Satellite records show a decline in ice extent over more than three decades, with a record minimum in September 2012. Results from the Pan‐Arctic Ice‐Ocean Modelling and Assimilation system (PIOMAS) suggest that the decline in extent has been accompanied by a decline in volume, but this has not been confirmed by data. Using new data from the European Space Agency CryoSat‐2 (CS‐2) mission, validated with in situ data, we generate estimates of ice volume for the winters of 2010/11 and 2011/12. We compare these data with current estimates from PIOMAS and earlier (2003–8) estimates from the National Aeronautics and Space Administration ICESat mission. Between the ICESat and CryoSat‐2 periods, the autumn volume declined by 4291 km3 and the winter volume by 1479 km3. This exceeds the decline in ice volume in the central Arctic from the PIOMAS model of 2644 km3 in the autumn, but is less than the 2091 km3 in winter, between the two time periods.

Uncertainty in the Pan‐Arctic Ice‐Ocean Modeling and Assimilation System (PIOMAS) Arctic sea ice volume record is characterized. A range of observations and approaches, including in situ ice thickness measurements, ICESat retrieved ice thickness, and model sensitivity studies, yields a conservative estimate for October Arctic ice volume uncertainty of 1.35 × 103 km3 and an uncertainty of the ice volume trend over the 1979–2010 period of 1.0 × 103 km3 decade–1. A conservative estimate of the trend over this period is −2.8 × 103 km3 decade–1. PIOMAS ice thickness estimates agree well with ICESat ice thickness retrievals (<0.1 m mean difference) for the area for which submarine data are available, while difference outside this area are larger. PIOMAS spatial thickness patterns agree well with ICESat thickness estimates with pattern correlations of above 0.8. PIOMAS appears to overestimate thin ice thickness and underestimate thick ice, yielding a smaller downward trend than apparent in reconstructions from observations. PIOMAS ice volume uncertainties and trends are examined in the context of climate change attribution and the declaration of record minima. The distribution of 32 year trends in a preindustrial coupled model simulation shows no trends comparable to those seen in the PIOMAS retrospective, even when the trend uncertainty is accounted for. Attempts to label September minima as new record lows are sensitive to modeling error. However, the September 2010 ice volume anomaly did in fact exceed the previous 2007 minimum by a large enough margin to establish a statistically significant new record.

A parallel ocean and ice model (POIM) in generalized orthogonal curvilinear coordinates has been developed for global climate studies. The POIM couples the Parallel Ocean Program (POP) with a 12-category thickness and enthalpy distribution (TED) sea ice model. Although the POIM aims at modeling the global ocean and sea ice system, the focus of this study is on the presentation, implementation, and evaluation of the TED sea ice model in a generalized coordinate system. The TED sea ice model is a dynamic thermodynamic model that also explicitly simulates sea ice ridging. Using a viscous plastic rheology, the TED model is formulated such that all the metric terms in generalized curvilinear coordinates are retained. Following the POP's structure for parallel computation, the TED model is designed to be run on a variety of computer architectures: parallel, serial, or vector. When run on a computer cluster with 10 parallel processors, the parallel performance of the POIM is close to that of a corresponding POP ocean-only model. Model results show that the POIM captures the major features of sea ice motion, concentration, extent, and thickness in both polar oceans. The results are in reasonably good agreement with buoy observations of ice motion, satellite observations of ice extent, and submarine observations of ice thickness. The model biases are within 8% in Arctic ice motion, within 9% in Arctic ice thickness, and within 14% in ice extent in both hemispheres. The model captures 56% of the variance of ice thickness along the 1993 submarine track in the Arctic. The simulated ridged ice has various thicknesses, up to 20 m in the Arctic and 16 m in the Southern Ocean. Most of the simulated ice is 1-3 m thick in the Arctic and 1-2 m thick in the Southern Ocean. The results indicate that, in the Atlantic-Indian sector of the Southern Ocean, the oceanic heating, mainly due to convective mixing, can readily exceed the atmospheric cooling at the surface in midwinter, thus forming a polynya. The results also indicate that the West Spitzbergen Current is likely to bring considerable oceanic heat (generated by lateral advection and vertical convection) to the Odden ice area in the Greenland Sea, an important factor for an often tongue-shaped ice concentration in that area.

We present a description of the ModelE2 version of the Goddard Institute for Space Studies (GISS) General Circulation Model (GCM) and the configurations used in the simulations performed for the Coupled Model Intercomparison Project Phase 5 (CMIP5). We use six variations related to the treatment of the atmospheric composition, the calculation of aerosol indirect effects, and ocean model component. Specifically, we test the difference between atmospheric models that have noninteractive composition, where radiatively important aerosols and ozone are prescribed from precomputed decadal averages, and interactive versions where atmospheric chemistry and aerosols are calculated given decadally varying emissions. The impact of the first aerosol indirect effect on clouds is either specified using a simple tuning, or parameterized using a cloud microphysics scheme. We also use two dynamic ocean components: the Russell and HYbrid Coordinate Ocean Model (HYCOM) which differ significantly in their basic formulations and grid. Results are presented for the climatological means over the satellite era taken from transient simulations starting from the preindustrial (1850) driven by estimates of appropriate forcings over the 20th Century. Differences in base climate and variability related to the choice of ocean model are large, indicating an important structural uncertainty. The impact of interactive atmospheric composition on the climatology is relatively small except in regions such as the lower stratosphere, where ozone plays an important role, and the tropics, where aerosol changes affect the hydrological cycle and cloud cover. While key improvements over previous versions of the model are evident, these are not uniform across all metrics.

Recent record lows of Arctic summer sea ice extent are found to be triggered by the Arctic atmospheric Dipole Anomaly (DA) pattern. This local, second–leading mode of sea–level pressure (SLP) anomaly in the Arctic produced a strong meridional wind anomaly that drove more sea ice out of the Arctic Ocean from the western to the eastern Arctic into the northern Atlantic during the summers of 1995, 1999, 2002, 2005, and 2007. In the 2007 summer, the DA also enhanced anomalous oceanic heat flux into the Arctic Ocean via Bering Strait, which accelerated bottom and lateral melting of sea ice and amplified the ice–albedo feedback. A coupled ice–ocean model was used to confirm the historical record lows of summer sea ice extent.

[1] This paper synthesizes a variety of atmospheric and oceanic data to examine the large-scale energy budget of the Arctic. Assessment of the atmospheric budget relies primarily on the ERA-40 reanalysis. The seasonal cycles of vertically integrated atmospheric energy storage and the convergence of energy transport from ERA-40, as evaluated for the polar cap (defined by the 70°N latitude circle), in general compare well with realizations from the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis over the period 1979-2001. However, shortcomings in top of atmosphere radiation, as compared to satellite data, and the net surface flux, contribute to large energy budget residuals in ERA-40. The seasonal cycle of atmospheric energy storage is strongly modulated by the net surface flux, which is also the primary driver of seasonal changes in heat storage within the Arctic Ocean. Averaged for an Arctic Ocean domain, the July net surface flux from ERA-40 of À100 W m À2 (i.e., into the ocean), associated with sea ice melt and oceanic sensible heat gain, exceeds the atmospheric energy transport convergence of 91 W m Citation: Serreze,

Ocean temperature profiles and satellite data have been analyzed for summertime sea surface temperature (SST) and upper ocean heat content variations over the past century, with a focus on the Arctic Ocean peripheral seas. We find that many areas cooled up to ∼0.5°C per decade during 1930–1965 as the Arctic Oscillation (AO) index generally fell, while these areas warmed during 1965–1995 as the AO index generally rose. Warming is particularly pronounced since 1995, and especially since 2000. Summer 2007 SST anomalies are up to 5°C. The increase in upper ocean summertime warming since 1965 is sufficient to reduce the following winter's ice growth by as much as 0.75 m. Alternatively, this heat may return to the atmosphere before any ice forms, representing a fall freeze‐up delay of two weeks to two months. This returned heat might be carried by winds over terrestrial tundra ecosystems, contributing to the local heat budget.

This model study examines the impact of an intense early August cyclone on the 2012 record low Arctic sea ice extent. The cyclone passed when Arctic sea ice was thin and the simulated Arctic ice volume had already declined ~40% from the 2007–2011 mean. The thin sea ice pack and the presence of ocean heat in the near surface temperature maximum layer created conditions that made the ice particularly vulnerable to storms. During the storm, ice volume decreased about twice as fast as usual, owing largely to a quadrupling in bottom melt caused by increased upward ocean heat transport. This increased ocean heat flux was due to enhanced mixing in the oceanic boundary layer, driven by strong winds and rapid ice movement. A comparison with a sensitivity simulation driven by reduced wind speeds during the cyclone indicates that cyclone‐enhanced bottom melt strongly reduces ice extent for about 2 weeks, with a declining effect afterward. The simulated Arctic sea ice extent minimum in 2012 is reduced by the cyclone but only by 0.15 × 106 km2 (4.4%). Thus, without the storm, 2012 would still have produced a record minimum.