Abstract

Submarine measurements of sea ice draft show that the ice has thinned in some parts of the Arctic Ocean at a remarkably high rate over the past few decades. The spatial pattern indicates that the thinning was a strong function of ice thickness, with the greatest thinning occurring where the ice was initially thickest. A similar relationship between sea ice thinning and the initial thickness is reproduced individually by three global climate models in response to increased levels of carbon dioxide in the models' atmosphere. All three models have weak trends in their surface winds and one model lacks ice dynamics altogether, implying that trends in the atmosphere or ice circulation are not necessary to produce a relatively high rate of thinning over the central Arctic or a thickness change that increases with the initial thickness. A general theory is developed to describe the thinning of sea ice subjected to climate perturbations, and it is found that the leading component of the thickness dependence of the thinning is due to the basic thermodynamics of sea ice. When perturbed, sea ice returns to its equilibrium thickness by adjusting its growth rate. The growth–thickness relationship is stabilizing and hence can be reckoned as a negative feedback. The feedback is stronger for thinner ice, which is known to adjust more quickly to perturbations than thicker ice. In addition, thinner ice need not thin much to increase its growth rate a great deal, thereby establishing a new equilibrium with relatively little change in thickness. In contrast, thicker ice must thin much more. An analysis of a series of models, with physics ranging from very simple to highly complex, indicates that this growth–thickness feedback is the key to explaining the models' relatively high rate of thinning in the central Arctic compared to thinner ice in the subpolar seas.

Abstract

The authors examine the net winter, summer, and annual mass balance of six glaciers along the northwest coast of North America, extending from Washington State to Alaska. The net winter (NWB) and net annual (NAB) mass balance anomalies for the maritime glaciers in the southern group, located in Washington and British Columbia, are shown to be positively correlated with local precipitation anomalies and storminess (defined as the rms of high-passed 500-mb geopotential anomalies) and weakly and negatively correlated with local temperature anomalies. The NWB and NAB of the maritime Wolverine glacier in Alaska are also positively correlated with local precipitation, but they are positively correlated with local winter temperature and negatively correlated with local storminess. Hence, anomalies in mass balance at Wolverine result mainly from the change in moisture that is being advected into the region by anomalies in the averaged wintertime circulation rather than from a change in storminess. The patterns of the wintertime 500-mb circulation and storminess anomalies associated with years of high NWB in the southern glacier group are similar to those associated with low NWB years at the Wolverine glacier, and vice versa.

The decadal ENSO-like climate phenomenon discussed by Zhang et al. has a large impact on the NWB and NAB of these maritime glaciers, accounting for up to 35% of the variance in NWB. The 500-mb circulation and storminess anomalies associated with this decadal ENSO-like mode resemble the Pacific–North American pattern, as do 500-mb composites of years of extreme NWB of South Cascade glacier in Washington and of Wolverine glacier in Alaska. Hence, the decadal ENSO-like mode affects precipitation in a crucial way for the NWB of these glaciers. Specifically, the decadal ENSO-like phenomenon strongly affects the storminess over British Columbia and Washington and the moisture transported by the seasonally averaged circulation into maritime Alaska. In contrast, ENSO is only weakly related to NWB of these glaciers because (i) the large-scale circulation anomalies associated with ENSO do not produce substantial anomalies in moisture advection into Alaska, and (ii) the storminess and precipitation anomalies associated with ENSO are far to the south of the southern glacier group.

Finally, the authors discuss the potential for short-term climate forecasts of the mass balance for the maritime glaciers in the northwest of North America.

Abstract

The Arctic surface circulation simulated by atmospheric general circulation models is assessed in the context of driving sea ice motion. A sea ice model is forced by geostrophic winds from eight models participating in the first Atmospheric Model Intercomparison Project (AMIP1), and the results are compared to simulations with the sea ice model forced by observed winds. The mean sea level pressure in the AMIP models is generally too high over the Arctic Ocean, except in the Beaufort and Chukchi Seas, where it is too low. This pattern creates anomalous winds that tend to transport too much ice away from the coast of Greenland and the Canadian Archipalego, and into the East Siberian Sea, producing a pattern of ice thickness in the Arctic that is rotated by roughly 180° relative to what is expected based on observations. AMIP winds also drive too little ice transport through Fram Strait and too much transport east of Svalbard by way of the Barents Sea. These errors in ice thickness and transport influence ice growth and melt rates and hence the freshwater flux into the ocean. Sensitivity experiments that test the model response to the wind composition show the ice thickness patterns depend primarily on the climatological mean annual cycle of the geostrophic winds. Daily wind variability is necessary to create sufficient ice deformation and open water, but the sea ice behavior is rather insensitive to the details of the daily variations.

Abstract

The mechanisms forcing variability in Southern Ocean sea ice and sea surface temperature from 600 years of a control climate coupled model integration are discussed. As in the observations, the leading mode of simulated variability exhibits a dipole pattern with positive anomalies in the Pacific sector associated with negative anomalies in the Atlantic. It is found that in the Pacific ocean circulation changes associated with variable wind forcing modify the ocean heat flux convergence and sea ice transport, resulting in sea surface temperature and sea ice anomalies. The Pacific ice and ocean anomalies persist over a number of years due to reductions in ocean shortwave absorption reinforcing the initial anomalies. In the Atlantic sector, no single process dominates in forcing the anomalies. Instead there are contributions from changing ocean and sea ice circulation and surface heat fluxes. While the absorbed solar radiation in the Atlantic is modified by the changing surface albedo, the anomalies are much shorter-lived than in the Pacific because the ocean circulation transports them northward, removing them from ice formation regions. Sea ice and ocean anomalies associated with the El Niño–Southern Oscillation and the Southern Annular Mode both exhibit a dipole pattern and contribute to the leading mode of ice and ocean variability.

Abstract

A coupled global climate model is used to evaluate processes that determine the equilibrium location of the sea-ice edge and its climatological annual cycle. The extent to which the wintertime ice edge departs from a symmetric ring around either pole depends primarily on coastlines, ice motion, and the melt rate at the ice–ocean interface. At any location the principal drivers of the oceanic heat flux that melts sea ice are absorbed solar radiation and the convergence of heat transported by ocean currents. The distance between the ice edge and the pole and the magnitude of the ocean heat flux convergence at the ice edge are inversely related. The chief exception to this rule is in the East Greenland Current, where the ocean heat flux convergence just east of the ice edge is relatively high but ice survives due to its swift southward motion and the protection of the cold southward-flowing surface water. In regions where the ice edge extends relatively far equatorward, absorbed solar radiation is the largest component of the ocean energy budget, and the large seasonal range of insolation causes the ice edge to traverse a large distance. In contrast, at relatively high latitudes, the ocean heat flux convergence is the largest component and it has a relatively small annual range, so the ice edge traverses a much smaller distance there. When the model is subject to increased CO₂ forcing up to twice preindustrial levels, the ocean heat flux convergence weakens near the ice edge in most places. This weakening reduces the heat flux from the ocean to the base of the ice and tends to offset the effects of increased radiative forcing at the ice surface, so the ice edge retreats less than it would otherwise.

Abstract

The authors examine the natural variability of the arctic climate system simulated by two very different models: the Geophysical Fluid Dynamics Laboratory (GFDL) global climate model, and an area-averaged model of the arctic atmosphere–sea ice–upper-ocean system called the polar cap climate model, the PCCM. A 1000-yr integration of the PCCM is performed in which the model is driven by a prescribed, stochastic atmospheric energy flux convergence (D), which has spectral characteristics that are identical to the spectra of the observed D. The standard deviation of the yearly mean sea ice thickness from this model is 0.85 m; the mean sea ice thickness is 3.1 m. In contrast, the standard deviation of the yearly averaged sea ice thickness in the GFDL climate model is found to be about 6% of the climatological mean thickness and only 24% of that simulated by the PCCM.

A series of experiments is presented to determine the cause of these disparate results. First, after changing the treatment of sea ice and snow albedo in the (standard) PCCM model to be identical thermodynamically to that in the GFDL model, the PCCM is driven with D from the GFDL control integration to demonstrate that the PCCM model produces an arctic climate similar to that of the GFDL model. Integrations of the PCCM are then examined in which the different prescriptions of the sea ice treatment (GFDL vs standard PCCM) and D (GFDL vs observed) are permutated. The results indicate that unarguable improvements in the treatment of sea ice in the GFDL climate model should amplify significantly the natural variability in this model. The authors present calculations that indicate the variability in the sea ice thickness is extremely sensitive to the spectrum of the atmospheric energy flux convergence. Specifically, the differences between the GFDL and observed D at timescales shorter than 3 yr are shown to have a significant impact on the sea ice variability on all timescales. A conservative best estimate for the amplitude of the natural variability in the arctic sea ice volume is presented; this estimate is a significant fraction (about 25%) of the mean sea ice thickness.

The results suggest that most of the global climate models that have been used to evaluate climate change may also have artificially quiescent natural variability in the Arctic.

Abstract

The sensitivity of global climate with respect to forcing is generally described in terms of the global climate feedback—the global radiative response per degree of global annual mean surface temperature change. While the global climate feedback is often assumed to be constant, its value—diagnosed from global climate models—shows substantial time variation under transient warming. Here a reformulation of the global climate feedback in terms of its contributions from regional climate feedbacks is proposed, providing a clear physical insight into this behavior. Using (i) a state-of-the-art global climate model and (ii) a low-order energy balance model, it is shown that the global climate feedback is fundamentally linked to the geographic pattern of regional climate feedbacks and the geographic pattern of surface warming at any given time. Time variation of the global climate feedback arises naturally when the pattern of surface warming evolves, actuating feedbacks of different strengths in different regions. This result has substantial implications for the ability to constrain future climate changes from observations of past and present climate states. The regional climate feedbacks formulation also reveals fundamental biases in a widely used method for diagnosing climate sensitivity, feedbacks, and radiative forcing—the regression of the global top-of-atmosphere radiation flux on global surface temperature. Further, it suggests a clear mechanism for the “efficacies” of both ocean heat uptake and radiative forcing.

Abstract

The temporal characteristics of Arctic sea ice extent and area are analyzed in terms of their lagged correlation in observations and a GCM ensemble. Observations and model output generally match, exhibiting a red-noise spectrum, where significant correlation (or memory) is lost within 2–5 months. September sea ice extent is significantly correlated with extent of the previous August and July, and thus these months show a predictive skill of the summer minimum extent. Beyond this initial loss of memory, there is an increase in correlation—a reemergence of memory—that is more ubiquitous in the model than observations. There are two distinct modes of memory reemergence in the model. The first, a summer-to-summer reemergence arises within the model from the persistence of thickness anomalies and their influence on ice area. The second, which is also seen in observations, is associated with anomalies in the growth season that originate in the melt season. This reemergence stems from the several-month persistence of SSTs. In the model memory reemergence is enhanced by the sea ice albedo feedback. The same mechanisms that give rise to reemergence also enhance the 1-month lagged correlation during summer and winter. The study finds the least correlation between successive months when the sea ice is most rapidly advancing or retreating.

Abstract

Sea ice predictability is a rapidly growing area of research, with most studies focusing on the Arctic. This study offers new insights by comparing predictability between the Arctic and Antarctic sea ice anomalies, focusing on the effects of regional differences in ice thickness and ocean dynamics. Predictability in simulated regional sea ice area and volume is investigated in long control runs of an Earth system model. Sea ice area predictability in the Arctic agrees with results from other studies, with features of decaying initial persistence and reemergence because of ocean mixed layer processes and memory in thick ice. In pan-Arctic averages, sea ice volume and the area covered by thick ice are the best predictors of September area for lead times greater than 2 months. In the Antarctic, area is generally the best predictor of future area for all times of year. Predictability of area in summer differs between the hemispheres because of unique aspects of the coupling between area and volume. Generally, ice volume only adds to the predictability of summer sea ice area in the Arctic. Predictability patterns vary greatly among different regions of the Arctic but share similar seasonality among regions of the Antarctic. Interactive ocean dynamics influence anomaly reemergence differently in the Antarctic than the Arctic, both for the total and regional area. In the Antarctic, ocean dynamics generally decrease the persistence of area anomalies, reducing predictability. In the Arctic, the presence of ocean dynamics improves ice area predictability, mainly through mixed layer depth variability.

Abstract

In the coastal mountains of western North America, most extreme precipitation is associated with atmospheric rivers (ARs), narrow bands of moisture originating in the tropics. Here we quantify how interannual variability in atmospheric rivers influences snowpack in the western United States in observations and a model. We simulate the historical climate with the Model for Prediction Across Scales (MPAS) with physics from the Community Atmosphere Model, version 5 [CAM5 (MPAS-CAM5)], using prescribed sea surface temperatures. In the global variable-resolution domain, regional refinement (at ~30 km) is applied to our region of interest and upwind over the northeast Pacific. To better characterize internal variability, we conduct simulations with three ensemble members over 30 years of the historical period. In the Cascade Range, with some exceptions, winters with more atmospheric river days are associated with less snowpack. In California’s Sierra Nevada, winters with more ARs are associated with greater snowpack. The slope of the linear regression of observed snow water equivalent (SWE) on reanalysis-based AR count has the same sign as that arrived at using the model, but is statistically significant in observations only for California. In spring, internal variance plays an important role in determining whether atmospheric river days appear to be associated with greater or less snowpack. The cumulative (winter through spring) number of atmospheric river days, on the other hand, has a relationship with spring snowpack, which is consistent across ensemble members. Thus, the impact of atmospheric rivers on winter snowpack has a greater influence on spring snowpack than spring atmospheric rivers in the model for both regions and in California consistently in observations.