The fourth version of the Community Climate System Model (CCSM4) was recently completed and released to the climate community. This paper describes developments to all CCSM components, and documents ...fully coupled preindustrial control runs compared to the previous version, CCSM3. Using the standard atmosphere and land resolution of 1° results in the sea surface temperature biases in the major upwelling regions being comparable to the 1.4°-resolution CCSM3. Two changes to the deep convection scheme in the atmosphere component result in CCSM4 producing El Niño–Southern Oscillation variability with a much more realistic frequency distribution than in CCSM3, although the amplitude is too large compared to observations. These changes also improve the Madden–Julian oscillation and the frequency distribution of tropical precipitation. A new overflow parameterization in the ocean component leads to an improved simulation of the Gulf Stream path and the North Atlantic Ocean meridional overturning circulation. Changes to the CCSM4 land component lead to a much improved annual cycle of water storage, especially in the tropics. The CCSM4 sea ice component uses much more realistic albedos than CCSM3, and for several reasons the Arctic sea ice concentration is improved in CCSM4. An ensemble of twentieth-century simulations produces a good match to the observed September Arctic sea ice extent from 1979 to 2005. The CCSM4 ensemble mean increase in globally averaged surface temperature between 1850 and 2005 is larger than the observed increase by about 0.4°C. This is consistent with the fact that CCSM4 does not include a representation of the indirect effects of aerosols, although other factors may come into play. The CCSM4 still has significant biases, such as the mean precipitation distribution in the tropical Pacific Ocean, too much low cloud in the Arctic, and the latitudinal distributions of shortwave and longwave cloud forcings.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We provide a high-level review of sea ice models used for climate studies and of the recent advances made with these models to understand sea ice predictability. Models currently in use for the ...Coupled Model Intercomparison Project and new developments coming online that will enable enhanced predictions are discussed. Previous work indicates that seasonal sea ice can be predicted based on mechanisms associated with long-lived ice thickness or ocean heat anomalies. On longer timescales, internal climate variability is an important source of uncertainty, although anthropogenic forcing is sizable, and studies suggest that anthropogenic signals have already emerged from internal climate noise. Using new analysis from the Multi-Model Large Ensemble, we show that while models differ in the magnitude and timing of predictable signals, many ice predictability characteristics are robust across multiple models. This includes the reemergence of predictable seasonal signals in ice area and the sizable uncertainty in predictions of ice-free Arctic timing associated with internal variability.
The extent and thickness of the Arctic sea ice cover has decreased dramatically in the past few decades with minima in sea ice extent in September 2007 and 2011 and climate models did not predict ...this decline. One of the processes poorly represented in sea ice models is the formation and evolution of melt ponds. Melt ponds form on Arctic sea ice during the melting season and their presence affects the heat and mass balances of the ice cover, mainly by decreasing the value of the surface albedo by up to 20%. We have developed a melt pond model suitable for forecasting the presence of melt ponds based on sea ice conditions. This model has been incorporated into the Los Alamos CICE sea ice model, the sea ice component of several IPCC climate models. Simulations for the period 1990 to 2007 are in good agreement with observed ice concentration. In comparison to simulations without ponds, the September ice volume is nearly 40% lower. Sensitivity studies within the range of uncertainty reveal that, of the parameters pertinent to the present melt pond parameterization and for our prescribed atmospheric and oceanic forcing, variations of optical properties and the amount of snowfall have the strongest impact on sea ice extent and volume. We conclude that melt ponds will play an increasingly important role in the melting of the Arctic ice cover and their incorporation in the sea ice component of Global Circulation Models is essential for accurate future sea ice forecasts.
Key Points
We have developed a melt pond model simulating the evolution of melt ponds
Our simulations are in agreement with observed ice extent and concentration
Our pond scheme is ready to be included in a coupled GCM
•Compares new CICE version 5 model options in a unified framework.•Combines age and volume analyses to discriminate among the effects of model parameterizations.•Variable drag coefficients produce a ...more realistic simulation of ice age.•Resolution of thicker ice in the ice thickness distribution is critical for proper volume and age simulation.•Analyses of thermodynamic processes explain differences among sensitivity runs.
New dynamics parameterizations in Version 5 of the Los Alamos Sea Ice Model, CICE, feature an anisotropic rheology and variable drag coefficients. This study investigates their effect on Arctic sea ice volume and age simulations, along with the effects of several pre-existing model options: a parameter that represents the mean cumulative area of ice participating in ridging, the resolution of the ice thickness distribution, and the resolution of the vertical temperature and salinity profiles.
By increasing shear stress between floes, the anisotropic rheology slows the ice motion, producing a thicker, older ice pack. The inclusion of variable drag coefficients, which depend on modeled roughness elements such as deformed ice and melt pond edges, leads to thinner ice and a more realistic simulation of sea ice age. Several feedback processes act to enhance differences among the runs. Notably, if less open water is produced mechanically through ice deformational processes, the simulated ice thins relative to runs with more mechanically produced open water. Thermodynamic processes can have opposing effects on ice age and volume; for instance, growth of new ice increases the volume while decreasing the age of the pack. Therefore, age data provides additional information useful for differentiating among process parameterization effects and sensitivities to other model parameters.
Resolution of thicker ice types is crucial for proper modeling of sea ice volume, because the volume of ice in the thicker ice categories determines the total ice volume. Model thickness categories tend to focus resolution for thinner ice; this paper demonstrates that 5 ice thickness categories are not enough to accurately resolve the ice thickness distribution for simulations of ice volume.
Passive microwave satellite observations of ice extent and concentration form the foundation of sea ice model evaluations, due to their wide spatial coverage and decades-long availability. ...Observations related to other model quantities are somewhat more limited but increasing as interest in high-latitude processes intensifies. Sea ice thickness, long judged a critical quantity in the physical system, is now being scrutinized more closely in sea ice model simulations as more expansive measurements become available. While albedo is often the first parameter chosen by modelers to adjust simulated ice thickness, this paper explores a set of less prominent parameters to which thickness is also quite sensitive. These include parameters associated with sea ice conductivity, mechanical redistribution, oceanic heat flux, and ice–ocean dynamic stress, in addition to shortwave radiation. Multiple combinations of parameter values can produce the same mean ice thickness using the Los Alamos Sea Ice Model, CICE. One of these “tuned” simulations is compared with a variety of observational data sets in both hemispheres. While deformed ice area compares well with the limited observations available for ridged ice, thickness measurements differ such that the model cannot agree with all of them simultaneously. Albedo and ice–ocean dynamic parameters that affect the turning of the ice relative to the ocean currents have the largest effect on ice thickness, of the parameters tested here. That is, sea ice thickness is highly sensitive to changes in external forcing by the atmosphere or ocean, and therefore serves as a sensitive diagnostic for high-latitude change.
In multicategory sea ice models the compressive strength of the ice pack is often assumed to be a function of the potential energy of pressure ridges. This assumption, combined with other standard ...features of ridging schemes, allows the ice strength to change dramatically on short timescales. In high‐resolution (∼10 km) sea ice models with a typical time step (∼1 hour), abrupt strength changes can lead to large internal stress gradients that destabilize the flow. The unstable flow is characterized by large oscillations in ice concentration, thickness, strength, velocity, and strain rates. Straightforward, physically motivated changes in the ridging scheme can reduce the likelihood of abrupt strength changes and improve stability. In simple test problems with flow toward and around topography, stability is significantly enhanced by eliminating the threshold fraction G* in the ridging participation function. Use of an exponential participation function increases the maximum stable time step at 10‐km resolution from less than 30 min to about 2 hours. Modifying the redistribution function to build thinner ridges modestly improves stability and also gives better agreement between modeled and observed thickness distributions. Allowing the ice strength to increase linearly with the mean ice thickness improves stability but probably underestimates the maximum stresses.
The sea ice simulation of the Community Climate System Model version 3 (CCSM3) T42-gx1 and T85-gx1 control simulations is presented and the influence of the parameterized sea ice thickness ...distribution (ITD) on polar climate conditions is examined. This includes an analysis of the change in mean climate conditions and simulated sea ice feedbacks when an ITD is included. It is found that including a representation of the subgrid-scale ITD results in larger ice growth rates and thicker sea ice. These larger growth rates represent a higher heat loss from the ocean ice column to the atmosphere, resulting in warmer surface conditions. Ocean circulation, most notably in the Southern Hemisphere, is also modified by the ITD because of the influence of enhanced high-latitude ice formation on the ocean buoyancy flux and resulting deep water formation. Changes in atmospheric circulation also result, again most notably in the Southern Hemisphere.
There are indications that the ITD also modifies simulated sea ice–related feedbacks. In regions of similar ice thickness, the surface albedo changes at 2XCO₂ conditions are larger when an ITD is included, suggesting an enhanced surface albedo feedback. The presence of an ITD also modifies the ice thickness–ice strength relationship and the ice thickness–ice growth rate relationship, both of which represent negative feedbacks on ice thickness. The net influence of the ITD on polar climate sensitivity and variability results from the interaction of these and other complex feedback processes.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We perform global simulations of the Los Alamos sea‐ice model, CICE, with a new thermodynamics component that has a fully prognostic, variable bulk salinity vertical profile based on mushy layer ...physics. The processes of gravity drainage, melt‐water flushing and snow‐ice formation are parameterized to allow the bulk salinity to evolve with time. We analyze the seasonal and spatial variation of sea‐ice bulk salinity, area, volume and thickness and compare these quantities to simulations using the previous thermodynamic component. Adjusting one of the gravity drainage parameters, we find good agreement between simulation results and fieldwork ice‐core observations of sea‐ice bulk salinity. As expected, bulk salinity is highest during periods of ice growth and lowest after periods of ice melt. In the northern hemisphere the new thermodynamics component produces thicker ice than the previous thermodynamics component. Of the nine major differences between the two models, differences in how salinities are calculated and how melt‐pond flushing is parameterized are the principal causes of this thickness difference. Thickness differences are smaller in the southern hemisphere than in the northern hemisphere since a greater fraction of ice melts, and differences cannot accumulate year‐on‐year. Model differences in how ice thickness changes and snow‐ice formation are calculated are the most important causes of the different thickness between the two thermodynamic components in the southern hemisphere. The melt‐pond area and volume are found to be highly sensitive to a parameter choice controlling drainage through macroscopic holes in the ice, in both hemispheres.
Key Points:
Sea‐ice salinity is simulated in the CICE global sea‐ice model
Gravity drainage, melt pond flushing and snow‐ice formation determine salinity
Reasonable agreement found between simulation results and sea‐ice core data
We present the analysis of global sympagic primary production (PP) from 300 years of pre-industrial and historical simulations of the E3SMv1.1-BGC model. The model includes a novel, eight-element sea ...ice biogeochemical component, MPAS-Seaice zbgc, which is resolved in three spatial dimensions and uses a vertical transport scheme based on internal brine dynamics. Modeled ice algal chlorophyll-a concentrations and column-integrated values are broadly consistent with observations, though chl-a profile fractions indicate that upper ice communities of the Southern Ocean are underestimated. Simulations of polar integrated sea ice PP support the lower bound in published estimates for both polar regions with mean Arctic values of 7.5 and 15.5 TgC/a in the Southern Ocean. However, comparisons of the polar climate state with observations, using a maximal bound for ice algal growth rates, suggest that the Arctic lower bound is a significant underestimation driven by biases in ocean surface nitrate, and that correction of these biases supports as much as 60.7 TgC/a of net Arctic PP. Simulated Southern Ocean sympagic PP is predominantly light-limited, and regional patterns, particularly in the coastal high production band, are found to be negatively correlated with snow thickness.
We have equipped the unstructured‐mesh global sea‐ice and ocean model FESOM2 with a set of physical parameterizations derived from the single‐column sea‐ice model Icepack. The update has ...substantially broadened the range of physical processes that can be represented by the model. The new features are directly implemented on the unstructured FESOM2 mesh, and thereby benefit from the flexibility that comes with it in terms of spatial resolution. A subset of the parameter space of three model configurations, with increasing complexity, has been calibrated with an iterative Green's function optimization method to test the impact of the model update on the sea‐ice representation. Furthermore, to explore the sensitivity of the results to different atmospheric forcings, each model configuration was calibrated separately for the NCEP‐CFSR/CFSv2 and ERA5 forcings. The results suggest that a complex model formulation leads to a better agreement between modeled and the observed sea‐ice concentration and snow thickness, while differences are smaller for sea‐ice thickness and drift speed. However, the choice of the atmospheric forcing also impacts the agreement of the FESOM2 simulations and observations, with NCEP‐CFSR/CFSv2 being particularly beneficial for the simulated sea‐ice concentration and ERA5 for sea‐ice drift speed. In this respect, our results indicate that parameter calibration can better compensate for differences among atmospheric forcings in a simpler model (i.e., sea‐ice has no heat capacity) than in more realistic formulations with a prognostic sea‐ice thickness distribution and sea ice enthalpy.
Plain Language Summary
The role of model complexity in determining the performance of sea‐ice numerical simulations is still not completely understood. Some studies suggest that a more sophisticated description of the sea‐ice physics leads to simulations that agree better with sea‐ice observations. Others, however, fail to establish a link between complex model formulations and improved model performance. Here, we investigate this open question by analyzing a set of sea‐ice simulations performed with a revised and improved sea‐ice model that features substantial modularity in terms of model complexity. Ten model parameters in three different model configurations are optimized to improve the agreement between model results and observations, allowing a fair comparison between model configurations with varying complexity. The model optimization is repeated for two different atmospheric forcings to shed light on the relationship between model complexity and other sources of uncertainty in the sea‐ice simulations, such as those associated with the atmospheric conditions. The results suggest that a more complex formulation of our model can lead to a more appropriate representation of sea ice concentration and snow thickness, while it is less relevant for sea‐ice thickness and drift.
Key Points
Increased sea‐ice model complexity can improve the simulated sea‐ice concentration and snow thickness
Sea‐ice thickness and drift are only weakly affected by model complexity
Parameter calibration can better compensate for differences between atmospheric forcings in a simpler model
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