Abstract

Abstract. During the winter of 2019/2020, as the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) project started its work, the Arctic Oscillation (AO) experienced some of its largest shifts, ranging from a highly negative index in November 2019 to an extremely positive index during January–February–March (JFM) 2020. The permanent positive AO phase for the 3 months of JFM 2020 was accompanied by a prevailing positive phase of the Arctic Dipole (AD) pattern. Here we analyze the sea ice thickness (SIT) distribution based on CryoSat-2/SMOS satellite-derived data augmented with results from the hindcast simulation by the fully coupled Regional Arctic System Model (RASM) from November 2019 through March 2020. A notable result of the positive AO phase during JFM 2020 was large SIT anomalies of up to 1.3 m that emerged in the Barents Sea (BS), along the northeastern Canadian coast and in parts of the central Arctic Ocean. These anomalies appear to be driven by nonlinear interactions between thermodynamic and dynamic processes. In particular, in the Barents and Kara seas (BKS), they are a result of enhanced ice growth connected with low-temperature anomalies and the consequence of intensified atmospherically driven sea ice transport and deformations (i.e., ice divergence and shear) in this area. The Davies Strait, the east coast of Greenland and the BS regions are characterized by convergence and divergence changes connected with thinner sea ice at the ice borders along with an enhanced impact of atmospheric wind forcing. Low-pressure anomalies that developed over the eastern Arctic during JFM 2020 increased northerly winds from the cold Arctic Ocean to the BS and accelerated the southward drift of the MOSAiC ice floe. The satellite-derived and simulated sea ice velocity anomalies, which compared well during JFM 2020, indicate a strong acceleration of the Transpolar Drift relative to the mean for the past decade, with intensified speeds of up to 6 km d−1. As a consequence, sea ice transport and deformations driven by atmospheric surface wind forcing accounted for the bulk of the SIT anomalies, especially in January 2020 and February 2020. RASM intra-annual ensemble forecast simulations with 30 ensemble members forced with different atmospheric boundary conditions from 1 November 2019 through 30 April 2020 show a pronounced internal variability in the sea ice volume, driven by thermodynamic ice-growth and ice-melt processes and the impact of dynamic surface winds on sea ice formation and deformation. A comparison of the respective SIT distributions and turbulent heat fluxes during the positive AO phase in JFM 2020 and the negative AO phase in JFM 2010 corroborates the conclusion that winter sea ice conditions in the Arctic Ocean can be significantly altered by AO variability.

Highlights

  • The temporal evolution of the Arctic sea ice thickness distribution is the result of complex and highly variable interactions within the pack ice and its interactions with atmospheric and oceanic processes (e.g., Belter et al, 2021)

  • The Arctic Oscillation (AO) pattern was defined as the leading mode from empirical orthogonal function analysis of the monthly mean sea-level pressure (SLP) during the 1979–2000 period over the domain 20–90◦ N. This domain and this reference period were used for the calculation of the spatial AO patterns to ensure comparability with the widely used AO index provided by the NOAA Climate Prediction Center (CPC), which is based on the AO pattern calculated for the mentioned reference period and the National Centers for Environmental Predictions (NCEP)/NCAR reanalysis data set

  • Averaged sea ice thicknesses from remotely sensed data based on the European Space Agency (ESA) CryoSat-2/SMOS data set allowed the determination of Arctic-wide sea ice thickness distributions between 15 October 2019 and 15 April 2020

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Summary

Introduction

The temporal evolution of the Arctic sea ice thickness distribution is the result of complex and highly variable interactions within the pack ice and its interactions with atmospheric and oceanic processes (e.g., Belter et al, 2021). As it is a regional climate model that is forced along the boundaries with a realistic global atmospheric reanalysis such as the National Centers for Environmental Predictions (NCEP) Coupled Forecast System (CFS) Reanalysis (CFSR), RASM offers a unique capability to reproduce the observed natural environmental conditions with respect to place and time Given such capabilities, we (i) evaluate the skill of RASM at reproducing the sea ice thickness distribution from CryoSat-2/SMOS satellite-derived data from November 2019 until March 2020, (ii) diagnose the observed evolution of sea ice, and (iii) investigate the mechanisms of and the interplay between thermodynamic growth and dynamic sea ice processes for a positive AO phase. We end this paper with results from the RASM ensemble forecasts to quantify the strength of internal variability driven by regional processes within the Arctic climate system and a comparison of RASM sea ice conditions and turbulent surface heat fluxes between the AO-positive winter of 2019/2020 and AO-negative winter of 2009/2010

Data and model setup
Atmospheric circulation and states of the AO and AD patterns
Sea ice thickness and extent
Model evaluation
Interpretation of the positive sea ice anomaly in the BS
Transpolar sea ice drift
Internal variability
Case study of positive and negative AO winters
Summary and conclusions
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