Evolution Of Arctic Sea Ice Research Articles

The evolution of the Fram Strait sea ice volume export decomposed by age: estimating with parameter-optimized sea ice-ocean model outputs

Sea ice export through the Fram Strait is crucial in the dynamic evolution of Arctic sea ice and can further modulate Arctic sea ice mass balance as well as the ocean thermohaline circulation. In this study, based on outputs from a parameter-optimized and fully physical ocean–sea ice coupled model and sea ice age observation, we estimate sea ice volume (SIV) flux and its age evolution via the Fram Strait. The estimate of mean annual SIV flux is about 1605 315 km3 yr−1 without a significant trend for 1979–2021. Combining with sea ice age data, the variation of the sea ice age and its corresponding SIV flux are obtained for 1984–2020. The SIV flux of 1st-year ice significantly increases as expected, but it still contributes very little to the total flux in the 2010s with a proportion of 3.5%. SIV fluxes of different ages in multi-year ice present diverse variations. The proportions of 2nd-year ice and 3rd-year ice in the annual SIV flux show an extreme increase from 6.8% and 25.0% in the 1980s to 49.0% and 38.8% in the 2010s, respectively, while the proportions of 4th-year ice and 5th-year and older (5+ year) ice significantly decrease from 22.8% and 45.0% in the 1980s to 7.1% and 1.6% in the 2010s, respectively. Meanwhile, the prevailing age of annual volume export via Fram Strait shifts from 4th-year and 5+ year ice to 2nd-year and 3rd-year ice around 2007/2008. It is worth noticing that the variation in Fram Strait ice export modulates the variation in Arctic SIV prior to 2008, but the reverse is true after 2008, indicating a decreasing influence of Fram Strait SIV export on Artic SIV variability with decreasing sea ice age. The results are beneficial to promote the understanding of the evolution of Fram Strait SIV export under the warming Arctic.

An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018

Abstract. The improved and updated Coupled Arctic Prediction System (CAPS) is evaluated using a set of Pan-Arctic prediction experiments for the year 2018. CAPS is built on the Weather Research and Forecasting model (WRF), the Regional Ocean Modeling System (ROMS), the Community Ice CodE (CICE), and a data assimilation based on the local error subspace transform Kalman filter. We analyze physical processes linking improved and changed physical parameterizations in WRF, ROMS, and CICE to changes in the simulated Arctic sea ice state. Our results show that the improved convection and boundary layer schemes in WRF result in an improved simulation of downward radiative fluxes and near-surface air temperature, which influences the predicted ice thickness. The changed tracer advection and vertical mixing schemes in ROMS reduce the bias in sea surface temperature and change ocean temperature and salinity structure in the surface layer, leading to improved evolution of the predicted ice extent (particularly correcting the late ice recovery issue in the previous CAPS). The improved sea ice thermodynamics in CICE have noticeable influences on the predicted ice thickness. The updated CAPS can better predict the evolution of Arctic sea ice during the melting season compared with its predecessor, though the prediction still has some biases at the regional scale. We further show that the updated CAPS can remain skillful beyond the melting season, which may have a potential value for stakeholders to make decisions for socioeconomic activities in the Arctic.

Regional variability of Arctic sea ice seasonal change climate indicators from a passive microwave climate data record

The seasonal evolution of Arctic sea ice can be described by the timing of key dates of sea ice concentration (SIC) change during its annual retreat and advance cycle. Here, we use SICs from a satellite passive microwave climate data record to identify the sea ice dates of opening (DOO), retreat (DOR), advance (DOA), and closing (DOC) and the periods of time between these events. Regional variability in these key dates, periods, and sea ice melt onset and freeze-up dates for 12 Arctic regions during the melt seasons of 1979–2016 is investigated. We find statistically significant positive trends in the length of the melt season (outer ice-free period) for most of the eastern Arctic, the Bering Sea, and Hudson and Baffin Bays with trends as large as 11.9 d decade−1 observed in the Kara Sea. Trends in the DOR and DOA contribute to statistically significant increases in the length of the open water period for all regions within the Arctic Ocean ranging from 3.9 to 13.8 d decade−1. The length of the ice retreat period (DOR−DOO) ranges from 17.1 d in the Sea of Okhotsk to 41 d in the Greenland Sea. The length of the ice advance period (DOC−DOA) is generally much shorter and ranges from 17.9 to 25.3 d in the Sea of Okhotsk and Greenland Sea, respectively. Additionally, we derive the extent of the seasonal ice zone (SIZ) and find statistically significant negative trends (SIZ is shrinking) in the Sea of Okhotsk, Baffin Bay, Greenland Sea, and Barents Sea regions, which are geographically open to the oceans and influenced by reduced winter sea ice extent. Within regions of the Arctic Ocean, statistically significant positive trends indicate that the extent of the SIZ is expanding as Arctic summer sea ice declines.

The potential of sea ice leads as a predictor for summer Arctic sea ice extent

Abstract. The Arctic sea ice extent throughout the melt season is closely associated with initial sea ice state in winter and spring. Sea ice leads are important sites of energy fluxes in the Arctic Ocean, which may play an important role in the evolution of Arctic sea ice. In this study, we examine the potential of sea ice leads as a predictor for summer Arctic sea ice extent forecast using a recently developed daily sea ice lead product retrieved from the Moderate-Resolution Imaging Spectroradiometer (MODIS). Our results show that July pan-Arctic sea ice extent can be predicted from the area of sea ice leads integrated from midwinter to late spring, with a prediction error of 0.28 million km2 that is smaller than the standard deviation of the observed interannual variability. However, the predictive skills for August and September pan-Arctic sea ice extent are very low. When the area of sea ice leads integrated in the Atlantic and central and west Siberian sector of the Arctic is used, it has a significantly strong relationship (high predictability) with both July and August sea ice extent in the Atlantic and central and west Siberian sector of the Arctic. Thus, the realistic representation of sea ice leads (e.g., the areal coverage) in numerical prediction systems might improve the skill of forecast in the Arctic region.

The unprecedented 2016-17 Arctic sea ice growth season: the crucial role of atmospheric rivers and longwave fluxes.

The 2016-17 Arctic sea ice growth season (October-March) exhibited the lowest end-of-season sea ice volume and extent of any year since 1979. An analysis of MERRA2 atmospheric reanalysis data and CERES radiative flux data reveals that a record warm and moist Arctic atmosphere supported the reduced sea ice growth through two pathways. First, numerous regional episodes of increased atmospheric temperature and moisture, transported from lower latitudes, increased the cumulative energy input from downwelling longwave surface fluxes. Second, in those same episodes, the efficiency that the atmosphere cooled radiatively to space was reduced, increasing the amount of energy retained in the Arctic atmosphere and reradiated back toward the surface. Overall, the Arctic radiative cooling efficiency shows a decreasing trend since 2000. The results presented highlight the increasing importance of atmospheric forcing on sea ice variability demonstrating that episodic Arctic atmospheric rivers, regions of elevated poleward water vapor transport, and the subsequent surface energy budget response is a critical mechanism actively contributing to the evolution of Arctic sea ice.

NeXtSIM: a new Lagrangian sea ice model

Abstract. The Arctic sea ice cover has changed drastically over the last decades. Associated with these changes is a shift in dynamical regime seen by an increase of extreme fracturing events and an acceleration of sea ice drift. The highly non-linear dynamical response of sea ice to external forcing makes modelling these changes and the future evolution of Arctic sea ice a challenge for current models. It is, however, increasingly important that this challenge be better met, both because of the important role of sea ice in the climate system and because of the steady increase of industrial operations in the Arctic. In this paper we present a new dynamical/thermodynamical sea ice model called neXtSIM that is designed to address this challenge. neXtSIM is a continuous and fully Lagrangian model, whose momentum equation is discretised with the finite-element method. In this model, sea ice physics are driven by the combination of two core components: a model for sea ice dynamics built on a mechanical framework using an elasto-brittle rheology, and a model for sea ice thermodynamics providing damage healing for the mechanical framework. The evaluation of the model performance for the Arctic is presented for the period September 2007 to October 2008 and shows that observed multi-scale statistical properties of sea ice drift and deformation are well captured as well as the seasonal cycles of ice volume, area, and extent. These results show that neXtSIM is an appropriate tool for simulating sea ice over a wide range of spatial and temporal scales.

Modeled Arctic sea ice evolution through 2300 in CMIP5 extended RCPs

Abstract. Almost all global climate models and Earth system models that participated in the Coupled Model Intercomparison Project 5 (CMIP5) show strong declines in Arctic sea ice extent and volume under the highest forcing scenario of the representative concentration pathways (RCPs) through 2100, including a transition from perennial to seasonal ice cover. Extended RCP simulations through 2300 were completed for a~subset of models, and here we examine the time evolution of Arctic sea ice in these simulations. In RCP2.6, the summer Arctic sea ice extent increases compared to its minimum following the peak radiative forcing in 2044 in all nine models. RCP4.5 demonstrates continued summer Arctic sea ice decline after the forcing stabilizes due to continued warming on longer timescales. Based on the analysis of these two scenarios, we suggest that Arctic summer sea ice extent could begin to recover if and when radiative forcing from greenhouse gas concentrations were to decrease. In RCP8.5 the Arctic Ocean reaches annually ice-free conditions in seven of nine models. The ensemble of simulations completed under the extended RCPs provide insight into the global temperature increase at which sea ice disappears in the Arctic and the reversibility of declines in seasonal sea ice extent.

A stochastic perturbation theory for non-autonomous systems

We develop a perturbation theory for a class of first order nonlinear non-autonomous stochastic ordinary differential equations that arise in climate physics. The perturbative procedure produces moments in terms of integral delay equations, whose order by order decay is characterized in a Floquet-like sense. Both additive and multiplicative sources of noise are discussed and the question of how the nature of the noise influences the results is addressed theoretically and numerically. By invoking the Martingale property, we rationalize the transformation of the underlying Stratonovich form of the model to an It$\hat{\text{o}}$ô form, independent of whether the noise is additive or multiplicative. The generality of the analysis is demonstrated by developing it both for a Brownian particle moving in a periodically forced quartic potential, which acts as a simple model of stochastic resonance, as well as for our more complex climate physics model. The validity of the approach is shown by comparison with numerical solutions. The particular climate dynamics problem upon which we focus involves a low-order model for the evolution of Arctic sea ice under the influence of increasing greenhouse gas forcing ΔF0. The deterministic model, developed by Eisenman and Wettlaufer [“Nonlinear threshold behavior during the loss of Arctic sea ice,” Proc. Natl. Acad. Sci. U.S.A. 106(1), 28–32 (2009)] exhibits several transitions as ΔF0 increases and the stochastic analysis is used to understand the manner in which noise influences these transitions and the stability of the system.

Arctic sea‐ice evolution as modeled by Max Planck Institute for Meteorology's Earth system model

We describe the evolution of Arctic sea ice as modeled by the Max Planck Institute for Meteorology's Earth System Model (MPI‐ESM). The modeled spatial distribution and interannual variability of the sea‐ice cover agree well with satellite observations and are improved relative to the model's predecessor ECHAM5/MPIOM. An evaluation of modeled sea‐ice coverage based on sea‐ice area gives, however, conflicting results compared to an evaluation based on sea‐ice extent and is additionally hindered by uncertainties in the observational record. Simulated trends in sea‐ice coverage for the satellite period range from more strongly negative than observed to positive. The observed evolution of Arctic sea ice is incompatible with modeled internal variability and probably caused by external forcing. Simulated drift patterns agree well with observations, but simulated drift speed is generally too high. Simulated sea‐ice volume agrees well with volume estimates of the PIOMAS reanalysis for the past few years. However, a preceding Arctic wide decrease in sea‐ice volume starts much earlier in MPI‐ESM than in PIOMAS. Analyzing this behavior in MPI‐ESM's ocean model MPIOM, we find that the modeled volume trend depends crucially on the specific choice of atmospheric reanalysis forcing, which casts some doubt on the reliability of estimates of volume trends. In our CMIP5 scenario simulations, we find a substantial delay in sea‐ice response to increasing CO2 concentration; a seasonally ice‐free Arctic can result for a CO2 concentration of around 500 ppm. Simulated winter sea‐ice coverage drops rapidly to near ice‐free conditions once the mean Arctic winter temperature exceeds −5°C.

Simulated Siberian snow cover response to observed Arctic sea ice loss, 1979–2008

The loss of Arctic sea ice has wide‐ranging impacts, some of which are readily apparent and some of which remain obscure. For example, recent observational studies suggest that terrestrial snow cover may be affected by decreasing sea ice. Here, we examine a possible causal link between Arctic sea ice and Siberian snow cover during the past 3 decades using a suite of experiments with the National Center for Atmospheric Research Community Atmospheric Model version 3. The experiments were designed to isolate the influence of surface conditions within the Arctic Ocean from other forcing agents such as low‐latitude sea surface temperatures and direct radiative effects of increasing greenhouse gases. Only those experiments that include the observed evolution of Arctic sea ice and sea surface temperatures result in increased snow depth over Siberia, while those that maintain climatological values for Arctic Ocean conditions result in no snow signal over Siberia. In the former, Siberian precipitation and air temperature both increase, but because surface air temperatures remain below freezing during most months, the snowpack thickens over this region. These results suggest that Arctic Ocean surface forcing is necessary and sufficient to induce a Siberian snow signal, and that other forcings in combination can modulate the strength and geographic extent of the response.

On the existence of stable seasonally varying Arctic sea ice in simple models

Within the framework of lower order thermodynamic theories for the climatic evolution of Arctic sea ice we isolate the conditions required for the existence of stable seasonally‐varying solutions, in which ice forms each winter and melts away each summer. This is done by constructing a two‐season model from the continuously evolving theory of Eisenman and Wettlaufer (2009) and showing that seasonally‐varying states are unstable under constant annual average short‐wave radiative forcing. However, dividing the summer season into two intervals (ice covered and ice free) provides sufficient freedom to stabilize seasonal ice. Simple perturbation theory shows that the condition for stability is determined by the timing of when the ice vanishes in summer and hence the relative magnitudes of the summer heat flux over the ocean versus over the ice. This scenario is examined within the context of greenhouse gas warming, as a function of which stability conditions are discerned.