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Future climate change and its impact on drought is critical for Uzbekistan, located in Central Asia, the world's largest arid zone. This study examines the evolving intensity of climate change and drought events using multi-model ensembles (MMEs) derived from the Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) simulated under the Representative Concentration Pathway and Shared Socioeconomic Pathway (RCP and SSP) scenarios. The projections show different rates of increase in temperature and precipitation under the RCPs and SSPs. Projected temperature increases are expected to reach up to 2–2.5 °C under SSP1-2.6, SSP2-4.5, and SSP3-7.0, by mid-century. By 2080–2099, an increase is projected of 2–3 °C in monthly mean temperatures throughout the year (SSP1-2.6), and a more pronounced increase in summer up to 3–4 °C (SSP2-4.5) and 4–6 °C (SSP3-7.0), with a marked contrast in conditions between the mountainous and desert regions of Uzbekistan. Regional changes in precipitation over the study periods show relatively little variability, except for FD, where notable trends are found. Under SSP1-2.6 and SSP2-4.5, the increase in precipitation is relatively modest, whereas the changes in SSP3-7.0 are more substantial, with some regions experiencing variations of up to 10–20 mm per period. The Standardized Precipitation Evapotranspiration Index (SPEI), calculated based on the projected temperature and precipitation, provides an estimate of future drought trends. Our results show increasing aridity under all scenarios by mid-century, with longer-term projections indicating stabilization around different SPEI values by 2100: RCP2.6 and SSP1-1.9 stabilize around −1.0; RCP4.5, RCP6.0, SSP2-4.5, and SSP3-7.0 stabilize around −1.5; while RCP8.5 and SSP5-8.5 scenarios project values of −2 or less by 2100. Notable differences in the SPEI index are found between lowland and foothill regions. In view of Uzbekistan's heavy reliance on agriculture and irrigation, which are the sectors that are expected to be mostly affected by climate change, our study provides a scientific basis for informed policy decision-making. This includes various aspects such as planning and management water resources, as well as the broader socioeconomic development of the country. [ABSTRACT FROM AUTHOR]

Methane emission in South Asia is poorly understood due to a lack of observations, despite being a major contributor to methane emissions globally. We present the first results of atmospheric CH4 inversions using air samples collected weekly at Nainital, India (NTL), and Comilla, Bangladesh (CLA), in addition to surface background flask measurements by NOAA, CSIRO and AGAGE using the MIROC4-ACTM. Our simulations span from 2000 to 2020 (considering the fixed "edge" effect), but the main analysis period is 2013–2020, when both the NTL and CLA datasets are available. An additional flux uncertainty reduction of up to 40% was obtained (mainly in the northern part of the Indian subcontinent), which enhanced our confidence in flux estimation and reaffirmed the significance of observations at the NTL and CLA sites. Our estimated regional flux was 64.0 ± 4.7 Tg-CH4 yr−1 in South Asia for the period 2013–2020. We considered two combinations of a priori fluxes that represented different approaches for CH4 emission from rice fields and wetlands. By the inversion, the difference in emissions between these combinations was notably reduced due to the adjustment of the CH4 emission from the agriculture, oil and gas, and waste sectors. At the same time, the discrepancy in wetland emissions, approximately 8 Tg-CH4 yr−1, remained unchanged. In addition to adjusting the annual totals, the inclusion of NTL/CLA observations in the inversion analysis modified the seasonal cycle of total fluxes, possibly due to the agricultural sector. While the a priori fluxes consisted of a single peak in August, the a posteriori values indicated double peaks in May and September. These peaks are highly likely associated with field preparation for summer crops and emissions from rice fields during the heading stage (panicle formation). The newly incorporated sites primarily exhibit sensitivity to the Indo-Gangetic Plain subregion, while coverage in southern India remains limited. Expanding the observation network is necessary, with careful analysis of potential locations using back-trajectory methods for footprint evaluation. [ABSTRACT FROM AUTHOR]

Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Emissions and atmospheric concentrations of CH4 continue to increase, maintaining CH4 as the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the factors explaining the well-observed atmospheric growth rate arise from diverse, geographically overlapping CH4 sources and from the uncertain magnitude and temporal change in the destruction of CH4 by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise and update the global CH4 budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH4 budget, integrating results of top-down CH4 emission estimates (based on in-situ and greenhouse gas observing satellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land-surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full datasets are available), for the previous decade of 2000–2009 and for the year 2020. The revision of the bottom-up budget in this edition benefits from important progress in estimating inland freshwater emissions, with better accounting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double accounting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double accounting that still exists (average of 23 Tg CH4 yr-1). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH4 yr-1 for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches. For the 2010–2019 decade, global CH4 emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH4 yr-1 (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH4 yr-1 or ~65 % are attributed to direct anthropogenic sources in the fossil, agriculture and waste and anthropogenic biomass burning (range 350–391 Tg CH4 yr-1or 63–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH4 yr-1 (range 9–40). Since 2012, global direct anthropogenic CH4 emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH4 yr-1) larger global emissions (669 Tg CH4 yr-1, range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH4 yr-1 in Saunois et al. (2016, 2020), respectively), and for the first time uncertainty in bottom-up and top-down budgets overlap. The latitudinal distribution from atmospheric inversion-based emissions indicates a predominance of tropical and southern hemisphere emissions (~65 % of the global budget, <30° N) compared to mid (30° N–60° N, ~30 % of emissions) and high-northern latitudes (60° N–90° N, ~4 % of global emissions). This latitudinal distribution is similar in the bottom-up budget though the bottom-up budget estimates slightly larger contributions for the mid and high-northern latitudes, and slightly smaller contributions from the tropics and southern hemisphere than the inversions. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH4 budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters. We identify five major priorities for improving the CH4 budget: i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH4 based on a robust classification of different types of emitting ecosystems; ii) further development of process-based models for inland-water emissions; iii) intensification of CH4 observations at local (e.g., FLUXNET-CH4 measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; iv) improvements of transport models and the representation of photochemical sinks in top-down inversions, and v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture and landfills) to improve source partitioning. The data presented here can be downloaded from https://doi.org/10.18160/GKQ9-2RHT (Martinez et al., 2024). [ABSTRACT FROM AUTHOR]

Monitoring the spatial distribution and trends in surface greenhouse gas (GHG) fluxes, as well as flux attribution to natural and anthropogenic processes, is essential to track progress under the Paris Agreement and to inform its Global Stocktake. This study updates earlier syntheses (Petrescu et al., 2020, 2021, 2023) and provides a consolidated synthesis of CH4 emissions using bottom-up (BU) and top-down (TD) approaches for the European Union (EU) and seven additional countries with large anthropogenic and/or natural emissions (USA, Brazil, China, India, Indonesia, Russia, and the Democratic Republic of Congo (DR Congo)). The work utilizes updated National GHG Inventories (NGHGIs) reported by Annex I Parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2023 and the latest available Biennial Update Reports (BURs) reported by non-Annex I Parties. The NGHGIs are considered in an integrated analysis that also relies on independent flux estimates from global inventory datasets, process-based models, inverse modeling and, when available, respective uncertainties. Whenever possible, it extends the period to 2021. Comparing NGHGIs with other approaches reveals that differences in the emission sources that are included in the estimate is a key source of divergence between approaches. A key system boundary difference is whether both anthropogenic and natural fluxes are included and, if they are, how fluxes belonging to these two sources are grouped/partitioned. Additionally, the natural fluxes are sensitive to the prior geospatial distribution of emissions in atmospheric inversions. Over the studied period, the total CH4 emissions in the EU, USA, and Russia show a steady decreasing trend since 1990, while for the non-EU emitters analyzed in this study, Brazil, China, India, Indonesia, and DR Congo, CH4 emissions have generally increased. In the EU, the anthropogenic BU approaches are reporting relatively similar mean emissions over 2015 to 2020 of 18.5 ± 2.7 Tg CH4 yr-1 for EDGAR v7.0, 16 Tg CH4 yr-1 for GAINS and 19 Tg CH4 yr-1 for FAOSTAT, with the NGHGI estimates of 15 ± 1.8 Tg CH4 yr-1. Inversions give higher emission estimates as they include natural emissions. Over the same period, the three high-resolution regional inversions report a mean emission of 21 (19-25) Tg CH4 yr-1, while the mean of six coarser-resolution global inversions results in emission estimates of 24 (23-25) Tg CH4 yr-1. The magnitude of BU natural emissions (peatland and mineral soils, lakes and reservoirs, geological and biomass burning) accounts for 6.6 Tg CH4 yr-1 (Petrescu et al., 2023a) and explains the differences between the TD inversions and the BU estimates of anthropogenic emissions (including NGHGIs). For the other Annex I Parties in this study (USA and Russia), over 2015 to 2020, the mean of the four anthropogenic BU approaches reports 18.5 (13-27.9) Tg CH4 yr-1 for Russia and 29.1 (23.5- Tg CH4 yr-1 for the USA, against total TD mean estimates of 37 (30-43) Tg CH4 yr-1 and 43.4 (42-48) Tg CH4 yr-1, respectively. The averaged BU and TD natural emissions account for 16.2 Tg CH4 yr-1 for Russia and 14.6 Tg CH4 yr-1 for the USA, partly explaining the gap between the BU anthropogenic and total TD emissions. For the non-Annex I Parties, anthropogenic CH4 estimates from UNFCCC BURs show large differences with the other global inventory-based estimates and even more with atmospheric-based ones. This poses an important potential challenge to monitoring the progress of the global CH4 pledge and the Global Stocktake, not only from the availability of data but also its accuracy. By systematically comparing the BU with TD methods, this study provides recommendations for more robust comparisons of available data sources and hopes to steadily engage more Parties in using observational methods to complement their UNFCCC inventories, as well as considering their natural emissions. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, future development needs to resolve knowledge gaps in both BU and TD approaches and to better quantify remaining uncertainty. Consequently, TD methods may emerge as a powerful tool for verifying emission inventories for CH4, and other GHGs and informing international climate policy. The referenced datasets related to figures are available at https://doi.org/10.5281/zenodo.10276087 (Petrescu et al., 2023b). [ABSTRACT FROM AUTHOR]

Coal use exacerbates several major environmental problems including build-up of greenhouse gases and air quality deterioration. Although Kuzbass (Siberia) is one of the largest exploited coal basins worldwide, the role of regional coal mining and processing in atmospheric pollution is unknown. We outlined the Kuzbass coal basin by spaceborne night-lights and revealed a regional, long-term tropospheric NO2 anomaly (2005–2018) by spaceborne NO2 column observations (hereafter ‒ NO2). The spatial agreement between NO2 and night-lights indicates that the anomaly is attributable to an agglomeration of coal quarries and the cities in Kuzbass, that are heavily reliant on coal. A positive relationship between NO2 and interannual coal production suggested that the anomaly was related to coal in Kuzbass; ~ 1.0% of annual coal production increase induced ~ 0.5–0.6% of NO2 enhancement. As coal production accelerated since 2010, NO2 exhibited strikingly similar annual increases over Kuzbass in 2010–2014 (7%) and 2015–2019 (15%), compared to 2005–2009. Conversely, Siberian cities lacking a coal industry followed the global trend of reducing NO2 for the same periods (−5% and −14%, respectively), driven by fuel combustion improvements. Overall, we demonstrated that coal mining, processing and utilization can induce distinct tropospheric NO2 anomalies, detectable from space. [ABSTRACT FROM AUTHOR]

We examined CH[sub 4] variability over different regions of India and the surrounding oceanic regions derived from thermal infrared (TIR) band observations by the Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observation SATellite (GOSAT) and simulated by the updated MIROC4.0-based Atmospheric Chemistry Tracer Model (MIROC4-ACTM) for the period 2009–2014. This study attempts to understand the sensitivity of the vertical profile retrievals at different layers of the troposphere and lower stratosphere, arising from the averaging kernels and a priori assumptions. We stress that this is of particular importance when the satellite derived products are analyzed using a different ACTMs from that is used as retrieval a priori. A comparison of modeled and retrieved CH[sub 4] vertical profiles shows the 22 vertical levels of GOSAT/TANSO-FTS TIR retrievals provide critical information about transport from the top of the boundary layer to the upper troposphere and lower stratosphere in a consistent manner. The mean model-GOSAT TIR CH[sub 4] mismatch is within 50 ppb, excepting 150 hPa and upward, where the sensitivity of GOSAT/TANSO-FTS TIR observations becomes very low. Convolution of the modeled profiles with GOSAT/TANSO-FTS TIR averaging kernels reduce the mismatch to below uncertainty. Distinct seasonal variations of CH[sub 4] have been observed at the upper atmospheric boundary layer (800 hPa), free troposphere (500 hPa), and upper troposphere (200 hPa) levels over northern and southern regions of India corresponding to the southwesterly monsoon (July–September) and post-monsoon (October–December) seasons. Analysis of the transport and emission contributions to CH[sub 4] suggests that the CH[sub 4] seasonal cycle over the Indian subcontinent is governed by both the heterogeneous distributions of surface emissions and the influence of the global monsoon divergent wind circulations. GOSAT/TANSO-FTS TIR observations provide additional information about CH[sub 4] observations in this region compared to what is known from in situ data, which is important for improving the accuracy of emission flux optimization. Based on two emission sensitivity simulations, we suggest that the emissions of CH[sub 4] from the India region is 51.2 ± 1.6 Tg yr[sup −1] during the period of 2009–2014. [ABSTRACT FROM AUTHOR]

We developed a high-resolution surface flux inversion system based on the global Lagrangian-Eulerian coupled tracer transport model composed of National Institute for Environmental Studies Transport Model (NIES-TM) and FLEXible PARTicle dispersion model (FLEXPART). The inversion system is named NTFVAR (NIES-TM-FLEXPART-variational) as it applies variational optimisation to estimate surface fluxes. We tested the system by estimating optimized corrections to natural surface CO2 fluxes to achieve best fit to atmospheric CO2 data collected by the global in-situ network, as a necessary step towards capability of estimating anthropogenic CO2 emissions. We employ the Lagrangian particle dispersion model (LPDM) FLEXPART to calculate the surface flux footprints of CO2 observations at a 0.1° × 0.1° spatial resolution. The LPDM is coupled to a global atmospheric tracer transport model (NIES-TM). Our inversion technique uses an adjoint of the coupled transport model in an iterative optimization procedure. The flux error covariance operator is being implemented via implicit diffusion. Biweekly flux corrections to prior flux fields were estimated for the years 2010-2012 from in-situ CO2 data included in the Observation Package (ObsPack) dataset. High-resolution prior flux fields were prepared using Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) for fossil fuel combustion, Global Fire Assimilation System (GFAS) for biomass burning, the Vegetation Integrative SImulator for Trace gases (VISIT) model for terrestrial biosphere exchange and Ocean Tracer Transport Model (OTTM) for oceanic exchange. The terrestrial biospheric flux field was constructed using a vegetation mosaic map and separate simulation of CO2 fluxes at daily time step by the VISIT model for each vegetation type. The prior flux uncertainty for terrestrial biosphere was scaled proportionally to the monthly mean Gross Primary Production (GPP) by the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17 product. The inverse system calculates flux corrections to the prior fluxes in the form of a relatively smooth field multiplied by high-resolution patterns of the prior flux uncertainties for land and ocean, following the coastlines and vegetation productivity gradients. The resulting flux estimates improve fit to the observations at continuous observations sites, reproducing both the seasonal variation and short-term concentration variability, including high CO2 concentration events associated with anthropogenic emissions. The use of high-resolution atmospheric transport in global CO2 flux inversion has the advantage of better resolving the transport from the mix of the anthropogenic and biospheric sources in densely populated continental regions and shows potential for better separation between fluxes from terrestrial ecosystems and strong localised sources such as anthropogenic emissions and forest fires. Further improvements in the modelling system are needed as the posterior fit is better than that by the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker only for a fraction of the monitoring sites, mostly at coastal and island locations experiencing mix of background and local flux signals. [ABSTRACT FROM AUTHOR]