Abstract
The interactions between boundary layer turbulence, including Langmuir turbulence, and submesoscale processes in the oceanic mixed layer are described using large-eddy simulations of the ...spindown of a temperature front in the presence of submesoscale eddies, winds, and waves. The simulations solve the surface-wave-averaged Boussinesq equations with Stokes drift wave forcing at a resolution that is sufficiently fine to capture small-scale Langmuir turbulence. A simulation without Stokes drift forcing is also performed for comparison. Spatial and spectral properties of temperature, velocity, and vorticity fields are described, and these fields are scale decomposed in order to examine multiscale fluxes of momentum and buoyancy. Buoyancy flux results indicate that Langmuir turbulence counters the restratifying effects of submesoscale eddies, leading to small-scale vertical transport and mixing that is 4 times greater than in the simulations without Stokes drift forcing. The observed fluxes are also shown to be in good agreement with results from an asymptotic analysis of the surface-wave-averaged, or Craik–Leibovich, equations. Regions of potential instability in the flow are identified using Richardson and Rossby numbers, and it is found that mixed gravitational/symmetric instabilities are nearly twice as prevalent when Langmuir turbulence is present, in contrast to simulations without Stokes drift forcing, which are dominated by symmetric instabilities. Mixed layer depth calculations based on potential vorticity and temperature show that the mixed layer is up to 2 times deeper in the presence of Langmuir turbulence. Differences between measures of the mixed layer depth based on potential vorticity and temperature are smaller in the simulations with Stokes drift forcing, indicating a reduced incidence of symmetric instabilities in the presence of Langmuir turbulence.
Critical point tracking is a core topic in scientific visualization for understanding the dynamic behaviour of time‐varying vector field data. The topological notion of robustness has been introduced ...recently to quantify the structural stability of critical points, that is, the robustness of a critical point is the minimum amount of perturbation to the vector field necessary to cancel it. A theoretical basis has been established previously that relates critical point tracking with the notion of robustness, in particular, critical points could be tracked based on their closeness in stability, measured by robustness, instead of just distance proximity within the domain. However, in practice, the computation of classic robustness may produce artifacts when a critical point is close to the boundary of the domain; thus, we do not have a complete picture of the vector field behaviour within its local neighbourhood. To alleviate these issues, we introduce a multilevel robustness framework for the study of 2D time‐varying vector fields. We compute the robustness of critical points across varying neighbourhoods to capture the multiscale nature of the data and to mitigate the boundary effect suffered by the classic robustness computation. We demonstrate via experiments that such a new notion of robustness can be combined seamlessly with existing feature tracking algorithms to improve the visual interpretability of vector fields in terms of feature tracking, selection and comparison for large‐scale scientific simulations. We observe, for the first time, that the minimum multilevel robustness is highly correlated with physical quantities used by domain scientists in studying a real‐world tropical cyclone dataset. Such an observation helps to increase the physical interpretability of robustness.
This paper introduces a new multilevel robustness framework for studying time‐varying vector fields, which can differentiate the behaviours of critical points in terms of their multiscale stability. This framework supports feature tracking, selection and comparison, and improves the visual interpretability of vector fields from scientific simulations.
Accounting for the diversity in El Niño Southern Oscillation (ENSO)'s spatial pattern, with the novel ENSO longitudinal index (ELI), we evaluate the simulation of its teleconnections to US winter ...precipitation extremes by seven global high‐resolution (HR) Earth System Models (ESM). Six (four) HR ESMs simulate the observed increase in precipitation extremes over Southwest US (Southeast US) during ELI‐defined El Niño events better than their low‐resolution counterparts, which are low‐biased. The stronger ENSO‐dependence over the Southwest US and Southeast US in those models is associated with an improved simulation of moisture flux into the regions and/or storm track activity there. HR ESMs, however, generally overestimate the increase in precipitation extremes over the Pacific‐Northwest during La Niña events. Model bias there is associated with bias in moisture transport into the region during La Niña events, which is amplified by the enhanced vertical mass fluxes in HR.
Plain Language Summary
El Niño Southern Oscillation (ENSO) comes in many flavors with diverse spatial pattern of ocean warming and associated heavy tropical rainfall over the deep tropical Pacific. This ENSO diversity is one of the major reasons behind our limited understanding and prediction of its global impacts, particularly on climate extremes. A recent approach to characterize the full spectrum of ENSO using a simple index shows promise in improving our understanding. Here, we use this new index to evaluate if new state‐of‐the‐art high resolution Earth System Models can capture the remote impacts of ENSO on US precipitation extremes in the winter. We find that some of these models can credibly simulate these ENSO teleconnections, across its many flavors, over the SE‐US and SW‐US, and generally improve upon their low resolution model counterparts.
Key Points
We evaluate seven high‐resolution (HR) Earth System Models' simulation of diverse El Niño Southern Oscillation (ENSO) teleconnections to US winter precipitation extremes
Simulation of ENSO‐dependent Southwest‐US (Southeast‐US) precipitation extremes is improved in six (four) models with resolution increase
This is due to improvements in ENSO dependent moisture transport, storm track activity, and vertical mass fluxes in HR models
Two Earth system models are analyzed to gain insight into the processes that govern projected changes in the South Asian monsoon. Warmer present‐day base state tropical SSTs contribute to coupled ...processes that produce greater future tropical Pacific warming in CESM2 with less of an increase in season‐mean monsoon precipitation compared to E3SMv2. This is attributed to changes in the large‐scale east‐west atmospheric Walker circulation, with relatively larger increases in precipitation and upper‐level divergence over the tropical Pacific and increases in upper‐level convergence over South Asia in CESM2. The stronger El Niño‐like response in CESM2, which increases Pacific precipitation and upper‐level divergence farther to the east, and larger future ENSO amplitude in E3SMv2, produce a greater relative increase in future monsoon‐ENSO connections in E3SMv2 compared to CESM2. This analysis indicates that the key processes that affect future monsoon‐ENSO connections are ENSO amplitude and size of the future tropical Pacific El Niño‐like response.
Plain Language Summary
Two different Earth system models are analyzed to investigate processes that contribute to possible future changes of South Asian monsoon precipitation and connections to ENSO. The stronger increase of precipitation in CESM2 over the tropical Pacific, due in part to a larger El Niño‐like response of Pacific SSTs, produces less of an increase in future monsoon precipitation in CESM2 compared to E3SMv2. The eastward shift of precipitation in CESM2, along with the larger increase of ENSO amplitude in E3SMv2, produce a stronger future monsoon‐ENSO connection in E3SMv2 compared to CESM2.
Key Points
Warmer future tropical Pacific SSTs in CESM2 compared to E3SMv2 produce less of an increase in South Asian monsoon precipitation through the Walker Circulation
Future monsoon‐ENSO connections are stronger in E3SMv2 compared to CESM2 due to larger increases in future ENSO amplitude and shifts in the Walker Circulation
Key processes that affect future monsoon‐ENSO connections are ENSO amplitude and the size of the future tropical Pacific El Niño‐like response
We propose GNN-Surrogate, a graph neural network-based surrogate model to explore the parameter space of ocean climate simulations. Parameter space exploration is important for domain scientists to ...understand the influence of input parameters (e.g., wind stress) on the simulation output (e.g., temperature). The exploration requires scientists to exhaust the complicated parameter space by running a batch of computationally expensive simulations. Our approach improves the efficiency of parameter space exploration with a surrogate model that predicts the simulation outputs accurately and efficiently. Specifically, GNN-Surrogate predicts the output field with given simulation parameters so scientists can explore the simulation parameter space with visualizations from user-specified visual mappings. Moreover, our graph-based techniques are designed for unstructured meshes, making the exploration of simulation outputs on irregular grids efficient. For efficient training, we generate hierarchical graphs and use adaptive resolutions. We give quantitative and qualitative evaluations on the MPAS-Ocean simulation to demonstrate the effectiveness and efficiency of GNN-Surrogate. Source code is publicly available at https://github.com/trainsn/GNN-Surrogate .
Abstract
We evaluate the simulated teleconnection of El Niño–Southern Oscillation (ENSO) to winter season precipitation extremes over the United States in a long (98 years) 1950 control ...high-resolution version (HR; 25-km nominal atmosphere model horizontal resolution) of the U.S. Department of Energy’s (DOE) Energy Exascale Earth System Model version 1 (E3SMv1). The model bias and spatial pattern of ENSO teleconnections to mean and extreme precipitation in HR overall are similar to the low-resolution model’s (LR; 110 km) historical simulation (four-member ensemble, 1925–59). However, over the southeastern United States (SE-U.S.), HR produces stronger El Niño–associated extremes, reducing LR’s model bias. Both LR and HR produce weaker than observed increase in storm track activity during El Niño events there, but HR improves the ENSO-associated variability of moisture transport over SE-U.S. During El Niño, stronger vertical velocities in HR produce stronger large-scale precipitation, causing larger latent heating of the troposphere that pulls in more moisture from the Gulf of Mexico into the SE-U.S. This positive feedback also contributes to the stronger mean and extreme precipitation response in HR. Over the Pacific Northwest, LR’s bias of stronger than observed La Niña associated extremes is amplified in HR. Both models simulate stronger than observed moisture transport from the Pacific Ocean into the region during La Niña years. The amplified HR bias there is due to stronger orographically driven vertical updrafts that create stronger large-scale precipitation, despite weaker La Niña–induced storm track activity.
Significance Statement
New high-resolution Earth system models (ESMs) solve mathematical equations of fluid flow at much smaller spatial scales than prevalent ESMs, and thus are prohibitively expensive to compute. However, they can be useful for simulating accurate details of regional climate extremes that are driven by naturally occurring climate oscillations like El Niño–Southern Oscillation (ENSO). Here, we evaluate the simulation of ENSO-driven precipitation extremes over the United States in the high-resolution version of the U.S. Department of Energy’s new Energy Exascale Earth System Model version 1. We find that the high-resolution model improves upon its low-resolution counterpart over the southeastern United States by producing a better transport of moisture into the region from the Gulf of Mexico during El Niño. Over the U.S. Pacific Northwest, the high-resolution model simulates the atmospheric flow in more detail over the complex mountainous terrain. However, it also brings in more moisture from the Pacific Ocean just like the low-resolution model. This causes it to produce precipitation extremes during La Niña years there that are stronger than that observed in the real world.
Abstract
Current submesoscale restratification parameterizations, which help set mixed layer depth in global climate models, depend on a simplistic scaling of frontal width shown to be unreliable in ...several circumstances. Observations and theory indicate that frontogenesis is common, but stable frontal widths arise in the presence of turbulence and instabilities that participate in keeping fronts at the scale observed, the arrested scale. Here we propose a new scaling law for arrested frontal width as a function of turbulent fluxes via the turbulent thermal wind (TTW) balance. A variety of large-eddy simulations (LES) of strain-induced fronts and TTW-induced filaments are used to evaluate this scaling. Frontal width given by boundary layer parameters drawn from observations in the General Ocean Turbulence Model (GOTM) are found qualitatively consistent with the observed range in regions of active submesoscales. The new arrested front scaling is used to modify the mixed layer eddy restratification parameterization commonly used in coarse-resolution climate models. Results in CESM-POP2 reveal the climate model’s sensitivity to the parameterization update and changes in model biases. A comprehensive multimodel study is in planning for further testing.
Significance Statement
The ocean surface plays a major role in the climate system, primarily through exchange in properties, such as in heat and carbon, between the ocean and atmosphere. Accurate model representation of ocean surface processes is crucial for climate simulations, yet they tend to be too small, fast, or complex to be resolved. Significant efforts lie in approximating these small-scale processes using reduced expressions that are solved by the model. This study presents an improved representation of the ocean surface in climate models by capturing some of the synergy that has been missing between the processes that define it. Results encourage further testing across a wider range of models to comprehensively evaluate the effects of this adjustment in climate simulations.
This study provides an overview of the coupled high‐resolution Version 1 of the Energy Exascale Earth System Model (E3SMv1) and documents the characteristics of a 50‐year‐long high‐resolution control ...simulation with time‐invariant 1950 forcings following the HighResMIP protocol. In terms of global root‐mean‐squared error metrics, this high‐resolution simulation is generally superior to results from the low‐resolution configuration of E3SMv1 (due to resolution, tuning changes, and possibly initialization procedure) and compares favorably to models in the CMIP5 ensemble. Ocean and sea ice simulation is particularly improved, due to better resolution of bathymetry, the ability to capture more variability and extremes in winds and currents, and the ability to resolve mesoscale ocean eddies. The largest improvement in this regard is an ice‐free Labrador Sea, which is a major problem at low resolution. Interestingly, several features found to improve with resolution in previous studies are insensitive to resolution or even degrade in E3SMv1. Most notable in this regard are warm bias and associated stratocumulus deficiency in eastern subtropical oceans and lack of improvement in El Niño. Another major finding of this study is that resolution increase had negligible impact on climate sensitivity (measured by net feedback determined through uniform +4K prescribed sea surface temperature increase) and aerosol sensitivity. Cloud response to resolution increase consisted of very minor decrease at all levels. Large‐scale patterns of precipitation bias were also relatively unaffected by grid spacing.
Plain Language Summary
The Energy Exascale Earth System Model (E3SM) is a relatively new fully coupled Earth system and climate model used in major international model simulation projects and mission‐defined efforts for the U.S. Department of Energy. This paper describes the first simulation of the model in its high‐resolution configuration. This higher‐resolution version is able to capture the most energetic motions in the ocean, which are poorly represented in standard resolution coupled climate models, as well as the largest of storms in the atmosphere. Evaluation of this simulation confirms the benefits of high resolution found by other models with a few notable exceptions. These discrepancies with other studies are interesting because they provide a richer understanding of how and why resolution affects model bias. Another key finding is that climate and aerosol sensitivity in E3SM is unaffected by resolution change. This affirms the usefulness of coarser‐resolution models for understanding global‐scale climate change. This study also confirms the benefits of increased resolution for studying fine‐scale features such as hurricanes and orographic precipitation. Finally, the high‐resolution version of E3SM is shown to compare favorably to its low‐resolution counterpart and to the models participating in Phase 5 of the Coupled Model Intercomparison Project.
Key Points
The high‐resolution E3SMv1 model was run for 50 years using 1950 forcing data according to the HighResMIP protocol
Higher resolution and associated retuning improved bias relative to coarser versions of E3SMv1, particularly in ocean and sea ice metrics
Aerosol and climate sensitivity were relatively unaffected by resolution change; resolution‐related tuning had a larger impact
While various parameterizations of vertical turbulent fluxes at different levels of complexity have been proposed, each has its own limitations. For example, simple first‐order closure schemes such ...as the K‐Profile Parameterization (KPP) lack energetic constraints; two‐equation models like k−ɛ $k-\varepsilon $ directly solve an equation for the turbulent kinetic energy but do not account for non‐diffusive fluxes, and high‐order closures that include the high‐order transport terms are computationally expensive. To address these, we extend the Assumed‐Distribution Higher‐Order Closure (ADC) framework originally proposed for the atmospheric boundary layer and apply it to the ocean surface boundary layer. By assuming a probability distribution function relationship between the vertical velocity and tracers, all second‐order and higher‐order moments are exactly constructed and turbulence closure is achieved in the ADC scheme. In addition, this ADC parameterization has full energetic constraints and includes non‐diffusive fluxes without the computational cost of a full higher‐order closure scheme. We have tested the ADC scheme against a combination of large eddy simulation (LES), KPP, and k−ɛ $k-\varepsilon $ for surface buoyancy‐driven convective mixing and found that the ADC scheme is robust with different vertical resolutions and compares well to the LES results.
Plain Language Summary
The upper ocean (order of few tens of meters depth from the surface) has a substantial influence on our climate and weather systems. Specifically, upper ocean mixing processes play a key role in modulating global heat budget in the ocean and atmosphere by mixing heat deeper into the ocean or warming the atmosphere above. Accurate representation of the effects of these mixing processes on the global climate and in ocean models is crucial for understanding our current and changing climate. However, current mixing schemes used in these models have shown significant biases. We present a new physically‐motivated mixing scheme for the upper ocean inspired by atmospheric mixing schemes. Results show that the proposed mixing scheme can simulate upper ocean mixing efficiently, suggesting its potential use in climate and ocean models to help reduce model biases.
Key Points
A new physically‐motivated, PDF‐based parameterization of ocean surface boundary layer turbulence is presented
The non‐diffusive fluxes are included naturally and the scheme provides a closed set of equations with realizable closure assumptions
The mixing scheme accurately predicts the effects of convective turbulence across different vertical resolutions
Assessing uncertainty in future climate projections requires understanding both internal climate variability and external forcing. For this reason, single‐model initial condition large ensembles ...(SMILEs) run with Earth System Models (ESMs) have recently become popular. Here we present a new 20‐member SMILE with the Energy Exascale Earth System Model version 1 (E3SMv1‐LE), which uses a “macro” initialization strategy choosing coupled atmosphere/ocean states based on inter‐basin contrasts in ocean heat content (OHC). The E3SMv1‐LE simulates tropical climate variability well, albeit with a muted warming trend over the twentieth century due to overly strong aerosol forcing. The E3SMv1‐LE's initial climate spread is comparable to other (larger) SMILEs, suggesting that maximizing inter‐basin ocean heat contrasts may be an efficient method of generating ensemble spread. We also compare different ensemble spread across multiple SMILEs, using surface air temperature and OHC. The Community Earth system Model version 1, the only ensemble which utilizes a “micro” initialization approach perturbing only atmospheric initial conditions, yields lower spread in the first ∼30 years. The E3SMv1‐LE exhibits a relatively large spread, with some evidence for anthropogenic forcing influencing spread in the late twentieth century. However, systematic effects of differing “macro” initialization strategies are difficult to detect, possibly resulting from differing model physics or responses to external forcing. Notably, the method of standardizing results affects ensemble spread: control simulations for most models have either large background trends or multi‐centennial variability in OHC. This spurious disequlibrium behavior is a substantial roadblock to understanding both internal climate variability and its response to forcing.
Plain Language Summary
Large year‐to‐year variations in climate can arise due to chaos in the climate system, and as a result “large ensemble” sets of climate model simulations have become a popular way to estimate the magnitude of those variations. A new ensemble with the Energy Exascale Earth System Model version 1 is presented here, where the simulations have been started with as wide a range of amounts of heat contained in different oceans as possible. This allows a large spread across ensemble members, even with relatively few simulations. However, it is hard to say if this is the absolute “best” way of creating an ensemble, since many models have unrealistic trends in ocean heat content that lead to problems with calculating the differences between ensemble spreads.
Key Points
We present a new large ensemble with the Energy Exascale Earth System Model v1 (the E3SMv1‐LE) initialized using ocean basin heat contrasts
Ensemble spread in the 20‐member E3SMv1‐LE is comparable to that in other, larger model ensembles
Model ensemble spread exhibits diverse behavior, resulting from initialization strategy, physical differences, and long‐term climate drift
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