The sea ice component of the Community Earth System Model version 2 (CESM2) contains new “mushy‐layer” physics that simulates prognostic salinity in the sea ice, with consequent modifications to sea ...ice thermodynamics and the treatment of melt ponds. The changes to the sea ice model and their influence on coupled model simulations are described here. Two simulations were performed to assess the changes in the vertical thermodynamics formulation with prognostic salinity compared to a constant salinity profile. Inclusion of the mushy layer thermodynamics of Turner et al. (2013, https://doi.org/10.1002/jgrc.20171) in a fully coupled Earth system model produces thicker and more extensive sea ice in the Arctic, with relatively unchanged sea ice in the Antarctic compared to simulations using a constant salinity profile. While this is consistent with the findings of uncoupled ice‐ocean model studies, the role of the frazil and congelation growth is more important in fully coupled simulations. Melt pond drainage is also an important contribution to simulated ice thickness differences as also found in the uncoupled simulations of Turner and Hunke (2015; https://doi.org/10.1002/2014JC010358). However, it is an interaction of the ponds and the snow fraction that impacts the surface albedo and hence the top melt. The changes in the thermodynamics and resulting ice state modify the ice‐ocean‐atmosphere fluxes with impacts on the atmosphere and ocean states, particularly temperature.
Plain Language Summary
We investigate the role of a new approach for sea ice thermodynamics in the Community Earth System Model, based on mushy‐layer theory. The new approach produces thicker sea ice in the Arctic with subsequent impacts on the atmosphere and ocean.
Key Points
The choice of sea ice thermodynamics impacts the sea ice mean state
The choice of sea ice thermodynamics has a modest impact on the coupled system
Scientific observations of sea ice began more than a century ago, but detailed sea-ice models appeared only in the latter half of the last century. The high albedo of sea ice is critical for the ...Earth’s heat balance, and ice motion across the ocean’s surface transports fresh water and salt. The basic components in a complete sea-ice model must include vertical thermodynamics and horizontal dynamics, including a constitutive relation for the ice, advection and deformational processes. This overview surveys topics in sea-ice modeling from the global climate modeling perspective, emphasizing work that significantly advanced the state of the art and highlighting promising new developments.
Sea ice and iceberg dynamic interaction Hunke, Elizabeth C.; Comeau, Darin
Journal of Geophysical Research,
20/May , Letnik:
116, Številka:
C5
Journal Article
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A model of iceberg motion has been implemented in the Los Alamos sea ice model (CICE). Individual bergs are tracked under the influence of winds, currents, sea surface tilt, Coriolis, and sea ice ...forcing. In turn, sea ice is affected by the presence of icebergs, primarily as obstacles that cause the sea ice to ridge on the upstream side or create open water on the downstream side of the bergs. Open water formed near icebergs due to sea ice ridging and blocking of sea ice advection increases level and ridged ice downstream of the bergs through increased frazil ice formation. Resulting anomalies in sea ice area and thickness (compared with a simulation without icebergs) are transported with the sea ice flow, expanding over time. Although local changes in the sea ice distribution may be important for smaller‐scale studies, these anomalies are small compared with the total volume of sea ice and their effect on climate‐scale variables appears to be insignificant.
Key Points
Dynamic interaction between sea ice and icebergs was implemented in CICE
Sea ice volume anomalies due to iceberg dynamics may be important locally
Sea ice volume anomalies are small at climate scales
A new discretization for the elastic-viscous-plastic (EVP) sea ice dynamics model incorporates metric terms to account for grid curvature effects in curvilinear coordinate systems. A fundamental ...property of the viscous-plastic ice rheology that is invariant under changes of coordinate system is utilized, namely the work done by internal forces, to derive an energy dissipative discretization of the divergence of the stress tensor that includes metric terms. Comparisons of simulations using an older EVP numerical model with the new formulation highlight the effect of the metric terms, which can be significant when ice deformation is allowed to affect the ice strength. (Author)
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
We present MPAS-Seaice, a sea-ice model which uses the Model for Prediction Across Scales (MPAS) framework and spherical centroidal Voronoi tessellation (SCVT) unstructured meshes. As well as SCVT ...meshes, MPAS-Seaice can run on the traditional quadrilateral grids used by sea-ice models such as CICE. The MPAS-Seaice velocity solver uses the elastic–viscous–plastic (EVP) rheology and the variational discretization of the internal stress divergence operator used by CICE, but adapted for the polygonal cells of MPAS meshes, or alternatively an integral (“finite-volume”) formulation of the stress divergence operator. An incremental remapping advection scheme is used for mass and tracer transport. We validate these formulations with idealized test cases, both planar and on the sphere. The variational scheme displays lower errors than the finite-volume formulation for the strain rate operator but higher errors for the stress divergence operator. The variational stress divergence operator displays increased errors around the pentagonal cells of a quasi-uniform mesh, which is ameliorated with an alternate formulation for the operator. MPAS-Seaice shares the sophisticated column physics and biogeochemistry of CICE and when used with quadrilateral meshes can reproduce the results of CICE. We have used global simulations with realistic forcing to validate MPAS-Seaice against similar simulations with CICE and against observations. We find very similar results compared to CICE, with differences explained by minor differences in implementation such as with interpolation between the primary and dual meshes at coastlines. We have assessed the computational performance of the model, which, because it is unstructured, runs with 70 % of the throughput of CICE for a comparison quadrilateral simulation. The SCVT meshes used by MPAS-Seaice allow removal of equatorial model cells and flexibility in domain decomposition, improving model performance. MPAS-Seaice is the current sea-ice component of the Energy Exascale Earth System Model (E3SM).
We compare three forcing data sets, all variants of National Centers for Environmental Prediction (NCEP) forcing, in global ice‐ocean simulations and evaluate them for use in Arctic model studies. ...The data sets include the standard Arctic Ocean Model Intercomparison Project (AOMIP) protocol, standard NCEP forcing fields, and the data set of Large and Yeager (2004). We explore their performance in Arctic simulations using a global, coupled, sea ice‐ocean model, and find that while these forcing data sets have many similarities, the resulting simulations present significant differences, most notably in ice thickness and ocean circulation. This underscores the sensitivity of Arctic sea ice and ocean to slight changes in environmental forcing parameters. This study also highlights the difficulties faced by the model intercomparison community attempting to disentangle simulation differences due to model physics from those caused by small differences in forcing parameters. Assessing the simulation uncertainty due to inaccuracies in the forcing data provides context for the simulation uncertainty associated with model physics.
Changes in the high‐latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to ...understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance‐based approach in which Sobol' sequences are used to efficiently sample the full 39‐dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. It is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.
Key Points:
A global sensitivity analysis accounting for nonlinearity and nonadditivity is applied to CICE
A quantitative ranking of the most important parameters driving output uncertainty in CICE is made
Main effects and interactions of the most important parameters are identified
To establish how well the new Community Climate System Model, version 4 (CCSM4) simulates the properties of the Arctic sea ice and ocean, results from six CCSM4 twentieth-century ensemble simulations ...are compared here with the available data. It is found that the CCSM4 simulations capture most of the important climatological features of the Arctic sea ice and ocean state well, among them the sea ice thickness distribution, fraction of multiyear sea ice, and sea ice edge. The strongest bias exists in the simulated spring-to-fall sea ice motion field, the location of the Beaufort Gyre, and the temperature of the deep Arctic Ocean (below 250 m), which are caused by deficiencies in the simulation of the Arctic sea level pressure field and the lack of deep-water formation on the Arctic shelves. The observed decrease in the sea ice extent and the multiyear ice cover is well captured by the CCSM4. It is important to note, however, that the temporal evolution of the simulated Arctic sea ice cover over the satellite era is strongly influenced by internal variability. For example, while one ensemble member shows an even larger decrease in the sea ice extent over 1981–2005 than that observed, two ensemble members show no statistically significant trend over the same period. It is therefore important to compare the observed sea ice extent trend not just with the ensemble mean or a multimodel ensemble mean, but also with individual ensemble members, because of the strong imprint of internal variability on these relatively short trends.
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Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Results from adding a tracer for age of sea ice to a sophisticated sea ice model that is widely used for climate studies are presented. The consistent simulation of ice age, dynamics, and ...thermodynamics in the model shows explicitly that the loss of Arctic perennial ice has accelerated in the past three decades, as has been seen in satellite‐derived observations. Our model shows that the September ice age average across the Northern Hemisphere varies from about 5 to 8 years, and the ice is much younger (about 2–3 years) in late winter because of the expansion of first‐year ice. We find seasonal ice on average comprises about 5% of the total ice area in September, but as much as 1.34 × 106 km2 survives in some years. Our simulated ice age in the late 1980s and early 1990s declined markedly in agreement with other studies. After this period of decline, the ice age began to recover, but in the final years of the simulation very little young ice remains after the melt season, a strong indication that the age of the pack will again decline in the future as older ice classes fail to be replenished. The Arctic ice pack has fluctuated between older and younger ice types over the past 30 years, while ice area, thickness, and volume all declined over the same period, with an apparent acceleration in the last decade.
Large-scale models now are more likely to include a realistic ice rheology (Hunke and Dukowicz 1997), multilayer thermodynamics (Bitz and Lipscomb 1999), and a multicategory thickness distribution ...(Bitz et al. 2001; Lipscomb 2001). For the special case of a nondivergent velocity field, (3) becomes a simple advection equation: and similarly for (5) and (6).\n The upwind scheme has several desirable properties: it is monotonicity preserving, compatible, simple, and fast. ...this version of MPDATA does not preserve the monotonicity of conserved fields and tracers.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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