The Hudson Bay Lowlands (HBL) are recognized as the second largest peatland complex in the world. Due to variability in peat thickness across a large and heterogeneous landscape, the existing carbon (C) storage estimates for the HBL may contain large uncertainty. Here, we use geospatial variables that are associated with HBL peat formation, age, accumulation, and occurrence to understand the driving factors for peat depth variability and map peat depth and C storage at 30m spatial resolution. The estimated average peat depth of HBL is 184(±48)  cm with 90% of values falling between 89 and 264 cm. Based on the spatially explicit peat depth, the HBL total C storage is estimated to be 30(±6)  Pg. Distance to the coastline is the most important indicator of peat depth where the depth increases with distance further away from Hudson Bay coastline, confirming that the time since peat formation is closely related to peat depth.

We conduct a high-resolution seismic tomography for the crustal P and S-wave velocities of Yunnan region in southwestern China. Waveforms recorded at 128 broadband stations from 131 regional earthquakes of moment magnitudes 3.9−5.5 occurring between 2009 and 2021 are used to obtain traveltime residuals by the cross-correlation between records and synthetics. Using the regional community velocity model SWChinaCVM‐1.0 as the initial model, we carry out a three-stage iterative adjoint tomography, progressing from the longer period band of 50–20 s to shorter-period bands of 30–10 s and 30–5 s. The final model shows general consistency in the spatial patterns of P- and S-wave velocity anomalies. Widespread low-velocity anomalies with high-Vp/Vs ratios in the mid and lower crust in the region suggest a mix of weak materials of the mid-lower crustal flow from under the Tibetan Plateau with hot materials of the upwelling from the deep mantle plume that led to the Emeishan Large Igneous Province. Localized velocity and Vp/Vs ratio anomalies also reveal that the Lijiang-Xiaojinhe Fault Zone appears to be confined in the upper crust, while the Anninghe-Zemuhe Fault Zone and the Xiaojiang Fault Zone are both whole-crust structures reaching the Moho interface. The Red River Fault Zone, a whole-crust fault, separates the Yangtze Craton to the northeast from the Indo-China Block to the southwest. The main fault zones, the decoupling between the crustal and uppermost mantle parts, and the wide-spreading weak mid-lower crustal materials mutually interact, all contributing to the tectonic evolution of the entire region.

Given that more than half of the world’s population currently resides in cities, further understanding of the potential impact of future climate change on urban areas is needed. In this regard, we project recent heatwave (HW) episodes in the Metropolitan Area of Barcelona (AMB) with future climate conditions until 2100 using the pseudo global warming (PGW) method. First, we determine all the HWs that occurred in the AMB during the last climatological period of 30 years (1991-2020) and simulate each individual event using the Weather and Research Forecasting (WRF) model at high-resolution. Then, these historical HW events are re-simulated with the modified atmospheric conditions of the mid-century (2041-2070) and the end-of-the-century (2071-2100) according to the scenario SSP370, in which CO2 emissions are projected to almost double from current levels by 2100 following a low emission reduction scenario. HW intensity is expected to increase by 2.5 °C and 4.2 °C in the mid- and end-of-the-century periods, respectively. Higher temperatures are related to stationary and stable synoptic patterns, which are projected to experience the greatest intensification in the future. The geopotential height at 500 hPa could increase up to 100 geopotential meters (gpm) by the end of the century, leading to values up to 6050 gpm, which indicates changes in thermodynamic and dynamic effects resulting in potentially warmer HW episodes. The results obtained can aid in understanding the expected changes for this century, which could facilitate the formulation of heat mitigation and adaptation strategies, particularly for the most exposed areas.

Root zone water storage capacity (SR) plays a fundamental role in determining the magnitude of evapotranspiration (ET) during both average and extreme climatic conditions. While methods exist to estimate SR globally at relatively fine spatial scales, the effects of uncertainty in these broad-scale estimates on evapotranspiration are largely unknown. We present a new method to efficiently describe the relationships between SR and evapotranspiration across all possible values of SR, for a given climate. This approach replaces computationally expensive model sensitivity analyses and provides a means for characterizing the importance of uncertainty and spatial variability in SR across various climates and timescales. To demonstrate the utility of our framework, we apply our approach to nine sites across the United States that vary in their seasonal climatology. In doing so, we show that evapotranspiration can be dramatically different between sites even with the same SR. For example, a very shallow SR (15 mm) would limit evapotranspiration to 27% of its maximum value (given no storage limitation) in some sites but to only 68% in others. Furthermore, if SR was estimated to be 250 mm with an uncertainty of +- 20%, the effect on estimated evapotranspiration in Eel (a site in Northern California) would be significant (+- 10%) but negligible in Boulder (a site in the Colorado Rockies). Furthermore, we find distinct site-specific SR–ET relationships that substantially impact how uncertainty and spatial variability in landscape distributions of SRaffect evapotranspiration patterns.

Resilience is the ability of a system to withstand stressors while preserving its structure and functions. Various performance- and attribute-based resilience assessment frameworks have been developed to understand the behaviour and properties of individual water systems. However, in integrated water systems, the increased complexity presents new challenges in the application of these frameworks. This study first reviews the key elements in both frameworks, including system indicators, thresholds, and resilience metrics, across urban water supply, drainage, groundwater, and river systems. Challenges are identified in deriving consistent indicators from siloed subsystem models, robust threshold selection, and resilience metrics synthesis as well as their usefulness for management. Based on the insights, a bottom-up resilience assessment framework for integrated water systems is developed. A water system integration model (WSIMOD) is employed to derive indicators for subsystems. Four performance-based resilience metrics are designed and applied to the indicators to facilitate intercomparison between subsystems. The application of the metrics crosses from event-level assessments for understanding system behaviour to annual-level evaluations of long-term performance, which are ultimately synthesised at the system level for multi-stakeholder decision-making. The efficacy of this framework is demonstrated through a case study in Luton, UK. The findings highlight river water quality as the least resilient subsystem that needs prioritised management. Sensitivity analysis is conducted to examine the impacts of thresholds on resilience results, with subsequent interpretation linking these metrics to specific decision variables for enhanced management. This framework can be extended through stakeholder engagement to improve system performance under deep uncertainties.

Ocean water turbidity, influenced by marine chlorophyll concentration, significantly alters the distribution of shortwave radiation in the water column. This work aims to assess the effects of turbidity on the upper-ocean physical properties and their subsequent impact on the atmosphere, using a coupled ocean-atmosphere regional model for the Mediterranean and Black Seas. We performed 11-year (2011-2021) twin-simulation experiments, based on different chlorophyll concentrations to estimate turbidity and the penetration of solar radiation in the ocean. The first simulation used a monthly climatology field of chlorophyll concentrations derived from satellite observations, whilst in the second experiment, the chlorophyll concentration was kept constant at 0.05 mg m−3, representing clear water turbidity conditions. Results show that radiative heating driven by ocean turbidity amplifies the seasonal cycle of temperature in the upper layers, leading to increased surface warming in summer and surface cooling in winter. Also, higher turbidity contributes to cooling in subsurface layers throughout the year due to its shading effect. The temperature response to turbidity in the water column is controlled by the mixed layer depth and a balance between (1) direct near-surface radiative heating due to chlorophyll solar absorption, and (2) indirect cooling resulting from vertical turbulent mixing processes with subsurface waters. The atmosphere moderates the seasonal sea surface temperature (SST) response to turbidity, primarily through changes in latent heat flux. Ultimately, our simulations suggest that increased turbidity enhances the Mediterranean overturning circulation, highlighting the necessity of incorporating realistic turbidity forcing into regional climate modeling studies.

Accurate hourly precipitation forecasting is essential for local socioeconomic activities. Here, a U-shaped neural network (U-Net) with a customized loss function for precipitation is proposed to enhance 1–24-hour precipitation post-processing over Beijing, China. 36 precipitation-related predictors, informed by local forecasters’ expertise and grounded in physical constraints, are utilized as inputs. Permutation importance (PI) and saliency maps are used to evaluate the average and specific influence of selected predictors on U-Net predictions. The findings indicate the inclusion of additional predictors is crucial for bias correction, resulting in improvements of ~14.3\% in critical success index for precipitation exceeding 10mm/h. PI analysis reveals that associated atmospheric variables function as important supplementary indicators, and saliency maps visualizations suggest that key region influencing a given grid-point extends to ~30 kilometers for 1–24-hour precipitation forecasting. It demonstrates that integrating knowledge-based predictors is promise in advancing precipitation post-processing, and further understanding and interpretation of model is crucial.

The representation of easterly waves (EWs) over the east Pacific Ocean (PEWs) in Coupled Model Intercomparison Project phase 6 (CMIP6) simulations is examined. Models are assessed based on their ability of reproducing PEW-related precipitation and its evolution. The leading patterns reveal a large spread in the representation of PEW structure and amplitude. A comparison of the models with the most realistic PEWs with those with unrealistically weak PEW skill showed that the more accurate simulations more effectively capture the mean state and EW thermodynamic structures over the northeast Pacific. Particularly, good models exhibit realistic PEW precipitation, weaker meridional mean state humidity gradients, larger mean state precipitation over the Panama Bight, and realistic convective moisture adjustment timescales. These results underscore the importance of accurately representing the sensitivity of PEW convection to anomalous moisture for the realistic representation of the waves.

Analysis of magnetotelluric (MT) data in the northern Appalachian region reveals various heterogeneities in lithospheric structure. By inverting a subset of the long-period Earthscope USArray MT data, we obtained a 3D electrical resistivity model that provides insights into the previously identified seismic low-velocity Northern Appalachian Anomaly (NAA). We compared the resistivity (inverse of conductivity) values with empirical conductivity models based on an olivine-orthopyroxene-clinopyroxene mantle mineral assemblage. Low-resistivity anomalies in the inverted model suggest an anomalously hot mantle in the northern and western edges of the NAA. Both in the north and west, resistivities in the uppermost mantle are sufficiently low to require a small fraction of partial melt. Additionally, we resolved a resistive feature in western New England that extends deep into the mantle lithosphere and is absent in seismic velocity models. We interpret this feature as dry and depleted lithospheric mantle. The observed heterogeneities in the lithosphere may reflect ongoing asthenospheric upwelling caused by combined effects of weakened lithosphere from past hotspot interactions and present edge-driven mantle convection.

Earth’s inner magnetosphere contains multiple electron populations influenced by different factors. The cold electrons of the plasmasphere, warm plasma that contributes to the ring current, and the relativistic plasma of the radiation belt often seem to behave independently. Using omni-directional flux and energy measurements from the HOPE and MagEIS instruments aboard the Van Allen Probes, we provide a detailed density and temperature description of the inner magnetosphere, offering a comprehensive statistical analysis of the entire Van Allen Probe era. While number density and temperature data at geosynchronous orbit are available, this study focuses on the inner magnetosphere (2 < L∗ < 6). Values of density and temperature are extracted by fitting energy and phase space density to obtain the distribution function. The fitted distributions are related to the zeroth and second moments to estimate the number density and temperature. Analysis has indicated that a two Maxwellian fit is sufficient over a wide range of L∗ and that there are two independent plasma populations. The more energetic population has a median number density of approximately 1.2 × 104 m−3 and a temperature of around 130 keV, with a temperature peak observed between L* = 4 and L*= 4.5. This population is relatively uniform in MLT. In contrast, the less energetic warm electron population has a median number density of about 2.5 × 104 m−3 and a temperature of 7.4 keV. Strong statistical trends in density and temperature across both L* and MLT are presented, along with potential sources driving these variations.

To gain a deep understanding of the soil-plant-atmosphere continuum, it is crucial for land surface models to consider plant-related processes. This study focuses on the role of leaf stomatal conductance, which is central to photosynthesis and transpiration. Previous research showed that existing stomatal conductance schemes yield divergent results within a land surface model (LSM), implying a knowledge gap. We further the investigation on the performance of five representative stomatal conductance schemes using a full-fledged LSM, VIC+, and conduct a comparison study over a period of one to two years at two U.S. sites, Blodgett and Duke. Our results reveal substantial differences in modeling outcomes for stomatal conductance, leaf water potential, and leaf CO2 concentration. To gain a better understanding of the divergence among the modeling results, we examine the reasonableness of hourly output variables. Boundaries for unreasonable results are first established using published data for similar conditions, combined with physical reasoning, and judgements thus circumventing limitations of the lack of observational data. Based on them, large portions of the output from these schemes are deemed unreasonable. Treating these schemes as equally plausible or of the same level of credence, we present a new approach in which the stomatal conductance is estimated by simultaneously using multiple plausible expressions derived from two different schemes. This results in the introduction of an additional variable that binds two schemes into a robust one. This new scheme gives a significantly lower percentage of unreasonable variable combinations in its outputs, demonstrating its effectiveness.

Cloud optical property retrievals from passive satellite imagers tend to be most accurate during the daytime due to the availability of visible and near-infrared solar reflectances. Infrared (IR) channels have a relative lack of spectral sensitivity to optically thick clouds and are heavily influenced by cloud-top temperature making accurate retrievals of cloud optical depth, cloud effective radius, and cloud water path more difficult at night. In this work, we examine whether the use of spatial context—information about the local structure and organization of cloud features—can help overcome these limitations of IR channels and provide more accurate estimates of nighttime cloud optical properties. We trained several neural networks to emulate the Advanced Baseline Imager (ABI) NOAA Daytime Cloud Optical and Microphysical Properties (DCOMP) algorithm using only IR channels. We then compared the neural networks to the NOAA operational daytime and nighttime products, and the Nighttime Lunar Cloud Optical and Microphysical Properties (NLCOMP) algorithm, which utilizes the low-light visible band on VIIRS in collocated imagery. These comparisons show that the use of spatial context can significantly improve estimates of nighttime cloud optical properties. The primary model we trained, U-NetCOMP, can reasonably match DCOMP during the day and significantly reduces artifacts associated with day/night terminator. We also find that U-NetCOMP estimates align more closely with NLCOMP at night compared to the nighttime NOAA operational products for ABI.

The state of the near-surface atmosphere, especially air temperature and specific humidity, has profound effects on human health, ecosystem function, and global energy flows. The accuracy of these products is important for weather forecasting, climate modeling, data assimilation, and trend assessment. The Atmospheric Infrared Sounder (AIRS) provides global products of near-surface air temperature and specific humidity estimates. These products have seen continuous improvements in accuracy, resulting in significant reductions in error rates. Despite these improvements, existing studies have not systematically validated AIRS near-surface products in both temporal and spatial perspectives, especially over oceans. This study aims to fill this gap by using the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) as a ground-based reference to evaluate AIRS near-surface air temperature and specific humidity over the ocean from the V7 Level 2 product. Our results show an overall underestimation of near-surface air temperature and specific humidity, with pronounced spatial patterns in the estimation errors. In addition, we observed higher uncertainties near land and found that the products perform better during winter and at night on a global scale, although there are regional exceptions. In terms of time scale, the estimation errors show remarkable stability over a 20-year period, demonstrating the ability of AIRS to capture general temporal characteristics. These findings underline the importance of validating and understanding the retrieval uncertainties of AIRS near-surface products, paving the way for improved climatological applications.

The directional derivative approach (DDA) has the potential to rapidly and accurately quantify emission distributions based on the directional derivative of satellite-observed column amounts with respect to the horizontal wind. From the first principles, this paper derives the DDA emission estimators with a range of complexity by vertically integrating the 3D continuity equation and simplifying the results under several assumptions and approximations. The connection and difference between the DDA and a widely used divergence method for emission estimation are highlighted. A key difference is that the DDA integrates from the surface to an intermediate altitude instead of to the top of the observed column. This leads to the inherent background removal of the DDA, in contrast to the explicit background removal necessitated by the divergence method theory. Linear fittings are used to account for the effects of topography, chemical reactions, and retrieval biases. Realistic estimators of NOx emissions using satellite-observed NO2 column amounts are proposed, leveraging external climatology of the NOx:NO2 ratio and its directional derivative. These estimators are evaluated within a WRF-CMAQ simulation of NOx by comparisons with the model NOx emissions. The DDA estimators consistently outperform the divergence method estimator, and the DDA estimator that considers both topography and chemistry features the lowest root mean square error (RMSE). Lessons learned from this study using synthetic model data can be readily applied to the usage of actual satellite observations for emission estimation.

Fluid production from dehydration reactions and fluid migration in the subducting slab impact various subduction processes, including intraslab and megathrust earthquakes, episodic slip and tremor, mantle wedge metasomatism, and arc-magma genesis. Quantifying those processes requires a good knowledge of the location and amount of fluid outflux at the top of the slab. Previous models of fluid migration indicate that compaction-pressure gradients induced by the dehydration reactions could drive updip intraslab fluid flow (Wilson et al., 2014). However, how the initial hydration in the oceanic mantle prior to subduction impacts the updip fluid flow has not been investigated. Here, we use a 2-D two-phase flow model to investigate this effect under various initial slab-mantle hydration states and slab thermal conditions, both of which impact the depth extent of the stability of hydrous minerals. We focus on the lateral shift between the site of dehydration reactions and the location of fluid outflux at the top of the slab due to intraslab-updip migration. Our results indicate that major updip fluid pathways form along the antigorite and chlorite dehydration fronts sub-parallel to the slab surface. This, in turn, promotes slab-fluid outflux at the slab surface as shallow as 30–40 km depths. This mechanism is more likely in young slabs (< ~30 Ma), in which the thickness of the hydrated mantle in the incoming oceanic mantle that is required to form the slab-parallel dehydration fronts is relatively small (< ~20 km) because of its warm condition and thus a relatively thin antigorite stability zone.

The concept of the ecological niche is fundamental to understanding species distributions but it often overlooks the critical role of demography in shaping said distributions. Conversely, demographic theory has traditionally neglected how vital rates vary across environments, limiting our understanding of population dynamics across species’ ranges. Over 50 years ago, Maguire proposed conceptualising the ecological niche as composed of multiple “demographic niches” corresponding to separate vital rates such as survival, development, and reproduction. Despite its potential for ecological research, Maguire’s perspective never really took off. Here, we introduce the “demographic niche concept” (DNC), a contemporary evolution of Maguire’s perspective, integrating recent advances in niche theory and demography to formalise the DNC. We first outline the theoretical foundations of the DNC, focusing on vital rate variation along environmental gradients and its role in shaping ecological niches. We then formalise the definition of the DNC, explore how demographic niches and their boundaries are determined, and introduce a framework for applying the DNC in environmental and geographic spaces. Importantly, we present the DNC as a tool for conservation planning by improving our ability to identify critical areas where specific demographic processes are vital for species persistence or absent under global change. Throughout this review, we also explore methods for modelling and integrating demographic niches and discuss their relevance for addressing global challenges such as climate change. By integrating the ecological niche with demographic theory, we propose testable hypotheses on how and why vital rates vary within niche space, the current frontier of the DNC. Finally, we identify key knowledge gaps and suggest research directions to address these gaps. By advancing the conceptual and methodological tools of the DNC, this review establishes a foundation for future empirical research and applications, offering new directions for ecological theory and conservation amidst a changing world.

To better understand the landscape dynamics and changes in habitat connectivity influenced by glacial and interglacial oscillations over the biodiversity-rich Hengduan Mountains (HM) region, high-resolution climate data for past periods are essential. We apply the non-hydrostatic limited-area model COSMO, with a resolution of 12 km over East Asia, to simulate the Last Glacial Maximum (LGM), a period characterized by a generally colder and drier climate compared to present-day conditions. We perform the downscaling with a novel approach for paleoclimate modelling, the Pseudo-Global Warming (PGW) method. The COSMO PGW simulation for the LGM shows that COSMO generally replicates the large-scale dynamics of the driving global climate model simulation in the colder climate. Both models suggest weaker Asian summer monsoon systems during this period. Consequently, regions such as the Bay of Bengal, and the South China Sea, which typically receive substantial monsoon rainfall, experience significantly reduced precipitation. However, despite these model similarities, the high-resolution COSMO simulation exhibits distinctive differences on a smaller scale—particularly over land. For instance, COSMO suggests a more pronounced southward shift of the jet stream during the LGM winter, with cooler conditions in southern China. Moreover, the COSMO simulation, despite the overall weaker summer monsoon circulation, features increased precipitation amounts for much of the HM. Additionally, COSMO suggests a more extensive increase in snowfall over the High Mountain Asia region. Our study suggests that the resource-saving PGW approach is a suitable method to bridge the gap between large-scale projections and regional climate impacts—also for past periods like the LGM.

In the summer of 2021, devastating river floods occurred in Western Europe as a result of extreme rainfall. At numerous bridges, debris accumulations were observed, exacerbating flooding upstream by impeding waterflow and sometimes contributing to bridge failure. Due to widespread building damage and flooding of settlements along the rivers, these accumulations differed markedly from classic logjams, with substantial amounts of man-made objects. A new database of clogged bridges in Belgium and Germany – described in a separate data descriptor – was analysed to characterise bridge clogging and determine the effect of bridge design, bridge location and hydraulic conditions. Nearly half of the debris volume consisted of man-made materials, including building rubble, anthropogenic wood and vehicles. This created remarkably dense accumulations, highlighting the importance of further studying debris accumulations of mixed composition. Examination of the relations between bridge design and accumulation volumes found bridges with narrow pier spacing (≤10 metres) more susceptible to forming large accumulations. Blocking by the deck and railing also played a prominent role, in conjunction with blocking by the piers, as peak water levels at most bridges (85%) reached or exceeded the deck. These findings can help to better understand bridge clogging effects on flood conditions, to design bridges with lower debris accumulation risks, and to inform future flood hazard assessments, flood risk mapping, and disaster response strategies, especially in urbanised regions.