Arctic Sea Ice Concentration Research Articles

Tropical cyclone activity over western North Pacific favors Arctic sea ice increase.

Teleconnections between the tropics and the Arctic have attracted a lot of scientific interest. However, the mechanisms by which tropical synoptic-scale systems influence the variability of Arctic sea ice remain unknown. In this study, we highlight the impacts of tropical cyclone (TC) activity over the western North Pacific (WNP) on Arctic Sea Ice Concentration (SIC) using observational evidence and climate model simulation experiments. Our findings demonstrate significant positive correlations between Accumulated Cyclone Energy (ACE) in the WNP and SIC in the Arctic-Pacific Sector (APS), particularly when considering a 30-day lag. The TC activity over the WNP induces Rossby wave train propagation towards the Arctic, leading to anomalous cyclonic circulation over the upper troposphere of the APS. The anomalous cyclone over the Arctic, on one hand, signifies the deepening of the Arctic polar vortex and diminishes adiabatic warming over the APS, subsequently inducing cooling and drying of the lower Arctic air. This process reduces downward longwave radiation, promoting an increase in September APS SIC. On the other hand, the anomalous cyclone over the Arctic hinders the export of sea ice and local melting processes throughout the Fram Strait. These findings contribute to a deeper comprehension of tropics-Arctic teleconnections.

Interdecadal Variation in the Impact of Arctic Sea Ice on El Niño–Southern Oscillation: The Role of Atmospheric Mean Flow

Abstract This study reveals the remarkable interdecadal changes in the influence of boreal winter Arctic sea ice concentration (SIC) anomalies (ASICAs) in the Greenland–Barents Seas on the subsequent El Niño–Southern Oscillation (ENSO) development. Winter ASICA is strongly associated with the subsequent winter ENSO before the late 1980s and after the late 2000s, but their connection is weak during the 1990s and the 2000s. The interdecadal variations in the influence of ASICA on ENSO are associated with changes in the spatial structure of the ASICA-induced North Pacific atmospheric anomalies. During high-correlation periods, winter SIC increases in the Greenland–Barents Seas lead to tropospheric cooling via the suppression of upward surface heat fluxes, which further trigger an atmospheric teleconnection from the Arctic to the North Pacific. The accompanying North Pacific Oscillation–like atmospheric anomalies result in sea surface temperature (SST) warming in the subtropical North Pacific, which extends southward into the tropical Pacific via wind–evaporation–SST feedback and leads to surface westerly anomalies over the tropical western Pacific in the following summer. The tropical western Pacific westerly wind anomalies impact the subsequent ENSO development via triggering positive Bjerknes air–sea interaction. During low-correlation periods, atmospheric anomalies over the North Pacific generated by the winter ASICA are located more northward and cannot induce marked subtropical North Pacific SST anomalies and thus have a weak impact on the following ENSO development. Numerical experiments suggest that the interdecadal variation in the spatial structure of the North Pacific atmospheric anomalies induced by the winter ASICA is partly attributed to change in the atmospheric mean flow. Significance Statement Arctic sea ice is an important component of the global climate system. Studies have shown that Arctic sea ice has decreased significantly in recent decades, which is considered to be one of the most important responses of Earth’s climate system to global climate change. A number of studies have shown that Arctic sea ice anomalies have significant impacts on weather and climate over mid–high latitudes through tropospheric and stratospheric processes. Recent studies have suggested that the effects of Arctic sea ice anomalies could extend to the tropics via air–sea interactions. El Niño–Southern Oscillation (ENSO) is the strongest air–sea coupled system in the tropics and can have a significant impact on climate over the globe. ENSO is also considered to be one of the most important sources of subseasonal–seasonal climate predictability over many parts of the globe. It is therefore important to study the factors for the ENSO occurrence. A recent study showed that Arctic sea ice anomalies during boreal winter in the Greenland–Barents Seas have a significant impact on the following winter ENSO. In this study, we further reveal that the influence of winter Arctic sea ice anomalies in the Greenland–Barents Seas on the subsequent ENSO is unstable and has undergone significant interdecadal variation. We then investigate the mechanisms underlying the interdecadal variation via observational analysis and numerical simulations. The results of this study not only have implications for improving the prediction of ENSO but also could improve our understanding of the physical link between high-latitude climate systems and tropical air–sea coupling systems.

Predictability and applicability evaluation of winter temperatures in China based on Eurasian Arctic sea ice concentrations in autumn

Predictability and applicability evaluation of winter temperatures in China based on Eurasian Arctic sea ice concentrations in autumn

Cold Season Cloud Response to Sea Ice Loss in the Arctic

Abstract Based on satellite observations, we investigate the cloud responses during the cold season, from October to the following March, to interannual fluctuations in Arctic sea ice concentrations during September and October. It is found that the cloud responses differ between the periods 2000-2018 and 1982-1999. From 2000-2018, increased Arctic Ocean cloud cover in October and November transitioned to significantly less cloud cover and cloud radiative forcing (CRF) from January to March, in response to lower sea ice concentrations in September and October. The lower CRF in January and March preconditions the sea ice and likely contributes to higher sea ice concentrations the following September. This suggests a negative cloud feedback on sea ice concentration, which may contribute to sea ice recovery after a dramatic decrease. In contrast, from 1982 to 1999, generally increased cloud cover and cloud radiative forcing persisted from October to March following lower sea ice concentrations in the preceding September and October. The cause of the different cloud responses to sea ice changes in these two time periods remains unclear and requires further investigation.

Statistical seasonal prediction of Arctic sea ice concentration based on spatiotemporal anomaly persistent method

Abstract Accurate prediction of Arctic sea ice is crucial for high-latitude and even mid-latitude climate prediction. It significantly affects atmospheric circulation, the environment, ecology, and maritime transport. This study developed a statistical prediction model to predict monthly Arctic sea ice concentration (SIC) for up to one year based on the season-reliant empirical orthogonal functions (SEOFs) technique. Its prediction skill was compared with that of a dynamical prediction model. The spatiotemporal pattern of sea ice anomalies, which exhibit strong seasonality and are maintained for a significant period above the seasonal time scale by atmosphere-ocean interactions, was extracted using SEOFs. A prediction model was constructed by extrapolating from the recent anomalous state of sea ice to predict the future. Experimental retrospective predictions with monthly time resolution for 1982–2021 were performed to validate the prediction skill of Arctic SIC and areal extent. Statistically significant prediction skills were achieved over several months, even up to six months, exceeding the skill of the dynamical model.

Influence of the Atlantic and Pacific Multidecadal Variability on Arctic Sea Ice in Pacemaker Simulations during 1920–2013

Abstract The Atlantic multidecadal variability (AMV) and Pacific multidecadal variability (PMV) can influence Arctic sea ice and modulate its trend, but to what extent the AMV and PMV can affect Arctic sea ice and which processes are dominant are not well understood. Here, we analyze the Community Earth System Model, version 1, idealized and time-varying pacemaker ensemble simulations to investigate these issues. These experiments show that the sea ice concentration varies mainly over the marginal Arctic Ocean, while the sea ice thickness variations occur over the entire Arctic Ocean. The internal components of AMV and PMV can enhance or weaken the decadal sea ice loss rates over the marginal Arctic Ocean by more than 50%. The AMV- or PMV-induced anomalous atmospheric energy transport and downward longwave radiation related to low clouds (thermodynamical processes) and sea ice motion (dynamical processes) contribute to the Arctic surface air temperature and sea ice concentration and thickness changes. Anomalous oceanic heat flux is mainly a response to rather than a cause of sea ice variations. The dynamic processes contribute to the winter Arctic sea ice variations as much as the thermodynamic processes, but they contribute less (more) to the summer Arctic sea ice variability than the thermodynamic processes over the marginal Arctic Ocean (parts of the central Arctic Ocean). Sea ice loss enhances air–sea heat fluxes, which cause oceanic heat convergence and warm near-surface air and the lower troposphere, which in turn melt more sea ice.

Large-Scale Climate Modes Drive Low-Frequency Regional Arctic Sea Ice Variability

Abstract Summer Arctic sea ice is declining rapidly but with superimposed variability on multiple time scales that introduces large uncertainties in projections of future sea ice loss. To better understand what drives at least part of this variability, we show how a simple linear model can link dominant modes of climate variability to low-frequency regional Arctic sea ice concentration (SIC) anomalies. Focusing on September, we find skillful projections from global climate models (GCMs) from phase 6 of the Coupled Model Intercomparison Project (CMIP6) at lead times of 4–20 years, with up to 60% of observed low-frequency variability explained at a 5-yr lead time. The dominant driver of low-frequency SIC variability is the interdecadal Pacific oscillation (IPO) which is positively correlated with SIC anomalies in all regions up to a lead time of 15 years but with large uncertainty between GCMs and internal variability realization. The Niño-3.4 index and Atlantic multidecadal oscillation have better agreement between GCMs of being positively and negatively related, respectively, with low-frequency SIC anomalies for at least 10-yr lead times. The large variations between GCMs and between members within large ensembles indicate the diverse simulation of teleconnections between the tropics and Arctic sea ice and the dependence on the initial climate state. Further, the influence of the Niño-3.4 index was found to be sensitive to the background climate. Our results suggest that, based on the 2022 phases of dominant climate variability modes, enhanced loss of sea ice area across the Arctic is likely during the next decade. Significance Statement The purpose of this study is to better understand the drivers of low-frequency variability of Arctic sea ice. Teasing out the complicated relationships within the climate system takes a large number of examples. Here, we use 42 of the latest generation of global climate models to construct a simple linear model based on dominant named climate features to predict regional low-frequency sea ice anomalies at a lead time of 2–20 years. In 2022, these modes of variability happen to be in the phases most conducive to low Arctic sea ice concentration anomalies. Given the context of the longer-term trend of sea ice loss due to global warming, our results suggest accelerated Arctic sea ice loss in the next decade.

The out‐of‐phase pattern of summer precipitation over northern China and the possible mechanisms

AbstractThis study explored the interannual characteristics of the out‐of‐phase pattern of summer precipitation over northern China during the past years, as well as the possible underlying mechanisms. The out‐of‐phase pattern is characterized by the positive precipitation anomaly in Northwest China (NWC) and the negative anomaly in North China (NC). Our analyses indicate that the variation of Asian westerly jet (AWJ) is found evidently associated with this out‐of‐phase pattern of summer precipitation. The meridional displacement of AWJ induces opposite trends in water vapour transport and circulation anomalies between NWC and NC, leading to the out‐of‐phase pattern. Further analyses suggest that the anomalies of Arctic sea ice concentration (SIC), North Atlantic and Northwest Pacific sea surface temperature (SST) can impact the variation of AWJ by triggering the Eurasian (EU), polar‐Eurasia (POL) and Pacific‐Japan (PJ) teleconnection patterns, respectively, which in turn modulate the formation of this pattern. The precipitation in NWC is primarily affected by the North Atlantic and Northwest Pacific SST anomalies, whereas the precipitation in NC is notably influenced by both SST anomalies and the Arctic SIC anomaly. However, the role of SST anomalies on precipitation in NWC and NC exhibits an evident contrast, leading to the out‐of‐phase pattern of summer precipitation.

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.

Strengthened impact of late autumn Arctic sea ice on Asian winter cold extremes after 1999/2000

Winter cold extremes (WCEs) frequently plague densely populated areas of Asia, leading to substantial economic losses and even fatalities. It has been found that the late autumn sea ice concentration (SIC) anomalies in the northern (SICN) and southern Arctic (SICS) are significantly positively and negatively correlated with the occurrence frequency of WCE in Asia, respectively (Wang and Su 2024). Our study demonstrates that the impacts of SICN and SICS have strengthened after 1999/2000. Specifically, before 1999/2000, the influences of SICN and SICS on the Asian WCE (AWCE) were relatively weak, possibly related to the weak intensity of SICS and the limited correlation between SICN and SICS. After 1999/2000, the interannual variability of SICS became larger and anti-correlated with that of SICN, resulting in a stronger teleconnection between the Arctic SIC and AWCE. It is revealed that after 1999/2000, the greater loss of SICS modified atmospheric stability through changes in surface heat fluxes and surface upward longwave radiation fluxes. This alteration weakened the magnitudes of westerly winds and increased the frequency of blocking events over the northern Eurasian continent, leading directly to a higher occurrence of cold extremes in Asia. These interdecadal differences in the influence of Arctic SIC on AWCE may be associated with long-term climate change.

Interdecadal variations in the interannual relationship between winter tropical Pacific SST and subsequent summer Arctic sea ice in early 2000s

AbstractThe present study investigates the interdecadal change in the interannual relationship between winter sea surface temperature (SST) in the tropical central‐eastern Pacific and the subsequent summer Arctic sea ice concentration (SIC) and its possible mechanism using 1979–2023 monthly SST and SIC data from Hadley Center and atmospheric reanalysis data from NCEP/NCAR. It is found that the relationship between the winter tropical Pacific SST and the subsequent summer SIC of Beaufort Sea experienced an obvious interdecadal change from a significant negative correlation to a weak and insignificant correlation before and after the early 2000s, respectively. Before the early 2000s, when winter SST warming (cooling) anomalies occurred in the central and eastern tropical Pacific, it could sustain into summer and enhance a Rossby wave propagating from Tropic to high latitude, forming negative (positive) pressure anomalies in the North Pacific. Influenced by such anomalies, it is conducive to maintaining negative (positive) SST anomalies in the North Pacific from winter to subsequent summer. The summer SST cooling (warming) in the North Pacific could form a favourable atmospheric circulation anomaly for less (more) SIC. In contrast, after the early 21st century, the strong SST anomalies in winter over the eastern tropical Pacific would weaken with time and subsequently diminish in spring, leading to the SST anomalies in the North Pacific persist to spring, which can hardly affect summer SIC anomaly in the Beaufort Sea. Therefore, the relationship collapses after the the early 2000s due to the weakened persistence of the eastern tropical Pacific SST anomaly.

PDED-ConvLSTM: Pyramid Dilated Deeper Encoder–Decoder Convolutional LSTM for Arctic Sea Ice Concentration Prediction

Arctic sea ice concentration plays a key role in the global ecosystem. However, accurate prediction of Arctic sea ice concentration remains a challenging task due to its inherent nonlinearity and complex spatiotemporal correlations. To address these challenges, we propose an innovative encoder–decoder pyramid dilated convolutional long short-term memory network (DED-ConvLSTM). The model is constructed based on the convolutional long short-term memory network (ConvLSTM) and, for the first time, integrates the encoder–decoder architecture of ConvLSTM (ED-ConvLSTM) with a pyramidal dilated convolution strategy. This approach aims to efficiently capture the spatiotemporal properties of the sea ice concentration and to enhance the identification of its nonlinear relationships. By applying convolutional layers with different dilation rates, the PDED-ConvLSTM model can capture spatial features at multiple scales and increase the receptive field without losing resolution. Further, the integration of the pyramid convolution module significantly enhances the model’s ability to understand complex spatiotemporal relationships, resulting in notable improvements in prediction accuracy and generalization ability. The experimental results show that the sea ice concentration distribution predicted by the PDED-ConvLSTM model is in high agreement with ground-based observations, with the residuals between the predictions and observations maintained within a range from −20% to 20%. PDED-ConvLSTM outperforms other models in terms of prediction performance, reducing the RMSE by 3.6% compared to the traditional ConvLSTM model and also performing well over a five-month prediction period. These experiments demonstrate the potential of PDED-ConvLSTM in predicting Arctic sea ice concentrations, making it a viable tool to meet the requirements for accurate prediction and provide technical support for safe and efficient operations in the Arctic region.

Connection between Barents Sea Ice in May and Early Summer Monsoon Rainfall in the South China Sea and Its Possible Mechanism

The impacts of Arctic sea ice on climate in middle and high latitudes have been extensively studied. However, its effects on climate in low latitudes, particularly on summer monsoon rainfall in the South China Sea (SCS), have received limited attention. Thus, this study investigates the connection between the Arctic sea ice concentration (SIC) anomaly and the early summer monsoon rainfall (ESMR) in the SCS and its underlying physical mechanism. The results reveal a significant positive correlation between the Barents Sea (BS) SIC in May and the ESMR in the SCS. When there is more (less) SIC in the Barents Sea (BS) during May, this results in a positive (negative) anomaly of the local turbulent heat flux, which lasts until June. This, in turn, excites an upward (downward) air motion anomaly in the vicinity of the BS, causing a corresponding downward (upward) motion anomaly over the Black Sea. Consequently, this triggers a wave train similar to the Eurasian (SEU) teleconnection, propagating eastward towards East Asia. The SEU further leads to an (a) upward (downward) motion anomaly and weakens (strengthens) the western Pacific subtropical high (WPSH) over the SCS, which is accompanied by a southwest adequate (scarce) water vapor anomaly transporting from the Indian Ocean, resulting in more (less) precipitation in the SCS. Furthermore, the response of ESMR in the SCS to the SIC in the BS is further verified by using the Community Atmosphere Model version 5.3 (CAM5.3). This study introduces novel precursor factors that influence the South China Sea summer monsoon (SCSSM), presenting a new insight for climate prediction in this region, which holds significant implications.

Influence of New Parameterization Schemes on Arctic Sea Ice Simulation

Two coupled climate models that participated in the CMIP6 project (Coupled Model Intercomparison Project Phase 6), the Earth System Model of Chinese Academy of Sciences version 2 (CAS-ESM2-0), and the Nanjing University of Information Science and Technology Earth System Model version 3 (NESM3) were assessed in terms of the impact of four new sea ice parameterization schemes. These four new schemes are related to air–ice heat flux, radiation penetration and absorption, melt ponds, and ice–ocean flux, respectively. To evaluate the effectiveness of these schemes, key sea ice variables with and without these new schemes, such as sea ice concentration (SIC) and sea ice thickness (SIT), were compared against observation and reanalysis products from 1980 to 2014. The simulations followed the design of historical experiments within the CMIP6 framework. The results revealed that both models demonstrated improvements in simulating Arctic SIC and SIT when the new parameterization schemes were implemented. The model bias of SIC in some marginal sea ice zones of the Arctic was reduced, especially during March. The SIT was increased and the transpolar gradient of SIT was reproduced. The changes in spatial patterns of SIC and SIT after adding new schemes bear similarities between the two coupled models. This suggests that the new schemes have the potential for broad application in climate models for simulation and future climate scenario projection, especially for those with underestimated SIT.

Unveiling the role of tropical Pacific on the emergence of ice-free Arctic projections

The observed sea ice concentration (SIC) over the Arctic has receded substantially in recent decades, and future model projections predict a seasonally ice-free Arctic in the second half of this century. Nevertheless, the impact of the Pacific on Arctic sea ice projections has yet to receive much attention. Observations show that summertime Arctic SIC growth events are related to the weakening of the Aleutian low and cooling events over the equatorial Pacific, and vice versa. We demonstrate that under various Coupled Model Intercomparison Project Phase 6 projections, the models in which the impact of El Niño-driven SIC loss is significantly higher than the La Niña-related SIC growth tend to turn seasonally ice-free by about 10–20 years ahead of the ensemble mean under high-emission future scenarios. We show how the non-linear impact of the El Niño Southern Oscillation (ENSO) on Arctic SIC resulted in a faster decline of summertime sea ice. The ENSO-related SIC changes in the multi-model ensemble mean of Arctic SIC are considerably lower than the internal variability and anthropogenic-driven changes. However, the asymmetric interannual ENSO effects over several decades and the resultant changes in surface heat fluxes over the Arctic lead to significant differences in the timing of sea ice extinction. Our results suggest that climate models must capture the realistic tropical Pacific–Arctic teleconnection to better predict the long-term evolution of the Arctic climate.

Differing roles of North Atlantic oceanic and atmospheric transports in the winter Eurasian Arctic sea-ice interannual-to-decadal variability

In recent decades, winter Arctic sea-ice concentration (SIC) has experienced a most prominent decline over Barents-Kara Seas (BKS). However, what regulates the time scale and spatial structure of the SIC variability over the Eurasian Arctic is unclear. Here, we find that the SIC variability over the Eurasian Arctic exhibits two major modes: decadal dipole mode with antiphase variation between the BKS and East Greenland (EG), and interannual monopole mode with in-phase variation between the BKS and EG. This decadal mode mainly results from interdecadal changes in ocean heat transports (OHTs) through Barents Sea Opening (BSO) and EG, lagging the Atlantic Multidecadal Oscillation by 7–16 years. The positive SIC dipole mode with a decrease over the BKS and an increase over the EG is also tied to the negative Arctic Oscillation comprised of Ural blocking and the negative North Atlantic Oscillation (NAO). However, the SIC loss of the interannual monopole mode mainly stems from the positive Arctic dipole comprised of Ural blocking and positive NAO through interannual changes in the BSO OHT and atmospheric moisture or heat transport. We further highlight that interannual atmospheric transports and BSO OHT associated with the Arctic dipole contribute to ~66% and ~34% of the interannual variability of the Eurasian Arctic SIC during 1960-2017, respectively. On decadal timescales, the relative contributions of atmospheric transports associated with Arctic Oscillation and OHT to the Eurasian Arctic SIC variability are ~19% and ~81%, respectively. Especially, the contribution of decadal atmospheric transports is significantly intensified during 2000–2017.

Impact of the Eurasian Zonal Circulation on the Interannual Variability of Winter Surface Air Temperature and Subseasonal Temperature Reversal in North China

AbstractThis study focuses on the interannual variability of winter mean surface air temperature (SAT) and subseasonal SAT reversal in North China, which have profound impacts on national life and economic activities. The analysis explores the temporal and spatial distribution characteristics of these variations and their relationship with the Eurasian Zonal Circulation (EZ). The findings reveal distinct interdecadal changes in North China’s winter mean SAT, along with a general subseasonal reversal of cold and warm conditions. In comparison to the North Atlantic oscillation/Arctic Oscillation, the EZ has a more significant influence on the interannual variability of winter and subseasonal reversals. Strong EZ years are associated with the weakening of the Siberian High and the Ural Blocking High, causing the East Asian Trough to move eastward, which leads to the retention of cold air from the north and increased SAT in North China. Conversely, weak EZ years exhibit opposite circulation patterns. The winter mean EZ is influenced by preceding autumn Arctic sea ice concentrations (SIC). Reduced autumn SIC results in decreased heat flux and reduced eddy energy, leading to substantial attenuation of the winter storm track across Scandinavia and Western Asia. This attenuation, mediated through the interaction between eddies and mean flow, triggers a slowdown of Eurasian zonal circulation and intensification of the Siberian High. These conditions favor a positive phase of the winter Scandinavia pattern, facilitating the transport of cold Arctic air into North China. Likewise, subseasonal reversals in SIC influence corresponding alterations in the EZ.

Two distinct declining trend of autumn Arctic sea ice concentration before and after 2002

This study investigates the Arctic sea ice concentration trend during 1979–2021 and explores why the autumn Arctic sea ice loss is accelerated after 2002 and its trend declining center shifts from the Chukchi Sea to the Barents-Kara-Laptev Seas. Attribution analysis reveals that the enhanced summer sea ice concentration negative trend in large part explains the autumn sea ice concentration accelerating reduction, whereas it is the trend center shift of increased downward longwave radiation that accounts for mostly of the autumn sea ice concentration decline center shift. Further analysis suggests the downward longwave radiation trend is closely related to large-scale atmospheric circulation changes. A tendency towards a dipole structure with an anticyclonic circulation over Greenland and the Arctic Ocean and a cyclonic circulation over Barents-Kara Seas enhances (suppresses) the downward longwave radiation over Western (Eastern) Arctic by warming and moistening (cooling and drying) the lower troposphere during 1979–2001. In comparison, a tendency towards a stronger Ural anticyclone combined with positive phase of the North Atlantic Oscillation pattern significantly promotes the increase of downward longwave radiation over Barents-Kara-Laptev Seas during 2002–2021. Our results set new insights into the Arctic sea ice variability and deepen our understanding of the climate change.

Effects of Arctic sea-ice concentration on turbulent surface fluxes in four atmospheric reanalyses

Abstract. A prerequisite for understanding the local, regional, and hemispherical impacts of Arctic sea-ice decline on the atmosphere is to quantify the effects of sea-ice concentration (SIC) on the turbulent surface fluxes of sensible and latent heat in the Arctic. We analyse these effects utilising four global atmospheric reanalyses, ERA5, JRA-55, MERRA-2, and NCEP/CFSR (including both the NCEP Climate Forecast System Reanalysis (CFSR) and the NCEP Climate Forecast System Version 2 (CFSv2)), and evaluate their uncertainties arising from inter-reanalysis differences in SIC and in the sensitivity of the turbulent surface fluxes to SIC. The magnitude of the differences in SIC is up to 0.15 but typically around 0.05 in most of the Arctic over all four seasons. Orthogonal-distance regression and ordinary-least-squares regression analyses indicate that the greatest sensitivity of both the latent and the sensible heat flux to SIC occurs in the cold season, November to April. For these months, using daily means of data, the average sensitivity is 400 W m−2 for the latent heat flux and over 800 W m−2 for the sensible heat flux per unit of SIC (change in SIC from 0 to 1), with differences between reanalyses that are as large as 300 W m−2 for the latent heat flux and 600 W m−2 for the sensible heat flux per unit of SIC. The sensitivity is highest for the NCEP/CFSR reanalysis. Comparing the periods 1980–2000 and 2001–2021, we find that the effect of SIC on turbulent surface fluxes has weakened owing to the increasing surface temperature of sea ice and sea-ice decline. The results also indicate signs of a decadal-scale improvement in the mutual agreement between reanalyses. The effect of SIC on turbulent surface fluxes arises mostly via the effect of SIC on atmosphere–surface differences in temperature and specific humidity, whereas the effect of SIC on wind speed (via surface roughness and atmospheric-boundary-layer stratification) partly cancels out in the turbulent surface fluxes, as the wind speed increases the magnitudes of both upward and downward fluxes.

Why Are Arctic Sea Ice Concentration in September and Its Interannual Variability Well Predicted over the Barents–East Siberian Seas by CFSv2?

Why Are Arctic Sea Ice Concentration in September and Its Interannual Variability Well Predicted over the Barents–East Siberian Seas by CFSv2?