Changes In Arctic Sea Ice Research Articles

Exploring the Use of Fengyun-3 Meteorological Satellite for Monitoring Sea Ice to Provide Services for Polar Navigation

This paper explores the feasibility and effectiveness of using the Fengyun-3 meteorological satellite to monitor sea ice, providing services for ships navigating in polar regions. Firstly, it analyzes the impact of Arctic sea ice changes on ship navigation and the importance of sea ice monitoring in route planning. Next, it provides a detailed introduction to the data sources and processing methods of the Fengyun-3 satellite, including radiometric calibration, geometric correction, image registration, and cropping. Subsequently, it discusses the characteristics of sea ice in the visible spectrum and successfully extracts sea ice information using MERSI-II data with land, cloud, and seawater masking techniques. The study indicates that the comprehensive use of multi-spectral data and other observation methods can significantly enhance sea ice monitoring capabilities. In the future, integrating more advanced technologies is expected to achieve refined identification and short-term prediction of sea ice movement, thereby providing more scientific and efficient support for ships navigating in polar regions, enhancing navigation safety and efficiency, and offering a scientific basis for the development of Arctic shipping routes.

Melt Pond Evolution along the MOSAiC Drift: Insights from Remote Sensing and Modeling

Melt ponds play a crucial role in the melting of Arctic sea ice. Studying the evolution of melt ponds is essential for understanding changes in Arctic sea ice. In this study, we used a revised sea ice model to simulate the evolution of melt ponds along the MOSAiC drift at a resolution of 10 m. A novel melt pond parameterization scheme simulates the movement of meltwater under the influence of gravity over a realistic sea ice topography. We evaluated different melt pond parameterization schemes based on remote sensing observations. The absolute deviation of the maximum pond coverage simulated by the new scheme is within 3%, while differences among parameterization schemes exceed 50%. Errors were found to be primarily due to the calculation of macroscopic meltwater loss, which is related to sea ice surface topography. Previous studies have indicated that sea ice with a lower surface roughness has a larger catchment area, resulting in larger pond coverage during the melt season. This study has identified an opposing mechanism: sea ice with lower surface roughness has a larger catchment area connected to the macroscopic flaws of the sea ice surface, which leads to more macroscopic drainage into the ocean and thereby a decrease in melt pond coverage. Experimental simulations showed that sea ice with 46% higher surface roughness, resulting in 12% less macroscopic drainage, exhibited a 38% higher maximum pond fraction. The presence of macroscopic flaws is related to the fragmentation of sea ice cover. As Arctic sea ice cover becomes increasingly fragmented and mobile, this mechanism will become more significant.

Capacity of a set of CMIP6 models to simulate Arctic sea ice drift

Abstract Evaluating CMIP6 model performance helps to improve the prediction of future changes in Arctic sea ice. We analyze the seasonal cycles, distribution, and evolution of sea ice in different regions from 1979 to 2014. We compare the output from selected CMIP6 models with reference data for sea ice motion. We also discuss the correlations between sea ice motion(SIM) and sea ice thickness (SIT) in reference data, and how CMIP6 models explain them. We select EC-Earth3, ACCESS-CM2, BCC-CSM2-MR, MPI-ESM1-2-HR, and NorESM2-LM for CMIP6 study. We compare outputs with reference data: Sea ice extent (SIE) from NSIDC; SIT from PIOMAS; and SIM from the IABP buoy data. Analytical techniques include Theil-Sen and Ordinary least squares (OLS) regression. Most selected CMIP6 models have seasonal cycles of SIM lagging behind IABP observations by 1-2 month and overestimate central Arctic SIM magnitude, with MPI-ESM1-2-HR having the highest discrepancy and NorESM2-LM lowest. The models show better simulation of SIM in the ice melting season than in the growing season. Models perform worse at capturing regional differences in SIM evolution and are overly conservative when simulating the increasing trend in ice motion, especially in coastal Arctic seas during summer. There is significant negative correlation between SIT and SIM in October.

Assessment of Arctic sea ice simulations in cGENIE model and projections under RCP scenarios

Simulating and predicting Arctic sea ice accurately remains an academic focus due to the complex and unclear mechanisms of Arctic sea ice variability and model biases. Meanwhile, the relevant forecasting and monitoring authorities are searching for models to meet practical needs. Given the previous ideal performance of cGENIE model in other fields and notable features, we evaluated the model’s skill in simulating Arctic sea ice using multiple methods and it demonstrates great potential and combined advantages. On this basis, we examined the direct drivers of sea-ice variability and predicted the future spatio-temporal changes of Arctic sea ice using the model under different Representative Concentration Pathways (RCP) scenarios. Further studies also found that Arctic sea ice concentration shows large regional differences under RCP 8.5, while the magnitude of the reduction in Arctic sea ice thickness is generally greater compared to concentration, showing a more uniform consistency of change.

Reconciling Roles of External Forcing and Internal Variability in Arctic Sea Ice Change on Different Time Scales

Abstract Two large ensemble simulations are adopted to investigate the relative contribution of external forcing and internal variability to Arctic sea ice variability on different time scales since 1960 by correcting the response error of models to external forcing using observational datasets. Our study suggests that previous approaches might overestimate the real impact of internal variability on Arctic sea ice change especially on long time scales. Our results indicate that in both March and September, internal variability plays a dominant role on all time scales over the twentieth century, while the anthropogenic signal on sea ice change can be steadily and consistently detected on a time scale of more than 20 years after the 2000s. We also reveal that the dominant mode of internal variability in March shows consistency across different time scales. On the contrary, the pattern of internal variability in September is highly nonuniform over the Arctic and varies across different time scales, indicating that sea ice internal variability in September at different time scales is driven by different factors.

An assessment of the CMIP6 performance in simulating Arctic sea ice volume flux via Fram Strait

Numerical models serve as an essential tool to investigate the causes and effects of Arctic sea ice changes. Evaluating the simulation capabilities of the most recent CMIP6 models in sea ice volume flux provides references for model applications and improvements. Meanwhile, reliable long-term simulation results of the ice volume flux contribute to a deeper understanding of the sea ice response to global climate change. In this study, the sea ice volume flux through six Arctic gateways over the past four decades (1979–2014) were estimated in combination of satellite observations of sea ice concentration (SIC) and sea ice motion (SIM) as well as the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) reanalysis sea ice thickness (SIT) data. The simulation capability of 17 CMIP6 historical models for the volume flux through Fram Strait were quantitatively assessed. Sea ice volume flux simulated from the ensemble mean of 17 CMIP6 models demonstrates better performance than that from the individual model, yet IPSL-CM6A-LR and EC-Earth3-Veg-LR outperform the ensemble mean in the annual volume flux, with Taylor scores of 0.86 and 0.50, respectively. CMIP6 models display relatively robust capability in simulating the seasonal variations of volume flux. Among them, CESM2-WACCM performs the best, with a correlation coefficient of 0.96 and a Taylor score of 0.88. Conversely, NESM3 demonstrates the largest deviation from the observation/reanalysis data, with the lowest Taylor score of 0.16. The variability of sea ice volume flux is primarily influenced by SIM and SIT, followed by SIC. The extreme large sea ice export through Fram Strait is linked to the occurrence of anomalously low air temperatures, which in turn promote increased SIC and SIT in the corresponding region. Moreover, the intensified activity of Arctic cyclones and Arctic dipole anomaly could boost the southward sea ice velocity through Fram Strait, which further enhance the sea ice outflow.

Comparison of Arctic and Antarctic sea ice spatial–temporal changes during 1979–2018

In recent years, the Arctic and Antarctic sea ice changes fluctuated significantly, and the overall trend of sea ice changes in the Arctic and Antarctic is opposite. However, studying the change characteristics and trends of the Arctic and Antarctic sea ice on more sensitive sea ice parameters and on smaller time scales still has high research value and significance. The Antarctic and Arctic sea ice spatial–temporal changes are studied and compared from the aspects of sea ice concentration, sea ice extent, first-year ice extent, multi-year ice extent, melting period and freezing period based on the daily sea ice concentration data during three periods of 1979–2018, 2009–2018, and 2014–2018, in order to more comprehensively reflect the Arctic and Antarctic sea ice changes. The results are as follows: Sea ice concentration showed a decreasing trend with the maximum negative change rate of −1.771 %/year in the Arctic and an increasing trend with the maximum positive change rate of 1.007 %/year in the Antarctic, and sea ice concentration in the Arctic and Antarctic showed a decreasing trend accompanied by the decrease of latitude. The Antarctic sea ice extent increased slowly, however, there was a more obvious decrease in the Arctic sea ice extent. The Arctic sea ice melting period increased 0.1564 days/year and the freezing period decreased 0.1646 days/year, and the Antarctic melting period increased 0.0402 days/year and the freezing period decreased −0.037 days/year. First-year ice extent in the Arctic and Antarctic showed a increasing trend, and multi-year ice extent showed a decreasing trend.

Seasonal Variations and Spatial Patterns of Arctic Cloud Changes in Association with Sea Ice Loss during 1950–2019 in ERA5

Abstract The dynamic and thermodynamic mechanisms that link retreating sea ice to increased Arctic cloud amount and cloud water content are unclear. Using the fifth generation of the ECMWF Reanalysis (ERA5), the long-term changes between years 1950–79 and 1990–2019 in Arctic clouds are estimated along with their relationship to sea ice loss. A comparison of ERA5 to CERES satellite cloud fractions reveals that ERA5 simulates the seasonal cycle, variations, and changes of cloud fraction well over water surfaces during 2001–20. This suggests that ERA5 may reliably represent the cloud response to sea ice loss because melting sea ice exposes more water surfaces in the Arctic. Increases in ERA5 Arctic cloud fraction and water content are largest during October–March from ∼950 to 700 hPa over areas with significant (≥15%) sea ice loss. Further, regions with significant sea ice loss experience higher convective available potential energy (∼2–2.75 J kg−1), planetary boundary layer height (∼120–200 m), and near-surface specific humidity (∼0.25–0.40 g kg−1) and a greater reduction of the lower-tropospheric temperature inversion (∼3°–4°C) than regions with small (<15%) sea ice loss in autumn and winter. Areas with significant sea ice loss also show strengthened upward motion between 1000 and 700 hPa, enhanced horizontal convergence (divergence) of air, and decreased (increased) relative humidity from 1000 to 950 hPa (950–700 hPa) during the cold season. Analyses of moisture divergence, evaporation minus precipitation, and meridional moisture flux fields suggest that increased local surface water fluxes, rather than atmospheric motions, provide a key source of moisture for increased Arctic clouds over newly exposed water surfaces during October–March. Significance Statement Sea ice loss has been shown to be a primary contributor to Arctic warming. Despite the evidence linking large sea ice retreat to Arctic warming, some studies have suggested that enhanced downwelling longwave radiation associated with increased clouds and water vapor is the primary reason for Arctic amplification. However, it is unclear how sea ice loss is linked to changes in clouds and water vapor in the Arctic. Here, we investigate the relationship between Arctic sea ice loss and changes in clouds using the ERA5 dataset. Improved knowledge of the relationship between Arctic sea ice loss and changes in clouds will help further our understanding of the role of the cloud feedback in Arctic warming.

Modeled variations in the inherent optical properties of summer Arctic ice and their effects on the radiation budget: a case based on ice cores from 2008 to 2016

Abstract. Variations in Arctic sea ice are apparent not only in its extent and thickness but also in its internal properties under global warming. The microstructure of summer Arctic sea ice changes due to varying external forces, ice age, and extended melting seasons, which affect its optical properties. Sea ice cores sampled in the Pacific sector of the Arctic obtained by the Chinese National Arctic Research Expedition (CHINARE) during the summers of 2008 to 2016 were used to estimate the variations in the microstructures and inherent optical properties (IOPs) of ice and determine the radiation budget of sea ice based on a radiative transfer model. The variations in the volume fraction of gas bubbles (Va) of the ice top layer were not significant, and the Va of the ice interior layer was significant. Compared with 2008, the mean Va of interior ice in 2016 decreased by 9.1 %. Meanwhile, the volume fraction of brine pockets increased clearly during 2008–2016. The changing microstructure resulted in the scattering coefficient of the interior ice decreasing by 38.4 % from 2008 to 2016, while no clear variations can be seen in the scattering coefficient of the ice top layer. These estimated ice IOPs fell within the range of other observations. Furthermore, we found that variations in interior ice were significantly related to the interannual changes in ice ages. At the Arctic basin scale, the changing IOPs of interior ice greatly changed the amount of solar radiation transmitted to the upper ocean even when a constant ice thickness is assumed, especially for thin ice in marginal zones, implying the presence of different sea ice bottom melt processes. These findings revealed the important role of the changing microstructure and IOPs of ice in affecting the radiation transfer of Arctic sea ice.

Arctic Sea Ice Loss Inducing Northwest Extension of Summer Precipitation over the Tibetan Plateau

Abstract Summer precipitation over the Tibetan Plateau (TP) has experienced obvious changes in recent decades, with significantly increased trends observed over the northern and western regions during 1961–2020. Results indicate that there are two pathways linking them to the reduced Arctic sea ice from late spring (April and May) to early summer (June and July). First, decreased sea ice in late spring favors wet and dry soil over the eastern Caspian Sea and northeastern TP by stimulating anomalous wavelike patterns and modulating snow melting. Furthermore, the soil moisture anomalies during late spring could maintain to July because of memory effects, strengthen the midlatitude Silk Road pattern, and contribute to the formation of a dipole mode around TP, characterized by a west cyclone and an east anticyclone. Second, through exciting an anomalous Rossby wave directly spreading from the Arctic to TP, a similar dipole pattern on 500-hPa geopotential height could be associated with the decline of sea ice in early summer. Consequently, enhanced southwesterly winds shown with the west cyclone cause more water vapor input to the western TP, while easterly anomalies along with the east anticyclone inhibit climatological westerly winds and prevent water vapor from outputting. This dipole pattern finally brings more moisture accumulated in the western and northern TP and increases the precipitation. This study links heterogeneous changes in summer precipitation over TP to Arctic sea ice changes and provides insights for further investigation into whether the TP could serve as a bridge connecting the Arctic and tropical climates.

Physical Links from Atmospheric Circulation Patterns to Barents–Kara Sea Ice Variability from Synoptic to Seasonal Timescales in the Cold Season

Abstract Previous findings show that large-scale atmospheric circulation plays an important role in driving Arctic sea ice variability from synoptic to seasonal time scales. While some circulation patterns responsible for Barents–Kara sea ice changes have been identified in previous works, the most important patterns and the role of their persistence remain unclear. Our study uses self-organizing maps to identify nine high-latitude circulation patterns responsible for day-to-day Barents–Kara sea ice changes. Circulation patterns with a high pressure center over the Urals (Scandinavia) and a low pressure center over Iceland (Greenland) are found to be the most important for Barents–Kara sea ice loss. Their opposite-phase counterparts are found to be the most important for sea ice growth. The persistence of these circulation patterns helps explain sea ice variability from synoptic to seasonal time scales. We further use sea ice models forced by observed atmospheric fields (including the surface circulation and temperature) to reproduce observed sea ice variability and diagnose the role of atmosphere-driven thermodynamic and dynamic processes. Results show that thermodynamic and dynamic processes similarly contribute to Barents–Kara sea ice concentration changes on synoptic time scales via circulation. On seasonal time scales, thermodynamic processes seem to play a stronger role than dynamic processes. Overall, our study highlights the importance of large-scale atmospheric circulation, its persistence, and varying physical processes in shaping sea ice variability across multiple time scales, which has implications for seasonal sea ice prediction. Significance Statement Understanding what processes lead to Arctic sea ice changes is important due to their significant impacts on the ecosystem, weather, and shipping, and hence our society. A well-known process that causes sea ice changes is atmospheric circulation variability. We further pin down what circulation patterns and underlying mechanisms matter. We identify multiple circulation patterns responsible for sea ice loss and growth to different extents. We find that the circulation can cause sea ice loss by mechanically pushing sea ice northward and bringing warm and moist air to melt sea ice. The two processes are similarly important. Our study advances understanding of the Arctic sea ice variability with important implications for Arctic sea ice prediction.

The Weakened Atlantic Meridional Overturning Circulation Diminishes Recent Arctic Sea Ice Loss

AbstractThe Arctic sea ice has been rapidly dwindling over the past four decades, significantly impacting the Arctic region and beyond. During the same period, the Atlantic meridional overturning circulation (AMOC) was also found in a declining trend. Here we investigate the role of the AMOC in the recent Arctic sea ice changes by comparing simulations from the Community Climate System Model version 4 with decelerated and stationary AMOCs under anthropogenic climate change. We find that the weakened AMOC can slow down the decline rates of Arctic sea ice area and volume by 36% and 22% between 1980 and 2020, respectively. The decelerated ocean circulation causes a reduction of northward Atlantic heat transport and hence a general interior ocean cooling in the Arctic Mediterranean, which helps alleviate the Arctic sea ice loss primarily through thermodynamic processes occurring at the base of the sea ice.

Responses in the Subpolar North Atlantic in Two Climate Model Sensitivity Experiments with Increased Stratospheric Aerosols

Abstract The subpolar North Atlantic (SPNA) shows contrasting responses in two sensitivity experiments with increased stratospheric aerosols, offering insight into the physical processes that may impact the Atlantic meridional overturning circulation (AMOC) in a warmer climate. In one, the upper ocean becomes warm and salty, but in the other it becomes cold and fresh. The changes are accompanied by diverging AMOC responses. The first experiment strengthens the AMOC, opposing the weakening trend in the reference simulation. The second experiment shows a much smaller impact. Both simulations use the Community Earth System Model with the Whole Atmosphere Community Climate Model component (CESM-WACCM) but differ in model versions and stratospheric aerosol specifications. Despite both experiments using similar approaches to increase stratospheric aerosols to counteract the rising global temperature, the contrasting SPNA and AMOC responses indicate a considerable dependency on model physics, climate states, and model responses to forcings. This study focuses on examining the physical processes involved with the impact of stratospheric aerosols on the SPNA salinity changes and their potential connections with the AMOC and the Arctic. We find that in both cases, increased stratospheric aerosols act to enhance the SPNA upper-ocean salinity by reducing freshwater export from the Arctic, which is closely tied to the Arctic sea ice changes. The impact on AMOC is primarily through the thermal component of the surface buoyancy fluxes, with negligible contributions from the freshwater component. These experiments shed light on the physical processes that dictate the important connections between the SPNA, the Arctic, the AMOC, and their subsequent feedbacks on the climate system.

Characteristics of extratropical cyclone variability in the Northern Hemisphere and their response to rapid changes in Arctic sea ice

Characteristics of extratropical cyclone variability in the Northern Hemisphere and their response to rapid changes in Arctic sea ice

Forced and internal components of observed Arctic sea-ice changes

Abstract. The Arctic sea-ice cover is strongly influenced by internal variability on decadal timescales, affecting both short-term trends and the timing of the first ice-free summer. Several mechanisms of variability have been proposed, but how these mechanisms manifest both spatially and temporally remains unclear. The relative contribution of internal variability to observed Arctic sea-ice changes also remains poorly quantified. Here, we use a novel technique called low-frequency component analysis to identify the dominant patterns of winter and summer decadal Arctic sea-ice variability in the satellite record. The identified patterns account for most of the observed regional sea-ice variability and trends, and they thus help to disentangle the role of forced and internal sea-ice changes over the satellite record. In particular, we identify a mode of decadal ocean–atmosphere–sea-ice variability, characterized by an anomalous atmospheric circulation over the central Arctic, that accounts for approximately 30 % of the accelerated decline in pan-Arctic summer sea-ice area between 2000 and 2012 but accounts for at most 10 % of the decline since 1979. For winter sea ice, we find that internal variability has dominated decadal trends in the Bering Sea but has contributed less to trends in the Barents and Kara seas. These results, which detail the first purely observation-based estimate of the contribution of internal variability to Arctic sea-ice trends, suggest a lower estimate of the contribution from internal variability than most model-based assessments.

Assessing CMIP6 simulations of Arctic sea ice drift: Role of near-surface wind and surface ocean current in model performance

Sea ice drift is a critical parameter for understanding the rapid changes in Arctic sea ice. Since the release of the Coupled Model Intercomparison Project Phase 6 (CMIP6), there has been a lack of quantitative analysis regarding CMIP6's simulation of Arctic sea ice drift. This study aims to assess the simulated Arctic sea ice drift from 1979 to 2014 by fifteen CMIP6 models against recent satellite retrievals, utilizing various quantitative indices. Additionally, the influence of near-surface wind and surface ocean current on model performance is further analyzed. The CMIP6 models capture several aspects of the observed Arctic sea ice drift climatology and variability. The seasonal patterns of sea ice drift speed in all models exhibit similarities with the observed data, and the models agree with the evaluation datasets, indicating that the seasonal evolution of sea ice drift corresponds to near-surface wind patterns. However, notable discrepancies are identified. All models overestimate sea ice drift speed, exceeding the observational data by 36%–97%. Fourteen out of fifteen models display larger seasonal variability (ranging from 0.74 to 1.28 km d−1) compared to the observed data (0.54 km d−1). Seven out of fifteen models exhibit a significant increasing trend in annual sea ice drift speed, similar to the observed trend of 0.58 km d−1 per decade, but with weaker trends (ranging from 0.11 to 0.33 km d−1 per decade). The remaining eight models reveal no statistically significant trend. The potential causes of such biases were further explored in this study. It suggests that the overestimation of sea ice drift speed in the models might be primarily attributed to the overestimation of near-surface wind speeds and their influence on sea ice drift speed. The models' overestimation of seasonal variability in near-surface wind speeds may account for the overestimation of seasonal variability in sea ice drift. The models' inability to represent the trend in sea ice drift speed may result from their failure to simulate an increasing trend in surface ocean current speed.

Northern Pacific extratropical cyclone variability and its linkage with Arctic sea ice changes

Extratropical cyclones are critical weather systems affecting climate change in mid and high-latitude regions. Researching the characteristics, patterns, and movements of extratropical cyclones is helpful for improved prediction and understanding of global climate change. Currently, there are still great difficulties in predicting extratropical cyclones. Also, few prior studies have investigated the potential impact of Arctic sea ice on extratropical cyclone activity (ECA). This study utilizes updated ERA5 data and an improved ECA identification method to reveal ECA in the Pacific. The results demonstrate that the Pacific ECA primarily occurs during the cold season (November to March), and the North Pacific region has the maximum ECA. More remarkably, a strong linkage exists between the preceding summer-fall anomalous changes in the Arctic sea ice and the cold season Pacific ECA. We discover that Arctic sea ice could modify the local pressure field, changing the southern boundary of the Pacific sector polar vortex in winter, which in turn influences the intensity of the westerly jet stream and eventually impacts the Pacific ECA during the cold season. Our exploration will provide references for further study and prediction of ECA in the Pacific Ocean.

The Effects of Discharge Changes in Siberian Rivers on Arctic Sea-Ice Melting

Arctic river discharge is one of the important factors affecting sea-ice melting of Arctic shelf seas. However, such effects have not been given much attention. In this study, the changes in discharge of the Ob, Yenisei, and Lena Rivers and the sea ice of the Kara and Laptev Seas during 1979–2019 were analyzed. Substantial increases in discharge and heat from the discharge and decreases in sea ice concentration (SIC) were detected. The effects of changes in discharge and riverine heat on sea ice changes were investigated. The results showed that the influence of the discharge, accumulated discharge, heat, and accumulated heat on SIC mainly occurred at the beginning and final stages of sea-ice melting. Discharge accelerated the melting of sea ice by increasing the absorption of solar radiation as the impurities contained in the discharge washed to the sea ice surface during the initial and late stages of sea-ice melting. Changes in cumulative riverine heat from May to September greatly contributed to the SIC changes in the Kara and Laptev Seas at the seasonal scale. The SIC reduced by 1% when the cumulative riverine heat increased by 213.2 × 106 MJ, 181.5 × 106 MJ, and 154.6 × 106 MJ in the Lena, Yenisei, and Ob Rivers, respectively, from May to September. However, even in the plume coverage areas in the Kara and Laptev Seas, discharge changes from the three rivers had a limited contribution to the reduction in SIC at annual scales. This work is helpful for understanding the changes in Arctic sea ice.

Ozone Monitoring Instrument (OMI) UV aerosol index data analysis over the Arctic region for future data assimilation and climate forcing applications

Abstract. Due to a lack of high-latitude ground-based and satellite-based data from traditional passive- and active-based measurements, the impact of aerosol particles on the Arctic region is one of the least understood factors contributing to recent Arctic sea ice changes. In this study, we investigated the feasibility of using the ultraviolet (UV) aerosol index (AI) parameter from the Ozone Monitoring Instrument (OMI), a semi-quantitative aerosol parameter, for quantifying spatiotemporal changes in UV-absorbing aerosols over the Arctic region. We found that OMI AI data are affected by an additional row anomaly that is unflagged by the OMI quality control flag and are systematically biased as functions of observing conditions, such as azimuth angle, and certain surface types over the Arctic region, resulting in an anomalous “ring” of climatologically high AI centered at about 70∘ N, surrounding an area of low AI over the pole. Two methods were developed in this study for quality-assuring the Arctic AI data. Using quality-controlled OMI AI data from 2005 through 2020, we found decreases in UV-absorbing aerosols in the spring months (April and May) over much of the Arctic region and increases in UV-absorbing aerosols in the summer months (June, July, and August) over northern Russia and northern Canada. Additionally, we found significant increases in the frequency and size of UV-absorbing aerosol events across the Arctic and high-Arctic (north of 80∘ N) regions for the latter half of the study period (2014–2020), driven primarily by a significant increase in boreal biomass-burning plume coverage.

Evaluation of Arctic Sea Ice Thickness from a Parameter-Optimized Arctic Sea Ice–Ocean Model

Sea ice thickness (SIT) presents comprehensive information on Arctic sea ice changes and their role in the climate system. However, our understanding of SIT is limited by a scarcity of observations and inaccurate model simulations. Based on simultaneous parameter optimization with a micro genetic algorithm, the North Atlantic/Arctic Ocean–Sea Ice Model (NAOSIM) has already demonstrated advantages in Arctic sea ice simulations. However, its performance in simulating pan-Arctic SITs remains unclear. In this study, a further evaluation of Arctic SITs from NAOSIM was conducted based on a comparison with satellite and in situ observations. Generally, NAOSIM can reproduce the annual cycle and downward trend in the sea ice volume. However, deficiencies can still be found in the simulation of SIT spatial patterns. NAOSIM overestimates the SIT of thinner ice (<1.5 m) in the Beaufort Sea, underestimates the SIT of thick ice (>1.5 m) in the central Arctic and is unable to capture the upward trend in the SIT in the north of the Canadian Archipelago as well as to reproduce the intensity of the observed SIT variability. In terms of SIT simulation, NAOSIM performs better as the time approaches the optimization window (2000–2012). Therefore, in the context of rapid changes in Arctic sea ice, how to optimize this model based on limited observations still remains a challenge.