• Climate change and impacts will continue to accelerate until the warming influences are reduced or offset by direct cooling approaches. • Direct climate cooling approaches have the potential to reduce local to global portions of human-induced warming influences. • GHG emission reduction and removal policies alone will take at least decades to halt warming, much less restore 20 th century conditions.

We developed a coupled overland and river model for modeling compound flooding using the kinematic wave approximation on inland sections of an unstructured mesh, and the diffusive wave approximation on riverine sections. A finite element method is used for spatial discretization and a Crank-Nicolson scheme is used for time discretization. A wetting and drying algorithm is implemented for improved efficiency in the model. Pluvial conditions and tidal conditions are implemented as source terms in the river model. The results show that effects from compound inundation could be captured using this framework.

The atmospheric CO2 growth rate is a fundamental measure of climate forcing. NOAA's growth rate estimates, derived from in situ observations at the marine boundary layer (MBL), serve as the benchmark in policy and science. However, NOAA's MBL-based method encounters challenges in accurately estimating the whole-atmosphere CO2 growth rate at sub-annual scales. We introduce the Growth Rate from Satellite Observations (GRESO) method as a complementary approach to estimate the whole-atmosphere CO2 growth rate utilizing satellite data. Satellite CO2 observations offer extensive atmospheric coverage that extends the capability of the current NOAA benchmark. We assess the sampling errors of the GRESO and NOAA methods using ten atmospheric transport model simulations. The simulations generate synthetic OCO-2 satellite and NOAA MBL data for calculating CO2 growth rates, which are compared against the global sum of carbon fluxes used as model inputs. We find good performance for the NOAA method (R = 0.93, RMSE = 0.12 ppm/year or 0.25 PgC/year). GRESO demonstrates lower sampling errors (R = 1.00; RMSE = 0.04 ppm/year or 0.09 PgC/year). Additionally, GRESO shows better performance at monthly scales than NOAA (R = 0.77 vs 0.47, respectively). Due to CO2's atmospheric longevity, the NOAA method accurately captures growth rates over five-year intervals. GRESO's robustness across partial coverage configurations (ocean or land data) shows that satellites can be promising tools for low-latency CO2 growth rate information, provided the systematic biases are minimized using in situ observations. Along with accurate and calibrated NOAA in situ data, satellite-derived growth rates can provide information about the global carbon cycle at sub-annual scales.

A document by Indronil Sarkar. Click on the document to view its contents.

A document by Rodrigue Tanguy. Click on the document to view its contents.

A document by Rodrigue Tanguy. Click on the document to view its contents.

Measurements of the surface ocean fugacity of carbon dioxide (fCO2) provide an important constraint on the global ocean carbon sink, yet the gap filling products developed so far to cope with the sparse observations are relatively coarse (1°x1° by 1 month). Here, we overcome this limitation by using the newly developed surface Ocean Carbon dioxide Neural Network (OceanCarbNN) method to estimate surface ocean fCO2 and the associated air sea CO2 fluxes (FCO2) at a resolution of 8-daily by 0.25°x0.25° (8D) over the period 1982 through 2022. The method reconstructs fCO2 with accuracy like that of low-resolution methods (~19 µatm) but improves it in the coastal ocean. Although global ocean CO2 uptake differs little, the 8D product captures 15\% more variance in FCO2. Most of this increase comes from the better-represented subseasonal scale variability, which is largely driven by the better resolved variability of the winds, but also contributed to by the better resolved fCO2. The high-resolution fCO2 is also able to capture the signal of short-lived regional events such as coastal upwelling and hurricanes. For example, the 8D product reveals that fCO2 was at least 25 µatm lower in the wake of Hurricane Maria (2017), the result of a complex interplay between the decrease in temperature, the entrainment of carbon-rich waters, and an increase in primary production. By providing new insights into the role of higher frequency variations of the ocean carbon sink and the underlying processes, the 8D product fills an important gap.

A document by Eugene Engmann. Click on the document to view its contents.

Addressing impacts of flash droughts (FDs) on the water-food nexus requires a understanding of FD mechanisms and drivers at the watershed level. Examining climatic drivers, dry and wet spell lengths from 1980 to 2019, we analyzed FD spatial and temporal characteristics, emphasizing areal extent, onset time, and duration. Our findings reveal substantial variations in FDs among different watersheds. Notably, watersheds in the Southern Hemisphere are witnessing expanding, faster-developing, and longer-lasting FDs, aligning with climate variations in precipitation and temperature. Additionally, at the watershed scale, the onset and duration of FDs are influenced by climatic drivers but remain unaffected by the duration of wet and dry periods. FD extents, however, correlate with both climatic conditions and wet and dry periods, underscoring watershed connectivity. Ultimately, our results underscore the necessity for research to comprehend the interplay between FDs and watershed characteristics and how it manifests in overall water resource management.

Surface and satellite observations of atmospheric methane show smooth seasonal behavior in the Southern Hemisphere driven by loss from the hydroxyl (OH) radical. However, observations in the Northern Hemisphere show a sharp mid-summer increase that is asymmetric with the Southern Hemisphere and not captured by the default configuration of the GEOS-Chem chemical transport model. Using an ensemble of 22 OH model estimates and 24 wetland emission inventories in GEOS-Chem, we show that the magnitude, latitudinal distribution, and seasonality of Northern Hemisphere wetland emissions are critical for reproducing the observed seasonality of methane in that hemisphere, with the interhemispheric OH ratio playing a lesser role. Reproducing the observed seasonality requires a wetland emission inventory with ~80 Tg a-1 poleward of 10°N including significant emissions in South Asia, and an August peak in boreal emissions persisting into autumn. In our 24-member wetland emission ensemble, only the LPJ-wsl MERRA-2 inventory has these attributes.

This study assessed total hydrocarbon content and polycyclic aromatic hydrocarbon content in Grönfjord (the Greenland Sea, Svalbard). The field study was held in marine expeditions of research vessel “Barentsburg” by the North-Western Branch of the Federal State Budget Institution, Research and Production Associaton «Typhoon» in summer periods of 2012 to 2022. In the framework of the field works simultaneous measurements of hydrological and hydrochemical characteristics of the water column were done. The data was analyzed using standard procedure in purpose to gather new information about the levels of hydrocarbons    (measured as total hydrocarbon contents), polycyclic aromatic hydrocarbons. The results showed pronounced interannual variations of total hydrocarbon contents and polycyclic aromatic hydrocarbons concentrations. Supposed that local natural sources contribute to elevated polycyclic aromatic hydrocarbons and total hydrocarbon content levels both in water and in sediments,  the levels of contamination do not signify exclusively anthropogenic influence on the sea-body. At the same time, some local elevated petroleum hydrocarbons concentrations, which were detected in the surface water layer, may be a sign of existing industrial activity affecting the waters of the fjord. Continuity of tasks starting from earlier expeditions indicates that many processes in the Norwegian Sea, Greenland Sea require further research.

More than a third of the world's population lives in drylands and is disproportionally at risk of hydrometeorological hazards such as drought and flooding. While existing studies have widely explored weather systems governing precipitation formation in humid regions, our understanding of the atmospheric processes generating precipitation in arid regions remains fragmented at best. Here we show, using a variety of precipitation datasets, that Rossby wave breaking is a key atmospheric driver of precipitation in arid regions worldwide. Rossby wave breaking contributes up to 90% of daily precipitation extremes and up to 80% of total precipitation amounts in arid regions equatorward and downstream of the midlatitude storm tracks. The relevance of Rossby wave breaking for precipitation increases with increasing land aridity. Contributions of wave breaking to precipitation dominate in the poleward and westward portions of arid subtropical regions during the cool season. Given the projected precipitation decline and the large uncertainty in projections of precipitation extremes in these regions, our findings imply that Rossby wave breaking plays a crucial role in projections and uncertainties of future precipitation changes in societally vulnerable regions that are exposed to both freshwater shortages and flood hazards.

Urban Land Surface Models (ULSMs) simulate energy and water exchanges between the urban surface and atmosphere. When part of numerical weather prediction, ULSMs provide a lower boundary for the atmosphere and improve the applicability of model results in the urban environment compared with non-urban land surface models. However, earlier systematic ULSM comparison projects assessed the energy balance but ignored the water balance which is coupled to the energy balance. Here, we analyze the water balance representation in 19 ULSMs participating in the Urban-PLUMBER project using results for 20 sites spread across a range of climates and urban form characteristics. As observations for most water fluxes are unavailable, we examine the water balance closure, flux timing, and magnitude with a score derived from seven indicators. We find that the water budget is only closed in 57% of the model-site combinations assuming closure when annual total incoming fluxes (precipitation and irrigation) fluxes are within 3% of the outgoing (all other) fluxes. Results show the timing is better captured than magnitude. No ULSM has passed all good water balance indicators for any site. Our results indicate models could be improved by explicitly verifying water balance closure and revising runoff parameterizations. By expanding ULSM evaluation to the water balance and related to latent heat flux performance, we demonstrate the benefits of evaluating processes with direct feedback mechanisms to the processes of interest.

J. Wei and Z. Wang made equal contributions to this work.*Corresponding authors:[email protected]; [email protected]; [email protected] imagery offers remarkable potential for various applications, including land monitoring and environmental assessment, thanks to its high spatial resolution and over 50 years of data records. However, the presence of atmospheric aerosols greatly hinders the precision of land classification and the quantitative retrieval of surface parameters. Notably, there has been no global retrieval of aerosol optical depth (AOD) from Landsat imagery that is needed for atmospheric correction, among other applications. To address this issue, this paper presents an innovative global AOD retrieval framework for Landsat imagery, propelled by atmospheric radiative transfer (ART) and enhanced GeoChronoTransformers (GCT) models incorporating multidimensional spatiotemporal sequence information and executed on the Google Earth Engine (GEE) cloud platform. We gathered all Landsat 8 and 9 images from their respective launch dates (February 2013 and September 2021) up to 2022, which were used to construct a robust ART-GCT-GEE model, and then rigorously validated the model performance across ~470 monitoring stations over land using diverse spatiotemporally independent methods. Leveraging information from multiple spectral channels, contributing to 58% according to the SHapley Additive exPlanation (SHAP) method, our results are highly consistent with observations (e.g., correlation coefficient = 0.863 and root-mean-square error = 0.096), suggesting that accurate historical and future AOD levels can be obtained. Around 81% and 50% of our AOD predictions meet the criteria of Moderate Resolution Imaging Spectroradiometer (MODIS) expected errors [±(0.05+20%)] and the Global Climate Observation System {[max(0.03, 10%)]}, respectively. Additionally, our model is less influenced by changes in surface conditions like topography and land cover. This allows us to generate spatially continuous AOD distributions with highly detailed and fine-scale information from dark to bright surfaces, especially for densely populated urban areas and expansive deserts with high aerosol loadings from both anthropogenic and natural sources.

Understanding past fire regimes and how they vary with climate, human activity, and vegetation patterns is fundamental to the mitigation and management of changing fire regimes as anthropogenic climate change progresses. Ash-derived trace elements and pyrogenic biomarkers from speleothems have recently been shown to record past fire activity in speleothems from both Australia and North America. This calls for an empirical study of ash geochemistry to aid the interpretation of speleothem palaeofire proxy records. Here we present analyses of leached ashes collected following fires in southwest and southeast Australia. We include a suite of inorganic elemental data from the water-soluble fraction of ash, as well as a selection of organic analytes (pyrogenic lipid biomarkers). We also present elemental data from leachates of soils collected from sites in southwest Australia. We demonstrate that the water-soluble fraction of ash differs from the water-soluble fraction of soils, with trace and minor element concentrations in ash leachates varying with combustion completeness (burn severity) and sample location. Changes in some lipid biomarker concentrations extracted from ashes may reflect burn severity. Our results contribute to building a process-based understanding of how speleothem geochemistry may record fire frequency and severity, and suggest that more research is needed to understand the transport pathways for the inclusion of pyrogenic biomarkers in speleothems. Our results also demonstrate that potential contaminant loads from ashes are much higher than from soils, with implications for the management of karst catchments, which are a critical water resource.

The widely-used Noah-MP land surface model (LSM) currently adopts snow albedo parameterizations that are semi-physical in nature with nontrivial uncertainties. To improve physical representations of snow albedo processes, a state-of-the-art snowpack radiative transfer model, the latest version of Snow, Ice, and Aerosol Radiative (SNICAR) model, is integrated into Noah-MP in this study. The coupled Noah-MP/SNICAR represents snow grain properties (e.g., shape and size), snow aging, and physics-based snow-aerosol-radiation interaction processes. We compare Noah-MP simulations employing the SNICAR scheme and the default semi-physical Biosphere-Atmosphere Transfer Scheme (BATS) against in-situ snow albedo observations at three Rocky Mountain field stations. The agreement between simulated and in-situ observed ground snow albedo in the broadband, visible, and near-infrared spectra is enhanced in Noah-MP/SNICAR simulations relative to Noah-MP/BATS simulations. The SNICAR scheme improves the temporal variability of modeled broadband snow albedo, with a nearly twofold higher correlation with observations (r=0.66) than the default BATS snow albedo scheme (r=0.37). The underestimated variability in Noah-MP/BATS is a result of inadequate physical linkage between snow albedo and environmental/snowpack conditions, which is substantially improved by the SNICAR scheme. Importantly, the Noah-MP/SNICAR model, with constraints of snow grain size from the MODIS snow covered area and grain size (MODSCAG) satellite data, physically represents and quantifies the snow albedo and absorption of shortwave radiation in response to snow grain size, non-spherical snow shapes, and light-absorbing particles (LAPs). The coupling framework of the Noah-MP/SNICAR model provides a means to reduce the bias in simulating snow albedo.

Estuaries are dynamic coastal features that support industry, food production, and recreation, and provide habitat for numerous animal species. Their typically low surface gradients make estuaries vulnerable to sea level rise, storms, and high river water discharge. This vulnerability combined with the large number of people who often live near estuaries has led to increasing efforts over recent decades to improve our understanding of how to minimize flooding and protect people and property. Despite these efforts, however, we still lack the tools to quantify the relationship between changes in estuarine morphology and flood risks. In particular, the interplay between bathymetric changes and water levels during storm conditions remains poorly quantified. To address this knowledge gap, we present a general enthalpy framework for modeling the evolution of estuaries that couples a low gradient subaerial topset and a subaqueous offshore region or foreset. Sediment transport in both the subaerial and subaqueous domains includes a non-linear term that relates sediment flux, local slope, and a threshold of motion. With this approach, we describe the evolution of the bathymetric profile and sediment partitioning between topset and foreset under a range of sea-level variations scenarios. We find that in some cases upstream sections of the topset can undergo erosion during periods of sea-level rise and deposition during sea-level fall, contradicting traditional stratigraphic models. These counterintuitive bathymetric changes could potentially lead to shifts in the location of maximum water levels along the estuary not accounted for by models of storm inundation.

A document by PRARABDH TIWARI. Click on the document to view its contents.

Nitrous oxide (N2O) is the most important stratospheric ozone-depleting agent based on current emissions and the third largest contributor to increased net radiative forcing. Increases in atmospheric N2O have been attributed primarily to enhanced soil N2O emissions. Critically, contributions from soils in the Northern High Latitudes (NHL, >50°N) remain poorly quantified despite their vulnerability to permafrost thawing induced by climate change. An ensemble of six terrestrial biosphere models suggests NHL soil N2O emissions doubled since the preindustrial 1860s, increasing on average by 2.0±1.0 Gg N yr-1 (p<0.01). This trend reversed after the 1980s because of reduced nitrogen fertilizer application in non-permafrost regions and increased plant growth due to CO2 fertilization suppressed emissions. However, permafrost soil N2O emissions continued increasing attributable to climate warming; the interaction of climate warming and increasing CO2 concentrations on nitrogen and carbon cycling will determine future trends in NHL soil N2O emissions.

Air pollution caused by various anthropogenic activities and biomass burning continues to be a major problem in India. To assess the effectiveness of current air pollution mitigation measures, we used a 3D global chemical transport model to analyze the projected optical depth of carbonaceous aerosol (AOD) in India under representative concentration pathways (RCP) 4.5 and 8.5 over the period 2000-2100. Our results show a decrease in future emissions, leading to a decrease in modeled AOD under both RCPs after 2030. The RCP4.5 scenario shows a 48-65% decrease in AOD by the end of the century, with the Indo-Gangetic Plain (IGP) experiencing a maximum change of ~25% by 2030 compared to 2010. Conversely, RCP8.5 showed an increase in AOD of ~29% by 2050 and did not indicate a significant decrease by the end of the century. Our study also highlights that it is likely to take three decades for current policies to be effective for regions heavily polluted by exposure to carbonaceous aerosols, such as the IGP and eastern India. We emphasize the importance of assessing the effectiveness of current policies and highlight the need for continued efforts to address the problem of air pollution from carbonaceous aerosols, both from anthropogenic sources and biomass burning, in India.

Reliable air temperature forecasts are necessary for mitigating the effects of droughts and Heatwaves. The numerical weather prediction(NWP) model forecasts have significant biases associated and therefore need post-processing. Post-processing of temperature forecasts using probabilistic approaches are lacking in India. In this study, we post-process the Global Ensemble Forecast System (GEFS) and EuropeanCentre for Medium Range Weather Forecasts (ECMWF) NWP model temperature forecasts for short to medium range time scales (1-7 days)using two probabilistic techniques, namely, Bayesian model averaging(BMA) and Nonhomogeneous gaussian regression (NGR). The post-processing techniques are evaluated for temperature (maximum and minimum) predictions across the Indian region. Results show that the probabilistic approaches considerably enhance the temperature predictions across India except the Himalayan regions. These techniques also comprehensively outperform the traditional post-processing techniques such as the running mean and simple linear regression. The NGR performs better than the BMA across all regions and is able to provide highly skillful temperature forecasts at higher lead times as well. Further, the study also assesses the implication of probabilistic post-processing Tmax forecast towards forecast enhancement of heatwaves (HW) in India. Post-processed Tmax forecasts revealed that the NGR approach considerably enhanced the HW prediction skill in India, especially in the northwestern and central Indian regions, considered highly prone to HW. The findings of this study will be useful in developing enhanced HW early warning and prediction systems in India.

The January 2022 eruption of Hunga Tonga-Hunga Ha’apai (HTHH) injected a huge amount (~150 Tg) of water vapour (H2O) into the stratosphere, along with small amount of SO2. An off-line 3-D chemical transport model (CTM) successfully reproduces the spread of the injected H2O through October 2023 as observed by the Microwave Limb Sounder (MLS). Dehydration in the 2023 Antarctic polar vortex caused the first substantial (~20 Tg) removal of HTHH H2O from the stratosphere. The CTM indicates that this process will dominate removal of HTHH H2O for the coming years, giving an overall e-folding timescale of 4 years; around 25 Tg of the injected H2O is predicted to still remain in the stratosphere by 2030. Following relatively low Antarctic column ozone in midwinter 2023 due to transport effects, additional springtime depletion due to H2O-related chemistry was small and maximised at the vortex edge (10 DU in column).

Background: The study aimed to determine latent patterns of geographical distribution of health-related air pollutants across the USA, and to evaluate real-world cumulative effects of these patterns on public health metrics. Methods: It was an ecological study using county-level data on the concentrations of 12 air pollutants (i.e., ozone, CO, NO2 and SO2, PM2.5 mass, and speciation, PM10 mass and speciation, HAPs, VOCs, NONOxNOy and lead) over 20 years, and the rigid measurements of population health including life expectancy at birth, age-specific mortality risks and cause-specific mortality rates (21 mutually exclusive disease groups). Latent class analysis (LCA) was used to identify the common clusters of life expectancy-associated air pollutants based on their concentration characteristics in the final studied counties (n=699). Multivariate linear regression analyses were then applied to assess the relationship between the LCA-derived clusters and health measurements with confounding adjustment. Results: PM2.5 mass, PM10 speciation, and NONOxNOy were associated with life expectancy and thus were included in LCA. Five clusters were identified: the ‘all low’ cluster (n=115, 16.5%), ‘all medium’ cluster (n=285, 40.8%), ‘high particulates’ cluster (n=152, 21.8%), ‘all high’ cluster (n=136, 19.5%) and ‘mixed profile’ cluster (n=11, 1.6%). Cluster with a more severe pollutant profile was associated with a decreasing life expectancy, an increasing mortality risk among the middle-aged and elderly populations (≥45 years), and an increasing mortality rate caused by chronic respiratory conditions, cardiovascular diseases, and neoplasms. Conclusions: Our study brings new perspectives of real-world geographical patterns of air pollution to explain health disparities across the USA.

A document by Gonçalo Duarte. Click on the document to view its contents.