Winter arctic sea ice volume decline: uncertainties reduced using passive microwave-based sea ice thickness

Arctic sea ice volume (SIV) is a key climate indicator and memory source in sea ice predictions and projections, yet suffering from large observational and model uncertainty. Here, we test whether passive microwave (PMW) data constrain the long-term evolution of Arctic SIV, as recently hypothesized. We find many commonalities in Arctic SIV changes from a PMW sea ice thickness (SIT) 1992-2020 time series reconstructed with a neural network algorithm trained on lidar altimetry, and the reference PIOMAS reanalysis: relatively low differences in SIV mean (4615 km3, 37%), SIV trends (46 km3, 17%), and phased variability (r2=0.55). Key to reduced differences is the consistent evolution of many SIV contributors: seasonal and perennial ice coverage, their SIT contrast, whereas perennial SIT provides the largest remaining uncertainty source. We argue that PMW includes useful SIT information, reducing SIV uncertainty. We foresee progress from sea ice reanalyses combining dynamical models and data assimilation of PMW SIT estimates, in addition to the already assimilated PWM sea ice concentration.

Passive microwave Arctic sea ice melt onset dates from the advanced horizontal range algorithm 1979-2022

The onset of the summer melt season is a key stage of the Arctic sea ice seasonal cycle and is an indicator of climate change. Surface melting of the bare or snow-covered sea ice is detected using passive microwave satellite observations. The data set presented here is a 44 year record of Arctic sea ice annual melt onset (MO) dates for 1979-2022 produced using an updated version of the Advanced Horizontal Range Algorithm (AHRA). This data product contains annual maps of the sea ice MO date and a set of descriptive statistics summarizing the data. This paper describes a new update of the AHRA methodology, now AHRA V5, including key changes to the algorithm starting date and sea ice mask methodology to improve estimates of early-season MO dates especially near the sea ice periphery. AHRA V5 data are suitable for monitoring trends in Arctic and regional sea ice MO dates and for process studies of atmosphere-sea ice interactions during the early spring and summer months.

Database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions

Since the early 2000s, sea ice has experienced an increased rate of decline in thickness, extent and age. This new regime, coined the 'New Arctic', is accompanied by a reshuffling of energy flows at the surface. Understanding of the magnitude and nature of this reshuffling and the feedbacks therein remains limited. A novel database is presented that combines satellite observations, model output, and reanalysis data with sea ice parcel drift tracks in a Lagrangian framework. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states, including remotely sensed surface energy budget terms. Additionally, flags indicate when sea ice parcels travel within cyclones, recording cyclone intensity and distance from the cyclone center. The quality of the ice parcel database was evaluated by comparison with sea ice mass balance buoys and correlations are high, which highlights the reliability of this database in capturing the seasonal changes and evolution of sea ice. This database has multiple applications for the scientific community; it can be used to study the processes that influence individual sea ice parcel time series, or to explore generalized summary statistics and trends across the Arctic.

The influence of sedimentation regime on natural radionuclide activity concentration in marine sediments of the East Siberian Arctic Shelf

Transport and accumulation of radionuclides in the Arctic depends on many biogeochemical processes, which are changing at accelerated rates due to climate change and human economic activity. We present the results of a study on the features distribution of some natural radionuclides in the marine sediments on the East Siberian Arctic Shelf collected during several expeditions from 2008 to 2019. Average activity concentration of ²³²Th, ⁴⁰K and ²²⁶Ra under the influence of different sedimentation regime increases from 40.7, 418 and 30.8 Bq/kg to 41.6, 423 and 34.9 Bq/kg respectively from coastal shelf marine sediments (<50% clay) to outer shelf marine sediments (>50% clay). Sediment particle size has a greater impact on radionuclides in the coastal shelf. An increase in the activity concentrations of ²³²Th and ²²⁶Ra with the increasing clay particles were found. On the outer shelf with a change in the sedimentation regime, the influence of the size composition decreased, at the same time, there is a correlation between the organic carbon concentration and the radionuclide activity concentration. Absolute maximums of natural radionuclide activity concentration (²³²Th = 70.9, ²²⁶Ra = 70.4, ⁴⁰K = 591 Bq/kg) were detected in the Chaun Bay. The highest activity concentration of ²²⁶Ra was found in paleo-river valleys marine sediments. A low ²³²Th/²²⁶Ra activity concentration ratio indicates the enrichment of paleo-river valleys marine sediments with ²²⁶Ra. In the deep-sea sediments of the shelf slope on the contrary paleo-river valleys, this ratio is greatly increased.

Comparison of Hemispheric and Regional Sea Ice Extent and Area Trends from NOAA and NASA Passive Microwave-Derived Climate Records

Three passive microwave-based sea ice products archived at the National Snow and Ice Data Center (NSIDC) are compared: (1) the NASA Team (NT) algorithm product, (2) Bootstrap (BT) algorithm product, and (3) a new version (Version 4) of the NOAA/NSIDC Climate Data Record (CDR) product. Most notable for the CDR Version 4 is the addition of the early passive microwave record, 1979 to 1987. The focus of this study is on long-term trends in monthly extent and area. In addition to hemispheric trends, regional analysis is also carried out, including use of a new Northern Hemisphere regional mask. The results indicate overall good consistency between the products, with all three products showing strong statistically significant negative trends in the Arctic and small borderline significant positive trends in the Antarctic. Regionally, the patterns are similar, except for a notable outlier of the NT area having a steeper trend in the Central Arctic, likely related to increasing surface melt. Other differences are due to varied approaches to quality control, e.g., weather filtering and correction of mixed land-ocean grid cells. Another factor, particularly in regards to NT trends with BT or CDR, is the inter-sensor calibration approach, which yields small discontinuities between the products. These varied approaches yield small differences in trends. In the Arctic, such differences are not critical, but in the Antarctic, where overall trends are near zero and borderline statistically significant, the differences are potentially important in the interpretation of trends.

Statistical modeling of sea ice concentration in the northwest region of the Antarctic Peninsula

Sea ice is one of the main components of the cryosphere that modifies the exchange of heat and moisture between the ocean and atmosphere, regulating the global climate. In this sense, it is important to identify the concentration of sea ice in different regions of Antarctica in order to measure the impact of environmental changes on the region's ecosystem. The objective of this study was to evaluate the performance of the multiple linear regression and Box-Jenkins methods for predicting the concentration of sea ice along the northwest coast of the Antarctic Peninsula. Sea ice concentration data from May to November for the period 1979-2018 were extracted from passive remote sensors including a scanning multichannel microwave radiometer, special sensor microwave imager, and special sensor microwave imager/sounder. Meteorological variables from the atmospheric reanalysis model ERA5 of the European Center for Medium-Range Weather Forecasts were used as predictor variables, and the leave-one-out cross-validation technique was used to calibrate and validate the models. It was found that both statistical models have similar performance when analyzing residual analysis results, root mean square error of cross-validation, and final accuracy and residual standard deviation, these responses being related to the regionalization of the study area and to the Box-Jenkins presents strong, homogeneous, and stable correlations in the time series modeled for each pixel.

Ice Concentration Retrieval from the Analysis of Microwaves: A New Methodology Designed for the Copernicus Imaging Microwave Radiometer

Over the last 25 years, the Arctic sea ice has seen its extent decline dramatically. Passive microwave observations, with their ability to penetrate clouds and their independency to sunlight, have been used to provide sea ice concentration (SIC) measurements since the 1970s. The Copernicus Imaging Microwave Radiometer (CIMR) is a high priority candidate mission within the European Copernicus Expansion program, with a special focus on the observation of the polar regions. It will observe at 6.9 and 10.65 GHz with 15 km spatial resolution, and at 18.7 and 36.5 GHz with 5 km spatial resolution. SIC algorithms are based on empirical methods, using the difference in radiometric signatures between the ocean and sea ice. Up to now, the existing algorithms have been limited in the number of channels they use. In this study, we proposed a new SIC algorithm called Ice Concentration REtrieval from the Analysis of Microwaves (IceCREAM). It can accommodate a large range of channels, and it is based on the optimal estimation. Linear relationships between the satellite measurements and the SIC are derived from the Round Robin Data Package of the sea ice Climate Change Initiative. The 6 and 10 GHz channels are very sensitive to the sea ice presence, whereas the 18 and 36 GHz channels have a better spatial resolution. A data fusion method is proposed to combine these two estimations. Therefore, IceCREAM will provide SIC estimates with the good accuracy of the 6+10GHz combination, and the high spatial resolution of the 18+36GHz combination.

Satellite-Based Prediction of Arctic Sea Ice Concentration Using a Deep Neural Network with Multi-Model Ensemble

Warming of the Arctic leads to a decrease in sea ice, and the decrease of sea ice, in turn, results in warming of the Arctic again. Several microwave sensors have provided continuously updated sea ice data for over 30 years. Many studies have been conducted to investigate the relationships between the satellite-derived sea ice concentration (SIC) of the Arctic and climatic factors associated with the accelerated warming. However, linear equations using the general circulation model (GCM) data, with low spatial resolution, cannot sufficiently cope with the problem of complexity or non-linearity. Time-series techniques are effective for one-step-ahead forecasting, but are not appropriate for future prediction for about ten or twenty years because of increasing uncertainty when forecasting multiple steps ahead. This paper describes a new approach to near-future prediction of Arctic SIC by employing a deep learning method with multi-model ensemble. We used the regional climate model (RCM) data provided in higher resolution, instead of GCM. The RCM ensemble was produced by Bayesian model averaging (BMA) to minimize the uncertainty which can arise from a single RCM. The accuracies of RCM variables were much improved by the BMA2 method, which took into consideration temporal and spatial variations to minimize the uncertainty of individual RCMs. A deep neural network (DNN) method was used to deal with the non-linear relationships between SIC and climate variables, and to provide a near-future prediction for the forthcoming 10 to 20 years. We adjusted the DNN model for optimized SIC prediction by adopting best-fitted layer structure, loss function, optimizer algorithm, and activation function. The accuracy was much improved when the DNN model was combined with BMA2 ensemble, showing the correlation coefficient of 0.888. This study provides a viable option for monitoring Arctic sea ice change of the near future.

Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models

The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO₃), mixed layer depth (MLD), euphotic layer depth (Z_eu), and sea ice concentration, by comparing results against a newly updated, quality-controlled in situ NPP database for the Arctic Ocean (1959-2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan-Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO₃, MLD, and Z_eu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice-free versus ice-influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO₃ was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling.

Validation of Remote Sensing Retrieval Products using Data from a Wireless Sensor-Based Online Monitoring in Antarctica

Of the modern technologies in polar-region monitoring, the remote sensing technology that can instantaneously form large-scale images has become much more important in helping acquire parameters such as the freezing and melting of ice as well as the surface temperature, which can be used in the research of global climate change, Antarctic ice sheet responses, and cap formation and evolution. However, the acquirement of those parameters is impacted remarkably by the climate and satellite transit time which makes it almost impossible to have timely and continuous observation data. In this research, a wireless sensor-based online monitoring platform (WSOOP) for the extreme polar environment is applied to obtain a long-term series of data which is site-specific and continuous in time. Those data are compared and validated with the data from a weather station at Zhongshan Station Antarctica and the result shows an obvious correlation. Then those data are used to validate the remote sensing products of the freezing and melting of ice and the surface temperature and the result also indicated a similar correlation. The experiment in Antarctica has proven that WSOOP is an effective system to validate remotely sensed data in the polar region.

How well must climate models agree with observations?

The usefulness of a climate-model simulation cannot be inferred solely from its degree of agreement with observations. Instead, one has to consider additional factors such as internal variability, the tuning of the model, observational uncertainty, the temporal change in dominant processes or the uncertainty in the forcing. In any model-evaluation study, the impact of these limiting factors on the suitability of specific metrics must hence be examined. This can only meaningfully be done relative to a given purpose for using a model. I here generally discuss these points and substantiate their impact on model evaluation using the example of sea ice. For this example, I find that many standard metrics such as sea-ice area or volume only permit limited inferences about the shortcomings of individual models.

Climate trends in the Arctic as observed from space

The Arctic is a region in transformation. Warming in the region has been amplified, as expected from ice-albedo feedback effects, with the rate of warming observed to be ∼0.60 ± 0.07°C/decade in the Arctic (>64°N) compared to ∼0.17°C/decade globally during the last three decades. This increase in surface temperature is manifested in all components of the cryosphere. In particular, the sea ice extent has been declining at the rate of ∼3.8%/decade, whereas the perennial ice (represented by summer ice minimum) is declining at a much greater rate of ∼11.5%/decade. Spring snow cover has also been observed to be declining by -2.12%/decade for the period 1967-2012. The Greenland ice sheet has been losing mass at the rate of ∼34.0 Gt/year (sea level equivalence of 0.09 mm/year) during the period from 1992 to 2011, but for the period 2002-2011, a higher rate of mass loss of ∼215 Gt/year has been observed. Also, the mass of glaciers worldwide declined at the rate of 226 Gt/year from 1971 to 2009 and 275 Gt/year from 1993 to 2009. Increases in permafrost temperature have also been measured in many parts of the Northern Hemisphere while a thickening of the active layer that overlies permafrost and a thinning of seasonally frozen ground has also been reported. To gain insight into these changes, comparative analysis with trends in clouds, albedo, and the Arctic Oscillation is also presented. How to cite this article:WIREs Clim Change 2014, 5:389�409. doi: 10.1002/wcc.277.

Climate-driven regime shifts in Arctic marine benthos

Climate warming can trigger abrupt ecosystem changes in the Arctic. Despite the considerable interest in characterizing and understanding the ecological impact of rapid climate warming in the Arctic, few long time series exist that allow addressing these research goals. During a 30-y period (1980-2010) of gradually increasing seawater temperature and decreasing sea ice cover in Svalbard, we document rapid and extensive structural changes in the rocky-bottom communities of two Arctic fjords. The most striking component of the benthic reorganization was an abrupt fivefold increase in macroalgal cover in 1995 in Kongsfjord and an eightfold increase in 2000 in Smeerenburgfjord. Simultaneous changes in the abundance of benthic invertebrates suggest that the macroalgae played a key structuring role in these communities. The abrupt, substantial, and persistent nature of the changes observed is indicative of a climate-driven ecological regime shift. The ecological processes thought to drive the observed regime shifts are likely to promote the borealization of these Arctic marine communities in the coming years.

Disturbance, colonization and development of Antarctic benthic communities

A decade has yielded much progress in understanding polar disturbance and community recovery-mainly through quantifying ice scour rates, other disturbance levels, larval abundance and diversity, colonization rates and response of benthos to predicted climate change. The continental shelf around Antarctica is clearly subject to massive disturbance, but remarkably across so many scales. In summer, millions of icebergs from sizes smaller than cars to larger than countries ground out and gouge the sea floor and crush the benthic communities there, while the highest wind speeds create the highest waves to pound the coast. In winter, the calm associated with the sea surface freezing creates the clearest marine water in the world. But in winter, an ice foot encases coastal life and anchor ice rips benthos from the sea floor. Over tens and hundreds of thousands of years, glaciations have done the same on continental scales-ice sheets have bulldozed the seabed and the zoobenthos to edge of shelves. We detail and rank modern disturbance levels (from most to least): ice; asteroid impacts; sediment instability; wind/wave action; pollution; UV irradiation; volcanism; trawling; non-indigenous species; freshwater inundation; and temperature stress. Benthic organisms have had to recolonize local scourings and continental shelves repeatedly, yet a decade of studies have demonstrated that they have (compared with lower latitudes) slow tempos of reproduction, colonization and growth. Despite massive disturbance levels and slow recolonization potential, the Antarctic shelf has a much richer fauna than would be expected for its area. Now, West Antarctica is among the fastest warming regions and its organisms face new rapid changes. In the next century, temperature stress and non-indigenous species will drastically rise to become dominant disturbances to the Antarctic life. Here, we describe the potential for benthic organisms to respond to disturbance, focusing particularly on what we know now that we did not a decade ago.