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
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
The Community Climate System Model, version 4 has revisions across all components. For sea ice, the most notable improvements are the incorporation of a new shortwave radiative transfer scheme and ...the capabilities that this enables. This scheme uses inherent optical properties to define scattering and absorption characteristics of snow, ice, and included shortwave absorbers and explicitly allows for melt ponds and aerosols. The deposition and cycling of aerosols in sea ice is now included, and a new parameterization derives ponded water from the surface meltwater flux. Taken together, this provides a more sophisticated, accurate, and complete treatment of sea ice radiative transfer. In preindustrial CO₂ simulations, the radiative impact of ponds and aerosols on Arctic sea ice is 1.1 W m−2annually, with aerosols accounting for up to 8 W m−2of enhanced June shortwave absorption in the Barents and Kara Seas and with ponds accounting for over 10 W m−2in shelf regions in July. In double CO₂ (2XCO₂) simulations with the same aerosol deposition, ponds have a larger effect, whereas aerosol effects are reduced, thereby modifying the surface albedo feedback. Although the direct forcing is modest, because aerosols and ponds influence the albedo, the response is amplified. In simulations with no ponds or aerosols in sea ice, the Arctic ice is over 1 m thicker and retains more summer ice cover. Diagnosis of a twentieth-century simulation indicates an increased radiative forcing from aerosols and melt ponds, which could play a role in twentieth-century Arctic sea ice reductions. In contrast, ponds and aerosol deposition have little effect on Antarctic sea ice for all climates considered.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•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.
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.
A better understanding of the role of sea ice for the changing climate of our planet is the central aim of the diagnostic Coupled Model Intercomparison Project 6 (CMIP6)-endorsed Sea-Ice Model ...Intercomparison Project (SIMIP). To reach this aim, SIMIP requests sea-ice-related variables from climate-model simulations that allow for a better understanding and, ultimately, improvement of biases and errors in sea-ice simulations with large-scale climate models. This then allows us to better understand to what degree CMIP6 model simulations relate to reality, thus improving our confidence in answering sea-ice-related questions based on these simulations. Furthermore, the SIMIP protocol provides a standard for sea-ice model output that will streamline and hence simplify the analysis of the simulated sea-ice evolution in research projects independent of CMIP. To reach its aims, SIMIP provides a structured list of model output that allows for an examination of the three main budgets that govern the evolution of sea ice, namely the heat budget, the momentum budget, and the mass budget. In this contribution, we explain the aims of SIMIP in more detail and outline how its design allows us to answer some of the most pressing questions that sea ice still poses to the international climate-research community.
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