rich groundwaters of eastern Belgium have been known for centuries, the exact origin of their gas is still unclear. This paper presents the results of a sampling campaign in Belgium (Spa, Stoumont, Malmedy): 30 samples of both carbogaseous and non-carbogaseous groundwaters were analyzed for major elements, CO2 content and carbon isotopic composition. Among them, 13 samples were also analyzed for 3He/4He and 4He/20Ne ratios. The combination of δ13C (between ca. -9‰ VPDB Vienna Pee Dee Belemnite and -2‰ VPDB), CO2/3He ratio (between 1.9×108 and 2.9×109) and 3He/4He (between 0.92 and 2.70 Ra) shows with a high level of confidence that the CO2 in the carbogaseous groundwater of Spa and Bru has a mantle origin. It can likely be attributed to the degassing of mantle from the neighboring Eifel volcanic fields, located at a distance of 100 km eastwards. The identity and nature of the deep-rooted fractures that act as CO2 transport pathways to the surface are still to be clarified, but several major thrust faults exist in the Rhenish Massif and could connect the Eifel volcanic fields with the studied area. [ABSTRACT FROM AUTHOR]

A series of attenuated atmospheric backscatter profiles were measured at 1572 nm by the CO 2 Sounder lidar during the eight flights of the 2017 ASCENDS/ABoVE (Active Sensing of CO 2 Emission over Nights, Days, and Seasons, Arctic Boreal Vulnerability Experiment) airborne campaign. In addition to measuring the column average CO 2 mixing ratio from the laser signals reflected by the ground, the CO 2 Sounder lidar also recorded the height-resolved attenuated atmospheric backscatter profiles beneath the aircraft. We have recently processed these vertical profiles with a 15 m vertical spacing and 1 s integration time along the flight path (∼ 200 m) for all the 2017 flights and have posted the results at NASA Distributed Active Archive Center (DAAC) for Biogeochemical Dynamics 10.3334/ORNLDAAC/2051 (Sun et al., 2022). This paper describes the measurement principles, the data processing technique, and the signal to noise ratios. [ABSTRACT FROM AUTHOR]

Ocean Alkalinity Enhancement (OAE) has recently gained attention as a potential method for negative emissions at gigatonne scale, with near-coast OAE operations being economically favorable due to proximity to mineral and energy sources. In this paper we study critical questions which determine the scale and viability of OAE: Which coastal locations are able to sustain a large flux of alkalinity at minimal pH and ΩArag (aragonite saturation) changes? What is the interference distance between adjacent OAE projects? How much CO2 is absorbed per unit of alkalinity added? How quickly does the induced CO2 deficiency equilibrate with the atmosphere? Using the LLC270 (0.3deg) ECCO global circulation model we find that the steady-state OAE rate varies over 1–2 orders of magnitude between different coasts and exhibits complex patterns and non-local dependencies which vary from region to region. In general, OAE in areas of strong coastal currents allow the largest fluxes and depending on the direction of coastal currents, neighboring OAE sites can exhibit dependencies as far as 400 km or more. We found that within relatively conservative constraints set on ∆pH or ∆Omega, most regional stretches of coastline are able to accommodate on the order of tens to hundreds of megatonnes of negative emissions within 300 km of the coast. We conclude that near-coastal OAE has the potential to scale globally to several GtCO2/yr of drawdown with conservative pH constraints, if the effort is spread over the majority of available coastlines. Depending on the location, we find a diverse set of equilibration kinetics, determined by the interplay of gas exchange and surface residence time. Most locations reach an uptake-efficiency plateau of 0.6–0.8mol CO2 per mol of alkalinity after 3–4 years, after which there is little further CO2 uptake. The most ideal locations, reaching an uptake of around 0.8 include north Madagascar, San Francisco, Brazil, Peru and locations close to the southern ocean such as Tasmania, Kerguelen and Patagonia, where the gas exchange appears to occur faster than the surface residence time. Some locations (e.g. Hawaii) take significantly longer to equilibrate (up to 8–10 years), though can still eventually achieve high uptake. If the alkalinity released advects into regions of significant downwelling (e.g. around Iceland) up to half of the OAE potential can be lost to bottom waters. [ABSTRACT FROM AUTHOR]

Forest environments contain a wide variety of airborne biological particles (bioaerosols), including pollen, fungal spores, bacteria, viruses, plant detritus, and soil particles. Forest bioaerosol plays a number of important roles related to plant and livestock health, human disease and allergenicity, and forest and wider ecology and are thought to influence biosphere–atmosphere interactions via warm and cold cloud formation. Despite the importance of bioaerosols, there are few measurements of forest aerosol, and there is a lack of understanding of how climate change will affect forest bioaerosol in the future. We installed low-cost optical particle counters (OPCs) to measure particles in the size range between 1 and 10 µ m (PM 10 –PM 1) for a period of 2 months in autumn 2018 at the Birmingham Institute of Forest Research (BIFoR) free-air carbon dioxide enrichment (FACE) facility. In the paper, we propose that the PM 10 –PM 1 (particulate matter) metric is a good proxy for bioaerosols because of the bioaerosol representative size range, the location of the study site (a woodland in a rural location), the field measurement taking place during the season of peak fungal activity, and the low hygroscopicity of the particles measured. The BIFoR FACE facility fumigates three 700 m 2 areas of the forest with an additional 150 ppm CO 2 above ambient levels with minimal impacts on other potential environmental drivers such as temperature, humidity, and wind. This experimental set-up enabled us to investigate the effect of environmental variables, including elevated CO 2 (e CO 2), on bioaerosol proxy concentrations and to evaluate the performance of the low-cost OPCs in a forested environment. Operating the low-cost OPCs during autumn 2018, we aimed to capture predominantly the fungal bioaerosol season. Across the experimental duration, the OPCs captured both temporal and spatial variation in bioaerosol concentrations. Aerosol concentrations were affected by changing temperatures and wind speeds but, contrary to our initial hypothesis, not by relative humidity. We detected no effect of the e CO 2 treatment on total bioaerosol concentrations, but a potential suppression of high-concentration bioaerosol events was detected under e CO 2. In-canopy atmospheric dispersion modelling indicates that the median spore dispersion distance is sufficiently small that there is little mixing between treatment and control experiments. Our data demonstrate the suitability of low-cost OPCs, interpreted with due caution, for use in forests and so opens the possibility of forest bioaerosol monitoring in a wider range of habitats to a wider range of researchers at a modest cost. [ABSTRACT FROM AUTHOR]

We describe the instrumentation, calibration, and uncertainty of the network of ground-based, in situ, cavity ring down spectroscopy (CRDS) greenhouse gas (GHG) measurements deployed in the Permian Basin. The primary goal of the network is to be used in conjunction with atmospheric transport modeling to determine methane emissions of the Delaware sub-basin of the Permian Basin oil and natural gas extraction area in Texas and New Mexico. Four of the measurements are based on tall communications towers, while one is on a building on a mountain ridge, with the recent addition of a small tower at that site. Although methane (CH4) is the primary species of interest, carbon dioxide (CO2), hydrogen sulfide (H2S), and the isotopic ratio of methane (δ13CH4) are also reported for a subset of the sites. Measurements were reported following the WMO X2004A scale for CH4 and the WMO X2019 scale for CO2. CRDS instruments were calibrated for CH4 and CO2 in the laboratory prior to deployment. For H2S , data were offset-corrected using the minimum 40 min running mean value of the day, and for δ13CH4 , calibrations were based on laboratory data. We describe the characteristics of the dataset with a set of illustrative analyses. Methane and carbon dioxide showed strong seasonality, with a well-defined diurnal cycle during the summer, which was opposed to the winter, when a diurnal cycle was absent. CH4 enhancements to the background, during the winter, are up to twice the summer values, which is attributed to the changes in boundary layer depth and wind speed. The largest CH4 enhancements occurred when winds blow from the center of the Delaware sub-basin, where most of the methane emissions come from. The magnitude of enhancements of CO2 did not present seasonality. H2S enhancements indicated a potential source northeast of the tower (Hobbs, New Mexico) where the inlet is installed. Isotopic ratios of methane indicated that oil and natural gas extraction is the source of local methane in the region. The hourly-averaged data, starting on 1 March 2020 and described in this paper, are archived at The Pennsylvania State University Data Commons at 10.26208/98y5-t941 (Monteiro et al., 2021). [ABSTRACT FROM AUTHOR]

Stationarity is a critical assumption in the eddy-covariance method that is widely used to calculate turbulent fluxes. Many methods have been proposed to diagnose non-stationarity attributed to external non-turbulent flows. In this paper, we focus on intrinsic non-stationarity (IN) attributed to turbulence randomness. The detrended fluctuation analysis is used to quantify IN of CO2 turbulent fluxes in the downtown of Beijing. Results show that the IN is common in CO2 turbulent fluxes and is a small-scale phenomenon related to the inertial sub-range turbulence. The small-scale IN of CO2 turbulent fluxes can be simulated by the Ornstein–Uhlenbeck (OU) process as a first approximation. Based on the simulation results, we find that the flux-averaging time should be greater than 27 s to avoid the effects of IN. Besides, the non-stationarity diagnosis methods that do not take into account IN would possibly make a wrong diagnosis with some parameters. [ABSTRACT FROM AUTHOR]

Global- and national-scale inventories of carbon dioxide (CO 2) emissions are important tools as countries grapple with the need to reduce emissions to minimize the magnitude of changes in the global climate system. The longest time series dataset on global and national CO 2 emissions, with consistency over all countries and all years since 1751, has long been the dataset generated by the Carbon Dioxide Information and Analysis Center (CDIAC), formerly housed at Oak Ridge National Laboratory. The CDIAC dataset estimates emissions from fossil fuel combustion and cement manufacture, by fuel type, using the United Nations energy statistics and global cement production data from the United States Geological Survey. Recently, the maintenance of the CDIAC dataset was transferred to Appalachian State University, and the dataset is now identified as CDIAC-FF. This paper describes the annual update of the time series of emissions with estimates through 2017; there is typically a 2- to 3-year time lag in the processing of the two primary datasets used for the estimation of CO 2 emissions. We provide details on two changes to the approach to calculating CO 2 emissions that have been implemented in the transition from CDIAC to CDIAC-FF: refinement in the treatment of changes in stocks at the global level and changes in the procedure to calculate CO 2 emissions from cement manufacture. We compare CDIAC-FF's estimates of CO 2 emissions with other global and national datasets and illustrate the trends in emissions (1990–2015) using a decomposition analysis of the Kaya identity. The decompositions for the top 10 emitting countries show that, although similarities exist, countries have unique factors driving their patterns of emissions, suggesting the need for diverse strategies to mitigate carbon emissions to meditate anthropogenic climate change. The data for this particular version of CDIAC-FF are available at 10.5281/zenodo.4281271 (Gilfillan et al., 2020a). [ABSTRACT FROM AUTHOR]

The Geostationary Carbon Cycle Observatory (GeoCarb) was selected as NASA's second Earth Venture Mission (EVM-2). The scientific objectives of GeoCarb are to advance our knowledge of the carbon cycle, in particular, landatmosphere fluxes of the greenhouse gases carbon dioxide (CO2) and methane (CH4), and the effects of these fluxes on the Earth's radiation budget. GeoCarb will retrieve column integrated amounts of CO2 (XCO2), CH4 (XCH4) and CO (XCO, important for understanding tropospheric chemistry), in addition to Solar-Induced Fluorescence (SIF), from hyperspectral resolution measurements in the O2 A-band at 0.76 µm, the weak CO2 band at 1.6 µm, the strong CO2 band at 2.06 µm, and a CH4/CO band at 2.32 µm. Unlike it's polar orbiting predecessors (OCO-2/3, GOSAT-1/2, TROPOMI), GeoCarb will be in a Geostationary orbit with a sub-satellite point centered over the Americas. This orbital configuration combined with its high spatial resolution imaging capabilities will provide an unprecedented view of these quantities on spatial and temporal scales accurate enough to resolve sources and sinks to improve land-atmosphere CO2 and CH4 flux calculations and reduce the uncertainty of these fluxes. This paper will present a description of the GeoCarb instrument and the L2 retrieval algorithms which will be followed by simulation experiments to determine a relatively comprehensive error budget for each target gas. Several sources of uncertainty will be explored including that from the instrument calibration parameters for radiometric gain, the instrument line shape (ILS), the polarization, and the geolocation pointing, in addition to, forward model parameters including that from meteorology and spectroscopy. The results indicate that the errors (1κ) are less than the instrument's multi-sounding precision requirements of 1.2 ppm, 10 ppb, and 12 ppb (10%), for XCO2, XCH4, and XCO, respectively. In particular, when considering the sources of uncertainty separately and in combination (all sources included), we find overall RMS errors of 1.06 ppm for XCO2, 8.2 ppb for XCH4, and 2.5 ppb for XCO, respectively. Additionally, we find that, as expected, errors in XCO2 and XCH4 are dominated by forward model and other systematic errors, while errors in XCO, like SIF, are dominated by measurement noise. [ABSTRACT FROM AUTHOR]

Predicting the responses of terrestrial ecosystem carbon to future global change strongly relies on our ability to model accurately the underlying processes at a global scale. However, terrestrial biosphere models representing the carbon and nitrogen cycles and their interactions remain subject to large uncertainties, partly because of unknown or poorly constrained parameters. Data assimilation is a powerful tool that can be used to optimise these parameters by confronting the model with observations. In this paper, we identify sensitive model parameters from a recent version of the ORCHIDEE land surface model that includes the nitrogen cycle. These sensitive parameters include ones involved in parameterisations controlling the impact of the nitrogen cycle on the carbon cycle and, in particular, the limitation of photosynthesis due to leaf nitrogen availability. We optimise these ORCHIDEE parameters against carbon flux data collected on sites from the Fluxnet network. However, optimising against present-day observations does not automatically give us confidence in the future projections of the model, given that environmental conditions are likely to shift compared to present-day. Manipulation experiments give us a unique look into how the ecosystem may respond to future environmental changes. One such experiment, the Free Air CO2 Enrichment experiment, provides a unique opportunity to assess vegetation response to increasing CO2 by providing data at ambient and elevated CO2 conditions. Therefore, to better capture the ecosystem response to increased CO2, we add the data from two FACE sites to our optimisations, in addition to the Fluxnet data. We use data from both CO2 conditions of the Free Air CO2 Enrichment experiment, which allows us to gain extra confidence in the model simulations using this set of parameters. We find that we are able to improve the magnitude of modelled productivity. Although we are unable to correct the interannual variability fully, we start to simulate possible progressive nitrogen limitation at one of the sites. Using an idealised simulation experiment based on increasing atmospheric CO2 by 1 % per year over 100 years, we find that the rate of CO2 fertilisation is much lower when Free Air CO2 Enrichment data has been in the optimisation. [ABSTRACT FROM AUTHOR]

The Paris Agreement to limit global warming to well below 2 ∘ C requires the ambitious reduction in greenhouse gas emissions and the balancing of remaining emissions through carbon sinks (i.e., the deployment of carbon dioxide removal or CDR). While ambitious climate mitigation scenarios until now primarily consider land-based CDR methods, there is growing concern about their potential to deliver sufficient CDR, and marine CDR options are receiving more and more interest. Based on idealized theoretical studies, ocean alkalinity enhancement (OAE) appears as a promising marine CDR method. However, the knowledge base is insufficient for a robust assessment of its practical feasibility, of its side effects, social and governance aspects, and monitoring and verification issues. A number of research efforts aims to improve this in a timely manner. We provide an overview on the current situation of developing OAE as a marine CDR method and describe the history that has led to the creation of the OAE research best practices guide. [ABSTRACT FROM AUTHOR]

The ocean carbon sink plays a critical role in climate, absorbing anthropogenic carbon from the atmosphere and mitigating climate change. The sink shows significant variability on decadal timescales, but estimates from models and observations disagree with one another, raising uncertainty over the magnitude of the sink, its variability, and its driving mechanisms. There is a need to reconcile observation-based estimates of air–sea CO2 fluxes with those of the changing ocean carbon inventory in order to improve our understanding of the sink, and doing so requires knowledge of how carbon is transported within the interior by the ocean circulation. Here we employ a recently developed optimal transformation method (OTM) that uses water-mass theory to relate interior changes in tracer distributions to transports and mixing and boundary forcings, and we extend its application to include carbon using synthetic data. We validate the method using model outputs from a biogeochemical state estimate, and we test its ability to recover boundary carbon fluxes and interior transports consistent with changes in heat, salt, and carbon. Our results show that the OTM effectively reconciles boundary carbon fluxes with interior carbon distributions when given a range of prior fluxes. The OTM shows considerable skill in its reconstructions, reducing root-mean-squared errors from biased priors between model "truth" and reconstructed boundary carbon fluxes by up to 71 %, with the bias of the reconstructions consistently ≤0.06 molCm-2yr-1 globally. Inter-basin transports of carbon also compare well with the model truth, with residuals <0.25 PgCyr-1 for reconstructions produced using a range of priors. The OTM has significant potential for application to reconcile observational estimates of air–sea CO2 fluxes with the interior accumulation of anthropogenic carbon. [ABSTRACT FROM AUTHOR]

The Bay of Marseille (BoM), located in the northwestern Mediterranean Sea, is affected by various hydrodynamic processes (e.g., Rhône River intrusion and upwelling events) that result in a highly complex local carbonate system. In any complex environment, the use of models is advantageous since it allows us to identify the different environmental forcings, thereby facilitating a better understanding. By combining approaches from two biogeochemical ocean models and improving the formulation of total alkalinity, we develop a more realistic representation of the carbonate system variables at high temporal resolution, which enables us to study air–sea CO 2 fluxes and seawater p CO 2 variations more reliably. We apply this new formulation to two particular scenarios that are typical for the BoM: (i) summer upwelling and (ii) Rhône River intrusion events. In both scenarios, our model was able to correctly reproduce the observed patterns of p CO 2 variability. Summer upwelling events are typically associated with a p CO 2 decrease that mainly results from decreasing near-surface temperatures. Furthermore, Rhône River intrusion events are typically associated with a p CO 2 decrease, although, in this case, the p CO 2 decrease results from a decrease in salinity and an overall increase in total alkalinity. While we were able to correctly represent the daily range of air–sea CO 2 fluxes, the present configuration of Eco3M_MIX-CarbOx does not allow us to correctly reproduce the annual cycle of air–sea CO 2 fluxes observed in the area. This pattern directly impacts our estimates of the overall yearly air–sea CO 2 flux as, even if the model clearly identifies the bay as a CO 2 sink, its magnitude was underestimated, which may be an indication of the limitations inherent in dimensionless models for representing air–sea CO 2 fluxes. [ABSTRACT FROM AUTHOR]

We report in-plume carbon dioxide (CO 2) concentrations and carbon isotope ratios during the 2021 eruption of Tajogaite volcano, island of La Palma, Spain. CO 2 measurements inform our understanding of volcanic contributions to the global climate carbon cycle and the role of CO 2 in eruptions. Traditional ground-based methods of CO 2 collection are difficult and dangerous, and as a result only about 5 % of volcanoes have been directly surveyed. We demonstrate that unpiloted aerial system (UAS) surveys allow for fast and relatively safe measurements. Using CO 2 concentration profiles we estimate the total flux during several measurements in November 2021 to be 1.76±0.20×103 to 2.23±0.26×104 t d -1. Carbon isotope ratios of plume CO 2 indicate a deep magmatic source, consistent with the intensity of the eruption. Our work demonstrates the feasibility of UASs for CO 2 surveys during active volcanic eruptions, particularly for deriving rapid emission estimates. [ABSTRACT FROM AUTHOR]

The effects of anthropogenic warming on the hydroclimate of California are becoming more pronounced with the increased frequency of multi-year droughts and flooding. As a past analog for the future, the Paleocene–Eocene Thermal Maximum (PETM) is a unique natural experiment for assessing global and regional hydroclimate sensitivity to greenhouse gas warming. Globally, extensive evidence (i.e., observations and climate models with high p CO 2) demonstrates hydrological intensification with significant variability from region to region (i.e., drier or wetter, greater frequency, and/or intensity of extreme events). Central California (paleolatitude ∼ 42° N), roughly at the boundary between dry subtropical highs and mid-latitude low-pressure systems, would have been particularly susceptible to shifts in atmospheric circulation and precipitation patterns/intensity. Here, we present new observations and climate model output on regional/local hydroclimate responses in central California during the PETM. Our findings, based on multi-proxy evidence within the context of model outputs, suggest a transition to an overall drier climate punctuated by increased precipitation during summer months along central coastal California during the PETM. [ABSTRACT FROM AUTHOR]

We present a new routine for calculating the non-local thermodynamic equilibrium (non-LTE) 15 µ m CO 2 cooling–heating of mesosphere and lower thermosphere in general circulation models. It uses the optimized models of the non-LTE in CO 2 for day and night conditions and delivers cooling–heating with an error not exceeding 1 K d -1 even for strong temperature disturbances. The routine uses the accelerated lambda iteration and opacity distribution function techniques for the exact solution of the non-LTE problem and is about 1000 times faster than the standard matrix and line-by-line solution. It has an interface for feedbacks from the model and is ready for implementation. It may use any quenching rate coefficient of the CO 2 (ν2) + O(3 P) reaction, handles large variations in O(3 P), and allows the user to vary the number of vibrational levels and bands to find a balance between the calculation speed and accuracy. The suggested routine can handle the broad variation in CO 2 both below and above the current volume mixing ratio, up to 4000 ppmv. This allows the use of this routine for modeling Earth's ancient atmospheres and the climate changes caused by increasing CO 2. [ABSTRACT FROM AUTHOR]

Atmospheric tracers are often used to interpret the local CO 2 budget, where measurements at a single height are assumed to represent local flux signatures. Alternatively, these signatures can be derived from direct flux measurements or by using fluxes derived from measurements at multiple heights. In this study, we contrast interpretation of surface CO 2 exchange from tracer measurements at a single height to measurements at multiple heights. Specifically, we analyse the ratio between atmospheric O 2 and CO 2 (exchange ratio, ER) above a forest. We consider the following two alternative approaches: the exchange ratio of the forest (ER forest) obtained from the ratio of the surface fluxes of O 2 and CO 2 derived from measurements at multiple heights, and the exchange ratio of the atmosphere (ER atmos) obtained from changes in the O 2 and CO 2 mole fractions over time measured at a single height. We investigate the diurnal cycle of both ER signals to better understand the biophysical meaning of the ER atmos signal. We have combined CO 2 and O 2 measurements from Hyytiälä, Finland, during spring and summer of 2018 and 2019 with a conceptual land–atmosphere model to investigate the behaviour of ER atmos and ER forest. We show that the CO 2 and O 2 signals as well as their resulting ERs are influenced by climate conditions such as variations in soil moisture and temperature, for example during the 2018 heatwave. We furthermore show that the ER atmos signal obtained from single-height measurements rarely represents the forest exchange directly, mainly because it is influenced by entrainment of air from the free troposphere into the atmospheric boundary layer. The influence of these larger-scale processes can lead to very high ER atmos values (even larger than 2), especially in the early morning. These high values do not directly represent carbon cycle processes, but are rather a mixture of different signals. We conclude that the ER atmos signal provides only a weak constraint on local-scale surface CO 2 exchange, and that ER forest above the canopy should be used instead. Single-height measurements always require careful selection of the time of day and should be combined with atmospheric modelling to yield a meaningful representation of forest carbon exchange. More generally, we recommend always measuring at multiple heights when using multi-tracer measurements to study surface CO 2 exchange. [ABSTRACT FROM AUTHOR]

Hydrofluorocarbons (HFCs) are powerful anthropogenic greenhouse gases (GHGs) with high global-warming potentials (GWPs). They have been widely used as refrigerants, insulation foam-blowing agents, aerosol propellants, and fire suppression agents. Since the mid-1990s, emissions of HFCs have been increasing rapidly as they are used in many applications to replace ozone-depleting chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs) whose consumption and production have been phased out under the Montreal Protocol (MP). Due to the high GWP of HFCs, the Kigali Amendment to the MP requires the phasedown of production and consumption of HFCs to gradually achieve an 80 %–85 % reduction by 2047, starting in 2019 for non-Article 5 (developed) countries with a 10 % reduction against each defined baseline and later schedules for Article 5 (developing) countries. In this study, we have examined long-term high-precision measurements of atmospheric abundances of five major HFCs (HFC-134a, HFC-143a, HFC-32, HFC-125, and HFC-152a) at Gosan station, Jeju Island, South Korea, from 2008 to 2020. Background abundances of HFCs gradually increased, and the inflow of polluted air masses with elevated abundances from surrounding source regions were detected over the entire period. From these pollution events, we inferred regional and country-specific HFC emission estimates using two independent Lagrangian particle dispersion models and Bayesian inversion frameworks (FLEXPART-FLEXINVERT + and NAME-InTEM). The spatial distribution of the derived "top-down" (measurement based) emissions for all HFCs shows large fluxes from megacities and industrial areas in the region. Our most important finding is that HFC emissions in eastern China and Japan have sharply increased from 2016 to 2018. The contribution of East Asian HFC emissions to the global total increased from 9 % (2008–2014) to 13 % (2016–2020). In particular, HFC emissions in Japan (Annex I country) rose rapidly from 2016 onward, with accumulated total inferred HFC emissions being ∼ 114 Gg yr -1 , which is ∼ 76 Gg yr -1 higher for 2016–2020 than the "bottom-up" (i.e., based on activity data and emission factors) emissions of ∼ 38 Gg yr -1 reported to the United Nations Framework Convention on Climate Change (UNFCCC). This is likely related to the increase in domestic demand in Japan for refrigerants and air-conditioning-system-related products and incomplete accounting. A downward trend of HFC emissions that started in 2019 reflects the effectiveness of the F-gas policy in Japan. Eastern China and South Korea, though not obligated to report to the UNFCCC, voluntarily reported emissions, which also show differences between top-down and bottom-up emission estimates, demonstrating the need for atmospheric measurements, comprehensive data analysis, and accurate reporting for precise emission management. Further, the proportional contribution of each country's CO 2 -equivalent HFC emissions has changed over time, with HFC-134a decreasing and HFC-125 increasing. This demonstrates the transition in the predominant HFC substances contributing to global warming in each country. [ABSTRACT FROM AUTHOR]

Accurate assessment of the size and distribution of carbon dioxide (CO 2) sources and sinks is important for efforts to understand the carbon cycle and support policy decisions regarding climate mitigation actions. Satellite retrievals of the column-averaged dry-air mole fractions of CO 2 (XCO 2) have been widely used to infer spatial and temporal variations in carbon fluxes through atmospheric inversion techniques. In this study, we present a global spatially resolved terrestrial and ocean carbon flux dataset for 2015–2022. The dataset was generated by the Global ObservatioN-based system for monitoring Greenhouse GAses (GONGGA) atmospheric inversion system through the assimilation of Orbiting Carbon Observatory-2 (OCO-2) XCO 2 retrievals. We describe the carbon budget, interannual variability, and seasonal cycle for the global scale and a set of TransCom regions. The 8-year mean net biosphere exchange and ocean carbon fluxes were - 2.22 ± 0.75 and - 2.32 ± 0.18 Pg C yr -1 , absorbing approximately 23 % and 24 % of contemporary fossil fuel CO 2 emissions, respectively. The annual mean global atmospheric CO 2 growth rate was 5.17 ± 0.68 Pg C yr -1 , which is consistent with the National Oceanic and Atmospheric Administration (NOAA) measurement (5.24 ± 0.59 Pg C yr -1). Europe has the largest terrestrial sink among the 11 TransCom land regions, followed by Boreal Asia and Temperate Asia. The dataset was evaluated by comparing posterior CO 2 simulations with Total Carbon Column Observing Network (TCCON) retrievals as well as Observation Package (ObsPack) surface flask observations and aircraft observations. Compared with CO 2 simulations using the unoptimized fluxes, the bias and root mean square error (RMSE) in posterior CO 2 simulations were largely reduced across the full range of locations, confirming that the GONGGA system improves the estimates of spatial and temporal variations in carbon fluxes by assimilating OCO-2 XCO 2 data. This dataset will improve the broader understanding of global carbon cycle dynamics and their response to climate change. The dataset can be accessed at 10.5281/zenodo.8368846 (Jin et al., 2023a). [ABSTRACT FROM AUTHOR]

Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC). Evidence-based decision-making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles. We follow methods as close as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. We compile monitoring datasets to produce estimates for key climate indicators related to forcing of the climate system: emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, the Earth's energy imbalance, surface temperature changes, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. The purpose of this effort, grounded in an open-data, open-science approach, is to make annually updated reliable global climate indicators available in the public domain (10.5281/zenodo.11388387, Smith et al., 2024a). As they are traceable to IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that, for the 2014–2023 decade average, observed warming was 1.19 [1.06 to 1.30] °C, of which 1.19 [1.0 to 1.4] °C was human-induced. For the single-year average, human-induced warming reached 1.31 [1.1 to 1.7] °C in 2023 relative to 1850–1900. The best estimate is below the 2023-observed warming record of 1.43 [1.32 to 1.53] °C, indicating a substantial contribution of internal variability in the 2023 record. Human-induced warming has been increasing at a rate that is unprecedented in the instrumental record, reaching 0.26 [0.2–0.4] °C per decade over 2014–2023. This high rate of warming is caused by a combination of net greenhouse gas emissions being at a persistent high of 53±5.4 Gt CO 2 e yr -1 over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that the rate of increase in CO2 emissions over the last decade has slowed compared to the 2000s, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for some of the indicators presented here. [ABSTRACT FROM AUTHOR]

For carbon dioxide concentration (XCO2) distribution, the improvement of spatial and temporal resolution is very important in some scientific studies (e.g., studies of the carbon cycle and assessment of carbon emissions based on top-down theory). However, carbon sniffing satellites based on passive theory (e.g., Gosat-2, OCO-2, and OCO-3) are susceptible to cloud and aerosol interference when the data are captured. Therefore, the data collected by carbon sniffing satellites have relatively low utilization, especially in some regions where data gaps exist. Here, we present the Carbon Dioxide Coverage (CDC) dataset, an innovative theory to obtain high spatial and temporal resolution maps of XCO2 distribution by combining spatial attributes and extracted temporal attributes from the GOSAT satellite series data. This theory is divided into the following three parts. Firstly, several background values in the raw GOSAT data were removed through data pre-processing, and for spatial attributes, GOSAT satellite data gap areas were filled by combining adjacent GOSAT data and empirical Bayesian kriging (EBK) theory in the study area. Secondly, for the temporal attributes, we constructed a time profile parameter library, based on the GOSAT data of the time series to extract the temporal parameters from a specific formula at each point of the study area. Finally, for the integration of temporal and spatial information, based on the GOSAT satellite data and the populated data based on spatial attributes, we assign the temporal parameter information from the time parameter library to each pixel location in the study area, combining the transfer component analysis (TCA) theory, and then combine the assigned parameters with specific formulas to complete the prediction of XCO2 distribution. For temporal resolution, both the GOSAT_FTS_L3_V2.95 and CDC datasets are monthly-averaged resolution datasets from 2010 to 2020. And for spatial resolution, the CDC dataset is 0.25° resolution with a significant improvement compared to GOSAT_FTS_L3_V2.95 which is 2.5° resolution. And the dataset contained 136 files. Besides, for the data validation part, we used OCO-2 satellite data from 2009 to 2020 and TCCON data at mid and low latitudes, respectively. This CDC dataset and the original data from the TCCON sites were compared on a monthly-averaged scale. And the results showed that R2 was 0.9686, and RMSE was 1.3811 ppm. We also derived statistical monthly averaged XCO2 from OCO-2 data and compared it with the data set from our theory. And our evaluation index R was greater than 0.7, by comparison with OCO-2 during 2014-2020. Finally, to assess the accuracy of the algorithm, we compared the predicted results with the input data for the period of 2009-2020. And the comparison results show that the mean value of R2 is 0.93 and the mean value of RMSE is 0.53 ppm during 2010-2020. Data gaps produced by sniffer satellites are disturbed by factors such as clouds and aerosols and can be filled by this mapping technique is mentioned in this paper. This technique improves the utilization of XCO2 and the accuracy and resolution of the CDC dataset is sufficient for scientific applications. And the CDC dataset is publicly available at https://doi.org/10.6084/m9.figshare.17826404.v4 (Zhang et al., 2022), which is of significance for a multitude of scientific carbon research. [ABSTRACT FROM AUTHOR]

This paper introduces GCAM-CDR 1.0, an integrated assessment model for climate policy based on the open-source Global Change Analysis Model (GCAM). GCAM-CDR extends GCAM v5.4 by enabling users to model additional carbon dioxide removal (CDR) technologies and additional policies and controls related to CDR. New CDR technologies include terrestrial enhanced weathering with basalt, ocean liming, and additional versions of direct air capture. New CDR policies and controls include integration of bioenergy with carbon capture and storage (BECCS) into the CDR market, interregional trade in CDR, exogenous control over the rate of growth of CDR, the ability to set independent targets for emissions abatement and CDR, and a variety of mechanisms for setting demand for CDR at the regional and/or global level. These extensions enhance users' ability to study the potential roles of CDR in climate policy. [ABSTRACT FROM AUTHOR]

The use of different primary energy sources in human society has led to two major polluting emissions in the environment: energy (mostly heat), and chemical substances (mostly carbon dioxide). In this paper, a new approach, based on the similarity between sensible heat and CO2 transfer properties, was used to determine the effect of anthropogenic heat release on the global air temperature. The total global anthropogenic emissions of sensible heat were divided into two separate streams: directly transferred to: (1) water and land, and (2) to the atmosphere. The direct emissions of heat to the atmosphere during the industrial era (years 1850-2018) were determined and their effect on the change of global atmospheric temperature was calculated. The global atmospheric temperature increase caused by anthropogenic heat emissions was estimated. The resulting calculations showed that at least half of the actual atmospheric temperature rise recorded during the last 170-year period, was due to the anthropogenic heat release. These results suggest that the temperature change of the atmosphere (global warming) is strongly affected by anthropogenic heat emissions. [ABSTRACT FROM AUTHOR]

We present a direct measurement of the impact of increased atmospheric CO 2 on the spectra of the Earth's longwave radiation obtained from space. The goal of this study is to experimentally confirm that the direct effects of CO 2 increase on the Earth's outgoing longwave spectra follow theoretical estimates, by developing a methodology that allows for a direct and more precise comparison between theory and observations. In this methodology, a search is performed to find selected ensembles of observed atmospheric vertical profiles of temperature and water vapor that are as close as possible to each other in terms of their values. By analyzing the spectral radiances measured from space by the Atmospheric Infrared Sounder (AIRS), corresponding to the selected ensembles of profiles, the effects of increased CO 2 on the spectra can be isolated from the temperature and water vapor effects. The results illustrate the impact of the increase in CO 2 on the longwave spectra and compare well with theoretical estimates. As far as the authors are aware, this is the first time that the spectral signature of the increase in CO 2 (isolated from temperature and water vapor changes) has been directly observed from space. [ABSTRACT FROM AUTHOR]

The Amazon rainforest plays a crucial role in global carbon storage, but a minor destabilization of these forests could result in considerable carbon loss. Among the external factors affecting vegetation, elevated CO 2 (eCO 2) levels have long been anticipated to have positive impacts on vegetation, including the direct enhancement of both photosynthesis and productivity and increasing water use efficiency. However, the overall impact of eCO 2 on the net carbon balance, especially concerning tree-mortality-induced carbon loss and recovery following extreme drought events, has remained elusive. Here, we use a process-based model that couples physiological CO 2 effects with demography and both drought mortality and resistance processes. The model was previously calibrated to reproduce observed drought responses of Amazon forest sites. The model results, based on factorial simulations with and without eCO 2 , reveal that eCO 2 enhances forest growth and promotes competition between trees, leading to more natural self-thinning of forest stands. This occurs following a growth–mortality trade-off response, although the growth outweighs the tree loss. Additionally, eCO 2 provides water-saving benefits, reducing the risk of tree mortality during drought episodes. However, extra carbon losses could still occur due to an eCO 2 -induced increase in background biomass density, leading to "more carbon available to lose" when severe droughts happen. Furthermore, we found that eCO 2 accelerates drought recovery and enhances drought resistance and resilience. By delving into the less-explored aspect of tree mortality response to eCO 2 , the model improvements advance our understanding of how carbon balance responds to eCO 2 , particularly regarding mechanisms of continuous competition-induced carbon loss vs. pulses of drought-induced carbon loss. These findings provide valuable insights into the intricate ways in which rising CO 2 influences forest carbon dynamics and vulnerability, offering a critical understanding of the Amazon rainforest's evolution amidst more frequent and intense extreme climate events. [ABSTRACT FROM AUTHOR]

The temporal variation of the carbonate system, air–sea CO 2 fluxes, and pH is analyzed in the southern Indian Ocean, south of the polar front, based on in situ data obtained from 1985 to 2021 at a fixed station (50°40 ′ S–68°25 ′ E) and results from a neural network model that reconstructs the fugacity of CO 2 (fCO2) and fluxes at monthly scale. Anthropogenic CO 2 (C ant) is estimated in the water column and is detected down to the bottom (1600 m) in 1985, resulting in an aragonite saturation horizon at 600 m that migrated up to 400 m in 2021 due to the accumulation of C ant. At the subsurface, the trend of C ant is estimated at +0.53±0.01 µ mol kg -1 yr -1 with a detectable increase in the trend in recent years. At the surface during austral winter the oceanic fCO2 increased at a rate close to or slightly lower than in the atmosphere. To the contrary, in summer, we observed contrasting fCO2 and dissolved inorganic carbon (C T) trends depending on the decade and emphasizing the role of biological drivers on air–sea CO 2 fluxes and pH inter-annual variability. The regional air–sea CO 2 fluxes evolved from an annual source to the atmosphere of 0.8 molC m -2 yr -1 in 1985 to a sink of -0.5 molC m -2 yr -1 in 2020. Over 1985–2020, the annual pH trend in surface waters of -0.0165±0.0040 per decade was mainly controlled by the accumulation of anthropogenic CO 2 , but the summer pH trends were modulated by natural processes that reduced the acidification rate in the last decade. Using historical data from November 1962, we estimated the long-term trend for fCO2 , C T , and pH, confirming that the progressive acidification was driven by the atmospheric CO 2 increase. In 59 years this led to a diminution of 11 % for both aragonite and calcite saturation state. As atmospheric CO 2 is expected to increase in the future, the pH and carbonate saturation state will decrease at a faster rate than observed in recent years. A projection of future C T concentrations for a high emission scenario (SSP5-8.5) indicates that the surface pH in 2100 would decrease to 7.32 in winter. This is up to -0.86 lower than pre-industrial pH and -0.71 lower than pH observed in 2020. The aragonite undersaturation in surface waters would be reached as soon as 2050 (scenario SSP5-8.5) and 20 years later for a stabilization scenario (SSP2-4.5) with potential impacts on phytoplankton species and higher trophic levels in the rich ecosystems of the Kerguelen Islands area. [ABSTRACT FROM AUTHOR]

By utilizing progress in millijoule-level pulsed fiber lasers operating in the 1.96 µm spectral range, we introduce a concept utilizing a spaceborne differential absorption barometric lidar designed to operate within the 1.96 µm CO2 absorption band for remote sensing of Martian atmospheric properties. Our focus is on the online wavelength situated in the trough region of two absorption lines, selected due to its insensitivity to laser frequency variations, thus mitigating the necessity for stringent laser frequency stability. Our investigation revolves around a compact lidar configuration, featuring reduced telescope dimensions and lower laser pulse energies. These adjustments are geared towards minimizing costs for potential forthcoming Mars missions. The core measurement objectives encompass the determination of column CO2 absorption optical depth, columnar CO2 abundance, surface atmospheric pressure, and vertical distributions of dust and cloud layers. Through the amalgamation of surface pressure data with atmospheric temperature insights garnered from sounders and utilizing the barometric formula, the prospect of deducing atmospheric pressure profiles becomes feasible. Simulation studies validate the viability of our approach. Notably, the precision of Martian surface pressure measurements is projected to surpass 1 Pa when the aerial dust optical depth is projected to be under 0.7, a typical airborne dust scenario on Mars, considering a horizontal averaging span of 10 km. [ABSTRACT FROM AUTHOR]

We present the role of CO 2 forcing in controlling Late Pliocene sea surface temperature (SST) change using six models from Phase 2 of the Pliocene Model Intercomparison Project (PlioMIP2) and palaeoclimate proxy data from the PlioVAR working group. At a global scale, SST change in the Late Pliocene relative to the pre-industrial is predominantly driven by CO 2 forcing in the low and mid-latitudes and non-CO 2 forcing in the high latitudes. We find that CO 2 is the dominant driver of SST change at the vast majority of proxy data sites assessed (17 out of 19), but the relative dominance of this forcing varies between all proxy sites, with CO 2 forcing accounting for between 27 % and 82 % of the total change seen. The dearth of proxy data sites in the high latitudes means that only two sites assessed here are predominantly forced by non-CO 2 forcing (such as changes to ice sheets and orography), both of which are in the North Atlantic Ocean. We extend the analysis to show the seasonal patterns of SST change and its drivers at a global scale and at a site-specific level for three chosen proxy data sites. We also present a new estimate of Late Pliocene climate sensitivity using site-specific proxy data values. This is the first assessment of site-specific drivers of SST change in the Late Pliocene and highlights the strengths of using palaeoclimate proxy data alongside model outputs to further develop our understanding of the Late Pliocene. We use the best available proxy and model data, but the sample sizes remain limited, and the confidence in our results would be improved with greater data availability. [ABSTRACT FROM AUTHOR]

Devising effective national-level climate action plans requires a more detailed understanding of the regional distribution of sources and sinks of greenhouse gases. Due to insufficient observations and modelling capabilities, India's current carbon source–sink estimates are uncertain. This study uses a high-resolution Lagrangian transport model to examine the potential of available CO 2 observations over India for inverse estimation of regional carbon fluxes. We use four different sites in India that vary in the measurement technique, frequency and spatial representation. These observations exhibit substantial seasonal (7.5 to 9.2 ppm) and intra-seasonal (2 to 12 ppm) variability. Our modelling framework, a high-resolution Weather Research and Forecasting Model combined with the Stochastic Time-Inverted Lagrangian Transport model (WRF–STILT), performs better in simulating seasonal (R2=0.50 to 0.96) and diurnal (R2=0.96) variability (for the Mohali station) of observed CO 2 than the current-generation global models (CarboScope, CarbonTracker and ECMWF EGG4). The seasonal CO 2 concentration variability in Mohali, associated with crop residue burning, is largely underestimated by the models. WRF–STILT captures the seasonal biospheric variability over Nainital better than the global models but underestimates the strength of the CO 2 uptake by crops. The choice of emission inventory in the modelling framework alone leads to significant biases in simulations (5 to 10 ppm), endorsing the need for accounting for emission fluxes, especially for non-background sites. Our study highlights the possibility of using the CO 2 observations from these Indian stations for deducing carbon flux information at regional (Nainital) and suburban to urban (Mohali, Shadnagar and Nagpur) scales with the help of a high-resolution model. On accounting for observed variability in CO 2 , the global carbon data assimilation system can benefit from the measurements from the Indian subcontinent. [ABSTRACT FROM AUTHOR]

We present the first results of a ground-based imaging experiment using a shortwave infrared spectral camera to quantify carbon dioxide (CO 2) emissions from a coal-fired power plant in Mannheim, Germany. The power plant emits more than 4.9 Mt CO 2 yr -1 and is a validation opportunity for the emission estimation technique. The camera is a hyperspectral imaging spectrometer that covers the spectral range from 900 to 2500 nm with a spectral resolution of 7 nm. We identify CO 2 enhancements from hourly averaged images using an iterative matched filter retrieval using the 2000 nm absorption band of CO 2. We present 11 plume images from 5 d in 2021 and 2022 covering a variety of ambient conditions. We design a forward model based on a three-dimensional, bent-over Gaussian plume rise simulation and compare our observed emission plumes with the forward model. The model depends on the parameters ambient wind velocity, wind direction, plume dispersion, and emission rate. We retrieve the emission rate by minimizing the least-squares difference between the measured and the simulated images. We find an overall reasonable agreement between the retrieved and expected emissions for power plant emission rates between 223 and 587 t CO 2 h -1. The retrieved emissions average 84 % of the expected emissions and have a mean relative uncertainty of 24 %. The technique works at wind speeds down to 1.4 m s -1 and can follow diurnal emission dynamics. We also include observations with unfavorable ambient conditions, such as background heterogeneity and acute observation angles. These conditions are shown to produce considerable biases in the retrieved emission rates, yet they can be filtered out reliably in most cases. Thus, this emission estimation technique is a promising tool for independently verifying reported emissions from large point sources and provides complementary information to existing monitoring techniques. [ABSTRACT FROM AUTHOR]