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Amélie Desmarais and L. Bruno Tremblay

Abstract

Uncertainties in the timing of a seasonal ice cover in the Arctic Ocean depend on model physics and parameterizations, natural variability at decadal time scales, and uncertainties in climate scenarios and forcings. We use the Gridded Monthly Sea Ice Extent and Concentration, 1850 Onward data product to assess the simulated decadal variability from the Community Earth System Model–Large Ensemble (CESM-LE) in the Pacific, Eurasian, and Atlantic sectors of the Arctic where a longer observational record exists. Results show that sea ice decadal (8–16 years) variability in CESM-LE is in agreement with the observational record in the Pacific sector of the Arctic, underestimated in the Eurasian sector of the Arctic, specifically in the East Siberian Sea, and slightly overestimated in the Atlantic sector of the Arctic, specifically in the Greenland Sea. Results also show an increase in variability at decadal time scales in the Eurasian and Pacific sectors during the transition to a seasonally ice-free Arctic, in agreement with the observational record although this increase is delayed by 10–20 years. If the current sea ice retreat in the Arctic continues to be Pacific-centric, results from the CESM-LE suggest that uncertainty in the timing of an ice-free Arctic associated with natural variability is realistic, but that a seasonal ice cover may occur earlier than projected.

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Karen L. Smith, Lorenzo M. Polvani, and L. Bruno Tremblay

Abstract

Given the rapidly changing Arctic climate, there is an urgent need for improved seasonal predictions of Arctic sea ice. Yet, Arctic sea ice prediction is inherently complex. Among other factors, wintertime atmospheric circulation has been shown to be predictive of summertime Arctic sea ice extent. Specifically, many studies have shown that the interannual variability of summertime Arctic sea ice extent (SIE) is anticorrelated with the leading mode of extratropical atmospheric variability, the Arctic Oscillation (AO), in the preceding winter. Given this relationship, the potential predictive role of stratospheric circulation extremes and stratosphere–troposphere coupling in linking the AO and Arctic SIE variability is examined. It is shown that extremes in the stratospheric circulation during the winter season, namely, stratospheric sudden warming (SSW) and strong polar vortex (SPV) events, are associated with significant anomalies in sea ice concentration in the Barents Sea in spring and along the Eurasian coastline in summer in both observations and a fully coupled, stratosphere-resolving general circulation model. Consistent with previous work on the AO, it is shown that SSWs, which are followed by the negative phase of the AO at the surface, result in sea ice growth, whereas SPVs, which are followed by the positive phase of the AO at the surface, result in sea ice loss, although the mechanisms in the Barents Sea and along the Eurasian coastline are different. The analysis suggests that the presence or absence of stratospheric circulation extremes in winter may play a nontrivial role in determining total September Arctic SIE when combined with other factors.

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Melissa Gervais, Eyad Atallah, John R. Gyakum, and L. Bruno Tremblay

Abstract

An important aspect of understanding the impacts of climate change on society is determining how the distribution of weather regimes will change. Arctic amplification results in greater warming over the Arctic compared to the midlatitudes, and this study examines how patterns of Arctic air masses will be affected. The authors employ the Community Earth System Model Large Ensemble (CESM-LE) RCP 8.5, consisting of 30 ensemble members run through the twenty-first century. Self-organizing maps are used to define archetypes of 850-hPa equivalent potential temperature anomalies with respect to a changing climate and assess changes in their frequency of occurrence. In the model, a pattern with negative anomalies over the central Arctic becomes less frequent in the future. There is also an increase in the frequency of patterns associated with an amplified ridge (trough) with positive (negative) anomalies over western (eastern) North America. It is hypothesized that the increase in frequency of such patterns is the result of enhanced forcing of baroclinic waves owing to reduced sea ice over the western Arctic. There is also a decline in patterns that have anomalously high over the North Atlantic, a pattern that is associated with intense ridging in the 500-hPa flow over the North Atlantic and colder over Europe. The authors relate the decrease of these patterns to an enhancement of the North Atlantic jet induced by a warming deficit in the North Atlantic Ocean.

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Rachel Kim, L. Bruno Tremblay, Charles Brunette, and Robert Newton

Abstract

Thinning sea ice cover in the Arctic is associated with larger interannual variability in the minimum sea ice extent (SIE). The current generation of forced or fully coupled models, however, has difficulty predicting SIE anomalies from the long-term trend, highlighting the need to better identify the mechanisms involved in the seasonal evolution of sea ice cover. One such mechanism is coastal divergence (CD), a proxy for ice thickness anomalies based on late winter ice motion, quantified using Lagrangian ice tracking. CD gains predictive skill through the positive feedback of surface albedo anomalies, mirrored in reflected solar radiation (RSR), during melt season. Exploring the dynamic and thermodynamic contributions to minimum SIE predictability, RSR, initial SIE (iSIE), and CD are compared as predictors using a regional seasonal sea ice forecast model for 1 July, 1 June, and 1 May forecast dates for all Arctic peripheral seas. The predictive skill of June RSR anomalies mainly originates from open water fraction at the surface; that is, June iSIE and June RSR have equal predictive skill for most seas. The finding is supported by the surprising positive correlation found between June melt pond fraction (MPF) and June RSR in all peripheral seas: MPF anomalies indicate the presence of ice or open water, which is key to creating minimum SIE anomalies. This contradicts models that show correlation between melt onset, MPF, and the minimum SIE. A hindcast model shows that for a 1 May forecast, CD anomalies have better predictive skill than RSR anomalies for most peripheral seas.

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Melissa Gervais, L. Bruno Tremblay, John R. Gyakum, and Eyad Atallah

Abstract

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 (0.25°) and low-resolution areal averaged (0.9° lat × 1.25° lon) precipitation fields. For example, the range of the representativeness errors is estimated, for annual extreme precipitation, to be from +16% to −12% in the low-resolution data, when station density is 5 stations per 0.9° lat × 1.25° lon grid box.

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Patricia DeRepentigny, L. Bruno Tremblay, Robert Newton, and Stephanie Pfirman

Abstract

The patterns of sea ice retreat in the Arctic Ocean are investigated using two global climate models (GCMs) that have profound differences in their large-scale mean winter atmospheric circulation and sea ice drift patterns. The Community Earth System Model Large Ensemble (CESM-LE) presents a mean sea level pressure pattern that is in general agreement with observations for the late twentieth century. The Community Climate System Model, version 4 (CCSM4), exhibits a low bias in its mean sea level pressure over the Arctic region with a deeper Icelandic low. A dynamical mechanism is presented in which large-scale mean winter atmospheric circulation has significant effect on the following September sea ice extent anomaly by influencing ice divergence in specific areas. A Lagrangian model is used to backtrack the 80°N line from the approximate time of the melt onset to its prior positions throughout the previous winter and quantify the divergence across the Pacific and Eurasian sectors of the Arctic. It is found that CCSM4 simulates more sea ice divergence in the Beaufort and Chukchi Seas and less divergence in the Eurasian seas when compared to CESM-LE, leading to a Pacific-centric sea ice retreat. On the other hand, CESM-LE shows a more symmetrical retreat between the Pacific, Eurasian, and Atlantic sectors of the Arctic. Given that a positive trend in the Arctic Oscillation (AO) index, associated with low sea level pressure anomalies in the Arctic, is a robust feature of GCMs participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5), these results suggest that the sea ice retreat in the Pacific sector could be amplified during the transition to a seasonal ice cover.

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Jan Sedlacek, Jean-François Lemieux, Lawrence A. Mysak, L. Bruno Tremblay, and David M. Holland

Abstract

The granular sea ice model (GRAN) from Tremblay and Mysak is converted from Cartesian to spherical coordinates. In this conversion, the metric terms in the divergence of the deviatoric stress and in the strain rates are included. As an application, the GRAN is coupled to the global Earth System Climate Model from the University of Victoria. The sea ice model is validated against standard datasets. The sea ice volume and area exported through Fram Strait agree well with values obtained from in situ and satellite-derived estimates. The sea ice velocity in the interior Arctic agrees well with buoy drift data. The thermodynamic behavior of the sea ice model over a seasonal cycle at one location in the Beaufort Sea is validated against the Surface Heat Budget of the Arctic Ocean (SHEBA) datasets. The thermodynamic growth rate in the model is almost twice as large as the observed growth rate, and the melt rate is 25% lower than observed. The larger growth rate is due to thinner ice at the beginning of the SHEBA period and the absence of internal heat storage in the ice layer in the model. The simulated lower summer melt is due to the smaller-than-observed surface melt.

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Melissa Gervais, John R. Gyakum, Eyad Atallah, L. Bruno Tremblay, and Richard B. Neale

Abstract

An intercomparison of the distribution and extreme values of daily precipitation between the National Center for Atmospheric Research Community Climate System Model, version 4 (CCSM4) and several observational/reanalysis data sources are conducted over the contiguous United States and southern Canada. The use of several data sources, from gridded station, satellite, and reanalysis products, provides a measure of errors in the reference datasets. An examination of specific locations shows that the global climate model (GCM) distributions closely match the observations along the East and West Coasts, with larger discrepancies in the Great Plains and Rockies. In general, the distribution of model precipitation is more positively skewed (more light and less heavy precipitation) in the Great Plains and the eastern United States compared to gridded station observations, a recurring error in GCMs. In the Rocky Mountains the GCMs generally overproduce precipitation relative to the observations and furthermore have a more negatively skewed distribution, with fewer lower daily precipitation values relative to higher values. Extreme precipitation tends to be underestimated in regions and time periods typically characterized by large amounts of convective precipitation. This is shown to be the result of errors in the parameterization of convective precipitation that have been seen in previous model versions. However, comparison against several data sources reveals that model errors in extreme precipitation are approaching the magnitude of the disparity between the reference products. This highlights the existence of large errors in some of the products employed as observations for validation purposes.

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Irina V. Gorodetskaya, L-Bruno Tremblay, Beate Liepert, Mark A. Cane, and Richard I. Cullather

Abstract

The impact of Arctic sea ice concentrations, surface albedo, cloud fraction, and cloud ice and liquid water paths on the surface shortwave (SW) radiation budget is analyzed in the twentieth-century simulations of three coupled models participating in the Intergovernmental Panel on Climate Change Fourth Assessment Report. The models are the Goddard Institute for Space Studies Model E-R (GISS-ER), the Met Office Third Hadley Centre Coupled Ocean–Atmosphere GCM (UKMO HadCM3), and the National Center for Atmosphere Research Community Climate System Model, version 3 (NCAR CCSM3). In agreement with observations, the models all have high Arctic mean cloud fractions in summer; however, large differences are found in the cloud ice and liquid water contents. The simulated Arctic clouds of CCSM3 have the highest liquid water content, greatly exceeding the values observed during the Surface Heat Budget of the Arctic Ocean (SHEBA) campaign. Both GISS-ER and HadCM3 lack liquid water and have excessive ice amounts in Arctic clouds compared to SHEBA observations. In CCSM3, the high surface albedo and strong cloud SW radiative forcing both significantly decrease the amount of SW radiation absorbed by the Arctic Ocean surface during the summer. In the GISS-ER and HadCM3 models, the surface and cloud effects compensate one another: GISS-ER has both a higher summer surface albedo and a larger surface incoming SW flux when compared to HadCM3. Because of the differences in the models’ cloud and surface properties, the Arctic Ocean surface gains about 20% and 40% more solar energy during the melt period in the GISS-ER and HadCM3 models, respectively, compared to CCSM3.

In twenty-first-century climate runs, discrepancies in the surface net SW flux partly explain the range in the models’ sea ice area changes. Substantial decrease in sea ice area simulated during the twenty-first century in CCSM3 is associated with a large drop in surface albedo that is only partly compensated by increased cloud SW forcing. In this model, an initially high cloud liquid water content reduces the effect of the increase in cloud fraction and cloud liquid water on the cloud optical thickness, limiting the ability of clouds to compensate for the large surface albedo decrease. In HadCM3 and GISS-ER, the compensation of the surface albedo and cloud SW forcing results in negligible changes in the net SW flux and is one of the factors explaining moderate future sea ice area trends. Thus, model representations of cloud properties for today’s climate determine the ability of clouds to compensate for the effect of surface albedo decrease on the future shortwave radiative budget of the Arctic Ocean and, as a consequence, the sea ice mass balance.

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