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Methane (CH 4) is a significant greenhouse gas in exacerbating climate change. Approximately 25 % of CH 4 is emitted from storage tanks. It is crucial to spatially explore the CH 4 emission patterns from storage tanks for efficient strategy proposals to mitigate climate change. However, due to the lack of publicly accessible storage tank locations and distributions, it is difficult to ascertain the CH 4 emission spatial pattern over a large-scale area. To address this problem, we generated a storage tank dataset (STD) by implementing a deep learning model with manual refinement based on 4403 high-spatial-resolution images (1–2 m) from the Gaofen-1, Gaofen-2, Gaofen-6, and Ziyuan-3 satellites over city regions in China with officially reported numerous storage tanks in 2021. STD is the first storage tank dataset for over 92 typical city regions in China. The dataset can be accessed at 10.5281/zenodo.10514151 (Chen et al., 2024). It provides a detailed georeferenced inventory of 14 461 storage tanks wherein each storage tank is validated and assigned the construction year (2000–2021) by visual interpretation of the collected high-spatial-resolution images, historical high-spatial-resolution images of Google Earth, and field survey. The inventory comprises storage tanks with various distribution patterns in different city regions. Spatial consistency analysis with the CH 4 emission product shows good agreement with storage tank distributions. The intensive construction of storage tanks significantly induces CH 4 emissions from 2005 to 2020, underscoring the need for more robust measures to curb CH 4 release and aid in climate change mitigation efforts. Our proposed dataset, STD, will foster the accurate estimation of CH 4 released from storage tanks for CH 4 control and reduction and ensure more efficient treatment strategies are proposed to better understand the impact of storage tanks on the environment, ecology, and human settlements. [ABSTRACT FROM AUTHOR]

To mitigate the impact of greenhouse gas (GHG) and air pollutant emissions, it is of utmost importance to understand where emissions occur. In the real world, atmospheric pollutants are produced by various human activities from point sources (e.g. power plants and industrial facilities) but also from diffuse sources (e.g. residential activities and agriculture). However, as tracking all these single sources of emissions is practically impossible, emission inventories are typically compiled using national-level statistics by sector, which are then downscaled at the grid-cell level using spatial information. In this work, we develop high-spatial-resolution proxies for use in downscaling the national emission totals for all world countries provided by the Emissions Database for Global Atmospheric Research (EDGAR). In particular, in this paper, we present the latest EDGAR v8.0 GHG, which provides readily available emission data at different levels of spatial granularity, obtained from a consistently developed GHG emission database. This has been achieved through the improvement and development of high-resolution spatial proxies that allow for a more precise allocation of emissions over the globe. A key novelty of this work is the potential to analyse subnational GHG emissions over the European territory and also over the United States, China, India, and other high-emitting countries. These data not only meet the needs of atmospheric modellers but can also inform policymakers working in the field of climate change mitigation. For example, the EDGAR GHG emissions at the NUTS 2 level (Nomenclature of Territorial Units for Statistics level 2) over Europe contribute to the development of EU cohesion policies, identifying the progress of each region towards achieving the carbon neutrality target and providing insights into the highest-emitting sectors. The data can be accessed at 10.2905/b54d8149-2864-4fb9-96b9-5fd3a020c224 specifically for EDGAR v8.0 (Crippa et al., 2023a) and 10.2905/D67EEDA8-C03E-4421-95D0-0ADC460B9658 for the subnational dataset (Crippa et al., 2023b). [ABSTRACT FROM AUTHOR]

Greenhouse gases monitoring is important to ensure climate goals are being achieved. This study unveils the potential of using atmospheric tall towers in direct flux measurements, bridging the gap between atmospheric and ecosystem monitoring networks. The ICOS Cities (PAUL) project aims to monitor CO2 emissions in urban areas, where concentrated emissions make them key targets for climate change mitigation. This study explores synergy between ICOS atmospheric and ecosystem networks by utilizing slow-response analysers (~2 sec) on tall atmospheric towers for ecosystem studies using the Eddy Covariance method. A standard setup with an ultrasonic anemometer and an infrared (IR) fast-response CO2 analyser was installed and compared with measurements from an existing cavity ring down spectroscopy (CRDS) analyser measuring CO2, CO, and CH4. Deployed on the 100 m Saclay tower near Paris, covering a 43.9 km² 80 % footprint with heavy traffic roads, a nearby heating plant, and a forest, the setup addressed technical challenges and height-induced complexities. Corrections for flux attenuation by high frequency losses were limited to <20 % on average for all stabilities, around 11 % for unstable conditions. Wavelet-based eddy covariance allowed 18–34 % more data exploitation than standard EC enabling the analysis of non-stationary fluxes, particularly from a point source such was the case of a heating plant. The estimated storage term produced by atmospheric profiling measurements reported an expected increase at night, destocking during the first half of the day. Storage term represented at times more than half of the surface flux. Elevated mean fluxes for CO2 (10 μmolm−2s−1) and CH4 (200 nmolm−2s−1) were observed from the heating plant wind direction during December and January. Conversely, the forest direction exhibited the strongest sink among all wind directions, with −4 μmolm−2s−1 during July and August. These results demonstrate the feasibility and versatility of utilizing atmospheric towers for urban emission monitoring, offering valuable insights for emission monitoring strategies worldwide. [ABSTRACT FROM AUTHOR]

Today, city planners design urban futures by considering environmental degradation and climate mitigation. Here, we studied the greenhouse gas fluxes of urban lawns and meadows and linked the observations with plant functional types and soil properties. In eight lawns and eight meadows in the Helsinki metropolitan area, Finland, carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) fluxes were measured using manual chambers, and plant functional types were recorded. Four of these sites, i.e. an irrigated lawn, an old mesic meadow, a non-irrigated lawn and a young dry meadow, were more intensively studied in 2021–2022. The process-based ecosystem model JSBACH was utilized together with the momentary observations collected approximately every second week on CO2 exchange to quantify the annual carbon (C) balance of these sites. On the remaining sites, we studied the initial dynamics of conversion from lawns to meadows by transforming parts of lawns to meadows in late 2020 and conducting measurements from 2020 to 2022. The mean photosynthetic production (GPP) of the irrigated lawn and mesic meadow was the highest in this study, whereas the dry meadow had the lowest GPP. The studied lawns were stronger C sinks compared to the meadows. However, the net exchange values were uncertain as the soils were not in equilibrium with the vegetation at all sites, which is common for urban habitats, and modelling the heterotrophic emissions was therefore challenging. The conversion from a lawn to a meadow did not affect the fluxes of CH4 and N2O. Moreover, the mesic meadow was more resistant to drought events than the non-irrigated lawn. Lastly, the proportion of herbaceous flowering plants other than grasses was higher in meadows than in lawns. Even though social and economic aspects also steer urban development, these results can guide planning when considering environmentally friendlier green spaces and carbon smartness. [ABSTRACT FROM AUTHOR]

The substantial climate change mitigation potential of restoring peatlands through rewetting and intensifying agriculture to reduce greenhouse gas (GHG) emissions is largely recognized. The green deal in Denmark aims at restoring 100 000 ha of peatlands by 2030. This area corresponds to more than half of the Danish peatland, with an expected reduction in GHG emissions of almost half of the entire land use, land use change and forestry (LULUFC) emissions. Recent advances established the functional relationship between hydrological regimes, i.e., water table depth (WTD), and CO2 and CH4 emissions. This builds the basis for science-based tools to evaluate and prioritize peatland restoration projects. With this article, we lay the foundation of such a development by developing a high-resolution WTD map for Danish peatlands. Further, we define WTD response functions (CO2 and CH4) fitted to Danish flux data to derive a national GHG emission estimate for peat soils. We estimate the annual GHG emissions to be 2.6 MtCO2-eq , which is around 15 % lower than previous estimates. Lastly, we investigate alternative restoration scenarios and identify substantial differences in the GHG reduction potential depending on the prioritization of fields in the rewetting strategy. If wet fields are prioritized, which is not unlikely in a context of a voluntary bottom-up approach, the GHG reduction potential is just 30 % for the first 10 000 ha with respect to a scenario that prioritizes drained fields. This underpins the importance of the proposed framework linking WTD and GHG emissions to guide a spatially differentiated peatland restoration. The choice of model type used to fit the CO2 WTD response function, the applied global warming potentials and uncertainties related to the WTD map are investigated by means of a scenario analysis, which suggests that the estimated GHG emissions and the reduction potential are associated with coefficients of variation of 13 % and 22 %, respectively. [ABSTRACT FROM AUTHOR]

Despite an increasing attention on the role of land in meeting countries' climate pledges under the Paris Agreement, the range of estimates of carbon fluxes from land use, land-use change, and forestry (LULUCF) in available databases is very large. A good understanding of the LULUCF data reported by countries under the United Nations Framework Convention on Climate Change (UNFCCC) – and of the differences with other datasets based on country-reported data – is crucial to increase confidence in land-based climate change mitigation efforts. Here we present a new data compilation of LULUCF fluxes of carbon dioxide (CO 2) on managed land, aiming at providing a consolidated view on the subject. Our database builds on a detailed analysis of data from national greenhouse gas inventories (NGHGIs) communicated via a range of country reports to the UNFCCC, which report anthropogenic emissions and removals based on the IPCC (Intergovernmental Panel on Climate Change) methodology. Specifically, for Annex I countries, data are sourced from annual GHG inventories. For non-Annex I countries, we compiled the most recent and complete information from different sources, including national communications, biennial update reports, submissions to the REDD + (reducing emissions from deforestation and forest degradation) framework, and nationally determined contributions. The data are disaggregated into fluxes from forest land, deforestation, organic soils, and other sources (including non-forest land uses). The CO 2 flux database is complemented by information on managed and unmanaged forest area as available in NGHGIs. To ensure completeness of time series, we filled the gaps without altering the levels and trends of the country reported data. Expert judgement was applied in a few cases when data inconsistencies existed. Results indicate a mean net global sink of -1.6 Gt CO 2 yr -1 over the period 2000–2020, largely determined by a sink on forest land (-6.4 Gt CO 2 yr -1), followed by source from deforestation (+4.4 Gt CO 2 yr -1), with smaller fluxes from organic soils (+0.9 Gt CO 2 yr -1) and other land uses (-0.6 Gt CO 2 yr -1). Furthermore, we compare our NGHGI database with two other sets of country-based data: those included in the UNFCCC GHG data interface, and those based on forest resources data reported by countries to the Food and Agriculture Organization of the United Nations (FAO) and used as inputs into estimates of GHG emissions in FAOSTAT. The first dataset, once gap filled as in our study, results in a net global LULUCF sink of -5.4 Gt CO 2 yr -1. The difference with the NGHGI database is in this case mostly explained by more updated and comprehensive data in our compilation for non-Annex I countries. The FAOSTAT GHG dataset instead estimates a net global LULUCF source of +1.1 Gt CO 2 yr -1. In this case, most of the difference to our results is due to a much greater forest sink for non-Annex I countries in the NGHGI database than in FAOSTAT. The difference between these datasets can be mostly explained by a more complete coverage in the NGHGI database, including for non-biomass carbon pools and non-forest land uses, and by different underlying data on forest land. The latter reflects the different scopes of the country reporting to FAO, which focuses on area and biomass, and to UNFCCC, which explicitly focuses on carbon fluxes. Bearing in mind the respective strengths and weaknesses, both our NGHGI database and FAO offer a fundamental, yet incomplete, source of information on carbon-related variables for the scientific and policy communities, including under the Global stocktake. Overall, while the quality and quantity of the LULUCF data submitted by countries to the UNFCCC significantly improved in recent years, important gaps still remain. Most developing countries still do not explicitly separate managed vs. unmanaged forest land, a few report implausibly high forest sinks, and several report incomplete estimates. With these limits in mind, the NGHGI database presented here represents the most up-to-date and complete compilation of LULUCF data based on country submissions to UNFCCC. Data from this study are openly available via the Zenodo portal (Grassi et al., 2022), at 10.5281/zenodo.7190601. [ABSTRACT FROM AUTHOR]

Atmospheric carbon dioxide (CO2) is the most significant greenhouse gas, and its concentration is continuously increasing, mainly as a consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change and for designing mitigation strategies aimed at stabilizing CO2 emissions. Satellites provide the most effective way to monitor the concentration of CO2 in the atmosphere. In this study, we utilized the concentration of the column-averaged dry-air mole fraction of CO2 , i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory-2 (OCO-2), and the net primary productivity (NPP) provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the anthropogenic CO2 emissions using the Generalized Regression Neural Network (GRNN) over East and West Asia. OCO-2 XCO2 , MODIS NPP, and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (2015–2019) were used in this study. The annual XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and seasonal variability. The XCO2 anomaly, NPP, and ODIAC emission datasets from 2015 to 2018 were then used to train the GRNN model, and, finally, the anthropogenic CO2 emissions were estimated for 2019 based on the NPP and XCO2 anomalies derived for the same year. The estimated and the ODIAC CO2 emissions were compared, and the results showed good agreement in terms of spatial distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO2 retrievals can be used to estimate the regional-scale anthropogenic CO2 emissions, and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration datasets. [ABSTRACT FROM AUTHOR]

Producing targeted climate information at the local scale, including major sources of climate change projection uncertainty for diverse emissions scenarios, is essential to support climate change mitigation and adaptation efforts. Here, we present the first chain of computationally efficient Earth System Model (ESM) emulators allowing to rapidly translate greenhouse gas emission pathways into spatially resolved annual-mean temperature anomaly field time series, accounting for both forced climate response and natural variability uncertainty at the local scale. By combining the global-mean, emissions-driven emulator MAGICC with the spatially resolved emulator MESMER, ESM-specific as well as constrained probabilistic emulated ensembles can be derived. This emulation chain can hence build on and extend large multi-ESM ensembles such as the ones produced within the 6th phase of the Coupled Model Intercomparison Project (CMIP6). The main extensions are threefold. (i) A more thorough sampling of the forced climate response and the natural variability uncertainty is possible with millions of emulated realizations being readily created. (ii) The same uncertainty space can be sampled for any emission pathway, which is not the case in CMIP6, where some of the most societally relevant strong mitigation scenarios have been run by only a small number of ESMs. (iii) Other lines of evidence to constrain future projections, including observational constraints, can be introduced, which helps to refine projected future ranges beyond the multi-ESM ensemble's estimates. In addition to presenting results from the coupled MAGICC-MESMER emulator chain, we carry out an extensive validation of MESMER, which is trained on and applied to multiple emission pathways for the first time in this study. The newly developed MAGICC-MESMER coupled emulator will allow unprecedented assessments of the implications of manifold emissions pathways at regional scale. [ABSTRACT FROM AUTHOR]

Atmospheric carbon dioxide (CO2) is the most significant greenhouse gas and its concentration is continuously increasing mainly as a consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change and designing mitigation strategies aimed at stabilizing the CO2 emissions. Satellites provide the most effective way to monitor the concentration of CO2 in the atmosphere. In this study, we utilized the concentration of column-averaged dry-air mole fraction of CO2 i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory 2 (OCO-2) to estimate the anthropogenic CO2 emissions using Generalized Regression Neural Network over East and West Asia. OCO-2 XCO2 and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (2015-2019) were used in this study. The annual XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and seasonal variabilities. Then the XCO2 anomaly and ODIAC emission datasets from 2015 to 2018 were used to train the GRNN model, and finally, the anthropogenic CO2 emissions were estimated for 2019 based on the XCO2 anomalies derived for the same year. The XCO2-based estimated and the ODIAC actual CO2 emissions were compared and the results showed a good agreement in terms of spatial distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO2 retrievals can be used to estimate the regional scale anthropogenic CO2 emissions and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration datasets. [ABSTRACT FROM AUTHOR]

Management of Earth's surface albedo is increasingly viewed as an important climate change mitigation strategy both on (Seneviratne et al., 2018) and off (Field et al., 2018; Kravitz et al., 2018) the land. Assessing the impact of a surface albedo change involves employing a measure like radiative forcing (RF) which can be challenging to digest for decision-makers who deal in the currency of CO2 -equivalent emissions. As a result, many researchers express albedo change (Δα) RFs in terms of their CO2 -equivalent effects, despite the lack of a standard method for doing so, such as there is for emissions of well-mixed greenhouse gases (WMGHGs; e.g., IPCC AR5, Myhre et al., 2013). A major challenge for converting Δα RFs into their CO2 -equivalent effects in a manner consistent with current IPCC emission metric approaches stems from the lack of a universal time dependency following the perturbation (perturbation "lifetime"). Here, we review existing methodologies based on the RF concept with the goal of highlighting the context(s) in which the resulting CO2 -equivalent metrics may or may not have merit. To our knowledge this is the first review dedicated entirely to the topic since the first CO2-eq. metric for Δα surfaced 20 years ago. We find that, although there are some methods that sufficiently address the time-dependency issue, none address or sufficiently account for the spatial disparity between the climate response to CO2 emissions and Δα – a major critique of Δα metrics based on the RF concept (Jones et al., 2013). We conclude that considerable research efforts are needed to build consensus surrounding the RF "efficacy" of various surface forcing types associated with Δα (e.g., crop change, forest harvest), and the degree to which these are sensitive to the spatial pattern, extent, and magnitude of the underlying surface forcings. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) research has traditionally required data collection and analysis using advanced and often expensive instruments, complex and proprietary software, and skilled technicians. Partly as a result, relatively little GHG research has been conducted in resource-constrained developing countries and a critical data gap exists in these regions. At the same time, these are the same countries and regions in which climate-change impacts will likely be strongest, and in which major science uncertainties are centered, given the importance of dryland and tropical systems to the global carbon cycle and climate. Increasingly, scientific communities have adopted appropriate technology and approach (AT&A) for GHG research, including low-cost and low-technology instruments, open source software and data, and participatory and networking-based research approaches. Adopting AT&A can mean acquiring data with fewer technical constraints and lower economic burden, and is thus a strategy for enhancing GHG research in developing countries. However, AT&A can be characterized by higher uncertainties; these can often be mitigated by carefully designing experimental set-up, providing clear protocols for data collection, and monitoring and validating the quality of obtained data. For implementing this approach in GHG research of developing countries, first, it is necessary to recognize the scientific and moral importance of AT&A. At the same time, new AT&A techniques should be identified and further developed. Finally, these processes should be promoted through training local staff and encouraged for wide use and further innovation in developing countries. [ABSTRACT FROM AUTHOR]

Parties to the Paris Agreement (PA, 2015) outline their planned contributions towards achieving the PA temperature goal to hold […] the increase in the global average temperature to well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C (Article 2.1.a, PA) in their Nationally Determined Contributions (NDCs). Most NDCs include targets to mitigate national greenhouse gas (GHG) emissions, which need quantifications to assess, i.a., whether the current NDCs collectively put us on track to reach the PA temperature goals or the gap in ambition to do so. We implemented the new open-source tool NDCmitiQ to quantify GHG mitigation targets defined in the NDCs for all countries with quantifiable targets on a disaggregated level, and to create corresponding national and global emissions pathways. In light of the five-year update cycle of NDCs and the global stocktake, the quantification of NDCs is an ongoing task for which NDCmitiQ can be used, as calculations can easily be updated upon submission of new NDCs. In this paper, we describe the methodologies behind NDCmitiQ and quantification challenges we encountered by addressing a wide range of aspects, including: target types and the input data from within NDCs; external time series of national emissions, population, and GDP; uniform approach vs. country specifics; share of national emissions covered by NDCs; how to deal with the Land Use, Land-Use Change and Forestry (LULUCF) component and the conditionality of pledges; establishing pathways from single year targets. For use in NDCmitiQ, we furthermore construct an emissions data set from the baseline emissions provided in the NDCs. Example use cases show how the tool can help to analyse targets on a national, regional, or global scale, and to quantify uncertainties caused by a lack of clarity in the NDCs. Results confirm that the conditionality of targets and assumptions on economic growth dominate uncertainty in mitigated emissions on a global scale, which are estimated as 49.2-55.7 Gt CO2 eq AR4 for 2030 (10th/90th percentiles, median: 52.4 Gt CO2 eq AR4; excl. LULUCF and bunker fuels). We estimate that 77 % of global 2017 emissions were emitted from sectors and gases covered by current NDCs (excl. the USA). [ABSTRACT FROM AUTHOR]

Natural peatlands are important carbon sinks and sources of methane (CH4). In contrast, drained peatlands turn from a carbon sink to a carbon source and potentially emit nitrous oxide (N2O). Rewetting of peatlands thus potentially implies climate change mitigation. However, data about the time span that is needed for the re-establishment of the carbon sink function by restoration are scarce. We therefore investigated the annual greenhouse gas (GHG) balances of three differently vegetated sites of a bog ecosystem 30 years after rewetting. All three vegetation communities turned out to be sources of carbon dioxide (CO2) ranging between 0.6 ± 1.43 t CO2 ha-2yr-1 (Sphagnum-dominated vegetation) and 3.09 ± 3.86 t CO 2ha-2yr-1 (vegetation dominated by heath). While accounting for the different global warming potential (GWP) of CO2, CH4 and N2O, the annual GHG balance was calculated. Emissions ranged between 25 and 53 t CO2-eq ha-1yr-1 and were dominated by large emissions of CH4 (22-51 t CO2-eq ha-1 yr-1), with highest rates found at purple moor grass (Molinia caerulea) stands. These are to our knowledge the highest CH4 emissions so far reported for bog ecosystems in temperate Europe. As the restored area was subject to large fluctuations in the water table, we assume that the high CH4 emission rates were caused by a combination of both the temporal inundation of the easily decomposable plant litter of purple moor grass and the plant-mediated transport through its tissues. In addition, as a result of the land use history, mixed soil material due to peat extraction and refilling can serve as an explanation. With regards to the long time span passed since rewetting, we note that the initial increase in CH4 emissions due to rewetting as described in the literature is not inevitably limited to a short-term period. [ABSTRACT FROM AUTHOR]

The article reports on the implementation of the HadGEM2-ES Coupled Model Intercomparison Project (CMIP5), a standard experimental protocol for studying the output of general circulation models (GCMs). It covers the greenhouse gas concentrations, aerosol precursors, stratospheric and tropospheric ozone assumptions, as well as implementation of land-use change and natural forcings for the experiments. It also details how the HadGEM2-ES ensemble members were initialised from the control run.

Afforestation and reforestation have become popular instruments of climate mitigation policy, as forests are known to store large quantities of carbon. However, they also modify the fluxes of energy, water and momentum at the land surface. Previous studies have shown that these biogeophysical effects can counteract the carbon drawdown and, in boreal latitudes, even overcompensate it due to large albedo differences between forest canopy and snow. This study investigates the role forest cover plays for global climate by conducting deforestation and afforestation experiments with the earth system model of the Max Planck Institute for Meteorology (MPI-ESM). Complete de forestation of the tropics (18.75° S-15° N) exerts a global warming of 0.4 °C due to an increase in CO2 concentration by initially 60 ppm and a decrease in evapotranspiration in the deforested areas. In the northern latitudes (45° N-90° N), complete deforestation exerts a global cooling of 0.25 °C after 100 years, while afforestation leads to an equally large warming, despite the counteracting changes in CO2 concentration. Earlier model studies are qualitatively confirmed by these findings. As the response of temperature as well as terrestrial carbon pools is not of equal sign at every land cell, considering forests as cooling in the tropics and warming in high latitudes seems to be true only for the spatial mean, but not on a local scale. [ABSTRACT FROM AUTHOR]

Oxygen (O2) plays a critical role for life on Earth, but over the last 50 years, O2 concentrations in the Ocean have decreased massively as a consequence of human activity. These activities include greenhouse gas emissions and nutrient discharge to coastal waters, with major impacts on Ocean biogeochemistry and ecology. This effect is particularly intense in O2 depleted tropical Ocean areas, also referred to as oxygen minimum zones (OMZ). These are sensitive areas as OMZs are connected to nutrient-rich coastal upwelling systems and support some of the world's most prolific fisheries. While continuous OMZ expansion would lead to a decreasing habitat for fish and other organisms, feedback mechanisms which counteract O2 depletion and stabilize the system's ecological functionality may however explain and sustain high productivity. Microbes are essential to regulating ocean biogeochemistry, oxygen and nutrient turnover and may those hold the key to stabilizing the oxygen budget to a certain extent. In this presentation, I will discuss the potential of microbes involved in the nitrogen cycle to mitigate Ocean oxygen depletion via three different feedback cycles from an integrated molecular, biogeochemical, and modeling perspective. [ABSTRACT FROM AUTHOR]

Scientists generally agree that, by 2100, the annual extraction of an average of 3.3 Gt of carbon equivalent from the atmosphere will be necessary to limit the increase in global average temperature to 2°C relative to pre-industrial levels (Smith et al., 2016). A greenhouse gas (GHG) removal technology (GGRT) is one that can remove a GHG from the atmosphere. Enhanced silicate rock weathering for long-term CO2 sequestration has considerable potential as a GGRT, but depends on the availability of suitable rocks coupled with proximity to locations where it can be applied. Extracting, processing, transporting and applying ground rock to land consume energy and thus generate GHG emissions, which must be balanced against the sequestration potential. In this study, we i) assess the environmental impacts of the practice, using existing basalt rock quarrying in Sao Paulo (SP) state by considering the emissions associated with the rock extraction, comminution, transport and application through a Life Cycle Assessment (LCA) approach; ii) estimate the contribution of the different processes involved with soil carbonation and enhanced weathering and iii) assess the potential net CO2 removal of Sao Paulo agricultural land through soil carbonation and weathering of basalt rock.Our results show that enhanced weathering and carbonation respectively emit around 75 and 135 kg CO2eq per tonne of CO2eq removed (considering an average quarry to field distance of 65 km.) We underline transportation as the principal process negatively affecting the practice and establish a limiting road travel distance from quarry to field of 540 ± 65 km for carbonation and 990 ± 116 km for enhanced weathering. Above these distances, the GHG emissions exceed the potential sequestration. The application of crushed basalt at 1 t/ha to all of Sao Paulo State's 12 million hectares of agricultural land could capture around 1.3 to 2.4 Mt CO2eq through carbonation and enhanced weathering, respectively. If 50 t/ha is applied, the sequestration potential could reach up to 40 to 76 % of the total Sao Paulo State emissions in 2016 through carbonation and enhanced weathering respectively, assuming no loss in the effectiveness of basalt at high application rates.This study lowers previous net sequestration estimates from Kantola et al. (2017) by around 50% and, as various techniques are increasingly promoted as efficient solutions to remove or sequester CO2, it emphasizes the need to consider the practice in its entirety to show the limitations and potential caveats. The holistic approach of LCA helps identify processes with the most impact and their significance. However, its accuracy and reliability is inevitably limited by the amount, quality and representativeness of the input data. Therefore, detailed studies on each process stage should be conducted to increase accuracy, focusing on local practices especially transport.References: 1. Kantola et al., 2017, Potential of global croplands and bioenergy crops for climate change mitigation through deployment for enhanced weathering. https://doi.org/http://dx.doi.org/10.1098/rsbl.2016.07142. Smith et al., 2016, Biophysical and economic limits to negative CO2 emissions. https://doi.org/10.1038/nclimate2870 [ABSTRACT FROM AUTHOR]

In 2016, 175 parties agreed to ambitious greenhouse gas reductions under the UNFCCC Paris agreement, yet estimates of greenhouse gas emissions and uptake by sinks remain uncertain. The capability to verify emissions reductions at sub-national to national scale and guide carbon mitigation policy is urgently needed to support climate mitigation. Atmospheric greenhouse gas measurements have a key role to play. The amount of greenhouse gases stored in the atmosphere is the by far the most well-known component of greenhouse gas budgets due to precise, accurate greenhouse gas records from a global network that comprises more than 400 stations, supported by over 80 nations. These measurements have been crucial to a range of scientific studies, but we have only begun to explore their potential to inform carbon mitigation policy. We will present results form a case study in New Zealand, which suggests that NZ's land biosphere absorbs much more carbon than expected from the National Inventory Report (NIR) and previous synthesis reports. The NIR is the official national reporting of our greenhouse gas emissions and removals that follows international protocols and must be submitted annually to meet our UNFCCC obligations. The atmospheric data suggests that much of this missing carbon sink occurs in Fiordland, a high rainfall region dominated by indigenous forests. In addition to the results of our pilot study will present plans to expand this effort through the newly funded CarbonWatch-NZ project. This five year programme will expand our national observing network to 11 permanent and 2 temporary stations, introduce urban and regional forest scale studies, leverage measurements of isotopes and carbonyl sulphide to understand source and sink processes, and integrate flux tower observations to understand the role of pastures. In addition to addressing the scientific findings of this study, we will discuss the process used to engage New Zealand's NIR and environmental policy communities, starting from the development phase of the project. This will include the strategies used to resolve differences between components of the carbon budget counted in the NIR and those detected by the inversion, communicate uncertainties, and plan research activities that meet policy needs. [ABSTRACT FROM AUTHOR]

The Paris Agreement (PA) is a landmark agreement because for the first time all 195participating countries are committed to take climate change mitigation and adaptationactions and track progress with implementation of such action through an enhancedtransparency framework with common modalities, procedures and guidelines. Whileregularly updated inventories of national anthropogenic GHG emissions, also referenced to asbottom-up estimates (more than three quarters being CO2 emissions from fossil fuel burningand cement production), will form the core of monitoring and tracking progress with effortsto reduce global emissions towards PA goals, so-called top-down atmospheric measurementsare required to provide observation-based evidence of emission trends in near-realtime at higher spatial and temporal resolution. The scientific community alreadybrings together top-down and bottom-up information in the yearly update of theglobal carbon budget, but no international system exists to monitor and supportemission reductions estimated from national inventories on an operational basisthat is useful for the following up on the Paris Agreement. Such a system shouldprovide information at scales from large power-plants and cities to regional areas andcountries. The European Commission, together with European Space Agency and supported by theEuropean Centre for Medium-Range Weather Forecasts, the European Organisation for theExploitation of Meteorological Satellites and international experts, is developing a newoperational capacity for monitoring and verifying anthropogenic CO2 emissions spelling outthe ingredients for a dedicated European CO2 Earth Observation service under theCopernicus Programme. Design studies are carried out to help define the underpinningrequirements and foundational building blocks of such a CO2 emission monitoringcapacity with: (i) atmospheric space-borne and in-situ measurements, (ii) near-realtime bottom-up CO2 emission maps, (iii) an operational data-assimilation systemintegrating top down and bottom-up information, (iv) a decision support tool forpolicymakers. The ultimate goal is to build an observation system with capabilities to monitoranthropogenic CO2 emissions with operational and internationally coordinated capabilitieslikely to be established by 2026. This can complement the enhanced transparency frameworkforeseen by the Paris Agreement, providing governments and citizens with actionableinformation on the anthropogenic CO2 actually emitted into the Earth System&#x2019;s atmosphere,such that they can reflect on the trends and on the effectiveness of the reduction measures andreact correspondingly. [ABSTRACT FROM AUTHOR]

AbstractFor effective adaptation and mitigation strategies of climate change, adequate data on greenhouse gases (GHGs) emissions from a wide range of land use and land cover types is a prerequisite. Such data is limited in Africa and especially in areas that face diverse land use changes because of human activities. In this study, we measured carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4) fluxes in the lowland part of South-Eastern Kenya, using the static chamber method and gas chromatography. Gas samples were collected from four dominant land uses: farmland, bushland, conservation area, and grazing land. Eight individual gas sampling campaigns were conducted targeting wet and dry season and transition periods in between covering a total of 12 months. Data on soil moisture and soil temperature were also collected to better understand the flux patterns. Conservation area recorded the highest average CO2 fluxes (74.2mg CO2 m-2 h-1) followed by bushland (46.2mg CO2 m-2 h-1), grazing land (45.6mg CO2 m-2 h-1) and lastly farmland (39.5mg CO2 m-2 h-1). CO2 fluxes across land use were found to vary significantly (p-value<0.001). Those in the conservation area were higher than the other land use by about 37%-47% on average. CO2 fluxes also varied significantly across seasons with highest fluxes recorded during the transition period from wet to dry season. N2O, on the other hand, was highest in the grazing land (1.2ug N2O m-2 h-1), followed by farmland (1.14 ug N2O m-2 h-1), conservation area (1.13ug N2O m-2 h-1) and lastly bushland (0.91ug N2O m-2 h-1). The differences between the land uses were not found to be significant. Most CH4 fluxes across all land use were below the detection limit ranging on average from -0.011mg CH4 m-2 h-1, -0.018mg CH4 m-2 h-1, -0.024mg CH4 m-2 h-1, -0.025mg CH4 m-2 h-1 for in the grazing area, bushland, conservation area, and farmland respectively. For N2O and CH4, there were no significant differences in fluxes across seasons. Our results showed a non-linear relationship of CO2, N2O and CH4 fluxes with increasing temperature. For soil moisture, the rate of CO2 emission increased with increasing soil moisture contents especially after a period of dry season. In the conservation area and grazing area, higher CO2 and N2O fluxes can be attributed to high carbon (C) and nitrogen (N) contents, a dense root network and high C input from grass decay.KEYWORDS: Static Chamber, Seasonal Variability, Climate Change, Bushland, Conservation, Grazing land, Farmland. [ABSTRACT FROM AUTHOR]

Northern Peatlands play an important role in the regulation of the atmospheric greenhousegas (GHG) balance. In Scotland, deep peats (&#x003E;0.5m) cover an area of approximately 18,000km2 and under undisturbed conditions they function as a net C sink due to organic matteraccumulation with low rates of organic decomposition. Modifications like drainage increaseaerobic decomposition which may lead to an enhancement of gaseous greenhouse gas (GHG)release. For this reason, many blanket bogs are protected by environmental legislation andthe number of restoration projects has increased over recent years. Restoration isundertaken without key underpinning knowledge of the impact of restoration uponGHG dynamics, particularly for sites restored from commercial forestry. The FlowCountry of Northern Scotland represents an extended area of blanket bogs and poolsof about 4,000 km2 and it hosts the largest stretch of deep peats in the country,some of them afforested, others nearly undisturbed or under active restoration,involving tree removal and site modification to aid rewetting of the peat surface. Wesummarise an investigation of the effect of forest-bog restoration practices focusing onthe inter-annual climatic variability driving carbon dioxide (CO2) and methane(CH4) exchange dynamics on a deep peat soil area located in the Royal Society forProtection of Birds (RSPB) nature reserve of Forsinard that has undergone restorationwith tree felling in 2003 and management promoting rewetting in April 2018. Fluxmeasurements were collected using different chambers techniques, with collarspositioned along a transect in which different vegetation types and micro topographywere represented (i.e. ridges, furrows, original surfaces and pools). Gas analysiswas performed both in-situ using a Los-Gatos Ultraportable Gas Analyser and bygas chromatography from static chambers. Our results show that CO2 exchange isprimarily controlled by soil moisture and microtopography (e.g. higher CO2 fluxes onridges), while undisturbed bog areas and pools could be regarded as hotspots formethane emissions. Average annual emission for undisturbed bog surface was 4.6gCO2eq m−2 y−1 and pools emitted 16g CO2eq m−2 y−1 in comparison to ridges(1.4g CO2eq m−2 y−1) and furrows (2.1g CO2eq m−2 y−1). Methane exchange isalso likely to be influenced by vegetation composition. Plots with abundance ofvascular plants like Tricophorum spp. and Eriophorum spp. showed higher CH4fluxes (12g CO2eq m−2 y−1) compared to Sphagnum dominated locations (4.1gCO2eq m−2 y−1). Preliminary interpretation from this study suggests restorationpractices, that aim to bring the peatland back to pristine or more natural (pre-forestry)conditions, cause an increase in methane emissions, since original surfaces and pools(which appear to be hotspots for this GHG) will extend post restoration. This studywill provide a better process-based understanding of the C and GHG dynamicsassociated with peatland restoration, and will make a significant contribution tobetter informing the development of rural land management policy, especially inrelation to climate change mitigation and reporting at the national and internationallevel. [ABSTRACT FROM AUTHOR]