Journal of Advances in Modeling Earth Systems最新文献

Ice cover in seasonally frozen lakes plays a crucial role in ecosystem dynamics and human activities, while also serving as a sensitive indicator of climate change. Accurate yet efficient modeling of lake ice timing and thickness is therefore increasingly important. This study presents a new ice module integrated into the existing air2water model, a hybrid physics-based/statistical model originally developed to predict lake surface water temperature (LSWT) using air temperature as its sole input. The extended model simulates ice cover dynamics, distinguishing between black ice (formed by direct lake water freezing) and white ice (from accumulated snow submerged and frozen over black ice). The extension preserves the original framework's key advantages: minimal input requirements (air temperature and precipitation), low parametrization (8–11 parameters), and physically based governing equations. Calibration was performed using an automatic optimization algorithm with a multi-objective performance metric combining LSWT and ice thickness Nash-Sutcliffe efficiency indices (NSEs). Evaluation employed long time series (1960–2023) of LSWT and ice thickness data from three Finnish lakes spanning different climatic conditions. When calibrated with equal weighting of LSWT and ice thickness NSEs, the model demonstrated robust performance across all lakes, with daily LSWT root mean square errors (RMSEs) near 1°C and ice thickness RMSEs of about 10 cm. Notably, consistent ice thickness predictions were achieved even when calibration relied solely on LSWT data. LSWT simulations also improved relative to the original model. These results show that the extended air2water model offers a competitive and data-efficient alternative to more complex lake ice models.

We introduce JAX-LaB, a differentiable, Python-based Lattice Boltzmann simulation library designed for modeling multiphase and multiphysics fluid dynamics problems in hydrologic, geologic, and engineered porous media settings. The library is designed as an extension to XLB (Ataei & Salehipour, 2024, https://doi.org/10.1016/j.cpc.2024.109187), and it is built on the JAX framework (Bradbury et al., 2018, http://github.com/jax-ml/jax). The library delivers a performant, hardware-agnostic implementation that seamlessly integrates with machine learning libraries and scales efficiently across CPUs, multi-GPU setups, and distributed environments. Multiphase interactions are modeled using the Shan-Chen pseudopotential method, coupled with an equation of state (EOS) to reproduce densities consistent with Maxwell's construction, enabling accurate simulation of flows with density ratios $��>�1�0^7�$ while maintaining low spurious currents. Fluid wetting is achieved using the “improved” virtual density scheme (Q. Li et al., 2019, https://doi.org/10.1103/physreve.100.053313), which enables precise control of contact angle on flat and curved surfaces, while eliminating non-physical films seen in the Shan-Chen virtual density scheme. This scheme integrates directly into the interaction force calculations, removing the need to handle fluid-fluid and fluid-solid forces separately. We validate the library's accuracy and performance through comprehensive analytical benchmarks, including Laplace's law, capillary rise in parallel plates, and multi-component cocurrent flow in a channel. We then use the code for several applications involving multicomponent and multiphase flows, including permeability estimation, injection of supercritical $��C�O_2�$ in a water-saturated Fontainebleau sandstone, and obtaining the characteristic curves for a sphere pack geometry. Finally, the single-GPU performance and multi-GPU scaling of the code are evaluated on both single-node and distributed systems. The library is open-source under the Apache license and available at https://github.com/piyush-ppradhan/JAX-LaB.

Accurately representing daily reservoir operations in large-scale hydrological and water resource modeling remains challenging due to both the complex and unclear nature of real-world operations and very limited availability of operation records for many reservoirs worldwide. To address this gap, this study introduces MODROM (MOdular Data-driven Reservoir Operation Model), a parsimonious reservoir parameterization scheme that conceptualizes reservoir operations through simple operation modules and their seasonal transitions. These operation modules are designed to be simple and parsimonious for easier generalizing from data-rich to data-scarce reservoirs. MODROM is calibrated using high-quality long-term operation records from 411 data-rich reservoirs across the contiguous United States (CONUS), and a Random Forest model is developed to provide calibrated parameters for data-scarce reservoirs based on a suite of static reservoir characteristics. Results demonstrate MODROM's strong and robust performance when calibrating using all available data for each reservoir, though the performance generally declines for reservoirs with larger regulation capacity. The generalization performance is strong under favorable sampling conditions but varies with different train-test splits due to the limited reservoir data set. Benchmarking against existing models shows that MODROM achieves enhanced performance, with a median Kling-Gupta Efficiency of approximately 0.5, compared to 0.4 for the best storage-based model and 0.2 for data-inferred model; this demonstrates distinct advantages in generalizing parameters to data-scarce reservoirs using readily available static reservoir characteristics, though the performance can be affected by train-test split due to limited reservoirs in the sample.

Ocean turbulence parameterization has principally been based on process-based approaches, seeking to embed physical principles so that coarser resolution calculations can capture the net influence of smaller scale unresolved processes. More recently there has been an increasing focus on the application of data-driven approaches to this problem. Here we consider the application of online learning to data-driven eddy parameterization, constructing an end-to-end automatically differentiable dynamical solver forced by a neural network (NN), and training the NN based on the dynamics of the combined hybrid system. This approach is applied to the classic barotropic Stommel-Munk gyre problem—a highly idealized configuration which nevertheless includes multiple flow regimes, boundary dynamics, and a separating jet, and therefore presents a challenging test case for the online learning approach. It is found that a NN which is suitably trained can lead to a coarse resolution NN parameterized model which is stable, and has both a reasonable mean state and intrinsic variability. This suggests that online learning is a powerful tool for studying the problem of ocean turbulence parameterization. A test of generalizability with a modified wind forcing shows some positive results. However a test of symmetry preservation demonstrates that the NN parameterized model fails to respect an intrinsic symmetry property of the underlying system.

The accuracy of global forecasting of atmospheric composition is essential for protecting public health and advancing climate research. Carbon monoxide (CO), a key pollutant with indirect greenhouse effects, requires timely and accurate prediction. We propose Duo-AttnOPNets, an operational framework that combines a deep learning-based forecasting module Duo-AttnForeNet with a training-free data assimilation module Duo-AttnVarNet. Duo-AttnForeNet employs CSLSTM blocks—built from ConvLSTM cells and dual attention mechanisms—to effectively capture spatio-temporal dynamics. Duo-AttnVarNet leverages automatic differentiation and GPU acceleration to enable efficient four-dimensional variational (4D-Var) assimilation without extensive manual adjoint coding. We evaluate Duo-AttnOPNets against the Integrated Forecasting System for Atmospheric Composition (C-IFS). Results show that Duo-AttnOPNets achieves comparable or superior accuracy in both forecasting and assimilation, while generating 5-day forecasts within seconds on a single GPU. These findings demonstrate its potential for real-time, scalable, and accurate CO forecasting, marking a promising advance in integrating deep learning with traditional variational methods for operational atmospheric modeling.

Aerosol-cloud interactions (aci) are the leading source of uncertainty in inferring climate sensitivity from the historical record. Earth system models (ESMs) struggle to represent aci because the processes responsible for these phenomena occur at much finer time and space scales than can be resolved by any ESM. Observational constraints provide key benchmarks to test ESMs, but cannot be used alone to fully understand aci processes except in very specific cases where causality is controlled; some degree of modeling is required to infer aci and estimate radiative forcing. Here, we generate and characterize a perturbed parameter ensemble (PPE) in version 3 of the Energy Exascale ESM (E3SMv3). We perturb 25 parameters that govern aci processes over 250 members and integrate the model over present-day and preindustrial aerosol emissions. We find that the process representation in E3SMv3 is flexible and can generate global-mean effective radiative forcings due to aci (ERFaci) ranging from −3.0 to +0.9 W m⁻². The positive ERFaci values simulated by a portion of the PPE are implausible and result from parameter combinations that produce unrealistic top-of-atmosphere energy fluxes. While global-mean cloud droplet number concentration always increases in response to anthropogenic aerosol, cloud liquid water path can both increase and decrease, suggesting that precipitation suppression is not the only aerosol-cloud adjustment represented by E3SMv3. Analysis of which processes control liquid cloud adjustment in the PPE points toward stratiform precipitation processes and aerosol activation, which is consistent with many previous ESMs, as well as the new two-moment convective cloud microphysics in E3SMv3.

Diapycnal mixing in the ocean interior has diverse numerical representations in current global ocean models. These representations affect the simulated transport and storage of oceanic tracers in ways that remain little studied. Here we present the impacts of three different tidal mixing representations in thousand-year-long simulations with the NEMO global ocean model at one-degree resolution. The first model experiment includes local bottom-intensified mixing at internal tide generation sites and a constant background diffusivity. The second explicitly includes both local and remote tidal mixing, with no background diffusivity. The third experiment is identical to the second but has the added contribution of bottom-trapped (subinertial) internal tides, known to be important in polar regions. The three simulations show broadly similar circulation and stratification but important regional differences. Explicit representation of remote tidal mixing strengthens the Atlantic Meridional Overturning Circulation by up to 1.5 × 10⁶ m³ s⁻¹. Inclusion of bottom-trapped internal tides reduces heat reaching Antarctica by eroding Circumpolar Deep Water at southern high latitudes, and reduces the mean age of the global deep (>2 km) ocean by 10%. The results call for more observational constraints on polar ocean mixing, and point to multi-faceted climatic repercussions of tidal mixing representations.

Parameterizing radiative transfer in climate models means navigating trade-offs between physical accuracy and conceptual clarity. However, currently available schemes sit at the extremes of this spectrum: correlated-k schemes are fast and accurate but rely on lookup tables which obscure the underlying physics and make such schemes difficult to modify, while gray radiation schemes are conceptually straightforward but introduce significant biases in atmospheric circulation. Here we introduce a Simple Spectral Model (SSM) for clear-sky longwave radiative transfer which bridges this “clarity-accuracy” gap. The SSM accomplishes this by representing the spectral structure of $��H_2�$O and C$��O_2�$ absorption using analytic fits at reference conditions, then uses simple functional forms to extend these fits to different atmospheric conditions. This, coupled to a simple, two-stream solver, yields a system of six equations and ten physically meaningful parameters which can solve for clear-sky longwave fluxes given atmospheric profiles of temperature and humidity. When implemented in an idealized aquaplanet GCM, the SSM produces zonal-mean climate states which accurately mimic the results using a benchmark correlated-k code. The SSM also alleviates the significant zonal-mean climate biases associated with using gray radiation, including an improved representation of radiative cooling profiles, tropopause structure, jet dynamics, and Hadley Cell characteristics both in control climates and in response to uniform warming. This work demonstrates that even a simple spectral representation of atmospheric absorption suffices to capture the essential physics of longwave radiative transfer. The SSM promises to be a valuable tool both for idealized climate modeling, and for teaching radiative transfer in the classroom.

Our goal in this study is to characterize the relationship between lower tropospheric environmental humidity and convective mass flux in the tropics. To do so, we have created gridded convective mass flux data sets from five global storm-resolving models (GSRMs). We have three principal findings. First, in humid environments, mass flux increases with height from the surface through the depth of the lower free troposphere, forming a “deep-inflow.” In dry environments, mass flux does not increase with height in the lower free troposphere. Second, mid-tropospheric mass flux increases nonlinearly with increasing lower tropospheric humidity, resembling a widely reported pickup in tropical precipitation. Third, increased lower tropospheric humidity is associated with reduced updraft buoyancy. To interpret these findings, we employ a simple three-equation parcel model with stochastic entrainment. The parcel model suggests that the response of convective mass flux to lower tropospheric humidity is governed by two effects: (a) survival, in which a greater share of entraining parcels ascend rather than detrain with greater humidity; and (b) dilution, in which the average entrainment rate among surviving parcels increases with environmental humidity. Together, survival and dilution account for the three mass flux responses to humidity.