Arctic Surface Air Temperature Research Articles

Decadal Relationship Between Arctic SAT and AMOC Changes Modulated by the North Pacific Oscillation

AbstractThe faster warming for Arctic Ocean surface air temperature (SAT) relative to that at lower latitude is connected with various processes, including local radiation feedback, poleward oceanic and atmospheric heat transport. It is unclear how combinations of different low‐frequency internal climate modes influence Arctic amplification on the decadal timescale. Here, the decadal Arctic SAT variation, its connection with the Atlantic meridional overturning circulation (AMOC) and possible underlying mechanisms, are investigated based on several independent observational proxies, pre‐industrial experiments, and historical large ensembles of two CMIP6 models. Our study suggests that AMOC and Arctic SAT vary in phase on the decadal timescale, whereas this relationship is insignificant at the interannual timescale. Further analysis shows that the AMOC accompanied with cross‐basin oceanic water/heat transport between Atlantic and Arctic would alter air–sea interface exchange over the melting ice regions, and then amplified poleward atmospheric heat and moisture transports. The resulting enhanced downward longwave radiation ultimately warms the Arctic SAT. Additionally, the decadal‐scale North Pacific Oscillation (NPO) can modulate the relationship between AMOC and Arctic SAT by influencing poleward moisture transport and cross‐basin circulation. Specifically, the phase shift of combined NPO and AMOC can contribute 14%–41% covariance relationship between AMOC and Arctic SAT. Our study provides potential sources for predicting the Arctic climate and constraining its uncertainty in future projections.

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.

Why Does the Ensemble Mean of CMIP6 Models Simulate Arctic Temperature More Accurately Than Global Temperature?

An accurate simulation and projection of future warming are needed for a proper policy response to expected climate change. We examine the simulations of the mean global and Arctic surface air temperatures by the CMIP6 (Climate Models Intercomparison Project phase 6) climate models. Most models overestimate the observed mean global warming. Only seven out of 19 models considered simulate global warming that is within ±15% of the observed warming between the average of the 2014–2023 and 1961–1990 reference period. Ten models overestimate global warming by more than 15% and only one of the models underestimates it by more than 15%. Arctic warming is simulated by the CMIP6 climate models much better than the mean global warming. The reason is an equal spread of over and underestimates of Arctic warming by the models, while most of the models overestimate the mean global warming. Eight models are within ±15% of the observed Arctic warming. Only three models are accurate within ±15% for both mean global and Arctic temperature simulations.

Quantifying Contributions of External Forcing and Internal Variability to Arctic Warming During 1900–2021

AbstractArctic warming has significant environmental and social impacts. Arctic long‐term warming trend is modulated by decadal‐to‐multidecadal variations. Improved understanding of how different external forcings and internal variability affect Arctic surface air temperature (SAT) is crucial for explaining and predicting Arctic climate changes. We analyze multiple observational data sets and large ensembles of climate model simulations to quantify the contributions of specific external forcings and various modes of internal variability to Arctic SAT changes during 1900–2021. We find that the long‐term trend and total variance in Arctic‐mean SAT since 1900 are largely forced responses, including warming due to greenhouse gases and natural forcings and cooling due to anthropogenic aerosols. In contrast, internal variability dominates the early 20th century Arctic warming and mid‐20th century Arctic cooling. Internal variability also explains ∼40% of the recent Arctic warming from 1979 to 2021. Unforced changes in Arctic SAT are largely attributed to two leading modes. The first is pan‐Arctic warming with stronger loading over the Eurasian sector, accounting for 70% of the unforced variance and closely related to the positive phase of the unforced Atlantic Multidecadal Oscillation (AMO). The second mode exhibits relatively weak warming averaged over the entire Arctic with warming over the North American‐Pacific sector and cooling over the Atlantic sector, explaining 10% of the unforced variance and likely caused by the positive phase of the unforced Interdecadal Pacific Oscillation (IPO). The AMO‐related changes dominate the unforced Arctic warming since 1979, while the IPO‐related changes contribute to the decadal SAT changes over the North American‐Pacific Arctic.

Modulated Trends in Arctic Surface Air Temperature Extremes as a Fingerprint of Climate Change

Abstract Strengthened by polar amplification, Arctic warming provides direct evidence for global climate change. This analysis shows how Arctic surface air temperature (SAT) extremes have changed throughout time. Using ERA5, we demonstrate a pan-Arctic (>60°N) significant upward SAT trend of +0.62°C decade−1 since 1979. Due to this warming, the warmest days of each month in the 1980s to 1990s would be considered average today, while the present coldest days would be regarded as normal in the 1980s to 1990s. Over 1979–2021, there was a 2°C (or 7%) reduction of pan-Arctic SAT seasonal cycle, which resulted in warming of the cold SAT extremes by a factor of 2 relative to the SAT trend and dampened trends of the warm SAT extremes by roughly 25%. Since 1979, autumn has seen the strongest increasing trends in daily maximum and minimum temperatures, as well as counts of days with SAT above the 90th percentile and decreasing trends in counts of days with SAT below the 10th percentile, consistent with rapid Arctic sea ice decline and enhanced air–ocean heat fluxes. The modulated SAT seasonal signal has a significant impact on the timing of extremely strong monthly cold and warm spells. The dampening of the SAT seasonal fluctuations is likely to continue to increase as more sea ice melts and upper-ocean warming persists. As a result, the Arctic winter cold SAT extremes may continue to exhibit a faster rate of change than that of the summer warm SAT extremes as the Arctic continues to warm. Significance Statement As a result of global warming, the Arctic Ocean’s sea ice is receding, exposing more and more areas to air–sea interactions. This reduces the range of seasonal changes in Arctic surface air temperatures (SAT). Since 1979, the reduced seasonal SAT signal has decreased the trend of warm SAT extremes by 25% over the background warming trend and doubled the trend of cold SAT extremes relative to SAT trends. A substantial number of warm and cold spells would not have been identified as exceptional if the reduction of the Arctic SAT seasonal amplitudes had not been taken into account. As the Arctic continues to warm and sea ice continues to diminish, seasonal SAT fluctuations will become more dampened, with the rate of decreasing winter SAT extremes exceeding the rate of increasing summer SAT extremes.

Relationship between the Stratospheric Arctic Vortex and Surface Air Temperature in the Midlatitudes of the Northern Hemisphere

Relationship between the Stratospheric Arctic Vortex and Surface Air Temperature in the Midlatitudes of the Northern Hemisphere

Decoupling of Arctic variability from the North Pacific in a warmer climate

The sea level pressure variability in the North Pacific modulates the climate of the Arctic and surrounding continents, substantially impacting ecosystems and indigenous communities. Our analysis based on data from the CESM2 Large Ensemble and different Model Intercomparison Project datasets reveals that the interannual variability of Arctic surface air temperature (SAT) gradually decouples from the contemporaneous atmospheric conditions over the North Pacific as external forcing increases in intensity in the future. Future projections show that the North Pacific-Arctic relationship during the fall season consistently weakens in magnitude until the end of this century, and in the 22nd and 23rd centuries, the relationship is negligible throughout the year. We show that under increased greenhouse gas emissions, the regional heat fluxes extensively control the Arctic temperature variability, and the strength of the projected North Pacific-Arctic relationship is strongly dependent on the Arctic sea ice extent. Our results suggest that under future warming, a strong coupling of Arctic SAT with the underlying ocean and a weakening of the meridional pressure gradient driven by an enhanced rate of sea ice retreat will weaken the interannual footprint of North Pacific variability on Arctic SAT. Therefore, we propose that the alarming rate of sea-ice decline over recent decades and projected in the near future could accelerate the rate of decoupling. Further, we suggest that mitigation strategies for the Arctic should focus on regional mechanisms operating on interannual and seasonal timescales.

An interdecadal change in the relationship between summer Arctic Oscillation and surface air temperature over the eastern Tibetan Plateau around the late 1990s

An interdecadal change in the relationship between summer Arctic Oscillation and surface air temperature over the eastern Tibetan Plateau around the late 1990s

Ice Freeze‐Up and Break‐Up in Arctic Rivers Observed With Satellite L‐Band Passive Microwave Data From 2010 to 2020

AbstractThe timing of ice freeze‐up and break‐up in the Arctic may be responding to climate change. Passive microwave remote sensing is a powerful technique for monitoring this timing. We processed low‐frequency microwave time series from the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission for a set of 31 satellite gauging reaches (SGRs) above 65°N between 2010 and 2020 to determine timing of freeze‐up and break‐up and annual river ice durations. We found indication of progressive ice cover reduction over more than half of the monitored river reaches, with possibly the fastest rate occurring over northeast Russia. Some rivers in high‐latitude North America experienced a slight increase in ice cover. Across the data set, we observed an average 2.2 days shift toward later ice freeze‐up in autumn and an average 0.6 days shift toward earlier ice break‐up in spring, resulting in an average decrease of 3.4 days in ice duration between 2010 and 2020. River reaches with the longest duration of ice cover appeared to have experienced the fastest rate of decrease. A possible reduction of the time lag between air temperature rise or fall and corresponding river ice break‐up and freeze‐up was also observed. Yet results on variability are carefuly interpreted given the short length of the time series (2010–2020) and the low statistical confidence rates calculated for the decadal tendency. Still outcomes are consistent with increases in global and Arctic surface air temperature. Following these time series over the next decade using passive microwave satellite sensors can monitor ice cover duration in the Arctic and will further determine temporal and regional trends.

A 600‐year marine record associated with the dynamics of the eastern Penny Ice Cap (Baffin Island, Nunavut, Canada)

ABSTRACTTwo composite sedimentary sequences sampled in the ice‐proximal (12CS) and ice‐distal (02CS) areas of Coronation Fjord (Baffin Island, Nunavut, Canada) were investigated in order to reconstruct the effect of climate variability on 600 years of changes in sediment transfer from the eastern Penny Ice Cap (PIC). Detrital proxies, and physical and sedimentological analyses revealed that glacial meltwater discharges led to frequent rapidly deposited layers (RDLs) in ice‐proximal settings. RDLs in ice‐distal settings involved the sudden release of a large quantity of sediment‐laden water during floods probably originating from adjacent fjords with large sandur deltas. Laminated sediments with ice‐rafted debris throughout the Little Ice Age interval in the ice‐proximal environment suggest that colder conditions promoted glacier growth, leading to successive episodes of turbid hyperpycnal meltwater plumes and iceberg calving in Coronation Fjord. Since 1850 ce, the accelerated Coronation retreat in response to modern warming has led to increased sedimentation rates, abrupt mineralogical and grain size proxy variations and more frequent RDLs. Similar trends between the detrital proxies of the ice‐proximal core and Atlantic Multidecadal Oscillation record and Arctic surface air temperature suggest high connectivity between atmospheric and sea surface temperature variations and PIC dynamics over the last 600 years.

Seasonal and regional contrasts of future trends in interannual arctic climate variability

Future changes in interannual variability (IAV) of Arctic climate indicators such as sea ice and precipitation are still fairly uncertain. Alongside global warming-induced changes in means, a thorough understanding of IAV is needed to more accurately predict sea ice variability, distinguish trends and natural variability, as well as to reduce uncertainty around the likelihood of extreme events. In this study we rank and select CMIP6 models based on their ability to replicate observations, and quantify simulated IAV trends (1981–2100) of Arctic surface air temperature, evaporation, precipitation, and sea ice concentration under continued global warming. We argue that calculating IAV on grid points before area-averaging allows for a more realistic picture of Arctic-wide changes. Large model ensembles suggest that on shorter time scales (30 years), IAV of all variables is strongly dominated by natural variability (e.g. 93% for sea ice area in March). Long-term trends of IAV are more robust, and reveal strong seasonal and regional differences in their magnitude or even sign. For example, IAV of surface temperature increases in the Central Arctic, but decreases in lower latitudes. Arctic precipitation variability increases more in summer than in winter; especially over land, where in the future it will dominantly fall as rain. Our results emphasize the need to address such seasonal and regional differences when portraying future trends of Arctic climate variability.

Newly reconstructed Arctic surface air temperatures for 1979–2021 with deep learning method

A precise Arctic surface air temperature (SAT) dataset, that is regularly updated, has more complete spatial and temporal coverage, and is based on instrumental observations, is critically important for timely monitoring and improving understanding of the rapid change in the Arctic climate. In this study, a new monthly gridded Arctic SAT dataset dated back to 1979 was reconstructed with a deep learning method by combining surface air temperatures from multiple data sources. The source data include the observations from land station of GHCN (Global Historical Climatology Network), ICOADS (International Comprehensive Ocean-Atmosphere Data Set) over the oceans, drifting ice station of Russian NP (North Pole), and buoys of IABP (International Arctic Buoy Programme). The last two are crucial for improving the representation of the in-situ observed temperatures within the Arctic. The newly reconstructed dataset includes monthly Arctic SAT beginning in 1979 and daily Arctic SAT beginning in 2011. This dataset would represent a new improvement in developing observational temperature datasets and can be used for a variety of applications.

Reduction in the Arctic Surface Warm Bias in the NCAR CAM6 by Reducing Excessive Low-Level Clouds in the Arctic

High-latitude low clouds in the Northern winter have been known to be closely related to the Arctic surface air temperature by controlling downward longwave radiation, but Earth system models often fail to accurately simulate this relationship. In this study, we conducted a series of model experiments to examine the role of winter high-latitude low-level clouds in determining the Arctic surface temperature. Our findings show that low-level clouds play a significant role in regulating the Arctic surface temperature. We used the NCAR CAM6 model and compared the results of an unforced simulation run with those of an experiment using an empirical low-level cloud scheme to alleviate the typical overestimation of the low cloud fraction of state-of-the-art general circulation models at high latitudes. The unforced simulation exhibited excessive downward longwave radiation in the Arctic, resulting in a significant warm bias compared to reanalysis data. On the other hand, the experiment using a modified scheme more closely resembled the reanalysis data in terms of low-level cloud simulation. Overall, our study underscores the importance of accurately representing low-level clouds in high-latitude regions to reduce surface temperature bias in the model.

Varying contributions of greenhouse gases, aerosols and natural forcings to Arctic land surface air temperature changes

The Arctic has warmed rapidly over the past century, with widespread negative impacts on local and surrounding environments. Previous studies have estimated the overall effects of individual groups of anthropogenic forcing agents on Arctic warming. However, the spatial patterns and temporal variabilities of the separate contributions of greenhouse gases (GHGs), natural forcing agents (NATs; solar radiation and volcanic activity combined) and other anthropogenic (OANT) forcing agents (which are dominated by aerosols) on Arctic land surface air temperatures remain underexamined. Here, we use CMIP6 (the Sixth Phase of the Coupled Model Intercomparison Project) models to quantify the separate contributions of GHGs, NATs and OANT forcing agents to Arctic land surface air temperature changes and analyze their spatial and temporal change patterns from 1915 to 2014. The results show that GHGs alone have warmed the Arctic by 2.72 °C/century (90% confidence interval: 1.42 °C–4.03 °C), 61.8% of which has been offset by OANT agents. The GHG-induced warming peaks are found in Ellesmere Island, Severnaya Zemlya and Svalbard (above 4 °C/century), while the largest cooling effects (above −2 °C/century) induced by OANT agents occurred in Krasnoyarsk Krai and Severnaya Zemlya. A further temporal evolution analysis indicates that the effects of GHGs and OANT forcings have been gradually and robustly detected over time; this increases our confidence in projecting future Arctic climate changes via CMIP6 models.

Present and future aerosol impacts on Arctic climate change in the GISS-E2.1 Earth system model

Abstract. The Arctic is warming 2 to 3 times faster than the global average, partly due to changes in short-lived climate forcers (SLCFs) including aerosols. In order to study the effects of atmospheric aerosols in this warming, recent past (1990–2014) and future (2015–2050) simulations have been carried out using the GISS-E2.1 Earth system model to study the aerosol burdens and their radiative and climate impacts over the Arctic (>60∘ N), using anthropogenic emissions from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases, while global annual mean greenhouse gas concentrations were prescribed and kept fixed in all simulations. Results showed that the simulations have underestimated observed surface aerosol levels, in particular black carbon (BC) and sulfate (SO42-), by more than 50 %, with the smallest biases calculated for the atmosphere-only simulations, where winds are nudged to reanalysis data. CMIP6 simulations performed slightly better in reproducing the observed surface aerosol concentrations and climate parameters, compared to the Eclipse simulations. In addition, simulations where atmosphere and ocean are fully coupled had slightly smaller biases in aerosol levels compared to atmosphere-only simulations without nudging. Arctic BC, organic aerosol (OA), and SO42- burdens decrease significantly in all simulations by 10 %–60 % following the reductions of 7 %–78 % in emission projections, with the Eclipse ensemble showing larger reductions in Arctic aerosol burdens compared to the CMIP6 ensemble. For the 2030–2050 period, the Eclipse ensemble simulated a radiative forcing due to aerosol–radiation interactions (RFARI) of -0.39±0.01 W m−2, which is −0.08 W m−2 larger than the 1990–2010 mean forcing (−0.32 W m−2), of which -0.24±0.01 W m−2 was attributed to the anthropogenic aerosols. The CMIP6 ensemble simulated a RFARI of −0.35 to −0.40 W m−2 for the same period, which is −0.01 to −0.06 W m−2 larger than the 1990–2010 mean forcing of −0.35 W m−2. The scenarios with little to no mitigation (worst-case scenarios) led to very small changes in the RFARI, while scenarios with medium to large emission mitigations led to increases in the negative RFARI, mainly due to the decrease in the positive BC forcing and the decrease in the negative SO42- forcing. The anthropogenic aerosols accounted for −0.24 to −0.26 W m−2 of the net RFARI in 2030–2050 period, in Eclipse and CMIP6 ensembles, respectively. Finally, all simulations showed an increase in the Arctic surface air temperatures throughout the simulation period. By 2050, surface air temperatures are projected to increase by 2.4 to 2.6 ∘C in the Eclipse ensemble and 1.9 to 2.6 ∘C in the CMIP6 ensemble, compared to the 1990–2010 mean. Overall, results show that even the scenarios with largest emission reductions leads to similar impact on the future Arctic surface air temperatures and sea-ice extent compared to scenarios with smaller emission reductions, implying reductions of greenhouse emissions are still necessary to mitigate climate change.

Relative contributions of internal atmospheric variability and surface processes to the interannual variations in wintertime Arctic surface air temperatures

AbstractSuperimposed on a warming trend, Arctic winter surface air temperature (SAT) exhibits substantial interannual variability, whose underlying mechanisms are unclear, especially regarding the role of sea-ice variations and atmospheric processes. Here, atmospheric reanalysis data and idealized atmospheric model simulations are used to reveal the mechanisms by which sea-ice variations and atmospheric anomalous conditions affect interannual variations in wintertime Arctic SAT. Results show that near-surface interannual warming in the Arctic is accompanied by comparable warming throughout large parts of the Arctic troposphere and large-scale anomalous atmospheric circulation patterns. Within the Arctic, changes in large-scale atmospheric circulations due to internal atmospheric variability explain a substantial fraction of interannual variation in SAT and tropospheric temperatures, which lead to an increase in moisture and downward longwave radiation, with the rest likely coming from sea ice-related and other surface processes. Arctic winter sea-ice loss allows the ocean to release more heat and moisture, which enhances Arctic warming; however, this effect on SAT is confined to the ice-retreat area and has a limited influence on large-scale atmospheric circulations.

Arctic Warming and Associated Sea Ice Reduction in the Early 20th Century Induced by Natural Forcings in MRI‐ESM2.0 Climate Simulations and Multimodel Analyses

AbstractDuring the early 20th century, the Arctic experienced a period of remarkable warming, often called the early 20th century warming (ETCW). However, the degree of the response to external climate forcing on the Arctic surface air temperature is not well understood. Climate simulations using a state‐of‐the‐art climate model of Meteorological Research Institute (MRI‐ESM2.0) and multimodel analyses were conducted to better understand ETCW. The MRI‐ESM2.0 historical simulations successfully reproduced the observed ETCW and the corresponding decreases in sea ice extent. Detection and attribution experiments using MRI‐ESM2.0 suggest that internal climate variability and external natural forcings by solar and volcanic activities had major influences on the model‐simulated ETCW, rather than external anthropogenic forcings. Multimodel analyses indicate that the Arctic warming trend during 1911–1940 induced by natural forcings is comparable to the unforced multidecadal internal variability, suggesting major contributions of the internal dynamics and natural forcings to ETCW.

The Role of Horizontal Temperature Advection in Arctic Amplification

AbstractThe wintertime (December–February) 1990–2016 Arctic surface air temperature (SAT) trend is examined using self-organizing maps (SOMs). The high-dimensional SAT dataset is reduced into nine representative SOM patterns, with each pattern exhibiting a decorrelation time scale of about 10 days and having about 85% of its variance coming from intraseasonal time scales. The trend in the frequency of occurrence of each SOM pattern is used to estimate the interdecadal Arctic winter warming trend associated with the SOM patterns. It is found that trends in the SOM patterns explain about one-half of the SAT trend in the Barents and Kara Seas, one-third of the SAT trend around Baffin Bay, and two-thirds of the SAT trend in the Chukchi Sea. A composite calculation of each term in the thermodynamic energy equation for each SOM pattern shows that the SAT anomalies grow primarily through the advection of the climatological temperature by the anomalous wind. This implies that a substantial fraction of Arctic amplification is due to horizontal temperature advection that is driven by changes in the atmospheric circulation. An analysis of the surface energy budget indicates that the skin temperature anomalies as well as the trend, although very similar to that of the SAT, are produced primarily by downward longwave radiation.

Subseasonal relationship between Arctic and Eurasian surface air temperature

The subseasonal relationship between Arctic and Eurasian surface air temperature (SAT) is re-examined using reanalysis data. Consistent with previous studies, a significant negative correlation is observed in cold season from November to February, but with a local minimum in late December. This relationship is dominated not only by the warm Arctic-cold Eurasia (WACE) pattern, which becomes more frequent during the last two decades, but also by the cold Arctic-warm Eurasia (CAWE) pattern. The budget analyses reveal that both WACE and CAWE patterns are primarily driven by the temperature advection associated with sea level pressure anomaly over the Ural region, partly cancelled by the diabatic heating. It is further found that, although the anticyclonic anomaly of WACE pattern mostly represents the Ural blocking, about 20% of WACE cases are associated with non-blocking high pressure systems. This result indicates that the Ural blocking is not a necessary condition for the WACE pattern, highlighting the importance of transient weather systems in the subseasonal Arctic-Eurasian SAT co-variability.

Multi-Aspect Assessment of CMIP6 Models for Arctic Sea Ice Simulation

AbstractThis paper evaluates the ability of 35 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) to simulate Arctic sea ice by comparing simulated results with observation from the aspects of spatial patterns and temporal variation. The simulation ability of each model is also quantified by the Taylor score and e score from these two aspects. Results show that biases between observed and simulated Arctic sea ice concentration (SIC) are mainly located in the East Greenland, Barents, and Bering Seas and the Sea of Okhotsk. The largest difference between the observed and simulated SIC spatial patterns occurs in September. Since the beginning of the twenty-first century, the ability of most models to simulate summer SIC spatial patterns has decreased. We also find that models with the Sea Ice Simulator (SIS) sea ice component in CMIP6 show a consistent larger positive simulation biases of SIC in the East Greenland and Barents Seas. In addition, for most models, the higher the model resolution is, the better the match between the simulated and observed spatial patterns of winter Arctic SIC is. Furthermore, this paper makes a detailed assessment for temporal variation of Arctic sea ice extent (SIE) with regard to climatological average, seasonal SIE, multiyear linear trend, and detrended standard deviation of SIE. The sensitivity of September Arctic SIE to a given change of Arctic surface air temperature over 1979–2014 in each model has also been investigated. Most models simulate a smaller loss of September Arctic SIE per degree of warming than is observed (1.37 × 106 km2 K−1).