Search

Advances in coastal modeling and computation provide the opportunity to examine non-hydrostatic and compressible fluid effects at very small scales, but the cost of these new capabilities and the accuracy of these models versus trusted non-hydrostatic codes has yet to be determined. Here the Coastal and Regional Ocean COmmunity model (CROCO, v1.2) and the National Center for Atmospheric Research large-eddy simulation (NCAR-LES) model are compared, with a focus on their simulation accuracy and computational efficiency. These models differ significantly in numerics and capabilities, so they are run on common classic problems of surface-forced, boundary-layer turbulence. In terms of accuracy, we compare turbulence statistics, including the effect of the explicit subgrid-scale (SGS) parameterization, the effect of the second (dilatational) viscosity, and the sensitivity to the speed of sound, which is used as part of the CROCO compressible turbulence formulation. To gauge how far CROCO is from the NCAR-LES, we first compare the NCAR-LES with two other non-hydrostatic Boussinesq approximation LES codes (PALM and Oceananigans), defining the notion and magnitude of accuracy for the LES and CROCO comparison. To judge efficiency of CROCO, strong and weak scaling simulation sets vary different problem sizes and workloads per processor, respectively. Additionally, the effects of 2D decomposition of CROCO and NCAR-LES and supercomputer settings are tested. In summary, the accuracy comparison between CROCO and the NCAR-LES is similar to the NCAR-LES compared to other LES codes. However, the additional capabilities of CROCO (e.g., nesting, non-uniform grid, and realism of ocean configuration in general) and its weakly compressible formulation come with roughly an order of magnitude of additional costs, despite efforts to reduce them by adjusting the second viscosity and speed of sound as far as accuracy allows. However, a new variant of the non-hydrostatic CROCO formulation is currently undergoing prototype testing and should enable faster simulations by releasing the stability constrain by the free surface. Overall, when the additional features of CROCO are needed (nesting, complex topography, etc.) additional costs are justified, while in idealized settings (a rectangular domain with periodic boundary conditions) the NCAR-LES is faster in arriving at nearly the same result. [ABSTRACT FROM AUTHOR]

This study evaluates the impact of increasing resolution on Arctic Ocean simulations using five pairs of matched low- and high-resolution models within the OMIP-2 (Ocean Model Intercomparison Project phase 2) framework. The primary objective is to assess whether a higher resolution can mitigate typical biases in low-resolution models and improve the representation of key climate-relevant variables. We reveal that increasing the horizontal resolution contributes to a reduction in biases in mean temperature and salinity and improves the simulation of the Atlantic water layer and its decadal warming events. A higher resolution also leads to better agreement with observed surface mixed-layer depth, cold halocline base depth and Arctic gateway transports in the Fram and Davis straits. However, the simulation of the mean state and temporal changes in Arctic freshwater content does not show improvement with increased resolution. Not all models achieve improvements for all analyzed ocean variables when spatial resolution is increased so it is crucial to recognize that model numerics and parameterizations also play an important role in faithful simulations. Overall, a higher resolution shows promise in improving the simulation of key Arctic Ocean features and processes, but efforts in model development are required to achieve more accurate representations across all climate-relevant variables. [ABSTRACT FROM AUTHOR]

Advances in coastal modeling and computation provide the opportunity for examining non-hydrostatic and compressible fluid effects at very small scales, but the cost of these new capabilities and the accuracy of these models versus trusted non-hydrostatic codes has yet to be determined. Here the Coastal and Regional Ocean COmmunity model (CROCO) and the NCAR Large-Eddy Simulations (NCAR-LES) code base are compared with a focus on their simulation accuracy and computational efficiency. These models differ significantly in numerics and capabilities, so they are run on common classic problems of surface-forced, boundary-layer turbulence. In accuracy, we compare turbulence statistics, including the effect of the explicit sub-grid scale (SGS) parameterization, the effect of the second (dilatational) viscosity and the sensitivity to the speed-of-sound, which is used as part of the CROCO compressible turbulence formulation. To gauge how far CROCO is from the NCAR-LES, we first compare the NCAR-LES with two other LES codes (PALM and Oceanigans). To judge efficiency of CROCO, strong and weak scaling simulation sets vary different problem sizes and workload per processor, respectively. Additionally, the effects of 2D decomposition of CROCO and NCAR-LES and supercomputer settings are tested. In sum, the accuracy comparison between CROCO and the NCAR-LES is similar to the NCAR-LES versus other LES codes. However, the additional capabilities of CROCO (e.g., nesting and realism) and its compressible turbulence formulation come with roughly an order of magnitude of additional costs despite efforts to reduce them by adjusting the second viscosity and sound speed as far as accuracy allows. [ABSTRACT FROM AUTHOR]

The ocean mixed layer is the interface between the ocean interior and the atmosphere or sea ice and plays a key role in climate variability. It is thus critical that numerical models used in climate studies are capable of a good representation of the mixed layer, especially its depth. Here we evaluate the mixed-layer depth (MLD) in six pairs of non-eddying (1 ∘ grid spacing) and eddy-rich (up to 1/16 ∘) models from the Ocean Model Intercomparison Project (OMIP), forced by a common atmospheric state. For model evaluation, we use an updated MLD dataset computed from observations using the OMIP protocol (a constant density threshold). In winter, low-resolution models exhibit large biases in the deep-water formation regions. These biases are reduced in eddy-rich models but not uniformly across models and regions. The improvement is most noticeable in the mode-water formation regions of the Northern Hemisphere. Results in the Southern Ocean are more contrasted, with biases of either sign remaining at high resolution. In eddy-rich models, mesoscale eddies control the spatial variability in MLD in winter. Contrary to a hypothesis that the deepening of the mixed layer in anticyclones would make the MLD larger globally, eddy-rich models tend to have a shallower mixed layer at most latitudes than coarser models do. In addition, our study highlights the sensitivity of the MLD computation to the choice of a reference level and the spatio-temporal sampling, which motivates new recommendations for MLD computation in future model intercomparison projects. [ABSTRACT FROM AUTHOR]

This study evaluates the impact of increasing resolution on Arctic Ocean simulations using five pairs of matched lowand high-resolution models within the OMIP-2 framework. The primary objective is to assess whether higher resolution can mitigate typical biases observed in low-resolution models and improve the representation of key climate-relevant variables. We reveal that increasing horizontal resolution contributes to a reduction in biases in mean temperature and salinity, and improves the simulation of the Atlantic Water layer and its decadal warming events. Higher resolution also leads to improved agreement with observed surface mixed layer depth, cold halocline base depth and Arctic gateway transports. However, the simulation of the mean state and temporal changes in Arctic freshwater content does not show improvement with increased resolution. While the use of higher resolution demonstrates positive outcomes for certain variables, it is crucial to recognize that model numerics and parameterizations also play a significant role in achieving faithful simulations. Overall, higher resolution shows promise in improving the simulation of key Arctic Ocean features and processes, but comprehensive model development is required to achieve more accurate representations across all climate-relevant variables. [ABSTRACT FROM AUTHOR]

The ocean mixed layer is the interface between the ocean interior and the atmosphere or sea ice, and plays a key role in climate variability. It is thus critical that numerical models used in climate studies are capable of a good representation of the mixed layer, especially its depth. Here we evaluate the mixed layer depth (MLD) in six pairs of non-eddying (1° resolution) and eddy-rich (up to 1/16°) models from the Ocean Model Intercomparison Project (OMIP), forced by a common atmospheric state. For model validation, we use an updated MLD dataset computed from observations using the OMIP protocol (a constant density threshold). In winter, low resolution models exhibit large biases in the deep water formation regions. These biases are reduced in eddy-rich models but not uniformly across models and regions. The improvement is most noticeable in the mode water formation regions of the northern hemisphere. Results in the Southern Ocean are more contrasted, with biases of either sign remaining at high resolution. In eddy-rich models, mesoscale eddies control the spatial variability of MLD in winter. Contrary to a hypothesis that the deepening of the mixed layer in anticyclones would make the MLD larger globally, eddy-rich models tend to have a shallower mixed layer at most latitudes than coarser models do. In addition, our study highlights the sensitivity of the MLD computation to the choice of a reference level and the spatio-temporal sampling, which motivates new recommendations for MLD computation in future model intercomparison projects. [ABSTRACT FROM AUTHOR]

We describe an idealized primitive-equation model for studying mesoscale turbulence and leverage a hierarchy of grid resolutions to make eddy-resolving calculations on the finest grids more affordable. The model has intermediate complexity, incorporating basin-scale geometry with idealized Atlantic and Southern oceans and with non-uniform ocean depth to allow for mesoscale eddy interactions with topography. The model is perfectly adiabatic and spans the Equator and thus fills a gap between quasi-geostrophic models, which cannot span two hemispheres, and idealized general circulation models, which generally include diabatic processes and buoyancy forcing. We show that the model solution is approaching convergence in mean kinetic energy for the ocean mesoscale processes of interest and has a rich range of dynamics with circulation features that emerge only due to resolving mesoscale turbulence. [ABSTRACT FROM AUTHOR]

We describe an idealized primitive equation model for studying mesoscale turbulence and leverage a hierarchy of grid resolutions to make eddy-resolving calculations on the finest grids more affordable. The model has intermediate complexity, incorporating basin-scale geometry with idealized Atlantic and Southern oceans, and with non-uniform ocean depth to allow for mesoscale eddy interactions with topography. The model is perfectly adiabatic and spans the equator, and thus fills a gap between quasi-geostrophic models, which cannot span two hemispheres, and idealized general circulation models, which generally have diabatic processes and buoyancy forcing. We show that the model solution is approaching convergence in mean kinetic energy for the ocean mesoscale processes of interest, and has a rich range of dynamics with circulation features that emerge only due to resolving mesoscale turbulence. [ABSTRACT FROM AUTHOR]

This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea ice simulations that are obtained following the OMIP-2 protocol (Griffies et al., 2016) and integrated for one cycle (1958–2018) of the JRA55-do atmospheric state and runoff dataset (Tsujino et al., 2018). Our goal is to assess the robustness of climate-relevant improvements in ocean simulations (mean and variability) associated with moving from coarse (∼ 1 ∘) to eddy-resolving (∼ 0.1 ∘) horizontal resolutions. The models are diverse in their numerics and parameterizations, but each low-resolution and high-resolution pair of models is matched so as to isolate, to the extent possible, the effects of horizontal resolution. A variety of observational datasets are used to assess the fidelity of simulated temperature and salinity, sea surface height, kinetic energy, heat and volume transports, and sea ice distribution. This paper provides a crucial benchmark for future studies comparing and improving different schemes in any of the models used in this study or similar ones. The biases in the low-resolution simulations are familiar, and their gross features – position, strength, and variability of western boundary currents, equatorial currents, and the Antarctic Circumpolar Current – are significantly improved in the high-resolution models. However, despite the fact that the high-resolution models "resolve" most of these features, the improvements in temperature and salinity are inconsistent among the different model families, and some regions show increased bias over their low-resolution counterparts. Greatly enhanced horizontal resolution does not deliver unambiguous bias improvement in all regions for all models. [ABSTRACT FROM AUTHOR]

Stochastic subgrid parameterizations enable ensemble forecasts of fluid dynamic systems and ultimately accurate data assimilation (DA). Stochastic advection by Lie transport (SALT) and models under location uncertainty (LU) are recent and similar physically based stochastic schemes. SALT dynamics conserve helicity, whereas LU models conserve kinetic energy (KE). After highlighting general similarities between LU and SALT frameworks, this paper focuses on their common challenge: the parameterization choice. We compare uncertainty quantification skills of a stationary heterogeneous data-driven parameterization and a non-stationary homogeneous self-similar parameterization. For stationary, homogeneous surface quasi-geostrophic (SQG; QG) turbulence, both parameterizations lead to high-quality ensemble forecasts. This paper also discusses a heterogeneous adaptation of the homogeneous parameterization targeted at a better simulation of strong straight buoyancy fronts. [ABSTRACT FROM AUTHOR]

In sea-ice-covered areas, the sea ice floe size distribution (FSD) plays an important role in many processes affecting the coupled sea–ice–ocean–atmosphere system. Observations of the FSD are sparse – traditionally taken via a painstaking analysis of ice surface photography – and the seasonal and inter-annual evolution of floe size regionally and globally is largely unknown. Frequently, measured FSDs are assessed using a single number, the scaling exponent of the closest power-law fit to the observed floe size data, although in the absence of adequate datasets there have been limited tests of this "power-law hypothesis". Here we derive and explain a mathematical technique for deriving statistics of the sea ice FSD from polar-orbiting altimeters, satellites with sub-daily return times to polar regions with high along-track resolutions. Applied to the CryoSat-2 radar altimetric record, covering the period from 2010 to 2018, and incorporating 11 million individual floe samples, we produce the first pan-Arctic climatology and seasonal cycle of sea ice floe size statistics. We then perform the first pan-Arctic test of the power-law hypothesis, finding limited support in the range of floe sizes typically analyzed in photographic observational studies. We compare the seasonal variability in observed floe size to fully coupled climate model simulations including a prognostic floe size and thickness distribution and coupled wave model, finding good agreement in regions where modeled ocean surface waves cause sea ice fracture. [ABSTRACT FROM AUTHOR]

Marine sediments have greatly improved our understanding of the climate system, but their interpretation often assumes that certain climate mechanisms operate consistently over all timescales of interest and that variability at one or few sample sites is representative of an oceanographic province. In this study, we test these assumptions using modern observations in an idealized manner mimicking paleo-reconstruction to investigate whether sea surface temperature and productivity proxy records in the Southern California Current System can be used to reconstruct Ekman upwelling. The method uses Extended Empirical Orthogonal Function (EEOF) analysis of covariation of alongshore windstress, chlorophyll and sea surface temperature as measured by satellites from 2002 to 2009. We find that EEOF1 does not reflect an Ekman upwelling pattern, but instead much broader California Current processes. EEOF2 and 3 reflect upwelling patterns, but these patterns are timescale dependent and are regional. Thus, the skill of using one site to reconstruct the large scale dominant patterns is spatially dependent. Lastly, we show that using multiple sites and/or multiple variables generally improve field reconstruction. These results together suggest caution is needed when attempting to extrapolate mechanisms that may be important on seasonal time scales (e.g. Ekman upwelling) to deeper time, but also the advantage of having multiple proxy records. [ABSTRACT FROM AUTHOR]

Marine sediments have greatly improved our understanding of the climate system, but their interpretation often assumes that certain climate mechanisms operate consistently over all timescales of interest and that variability at one or a few sample sites is representative of an oceanographic province. In this study, we test these assumptions using modern observations in an idealized manner mimicking paleo-reconstruction to investigate whether sea surface temperature and productivity proxy records in the Southern California Current System can be used to reconstruct Ekman upwelling. The method uses extended empirical orthogonal function (EEOF) analysis of the covariation of alongshore wind stress, chlorophyll, and sea surface temperature as measured by satellites from 2002 to 2009. We find that EEOF1 does not reflect an Ekman upwelling pattern but instead much broader California Current processes. EEOF2 and 3 reflect upwelling patterns, but these patterns are timescale dependent and regional. Thus, the skill of using one site to reconstruct the large-scale dominant patterns is spatially dependent. Lastly, we show that using multiple sites and/or multiple variables generally improves field reconstruction. These results together suggest that caution is needed when attempting to extrapolate mechanisms that may be important on seasonal timescales (e.g., Ekman upwelling) to deeper time but also the advantage of having multiple proxy records. [ABSTRACT FROM AUTHOR]

Stochastic subgrid parameterizations enable ensemble forecasts of fluid dynamics systems and ultimately accurate data assimilation. Stochastic Advection by Lie Transport (SALT) and models under Location Uncertainty (LU) are recent and similar physically-based stochastic schemes. SALT dynamics conserve helicity whereas LU models conserve kinetic energy. After highlighting general similarities between LU and SALT frameworks, this paper focuses on their common challenge: the parameterization choice. We compare uncertainty quantification skills of a stationary heterogeneous data-driven parameterization and a non-stationary homogeneous self-similar parameterization. For stationary, homogeneous Surface Quasi-Geostrophic (SQG) turbulence, both parameterizations lead to high quality ensemble forecasts. This paper also discusses a heterogeneous adaptation of the homogeneous parameterization targeted at better simulation of strong straight buoyancy fronts. [ABSTRACT FROM AUTHOR]

Correlation does not necessarily imply a causation, but in climatology and paleoclimatology, correlation is used to identify potential cause-and-effect relationships because linking mechanisms are difficult to observe. Confounding by an often unknown outside variable that drives the sets of observables is one of the major factors that lead to correlations that are not the result of causation. Here we show how autoregressive (AR) models can be used to examine lead-lag relationships - helpful in assessing cause and effect - of paleoclimate variables while addressing two other challenges that are often encountered in paleoclimate data: unevenly spaced data; and switching between regimes at unknown times. Specifically, we analyze multidimensional paleoclimate proxies, sea surface temperature (SST), C37, ∂15N, and %N from the central Peru margin to find their correlations and changes in their variability over the Holocene epoch. The four proxies are sampled at high-resolution but are not synchronously sampled at all possible locations. The multidimensional records are treated as evenly spaced data with missing parts, and the missing values are filled by the Kalman filter expected values. We employ hidden Markov models (HMM) and autoregressive HMM (AR-HMM) to address the potential that the degree of variability and the correlations between in these proxies appears to show changes over time. The HMM, which is not autoregressive, shows instantaneous correlations between observables in two regimes. However, our investigation of lead-lag relationships using the AR-HMM shows that the cross-correlations do not indicate a causal link. Each of the four proxies has predictability on decadal timescales, but none of the proxies is a good predictor of any other, so we hypothesize that a common unobserved variable - or a set of variables - is driving the instantaneous relationships among these four proxies, revealing probable confounding without prior knowledge of potential confounding variable(s). These findings suggest that the variability at this site is remotely driven by processes such as those causing the Pacific Decadal Oscillation, rather than locally driven by processes such as increased or decreased vertical mixing of nutrients. [ABSTRACT FROM AUTHOR]

We quantify skill and uncertainty in observing the statistics of natural variability using observing system simulation experiments on an ensemble of climate simulations and an observing strategy of in situ measurements and objective mapping. The targeted statistic is the 0–700 m global ocean heat content anomaly as observed by the In Situ Analysis System 2013 (ISAS13) strategy of a long, equilibriated simulation of the Community Climate System Model (CCSM) version 3.5. Subannual variability is found to be significantly contaminated by the observing strategy, especially before 2005, primarily due to the sparseness and seasonality in the number and location of pre-Argo observations. However, one-year running means from 2005 onward are found to faithfully capture the natural variability of the model's true ocean heat content variability. During these years, synthetic observed annual running means are strongly correlated with the actual annual running means of the model, with a median correlation of 95 %, versus only 60 % for the observational record before 2005. When scaled to account for the fact that the real ocean is more variable than the model, root mean square errors in observing the annual-running mean natural variability of the global ocean heat content are estimated to be 6.2 ZJ for the pre-Argo era (1990–2005) and 2.1 ZJ for the Argo era (2005–2013) with relative signal-to-noise ratios of 1.9 and 14.7. Combining the estimated, scaled uncertainties of the observing strategy with its estimated trend, the 1990–2013 trend in global ocean heat content is found to be 5.3 ± 1.0 ZJ/yr. [ABSTRACT FROM AUTHOR]