Search for: All records

Abstract The Last Ice Area—located to the north of Greenland and the northern Canadian Arctic Archipelago—is expected to persist as the central Arctic Ocean becomes seasonally ice-free within a few decades. Projections of the Last Ice Area, however, have come from relatively low resolution Global Climate Models that do not resolve sea ice export through the waterways of the Canadian Arctic Archipelago and Nares Strait. Here we revisit Last Ice Area projections using high-resolution numerical simulations from the Community Earth System Model, which resolves these narrow waterways. Under a high-end forcing scenario, the sea ice of the Last Ice Area thins and becomes more mobile, resulting in a large export southward. Under this potentially worst-case scenario, sea ice of the Last Ice Area could disappear a little more than one decade after the central Arctic Ocean has reached seasonally ice-free conditions. This loss would have profound impacts on ice-obligate species. 

Abstract Observations show predictive skill of the minimum sea ice extent (Min SIE) from late winter anomalous offshore ice drift along the Eurasian coastline, leading to local ice thickness anomalies at the onset of the melt season—a signal then amplified by the ice–albedo feedback. We assess whether the observed seasonal predictability of September sea ice extent (Sept SIE) from Fram Strait Ice Area Export (FSIAE; a proxy for Eurasian coastal divergence) is present in global climate model (GCM) large ensembles, namely the CESM2-LE, GISS-E2.1-G, FLOR-LE, CNRM-CM6-1, and CanESM5. All models show distinct periods where winter FSIAE anomalies are negatively correlated with the May sea ice thickness (May SIT) anomalies along the Eurasian coastline, and the following Sept Arctic SIE, as in observations. Counterintuitively, several models show occasional periods where winter FSIAE anomalies are positively correlated with the following Sept SIE anomalies when the mean ice thickness is large, or late in the simulation when the sea ice is thin, and/or when internal variability increases. More important, periods with weak correlation between winter FSIAE and the following Sept SIE dominate, suggesting that summer melt processes generally dominate over late-winter preconditioning and May SIT anomalies. In general, we find that the coupling between the winter FSIAE and ice thickness anomalies along the Eurasian coastline at the onset of the melt season is a ubiquitous feature of GCMs and that the relationship with the following Sept SIE is dependent on the mean Arctic sea ice thickness. 

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. 

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. 

This dataset contains data used in the paper: Revisiting the Last Ice Area Projections from a High-Resolution Global Earth System Model - Fol et al (2025). Results are organized in excel files or numpy arrays with the dataset name, variable and ensemble member (for simulations) in the name of the file. See below for more information on what variables are included in the files and their structure.  CESM_HR :  CESM_HR_SIAFluxes - per ensemble member: Timeseries of monthly SIA flux per gate. CESM_HR_fluxesCAADiv.npy, _fluxesQEIDiv.npy, _fuxesQEIDivMeltSeason.npy : Timeseries of annual divergence over the Queen Elizabeth Islands and the Southern Canadian Arctic Archipelago derived from monthly SIA fluxes at the entry and exit gates. CESM_HR_ThicknessDistribution.xlsx : Thickness distribution for the LIA-N, QEI, and CAA-S computed from the simulated thickness distribution (aicen001, aicen002, aicen003, aicen004, aicen005). CESM_HR_tendencies - per ensemble member- per region (LIA-N, QEI, CAA-S). Timeseries of melt season integrated thermodynamic, dynamic (advection and ridging terms) sea ice area loss. CESH_HR_sitPanArctic - per ensemble member: Timeseries of pan-Arctic mean may sea ice thickness. CESM_HR_sieSept and CESM_HR_sieMarch - per ensemble member- per region (LIA-N, QEI, CAA-S) and pan-Arctic : Timeseries of March or September sea ice extent, sea ice area. CESM_HR_sic - per ensemble member- per region (LIA-N, QEI, CAA-S): Timeseries of mean sea ice concentration for grid cells having more than 15% of SIC (no open water).  CESM_HR_meltSeason - per ensemble member- per region (LIA-N, QEI, CAA-S): Timeseries of annual freeze and melt onset dates allowing the definition of the melt season based on the thermodynamic sea ice area tendency crossing 0.  CESM_HR_mean_mayThickness.npy and CESM_HR_meanseptconc.npy: Results for map of the mean september sea ice concentration and may ice thickness for 1981-2000, 2001-2020, 2021-2040 and 2041-2060. CESM_LR :  CESM_LR_sieSept - per region (LIA-N, QEI, CAA-S) and pan-Arctic: Timeseries of September sea ice extent and sea ice area. CESM_LR_sitPanArctic.xlsx: Timeseries of pan-Arctic mean May sea ice thickness. CESM_LR_tendencies - per region (LIA-N, QEI, CAA-S). Timeseries of melt season integrated thermodynamic, dynamic (advection and ridging terms) sea ice area loss. CESM_LR_meltSeason - per region (LIA-N, QEI, CAA-S): Timeseries of annual freeze and melt onset dates allowing the definition of the melt season based on the thermodynamic sea ice area tendency crossing 0. CESM2_LE:  CESM2_LE_CAA_sept, _LIAN_sept, _QEI_sept, panArctic: Mean September sea ice extent and sea ice area per region. There is one excel tab per ensemble member in each file. CESM2_LE_pan_Arctic_hi_may: Pan-Arctic mean May sea ice thickness. There is one excel tab per ensemble member in each file. PIOMAS:  PIOMAS_panArctic_hi.xlsx: Timeseries of mean may sea ice thickness. PIOMAS_mean_1981_2000_mean_mayThickness.npy : Results for map of the mean May ice thickness for 1981-2000 and 2001-2020. Observations: CIS_marchSept_1982_1990_sie - per region (QEI and CAA-S) :  Timeseries of March and September mean sea ice extent and area. CIS ice charts do not fully cover the LIA-N.  NSDICCDR_1979_2023_sia - per region (LIA-N, QEI, CAA-S) and pan-Arctic : Timeseries of March and September mean sea ice extent and area. NSDICCDR_1981_2000_mean_septConc.npy: Results for map of the mean September sea ice concentration for 1981-2000 and 2001-2020. NSDICCDR_1981_2000_LIAN_sic.xlsx: Timeseries of monthly mean sea ice concentration in the LIA-N. NSDICCDR_1981_2000_QEI_sic.xlsx: Timeseries of monthly mean sea ice concentration in the QEI. These results are derived from the following datasets:  Ensemble members 1 and 3 of simulations from the high-resolution Community Earth System Model version 1.3 (CESM1.3-HR) produced for the International Laboratory for High-Resolution Earth System Prediction (iHESP) by the Qingdao National Laboratory for Marine Science and Technology (QNLM), Texas A&M University (TAMU), and the U.S. National Center for Atmospheric Research (NCAR). The lower resolution simulation is also used (CESM1.3-LR). (Chang et al., 2020; Zhang et al., 2020). The 100-ensemble members Community Earth System Model (version 2) Large Ensemble (CESM2-LE) (Danabasoglu et al., 2020). Satellite-derived monthly mean SIA fluxes through entry and exit gates of the CAA and Nares Strait (Howell et a., 2019; 2021; 2023; 2024; Smedsrud et al., 2017; Kwok, 2006). The National Snow and Ice Data Center (NSIDC) Climate Data Record (CDR) (version 4) sea ice concentration, stored on a 25 x 25 km polar stereographic grid centered on the North Pole from 1979 to 2023 (Meier et al., 2021). The gridded version of the regional Canadian Ice Service (CIS) Digital Archive ice charts from the Eastern and Western Arctic regions (Tivy et al., 2011). The Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) assimilated sea ice concentration and derived ice thickness distribution estimates in the Arctic from 1978 to 2022 (Zhang et al., 2000).   

We apply the Canny edge algorithm to imagery from the Utqiaġvik coastal sea ice radar system (CSIRS) to identify regions of open water and sea ice and quantify ice concentration. The radar-derived sea ice concentration (SIC) is compared against the (closest to the radar field of view) 25 km resolution NSIDC Climate Data Record (CDR) and the 1 km merged MODIS-AMSR2 sea ice concentrations within the ∼11 km field of view for the year 2022–2023, when improved image contrast was first implemented. The algorithm was first optimized using sea ice concentration from 14 different images and 10 ice analysts (140 analyses in total) covering a range of ice conditions with landfast ice, drifting ice, and open water. The algorithm is also validated quantitatively against high-resolution MODIS-Terra in the visible range. Results show a correlation coefficient and mean bias error between the optimized algorithm, the CDR and MODIS-AMSR2 daily SIC of 0.18 and 0.54, and ∼−1.0 and 0.7%, respectively, with an averaged inter-analyst error of ±3%. In general, the CDR captures the melt period correctly and overestimates the SIC during the winter and freeze-up period, while the merged MODIS-AMSR2 better captures the punctual break-out events in winter, including those during the freeze-up events (reduction in SIC). Remnant issues with the detection algorithm include the false detection of sea ice in the presence of fog or precipitation (up to 20%), quantified from the summer reconstruction with known open water conditions. The proposed technique allows for the derivation of the SIC from CSIRS data at spatial and temporal scales that coincide with those at which coastal communities members interact with sea ice. Moreover, by measuring the SIC in nearshore waters adjacent to the shoreline, we can quantify the effect of land contamination that detracts from the usefulness of satellite-derived SIC for coastal communities. 

We quantify changes in break‐up events of landfast ice in the transition from a perennial to a seasonal sea ice cover in the Arctic. A break‐up event is defined as a time when coastal sea ice concentration drops below 95% after a minimum period of 10 days of stable ice conditions. To this end we analyze output diagnostics from the Community Earth System Model (Version 1) – Large Ensemble from 1920 to 2080, focusing on six coastal communities of Alaska, Chukotka, and the Kamtchatka Peninsula: Utqiaġvik, Point Hope, Gambell, Novoye Chaplino, Sireniki, and Pakhachi. Model results generally agree with the satellite record with open water formation along the coastline associated with sustained offshore winds, although the sensitivity of CESM1‐LE is higher than that of observations due to the absence of a landfast ice parameterization in CESM1‐ LE. Specifically, we see a linear relationship between the magnitude of the opening and offshore surface wind stresses integrated over the 10 days prior to the opening event, (p‐value < 0.01). While the break‐up event frequency increases (5.53 × 10−5 events/day/year for Utqiagvik) in the 21st century due to the thin- ning, or weakening, of the landfast ice cover, the total number of winter break‐up events decreases due to a shortening of the winter season (mean of ‐5.3 days/decade). 

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.