An attempt has been made to quantitatively analyze different degrees of environmental drought events, given the limited scientific understanding of environmental droughts, which hinders practical assessment efforts. This study thus aims to rigorously develop and assess the applicability of a novel heuristic method in conjunction with creating an \cite{Srivastava_2023}. The heuristic method evaluates the combined influences of drought duration and water shortage levels, providing crucial insights into the environmental flow requirements amidst climate change. The Minimum in-stream Flow Requirements (MFR) is first defined as the threshold value essential for sustaining the river basin's ecological functions, aligning with Tennant’s environmental flow concept. Establishing MFR enables a balance between water resource utilization and ecological preservation, fostering sustainable water management. To comprehensively assess the eco-status, the study defined the High Flow Season (HFS) and the Low Flow Season (LFS). Drought status is then determined by comparing MFR with observed streamflow rate, quantifying negative differences as environmental droughts. Drought Duration Length (DDL) and Water Shortage Level (WSL) are introduced as functions of environmental drought. DDL categorizes consecutive months into four classes: DDL 1 (1-3 months), DDL 2 (4-6 months), DDL 3 (7-12 months), and DDL 4 (>12 months). WSL is determined by the most significant water deficit observed during DDL, classified into four categories: WSL 1 (<40%), WSL 2 (40-60%), WSL 3 (60-80%), and WSL 4 (>80%). Integrating DDL and WSL yields an index classifying environmental drought events into slight, moderate, severe, and extreme levels. The index value is obtained by comparing DDL and WSL values and selecting the maximum. The study enhances the scientific rigor of environmental drought identification and analysis, contributing to understanding drought impacts and effective mitigation strategies.

The geostationary Himawari-8 satellite offers a unique opportunity to monitor sub-daily thermal dynamics over Asia and Oceania, and several operational land surface temperature (LST) retrieval algorithms have been developed for this purpose. However, studies have reported inconsistency between LST data obtained from geostationary and polar-orbiting platforms, particularly for daytime LST, which usually shows directional artefacts and can be strongly impacted by viewing and illumination geometries and shadowing effects. To overcome this challenge, Solar Zenith Angle (SZA) serves as an ideal physical variable to quantify systematic differences between platforms. Here we presented an SZA-based Calibration (SZAC) method to operationally calibrate the daytime component of a split-window retrieved Himawari-8 LST (referred to here as the baseline). SZAC describes the spatial heterogeneity and magnitude of diurnal LST discrepancies from different products. The SZAC coefficient was spatiotemporally optimised against highest-quality assured (error < 1 K) pixels from the MODerate-resolution Imaging Spectroradiometer (MODIS) daytime LST between 01/Jan/2016 and 31/Dec/2020. We evaluated the calibrated LST data, referred to as the Australian National University LST with SZAC (ANUSZAC), against MODIS LST and the Visible Infrared Imaging Radiometer Suite (VIIRS) LST, as well as in-situ LST from the OzFlux network. Two peer Himawari-8 LST products from Chiba University and the Copernicus Global Land Service were also collected for comparisons. The median daytime bias of ANUSZAC LST against Terra-MODIS LST, Aqua-MODIS LST and VIIRS LST was 1.52 K, 0.98 K and -0.63 K, respectively, which demonstrated improved performance compared to baseline (5.37 K, 4.85 K and 3.02 K, respectively) and Chiba LST (3.71 K, 2.90 K and 1.07 K, respectively). All four Himawari-8 LST products showed comparable performance of unbiased root mean squared error (ubRMSE), ranging from 2.47 to 3.07 K, compared to LST from polar-orbiting platforms. In the evaluation against in-situ LST, the overall mean values of bias (ubRMSE) of baseline, Chiba, Copernicus and ANUSZAC LST during daytime were 4.23 K (3.74 K), 2.16 K (3.62 K), 1.73 K (3.31 K) and 1.41 K (3.24 K), respectively, based on 171,289 hourly samples from 20 OzFlux sites across Australia between 01/Jan/2016 and 31/Dec/2020. In summary, the SZAC method offers a promising approach to enhance the reliability of geostationary LST retrievals by incorporating the spatiotemporal characteristics observed by accurate polar-orbiting LST data. Furthermore, it is possible to extend SZAC for LST estimation by using data acquired by geostationary satellites in other regions, e.g., Europe, Africa and Americas, as this could improve our understanding of the error characteristics of overlapped geostationary imageries, allowing for targeted refinements and calibrations to further enhance applicability.KeywordsLand surface temperature; Geostationary; Himawari-8; Diurnal temperature cycle; Calibration; Solar zenith angle; MODIS; VIIRS

Black carbon (BC) has several direct, indirect, semi-direct, and microphysical effects on the Earth’s climate system. Analyses of the decade-long measurement of BC aerosols at Varanasi (from 2009 to 2021) was done to understand its impact on radiative balance. General studies suggest that the daily BC mass concentration (mean of 9.18±6.53 µg m–3) ranges from 0.07 to 46.23 µg m–3 and show a strong interannual and intra-annual variation over the 13-year study period. Trend analyses suggest that the interannual variability of BC shows significant decreasing trend (-0.47 µg m–3 yr-1) over the station. The decreasing trend is maximum during the post-monsoon (-1.86 µg m–3 yr-1) and minimum during the pre-monsoon season (-0.31 µg m–3 yr-1). The radiative forcing caused specifically by BC (BC-ARF) at the top of the atmosphere (TOA), surface (SUR), and within the atmosphere (ATM) is found to be 10.3 ± 6.4, -30.1 ± 18.9, and 40.5 ± 25.2 Wm−2, respectively. BC-ARF shows strong interannual variability with a decreasing trend at the TOA (–0.47 Wm–2 yr-1) and ATM ((–1.94 Wm–2 yr-1) forcing, while it showed an increasing trend at the SUR (1.33 Wm–2 yr-1). To identify the potential source sectors and the transport pathways of BC aerosols, concentrated weighted trajectories (CWT) and potential source contribution function (PSCF) analyses have been conducted over the station. These analyses revealed that the primary source of pollution at Varanasi originate from the upper IGP, lower IGP, and central India.

The Chinese Loess Plateau (CLP) in northern China serves one of the most prominent loess records in the world. The CLP is an extensive record of changes in past aeolian dust activity in East Asia; however, the interpretation of the loess records is hampered by ambiguity regarding the origin of loess-forming dust and an incomplete understanding of the circulation forcing dust accumulation. In this study, we used a novel modeling approach combining a dust emission model FLEXDUST with simulated back trajectories from FLEXPART to trace the dust back to where it was emitted. Over 21 years (1999-2019), we modeled back trajectories for fine (~ 2mu) and super-coarse (~ 20mu) dust particles at six CLP sites during the peak dust storm season from March to May. The source receptor relationship from FLEXPART is combined with the dust emission inventory from FLEXDUST to create site-dependent high-resolution maps of the source contribution of deposited dust. The nearby dust-emission areas dominate the source contribution at all sites. Wet deposition is important for dust deposition at all sites, regardless of dust size. Non-negligible amounts of dust from distant emission regions could be wet deposited on the CLP following high-level tropospheric transport, with the super-coarse dust preferentially from emission areas upwind of sloping topography. On an interannual scale, the phase of the Arctic Oscillation (AO) in winter was found to have a strong impact on the deposition rate on the CLP, while the strength of the East Asian Winter Monsoon was less influential.

The role that continental margin sediments play in the global carbon cycle and the mitigation of climate change is currently not well understood. Recent research has indicated that these sediments might store large amounts of organic carbon; however, Blue Carbon research continues to focus on vegetated coastal ecosystems as actionable Blue Carbon. Marine sediments are considered emerging Blue Carbon ecosystems, but to decide whether they are actionable requires better quantifications of organic carbon stocks, accumulation rates, and the mitigation potential from avoided emissions. To close some of these knowledge gaps, we spatially predicted organic carbon content, dry bulk density and sediment accumulation rates across the Norwegian margin. The resulting predictions were used to estimate organic carbon stocks in surface sediments and their accumulation rates. We found that organic carbon stocks are two orders of magnitude higher than those of vegetated coastal ecosystems and comparable to terrestrial ecosystems in Norway. Accumulation rates of organic carbon are spatially highly variable and linked to geomorphology and associated sedimentary processes. We identify shelf valleys with a glacial origin as hotspots of organic carbon accumulation with a potentially global role due to their widespread occurrence on formerly glaciated continental margins. The complex and heterogenous nature of continental margins regarding organic carbon accumulation means that to close existing knowledge gaps requires detailed spatial predictions that account for those complexities. Only in this way will it be possible to evaluate whether margin sediments might be actionable Blue Carbon ecosystems.

Optical and acoustic sensors have been widely used in laboratory experiments and field studies to investigate suspended particulate matter concentration and particle size over the last four decades. Both methods face a serious challenge as laboratory and in-situ calibrations are usually required. Furthermore, in coastal and estuarine environments, the coexistence of mud and sand often results in multimodal particle size distributions, amplifying erroneous measurements. This paper proposes a new approach of combining a pair of optical-acoustic signals to estimate the total concentration and sediment composition of a mud/sand mixture in an efficient way without an extensive calibration. More specifically, we first carried out a set of 54 bimodal size regime experiments to derive empirical functions of optical-acoustic signals, concentrations, and mud/sand fractions. The functionalities of these relationships were then tested and validated using more complex multimodal size regime experiments over 30 optical-acoustic pairs of 5 wavelengths (420, 532, 620, 700, 852 nm) and 6 frequencies (0.5, 1, 2, 4, 6, 8 MHz). In the range of our data, without prior knowledge of particle size distribution, combinations between optical wavelengths 620-700 nm and acoustic frequencies 4-6 MHz predict mud/sand fraction and total concentration with the variation < 10% for the former and < 15% for the later. This approach therefore enables the robust estimation of suspended sediment concentration and composition, which is particularly useful in cases where calibration data is insufficient.

Understanding the dynamics of sulfur dioxide (SO2) degassing is of primary importance to track temporal variations of volcanic activity. We develop here an algorithm to automatically estimate daily SO2 masses flux from space-borne hyperspectral images (such as those provided by Sentinel-5P/TROPOMI) without requiring prior knowledge of plume direction or speed. The method computes a linear regression, as a function of distance, of SO2 mass integrated in a series of nested circular domains centered on the volcano. An additional term proportional to the square of the distance, depending solely on a cutoff on the minimum reliable pixel column amount, allows for estimating pixel noise and posterior uncertainty. A statistical test is introduced to automatically detect occurrences of volcanic degassing, by comparing estimated flux and its associated uncertainty. After inversion, a single multiplication by plume speed suffices to deduce the SO2 mass flux, without requiring to re-run the inversion. This way, a range of plume speed scenarios can be easily explored. The method is suited for weakly degassing sources or high-latitude volcanoes. It is applied to two case-studies, where temporal correlation between degassing and seismic energy is highlighted: (a) a one-year-long period of intense degassing at Etna, Italy (2021), and (b) a two-years-long period including three eruptions at Piton de la Fournaise, La Réunion (2021–2023). The method is open-source, and is implemented as an interactive tool within the VolcPlume web portal, facilitating near-real-time exploitation of the TROPOMI archive for both volcano monitoring and assessment of volcanogenic atmospheric hazards.

Optimising hydropower operations to balance economic profitability and support functioning ecosystem services is integral to river management policy. In this article, we propose a multi-objective optimization framework for small hydropower plants (SHPs) to evaluate trade-offs among environmental flow scenarios. Specifically, we examine the balance between short-term losses in hydropower generation and the potential for compensatory benefits in the form of revenue from recreational ecosystem services, irrespective of the direct beneficiary. Our framework integrates a fish habitat model, a hydropower optimization model, and a recreational ecosystem service model to evaluate each environmental flow scenario. The optimisation process gives three outflow release scenarios, informed by previous streamflow realisations (dam inflow), and designed environmental flow constraints. The framework is applied and tested for the river Kuusinkijoki in North-eastern Finland, which is a habitat for migratory brown trout and grayling populations. We show that the revenue loss due to the environmental flow constraints arises through a reduction in revenue per generated energy unit and through a reduction in turbine efficiency. Additionally, the simulation results reveal that all the designed environmental flow constraints cannot be met simultaneously. Under the environmental flow scenario with both minimum flow and flow ramping rate constraints, the annual hydropower revenue decreases by 16.5%. An annual increase of 8% in recreational fishing visits offsets the revenue loss. The developed framework provides knowledge of the costs and benefits of hydropower environmental flow constraints and guides the prioritizing process of environmental measures.

Heterogeneous chemical cycles of pyrogenic nitrogen and halides influence tropospheric ozone and affect the stratosphere during extreme pyrocumulonimbus (PyroCB) events. We report field-derived N2O5 uptake coefficients, γ(N2O5), and ClNO2 yields, φ(ClNO2), from two aircraft campaigns observing fresh smoke in the lower and mid troposphere and processed/aged smoke in the upper troposphere and lower stratosphere (UTLS). Derived φ(ClNO2) varied across the full 0–1 range but was typically < 0.5 and smallest in a PyroCB (< 0.05). Derived γ(N2O5) was low in agricultural smoke (0.2–3.6 ×10-3), extremely low in mid-tropospheric wildfire smoke (0.1 × 10-3), but larger in PyroCB processed smoke (0.7–5.0 × 10–3). Aged BB aerosol in the UTLS had a higher median γ(N2O5) of 17 × 10–3 that increased with sulfate and liquid water, but that was nevertheless 1–2 orders of magnitude lower than values for aqueous sulfuric aerosol used in stratospheric models.

A document by Liguang Jiang. Click on the document to view its contents.

Comprehending the intricate interplay between atmospheric aerosols and water vapour in subsaturated regions is vital for accurate modelling of aerosol–cloud–radiation–climate dynamics. But the microphysical mechanisms governing these interactions with ambient aerosols remain inadequately understood. Here we report results from high-altitude, relatively pristine site in Western-Ghats of India during monsoon, serving as a baseline for climate processes in one of the world’s most polluted regions. Utilizing a novel quartz crystal microbalance (QCM) approach, we conducted size-resolved sampling to analyse humidity-dependent growth factors, hygroscopicity, deliquescence behaviour, and aerosol liquid water content (ALWC). Fine-mode aerosols (≤2.5 μm) exhibited size-dependent interactions with water vapour, contributing significantly to ALWC. Deliquescence was observed in larger aerosols (>180 nm), influenced by organic species, with deliquescence relative humidity (DRH) lower than that of pure inorganic salts. This research highlights the significance of understanding ambient aerosol-water interactions and hygroscopicity for refining climate models in subsaturated conditions.

The land sink of anthropogenic carbon emissions, a crucial component of mitigating climate change, is primarily attributed to the CO₂ fertilization effect on global gross primary productivity (GPP). However, direct observational evidence of this effect remains scarce, hampered by challenges in disentangling the CO₂ fertilization effect from other long-term drivers, particularly climatic changes. Here, we introduce a novel statistical approach to separate the CO₂ fertilization effect on GPP and daily maximum net ecosystem production (NEPmax) using eddy covariance records across 38 extratropical forest sites. We find the median stimulation rate of GPP and NEPmax to be 16.4 ± 4% and 17.2 ± 4% per 100 ppm increase in atmospheric CO₂ across these sites, respectively. To validate the robustness of our findings, we test our statistical method using factorial simulations of an ensemble of process-based land surface models. We acknowledge that additional factors, including nitrogen deposition and land management, may impact plant productivity, potentially confounding the attribution to the CO₂ fertilization effect. Assuming these site-specific effects offset to some extent across sites as random factors, the estimated median value still reflects the strength of the CO₂ fertilization effect. However, disentanglement of these long-term effects, often inseparable by timescale, requires further causal research. Our study provides direct evidence that the photosynthetic stimulation is maintained under long-term CO₂ fertilization across multiple eddy covariance sites. Such observation-based quantification is key to constraining the long-standing uncertainties in the land carbon cycle under rising CO₂ concentrations.

In celebrating its first decade of existence, a Chilean research center organized an open forum on climate and socio-environmental resilience engaging participants -both speakers and audience- from within and outside the academic community. Held the first week of September 2023, the symposum filled a void of events addressing climate and resilience research and its bi-directional links with society.

In various research fields such as hydrogeology, environmental science and energy engineering, geological formations with fractures are frequently encountered. Accurately characterizing these fractured media is of paramount importance when it comes to tasks that demand precise predictions of liquid flow and the transport of solute and energy within them. Since directly measuring fractured media poses inherent challenges, data assimilation (DA) techniques are typically employed to derive inverse estimates of media properties using observed state variables like hydraulic head, concentration, and temperature. Nonetheless, the considerable difficulties arising from the strong heterogeneity and non-Gaussian nature of fractured media have diminished the effectiveness of existing DA methods. In this study, we formulate a novel DA approach known as PEDL (parameter estimator with deep learning) that harnesses the capabilities of DL to capture nonlinear relationships and extract non-Gaussian features. To evaluate PEDL’s performance, we conduct two numerical case studies with increasing complexity. Our results unequivocally demonstrate that PEDL outperforms three popular DA methods: ensemble smoother with multiple DA (ESMDA), iterative local updating ES (ILUES), and ES with DL-based update (ESDL). Sensitivity analyses confirm PEDL’s validity and adaptability across various ensemble sizes and DL model architectures. Moreover, even in scenarios where structural difference exists between the accurate reference model and the simplified forecast model, PEDL adeptly identifies the primary characteristics of fracture networks.

Unlike the radiative forcing linked to CO2 and its cumulative storage in oceans since the start of the industrial era around two centuries ago, the Sun has heated the Earth for billions of years without accumulation and dramatic temperature drift. To overcome this obviously illogical difference in evolution, we first analyze several reasons showing that the current universally adopted relationship between carbon dioxide and global warming does not respect the fundamentals of Chemistry, Physics, and Thermodynamics. A recently proposed alternative mechanism, based on these hard sciences, is briefly recalled. In this new mechanism, heat on Earth is managed by water and its solid-liquid and liquid-vapor interphases equilibria before radiative elimination in space. Today, anthropogenic heat is increasingly seen as a complement to the solar heating although it is neglected in the universally adopted consensus. Anthropogenic heat releases are generally estimated from global energy consumption. A broader list of sources is established that includes the capture of solar thermal infrared radiations by artificial installations, including those acting as greenhouses. Three qualitative scenarios are proposed in which climate change depends on whether the ratio of anthropogenic heat releases relative to solar thermal contributions remains negligible, is acceptable or becomes so large that it could shorten the time until the next ice age. Currently, global temperature and ocean level are still very low compared to those in distant past. On the other hand, ice disappearance is indisputable, particularly at the levels of glaciers, floating ice, and permafrost. These features fit the scenario in which temperature continued to fluctuate as it did during the last 8,000 years of the current Holocene interglacial plateau while local rains, winds, floodings, droughts, etc., worsen in magnitude and frequency to help ice melt and evaporation manage excess heat. Policymakers should not wait to discover that decreasing atmospheric carbon dioxide has little effect on the worsening of climate events to begin mitigating of anthropogenic heat with the help of hard sciences scientists to work on quantification. Key points • Carbon dioxide-based radiative forcing as source of global warming does not resist to critical analysis based on fundamentals of chemistry, physics and thermodynamics • Thermal properties of water, water interphase exchanges, formation of clouds and radiative elimination to space control heat supplies and climate changes since water is present on Earth • Anthropogenic heat releases should not affect much temperature and ocean levels provided they remain negligible relative to solar heat supplies, but heat-dispersing local climatic vents should increase in strength and frequency

Peatland ecosystems are unsurpassed in their carbon-storing capacity. However, they can be hotspots for emissions of greenhouse gases (GHGs) depending on soil water saturation and oxygen status. Using automated floating chambers, we investigated the spatiotemporal variability of CH4 and CO2 fluxes and their environmental drivers from inundated areas in a temperate, rich fen. We distinguished between two areas: one with continuous inundation, caused by upwelling groundwater and a lower-lying area with periodic inundation by flooding from an adjacent stream. Using hourly measurements, we found mean effluxes of CH4 and CO2 to be 0.16 and 1.23 g C m-2 d-1 between October and May with more than a 10-fold variation between observations. For CO2, efflux were higher in the periodically inundated area compared to the continuously inundated area. In contrast, CH4 fluxes were higher, and dominated by ebullition, at the area with continuous inundation. Both fluxes increased with soil temperature and wind speed. Advective and diffusive fluxes of CH4 and CO2 associated to groundwater upwelling and upwards diffusion of dissolved gases from shallow groundwater (0.5-0.8 meters below ground level) contributed negligibly to the measured fluxes, suggesting that the emitted GHGs were produced close to the terrain. Our data highlight the large spatiotemporal variation of CO2 and CH4 emissions from fens due to variations in hydrology and topography affecting GHG production near the soil surface. Particularly, the temporary dynamics of soil inundation played a major role in controlling the contribution by CO2 and CH4 to wetland GHG release.

Grade Level: [Suitable grade level, e.g., 6-8]Duration: 50 minutes

Machine-learned uncertainty quantification (ML-UQ) has become a hot topic in environmental science, especially for neural networks.  Scientists foresee the use of ML-UQ to make better decisions and assess the trustworthiness of the ML model.  However, because ML-UQ is a new tool, its limitations are not yet fully appreciated.  For example, some types of uncertainty are fundamentally unresolvable, including uncertainty that arises from data being out of sample, i.e., outside the distribution of the training data.  While it is generally recognized that ML-based point predictions (predictions without UQ) do not extrapolate well out of sample, this awareness does not exist for ML-based uncertainty.  When point predictions have a large error, instead of accounting for this error by producing a wider confidence interval, ML-UQ often fails just as spectacularly.  We demonstrate this problem by training ML with five different UQ methods to predict shortwave radiative transfer.  The ML-UQ models are trained with real data but then tasked with generalizing to perturbed data containing, e.g., fictitious cloud and ozone layers.  We show that ML-UQ completely fails on the perturbed data, which are far outside the training distribution.  We also show that when the training data are lightly perturbed -- so that each basis vector of perturbation has a little variation in the training data -- ML-UQ can extrapolate along the basis vectors with some success, leading to much better (but still somewhat concerning) performance on the validation and testing data.  Overall, we wish to discourage overreliance on ML-UQ, especially in operational environments.

[ Predicting the spatiotemporal distribution of rainfall remains a key challenge in Tropical Meteorology, partly due to an incomplete understanding of the effects of different environmental factors on atmospheric convection. In this work, we use numerical simulations of tropical ocean domains to study how rainfall responds to imposed localized thermal and mechanical forcings to the atmosphere. We use the Normalized Gross Moist Stability—NGMS—to quantify the net precipitation response associated with a given net atmospheric heating. We find that NGMS values differ considerably for different forcings, but show that the relationship between precipitation and column relative humidity collapses along a universal curve across all of them. We also show that the vertical component of the Gross Moist Stability only approximates the NGMS well at scales larger than a couple hundred kilometers, indicating that general horizontal mixing processes are not negligible at smaller scales. ]

Vegetation Gross Primary Productivity (GPP) is the single largest carbon flux of the terrestrial biosphere which, in turn, is responsible for sequestering $25-30\%$ of anthropogenic carbon dioxide emissions. The ability to model GPP is therefore critical for calculating carbon budgets as well as understanding climate feedbacks. Earth System Models (ESMs) have the capability to simulate GPP but vary greatly in their individual estimates, resulting in large uncertainties. We describe a Machine Learning (ML) approach to investigate two key factors responsible for differences in simulated GPP quantities from ESMs: the relative importance of different atmospheric drivers and differences in the representation of land surface processes. We describe the different steps in the development of our interpretable Machine Learning (ML) framework including the choice of algorithms, parameter tuning, training and evaluation. Our results show that ESMs largely agree on the physical climate drivers responsible for GPP as seen in the literature, for instance drought variables in the Mediterranean region or radiation and temperature in the Arctic region. However differences do exist since models don’t necessarily agree on which individual variable is most relevant for GPP. We also explore a distance measure to attribute GPP differences to climate influences versus process differences and provide examples for where our methods work (South Asia, Mediterranean)and where they are inconclusive (Eastern North America).

This paper presents a method to analyze and improve the set of equations constituting a rainfall-runoff model structure based on a combination of a data assimilation algorithm and polynomial updates to the state equations. The method, which we have called “Data Assimilation Informed model Structure Improvement” (DAISI) is generic, modular, and demonstrated with an application to the GR2M model and 201 catchments in South-East Australia. Our results show that the updated model generated with DAISI generally performed better for all metrics considered included KGE, NSE on log transform flow and flow duration curve bias. In addition, the modelled elasticity of runoff to rainfall is higher in the updated model, which suggests that the structural changes could have a significant impact on climate change simulations. Finally, the DAISI diagnostic identified a reduced number of update configurations in the GR2M structure with distinct regional patterns in three sub-regions of the modelling domain (Western Victoria, central region, and Northern New South Wales). These configurations correspond to specific polynomials of the state variables that could be used to improve equations in a revised model. Several potential improvements of DAISI are proposed including the use of additional observed variables such as actual evapotranspiration to better constrain the model internal fluxes.

Mercury (Hg) is a naturally occurring element that has been greatly enriched in the environment by activities like mining and fossil fuel combustion. Despite commonalities in some CO2 and Hg emission sources, the implications of long-range climate scenarios for anthropogenic Hg emissions have yet to be explored. Here, we present comprehensive projections of anthropogenic Hg emissions (2020-2300) and evaluate impacts on global atmospheric Hg deposition. Projections are based on four shared socioeconomic pathway (SSP) narratives ranging from sustainable reductions in resource and energy intensity to rapid economic growth driven by abundant fossil fuel exploitation. There is a greater than two-fold difference in cumulative anthropogenic Hg emissions between the lower-bound (110 Gg) and upper-bound (230 Gg) scenarios. Hg releases to land and water are approximately six times those of direct emissions to air (600-1470 Gg). At their peak, anthropogenic Hg emissions reach 2200-2600 Mg a-1 sometime between 2010 (baseline) and 2030, depending on the SSP scenario. Coal combustion is the largest determinant of differences in Hg emissions among scenarios. Decoupling of Hg and CO2 emissions sources occurs under low- to mid-range scenarios, though contributions from artisanal and small-scale gold mining remain uncertain. A projected future shift in speciation of Hg emissions toward lower gaseous elemental Hg (Hg0) and higher divalent Hg (HgII) will result in a higher fraction of locally-sourced Hg deposition. Projected re-emissions of previously deposited anthropogenic Hg follow a similar temporal trajectory to primary emissions, amplifying benefits of primary Hg emissions reductions under the most stringent mitigation scenarios.

In the face of a changing climate, the understanding, predictions and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long-term adaptation strategies, and guide mitigation approaches. Increasingly complex socio-economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high-resolution modeling of global-to-local Earth system processes across timescales, a reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science-to-service pathways and co-production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally-funded U.S. modeling groups outline here perspectives on a six-pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support the next generation of climate services.

A document by Yi Yang. Click on the document to view its contents.