Synoptic-scale variability in the Beaufort High and spring ice opening in the Beaufort Sea

This study examines the role of synoptic-scale variability in the Beaufort High in spring ice opening in the Beaufort Sea using data over the 2000–2019 period. A few days before ice opening in spring, the Beaufort High begins to strengthen, deform, and shift eastward from the Chukchi Sea to the western Canadian Arctic Archipelago due to warming in eastern Siberia, and the Aleutian Low is also enhanced. The pressure gradient between the Aleutian Low and the Beaufort High increases rapidly, promoting strong southeasterly winds. As the Beaufort High shifts eastward, the anticyclonic sea ice circulation in the Beaufort Sea tends to be an asymmetrical gyre; as a result, sea ice in the eastern Beaufort Sea is exported toward the western Beaufort Sea without supplementation from the north. Sea ice volume budget analysis indicates that when southeasterly winds are prevailing, wind-forced ice advection and divergence dominate the ice opening in the southeastern Beaufort Sea. Although both the basal and lateral melting are promoted during the ice opening, their contributions to the reduction of ice volume are negligible

Temporal evolution of a geostrophic current under sea ice: analytical and numerical solutions

Author Posting. © American Meteorological Society, 2022. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 52(6),(2022): 1191-1204, https://doi.org/10.1175/jpo-d-21-0242.1.A simplified quasigeostrophic (QG) analytical model together with an idealized numerical model are used to study the effect of uneven ice–ocean stress on the temporal evolution of the geostrophic current under sea ice. The tendency of the geostrophic velocity in the QG model is given as a function of the lateral gradient of vertical velocity and is further related to the ice–ocean stress with consideration of a surface boundary layer. Combining the analytical and numerical solutions, we demonstrate that the uneven stress between the ice and an initially surface-intensified, laterally sheared geostrophic current can drive an overturning circulation to trigger the displacement of isopycnals and modify the vertical structure of the geostrophic velocity. When the near-surface isopycnals become tilted in the opposite direction to the deeper ones, a subsurface velocity core is generated (via geostrophic setup). This mechanism should help understand the formation of subsurface currents in the edge of Chukchi and Beaufort Seas seen in observations. Furthermore, our solutions reveal a reversed flow extending from the bottom to the middepth, suggesting that the ice-induced overturning circulation potentially influences the currents in the deep layers of the Arctic Ocean, such as the Atlantic Water boundary current.This work was funded by the National Key Research and Development Program of China (Grant 2017YFA0604600), the National Natural Science Foundation of China (Grant 41676019), the Fundamental Research Funds for the Central Universities (Grant 2019B81214), the Postgraduate Research and Practice Innovation Program of Jiangsu Province (Grant KYCX19_0384), and the National Science Foundation (MAS, Grants OPP-1822334, OCE-2122633)

Model‐simulated interannual variability of Lake Erie ice cover, circulation, and thermal structure in response to atmospheric forcing, 2003–2012

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/100281/1/jgrc20312.pd

Severe Ice Cover on Great Lakes During Winter 2008–2009

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94591/1/eost17090.pd

Interannual variability of Great Lakes ice cover and its relationship to NAO and ENSO

The impacts of North Atlantic Oscillation (NAO) and El Niño–Southern Oscillation (ENSO) on Great Lakes ice cover were investigated using lake ice observations for winters 1963–2010 and National Centers for Environmental Prediction reanalysis data. It is found that both NAO and ENSO have impacts on Great Lakes ice cover. The Great Lakes tend to have lower (higher) ice cover during the positive (negative) NAO. El Niño events are often associated with lower ice cover. The influence of La Niña on Great Lakes ice cover is intensity-dependent: strong (weak ) La Niña events are often associated with lower (higher) ice cover. The interference of impacts of ENSO and NAO complicates the relationship between ice cover and either of them. The nonlinear effects of ENSO on Great Lakes ice cover are important in addition to NAO effects. The correlation coefficient between the quadratic Nino3.4 index and ice cover (−0.48) becomes significant at the 99% confidence level. The nonlinear response of Great Lakes ice cover to ENSO is mainly due to the phase shift of the teleconnection patterns during the opposite phases of ENSO. Multiple-variable nonlinear regression models were developed for ice coverage. Using the quadratic Nino3.4 index instead of the index itself can significantly improve the prediction of Great Lakes ice cover (the correlation between the modeled and observed increases from 0.35 to 0.51). Including the interactive term NAO·Nino3.4² further improves the prediction skill (the correlation increases from 0.51 to 0.59). The analysis is also applied to individual lakes. The model for Lake Michigan has the highest prediction skill, while Lake Erie has the smallest skill

Development and validation of a preoperative MRI-based radiomics nomogram to predict progression-free survival in patients with clival chordomas

ObjectivesThe aim of this study was to establish and validate a MRI-based radiomics nomogram to predict progression-free survival (PFS) of clival chordoma.MethodsA total of 174 patients were enrolled in the study (train cohort: 121 cases, test cohort: 53 cases). Radiomic features were extracted from multiparametric MRIs. Intraclass correlation coefficient analysis and a Lasso and Elastic-Net regularized generalized linear model were used for feature selection. Then, a nomogram was established via univariate and multivariate Cox regression analysis in the train cohort. The performance of this nomogram was assessed by area under curve (AUC) and calibration curve.ResultsA total of 3318 radiomic features were extracted from each patient, of which 2563 radiomic features were stable features. After feature selection, seven radiomic features were selected. Cox regression analysis revealed that 2 clinical factors (degree of resection, and presence or absence of primary chordoma) and 4 radiomic features were independent prognostic factors. The AUC of the established nomogram was 0.747, 0.807, and 0.904 for PFS prediction at 1, 3, and 5 years in the train cohort, respectively, compared with 0.582, 0.852, and 0.914 in the test cohort. Calibration and risk score stratified survival curves were satisfactory in the train and test cohort.ConclusionsThe presented nomogram demonstrated a favorable predictive accuracy of PFS, which provided a novel tool to predict prognosis and risk stratification. Our results suggest that radiomic analysis can effectively help neurosurgeons perform individualized evaluations of patients with clival chordomas

Simulating the 1998 spring bloom in Lake Michigan using a coupled physical‐biological model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95047/1/jgrc12526.pd

Interannual variability of Great Lakes ice cover and its relationship to NAO and ENSO

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94684/1/jgrc12031.pd

Summer Changes in Water Mass Characteristics and Vertical Thermohaline Structure in the Eastern Chukchi Sea, 1974–2017

Hydrographic data from the World Ocean Database 2013 and the Chinese National Arctic Research Expedition were used to investigate the summertime changes in the eastern Chukchi Sea from 1974 to 2017. Owing to the Pacific inflow and timing of the sea ice retreat, water masses and vertical thermohaline structures in the eastern Chukchi Sea have changed but with regional differences. The entire eastern Chukchi Sea warmed up with significant temperature increase in the central shelf; however, the surface and bottom salinity in the southern, central, and northern shelves exhibited different trends. The northward extension of the Pacific Summer Water after 1997 influenced the summer hydrography significantly. Moreover, the data reveal changes in the characteristics of various water masses. Both Bering Summer Water (BSW) and Pacific Winter Water in the deeper layer became saltier, whereas the Alaskan Coastal Water in the upper layer became fresher after 1997. The previous definition of the BSW should be modified to include the warming water mass in the southern Chukchi Sea in the more recent years. Furthermore, the vertical thermohaline structure over the Chukchi shelves experienced considerable changes in its characteristics due to the combined effects of the Pacific inflow and surface forcing

Temporal and Spatial Variability of Great Lakes Ice Cover, 1973–2010

In this study, temporal and spatial variability of ice cover in the Great Lakes are investigated using historical satellite measurements from 1973 to 2010. The seasonal cycle of ice cover was constructed for all the lakes, including Lake St. Clair. A unique feature found in the seasonal cycle is that the standard deviations (i.e., variability) of ice cover are larger than the climatological means for each lake. This indicates that Great Lakes ice cover experiences large variability in response to predominant natural climate forcing and has poor predictability. Spectral analysis shows that lake ice has both quasi-decadal and interannual periodicities of;8 and ~4 yr. There was a significant downward trend in ice coverage from 1973 to the present for all of the lakes, with Lake Ontario having the largest, and Lakes Erie and St. Clair having the smallest. The translated total loss in lake ice over the entire 38-yr record varies from 37% in Lake St. Clair (least) to 88% in Lake Ontario (most). The total loss for overall Great Lakes ice coverage is 71%, while Lake Superior places second with a 79% loss. An empirical orthogonal function analysis indicates that a major response of ice cover to atmospheric forcing is in phase in all six lakes, accounting for 80.8% of the total variance. The second mode shows an out-of-phase spatial variability between the upper and lower lakes, accounting for 10.7% of the total variance. The regression of the first EOF-mode time series to sea level pressure, surface air temperature, and surface wind shows that lake ice mainly responds to the combined Arctic Oscillation and El Nin˜ o–Southern Oscillation patterns