The eastern tropical Pacific (ETP) coastal region, one of the rainiest places on Earth, is characterized by distinct diurnal offshore rainfall propagation primarily driven by mesoscale convective systems (MCSs). Previous studies have shown that MCS initiation in this region peaks in the early morning, with diurnally generated gravity waves from the Andes proposed as a key triggering mechanism. Additionally, enhanced low-level moisture has been hypothesized to be a crucial contributing factor in MCS development. However, the relative roles of these processes in triggering MCSs over the region remain insufficiently quantified. This study explores these mechanisms using an ensemble-based satellite data assimilation experiment focused on a representative nocturnal MCS event. Our results reveal a clear pre-MCS cooling trend in the lower troposphere, linked to gravity waves generated by afternoon inland convection. Concurrently, substantial low-level moistening occurs, driven by increased surface latent heat flux and horizontal moisture advection from the Panama low-level jet. These processes together destabilize the lower troposphere, creating favorable thermodynamic conditions for convection. Additionally, an offshore anabatic surface front enhances low-level convergence, promoting vertical lifting of unstable air and providing dynamic support for MCS initiation. Sensitivity experiments further demonstrate that MCS development is highly sensitive to low-level moisture availability, which is strongly influenced by surface wind-induced evaporation.

Tropical forest tree mortality is increasing due to more severe droughts, yet our understanding of how tree traits and life strategies are linked to drought stress has been limited by measurement scarcity. The BIONTE (BIOmass and NuTrient Experiment) near Manaus, Brazil hosts one of the world's largest sap flow installations, with sensors in 90 canopy trees across a wood density gradient monitored since June 2022. The 2023 El Ni & ntilde;o drought provided a unique opportunity to evaluate how water availability impacts tree transpiration. An interpretable machine learning framework was used to study the complex interactions between transpiration and multiple environmental variables such as soil water availability and vapor pressure deficit (VPD), and how these interactions vary with wood density and individual trees. We found varying responses of transpiration from different trees during the El Ni & ntilde;o drought. Transpiration generally increased with temperature, with stronger effects in wetter areas and in trees with low to medium wood density. However, this response was modulated by stomatal sensitivity to VPD, which constrained transpiration under high atmospheric demand, particularly in intermediate-moisture area. The inflection in transpiration rate at high temperatures (>32 degrees C) underscores the role of stomatal and hydraulic regulation in limiting water loss and protecting trees from excessive evaporative demand. Analysis of soil water contribution to transpiration revealed unimodal patterns in wetter area, with peak contributions near 0.45 cm(3) cm(-)(3) of surface soil water and declining or flat responses beyond that threshold, suggesting a shift from water- to energy-limited transpiration. In contrast, drier areas exhibited limited transpiration sensitivity to soil water conditions and minimal trait-based variation in VPD responses, indicating supply-limited conditions. Despite higher wood density trees being generally more resilient, this study shows diverse tree drought resilience, prompting further investigation into the specific traits and dynamics between environmental variables in regulating transpiration and other physiological processes in trees.

Since 1980, tropical cyclones have migrated poleward, but it remains unclear whether this trend reflects long-term climate change or temporary climate variability. Here we investigate the drivers of this poleward migration using multiple observational datasets and global models that permit tropical cyclones. We show that a tripolar pattern of Pacific sea surface temperature variability strongly modulates the interannual variation of cyclone latitudes and largely drove the poleward migration over 1980-2024. The tripolar pattern influences tropical cyclones more effectively than either the El Ni & ntilde;o/Southern Oscillation or the Hadley circulation. When its effects are removed, poleward migration is negligible. When it shows negative trends, the model simulates equatorward migration. As the pattern exhibits alternating multi-decadal trends but no long-term trend since 1970, its recent trend-and the associated poleward migration-is unlikely to persist. In ensemble projections under a warming scenario, tropical cyclone activity decreases overall, leading to fewer occurrences at high latitudes despite the poleward expansion of the Hadley cell. These results indicate that climate variability has played a dominant role in the observed poleward migration of tropical cyclones, and that future changes may differ markedly from the recent multi-decadal trends.

Abstract Tropical cyclone (TC) rapid intensification (RI) is driven by complex interactions of TCs with multiple environmental factors. Using observations and two HighResMIP models (CNRM‐CM6‐1‐HR and HadGEM3‐GC31‐HM) that better simulate TCRI, we examine how six environmental factors jointly influence TCRI. Observations show that while TCRI occurrence probability increases with the number of supportive conditions, it is strongly suppressed when even a few unsupportive ones are present. For the same number of supportive conditions, the number of unsupportive conditions largely controls the TCRI occurrence. In the models, the sensitivity of TCRI occurrence frequency to the number of supportive and unsupportive conditions is found to be weaker than the observed, caused by unrealistic representation of the effect of vertical wind shear and low free‐troposphere humidity on TCRI. Our results emphasize that unsupportive environmental conditions must be explicitly considered alongside supportive ones when diagnosing and predicting RI.

Riverine dissolved organic carbon (DOC), primarily sourced from soil organic carbon (SOC), plays a crucial role in regional and global carbon cycles. However, the complexities of the underlying mechanisms and limited observations present significant challenges for predictive understanding of DOC at regional or larger scales. Recently, we developed a machine learning-based (ML) map of DOC transformation rates, bridging the gap between SOC and DOC leaching flux and simplifying terrestrial DOC representation. Building on this advancement, we introduce ELM-MOSART-DOC, a DOC module integrated into the riverine component of the Energy Exascale Earth System Model (E3SM)-the Model for Scale Adaptive River Transport (MOSART). ELM-MOSART-DOC simulates DOC transport and transformation across both headwater streams and river networks, including those managed. Model validation demonstrates the ability of ELM-MOSART-DOC to accurately capture long-term average DOC concentrations, with Kling-Gupta Efficiency (KGE) scores of 0.58 and 0.76 at large and local stations, respectively. We further assess the impact of reservoirs through different simulation schemes, revealing that reservoirs significantly alter DOC fluxes by regulating streamflow patterns and promoting DOC mineralization. Model simulations indicate that reservoirs reduce total DOC flux from the Mississippi River into the ocean by 7.5%, with the long-term average annual export decreasing from 3.34 to 3.14 teragrams (Tg) per year. ELM-MOSART-DOC integrates process-based modeling with ML parameterization to enhance the predictive understanding of riverine biogeochemical processes. This approach reduces uncertainties in modeling regional and global carbon cycle ESMs and provides new insights into carbon cycling and its implications for global environmental change.

Abstract Machine learning (ML)‐based models have demonstrated high skill and computational efficiency, often outperforming conventional physics‐based models in weather and subseasonal predictions. While prior studies have assessed their fidelity in capturing synoptic‐scale atmospheric dynamics, their performance across timescales and under out‐of‐distribution forcing, such as +3K or +4K uniform‐warming forcings, and the sources of biases remain elusive, to establish the model's reliability for Earth science. Here, we design three sets of experiments targeting synoptic‐scale phenomena, interannual variability, and out‐of‐distribution uniform‐warming forcings. We evaluate the Neural General Circulation Model (NeuralGCM), a hybrid model integrating a dynamical core with ML‐based component, against observations and physics‐based Earth system models (ESMs). At the synoptic scale, NeuralGCM captures the evolution and propagation of extratropical cyclones with performance comparable to ESMs. At the interannual scale, when forced by El Niño‐Southern Oscillation sea surface temperature (SST) anomalies, NeuralGCM successfully reproduces associated teleconnection patterns but exhibits deficiencies in capturing nonlinear response. Under out‐of‐distribution uniform‐warming forcings, NeuralGCM simulates similar responses in global‐average temperature and precipitation and reproduces large‐scale tropospheric circulation features similar to those in ESMs. Notable weaknesses include overestimating the tracks and spatial extent of extratropical cyclones, biases in the teleconnected wave train triggered by tropical SST anomalies, and differences in upper‐level warming and stratospheric circulation responses to SST warming compared to physics‐based ESMs. The causes of these weaknesses were explored. Despite the noted weaknesses, NeuralGCM reproduces responses across experiments reasonably and performs comparably to ESMs. By integrating a dynamical core with ML, NeuralGCM shows potential for developing ML‐based ESMs.

Abstract The global ocean meridional overturning circulation (GMOC) is central for ocean transport and climate variations. However, a comprehensive picture of its historical mean state and variability remains vague due to limitations in modelling and observing systems. Incorporating observations into models offers a viable approach to reconstructing climate history, yet achieving coherent estimates of GMOC has proven challenging due to difficulties in harmonizing ocean stratification. Here, we demonstrate that multiscale data assimilation that integrates atmospheric and oceanic observations into two coupled models in a dynamically consistent way robustly retrieves the past 80-year GMOC. By diagnosing volcanic eruption-induced North Atlantic cooling and surface buoyancy loss, we show historic volcanic eruption events are imprinted in variability of the rebuilt GMOC. The Mt. Pinatubo (1991) volcanic eruption produces a multidecadal characteristic of the overturning evolution by the chain of enhancing diapycnal mixing - strengthening deep convection - mediated by eddy activities.

Abstract Mesoscale convective systems (MCSs) are powerful contributors to extreme precipitation, often resulting in flash floods. However, the extent to which these systems are influenced by natural modes of climate variability is still not fully understood. In this study, we found that the occurrence of extreme precipitation from MCSs nearly doubles during the intense positive phase of the Baroclinic Annular Mode (BAM). The positive phase of BAM slows down the zonal translation speed of extratropical cyclone (ETC) trajectories, creating a favorable dynamical environment for extreme MCS precipitation. Furthermore, we discovered that when ETCs and MCSs coincide, they account for more than 90% of extreme precipitation during springtime in the U.S. These multiscale interactions between the planetary‐scale BAM, synoptic‐scale extratropical cyclones, and MCSs manifest as significant regional impacts on extreme precipitation.

Abstract Accurate representation of groundwater table depth (GWTD) is crucial for simulating hydrological cycling in Earth system models (ESM). Nevertheless, there is a notable gap in the literature regarding the validation of GWTD simulations in ESMs and their subsequent impact on downstream hydrological components. This study explores the calibration of parameterization of global GWTD using machine learning within the Energy Exascale Earth System Model (E3SM) Land Model (ELM). Despite achieving significant gains in simulating GWTD through calibration, offline ELM simulations unexpectedly show that these improvements do not translate to substantial enhancements in model performance for other key hydrological variables, including soil moisture (SM), runoff, groundwater contribution to runoff or base flow index (BFI), and evapotranspiration and its partitioning. The performance in SM and runoff was even degraded in some regions, while BFI was mostly overestimated. Although there is significant improvement in GWTD within the critical range of 1–5 m, where groundwater traditionally influences land surface energy fluxes, these improvements occurred mostly in humid areas where the impact of GWTD on surface processes is minimal. Although the impacts of model calibration are generally small in offline ELM simulations, coupled land‐atmosphere simulations exhibit much stronger responses to GWTD calibration, highlighting the role of land‐atmosphere feedbacks in Earth system modeling. These findings underscore the need for integrated calibration strategies that simultaneously optimize multiple hydrological variables. However, if a single‐variable approach is necessary, it is crucial to establish clear priorities for calibration, identifying the most critical variables that have the greatest impact on overall model performance.

Mesoscale convective systems (MCSs) produce more than half of tropical rainfall and are central to the global hydrologic cycle. As the climate warms, environments favorable for MCSs may become more common; however, limited observational records hamper understanding of how MCSs respond to variations and changes in their environments. Here, we evaluate how well MCSs are represented in ERA5, a widely used global high-resolution reanalysis product. Using PyFLEXTRKR, which jointly tracks top-of-atmosphere infrared brightness temperature and surface precipitation, we identified MCSs in ERA5 and compared them with those identified in satellite observations using the same detection algorithm. This comparison analysis spans 2007-2020 using hourly data at 0.25 degrees horizontal resolution focusing over the tropics. ERA5 reproduces observed brightness-temperature statistics and captures the geographic distribution and seasonal and diurnal cycles of MCS cold cloud shields. However, ERA5 precipitation exhibits an intensity bias-too much light rain and too little heavy rain-which shifts the rain-rate distribution and reduces the frequency of MCSs relative to observations. Within MCSs, ERA5 precipitation exhibits the same pattern of bias, yielding a systematic underestimation of MCS precipitation intensity. Consistent with these biases, ERA5 underestimates the contribution of MCS to tropical rainfall by 25%-34% in key regions. Overall, ERA5 is suitable for studying MCS cold cloud-shield climatology and evolution, but precipitation-based MCS characteristics (including event-level precipitation features and the geospatial distribution of MCS precipitation) should be interpreted with caution. These findings clarify which aspects of MCS behavior are robustly represented in ERA5 for climatological applications.

Despite the well-documented impact of spatial resolution on the model extreme precipitation intensity (EPI), it is unclear whether and how this might influence the relationship between EPI and meteorological conditions. In this study, we assess how this relationship is represented in the Energy Exascale Earth System Model (E3SM) at low (110 km), high (25 km), and ultra-high (3.25 km, SCREAM) resolutions, using the Stage IV and IMERG data as observational references. The convection-permitting SCREAM performs well in capturing the EPI variations with temperature and saturation deficit under a wide range of atmospheric saturation levels. When the near-surface atmosphere is close to saturation, observations show a continuous increase in EPI with temperature, a pattern qualitatively well reproduced by SCREAM, although its short simulation of 40 days limits the range of temperature variation needed for a quantitative scaling rate comparison. In contrast, E3SM at 110 and 25 km resolutions produces an increase of EPI with temperature, followed by an erroneous negative scaling at high temperatures, even in saturated conditions. These comparisons are robust across all sample regions covering the midlatitudes, tropics, high altitudes, and mountainous areas, revealing that unless the spatial resolution is high enough to resolve deep convection, model physics dominates over spatial resolution in improving the performance in capturing the emergent relationship between EPI and the environmental drivers. Among other factors, our analyses indicate that the biased EPI scaling with temperature in the low- and high-resolution E3SM simulations might be related to deficiencies in parameterizing the dependence of atmospheric convection on relative humidity.

Abstract Accurate estimation of terrestrial evapotranspiration (ET) is vital for understanding global water and energy cycles. However, current global ET estimations are not well constrained. This study introduces an integrated framework combining the Maximum Entropy Production (MEP) theory with Random Forest (RF) model to improve global ET estimation. Specifically, in contrast to direct ET estimation by the RF model, the integrated framework (MEP‐RF) trains to predict error of MEP‐simulated ET. MEP‐RF outperforms RF in spatiotemporal extrapolation. Attribution analysis with in situ observations reveals that the inputs of MEP are the most critical variables for the ET process, including net radiation, vegetated area, soil moisture, and surface temperature. We further drive MEP‐RF with global reanalysis and satellite data sets of these four inputs, yielding a global mean terrestrial ET of 548 mm/year, with 77% attributed to transpiration. The global ET increased at a rate of 0.85 mm/year per year during 2003–2021, primarily due to vegetation greening rather than rising temperature, while decreasing soil moisture led to decreasing regional ET. The integrated framework provides a novel approach for the estimation of global ET without the need for hard‐to‐obtain and thus uncertain inputs, such as wind speed, surface roughness, aerodynamic and canopy stomatal resistance. Therefore, MEP‐RF offers an independent method on existing global ET products. It represents a promising physically based approach that can be incorporated into Earth System Models to enhance water and energy cycle simulations.

High-resolution precipitation data, generated through dynamical downscaling (DD) or statistical downscaling (SD) of global climate model output, provide critical information for regional climate assessment and adaptation planning. Most downscaling development and validation have focused on accurate gridscale precipitation construction and ignored the spatial structure of precipitation across model grids and at the event scale. However, many applications, e.g., hydrologic modeling and the analysis using the downscaled precipitation, require a reasonable representation of the spatial structure of precipitation within watersheds. Therefore, a set of standard metrics to evaluate the representation of the spatial structure of individual storms across diverse downscaled precipitation products is desired. To address this need, we conducted an object-based evaluation of precipitation in decades-long DD and SD products over the contiguous United States (CONUS). Specifically, we evaluate their ability to reproduce various features of precipitation objects in the observations: total volume, precipitation area, peak intensity, and spatial structure. Multiple metrics (bias, Perkins score, and nonparametric statistical tests) are used to quantify model performance. Our evaluation reveals notable variations in performance among individual products across different climate zones and seasons, as well as between extreme and nonextreme events. In general, most DD products exhibit balanced performance across the four precipitation object features, while SD products vary more significantly in their performance across products. Based on this comprehensive evaluation, we provide guidance on choosing downscaled products for specific regions, seasons, and precipitation object features. These findings and recommendations can inform precipitation-relevant modeling and analysis over CONUS, guide future downscaling technique developments, and provide actionable information for climate impact assessment and adaptation.

Summertime tropical upper-tropospheric troughs (TUTTs) provide a unified framework to better understand how extratropical and tropical forcings jointly modulate basin-scale tropical cyclone (TC) activity. In this study, we examine future changes in TUTTs and their implications for TC activity. Multimodel ensemble-mean projections from 45 Coupled Model Intercomparison Project phase 6 (CMIP6) models suggest a contraction of the Pacific TUTT and an expansion of the Atlantic TUTT as the climate warms. Consistently, future changes in environment-based TC indices indicate that the large-scale conditions will become more favorable for TC genesis and intensification over the central North Pacific but less favorable over the tropical North Atlantic and Gulf of Mexico. Utilizing a TC-permitting large-ensemble dataset [i.e., the Database for Policy Decision-Making for Future Climate Change (d4PDF)] that adequately captures the observed interannual TUTT-TC relationships, we further confirm the impacts of projected TUTT changes on the TC activity in a warmer climate. In contrast, the TUTT-TC relationship is poorly represented in most CMIP6 High-Resolution Model Intercomparison Project (HighResMIP) models; such deficiencies call for caution when assessing future TC risk based on explicitly tracked TCs in these models. Additionally, CMIP6 projections show large intermodel spread in TUTT changes, implying uncertainty in projected TC activity, especially over the central-to-eastern Pacific and the North Atlantic. This intermodel spread is associated with interhemispheric sea surface temperature warming asymmetry, which leads to a meridional shift of the intertropical convergence zone (ITCZ) and the simultaneous weakening or strengthening of TUTTs in the North Pacific and North Atlantic. The potential contributions of anthropogenic aerosol forcing and oceanic circulation to this interhemispheric warming asymmetry are briefly examined.

Mesoscale convective systems (MCS) and low-pressure systems (LPS) are both strongly associated with precipitation across the regions where they occur, particularly within global monsoon systems; however, their co-occurrence and its relationship to precipitation have not been systematically examined. Here, we use LPS and MCS trackers to detect compound MCS and LPS events in five monsoon regions and assess the association of this co-occurrence with anomalies of winds, precipitation, and other atmospheric variables. Additionally, we investigate the spatial distribution of precipitating MCS and LPS events. Our results show that most (similar to 60%) MCS and LPS co-occurrences are located in the lower latitudes, where they contribute up to 40% of annual precipitation. We find that compound events generally produce more extreme precipitation than MCS-only or LPS-only events. Furthermore, our assessment of the synoptic and mesoscale composites reveals that the underlying dynamics of compound events exhibit anomalously positive convective available potential energy and an anomalously low-pressure within the location of the event. In terms of the synoptic environment of the features, the compound MCS and LPS events are associated with inverted troughs in three out of five monsoon locations assessed.

Saturation excess and infiltration excess are two primary surface runoff generation mechanisms governing the timing and magnitude of streamflow at the catchment and larger scales. Despite their frequent co-occurrence and interconnections within catchments, most existing runoff schemes treat these mechanisms separately, following different theoretical paths. This study addresses this theoretical inconsistency by introducing a unified runoff scheme that integrates both mechanisms into a coherent framework. The scheme mathematically expresses both saturation and infiltration excess as functions of the probabilistic distribution of soil water storage, allowing dynamic transitions between mechanisms both in space and time based on the evolving soil water storage distribution during storm events. To demonstrate the applicability of this scheme, we developed a simple hydrologic model and tested it in 181 natural catchments over the U.S., spanning a range of humid to arid climates, and obtained Kling-Gupta efficiencies above 0.5 for 90 % and 70 % of the catchments during the parameter determination and validation periods, respectively. Results show that the model effectively captures the relative dominance of infiltration or saturation excess runoff at the event, seasonal, and annual scales. For instance, model results suggest that infiltration excess runoff dominates where the climate is arid and seasonal evaporative energy and precipitation are in phase, whilst saturation excess runoff dominates under other climate conditions. This unified scheme establishes a new foundation for enhancing the predictive understanding of runoff and other hydrological processes across diverse climates.

Spinning up fully coupled Earth system models is essential for establishing stable and physically consistent initial conditions, yet it remains one of the most computationally demanding steps in the modeling workflow. Here we assess and document coupled spinup behavior and characteristic equilibration timescales in version 3 of the Energy Exascale Earth System Model (E3SMv3) at standard resolution using a 2000-year fully coupled preindustrial benchmark simulation, and evaluate an alternating fully coupled–forced ocean–sea-ice (FC–FOSI) spin-up strategy. The benchmark shows that fast components, including the atmosphere, land surface energy balance, and upper ocean, approach a quasiequilibrated state within approximately 200–300 years, while the deep ocean continues to adjust on much longer timescales with weak residual drift. We test two FC–FOSI configurations that target an approximately 40% reduction in total computational cost relative to continuous fully coupled simulations by alternating 10 and 50 years of fully coupled integration with 60 and 160 years of forced ocean–sea-ice integration, respectively. During the alternating phase, the forcing-update cadence primarily affects transient subsurface ocean adjustment, most notably ocean heat content. After recoupling, both configurations reproduce the large-scale mean state, spatial patterns, and key modes of variability of the fully coupled benchmark. Our results demonstrate that a carefully designed FC–FOSI framework in E3SMv3, using alternation intervals within the tested ranges of 10–50 years for fully coupled integration and 60–160 years for forced ocean–sea-ice integration, offers a viable pathway to reduce computational cost while maintaining coupled initial conditions comparable to those from a continuous fully coupled spin-up.