Representing reservoir operations in large-scale hydrological models remains difficult due to complex release decisions and scarce operational records. Here, we develop PLSTM-Reg v1.0, a regional physics-encoded deep learning framework that explicitly enforces mass balance and operational constraints to simulate reservoir operations across diverse systems. The framework is evaluated using 256 representative reservoirs across the Contiguous United States, focusing on three core capabilities: temporal generalization to unseen periods, spatial transfer to unseen reservoirs, and historical data reconstruction in data-scarce reservoirs. Under temporal testing, the regional model improves 1 d-ahead release forecasts from a median Kling–Gupta Efficiency (KGE) of 0.83 to 0.96 relative to local counterparts (i.e., models trained exclusively on site-specific records), and reduces poorly simulated cases (KGE<0.8) from 41.8 % to 2.3 %. For long-term simulation, storage performance reaches a median KGE of 0.79, a modest gain over local models (0.76) but with notable robustness for reservoirs with large capacity. When transferred to unseen reservoirs, the model substantially outperforms widely used rule-based schemes: median KGE rises from 0.55 (best benchmark) to 0.73 for release and from 0.22 to 0.59 for storage, and the proportion of usable simulations (KGE>0.5) increases from 56.6 % to 89.8 % for release and 14.4 % to 61.7 % for storage. In historical storage reconstruction, incorporating monthly satellite-derived surface area as a proxy for missing ground-based records enables reconstruction accuracy comparable to local models. These results demonstrate that cross-reservoir deep learning combined with physical knowledge provides a scalable scheme for representing human water management within large-scale hydrological and land surface models.
Atmosphere–sea ice–ocean interactions are vital for understanding past and future changes in the Arctic climate system. Consistent heat-flux exchange across model-component interfaces is essential for investigating these interactions with coupled models on longer timescales. Here, we present the improved version of the Coupled Arctic Prediction System (CAPS), in which heat-flux exchange between the atmosphere (WRF) and ocean–sea ice (ROMS–CICE) components is made consistent across coupling interfaces. A set of pan-Arctic simulations covering the period of Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) were conducted with the revised CAPS. The revised coupling substantially reduces interfacial heat-flux residuals and improves the simulation of Arctic sea-ice conditions relative to the original coupling, which produces enhanced winter ice growth and reduced summer ice retreat during the 14-month simulation. CAPS was further evaluated against comprehensive MOSAiC observations of the atmosphere, ocean, snow, and sea ice. The model reproduces the broad evolution of Arctic conditions along the MOSAiC drift, although biases remain, partly associated with errors in simulated synoptic systems and unresolved polar physical processes. The constrained-atmosphere experiment with spectral nudging shows improved agreement with observations in several quantitative metrics, particularly for atmospheric profiles and ocean temperature. However, sensitivity experiments indicate that these improvements depend on the vertical extent and strength of nudging and should not be attributed to the revised coupling or model physics alone. Remaining biases in atmospheric humidity, surface turbulent heat fluxes, snow accumulation, and ocean salinity highlight priorities for further development of atmospheric boundary-layer, cloud radiation, snow/sea ice, and coupled surface processes in CAPS and other Arctic prediction systems.
This study presents a novel hybrid implicit–explicit time-integration strategy for simulating injection-induced fault reactivation within a fully coupled poroelastodynamic extended finite element (XFEM) framework. Modeling these systems is inherently challenging due to the extreme separation of temporal scales – ranging from hours to days for fluid diffusion to milliseconds for dynamic rupture. Although the fully implicit formulation can provide enhanced numerical stability, stability alone does not guarantee sufficient temporal accuracy during nonlinear fault rupture. The results demonstrate close agreement between simulations with minimum time-step sizes of 10−5 and 10−6 s for the investigated slip and event-scale observables on the adopted spatial mesh, indicating temporal convergence for these quantities at 10−5 s. This time-step size is substantially smaller than the mesh-based explicit stability estimate. To reduce computational cost, the proposed strategy employs an implicit solver for quasi-static loading and nucleation and switches to a central-difference-based mechanical solver with implicit pressure integration once a prescribed slip-velocity threshold is exceeded. At a switching threshold of 0.01 m s−1, the hybrid simulation requires approximately 10.0 h, compared with 26.4 h for the fully implicit reference, corresponding to a computational-time reduction of 62.12 %. The hybrid strategy reproduces the overall rupture sequence, stress evolution, final accumulated fault slip, and seismic moment. However, differences remain in the timing and magnitude of peak slip velocities, rupture-propagation speeds, and transient particle motion. These findings demonstrate the computational potential of the hybrid formulation for the investigated two-dimensional configuration, while highlighting the need for further assessment of transient dynamic accuracy and computational performance across different spatial discretizations and geological conditions.
Rapid advances in computational power over the past decade have enabled global, kilometer-scale simulations that realistically represent open-ocean submesoscale dynamics. However, the ability of global ocean models to represent coastal submesoscale dynamics – where flows are more heterogeneous and strongly shaped by coastlines, bathymetry, and buoyancy inputs – remains largely unexplored. This study is the first of two companion papers comparing coastal submesoscale processes representation in two models with very different numerical approaches: MPAS-O, an unstructured grid global ocean model, and ROMS, a structured grid regional model. This study is the first idealized evaluation of MPAS-O’s representation of coastal submesoscale dynamics using observed conditions in the Mississippi-Atchafalaya (M-A) River plume as a template for initial conditions, with the previously validated ROMS model serving as a benchmark. An important motivation for this study is to examine the suitability of models designed for global ocean simulations in reproducing energetic coastal flows. We compare statistical metrics based on flow invariants – total strain, relative vorticity, and divergence – as well as velocity gradients in frontal coordinates, across horizontal resolutions ranging from 10 km to 100 m within an idealized model domain. We find that the representation of submesoscale baroclinic instabilities is highly similar across all resolutions such that the impact of model choice is smaller than the choice of spatial grid resolution. We also compare numerical and physical mixing between models using online diagnostics based on discrete variance decay. We find that ROMS generally has more numerical mixing and less physical mixing than MPAS-O across all resolutions. Trends in numerical mixing likely arise from MPAS-O's flux-corrected transport tracer advection scheme, which exhibits a stronger variance-producing response relative to the MPDATA scheme used in the ROMS simulations. Trends in physical mixing likely arise from differences in each model’s configuration of the vertical mixing scheme (the K-Profile Parameterization). A companion paper extends this idealized model-model comparison to realistic simulations of the M-A River plume.
The integration of stiff atmospheric chemical systems is a major computational cost in Earth System Models and poses challenges for numerical stability as chemical complexity and temporal resolution increase. In this study, we implement a semi-implicit two-stage Rosenbrock method (ROS2) in the Energy Exascale Earth System Model version 1 (E3SMv1) and evaluate it as an alternative chemistry time-integration scheme. ROS2 is tested with two interactive chemical mechanisms of substantially different size: chemUCI (60 prognostic species) and trop_strat_mozart_mam4 (155 prognostic species). Numerical experiments include short simulations to quantify computational cost, a box-model timestep-refinement analysis, and one-year integrations to examine numerical stability and solution sensitivity. At the same 180 s chemistry timestep, ROS2 requires approximately 33 % less chemistry integration time than the original iterative implicit solver on the tested Cori/KNL CPU platform. The magnitude of this speedup is configuration dependent. Global-mean surface-ozone differences among the tested configurations are small (≤ 1.03 ppb), and the one-year integrations show no evidence of numerical instability. Spatial and diurnal differences in ozone, NO, NO2, and OH are species- and mechanism-dependent, indicating sensitivity to both timestep and solver formulation rather than a common systematic bias. These results support ROS2 as a lower-cost alternative to the current implicit integrator when E3SM chemistry is operated at short timesteps; additional timestep-refinement tests and benchmarking on contemporary CPU/GPU systems are needed to quantify coupled-model convergence and performance portability.
