Journal of Advances in Modeling Earth Systems最新文献

Nutrient resorption from senescing leaves can significantly affect plant nutrient status and growth, making it an important process for carbon-cycle predictions for land surface models (LSMs). Based on a recent analysis of global nutrient resorption patterns from trait data, we develop a dynamic scheme of nitrogen (N) resorption driven by leaf structural and environmental factors, and test its effect on present-day global simulations for woody plant functional types (PFTs) using the QUINCY biosphere model. Consistent with observations, we predict higher N resorption for the deciduous PFT compared to the evergreen PFTs, while at the same time reproducing the global gradient of decrease in resorption with key environmental drivers such as air temperature within each PFT. As a result, the novel scheme increases N resorption in N-limited plants, enhancing stored N for the subsequent year and reducing internal N limitation. This has cascading implications for ecosystem nutrient pools, plant productivity and, to a limited extent, the response of carbon and N cycling to elevated CO₂. The new scheme contributes to the development of an ecologically realistic representation of nutrient resorption in an LSM, with implications for both present day and future N limitation of the terrestrial biosphere.

Process-based hydrologic simulations in large domains generally require intensive computing resources. In this study, we implement various parallelization approaches within a process-based hydrologic solver, SUMMA, including the Message Passing Interface (MPI), Open Multi-Processing (OMP), and the Actor Model, to enable high-performance computing for large-domain hydrologic simulations. We provide detailed guidelines on these implementations to assist hydrologists in parallelizing their models effectively. Using a hydrologic simulation over North America as a case study, we compare the scalability, computational cost, input/output performance, and coupling capabilities of these parallel approaches with the original sequential approach. Our results show that the SUMMA-MPI exhibits linear scaling up to 1,024 cores, whereas SUMMA-OMP is only recommended for smaller numbers of cores. The MPI approach exhibited a straggler effect, resulting in core utilization of only 80%. To address this, we introduced a load-balancing calibration based on historical runs, which increases SUMMA-MPI core usage to 95% and thereby mitigates the straggler effect. With regard to coupling capabilities, MPI is the most effective for large-scale simulations involving multiple nodes and extensive core counts, supporting strong coupling and synchronization. The Actor Model reveals its excellent fault tolerance that enables automatic modification and recommencement of specific Grouped Response Units (GRUs) rather than restarting the entire simulation in the event of a failure within the simulation. Through this study, the implementation details of multiple parallelization schemes are documented and their advantages and limitations are discussed, which provides parallel computing insights for advancing computational hydrology in the Earth System Science community.

Carbon–nitrogen coupling is a critical constraint for improving carbon cycle and climate simulations in Earth system models (ESMs), yet large uncertainties hinder inter-model comparisons. Here, we present CNit v2.0, an updated representation of the carbon–nitrogen cycle in MAGICC—a widely used reduced-complexity model (RCM). CNit v2.0 is calibrated to emulate carbon–nitrogen cycle dynamics in various ESMs across historical, idealized (1pctCO2, 1pctCO2-bgc), and multiple Shared Socioeconomic Pathway (SSP) experiments, demonstrating strong emulation performance. The global annual-mean emulation from historical to SSP5-8.5 (1850–2100) reveals increasing nitrogen limitation on net primary production (NPP), with a multi-model mean inhibition of 10.2 ± 5.6% by 2100 due to nitrogen deficits limiting plant uptake. The stronger CO₂ fertilization effect in carbon-only (C-only) ESMs exceeds the mitigating influence of nitrogen limitation in CN-coupled ESMs, implying a risk of continued NPP overestimation in C-only ESMs—even if a nitrogen cycle is later added—due to insufficient constraints on CO₂ sensitivity. The climate response of litter production is sign-changing between C-only (inhibition) and CN-coupled (enhancement) ESMs, suggesting nitrogen effects may be misattributed as climate effects in C-only ESMs. Divergent climate responses and nitrogen effects on litter decomposition—particularly litter respiration and labile soil organic matter decomposition—are the primary drivers of total heterotrophic respiration differences between C-only and CN-coupled ESMs. Alongside NPP, these factors shape distinct carbon cycle dynamics. While nitrogen pools and fluxes generally follow carbon trends, they exhibit greater inter-model spread. In light of the calibration updates, we propose practical strategies to improve carbon cycle calibration in future RCMs.

Estimating and modeling background-error covariances remains a core challenge in variational data assimilation (DA). Operational systems typically approximate these covariances by transformations that separate geostrophically balanced components from unbalanced inertia-gravity modes—an approach well-suited for the midlatitudes but less applicable in the tropics, where different physical balances prevail. This study estimates background-error covariances in a reduced-dimension latent space learned by a neural-network autoencoder (AE). The AE was trained using 40 years of ERA5 reanalysis data, enabling it to capture flow-dependent atmospheric balances from a diverse set of weather states. We demonstrate that performing DA in the latent space yields analysis increments that preserve multivariate horizontal and vertical physical balances in both tropical and midlatitude atmosphere. Assimilating a single 500 hPa geopotential height observation in the midlatitudes produces increments consistent with geostrophic and thermal wind balance, while assimilating a total column water vapor observation with a positive departure in the nearly-saturated tropical atmosphere generates an increment resembling the tropical response to (latent) heat-induced perturbations. The resulting increments are localized and flow-dependent, and shaped by orography and land-sea contrasts. Forecasts initialized from these analyses exhibit realistic weather evolution, including the excitation of an eastward-propagating Kelvin wave in the tropics. Finally, we explore the transition from using synthetic ensembles and a climatology-based background error covariance matrix to an operational ensemble of data assimilations. Despite significant compression-induced variance loss in some variables, latent-space assimilation produces balanced, flow-dependent increments—highlighting its potential for ensemble-based latent-space 4D-Var.

Several different time periods of the Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) are isolated for examining how the depiction of tradewind marine shallow cumuli in single-column models (SCMs) is affected by choices about model physics. The periods of interest are times when the NOAA Research Vessel Ronald H. Brown and research aircraft WP-3D Orion were collocated, enabling verification of initial conditions and large-scale forcing (advective) tendencies constructed using gridded data from the fifth generation ECMWF atmospheric reanalysis (ERA5). To demonstrate how this new ATOMIC test case can be used to guide model development, three parameterization suites of the NOAA Unified Forecast System are evaluated within the Common Community Physics Package Single Column Model (CCPP SCM). Calculations are also performed using a large-eddy simulation (LES) to further bridge the gap between observations and SCM output, all of which are separated into regimes of either relatively active (“cloudy”) or inactive (“clear”) marine shallow cumuli. In both regimes tested, the parameterization suites tend to: (a) generate an unrealistic skewed or bimodal distribution of cloud fraction, (b) overestimate light to moderate rain rates, (c) produce an erroneously cold and dry boundary layer, and (d) produce higher-than-observed cloud tops. Results show that modifying the treatment of cloud fraction as well as increasing spatial and temporal resolution help bring the SCM more in line with observations. In addition, evidence is found to suggest that some of the remaining model biases may stem from intrinsic differences in the spatio-temporal sampling properties of the observations versus SCM output.

We have addressed convective self-aggregation (CSA) in steady and oscillating sea surface temperature (SST) and solar radiation (SOLIN) cloud-resolving model simulations in a non-rotating radiative-convective equilibrium (RCE) framework. Our experiment designs are motivated by land-ocean heterogeneity of atmospheric convection. The steady and oscillating forcings are idealizations of ocean and land conditions, respectively, based on their differences in heat capacities. In both kinds of simulations, the diurnal mean SST and SOLIN are the same, and both SST and SOLIN are only varied in time (i.e., they are spatially homogeneous at any given time). We find that diurnally oscillating forcing accelerates CSA. Stronger long-wave cooling in dry regions at night and during the warm SST phase (late afternoon) both allow the long-wave feedback, known to favor aggregation, to intensify compared to steady forcing simulations. In addition to the long-wave, reduced short-wave warming in dry regions (during the day) further enhances radiative cooling there compared to moist regions. Overall, the radiative cooling is enhanced in dry regions compared to neighboring moist convective regions. A dry subsidence is driven by this net radiative (short-wave plus long-wave) cooling, consistent with earlier work on CSA. Stronger radiative cooling allows stronger subsidence which allows low-level circulation to more efficiently transport moisture and energy up-gradient, driving convection to aggregate faster. We also note a sensitivity of our experimental setup to initial conditions, more so at warmer SST. This stochastic behavior might be critical in reconciling the differences of opinion regarding the response of convection aggregation to oscillating SST forcing.

Despite advances in high performance computing, accurate numerical simulations of global atmospheric dynamics remain a challenge. The resolution required to fully resolve the vast range scales as well as the strong coupling with—often not fully-understood—physics renders such simulations computationally infeasible over time horizons relevant for long-term climate risk assessment. While data-driven parameterizations have shown some promise of alleviating these obstacles, the scarcity of high-quality training data and their lack of long-term stability typically hinders their ability to capture the risk of rare extreme events. In this work we present a general strategy for training variational (probabilistic) neural network models to non-intrusively correct under-resolved long-time simulations of turbulent climate systems. The approach is based on the paradigm introduced by Barthel Sorensen et al. (2024, https://doi.org/10.1029/2023ms004122) which involves training a post-processing correction operator on under-resolved simulations nudged toward a high-fidelity reference. Our variational framework enables us to learn the dynamics of the underlying system from very little training data and thus drastically improve the extrapolation capabilities of the previous deterministic state-of-the art—even when the statistics of that training data are far from converged. We investigate and compare three recently introduced variational network architectures and illustrate the benefits of our approach on an anisotropic quasi-geostrophic flow. For this prototype model our approach is able to not only accurately capture global statistics, but also the anistropic regional variation and the statistics of multiple extreme event metrics—demonstrating significant improvement over previously introduced deterministic architectures.

Land surface models (LSMs) are essential tools for simulating the coupled climate system, representing the dynamics of water, energy, and carbon fluxes on land and their interaction with the atmosphere. However, parameterizing sub-grid processes at the scales relevant to climate models ($���\sim ��$10–100 km) remains a considerable challenge. The parameterizations typically have a large number of unknown and often correlated parameters, making calibration and uncertainty quantification difficult. Moreover, many existing LSMs are not readily adaptable to the incorporation of modern machine learning (ML) parameterizations trained with in situ and satellite data. This article presents the first version of ClimaLand, a new LSM designed for overcoming these limitations, including a description of the core equations underlying the model, the results of an extensive set of validation exercises, and an assessment of the computational performance of the model. We show that ClimaLand can leverage graphics processing units for computational efficiency, and that its modular architecture and high-level programming language, Julia, allows for integration with ML libraries. In the future, this will enable efficient simulation, calibration, and uncertainty quantification with ClimaLand.

Langmuir turbulence affects turbulent mixing in the ocean boundary layers and its effects require parameterizations in ocean circulation models. Most existing Langmuir turbulence parameterizations focus on the surface boundary layer in open oceans. In the shallow waters of coastal oceans, a surface boundary layer may interact and even merge with a bottom boundary layer. It is unclear how existing Langmuir turbulence parameterizations perform under such complex conditions. Here we assess the performance of two recent Langmuir turbulence parameterizations in an idealized case of merging boundary layers against turbulence-resolving large-eddy simulations (LES). In addition to assessing the solutions of free runs of single-column model (SCM) simulations, in which errors in the mean fields and turbulent fluxes are entangled, we also compare the simulated turbulent fluxes in SCM simulations with their mean fields nudged to those of the LES. In doing so, we focus on the parameterized turbulent fluxes in different parameterizations given the perfect mean fields. Our comparison highlights the tendency of parameterizations to deviate from the LES at each time instance, and thereby reveals the deficiencies of parameterizations in an instantaneous sense. It is shown that both parameterizations overestimate the near-bottom turbulent momentum flux when velocity shear is correct, resulting in too weak near-bottom shear in a free run. Consistent with previous studies, a down-Stokes drift shear momentum flux is necessary for capturing the momentum flux due to Langmuir turbulence but still misses the nonlocal momentum flux when coherent Langmuir supercells form.