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  1. Abstract

    In recent decades, the Arctic minimum sea ice extent has transitioned from a predominantly thick multiyear ice cover to a thinner seasonal ice cover. We partition the total (observed) Arctic summer area loss into thermodynamic and dynamic (convergence, ridging, and export) sea ice area loss during the satellite era from 1979 to 2021 using a Lagrangian sea ice tracking model driven by satellite-derived sea ice velocities. Results show that the thermodynamic signal dominates the total summer ice area loss and the dynamic signal remains small (∼20%) even in 2007 when dynamic loss was largest. Sea ice loss by compaction (within pack ice convergence) dominates the dynamic area loss, even in years when the export is largest. Results from a simple (Ekman) free-drift sea ice model, supported by results from the Lagrangian model, suggest that nonlinear effects between dynamic and thermodynamic area loss can be important for large negative anomalies in sea ice extent, in accord with previous modeling studies. A detailed analysis of two all-time record minimum years (2007 and 2012)—one with a semipermanent high in the southern Beaufort Sea and the other with a short-lived but extreme storm in the Pacific sector of the Arctic in late summer—shows that compaction by Ekman convergence together with large thermodynamic melt in the marginal ice zone dominated the sea ice area loss in 2007 whereas, in 2012, it was dominated by Ekman divergence amplified by sea–ice albedo feedback—together with an early melt onset. We argue that Ekman divergence from more intense summer storms when the sun is high above the horizon is a more likely mechanism for a “first-time” ice-free Arctic.

     
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  2. Abstract

    We compare the vertical hydrography of the Community Earth System Model Large Ensemble (CESM1‐LE) with observations from two specific periods: the Arctic Ice Dynamics Joint Experiment (AIDJEX; 1975–1976) and Ice‐Tethered Profilers (ITP; 2004–2018). A comparison between simulated and observed salinity and potential temperature profiles highlights two key model biases in all ensemble members: (a) an absence of Pacific Waters in the water column and (b) a slight deepening of the May mixed layer contrary to observations, which show a large reduction in the mixed‐layer depth and an increase in stratification over the same time period. We examine processes controlling the sea ice mass balance using a one‐dimensional vertical heat budget in the light of the model limitations implied by these two biases. Results indicate that remnant solar heat trapped beneath the halocline is mostly ventilated to the surface by mixing before the following melt season. Furthermore, we find that vertical advection associated with Ekman pumping has only a small effect on the vertical heat transport, even in early fall when the winds are strong and the pack ice is weak. Lastly, we estimate the impact of the missing Pacific Waters at 0.40 m of reduced winter ice growth.

     
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  3. Abstract. Free-drift estimates of sea ice motion are necessary to produce a seamless observational record combining buoy and satellite-derived sea ice motionvectors. We develop a new parameterization for the free drift of sea ice based on wind forcing, wind turning angle, sea ice state variables(thickness and concentration), and estimates of the ocean currents. Given the fact that the spatial distribution of the wind–ice–ocean transfercoefficient has a similar structure to that of the spatial distribution of sea ice thickness, we take the standard free-drift equation and introducea wind–ice–ocean transfer coefficient that scales linearly with ice thickness. Results show a mean bias error of −0.5 cm s−1(low-speed bias) and a root-mean-square error of 5.1 cm s−1, considering daily buoy drift data as truth. This represents a 35 %reduction of the error on drift speed compared to the free-drift estimates used in the Polar Pathfinder dataset (Tschudi et al., 2019b). Thethickness-dependent transfer coefficient provides an improved seasonality and long-term trend of the sea ice drift speed, with a minimum (maximum)drift speed in May (October), compared to July (January) for the constant transfer coefficient parameterizations which simply follow the peak inmean surface wind stresses. Over the 1979–2019 period, the trend in sea ice drift in this new model is +0.45 cm s−1 per decadecompared with +0.39 cm s−1 per decade from the buoy observations, whereas there is essentially no trend in a free-driftparameterization with a constant transfer coefficient (−0.09 cm s−1 per decade) or the Polar Pathfinder free-drift input data(−0.01 cm s−1 per decade). The optimal wind turning angle obtained from a least-squares fitting is 25∘, resulting in a meanerror and a root-mean-square error of +3 and 42∘ on the direction of the drift, respectively. The ocean current estimates obtained from theminimization procedure resolve key large-scale features such as the Beaufort Gyre and Transpolar Drift Stream and are in good agreement with oceanstate estimates from the ECCO, GLORYS, and PIOMAS ice–ocean reanalyses, as well as geostrophic currents from dynamical ocean topography, with aroot-mean-square difference of 2.4, 2.9, 2.6, and 3.8 cm s−1, respectively. Finally, a repeat of the analysis on two sub-sections of thetime series (pre- and post-2000) clearly shows the acceleration of the Beaufort Gyre (particularly along the Alaskan coastline) and an expansion ofthe gyre in the post-2000s, concurrent with a thinning of the sea ice cover and the observed acceleration of the ice drift speed and oceancurrents. This new dataset is publicly available for complementing merged observation-based sea ice drift datasets that include satellite and buoydrift records. 
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