Journal of Advances in Modeling Earth Systems最新文献

Methane (CH₄) is globally the second most critical greenhouse gas after carbon dioxide, contributing to 16%–25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH₄ fluxes is examined at 14 FLUXNET- CH₄ sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance-based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. We found that parameters linked to CH₄ production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET-CH₄ observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques.

Vegetation gross primary productivity (GPP) is the single largest carbon flux of the terrestrial biosphere which, in turn, is responsible for sequestering 25%–30% of anthropogenic carbon dioxide emissions. The ability to model GPP is therefore critical for calculating carbon budgets as well as understanding climate feedbacks. Earth system models (ESMs) have the capability to simulate GPP but vary greatly in their individual estimates, resulting in large uncertainties. We describe a machine learning (ML) approach to investigate two key factors responsible for differences in simulated GPP quantities from ESMs: the relative importance of different atmospheric drivers and differences in the representation of land surface processes. We describe the different steps in the development of our interpretable ML framework including the choice of algorithms, parameter tuning, training and evaluation. Our results show that ESMs largely agree on the physical climate drivers responsible for GPP as seen in the literature, for instance drought variables in the Mediterranean region or radiation and temperature in the Arctic region. However differences do exist since models don't necessarily agree on which individual variable is most relevant for GPP. We also explore a distance measure to attribute GPP differences to climate influences versus process differences and provide examples for where our methods work (South Asia, Mediterranean) and where they are inconclusive (Eastern North America).

Satellite microwave radiance observations are strongly sensitive to sea ice, but physical descriptions of the radiative transfer of sea ice and snow are incomplete. Further, the radiative transfer is controlled by poorly-known microstructural properties that vary strongly in time and space. A consequence is that surface-sensitive microwave observations are not assimilated over sea ice areas, and sea ice retrievals use heuristic rather than physical methods. An empirical model for sea ice radiative transfer would be helpful but it cannot be trained using standard machine learning techniques because the inputs are mostly unknown. The solution is to simultaneously train the empirical model and a set of empirical inputs: an “empirical state” method, which draws on both generative machine learning and physical data assimilation methodology. A hybrid physical-empirical network describes the known and unknown physics of sea ice and atmospheric radiative transfer. The network is then trained to fit a year of radiance observations from Advanced Microwave Scanning Radiometer 2, using the atmospheric profiles, skin temperature and ocean water emissivity taken from a weather forecasting system. This process estimates maps of the daily sea ice concentration while also learning an empirical model for the sea ice emissivity. The model learns to define its own empirical input space along with daily maps of these empirical inputs. These maps represent the otherwise unknown microstructural properties of the sea ice and snow that affect the radiative transfer. This “empirical state” approach could be used to solve many other problems of earth system data assimilation.

Subgrid-scale processes, such as atmospheric gravity waves (GWs), play a pivotal role in shaping the Earth's climate but cannot be explicitly resolved in climate models due to limitations on resolution. Instead, subgrid-scale parameterizations are used to capture their effects. Recently, machine learning (ML) has emerged as a promising approach to learn parameterizations. In this study, we explore uncertainties associated with a ML parameterization for atmospheric GWs. Focusing on the uncertainties in the training process (parametric uncertainty), we use an ensemble of neural networks to emulate an existing GW parameterization. We estimate both offline uncertainties in raw NN output and online uncertainties in climate model output, after the neural networks are coupled. We find that online parametric uncertainty contributes a significant source of uncertainty in climate model output that must be considered when introducing NN parameterizations. This uncertainty quantification provides valuable insights into the reliability and robustness of ML-based GW parameterizations, thus advancing our understanding of their potential applications in climate modeling.

Current eddy-permitting and eddy-resolving ocean models require dissipation to prevent a spurious accumulation of enstrophy at the grid scale. We introduce a new numerical scheme for momentum advection in large-scale ocean models that involves upwinding through a weighted essentially non-oscillatory (WENO) reconstruction. The new scheme provides implicit dissipation and thereby avoids the need for an additional explicit dissipation that may require calibration of unknown parameters. This approach uses the rotational, “vector invariant” formulation of the momentum advection operator that is widely employed by global general circulation models. A novel formulation of the WENO “smoothness indicators” is key for avoiding excessive numerical dissipation of kinetic energy and enstrophy at grid-resolved scales. We test the new advection scheme against a standard approach that combines explicit dissipation with a dispersive discretization of the rotational advection operator in two scenarios: (a) two-dimensional turbulence and (b) three-dimensional baroclinic equilibration. In both cases, the solutions are stable, free from dispersive artifacts, and achieve increased “effective” resolution compared to other approaches commonly used in ocean models.

Canonical understanding based on general circulation models (GCMs) is that the atmospheric circulation response to midlatitude sea-surface temperature (SST) anomalies is weak compared to the larger influence of tropical SST anomalies. However, the ∼100-km horizontal resolution of modern GCMs is too coarse to resolve strong updrafts within weather fronts, which could provide a pathway for surface anomalies to be communicated aloft. Here, we investigate the large-scale atmospheric circulation response to idealized Gulf Stream SST anomalies in Community Atmosphere Model (CAM6) simulations with 14-km regional grid refinement over the North Atlantic, and compare it to the responses in simulations with 28-km regional refinement and uniform 111-km resolution. The highest resolution simulations show a large positive response of the wintertime North Atlantic Oscillation (NAO) to positive SST anomalies in the Gulf Stream, a 0.4-standard-deviation anomaly in the seasonal-mean NAO for 2°C SST anomalies. The lower-resolution simulations show a weaker response with a different spatial structure. The enhanced large-scale circulation response results from an increase in resolved vertical motions with resolution and an associated increase in the influence of SST anomalies on transient-eddy heat and momentum fluxes in the free troposphere. In response to positive SST anomalies, these processes lead to a stronger and less variable North Atlantic jet, as is characteristic of positive NAO anomalies. Our results suggest that the atmosphere responds differently to midlatitude SST anomalies in higher-resolution models and that regional refinement in key regions offers a potential pathway to improve multi-year regional climate predictions based on midlatitude SSTs.

Modeled geospatial Lagrangian trajectories are widely used in Earth Science, including in oceanography, atmospheric science and marine biology. The typically large size of these data sets makes them arduous to analyze, and their underlying pathways challenging to identify. Here, we show that we can use a machine learning unsupervised k-means++ clustering method combined with expert aggregation of clusters to identify the pathways of the Labrador Current from a large set of modeled Lagrangian trajectories. The presented method requires simple pre-processing of the data, including a Cartesian correction on longitudes and a principal component analysis reduction. The clustering is performed in a kernelized space and uses a larger number of clusters than the number of expected pathways. To identify the main pathways, similar clusters are grouped into pathway categories by experts in the circulation of the region of interest. We find that the Labrador Current mainly follows a westward-flowing and an eastward retroflecting pathway (20% and 50% of the flow, respectively) that compensate each other through time in a see-saw behavior. These pathways experience a strong variability (representing through time 4%–42% and 24%–73% of the flow, respectively). Two thirds of the retroflection occurs at the tip of the Grand Banks, and one quarter at Flemish Cap. The westward pathway is mostly fed by the on-shelf branch of the Labrador Current, and the eastward pathway by the shelf-break branch. Among the pathways of secondary importance, we identify a previously unreported one that feeds the subtropics across the Gulf Stream.

Satellite altimetry combined with data assimilation and optimal interpolation schemes have deeply renewed our ability to monitor sea surface dynamics. Recently, deep learning schemes have emerged as appealing solutions to address space-time interpolation problems. However, the training of state-of-the-art neural schemes on real-world case-studies is hindered by the sparse space-time coverage of the sea surface of real altimetry data set. Here, we introduce an innovative approach that leverages state-of-the-art ocean models to train simulation-based neural schemes for the mapping of sea surface height and demonstrate their performance on real altimetry data sets. We analyze further how the ocean simulation data set used during the training phase impacts this performance. This experimental analysis covers both the resolution from eddy-present configurations to eddy-rich ones, forced simulations versus reanalyzes using data assimilation and tide-free versus tide-resolving simulations. Our benchmarking framework focuses on a Gulf Stream region for a realistic 5-altimeter constellation using NEMO ocean simulations and 4DVarNet mapping schemes. All simulation-based 4DVarNets outperform the operational observation-driven and reanalysis products, namely DUACS and GLORYS. The more realistic the ocean simulation data set used during the training phase, the better the mapping. The best 4DVarNet mapping was trained from an eddy-rich and tide-free simulation data sets. It improves the resolved longitudinal scale from 151 km for DUACS and 241 km for GLORYS to 98 km and reduces the root mean square error by 23% and 61%. These results open research avenues for new synergies between ocean modeling and ocean observation using learning-based approaches.

Mesoscale eddies modulate the stratification, mixing, tracer transport, and dissipation pathways of oceanic flows over a wide range of spatiotemporal scales. The parameterization of buoyancy and momentum fluxes associated with mesoscale eddies thus presents an evolving challenge for ocean modelers, particularly as modern climate models approach eddy-permitting resolutions. Here we present a parameterization targeting such resolutions through the use of a subgrid mesoscale eddy kinetic energy budget (MEKE) framework. Our study presents two novel insights: (a) both the potential and kinetic energy effects of eddies may be parameterized via a kinetic energy backscatter, with no Gent-McWilliams along-isopycnal transport; (b) a dominant factor in ensuring a physically-accurate backscatter is the vertical structure of the parameterized momentum fluxes. We present simulations of 1/2° and 1/4° resolution idealized models with backscatter applied to the equivalent barotropic mode. Remarkably, the global kinetic and potential energies, isopycnal structure, and vertical energy partitioning show significantly improved agreement with a 1/32° reference solution. Our work provides guidance on how to parameterize mesoscale eddy effects in the challenging eddy-permitting regime.

The new generation of heterogeneous CPU/GPU computer systems offer much greater computational performance but are not yet widely used for climate modeling. One reason for this is that traditional climate models were written before GPUs were available and would require an extensive overhaul to run on these new machines. In addition, even conventional “high–resolution” simulations don't currently provide enough parallel work to keep GPUs busy, so the benefits of such overhaul would be limited for the types of simulations climate scientists are accustomed to. The vision of the Simple Cloud-Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) project is to create a global atmospheric model with the architecture to efficiently use GPUs and horizontal resolution sufficient to fully take advantage of GPU parallelism. After 5 years of model development, SCREAM is finally ready for use. In this paper, we describe the design of this new code, its performance on both CPU and heterogeneous machines, and its ability to simulate real-world climate via a set of four 40 day simulations covering all 4 seasons of the year.