Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere

The Arctic sea ice has retreated rapidly in the past few decades, which is believed to be driven by various dynamic and thermodynamic processes in the atmosphere. The newly open water resulted from sea ice decline in turn exerts large influence on the atmosphere. Therefore, this study aims to investigate the causality between multiple atmospheric processes and sea ice variations using three distinct data-driven causality approaches that have been proposed recently: Temporal Causality Discovery Framework Non-combinatorial Optimization via Trace Exponential and Augmented lagrangian for Structure learning (NOTEARS) and Directed Acyclic Graph-Graph Neural Networks (DAG-GNN). We apply these three algorithms to 39 years of historical time-series data sets, which include 11 atmospheric variables from ERA-5 reanalysis product and passive microwave satellite retrieved sea ice extent. By comparing the causality graph results of these approaches with what we summarized from the literature, it shows that the static graphs produced by NOTEARS and DAG-GNN are relatively reasonable. The results from NOTEARS indicate that relative humidity and precipitation dominate sea ice changes among all variables, while the results from DAG-GNN suggest that the horizontal and meridional wind are more important for driving sea ice variations. However, both approaches produce some unrealistic cause-effect relationships. Additionally, these three methods cannot well detect the delayed impact of one variable on another in the Arctic. It also turns out that the results are rather sensitive to the choice of hyperparameters of the three methods. As a pioneer study, this work paves the way to disentangle the complex causal relationships in the Earth system, by taking the advantage of cutting-edge Artificial Intelligence technologies.

Acceleration of western Arctic sea ice loss linked to the Pacific North American pattern

Recent rapid Arctic sea-ice reduction has been well documented in observations, reconstructions and model simulations. However, the rate of sea ice loss is highly variable in both time and space. The western Arctic has seen the fastest sea-ice decline, with substantial interannual and decadal variability, but the underlying mechanism remains unclear. Here we demonstrate, through both observations and model simulations, that the Pacific North American (PNA) pattern is an important driver of western Arctic sea-ice variability, accounting for more than 25% of the interannual variance. Our results suggest that the recent persistent positive PNA pattern has led to increased heat and moisture fluxes from local processes and from advection of North Pacific airmasses into the western Arctic. These changes have increased lower-tropospheric temperature, humidity and downwelling longwave radiation in the western Arctic, accelerating sea-ice decline. Our results indicate that the PNA pattern is important for projections of Arctic climate changes, and that greenhouse warming and the resultant persistent positive PNA trend is likely to increase Arctic sea-ice loss.

Annually resolved Atlantic sea surface temperature variability over the past 2,900 y

Atlantic multidecadal sea surface temperature variability (AMV) strongly influences the Northern Hemisphere’s climate, including the Arctic. Here using a well-dated annually laminated lake sediment core, we show that the AMV exerts a strong influence on High-Arctic climate during the instrumental period (past ∼150 y) through atmospheric teleconnection. This highly resolved climate archive is then used to produce the first AMV reconstruction spanning the last ∼3 millennia at unprecedented temporal resolution. Our terrestrial record is significantly correlated to several sea surface temperature proxies in the Atlantic, highlighting the reliability of this record as an annual tracer of the AMV. The results show that the current warmth in sea surface temperature is unseen in the context of the past ∼3 millennia.

Global warming due to anthropogenic factors can be amplified or dampened by natural climate oscillations, especially those involving sea surface temperatures (SSTs) in the North Atlantic which vary on a multidecadal scale (Atlantic multidecadal variability, AMV). Because the instrumental record of AMV is short, long-term behavior of AMV is unknown, but climatic teleconnections to regions beyond the North Atlantic offer the prospect of reconstructing AMV from high-resolution records elsewhere. Annually resolved titanium from an annually laminated sedimentary record from Ellesmere Island, Canada, shows that the record is strongly influenced by AMV via atmospheric circulation anomalies. Significant correlations between this High-Arctic proxy and other highly resolved Atlantic SST proxies demonstrate that it shares the multidecadal variability seen in the Atlantic. Our record provides a reconstruction of AMV for the past ∼3 millennia at an unprecedented time resolution, indicating North Atlantic SSTs were coldest from ∼1400–1800 CE, while current SSTs are the warmest in the past ∼2,900 y.

High-Resolution COSMO-CLM Modeling and an Assessment of Mesoscale Features Caused by Coastal Parameters at Near-Shore Arctic Zones (Kara Sea)

Coastal Arctic regions are characterized by severe mesoscale weather events that include extreme wind speeds, and the rugged shore conditions, islands, and mountain ranges contribute to mesoscale event formation. High-resolution atmospheric modeling is a suitable tool to reproduce and estimate some of these events, and so the regional non-hydrostatic climate atmospheric model COSMO-CLM (Consortium for Small-scale Modeling developed within the framework of the international science group CLM-Community) was used to reproduce mesoscale circulation in the Arctic coast zone under various surface conditions. Mid-term experiments were run over the Arctic domain, especially over the Kara Sea region, using the downscaling approach, with ≈12 km and ≈3 km horizontal grid sizes. The best model configuration was determined using standard verification methods; however, the model run verification process raised questions over its quality and aptness based on the high level of small-scale coastline diversity and associated relief properties. Modeling case studies for high wind speeds were used to study hydrodynamic mesoscale circulation reproduction, and we found that although the model could not describe the associated wind dynamic features at all scales using ≈3 km resolution, it could simulate different scales of island wind shadow effects, tip jets, downslope winds, vortex chains, and so on, quite realistically. This initial success indicated that further research could reveal more about the detailed properties of mesoscale circulations and extreme winds by applying finer resolution modeling.

The Role of Synoptic Cyclones for the Formation of Arctic Summer Circulation Patterns as Clustered by Self-Organizing Maps

Contribution of extra-tropical synoptic cyclones to the formation of mean summer atmospheric circulation patterns in the Arctic domain (≥60° N) was investigated by clustering dominant Arctic circulation patterns based on daily mean sea-level pressure using self-organizing maps (SOMs). Three SOM patterns were identified; one pattern had prevalent low-pressure anomalies in the Arctic Circle (SOM1), while two exhibited opposite dipoles with primary high-pressure anomalies covering the Arctic Ocean (SOM2 and SOM3). The time series of their occurrence frequencies demonstrated the largest inter-annual variation in SOM1, a slight decreasing trend in SOM2, and the abrupt upswing after 2007 in SOM3. Analyses of synoptic cyclone activity using the cyclone track data confirmed the vital contribution of synoptic cyclones to the formation of large-scale patterns. Arctic cyclone activity was enhanced in the SOM1, which was consistent with the meridional temperature gradient increases over the land–Arctic ocean boundaries co-located with major cyclone pathways. The composite daily synoptic evolution of each SOM revealed that all three SOMs persisted for less than five days on average. These evolutionary short-term weather patterns have substantial variability at inter-annual and longer timescales. Therefore, the synoptic-scale activity is central to forming the seasonal-mean climate of the Arctic.

Winter storms accelerate the demise of sea ice in the Atlantic sector of the Arctic Ocean

A large retreat of sea-ice in the 'stormy' Atlantic Sector of the Arctic Ocean has become evident through a series of record minima for the winter maximum sea-ice extent since 2015. Results from the Norwegian young sea ICE (N-ICE2015) expedition, a five-month-long (Jan-Jun) drifting ice station in first and second year pack-ice north of Svalbard, showcase how sea-ice in this region is frequently affected by passing winter storms. Here we synthesise the interdisciplinary N-ICE2015 dataset, including independent observations of the atmosphere, snow, sea-ice, ocean, and ecosystem. We build upon recent results and illustrate the different mechanisms through which winter storms impact the coupled Arctic sea-ice system. These short-lived and episodic synoptic-scale events transport pulses of heat and moisture into the Arctic, which temporarily reduce radiative cooling and henceforth ice growth. Cumulative snowfall from each sequential storm deepens the snow pack and insulates the sea-ice, further inhibiting ice growth throughout the remaining winter season. Strong winds fracture the ice cover, enhance ocean-ice-atmosphere heat fluxes, and make the ice more susceptible to lateral melt. In conclusion, the legacy of Arctic winter storms for sea-ice and the ice-associated ecosystem in the Atlantic Sector lasts far beyond their short lifespan.

The Arctic's sea ice cover: trends, variability, predictability, and comparisons to the Antarctic

As assessed over the period of satellite observations, October 1978 to present, there are downward linear trends in Arctic sea ice extent for all months, largest at the end of the melt season in September. The ice cover is also thinning. Downward trends in extent and thickness have been accompanied by pronounced interannual and multiyear variability, forced by both the atmosphere and ocean. As the ice thins, its response to atmospheric and oceanic forcing may be changing. In support of a busier Arctic, there is a growing need to predict ice conditions on a variety of time and space scales. A major challenge to providing seasonal scale predictions is the 7-10 days limit of numerical weather prediction. While a seasonally ice-free Arctic Ocean is likely well within this century, there is much uncertainty in the timing. This reflects differences in climate model structure, the unknown evolution of anthropogenic forcing, and natural climate variability. In sharp contrast to the Arctic, Antarctic sea ice extent, while highly variable, has increased slightly over the period of satellite observations. The reasons for this different behavior remain to be resolved, but responses to changing atmospheric circulation patterns appear to play a strong role.

Arctic sea ice trends, variability and implications for seasonal ice forecasting

September Arctic sea ice extent over the period of satellite observations has a strong downward trend, accompanied by pronounced interannual variability with a detrended 1 year lag autocorrelation of essentially zero. We argue that through a combination of thinning and associated processes related to a warming climate (a stronger albedo feedback, a longer melt season, the lack of especially cold winters) the downward trend itself is steepening. The lack of autocorrelation manifests both the inherent large variability in summer atmospheric circulation patterns and that oceanic heat loss in winter acts as a negative (stabilizing) feedback, albeit insufficient to counter the steepening trend. These findings have implications for seasonal ice forecasting. In particular, while advances in observing sea ice thickness and assimilating thickness into coupled forecast systems have improved forecast skill, there remains an inherent limit to predictability owing to the largely chaotic nature of atmospheric variability.

Arctic cyclone water vapor isotopes support past sea ice retreat recorded in Greenland ice

Rapid Arctic warming is associated with important water cycle changes: sea ice loss, increasing atmospheric humidity, permafrost thaw, and water-induced ecosystem changes. Understanding these complex modern processes is critical to interpreting past hydrologic changes preserved in paleoclimate records and predicting future Arctic changes. Cyclones are a prevalent Arctic feature and water vapor isotope ratios during these events provide insights into modern hydrologic processes that help explain past changes to the Arctic water cycle. Here we present continuous measurements of water vapor isotope ratios (δ¹⁸O, δ²H, d-excess) in Arctic Alaska from a 2013 cyclone. This cyclone resulted in a sharp d-excess decrease and disproportional δ¹⁸O enrichment, indicative of a higher humidity open Arctic Ocean water vapor source. Past transitions to warmer climates inferred from Greenland ice core records also reveal sharp decreases in d-excess, hypothesized to represent reduced sea ice extent and an increase in oceanic moisture source to Greenland Ice Sheet precipitation. Thus, measurements of water vapor isotope ratios during an Arctic cyclone provide a critical processed-based explanation, and the first direct confirmation, of relationships previously assumed to govern water isotope ratios during sea ice retreat and increased input of northern ocean moisture into the Arctic water cycle.

Greenland ice sheet mass balance: a review

Over the past quarter of a century the Arctic has warmed more than any other region on Earth, causing a profound impact on the Greenland ice sheet (GrIS) and its contribution to the rise in global sea level. The loss of ice can be partitioned into processes related to surface mass balance and to ice discharge, which are forced by internal or external (atmospheric/oceanic/basal) fluctuations. Regardless of the measurement method, observations over the last two decades show an increase in ice loss rate, associated with speeding up of glaciers and enhanced melting. However, both ice discharge and melt-induced mass losses exhibit rapid short-term fluctuations that, when extrapolated into the future, could yield erroneous long-term trends. In this paper we review the GrIS mass loss over more than a century by combining satellite altimetry, airborne altimetry, interferometry, aerial photographs and gravimetry data sets together with modelling studies. We revisit the mass loss of different sectors and show that they manifest quite different sensitivities to atmospheric and oceanic forcing. In addition, we discuss recent progress in constructing coupled ice-ocean-atmosphere models required to project realistic future sea-level changes.

Spatially mapped reductions in the length of the Arctic sea ice season

Satellite data are used to determine the number of days having sea ice coverage in each year 1979-2013 and to map the trends in these ice-season lengths. Over the majority of the Arctic seasonal sea ice zone, the ice season shortened at an average rate of at least 5 days/decade between 1979 and 2013, and in a small area in the northeastern Barents Sea the rate of shortening reached over 65 days/decade. The only substantial non-coastal area with lengthening sea ice seasons is the Bering Sea, where the ice season lengthened by 5-15 days/decade. Over the Arctic as a whole, the area with ice seasons shortened by at least 5 days/decade is 12.4 × 10⁶ km², while the area with ice seasons lengthened by at least 5 days/decade is only 1.1 × 10⁶ km². The contrast is even greater, percentage-wise, for higher rates.

KEY POINTS

Sea ice seasons have shortened by at least 5 days/decade over most of the ArcticAcross 1.9 million km² ice seasons have shortened by at least 25 days/decadeCounter to most of the Arctic ice seasons have lengthened in the Bering Sea.