Abstract. The main characteristics of the new version 1.2 of the three-dimensional Earth system model of intermediate complexity LOVECLIM are briefly described. LOVE-CLIM 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 acCorrespondence to: H. Goosse (hugues.goosse@uclouvain.be) count 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 decades.

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

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 inter-model spread persists in the simulated summer sea ice losses over the 21st century for a given forcing scenario. The initial 1979–2010 sea ice properties (including the 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 SSIE anomalies (compared 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). In a new diagram (that does not consider the time as an independent variable) we show that the transition towards ice-free conditions is actually occuring in a very similar manner for all models. For these reasons, some quantities that do not explicitly depend on time, such as the year at which SSIE drops below a certain threshold, are likely to be constrained. 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 (between 2041 and 2060 for a high climate forcing scenario)

Abstract. 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 quasi-geostrophic model. The oceanic component is CLIO3, which is made up of an ocean general circulation model coupled to a comprehensive thermodynamic-dynamic sea-ice model. Its horizontal resolution is 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 oceanic carbon cycle is represented in 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 ice-atmosphere 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. LOVECLIM 1.2 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, 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 decades.

[1] 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.

Abstract.It is standard to compare climate model results covering the past millennium and reconstructions based on various archives in order to test the ability of models to reproduce the observed climate variability. Up to now, glacier length fluctuations have not been used systematically in this framework even though they offer information on multidecadal to centennial variations complementary to other records. One reason is that glacier length depends on several complex factors and so cannot be directly linked to the simulated climate. However, climate model skill can be measured by comparing the glacier length computed by a glacier model driven by simulated temperature and precipitation to observed glacier length variations. This is done here using the version 1.0 of the Open Global Glacier Model (OGGM) forced by fields derived from a range of simulations performed with global climate models over the past millennium. The glacier model is applied to a set of Alpine glaciers for which observations cover at least the 20th century. The observed glacier length fluctuations are generally well within the range of the simulations driven by the various climate model results, showing a general consistency with this ensemble of simulations. Sensitivity experiments indicate that the results are much more sensitive to the simulated climate than to OGGM parameters. This confirms that the simulations of glacier length can be used to evaluate the climate model performance, in particular the simulated summer temperatures that largely control the glacier changes in our region of interest. Simulated glacier length is strongly influenced by the internal variability in the system, putting limitations on the model-data comparison for some variables like the trends over the 20th century in the Alps. Nevertheless, comparison of glacier length fluctuations on longer timescales, for instance between the 18th century and the late 20th century, appear less influenced by the natural variability and indicate clear differences in the behaviour of the various climate models.

The climate of the Marine Isotopic Stage 13 (MIS-13) is explored in the fully coupled atmosphereocean 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.''

Abstract. It is standard to compare climate model results covering the past millennium and reconstructions based on various archives in order to test the ability of models to reproduce the observed climate variability. Up to now, glacier length fluctuations have not been used systematically in this framework even though they offer information on multi-decadal to centennial variations complementary to other records. One reason is that glacier length depends on several complex factors and so cannot be directly linked to the simulated climate. However, climate model skill can be measured by comparing the glacier length computed by a glacier model driven by simulated temperature and precipitation to observed glacier length variations. This is done here using the version 1.0 of Open Global Glacier Model (OGGM) forced by fields derived from a range of simulations performed with global climate models over the past millennium. The glacier model is applied to a set of Alpine glaciers for which observations cover at least the 20th century. The observed glacier length fluctuations are generally well within the range of the simulations driven by the various climate model results, showing a general consistency with this ensemble of simulations. Sensitivity experiments indicate that the results are much more sensitive to the simulated climate than to OGGM parameters. This confirms that the simulations of glacier length can be used to evaluate the climate model performance, in particular the summer temperatures that largely control the glacier changes in our region of interest. Simulated glacier length is strongly influenced by the internal variability of the system, putting limitations on the model-data comparison for some variables like the trends over the 20th century in the Alps. Nevertheless, comparison of glacier length fluctuations on longer timescales, for instance between the 18th century and the late 20th century, appear less influenced by the natural variability and indicate clear differences in the behaviour of the various climate models.