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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]

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]

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]

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]

The interactions between the terrestrial biosphere, atmospheric chemistry, and climate involve complex feedbacks that have traditionally been modeled separately. We present a new framework that couples the Yale Interactive terrestrial Biosphere (YIBs) model, a dynamic plant-chemistry model, with the RegCM-Chem model. RegCM-Chem–YIBs integrates meteorological variables and atmospheric chemical composition from RegCM-Chem with land surface parameters from YIBs. The terrestrial carbon flux calculated by YIBs is fed back into RegCM-Chem interactively, thereby representing the interactions between fine particulate matter (PM 2.5), ozone (O 3), and carbon dioxide (CO 2). For testing purposes, we carry out a 1-year simulation (2016) at a 30 km horizontal resolution over East Asia with RegCM-Chem–YIBs. The model accurately captures the spatio-temporal distribution of climate, chemical composition, and ecological parameters. In particular, the estimated O 3 and PM 2.5 are consistent with ground observations, with correlation coefficients (R) of 0.74 and 0.65, respectively. The simulated CO 2 concentration is consistent with observations from six sites (R ranged from 0.89 to 0.97) and exhibits a similar spatial pattern when compared with carbon assimilation products. RegCM-Chem–YIBs produces reasonably good gross primary productivity (GPP) and net primary productivity (NPP), showing seasonal and spatial distributions consistent with satellite observations, and mean biases (MBs) of 0.13 and 0.05 kg C m -2 yr -1. This study illustrates that RegCM-Chem–YIBs is a valuable tool to investigate coupled interactions between the terrestrial carbon cycle, atmospheric chemistry, and climate change at a higher resolution on a regional scale. [ABSTRACT FROM AUTHOR]

The Southern Ocean plays an important role in the exchange of carbon between the atmosphere and oceans and is a critical region for the ocean uptake of anthropogenic CO 2. However, estimates of the Southern Ocean air–sea CO 2 flux are highly uncertain due to limited data coverage. Increased sampling in winter and across meridional gradients in the Southern Ocean may improve machine learning (ML) reconstructions of global surface ocean p CO 2. Here, we use a large ensemble test bed (LET) of Earth system models and the " p CO 2 -Residual" reconstruction method to assess improvements in p CO 2 reconstruction fidelity that could be achieved with additional autonomous sampling in the Southern Ocean added to existing Surface Ocean CO 2 Atlas (SOCAT) observations. The LET allows for a robust evaluation of the skill of p CO 2 reconstructions in space and time through comparison to "model truth". With only SOCAT sampling, Southern Ocean and global p CO 2 are overestimated, and thus the ocean carbon sink is underestimated. Incorporating uncrewed surface vehicle (USV) sampling increases the spatial and seasonal coverage of observations within the Southern Ocean, leading to a decrease in the overestimation of p CO 2. A modest number of additional observations in Southern Hemisphere winter and across meridional gradients in the Southern Ocean leads to an improvement in reconstruction bias and root-mean-squared error (RMSE) of as much as 86 % and 16 %, respectively, as compared to SOCAT sampling alone. Lastly, the large decadal variability of air–sea CO 2 fluxes shown by SOCAT-only sampling may be partially attributable to undersampling of the Southern Ocean. [ABSTRACT FROM AUTHOR]

The late Taro Takahashi (Lamont-Doherty Earth Observatory (LDEO), Columbia University) and colleagues provided the first near-global monthly air–sea CO 2 flux climatology in Takahashi et al. (1997), based on available surface water partial pressure of CO 2 measurements. This product has been a benchmark for uptake of CO 2 in the ocean. Several versions have been provided since, with improvements in procedures and large increases in observations, culminating in the authoritative assessment in Takahashi et al. (2009a, b). Here we provide and document the last iteration using a greatly increased dataset (SOCATv2022) and determining fluxes using air–sea partial pressure differences as a climatological reference for the period 1980–2021 (Fay et al., 2023, 10.25921/295g-sn13). The resulting net flux for the open ocean region is estimated as -1.79±0.7 Pg C yr -1 , which compares well with other global mean flux estimates. While global flux results are consistent, differences in regional means and seasonal amplitudes are discussed. Consistent with other studies, we find the largest differences in the data-sparse southeast Pacific and Southern Ocean. [ABSTRACT FROM AUTHOR]

Since the beginning of the industrial revolution, atmospheric carbon dioxide (CO 2) concentrations have risen steadily and have induced a decrease of the averaged surface ocean pH by 0.1 units, corresponding to an increase in ocean acidity of about 30 %. In addition to ocean warming, ocean acidification poses a tremendous challenge to some marine organisms, especially calcifiers. The need for long-term oceanic observations of pH and temperature is a key element to assess the vulnerability of marine communities and ecosystems to these pressures. Nearshore productive environments, where a large majority of shellfish farming activities are conducted, are known to present pH levels as well as amplitudes of daily and seasonal variations that are much larger than those observed in the open ocean. Yet, to date, there are very few coastal observation sites where these parameters are measured simultaneously and at high frequency. To bridge this gap, an observation network was initiated in 2021 in the framework of the CocoriCO 2 project. Six sites were selected along the French Atlantic and Mediterranean coastlines based on their importance in terms of shellfish production and the presence of high- and low-frequency monitoring activities. At each site, autonomous pH sensors were deployed, both inside and outside shellfish production areas, next to high-frequency CTD (conductivity–temperature–depth) probes operated through two operating monitoring networks. pH sensors were set to an acquisition rate of 15 min, and discrete seawater samples were collected biweekly in order to control the quality of pH data (laboratory spectrophotometric measurements) as well as to measure total alkalinity and dissolved inorganic carbon concentrations for full characterization of the carbonate system. While this network has been up and running for more than 2 years, the acquired dataset has already revealed important differences in terms of pH variations between monitored sites related to the influence of diverse processes (freshwater inputs, tides, temperature, biological processes). Data are available at 10.17882/96982 (Petton et al., 2023a). [ABSTRACT FROM AUTHOR]

We investigate the use of atmospheric oxygen (O 2) and carbon dioxide (CO 2) measurements for the estimation of the fossil fuel component of atmospheric CO 2 in the UK. Atmospheric potential oxygen (APO) – a tracer that combines O 2 and CO 2 , minimizing the influence of terrestrial biosphere fluxes – is simulated at three sites in the UK, two of which make APO measurements. We present a set of model experiments that estimate the sensitivity of APO simulations to key inputs: fluxes from the ocean, fossil fuel flux magnitude and distribution, the APO baseline, and the exchange ratio of O 2 to CO 2 fluxes from fossil fuel combustion and the terrestrial biosphere. To estimate the influence of uncertainties in ocean fluxes, we compare three ocean O 2 flux estimates from the NEMO–ERSEM, the ECCO–Darwin ocean model, and the Jena CarboScope (JC) APO inversion. The sensitivity of APO to fossil fuel emission magnitudes and to terrestrial biosphere and fossil fuel exchange ratios is investigated through Monte Carlo sampling within literature uncertainty ranges and by comparing different inventory estimates. We focus our model–data analysis on the year 2015 as ocean fluxes are not available for later years. As APO measurements are only available for one UK site at this time, our analysis focuses on the Weybourne station. Model–data comparisons for two additional UK sites (Heathfield and Ridge Hill) in 2021, using ocean flux climatologies, are presented in the Supplement. Of the factors that could potentially compromise simulated APO-derived fossil fuel CO 2 (ffCO 2) estimates, we find that the ocean O 2 flux estimate has the largest overall influence at the three sites in the UK. At times, this influence is comparable in magnitude to the contribution of simulated fossil fuel CO 2 to simulated APO. We find that simulations using different ocean fluxes differ from each other substantially. No single model estimate, or a model estimate that assumed zero ocean flux, provided a significantly closer fit than any other. Furthermore, the uncertainty in the ocean contribution to APO could lead to uncertainty in defining an appropriate regional background from the data. Our findings suggest that the contribution of non-terrestrial sources needs to be better accounted for in model simulations of APO in the UK to reduce the potential influence on inferred fossil fuel CO 2 using APO. [ABSTRACT FROM AUTHOR]

The response of the global climate–carbon-cycle system to anthropogenic perturbations happens differently at different timescales. The unravelling of the memory structure underlying this timescale dependence is a major challenge in climate research. Recently the widely applied α – β – γ framework proposed by to quantify climate–carbon-cycle feedbacks has been generalized to account also for such internal memory. By means of this generalized framework, we investigate the timescale dependence of the airborne fraction for a set of Earth system models that participated in CMIP5 (Coupled Model Intercomparison Project Phase 5). The analysis is based on published simulation data from C 4 MIP-type (Coupled Climate–Carbon Cycle Model Intercomparison) experiments with these models. Independently of the considered scenario, the proposed generalization describes at global scale the reaction of the climate–carbon system to sufficiently weak perturbations. One prediction from this theory is how the timescale-resolved airborne fraction depends on the underlying feedbacks between climate and the carbon cycle. These feedbacks are expressed as timescale-resolved functions depending solely on analogues of the α , β , and γ sensitivities, introduced in the generalized framework as linear response functions. In this way a feedback-dependent quantity (airborne fraction) is predicted from feedback-independent quantities (the sensitivities). This is the key relation underlying our study. As a preparatory step, we demonstrate the predictive power of the generalized framework exemplarily for simulations with the Max Planck Institute (MPI) Earth System Model. The whole approach turns out to be valid for perturbations of up to an about 100 ppm CO 2 rise above the pre-industrial level; beyond this value the response becomes non-linear. By means of the generalized framework we then derive the timescale dependence of the airborne fraction from the underlying climate–carbon-cycle feedbacks for an ensemble of CMIP5 models. Our analysis reveals that for all studied CMIP5 models (1) the total climate–carbon-cycle feedback is negative at all investigated timescales, (2) the airborne fraction generally decreases for increasing timescales, and (3) the land biogeochemical feedback dominates the model spread in the airborne fraction at all these timescales. Qualitatively similar results were previously found by employing the original α – β – γ framework to particular perturbation scenarios, but our study demonstrates that, although obtained from particular scenario simulations, they are characteristics of the coupled climate–carbon-cycle system as such, valid at all considered timescales. These more general conclusions are obtained by accounting for the internal memory of the system as encoded in the generalized sensitivities, which in contrast to the original α , β , and γ are scenario-independent. [ABSTRACT FROM AUTHOR]

The determination of dissolved gases (O2 , CO2 , CH4 , N2O , N2) in surface waters allows the estimation of biological processes and greenhouse gas fluxes in aquatic ecosystems. Mercuric chloride (HgCl2) has been widely used to preserve water samples prior to gas analysis. However, alternates are needed because of the environmental impacts and prohibition of mercury. HgCl2 is a weak acid and interferes with dissolved organic carbon (DOC). Hence, we tested the effect of HgCl2 and two substitutes (copper(II) chloride – CuCl2 – and silver nitrate – AgNO3), as well as storage time (24 h to 3 months) on the determination of dissolved gases in low-ionic-strength and high-DOC water from a typical boreal lake. Furthermore, we investigated and predicted the effect of HgCl2 on CO2 concentrations in periodic samples from another lake experiencing pH variations (5.4–7.3) related to in situ photosynthesis. Samples fixed with inhibitors generally showed negligible O2 consumption. However, effective preservation of dissolved CO2 , CH4 and N2O for up to 3 months prior to dissolved gas analysis was only achieved with AgNO3. In contrast, HgCl2 and CuCl2 caused an initial increase in CO2 and N2O from 24 h to 3 weeks followed by a decrease from 3 weeks to 3 months. The CO2 overestimation, caused by HgCl2 acidification and a shift in the carbonate equilibrium, can be calculated from predictions of chemical speciation. Errors due to CO2 overestimation in HgCl2 -preserved water, sampled from low-ionic-strength and high-DOC freshwater, which is common in the Northern Hemisphere, could lead to an overestimation of the CO2 diffusion efflux by a factor of > 20 over a month or a factor of 2 over the ice-free season. The use of HgCl2 and CuCl2 for freshwater preservation should therefore be discontinued. Further testing of AgNO3 preservation should be performed under a large range of freshwater chemical conditions. [ABSTRACT FROM AUTHOR]

Coastal and estuarine ecosystems fringing the North Pacific Ocean are particularly vulnerable to ocean acidification, hypoxia, and intense marine heatwaves as a result of interactions among natural and anthropogenic processes. Here, we characterize variability during a seasonally resolved cruise time series (2014–2018) in the southern Salish Sea (Puget Sound, Strait of Juan de Fuca) and nearby coastal waters for select physical (temperature, T ; salinity, S) and biogeochemical (oxygen, O 2 ; carbon dioxide fugacity, f CO 2 ; aragonite saturation state, Ωarag) parameters. Medians for some parameters peaked (T , Ωarag) in surface waters in summer, whereas others (S , O 2 , f CO 2) changed progressively across spring–fall, and all parameters changed monotonically or were relatively stable at depth. Ranges varied considerably for all parameters across basins within the study region, with stratified basins consistently the most variable. Strong environmental anomalies occurred during the time series, allowing us to also qualitatively assess how these anomalies affected seasonal patterns and interannual variability. The peak temperature anomaly associated with the 2013–2016 northeast Pacific marine heatwave–El Niño event was observed in boundary waters during the October 2014 cruise, but Puget Sound cruises revealed the largest temperature increases during the 2015–2016 timeframe. The most extreme hypoxia and acidification measurements to date were recorded in Hood Canal (which consistently had the most extreme conditions) during the same period; however, they were shifted earlier in the year relative to previous events. During autumn 2017, after the heat anomaly, a distinct carbonate system anomaly with unprecedentedly low Ωarag values and high f CO 2 values occurred in parts of the southern Salish Sea that are not normally so acidified. This novel "CO 2 storm" appears to have been driven by anomalously high river discharge earlier in 2017, which resulted in enhanced stratification and inferred primary productivity anomalies, indicated by persistently and anomalously high O 2 , low f CO 2 , and high chlorophyll. Unusually, this CO 2 anomaly was decoupled from O 2 dynamics compared with past Salish Sea hypoxia and acidification events. The complex interplay of weather, hydrological, and circulation anomalies revealed distinct multi-stressor scenarios that will potentially affect regional ecosystems under a changing climate. Further, the frequencies at which Salish cruise observations crossed known or preliminary species' sensitivity thresholds illustrates the relative risk landscape of temperature, hypoxia, and acidification anomalies in the southern Salish Sea in the present day, with implications for how multiple stressors may combine to present potential migration, survival, or physiological challenges to key regional species. The Salish cruise data product used in this publication is available at https://doi.org/10.25921/zgk5-ep63 (Alin et al., 2022), with an additional data product including all calculated CO 2 system parameters available at https://doi.org/10.25921/5g29-q841 (Alin et al., 2023). [ABSTRACT FROM AUTHOR]

Methane (CH4) is a powerful greenhouse gas with a global warming potential 84 times higher than carbon dioxide (CO2) over 20 years. CH4 is produced from many natural and anthropogenic sources which can be further classified as biogenic or thermogenic in origin. The largest biogenic sources result from anaerobic decay such as wetlands, melting permafrost, or the breakdown of organic matter in the guts of ruminant animals. Thermogenic CH4 is generated during the breakdown of organic matter at high temperatures and pressure within the Earth's crust, a process which also produces more complex trace hydrocarbons such as ethane (C2H6) and propane (C3H8). Emissions of thermogenic CH4 are dominated by the fossil fuel energy sector, and the presence of elevated C2H6 along with CH4 can be used to distinguish oil and gas emissions from biogenic sources. This work outlines the development and deployment of an Unmanned Aerial System (UAS) outfitted with a fast (1 Hz) and sensitive (1–2 ppb s-1) CH4 & C2H6 sensor and ultrasonic anemometer. The UAV platform is a vertical-takeoff, hexarotor vehicle capable of vertical profiling to 120 m altitude and plume sampling across scales up to 1 km. This system has been used for direct quantification of point sources, as well as distributed emitters such as landfills, with source rates as low as 0.04 kg h-1 and up to 1500 kg h-1. Simultaneous measurements of CH4 and C2H6 mixing ratios, vector winds, and positional data allows for source classification (biogenic versus thermogenic), differentiation, and emission rates without the need for modeling or a priori assumptions about winds, vertical mixing, or other environmental conditions. The UAS has been deployed throughout the Southwest United States for system validation and targeted quantification of various sources emitting at or below the detection limits of other aircraft and satellite systems. This system offers a direct, repeatable method of horizontal and vertical profiling of emission plumes at scales that provide complementary information for regional aerial surveys as well as local ground-based monitoring. [ABSTRACT FROM AUTHOR]

Ocean liming has gained attention as a potential solution to mitigate climate change by actively removing carbon dioxide (CO2) from the atmosphere. The addition of hydrated lime (Ca(OH)2) into oceanic surface water leads to an increase in alkalinity, which in turn promotes the uptake and sequestration of atmospheric CO2. Despite the potential of this technique, its effects on the marine ecosystem are still far to be understood, and there is currently no information on the potential impacts on the concentration and quality of Dissolved Organic Matter (DOM), that is the largest, the most complex and yet the least understood mixture of organic molecules on Earth. The aim of this study is to provide the first experimental evidence about the potential effects of pH peaks, that might be generated by the Ca(OH)2 dissolution in seawater, on DOM dynamics by assessing changes in its concentration and optical properties (absorption and fluorescence). To investigate the effects of liming on DOM pools with different concentrations and quality, seawater was collected from two contrasting environments: the oligotrophic Mediterranean Sea (MedSea), known for its Dissolved Organic Carbon (DOC) concentration comparable to that observed in the oceans, and the eutrophic Baltic Sea (BalSea), characterized by high DOM concentration mostly of terrestrial origin. Ca(OH)2 was added in both waters, to reach a pH of 9 and 10. Our findings reveal that the addition of hydrated lime has a noticeable effect on DOM dynamics in both the MedSea and BalSea, determining a reduction in DOC concentration and a change in the optical properties (absorption and fluorescence) of DOM. Thes effects, detectable at pH 9, become significant at pH 10 and are more pronounced in the MedSea than in the BalSea. These potential short-term effects should be considered within the context of the physico-chemical properties of seawater and the seasonal variability. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) emissions from rivers are globally relevant, but quantification of these emissions comes with considerable uncertainty. Quantification of ecosystem-scale emissions is challenged by both spatial and short-term temporal variability. We measured spatio-temporal variability of CO 2 and CH 4 fluxes from a 1 km long reach of the lowland river Elbe in Germany over 3 d to establish which factor is more relevant to be taken into consideration: small-scale spatial variability or short-term temporal variability of CO 2 and CH 4 fluxes. GHG emissions from the river reach studied were dominated by CO 2 , and 90 % of total emissions were from the water surface, while 10 % of emissions were from dry fallen sediment at the side of the river. Aquatic CO 2 fluxes were similar at different habitats, while aquatic CH 4 fluxes were higher at the side of the river. Artificial structures to improve navigability (groynes) created still water areas with elevated CH 4 fluxes and lower CO 2 fluxes. CO 2 fluxes exhibited a clear diurnal pattern, but the exact shape and timing of this pattern differed between habitats. By contrast, CH 4 fluxes did not change diurnally. Our data confirm our hypothesis that spatial variability is especially important for CH 4 , while diurnal variability is more relevant for CO 2 emissions from our study reach of the Elbe in summer. Continuous measurements or at least sampling at different times of the day is most likely necessary for reliable quantification of river GHG emissions. [ABSTRACT FROM AUTHOR]

Almost 95 % of all terrestrial plant species form symbioses with mycorrhizal fungi that mediate plant–soil interactions: mycorrhizae facilitate plant nitrogen (N) acquisition and are, therefore, vital for plant growth, but they also build a pathway for plant-assimilated carbon (C) into the rhizosphere. Therefore, mycorrhizae likely play an important role in shaping the response of ecosystems to environmental changes such as rising atmospheric carbon dioxide (CO2) concentrations, which can increase plant N demand and the transfer of plant C assimilation to the soil. While the importance of mycorrhizal fungi is widely recognised, they are rarely represented in current terrestrial biosphere models (TBMs) explicitly. Here, we present a novel, dynamic plant–mycorrhiza–soil model as part of the QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) TBM. This new model is based on mycorrhizal functional types that either actively mine soil organic matter (SOM) for N or enhance soil microbial activity through increased transfer of labile C into the rhizosphere, thereby (passively) priming SOM decomposition. Using the Duke Free-Air CO2 Enrichment (FACE) experiment, we show that mycorrhizal fungi can have important effects on projected SOM turnover and plant nutrition under ambient as well as elevated- CO2 treatments. Specifically, we find that including enhanced active mining of SOM for N in the model allows one to more closely match the observations with respect to observed decadal responses of plant growth, plant N acquisition and soil C dynamics to elevated CO2 , whereas a simple enhancement of SOM turnover by increased below-ground C transfer of mycorrhizae is unable to replicate the observed responses. We provide an extensive parameter uncertainty study to investigate the robustness of our findings with respect to model parameters that cannot readily be constrained by observations. Our study points to the importance of implementing mycorrhizal functionalities in TBMs as well as to further observational needs to better constrain mycorrhizal models and to close the existing major knowledge gaps in actual mycorrhizal functioning. [ABSTRACT FROM AUTHOR]

Microbial release of CO 2 from soils to the atmosphere reflects how environmental conditions affect the stability of soil organic matter (SOM), especially in massive organic-rich ecosystems like the peatlands and grasslands of the Qinghai–Tibetan Plateau (QTP). Radiocarbon (14 C) is an important tracer of the global carbon cycle and can be used to understand SOM dynamics through the estimation of time lags between carbon fixation and respiration, often assessed with metrics such as age and transit time. In this study, we incubated peatland and grassland soils at four temperature (5, 10, 15 and 20 °C) and two water-filled pore space (WFPS) levels (60 % and 95 %) and measured the 14 C signature of bulk soil and heterotrophic respired CO 2. We compared the relation between the Δ14 C of the bulk soil and the Δ14 CO 2 of respired carbon as a function of temperature and WFPS for the two soils. To better interpret our results, we used a mathematical model to analyse how the calculated number of pools, decomposition rates of carbon (k), transfer (α) and partitioning (γ) coefficients affect the Δ14 C bulk and Δ14 CO 2 relation, with their respective mean age and mean transit time. From our incubations, we found that 14 C values in bulk and CO 2 from peatland were significantly more depleted (old) than from grassland soil. Our results showed that changes in temperature did not affect the Δ14 C values of heterotrophic respired CO 2 in either soil. However, changes in WFPS had a small effect on the 14 CO 2 in grassland soils and a significant influence in peatland soils, where higher WFPS levels led to more depleted Δ14 CO 2. In our models, the correspondence between Δ14 C, age and transit time highly depended on the internal dynamics of the soil (k , α , γ and number of pools) as well as on model structure. We observed large differences between slow and fast cycling systems, where low values of decomposition rates modified the Δ14 C values in a non-linear pattern due to the incorporation of modern carbon (14 C bomb) in the soil. We concluded that the stability of carbon in the peatland and grassland soils of the QTP depends strongly on the direction of change in moisture and how it affects the rates of SOM decomposition, while temperature regulates the number of fluxes. Current land cover modification (desiccation) in Zoigê peatlands and climate change occurring on the QTP might largely increase CO 2 fluxes along with the release of old carbon to the atmosphere potentially shifting carbon sinks into sources. [ABSTRACT FROM AUTHOR]

Since a pH sensor has become available that is principally suitable for use on demanding autonomous measurement platforms, the marine CO 2 system can be observed independently and continuously by Biogeochemical Argo floats. This opens the potential to detect variability and long-term changes in interior ocean inorganic carbon storage and quantify the ocean sink for atmospheric CO 2. In combination with a second parameter of the marine CO 2 system, pH can be a useful tool to derive the surface ocean CO 2 partial pressure (p CO 2). The large spatiotemporal variability in the marine CO 2 system requires sustained observations to decipher trends and study the impacts of short-term events (e.g., eddies, storms, phytoplankton blooms) but also puts a high emphasis on the quality control of float-based pH measurements. In consequence, a consistent and rigorous quality control procedure is being established to correct sensor offsets or drifts as the interpretation of changes depends on accurate data. By applying current standardized routines of the Argo data management to pH measurements from a pH / O 2 float pilot array in the subpolar North Atlantic Ocean, we assess the uncertainties and lack of objective criteria associated with the standardized routines, notably the choice of the reference method for the pH correction (CANYON-B, LIR-pH, ESPER-NN, and ESPER-LIR) and the reference depth for this adjustment. For the studied float array, significant differences ranging between ca. 0.003 pH units and ca. 0.04 pH units are observed between the four reference methods which have been proposed to correct float pH data. Through comparison against discrete and underway pH data from other platforms, an assessment of the adjusted float pH data quality is presented. The results point out noticeable discrepancies near the surface of > 0.004 pH units. In the context of converting surface ocean pH measurements into p CO 2 data for the purpose of deriving air–sea CO 2 fluxes, we conclude that an accuracy requirement of 0.01 pH units (equivalent to a p CO 2 accuracy of 10 µ atm as a minimum requirement for potential future inclusion in the Surface Ocean CO 2 Atlas, SOCAT, database) is not systematically achieved in the upper ocean. While the limited dataset and regional focus of our study do not allow for firm conclusions, the evidence presented still calls for the inclusion of an additional independent pH reference in the surface ocean in the quality control routines. We therefore propose a way forward to enhance the float pH quality control procedure. In our analysis, the current philosophy of pH data correction against climatological reference data at one single depth in the deep ocean appears insufficient to assure adequate data quality in the surface ocean. Ideally, an additional reference point should be taken at or near the surface where the resulting p CO 2 data are of the highest importance to monitor the air–sea exchange of CO 2 and would have the potential to very significantly augment the impact of the current observation network. [ABSTRACT FROM AUTHOR]

Mitigating the global climate crisis and its consequences, such as more frequent and severe droughts, is one of the major challenges for future agriculture. Therefore, identifying land use systems and management practices that reduce greenhouse gas (GHG) emissions and promote water use efficiency (WUE) is crucial. This, however, requires accurate and precise measurements of carbon dioxide (CO 2) fluxes and evapotranspiration (ET). Despite that, commercial systems to measure CO 2 and ET fluxes are expensive and thus often exclude research in ecosystems within the Global South. This is especially true for research and data of agroecosystems in these areas, which are to date still widely underrepresented. Here, we present a newly developed low-cost, non-dispersive infrared (NDIR)-based CO 2 and ET flux measurement device (∼ EUR 200) that provides reliable, accurate and precise CO 2 and ET flux measurements in conjunction with manually operated closed chambers. To validate the system, laboratory and field validation experiments were performed, testing multiple different low-cost sensors. We demonstrate that the system delivers accurate and precise CO 2 and ET flux measurements using the K30 FR NDIR (CO 2) and SHT31 (RH, relative humidity) sensor. An additional field trial application demonstrated its longer-term stability (> 3 months) and ability to obtain valid net ecosystem C balances (NECBs) and WUE. This was the case, even though environmental conditions at the field trial application site in sub-Saharan Africa were rather challenging (e.g., extremely high temperatures, humidity and rainfall). Consequently, the developed low-cost CO 2 and ET flux measurement device not only provides reasonable results but also might help with democratizing science and closing current data gaps. [ABSTRACT FROM AUTHOR]

To design a monitoring network for estimating CO 2 fluxes in an urban area, a high-resolution observing system simulation experiment (OSSE) is performed using the transport model Graz Mesoscale Model (GRAMMv19.1) coupled to the Graz Lagrangian Model (GRALv19.1). First, a high-resolution anthropogenic emission inventory which is considered as the truth serves as input to the model to simulate CO 2 concentration in the urban atmosphere on 10 m horizontal resolution in a 12.3 km × 12.3 km domain centred in Heidelberg, Germany. By sampling the CO 2 concentration at selected stations and feeding the measurements into a Bayesian inverse framework, CO 2 fluxes on a neighbourhood scale are estimated. Different configurations of possible measurement networks are tested to assess the precision of posterior CO 2 fluxes. We determine the trade-off between the quality and quantity of sensors by comparing the information content for different set-ups. Decisions on investing in a larger number or in more precise sensors can be based on this result. We further analyse optimal sensor locations for flux estimation using a Monte Carlo approach. We examine the benefit of additionally measuring carbon monoxide (CO). We find that including CO as tracer in the inversion enables the disaggregation of different emission sectors. Finally, we quantify the benefit of introducing a temporal correlation into the prior emissions. The results of this study have implications for an optimal measurement network design for a city like Heidelberg. The study showcases the general usefulness of the inverse framework developed using GRAMM/GRAL for planning and evaluating measurement networks in an urban area. [ABSTRACT FROM AUTHOR]

We describe the development of the tangent linear (TL) and adjoint models of the Model for Prediction Across Scales (MPAS)- CO2 transport model, which is a global online chemical transport model developed upon the non-hydrostatic Model for Prediction Across Scales – Atmosphere (MPAS-A). The primary goal is to make the model system a valuable research tool for investigating atmospheric carbon transport and inverse modeling. First, we develop the TL code, encompassing all CO2 transport processes within the MPAS- CO2 forward model. Then, we construct the adjoint model using a combined strategy involving re-calculation and storage of the essential meteorological variables needed for CO2 transport. This strategy allows the adjoint model to undertake a long-period integration with moderate memory demands. To ensure accuracy, the TL and adjoint models undergo vigorous verifications through a series of standard tests. The adjoint model, through backward-in-time integration, calculates the sensitivity of atmospheric CO2 observations to surface CO2 fluxes and the initial atmospheric CO2 mixing ratio. To demonstrate the utility of the newly developed adjoint model, we conduct simulations for two types of atmospheric CO2 observations, namely the tower-based in situ CO2 mixing ratio and satellite-derived column-averaged CO2 mixing ratio (XCO2). A comparison between the sensitivity to surface flux calculated by the MPAS- CO2 adjoint model with its counterpart from CarbonTracker–Lagrange (CT-L) reveals a spatial agreement but notable magnitude differences. These differences, particularly evident for XCO2 , might be attributed to the two model systems' differences in the simulation configuration, spatial resolution, and treatment of vertical mixing processes. Moreover, this comparison highlights the substantial loss of information in the atmospheric CO2 observations due to CT-L's spatial domain limitation. Furthermore, the adjoint sensitivity analysis demonstrates that the sensitivities to both surface flux and initial CO2 conditions spread out throughout the entire Northern Hemisphere within a month. MPAS- CO2 forward, TL, and adjoint models stand out for their calculation efficiency and variable-resolution capability, making them competitive in computational cost. In conclusion, the successful development of the MPAS- CO2 TL and adjoint models, and their integration into the MPAS- CO2 system, establish the possibility of using MPAS's unique features in atmospheric CO2 transport sensitivity studies and in inverse modeling with advanced methods such as variational data assimilation. [ABSTRACT FROM AUTHOR]

We summarise current important and well-established open issues related to the depletion of stratospheric ozone and discuss some newly emerging challenges. The ozone layer is recovering from the effects of halogenated source gases due to the continued success of the Montreal Protocol despite recent renewed production of controlled substances and the impact of uncontrolled very short-lived substances. The increasing atmospheric concentrations of greenhouse gases, such as carbon dioxide, methane (CH 4) and nitrous oxide (N 2 O), have large potential to perturb stratospheric ozone in different ways, but their future evolutions, and hence impacts, are uncertain. Ozone depletion through injection of smoke particles has been observed following recent Australian wildfires. Further perturbations to the ozone layer are currently occurring through the unexpected injection of massive amounts of water vapour from the Hunga Tonga–Hunga Ha'apai volcano in 2022. Open research questions emphasise the critical need to maintain, if not expand, the observational network and to address the impending "satellite data gap" in global, height-resolved observations of stratospheric trace gases and aerosols. We will, in effect, be largely blind to the stratospheric effects of similar wildfire and volcanic events in the near future. Complex Earth system models (ESMs) being developed for climate projections have the stratosphere as an important component. However, the huge computational requirement of these models must not result in an oversimplification of the many processes affecting the ozone layer. Regardless, a hierarchy of simpler process models will continue to be important for testing our evolving understanding of the ozone layer and for providing policy-relevant information. [ABSTRACT FROM AUTHOR]