A New Perspective on Four Decades of Changes in Arctic Sea Ice from Satellite Observations

Arctic sea ice characteristics have been changing rapidly and significantly in the last few decades. Using a long-term time series of sea ice products from satellite observations—the extended AVHRR Polar Pathfinder (APP-x)—trends in sea ice concentration, ice extent, ice thickness, and ice volume in the Arctic from 1982 to 2020 are investigated. Results show that the Arctic has become less ice-covered in all seasons, especially in summer and autumn. Arctic sea ice thickness has been decreasing at a rate of −3.24 cm per year, resulting in an approximate 52% reduction in thickness from 2.35 m in 1982 to 1.13 m in 2020. Arctic sea ice volume has been decreasing at a rate of −467.7 km3 per year, resulting in an approximate 63% reduction in volume, from 27,590.4 km3 in 1982 to 10,305.5 km3 in 2020. These trends are further examined from a new perspective, where the Arctic Ocean is classified into open water, perennial, and seasonal sea ice-covered areas based on sea ice persistence. The loss of the perennial sea ice-covered area is a major factor in the total sea ice loss in all seasons. If the current rates of sea ice changes in extent, concentration, and thickness continue, the Arctic is expected to have ice-free summers by the early 2060s.

Pathways between Climate, Fish, Fisheries, and Management: A Conceptual Integrated Ecosystem Management Approach

The abundance and distribution of marine fishes is influenced by environmental conditions, predator–prey relationships, multispecies interactions, and direct human impacts, such as fishing. The adaptive response of the system depends on its structure and the pathways that link environmental factors to the taxon in question. The “Star Diagram” is a socio-ecological model of marine ecosystems that depicts the general pathways between climate, fish, and fisheries, and their intersection with climate policy and resource management. We illustrate its use by identifying the key factors, pathways and drivers that influence walleye pollock, crab, and sockeye salmon, under a warming scenario on the eastern Bering Sea shelf. This approach predicts that all three species will see reduced populations under a long-term warming scenario. Going forward, the challenge to managers is to balance the magnitude of the effect of harvest and the adaptability of their management system, with the scale and degree of resilience and the behavioral, physiological, or evolutionary adaptation of the ecosystem and its constituents. The Star Diagram provides a novel conceptual construct that managers can use to visualize and integrate the various aspects of the system into a holistic, socio-ecological management framework.

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.

Influence of Arctic sea-ice variability on Pacific trade winds

A conceptual model connecting seasonal loss of Arctic sea ice to midlatitude extreme weather events is applied to the 21st-century intensification of Central Pacific trade winds, emergence of Central Pacific El Nino events, and weakening of the North Pacific Aleutian Low Circulation. According to the model, Arctic Ocean warming following the summer sea-ice melt drives vertical convection that perturbs the upper troposphere. Static stability calculations show that upward convection occurs in annual 40- to 45-d episodes over the seasonally ice-free areas of the Beaufort-to-Kara Sea arc. The episodes generate planetary waves and higher-frequency wave trains that transport momentum and heat southward in the upper troposphere. Regression of upper tropospheric circulation data on September sea-ice area indicates that convection episodes produce wave-mediated teleconnections between the maximum ice-loss region north of the Siberian Arctic coast and the Intertropical Convergence Zone (ITCZ). These teleconnections generate oppositely directed trade-wind anomalies in the Central and Eastern Pacific during boreal winter. The interaction of upper troposphere waves with the ITCZ air-sea column may also trigger Central Pacific El Nino events. Finally, waves reflected northward from the ITCZ air column and/or generated by triggered El Nino events may be responsible for the late winter weakening of the Aleutian Low Circulation in recent years.

Midwinter Arctic leads form and dissipate low clouds

Leads are a key feature of the Arctic ice pack during the winter owing to their substantial contribution to the surface energy balance. According to the present understanding, enhanced heat and moisture fluxes from high lead concentrations tend to produce more boundary layer clouds. However, described here in our composite analyses of diverse surface- and satellite-based observations, we find that abundant boundary layer clouds are associated with low lead flux periods, while fewer boundary layer clouds are observed for high lead flux periods. Motivated by these counterintuitive results, we conducted three-dimensional cloud-resolving simulations to investigate the underlying physics. We find that newly frozen leads with large sensible heat flux but low latent heat flux tend to dissipate low clouds. This finding indicates that the observed high lead fractions likely consist of mostly newly frozen leads that reduce any pre-existing low-level cloudiness, which in turn decreases downwelling infrared flux and accelerates the freezing of sea ice.

Seasonal Variations of Arctic Low-Level Clouds and Its Linkage to Sea Ice Seasonal Variations

Using CALIPSO-CloudSat-Clouds and the Earth's Radiant Energy System-Moderate Resolution Imaging Spectrometer data set, this study documents the seasonal variation of sea ice, cloud, and atmospheric properties in the Arctic (70°N-82°N) for 2007-2010. A surface-type stratification-consisting Permanent Ocean, Land, Permanent Ice, and Transient Sea Ice-is used to investigate the influence of surface type on low-level Arctic cloud liquid water path (LWP) seasonality. The results show significant variations in the Arctic low-level cloud LWP by surface type linked to differences in thermodynamic state. Subdividing the Transient Ice region (seasonal sea ice zone) by melt/freeze season onset dates reveals a complex influence of sea ice variations on low cloud LWP seasonality. We find that lower tropospheric stability is the primary factor affecting the seasonality of cloud LWP. Our results suggest that variations in sea ice melt/freeze onset have a significant influence on the seasonality of low-level cloud LWP by modulating the lower tropospheric thermal structure and not by modifying the surface evaporation rate in late spring and midsummer. We find no significant dependence of the May low-level cloud LWP peak on the melt/freeze onset dates, whereas and September/October low-level cloud LWP maximum shifts later in the season for earlier melt/later freeze onset regions. The Arctic low cloud LWP seasonality is controlled by several surface-atmosphere interaction processes; the importance of each varies seasonally due to the thermodynamic properties of sea ice. Our results demonstrate that when analyzing Arctic cloud-sea ice interactions, a seasonal perspective is critical.

High cloud coverage over melted areas dominates the impact of clouds on the albedo feedback in the Arctic

Warming in the Arctic is larger than the global average. A primary reason for this Arctic Amplification is the albedo feedback. The contrasting albedo of sea ice and dark melted surface areas is the key component of albedo feedback. Cloud coverage over the changing surface and the response of the clouds to the changing surface conditions will modify the change in planetary albedo when sea ice melts. Space-based lidar measurements provide a unique opportunity for cloud measurements in the Arctic. The response of clouds to the changing sea ice concentration was directly observed. Based on CALIPSO satellite observations of cloud properties, this study found that cloud coverage in ice-free regions in the Arctic linearly increased with the area of ice-free water during the melt seasons in the past 10 years, while sea ice coverage varies significantly year-to-year. The observations suggest that when sea-ice retreats, cloud fraction of the ice-free region remains fixed at nearly 81%. The high cloud coverage over melted areas significantly reduces the albedo feedback. These results indicate that space-based lidar cloud and surface observations of the Arctic can help constrain and improve climate models.

Observing and Modeling Ice Sheet Surface Mass Balance

Surface mass balance (SMB) provides mass input to the surface of the Antarctic and Greenland Ice Sheets and therefore comprises an important control on ice sheet mass balance and resulting contribution to global sea level change. As ice sheet SMB varies highly across multiple scales of space (meters to hundreds of kilometers) and time (hourly to decadal), it is notoriously challenging to observe and represent in models. In addition, SMB consists of multiple components, all of which depend on complex interactions between the atmosphere and the snow/ice surface, large-scale atmospheric circulation and ocean conditions, and ice sheet topography. In this review, we present the state-of-the-art knowledge and recent advances in ice sheet SMB observations and models, highlight current shortcomings, and propose future directions. Novel observational methods allow mapping SMB across larger areas, longer time periods, and/or at very high (subdaily) temporal frequency. As a recent observational breakthrough, cosmic ray counters provide direct estimates of SMB, circumventing the need for accurate snow density observations upon which many other techniques rely. Regional atmospheric climate models have drastically improved their simulation of ice sheet SMB in the last decade, thanks to the inclusion or improved representation of essential processes (e.g., clouds, blowing snow, and snow albedo), and by enhancing horizontal resolution (5-30 km). Future modeling efforts are required in improving Earth system models to match regional atmospheric climate model performance in simulating ice sheet SMB, and in reinforcing the efforts in developing statistical and dynamic downscaling to represent smaller-scale SMB processes.

Global and Arctic climate sensitivity enhanced by changes in North Pacific heat flux

Arctic amplification is a consequence of surface albedo, cloud, and temperature feedbacks, as well as poleward oceanic and atmospheric heat transport. However, the relative impact of changes in sea surface temperature (SST) patterns and ocean heat flux sourced from different regions on Arctic temperatures are not well constrained. We modify ocean-to-atmosphere heat fluxes in the North Pacific and North Atlantic in a climate model to determine the sensitivity of Arctic temperatures to zonal heterogeneities in northern hemisphere SST patterns. Both positive and negative ocean heat flux perturbations from the North Pacific result in greater global and Arctic surface air temperature anomalies than equivalent magnitude perturbations from the North Atlantic; a response we primarily attribute to greater moisture flux from the subpolar extratropics to Arctic. Enhanced poleward latent heat and moisture transport drive sea-ice retreat and low-cloud formation in the Arctic, amplifying Arctic surface warming through the ice-albedo feedback and infrared warming effect of low clouds. Our results imply that global climate sensitivity may be dependent on patterns of ocean heat flux in the northern hemisphere.

Active sensor synergy for arctic cloud microphysics

In this study, we focus on the retrieval of liquid and ice-phase cloud microphysics from spaceborne and ground-based lidar-cloud radar synergy. As an application of the cloud retrieval algorithm developed for the EarthCARE satellite mission (JAXA-ESA) [1], the derived statistics of cloud microphysical properties in high latitudes and their relation to the Arctic climate are investigated.

Evidence for ice-ocean albedo feedback in the Arctic Ocean shifting to a seasonal ice zone

Ice-albedo feedback due to the albedo contrast between water and ice is a major factor in seasonal sea ice retreat, and has received increasing attention with the Arctic Ocean shifting to a seasonal ice cover. However, quantitative evaluation of such feedbacks is still insufficient. Here we provide quantitative evidence that heat input through the open water fraction is the primary driver of seasonal and interannual variations in Arctic sea ice retreat. Analyses of satellite data (1979–2014) and a simplified ice-upper ocean coupled model reveal that divergent ice motion in the early melt season triggers large-scale feedback which subsequently amplifies summer sea ice anomalies. The magnitude of divergence controlling the feedback has doubled since 2000 due to a more mobile ice cover, which can partly explain the recent drastic ice reduction in the Arctic Ocean.

New insight of Arctic cloud parameterization from regional climate model simulations, satellite-based and drifting station data

Cloud observations from the CloudSat and CALIPSO satellites helped to explain the reduced total cloud cover (Ctot) in the atmospheric regional climate model HIRHAM5 with modified cloud physics. Arctic climate conditions are found to be better reproduced with (1) a more efficient Bergeron-Findeisen process and (2) more generalized subgrid-scale variability of total water content. As a result, the annual cycle of Ctot is improved over sea ice, associated with an almost 14% smaller area average than in the control simulation. The modified cloud scheme reduces the Ctot bias with respect to the satellite observations. Except for autumn, the cloud reduction over sea ice improves low-level temperature profiles compared to drifting station data. The HIRHAM5 sensitivity study highlights the need for improving accuracy of low-level (< 700m) cloud observations, as these clouds exert a strong impact on the near-surface climate.

Covariance between Arctic sea ice and clouds within atmospheric state regimes at the satellite footprint level

Understanding the cloud response to sea ice change is necessary for modeling Arctic climate. Previous work has primarily addressed this problem from the interannual variability perspective. This paper provides a refined perspective of sea ice-cloud relationship in the Arctic using a satellite footprint-level quantification of the covariance between sea ice and Arctic low cloud properties from NASA A-Train active remote sensing data. The covariances between Arctic low cloud properties and sea ice concentration are quantified by first partitioning each footprint into four atmospheric regimes defined using thresholds of lower tropospheric stability and midtropospheric vertical velocity. Significant regional variability in the cloud properties is found within the atmospheric regimes indicating that the regimes do not completely account for the influence of meteorology. Regional anomalies are used to account for the remaining meteorological influence on clouds. After accounting for meteorological regime and regional influences, a statistically significant but weak covariance between cloud properties and sea ice is found in each season for at least one atmospheric regime. Smaller average cloud fraction and liquid water are found within footprints with more sea ice. The largest-magnitude cloud-sea ice covariance occurs between 500 m and 1.2 km when the lower tropospheric stability is between 16 and 24 K. The covariance between low cloud properties and sea ice is found to be largest in fall and is accompanied by significant changes in boundary layer temperature structure where larger average near-surface static stability is found at larger sea ice concentrations.

Observational determination of albedo decrease caused by vanishing Arctic sea ice

The decline of Arctic sea ice has been documented in over 30 y of satellite passive microwave observations. The resulting darkening of the Arctic and its amplification of global warming was hypothesized almost 50 y ago but has yet to be verified with direct observations. This study uses satellite radiation budget measurements along with satellite microwave sea ice data to document the Arctic-wide decrease in planetary albedo and its amplifying effect on the warming. The analysis reveals a striking relationship between planetary albedo and sea ice cover, quantities inferred from two independent satellite instruments. We find that the Arctic planetary albedo has decreased from 0.52 to 0.48 between 1979 and 2011, corresponding to an additional 6.4 ± 0.9 W/m(2) of solar energy input into the Arctic Ocean region since 1979. Averaged over the globe, this albedo decrease corresponds to a forcing that is 25% as large as that due to the change in CO2 during this period, considerably larger than expectations from models and other less direct recent estimates. Changes in cloudiness appear to play a negligible role in observed Arctic darkening, thus reducing the possibility of Arctic cloud albedo feedbacks mitigating future Arctic warming.

Feedbacks and Interactions: From the Arctic Cryosphere to the Climate System

Changes in the Arctic’s climate are a result of complex interactions between the cryosphere, atmosphere, ocean, and biosphere. More feedbacks from the cryosphere to climate warming are positive and result in further warming than are negative, resulting in a reduced rate of warming or cooling. Feedbacks operate at different spatial scales; many, such as those operating through albedo and evapotranspiration, will have significant local effects that together could result in global impacts. Some processes, such as changes in carbon dioxide (CO₂) emissions, are likely to have very small global effects but uncertainty is high whereas others, such as subsea methane (CH₄) emissions, could have large global effects. Some cryospheric processes in the Arctic have teleconnections with other regions and major changes in the cryosphere have been largely a result of large-scale processes, particularly atmospheric and oceanic circulation. With continued climate warming it is highly likely that the cryospheric components will play an increasingly important climatic role. However, the net effect of all the feedbacks is difficult to assess because of the variability in spatial and temporal scales over which they operate. Furthermore, general circulation models (GCMs) do not include all major feedbacks while those included may not be accurately parameterized. The lack of full coupling between surface dynamics and the atmosphere is a major gap in current GCMs.