Journal of Advances in Modeling Earth Systems最新文献

Parameterizations of $��O��\left(��1�-�10��\right)��$km submesoscale mixed layer instabilities in General Circulation Models (GCMs) represent the effects of unresolved vertical buoyancy fluxes (VBF) in the ocean mixed layer. These submesoscale flows interact non-linearly with mesoscale and boundary layer turbulence, and it is challenging to account for all the relevant processes in physics-based parameterizations. In this work, we present a data-driven approach for the submesoscale parameterization, that relies on a Convolutional Neural Network (CNN) trained to predict mixed layer VBF as a function of relevant large-scale variables. The data used for training is given from 12 regions sampled from the global high-resolution MITgcm-LLC4320 simulation. When compared with the baseline of a submesoscale physics-based parameterization, the CNN demonstrates high offline skill across all regions, seasons, and filter scales tested in this study. During seasons when submesoscales are most active, which generally corresponds to winter and spring months, we find that the CNN prediction skill tends to be lower than in summer months. The CNN exhibits a dependency on the large scale strain field, a variable closely related to frontogenesis, which is currently missing from the submesoscale parameterizations in GCMs.

In proximity of the Antarctic ice sheet, oceanic motions with different spatial structures contribute to the transport of warm water onto the continental shelf, fueling submarine ice-shelf melting. Emerging evidence suggests that km-sized ocean submesoscale fronts and eddies significantly impact ice-ocean interactions on timescale of a few hours to a few days, with profound implications for the melting regime. However, ocean models often rely on parameterizations to represent these fine-scale, high-frequency processes because of the coarse resolution. This results in uncertainties in heat transfer mechanisms at the ice-ocean boundary which often leads to conservative estimations of melt rates. Here, we assess the impact of sub-kilometer model resolution on capturing ocean features at different scales in the Amundsen Sea Embayment, West Antarctica, and their impact on the melting regime of local ice shelves. While the simulations do not show substantial differences in large-scale ocean dynamics, we find that using a sub-kilometer resolution is necessary to resolve ocean eddies beneath ice shelves. Furthermore, the ocean kinetic energy using 200 m resolution is approximately three times higher than when using a 1 km resolution. This increase is driven by significantly stronger submesoscale activity, both in the open ocean and beneath the ice shelves, leading to improved model-data agreement, particularly in capturing high melt rates observed at the grounding line.

This study examines how different structural choices in bulk microphysics schemes impact the simulation of warm rain initiation. A single liquid category (SLC) approach prognosing up to four moments of a single drop size distribution (DSD) is compared to the traditional two-category, two-moment approach with separate DSDs for cloud and rain (four total prognostic variables). Different methods for calculating tendencies of the prognostic variables from drop collision-coalescence are also tested: a discretized numerical-integration approach, machine learning via neural networks, lookup tables, and traditional power law fits. Relative to simulations using a bin microphysics model, SLC gives smaller error overall than the two-category approach when numerical integration is used to calculate the collision-coalescence tendencies for both. Replacing the numerical integration with a pre-computed lookup table reduces computational cost with little loss of accuracy. However, using fitted power laws with SLC to represent the collision-coalescence tendencies substantially reduces accuracy and leads to an order of magnitude increase in error. It is also demonstrated that with SLC, reasonably accurate solutions are obtained using only three prognostic moments, while a two-moment SLC scheme leads to substantial error. Overall, both the choice of prognostic moments (e.g., SLC vs. two-category) and method to calculate the collision-coalescence tendencies are important to consider for minimizing errors in bulk schemes. SLC with a sufficiently detailed calculation of the collision-coalescence tendencies provides accurate solutions for a reasonable computational cost, providing a viable alternative to the traditional two-category, two-moment approach for bulk microphysics.

This study presents OceanCastNet (OCN), a machine learning approach for wave forecasting that incorporates wind and wave fields to predict significant wave height, mean wave period, and mean wave direction. We evaluate OCN's performance against the operational ECWAM model using two independent data sets: NDBC buoy and Jason-3 satellite observations. NDBC station validation indicates OCN performs better at 24 stations compared to ECWAM's 10 stations, and Jason-3 satellite validation confirms similar accuracy across 228-hr forecasts. OCN successfully captures wave patterns during extreme weather conditions, demonstrated through Typhoon Goni with prediction errors typically within $��\pm �$0.5 m. The approach also offers computational efficiency advantages. The results suggest that machine learning approaches can achieve performance comparable to conventional wave forecasting systems for operational wave prediction applications.

The skill of numerical weather forecasts strongly depends on the quality of the initial conditions (analyses), which are created by assimilating observations into previous short-range model forecasts. Therefore, it is important to carefully assess the influence of different observations on the analysis. The degrees of freedom for signal (DFS) is a useful metric for quantifying this influence. While DFS has long been used in variational data assimilation (DA) systems, its application in ensemble-based DA systems remains limited. In this study, we propose two novel approaches for estimating the DFS in ensemble-based systems. One approach uses the weighting vector calculated in ensemble transform Kalman filters, while the other uses the innovation vector and observation-space increment vector. We also propose a new strategy for implementing the DFS approaches in the presence of domain localization, which first estimates DFS locally and then aggregates the results to derive a global DFS value for each observation. Our numerical results show that the DFS per observation decreases as the localization radius increases. More generally, the proposed DFS approaches and implementation strategy have the potential to be used in practice to inform the optimization of observation networks and DA systems.

Accurately predicting terrestrial ecosystem responses to climate change over long-timescales is crucial for addressing global challenges. This relies on mechanistic modeling of ecosystem processes through land surface models (LSMs). Despite their importance, LSMs face significant uncertainties due to poorly constrained parameters, especially in carbon cycle predictions. This paper reviews the progress made in using data assimilation (DA) for LSM parameter optimization, focusing on carbon-water-vegetation interactions, as well as discussing the technical challenges faced by the community. These challenges include identifying sensitive model parameters and their prior distributions, characterizing errors due to observation biases and model-data inconsistencies, developing observation operators to interface between the model and the observations, tackling spatial and temporal heterogeneity as well as dealing with large and multiple data sets, and including the spin-up and historical period in the assimilation window. We outline how machine learning (ML) can help address these issues, proposing different avenues for future work that integrate ML and DA to reduce uncertainties in LSMs. We conclude by highlighting future priorities, including the need for international collaborations, to fully leverage the wealth of available Earth observation data sets, harness ML advances, and enhance the predictive capabilities of LSMs.

Data assimilation of observations into full atmospheric states is essential for weather forecast model initialization. Recently, methods for deep generative data assimilation have been proposed which allow for using new input data without retraining the model. They could also dramatically accelerate the costly data assimilation process used in operational regional weather models. Here, in a central US testbed, we demonstrate the viability of Score-based Data Assimilation (SDA) in the context of realistically complex km-scale weather. We train an unconditional diffusion model to generate snapshots of a state-of-the-art km-scale analysis product, the High Resolution Rapid Refresh. Then, using SDA to incorporate sparse weather station data, the model produces maps of precipitation and surface winds. The generated fields display physically plausible structures, such as gust fronts, and sensitivity tests confirm learned physics through multivariate relationships. Preliminary skill analysis shows the approach already outperforms a naive baseline of the High-Resolution Rapid Refresh system itself. By incorporating observations from 40 weather stations, 10% lower RMSEs on left-out stations are attained. Despite some lingering imperfections such as insufficiently disperse ensemble DA estimates, we find the results overall an encouraging proof of concept, and the first at km-scale. It is a ripe time to explore extensions that combine increasingly ambitious regional state generators with an increasing set of in situ, ground-based, and satellite remote sensing data streams.

Tropical climate and weather systems are integral to the global hydrological cycle. Yet, their detected and projected trends are highly uncertain in part due to shortcomings of Earth system models. This study uses novel aquaplanet simulations with different sea-surface temperature and with convection-permitting resolution in the tropics to simulate the response of tropical phenomena to globally uniform warming. The results show a complex response within the tropical circulation. With warming, the tropical troposphere warms and expands vertically alongside a weaker and broader Hadley cell. Simultaneously, the Intertropical Convergence Zone (ITCZ) narrows and strengthens, though its location shows an unclear response to warming. Both dynamic and thermodynamic processes play a role in the ITCZ response, exhibiting patterned changes in vertical velocity and magnitude changes in water vapor. Clouds also show a complex response, particularly with tropical cloud ice showing a non-linear response to tropical warming and likely causing non-linear changes in the radiation budget. At finer scales, tropical precipitation extremes increase non-linearly with warming, exceeding Clausius-Clapeyron scaling at higher percentiles. The prevalence of non-linear responses highlights the necessity of considering multiple scenarios when investigating the response of tropical phenomena to globally uniform warming. This research provides valuable insights into tropical climate sensitivity in a framework that captures multi-scale processes from global to convective scales.

This study proposes a cost-effective convective-scale static background-error covariance matrix (CSS B) approach and evaluates the approach for direct radar reflectivity data assimilation (DA) using the Finite-Volume Cubed-Sphere limited-area model (FV3-LAM)-based DA system. The relative importance of cross-variable correlations between model variables is first identified. To reduce the variational minimization cost, B_CRITICAL is established by only including the 43 critical cross-correlations for convective scales. Compared to the baseline CSS B that consists of all 66 cross-correlations, B_CRITICAL is cost-effective, saving the minimization cost by ∼28.3% while achieving similar performance. Emulating the Rapid Refresh Forecast System, the impacts of incorporating B_CRITICAL into the Gridpoint Statistical Interpolation (GSI)-based three-dimensional variational (3DVar) and hybrid ensemble–variational (EnVar) DA frameworks are explored for a convective storm event. Three experiments using B_CRITICAL (Exp-3DVar), a pure ensemble-based B (Exp-EnVar), and a blended B with a 30% weighting factor for B_CRITICAL (Exp-Hybrid) are conducted. Detailed comparisons and evaluations show the superiority of B_CRITICAL over ensemble-based B in adding observed reflectivity and the positive impacts of hybridizing B_CRITICAL and pure ensemble-based B on analysis and forecast improvements. Applying B_CRITICAL to observed clear-air areas for the first time facilitates spurious background reflectivity reduction. The analyses in Exp-Hybrid match reflectivity and low-level temperature observations better than Exp-3DVar, followed by Exp-EnVar. In the subsequent forecasts, Exp-3DVar better predicts the storm spin-up than Exp-EnVar. Exp-Hybrid better maintains the added storms and suppresses the overestimated storms compared to Exp-3DVar to achieve the highest forecast skill.

Biogeochemical (BGC) ocean models are simplified representations of complex coupled processes, usually resulting in a large number of parameters, that need to be calibrated. In general, these parameters are constrained relying on incomplete and very heterogeneous sets of data. In addition, as biogeochemical tracers strongly depend on ocean circulation, the spatio-temporal uncertainties in the physical forcing can bias the circulation, which makes the calibration of ocean carbon models challenging. This study addresses the calibration of ocean biogeochemical models when dealing with imperfect physical forcings and sparse observations. We design a numerical testbed based on a simple BGC box model. It comprises different uncertainty scenarios for the physical forcing as well as different observation configurations of the considered nutrient, phytoplankton, zooplankton, detritus dynamics. We propose and benchmark a learning-based scheme against a variational data assimilation (DA) approach. The former frames the calibration as learning a neural operator between observations and model parameters. The experiments revealed that the DA-based calibration is highly sensitive to imperfect physical forcing and limited observations, often leading to significant estimation errors in BGC parameters. Conversely, the learning-based approach demonstrated a greater robustness in parameter estimation and simulated BGC patterns. We discuss further how these results could transfer to more realistic BGC models and real observing systems.