The climate of the MIS-13 interglacial according to HadCM3

The climate of the Marine Isotopic Stage 13 (MIS-13) is explored in the fully coupled atmosphere–ocean general circulation model the Hadley Centre Coupled Model, version 3 (HadCM3). It is found that the strong insolation forcing at the time imposed a strengthened land–ocean thermal contrast, resulting in an intensified summer monsoon over Asia. The addition of land ice over North America and Eurasia results in a stationary wave feature across the Eurasian continent. This leads to a high pressure anomaly over the Sea of Japan with increased advection of warm moist air onto the Chinese landmasses. This in turn reinforces the East Asian summer monsoon (EASM), highlighting the counterintuitive notion that, depending on the background insolation and its size, ice can indeed contribute to strengthening the EASM. The modeling results support the geological record indication of a strong EASM 500 000 years ago. Furthermore, Arctic Oscillation, El Niño–Southern Oscillation, and Indian Ocean dipole–like teleconnection features are discussed in the MIS-13 environment. It is shown that the change in the tropical Pacific sea surface temperature has the potential to impact the North Atlantic climate through an atmospheric “bridge.

On the formulation of snow thermal conductivity in large-scale sea ice models

An assessment of the performance of a state-of-the-art large-scale coupled sea iceocean model, including a new snow multilayer thermodynamic scheme, is performed. Four 29 year long simulations are compared against each other and against sea ice thickness and extent observations. Each simulation uses a separate parameterization for snow thermophysical properties. The first simulation uses a constant thermal conductivity and prescribed density profiles. The second and third parameterizations use typical power-law relationships linking thermal conductivity directly to density (prescribed as in the first simulation). The fourth parameterization is newly developed and consists of a set of two linear equations relating the snow thermal conductivity and density to the mean seasonal wind speed. Results show that simulation 1 leads to a significant overestimation of the sea ice thickness due to overestimated thermal conductivity, particularly in the Northern Hemisphere. Parameterizations 2 and 4 lead to a realistic simulation of the Arctic sea ice mean state. Simulation 3 results in the underestimation of the sea ice basal growth in both hemispheres, but is partly compensated by lateral growth and snow ice formation in the Southern Hemisphere. Finally, parameterization 4 improves the simulated Snow Depth Distributions by including snow packing by wind, and shows potential for being used in future works. The intercomparison of all simulations suggests that the sea ice model is more sensitive to the snow representation in the Arctic than it is in the Southern Ocean, where the sea ice thickness is not driven by temperature profiles in the snow

Description of the Earth system model of intermediate complexity LOVECLIM, version 1.2.

The main characteristics of the new version 1.2 of the three-dimensional Earth system model of intermediate complexity LOVECLIM are briefly described. LOVECLIM 1.2 includes representations of the atmosphere, the ocean and sea ice, the land surface (including vegetation), the ice sheets, the icebergs and the carbon cycle. The atmospheric component is ECBilt2, a T21, 3-level quasigeostrophic model. The ocean component is CLIO3, which consists of an ocean general circulation model coupled to a comprehensive thermodynamic-dynamic sea-ice model. Its horizontal resolution is of 3◦ by 3◦, and there are 20 levels in the ocean. ECBilt-CLIO is coupled to VECODE, a vegetation model that simulates the dynamics of two main terrestrial plant functional types, trees and grasses, as well as desert. VECODE also simulates the evolution of the carbon cycle over land while the ocean carbon cycle is represented by LOCH, a comprehensive model that takes into account both the solubility and biological pumps. The ice sheet component AGISM is made up of a three-dimensional thermomechanical model of the ice sheet flow, a visco-elastic bedrock model and a model of the mass balance at the iceatmosphere and ice-ocean interfaces. For both the Greenland and Antarctic ice sheets, calculations are made on a 10 km by 10 km resolution grid with 31 sigma levels. LOVECLIM1.2 reproduces well the major characteristics of the observed climate both for present-day conditions and for key past periods such as the last millennium, the mid-Holocene and the Last Glacial Maximum. However, despite some improvements compared to earlier versions, some biases are still present in the model. The most serious ones are mainly located at low latitudes with an overestimation of the temperature there, a too symmetric distribution of precipitation between the two hemispheres, and an overestimation of precipitation and vegetation cover in the subtropics. In addition, the atmospheric circulation is too weak. The model also tends to underestimate the surface temperature changes (mainly at low latitudes) and to overestimate the ocean heat uptake observed over the last decade

Climate projections of Arctic sea ice

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Constraining projections of summer Arctic sea ice

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On the representation of snow in large scale sea ice models

International audienceAn assessment of the performance of a state-of-the-art large-scale coupled sea ice -ocean model, including a new snow multi-layer thermodynamic scheme, in simulating the sea ice thickness and extent over the past three decades in both hemispheres, is performed. Four simulations from the model are compared against each other and against submarine, airborne and satellite observations. Each simulation uses a separate formulation for snow apparent thermal conductivity and density. In the first experiment, the snow density profile is prescribed from observations and the thermal conductivity is constant and equal to 0.31 W m-1 K-1, a typical value for such models. Formulations (2) and (3) are typical power-law relationships linking thermal conductivity directly to density (prescribed as in simulation (1)). Parameterization (4) is newly developed and consists of a set of two linear equations relating the snow thermal conductivity and density to the mean seasonal wind speed. We show that the first simulation leads to an overestimation of the sea ice thickness due to overestimated snow thermal conductivity, particularly in the Northern Hemisphere. Formulation (2) leads to a realistic simulation of the Arctic sea ice mean state while (3) provides the minimum deviations with respect to sea ice extent and thickness observations in the Southern Ocean. Parameterization (4), accounting for the snow packing process in a simple way, is the most promising formulation. In particular, this formulation improves the simulated large-scale snow depth probability density functions. The intercomparison of all simulations suggests that the sea ice model is more sensitive to the snow representation in the Arctic than it is in the Southern Ocean, where both the simulated sea ice mean state and variability seem to be dominantly driven by the ocean

Changes in atmospheric CO2 concentration over the past two millennia: contribution of climate variability, land-use and Southern Ocean dynamics

The fluctuations of atmospheric CO2 concentrations over the preindustrial Common Era are generally attributed to changes in land carbon storage, caused primarily by changes in surface air temperature but also by changes in land use. This dominant influence of the land carbon cycle is consistent with the negative correlation between atmospheric CO2 concentrations and δ13CO2 variations recorded in ice cores. By performing an ensemble of sensitivity experiments with the LOVECLIM model, we confirm the potentially large role that temperature changes have on the land carbon cycle. However, this process alone cannot explain the magnitude of the reconstructed atmospheric CO2 and δ13CO2 variations. In particular, even when the model is constrained to follow reconstructed temperature changes by data assimilation, and when applying relatively large values of the climate-carbon feedback parameter, it can only explain about 50% of the atmospheric CO2 decrease between the 12th and the seventeenth century. We find that land use changes are likely responsible for most of the observed long term atmospheric CO2 trend over the first millennium of the Common Era, and for up to 30% of the decrease observed after 1600 CE. In addition, in our experiments, changes in southern hemisphere westerly winds induce slightly smaller changes in atmospheric CO2 concentrations than those associated with land use change, and variations in δ13CO2 of the same order of magnitude as the observed ones. Combining the effects of changes in temperature, land use and winds over the Southern Ocean provides a reasonable agreement with reconstructions for atmospheric CO2 concentrations and δ13CO2, especially for the low CO2 values observed during the seventeenth century. This underlines the important contribution of both land and ocean carbon processes. Nevertheless, some uncertainties remain on the origin of the relatively high CO2 concentrations reconstructed during the eleventh and sixteenth centuries

21st century changes in the Arctic sea ice cover: constraining CMIP5 sea ice projections

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Constraining projections of summer Arctic sea ice

International audienceAbstract. We examine the recent (1979–2010) and future (2011–2100) characteristics of the summer Arctic sea ice cover as simulated by 29 Earth system and general circulation models from the Coupled Model Intercomparison Project, phase 5 (CMIP5). As was the case with CMIP3, a large intermodel spread persists in the simulated summer sea ice losses over the 21st century for a given forcing scenario. The 1979–2010 sea ice extent, thickness distribution and volume characteristics of each CMIP5 model are discussed as potential constraints on the September sea ice extent (SSIE) projections. Our results suggest first that the future changes in SSIE with respect to the 1979–2010 model SSIE are related in a complicated manner to the initial 1979–2010 sea ice model characteristics, due to the large diversity of the CMIP5 population: at a given time, some models are in an ice-free state while others are still on the track of ice loss. However, in phase plane plots (that do not consider the time as an independent variable), we show that the transition towards ice-free conditions is actually occurring in a very similar manner for all models. We also find that the year at which SSIE drops below a certain threshold is likely to be constrained by the present-day sea ice properties. In a second step, using several adequate 1979–2010 sea ice metrics, we effectively reduce the uncertainty as to when the Arctic could become nearly ice-free in summertime, the interval [2041, 2060] being our best estimate for a high climate forcing scenario