Abstract. We present a selection of methodologies for using the palaeo-climate model component of the Coupled Model Intercomparison Project (Phase 5) (CMIP5) to attempt to constrain future climate projections using the same models. The constraints arise from measures of skill in hindcasting palaeo-climate changes from the present over three periods: the Last Glacial Maximum (LGM) (21 000 yr before present, ka), the mid-Holocene (MH) (6 ka) and the Last Millennium (LM) (850-1850 CE). The skill measures may be used to validate robust patterns of climate change across scenarios or to distinguish between models that have differing outcomes in future scenarios. We find that the multi-model ensemble of palaeo-simulations is adequate for addressing at least some of these issues. For example, selected benchmarks for the LGM and MH are correlated to the rank of future projections of precipitation/temperature or sea ice extent to indicate that models that produce the best agreement with palaeo-climate information give demonstrably different future results than the rest of the models. We also explore cases where comparisons are strongly dependent on uncertain forcing time series or show important non-stationarity, making direct inferences for the future problematic. Overall, we demonstrate that there is a strong potential for the palaeo-climate simulations to help inform the future projections and urge all the modelling groups to complete this subset of the CMIP5 runs.Published by Copernicus Publications on behalf of the European Geosciences Union.

A dynamic sea ice model based on granular material rheology is presented. The sea ice model is coupled to both a mixed layer ocean model and a one-layer thermodynamic atmospheric model, which allows for an ice albedo feedback. Land is represented by a 6-m thick layer with a constant base temperature. A 10-year integration including both thermodynamic and dynamic effects and incorporating prescribed climatological wind stress and ocean current data was performed in order for the model to reach a stable periodic seasonal cycle. The commonly observed lead complexes, along which sliding and opening of adjacent ice floes occur in the Arctic sea ice cover, are well reproduced in this simulation. In particular, shear lines extending from the western Canadian Archipelago toward the central Arctic, often observed in winter satellite images, are present. The ice edge is well positioned both in winter and summer using this thermodynamically coupled ocean-ice-atmosphere model. The results also yield a sea ice circulation and thickness distribution over the Arctic, which are in good agreement with observations. The model also produces an increase in ice formation associated with the dilatation of the ice medium along sliding lines. In this model, incident energy absorbed by the ocean melts ice laterally and warms the mixed layer, causing a smaller ice retreat in the summer. This cures a problem common to many existing thermodynamic-dynamic sea ice models.

[1] A time series of summer fresh water content anomalies (FWCA) over the Laptev and East Siberian sea shelves was constructed from historical hydrographic records for the period from 1920 to 2005. Results from a multiple regression between FCWA and various atmospheric and oceanic indices show that the fresh water content on the shelves is mainly controlled by atmospheric vorticity on quasi-decadal timescales. When the vorticity of the atmosphere on the shelves is antycyclonic, approximately 500 km 3 of fresh water migrates from the eastern Siberian shelf to the Arctic Ocean through the northeastern Laptev Sea. When the vorticity of the atmosphere is cyclonic, this fresh water remains on the southern Laptev and East Siberian sea shelves. This FWCA represents approximately 35% of the total fresh water inflow provided by river discharge and local sea-ice melt, and is about ten times larger than the standard deviation of the Lena River summer long-term mean discharge. However, the large interannual and spatial variability in the fresh water content of the shelves, as well as the spatial coverage of the hydrographic data, makes it difficult to detect the long-term tendency of fresh water storage associated with climate change.

This study focuses on errors in extreme precipitation in gridded station products incurred during the upscaling of station measurements to a grid, referred to as representativeness errors. Gridded precipitation station analyses are valuable observational data sources with a wide variety of applications, including model validation. The representativeness errors associated with two gridding methods are presented, consistent with either a point or areal average interpretation of model output, and it is shown that they differ significantly (up to 30%). An experiment is conducted to determine the errors associated with station density, through repeated gridding of station data within the United States using subsequently fewer stations. Two distinct error responses to reduced station density are found, which are attributed to differences in the spatial homogeneity of precipitation distributions. The error responses characterize the eastern and western United States, which are respectively more and less homogeneous. As the station density decreases, the influence of stations farther from the analysis point increases, and therefore, if the distributions are inhomogeneous in space, the analysis point is influenced by stations with very different precipitation distributions. Finally, ranges of potential percent representativeness errors of the median and extreme precipitation across the United States are created for high-resolution (0.258) and low-resolution areal averaged (0.98 lat 3 1.258 lon) precipitation fields. For example, the range of the representativeness errors is estimated, for annual extreme precipitation, to be from 116% to 212% in the low-resolution data, when station density is 5 stations per 0.98 lat 3 1.258 lon grid box.

[1] Summer hydrographic data show a dramatic warming of the bottom water layer over the eastern Siberian shelf coastal zone (<10 m depth), since the mid-1980s, by 2.1°C. We attribute this warming to changes in the Arctic atmosphere. The enhanced summer cyclonicity results in warmer air temperatures and a reduction in ice extent, mainly through thermodynamic melting. This leads to a lengthening of the summer open-water season and to more solar heating of the water column. The permafrost modeling indicates, however, that a significant change in the permafrost depth lags behind the imposed changes in surface temperature, and after 25 years of summer seafloor warming (as observed from 1985 to 2009), the upper boundary of permafrost deepens only by ∼1 m. Thus, the observed increase in temperature does not lead to a destabilization of methane-bearing subsea permafrost or to an increase in methane emission. The CH 4 supersaturation, recently reported from the eastern Siberian shelf, is believed to be the result of the degradation of subsea permafrost that is due to the long-lasting warming initiated by permafrost submergence about 8000 years ago rather than from those triggered by recent Arctic climate changes. A significant degradation of subsea permafrost is expected to be detectable at the beginning of the next millennium. Until that time, the simulated permafrost table shows a deepening down to ∼70 m below the seafloor that is considered to be important for the stability of the subsea permafrost and the permafrost-related gas hydrate stability zone.