Article

Observed Arctic sea-ice loss directly follows anthropogenic CO2 emission

Authors:
  • University of Hamburg and Max Planck Institute for Meteorology
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Abstract

Arctic sea ice is retreating rapidly, raising prospects of a future ice-free Arctic Ocean during summer. Since climate-model simulations of the sea-ice loss differ substantially, we here use a robust linear relationship between monthly-mean September sea-ice area and cumulative CO2 emissions to infer the future evolution of Arctic summer sea ice directly from the observational record. The observed linear relationship implies a sustained loss of 3 ± 0.3 m(2) of September sea-ice area per metric ton of CO2 emission. Based on this sensitivity, Arctic sea-ice will be lost throughout September for an additional 1000 Gt of CO2 emissions. Most models show a lower sensitivity, which is possibly linked to an underestimation of the modeled increase in incoming longwave radiation and of the modeled Transient Climate Response.

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... The recent warming and sea ice melting in the Arctic have been attributed largely to humaninduced increases in greenhouse gas concentrations (Serreze and Barry 2011;Screen et al. 2021;IPCC 2021). However, global climate models as a group show a more gradual decline in Arctic sea ice (e.g., Kay et al. 2011;Day et al. 2012;Stroeve et al. 2012;Swart et al. 2015;Notz and Stroeve 2016) and more spatially uniform warming in the Arctic atmosphere (Ding et al. 2017(Ding et al. , 2019Topel et al. 2019) than observations when the models are forced by anthropogenic forcing. The causes of this discrepancy remain unclear but leading candidates are a) the inability of models to capture important feedback processes to anthropogenic forcing (Liu et al. 2013;Rosenblum and Eisenman 2016;Notz and Stroeve 2016), b) imprecise natural and anthropogenic aerosol forcings applied in models (Fyfe et al. 2021), and c) internal variability that contributed substantially to the observed strong downward trend of sea ice (Winton 2011;Kay et al. 2011;Sigmond and Fyfe 2016;Meehl et al. 2018;Baxter et al. 2019;Huang et al. 2021) in the past decades. ...
... However, global climate models as a group show a more gradual decline in Arctic sea ice (e.g., Kay et al. 2011;Day et al. 2012;Stroeve et al. 2012;Swart et al. 2015;Notz and Stroeve 2016) and more spatially uniform warming in the Arctic atmosphere (Ding et al. 2017(Ding et al. , 2019Topel et al. 2019) than observations when the models are forced by anthropogenic forcing. The causes of this discrepancy remain unclear but leading candidates are a) the inability of models to capture important feedback processes to anthropogenic forcing (Liu et al. 2013;Rosenblum and Eisenman 2016;Notz and Stroeve 2016), b) imprecise natural and anthropogenic aerosol forcings applied in models (Fyfe et al. 2021), and c) internal variability that contributed substantially to the observed strong downward trend of sea ice (Winton 2011;Kay et al. 2011;Sigmond and Fyfe 2016;Meehl et al. 2018;Baxter et al. 2019;Huang et al. 2021) in the past decades. An answer to this question has important implications not only for the interpretation of the past Arctic climate change (Deser and Teng 2008;Olonscheck et al. 2019) but also for future projections for the Arctic, such as the question of when we will see the first ice free summer in the Arctic under continued anthropogenic forcing (Wang and Overland 2009;Jahn et al. 2018;Notz and SIMIP Community 2020). ...
... Although it was suggested that most climate models may have lower sensitivity to anthropogenic forcing in the Arctic than observations (Notz and Stroeve 2016;Rosenblum and Eisenman 2016; Notz and SIMIP Community 2020), here we emphasize that this conclusion may be premature considering that the discrepancy between the forced change and the observed counterpart may not only be determined by models' sensitivity to external radiation forcing. Here we show that observed changes in the Arctic are partially attributable to the response to the large scale atmospheric circulation change. ...
Article
Over the past decades, Arctic climate has exhibited significant changes characterized by strong Pan-Arctic warming and a large scale wind shift trending toward an anticyclonic anomaly centered over Greenland and the Arctic ocean. Recent work has suggested that this wind change is able to warm the Arctic atmosphere and melt sea ice through dynamical-driven warming, moistening and ice drift effects. However, previous examination of this linkage lacks a capability to fully consider the complex nature of the sea ice response to the wind change. In this study, we perform a more rigorous test of this idea by using a coupled high-resolution modelling framework with observed winds nudged over the Arctic that allows for a comparison of these wind-induced effects with observations and simulated effects forced by anthropogenic forcing. Our nudging simulation can well capture observed variability of atmospheric temperature, sea ice and the radiation balance during the Arctic summer and appears to simulate around 30% of Arctic warming and sea ice melting over the whole period (1979-2020) and more than 50% over the period 2000 to 2012, which is the fastest Arctic warming decade in the satellite era. In particular, in the summer of 2020, a similar wind pattern reemerged to induce the second-lowest sea ice extent since 1979, suggesting that large scale wind changes in the Arctic is essential in shaping Arctic climate on interannual and interdecadal time scales and may be critical to determine Arctic climate variability in the coming decades.
... Various researchers -including Johannessen (2008), Notz and Stroeve (2016), and Stroeve and Notz (2018) -have identified a linear empirical relationship between observed Arctic sea ice area and atmospheric CO 2 cumulative emissions. This linear carbon-trend relationship, which fits remarkably well in the observed data, can be expressed as: ...
... For each measure, the historical data cluster quite tightly around the fitted carbon trends. This remarkably robust linearity has been noted for sea-ice area in the literature (e.g., Notz and Stroeve, 2016), and here we generalize this result to other measures. In percentage terms, Arctic sea-ice coverage and thickness have trended downward at a similar rate, with SIE, SIA, and SIT falling by about 50% over the sample. ...
... In this section, we implement a second major robustness check by reexamining the carbon-trend estimates and forecasts using the atmospheric CO 2 concentration (measured in parts per million, ppm) as a different measure of cumulative CO 2 . 14 Although CO 2 emissions data are widely used for estimate the Arctic climate sensitivity Notz and Stroeve (2016), atmospheric CO 2 concentration has several advantages as a measure of carbon for this purpose. First, it is the most relevant metric for assessing the amount of heat-trapping gasses in the atmosphere, which is the source of global warming and Arctic ice melt. ...
Preprint
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Rapidly diminishing Arctic summer sea ice is a strong signal of the pace of global climate change. We provide point, interval, and density forecasts for four measures of Arctic sea ice: area, extent, thickness, and volume. Importantly, we enforce the joint constraint that these measures must simultaneously arrive at an ice-free Arctic. We apply this constrained joint forecast procedure to models relating sea ice to cumulative carbon dioxide emissions and models relating sea ice directly to time. The resulting "carbon-trend" and "time-trend" projections are mutually consistent and predict an effectively ice-free summer Arctic Ocean by the mid-2030s with an 80% probability. Moreover, the carbon-trend projections show that global adoption of a lower emissions path would likely delay the arrival of a seasonally ice-free Arctic by only a few years.
... The observed loss of Arctic sea ice has been shown to be tightly coupled to increasing global mean surface air temperature (42,43) and cumulative anthropogenic CO 2 emissions (44). This metric of sea ice sensitivity to CO 2 and global warming is commonly used by the sea ice community and has even been proposed as a way to reduce the uncertainty range of future sea ice evolution (44,45). ...
... The observed loss of Arctic sea ice has been shown to be tightly coupled to increasing global mean surface air temperature (42,43) and cumulative anthropogenic CO 2 emissions (44). This metric of sea ice sensitivity to CO 2 and global warming is commonly used by the sea ice community and has even been proposed as a way to reduce the uncertainty range of future sea ice evolution (44,45). Previous literature has shown that models usually simulate a lower sensitivity of Arctic sea ice loss per degree of global warming than has been observed (42,44), with accurate Arctic sea ice retreat only in CMIP5 runs that have too much global warming, which suggests that models may be getting the right Arctic sea ice retreat for the wrong reasons (10). ...
... This metric of sea ice sensitivity to CO 2 and global warming is commonly used by the sea ice community and has even been proposed as a way to reduce the uncertainty range of future sea ice evolution (44,45). Previous literature has shown that models usually simulate a lower sensitivity of Arctic sea ice loss per degree of global warming than has been observed (42,44), with accurate Arctic sea ice retreat only in CMIP5 runs that have too much global warming, which suggests that models may be getting the right Arctic sea ice retreat for the wrong reasons (10). More recently, the CMIP6 multimodel ensemble mean was shown to provide a more realistic estimate of the sensitivity of September Arctic sea ice area to a given amount of anthropogenic CO 2 emissions and global warming compared with earlier CMIP experiments (9). ...
Article
The mechanisms underlying decadal variability in Arctic sea ice remain actively debated. Here, we show that variability in boreal biomass burning (BB) emissions strongly influences simulated Arctic sea ice on multidecadal time scales. In particular, we find that a strong acceleration in sea ice decline in the early 21st century in the Community Earth System Model version 2 (CESM2) is related to increased variability in prescribed BB emissions in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) through summertime aerosol-cloud interactions. Furthermore, we find that more than half of the reported improvement in sea ice sensitivity to CO2 emissions and global warming from CMIP5 to CMIP6 can be attributed to the increased BB variability, at least in the CESM. These results highlight a new kind of uncertainty that needs to be considered when incorporating new observational data into model forcing while also raising questions about the role of BB emissions on the observed Arctic sea ice loss.
... Younger and thinner ice is replacing thick multi-year sea ice (Kwok and Rothrock, 2009;Hansen et al., 2013;Rosel et al., 2018). Mechanisms contributing to sea ice changes include increased anthropogenic greenhouse gases (Notz and Stroeve, 2016;Dai et al., 2019), sea-ice-albedo feedback (Perovich and Polashenski, 2012), increased warm and moist air intrusion into the Arctic (Boisvert et al., 2016;Woods and Caballero, 2016;Graham et al., 2017), radiative feedbacks associated with cloudiness and humidity (Kapsch et al., 2013;Morrison et al., 2018), and increased ocean heat transport (Nummelin et al., 2017;Taylor et al., 2018). However, one of the least understood factors of Arctic change is the impact of aerosols on sea ice albedo and concentration (IPCC, 2021a). ...
... Younger and thinner ice is replacing thick multi-year sea ice (Kwok and Rothrock, 2009;Hansen et al., 2013;Rosel et al., 2018). Mechanisms contributing to sea ice changes include increased anthropogenic greenhouse gases (Notz and Stroeve, 2016;Dai et al., 2019), sea-ice-albedo feedback (Perovich and Polashenski, 2012), increased warm and moist air intrusion into the Arctic (Boisvert et al., 2016;Woods and Caballero, 2016;Graham et al., 2017), radiative feedbacks associated with cloudiness and humidity (Kapsch et al., 2013;Morrison et al., 2018), and increased ocean heat transport (Nummelin et al., 2017;Taylor et al., 2018). However, one of the least understood factors of Arctic change is the impact of aerosols on sea ice albedo and concentration (IPCC, 2021a). ...
Article
We present an Arctic aerosol optical depth (AOD) climatology and trend analysis for 2003–2019 spring and summertime periods derived from a combination of multi-agency aerosol reanalyses, remote-sensing retrievals, and ground observations. This includes the U.S. Navy Aerosol Analysis and Prediction System ReAnalysis version 1 (NAAPS-RA v1), the NASA Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and the Copernicus Atmosphere Monitoring Service ReAnalysis (CAMSRA). Spaceborne remote-sensing retrievals of AOD are considered from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging SpectroRadiometer (MISR), and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Ground-based data include sun photometer data from AErosol RObotic NETwork (AERONET) sites and oceanic Maritime Aerosol Network (MAN) measurements. Aerosol reanalysis AODs and spaceborne retrievals show consistent climatological spatial patterns and trends for both spring and summer seasons over the lower Arctic (60–70∘ N). Consistent AOD trends are also found for the high Arctic (north of 70∘ N) from reanalyses. The aerosol reanalyses yield more consistent AOD results than climate models, can be verified well with AERONET, and corroborate complementary climatological and trend analysis. Speciated AODs are more variable than total AOD among the three reanalyses and a little more so for March–May (MAM) than for June–August (JJA). Black carbon (BC) AOD in the Arctic comes predominantly from biomass burning (BB) sources in both MAM and JJA, and BB overwhelms anthropogenic sources in JJA for the study period. AOD exhibits a multi-year negative MAM trend and a positive JJA trend in the Arctic during 2003–2019, due to an overall decrease in sulfate/anthropogenic pollution and a significant JJA increase in BB smoke. Interannual Arctic AOD variability is significantly large, driven by fine-mode and, specifically, BB smoke, with both smoke contribution and interannual variation larger in JJA than in MAM. It is recommended that climate models should account for BB emissions and BB interannual variabilities and trends in Arctic climate change studies.
... Indeed, the rate of warming is amplified with both elevation and latitude, such that high-mountain and arctic environments have experienced more rapid changes in temperature than those at lower elevations and latitudes (Mountain Research Initiative EDW Working Group, 2015;Pepin et al., 2015). In many alpine and arctic ecosystems, surface air temperature has more than doubled that of the global average (e.g., Beniston et al., 1997;Jones and Moberg, 2003;Notz and Stroeve, 2016;Richter-Menge et al., 2017), with approximately 1.2 times faster increases in annual mean temperatures from 1961 to 2010 (Wang et al., 2016). This temperature increase has been particularly marked in the last few decades, especially in spring and summer (e.g., Ceppi et al., 2012;Marty and Meister, 2012;Overland et al., 2018). ...
... Climate warming also has shown contrasting trends between Arctic and Antarctic regions (see Meredith et al., 2019 for a review). Unlike the Arctic, which has warmed by 2 CÀ3 C over the last century (Overland et al., 2014;Notz and Stroeve, 2016;Richter-Menge et al., 2017), regions of Antarctica have experienced more pronounced variation, with warming over parts of West Antarctica and no significant changes over East Antarctica (Nicolas and Bromwich, 2014;Jones et al., 2016). Indeed, Turner et al. (2016) reported that warming in the Antarctic Peninsula has stopped in the last decade, which seems to be a consequence of a short-term natural climate variability, and new warming phases are expected to occur across the Antarctic Peninsula (Lee et al., 2017). ...
Chapter
Alpine and arctic environments are predicted to be strongly influenced by climate change because their cold-adapted species may be sensitive to rapid warming. Genetic diversity, phenotypic plasticity and dispersal ability of seeds might be crucial for species to persist and/or migrate in these habitats. We reviewed the literature to synthetize current knowledge on seed-trait responses to direct and indirect effects of climate warming. Most experimental and observational studies we reviewed have focused on the effects of warming on seed germination, while other seed functions have received less attention. Overall, there is compelling evidence that increasing temperatures and water stress decreases the number, size and germination of seeds, suggesting that the net effect of warming will depend mostly on changes in water availability. These responses to climate change have been evaluated mainly in alpine temperate and arctic life zones, while alpine-tropical mountains have been largely neglected.
... T here is observational and modeling evidence that the recent retreat of Arctic sea ice has been driven by anthropogenic greenhouse gas emissions [1][2][3] , and climate projections using multiple global climate models (GCMs) suggest that near ice-free conditions will emerge in the Arctic Ocean in September by the middle of this century [4][5][6][7][8] . Overall, the reproducibility of the distribution and past variations of Arctic sea ice has much improved in the recent GCM generation [1][2][3]8,9 . ...
... T here is observational and modeling evidence that the recent retreat of Arctic sea ice has been driven by anthropogenic greenhouse gas emissions [1][2][3] , and climate projections using multiple global climate models (GCMs) suggest that near ice-free conditions will emerge in the Arctic Ocean in September by the middle of this century [4][5][6][7][8] . Overall, the reproducibility of the distribution and past variations of Arctic sea ice has much improved in the recent GCM generation [1][2][3]8,9 . However, the CMIP6 GCMs still have difficulties in reproducing sea ice in more localized region such as the Barents-Kara Sea (Fig. 1d). ...
Article
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Decline in winter sea-ice concentration (SIC) in the Barents-Kara Sea significantly impacts climate through increased heat release to the atmosphere. However, the past Barents-Kara SIC decrease rate is underestimated in the majority of Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models. Here we show that climate model simulations can reproduce the Barents-Kara SIC trend for 1970–2017 when sea surface temperature (SST) variability in the Gulf Stream region is constrained by observations. The constrained warming of the Gulf Stream strengthens ocean heat transport to the Barents-Kara Sea that enhances the SIC decline. The linear trends between the SIC and SST are highly correlated in the CMIP6 ensemble, suggesting that the externally forced component of the Gulf Stream SST increase explains up to 56% of the forced Barents-Kara SIC trend. Therefore, future warming of the Gulf Stream can be an essential pacemaker of the SIC decline.
... The Arctic is now warming three times faster than the global average (AMAP 2021). As a result, Arctic ecosystems are showing fundamental changes in hydrology, snow and ice dynamics and vegetation (Notz and Stroeve 2016, Box et al. 2019, Richter-Menge et al. 2020, AMAP 2021. Apart from gradual temperature increases, the occurrence of extreme conditions is increasing, including heavy precipitation, drought spells and heat waves (AMAP 2021, IPCC 2021. ...
... Arctic sea ice is a key component of the polar climate system, acting as a barrier which both reflects incoming solar radiation and regulates the rate of energy exchange between the atmosphere and ocean. Over the past four decades, however, it can be seen as a direct barometer for climate change, having suffered significant losses in areal extent across all seasons (Stroeve and Notz, 2018), linked to the long-term increase in anthropogenic CO 2 emissions (Notz and Stroeve, 2016). Such sea ice decline has profound implications for regional Northern Hemisphere circulation patterns (Francis et al., 2009;Cohen et al., 2014Cohen et al., , 2020, ecological productivity (Sakshaug et al., 1994;Stirling, 1997;Stroeve et al., 2021), and coastal communities (Fritz et al., 2017;Larsen et al., 2021) in present and future decades, as many model studies now predict the increasing likelihood of a season-ally ice-free Arctic Ocean occurring before the middle of this century Jahn, 2018;Notz and SIMIP-Community, 2020;Årthun et al., 2021). ...
Article
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The indirect effect of winter Arctic Oscillation (AO) events on the following summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer sea ice forecasts in a large number of coupled climate models show a considerable drop in predictive skill for forecasts initialised prior to the date of melt onset in spring, suggesting that some drivers of sea ice variability on longer timescales may not be well represented in these models. To this end, we introduce an unsupervised learning approach based on cluster analysis and complex networks to establish how well the latest generation of coupled climate models participating in phase 6 of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP6) are able to reflect the spatio-temporal patterns of variability in Northern Hemisphere winter sea-level pressure and Arctic summer sea ice concentration over the period 1979–2020, relative to ERA5 atmospheric reanalysis and satellite-derived sea ice observations, respectively. Two specific global metrics are introduced as ways to compare patterns of variability between models and observations/reanalysis: the adjusted Rand index – a method for comparing spatial patterns of variability – and a network distance metric – a method for comparing the degree of connectivity between two geographic regions. We find that CMIP6 models generally reflect the spatial pattern of variability in the AO relatively well, although they overestimate the magnitude of sea-level pressure variability over the north-western Pacific Ocean and underestimate the variability over northern Africa and southern Europe. They also underestimate the importance of regions such as the Beaufort, East Siberian, and Laptev seas in explaining pan-Arctic summer sea ice area variability, which we hypothesise is due to regional biases in sea ice thickness. Finally, observations show that historically, winter AO events (negatively) covary strongly with summer sea ice concentration in the eastern Pacific sector of the Arctic, although now under a thinning ice regime, both the eastern and western Pacific sectors exhibit similar behaviour. CMIP6 models however do not show this transition on average, which may hinder their ability to make skilful seasonal to inter-annual predictions of summer sea ice.
... But what about the summer ice? It was already projected in 2004 that 80% of the summer (September) ice could melt in response to a doubling of CO 2 , recently updated by many subsequent studies (e.g., Notz and Stroeve, 2016 ;Davy and Ouetten, 2020 ;Johannessen et al., 2020 ;SIMIP Community, 2020 ). Furthermore, a statistical analysis between the September SIE and the ln(CO 2 /CO 2 r), which is the empirical law for longwave radiation back to space from the surface ( Myhr et al., 1998 ), for the period 1901-2010, where r is the reference level of CO 2 in 1901, indicates that all the summer ice would melt if the CO 2 level in the atmosphere reaches 502 ppm ( Johannessen et al., 2020 , their Fig. ...
Article
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The global population during the last 100 years has increased from 2 to 7.7 billion, causing an increase in greenhouse gases in the atmosphere. In order to see how population increase is directly related to physical variables of the climate, this Perspective article places observations and scenarios of climate change into context and puts forth a statistical modeling study on how the sensitive Arctic climate responds to the increasing population. The relationships between population, Arctic sea-ice extent (SIE) and surface air temperature (SAT) are very strong, with the increasing population explaining 96% of the decreasing SIE and about 80% of the increasing SAT in the Arctic. Our projection for the SIE using the population as a “proxy predictor” for a projected population of 10 billion people on the Earth in 2100, yields a SIE of 9.30 and 8.21 million km² for a linear and squared relationship, respectively, indicating no “tipping point” for the annual ice extent in this century. This adds another dimension to climate understanding for the public at large using population as a proxy variable, instead of the more abstract CO2 parameter. This also indicates that it is important to attempt to limit the ongoing increase in population, which is the main cause of the greenhouse gas emissions, in addition to reducing per capita emissions by an exponential increase in implementing renewable energy, a formidable challenge in this century.
... More surprisingly, the reverse influence of sea-ice area and volume on temperature and ocean heat transport also exists and is sometimes larger than the reverse influence depending on the quantity (sea-ice area or volume) and the month of the year (March or September). This two-way influence indicates that the current decrease in Arctic sea-ice area and volume is not solely related to air temperature and, consequently to greenhouse gas emissions (Notz & Stroeve, 2016), but is also potentially driven by feedback mechanisms between sea ice, the atmosphere and the ocean Pithan & Mauritsen, 2014). ...
Article
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Plain Language Summary The Arctic has been warming at a larger rate than the rest of the world, resulting in a substantial loss of sea ice since the late 1970s. This has had and will continue to have an impact on our climate and societies. The exact causes of the ongoing sea‐ice loss are not entirely known, and understanding them is important in order to better prepare our societies for future climate changes. In our study, we apply a relatively novel approach that quantifies the cause‐effect relationships between Arctic sea ice and its potential drivers. We make use of a large range of model simulations performed with the EC‐Earth3 global climate model covering the period 1970‐2100. We find that air temperature, sea‐surface temperature, and the transport of heat by the ocean are important drivers of the ongoing and future retreat of Arctic sea ice. Conversely, changes in Arctic sea ice also affect the three former quantities. Our study demonstrates the performance of causal inference methods in the quest for a better understanding of relationships between climate variables. The geophysical and climate communities could greatly benefit from using these methods more intensively.
... Although the recent decline in Icelandic driftwood supply corresponds with a large-scale reduction in boreal wood harvesting in the former Soviet Union (Hellmann et al., 2015), economic factors alone cannot explain the observed changes. In fact, the contraction of sea-ice extent and variation in ocean currents (Fig. 5), mainly driven by anthropogenic warming (Notz and Stroeve, 2016), likely increased the risk of driftwood to sink before reaching multiyear sea-ice or shallow coastlines. Only when incorporated in multiyear sea-ice, driftwood can be transported thousands of kilometres before being deposited (Funder et al., 2011). ...
Article
Driftwood supply was a pivotal factor for the Norse expansion in medieval times and still exhibits an essential resource for Arctic settlements. The physical causes and societal consequences of long-term changes in the distribution of Arctic driftwood are, however, poorly understood. Here, we use dendrochronology to reconstruct the age and origin of 289 driftwood samples that were collected at remote shorelines in northeast Iceland. Based on 240 reference tree-ring width chronologies from the boreal forest zone, and an overall provenance success of 73%, we show that most of the driftwood is pine and larch from the Yenisei catchment in central Siberia. Our study reveals an abrupt decline in the amount of driftwood reaching Iceland since the 1980s, which is corroborated by the experience of local farmers and fishers. Despite the direct and indirect effects of changes in both, logging activity across Siberia as well as Arctic Ocean currents, the predicted amount of sea-ice loss under anthropogenic global warming is likely to terminate Iceland's driftwood supply by 2060 CE.
... Changes in Antarctic sea ice cover can affect heat, moisture, and gas exchanges between the atmosphere and ocean (Raphael, 2003;Kurtz et al., 2011;Søren et al., 2011), freshwater input, ocean circulation (Aagaard and Carmack, 1989;Kirkman and Bitz, 2011;Ferrari et al., 2014), local weather systems, and global climate change (Vihma, 2014;Smith et al., 2017;Ayres and Screen, 2019). Contrary to the rapid decline of the Arctic sea ice extent (SIE) in the context of global warming (Stroeve et al., 2007;Notz and Stroeve, 2016;Serreze and Meier, 2019), Antarctic SIE displays a modest increasing trend of ~1.0% ± 0.5% per decade since late 1978 (Parkinson, 2019), masking significant interannual and regional variations (Liu et al., 2004;Stammerjohn and Maksym, 2016;Yuan et al., 2017;Maksym, 2019). Annual mean Antarctic SIE hit a record high in 2014 (12.8 million km 2 ) after a long-term increase since 1978 and then plunged to a record low in 2017 (10.7 million km 2 ). ...
Article
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Seasonal minimum Antarctic sea ice extent (SIE) in 2022 hit a new record low since recordkeeping began in 1978 of 1.9 million km2 on 25 February, 0.17 million km2 lower than the previous record low set in 2017. Significant negative anomalies in the Bellingshausen/Amundsen Seas, the Weddell Sea, and the western Indian Ocean sector led to the new record minimum. The sea ice budget analysis presented here shows that thermodynamic processes dominate sea ice loss in summer through enhanced poleward heat transport and albedo-temperature feedback. In spring, both dynamic and thermodynamic processes contribute to negative sea ice anomalies. Specifically, dynamic ice loss dominates in the Amundsen Sea as evidenced by sea ice thickness (SIT) change, while positive surface heat fluxes contribute most to sea ice melt in the Weddell Sea.
... Though details of sea ice processes and interactions with the ocean and atmosphere are still not completely understood, the shrinking and thinning of Arctic sea ice has a clear fingerprint from rising concentrations of atmospheric greenhouse gases. Notz and Stroeve (2016) examined the linear relationship between September sea ice decline and cumulative CO 2 concentrations. When this evaluation was expanded to all months of the year, it indicated that all calendar months demonstrate a clear linear relationship, though the relationship is strongest in September. ...
Article
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Sea ice is an essential component of the Arctic climate system. The Arctic sea ice cover has undergone substantial changes in the past 40+ years, including decline in areal extent in all months (strongest during summer), thinning, loss of multiyear ice cover, earlier melt onset and ice retreat, and later freeze-up and ice advance. In the past 10 years, these trends have been further reinforced, though the trends (not statistically significant at p <0.05) in some parameters (e.g., extent) over the past decade are more moderate. Since 2011, observing capabilities have improved significantly, including collection of the first basin-wide routine observations of sea ice freeboard and thickness by radar and laser altimeters (except during summer). In addition, data from a year-long field campaign during 2019–2020 promises to yield a bounty of in situ data that will vastly improve understanding of small-scale processes and the interactions between sea ice, the ocean, and the atmosphere, as well as provide valuable validation data for satellite missions. Sea ice impacts within the Arctic are clear and are already affecting humans as well as flora and fauna. Impacts outside of the Arctic, while garnering much attention, remain unclear. The future of Arctic sea ice is dependent on future CO2 emissions, but a seasonally ice-free Arctic Ocean is likely in the coming decades. However, year-to-year variability causes considerable uncertainty on exactly when this will happen. The variability is also a challenge for seasonal prediction.
... During the 21st century, the Arctic mean surface air temperature has increased by twice the global average, with 2014-2018 temperatures exceeding any recorded value since 1900 (Notz and Stroeve 2016;Overland et al. 2018). Extreme air temperature anomalies have also increased, with temperatures in 2016 and 2018 over 6 • C higher than the 1981-2010 baseline (Overland et al. 2019). ...
Article
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Arctic ecosystems are at risk to climate impacts, challenging existing conservation measures such as protected areas. This study aims to describe the ecological dynamics of the Canadian Beaufort Sea Shelf (BSS) ecosystem and the Tarium Niryutait Marine Protected Area (TNMPA) under historical changes in sea surface temperature and sea ice extent. Using Ecopath with Ecosim, we compared the status of the BSS between two time periods, 1970-1974 and 2008-2012, and against four ecosystem models (Eastern Chukchi Sea, Barents Sea, Eastern Bering Sea, Gulf of Alaska) to inform the relative long-term health and status of Arctic marine ecosystems. We find that relative to the comparable ecosystems, the BSS had a greater proportion of biomass from pelagic primary and secondary producers, and limited production from higher trophic levels. Estimates of trophic structure indices for the BSS indicate temporal ecosystem stability, and no loss in diversity. While beluga whales are a focus of the TNMPA management plan, they are not considered a key component of the modeled food web. Rather, Arctic and polar cods (main beluga prey group), arthropods, large copepods, micro-zooplankton, and herring and smelt, were identified as keystone species and warrant attention as proxies for both beluga whales and ecosystem health.
... Observation results also indicate that SIE is changing significantly faster than anticipated based on the modeling studies generated for the Intergovernmental Panel for Climate Change Fifth Assessment Report (IPCC 2013). This retreat may result in a transition to ice-free conditions in late summers by the middle of the 21st century (Gagné et al. 2015;Notz and Stroeve 2016;IPCC 2021). The declining trends in Arctic sea ice show seasonal differences (Onarheim et al. 2018), which are resulted from diverse feedback mechanisms (Serreze and Barry 2011;Stroeve et al. 2012b;Döscher et al. 2014). ...
Article
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This study investigates the association of spring (April–May) Arctic sea ice melt with simultaneous surface air temperature (SAT) over mid-high latitudes of Eurasia from 1979 to 2019 by using observational datasets and simulation experiments. The results show that spring SAT anomalies associated with Arctic sea ice melt display a dipole pattern over Eurasia. A high Arctic sea ice melt corresponds to positive SAT anomalies over northern Eurasia and negative SAT anomalies over most of Asia. The 500 hPa geopotential height anomalies exhibit a wave train structure, and a dominant positive center is located over the Ural Mountains with two negative centers over East Asia and western Europe. This atmospheric circulation anomaly differs from the traditional Eurasian pattern and the North Atlantic-Eurasian teleconnection pattern due to their different spatial modes. Simulation experiments forced by Arctic sea ice anomalies reproduce the major characteristics of observational associations. Observations and numerical simulations indicate that high Arctic sea ice melt years are often associated with heavy sea ice in winter-spring, which is favorable for the occurrence of Arctic anticyclonic circulation anomaly and lead to a positive SAT anomaly in the Arctic. The Arctic warming not only strengthens polar zonal westerly winds by increasing local baroclinicity, but also weakens zonal winds in mid-latitude through a reduction meridional temperature gradient. It may contribute to the Arctic anticyclonic anomalies enhancement, and then induces a wave train southeastward propagating into the mid-low latitudes. This configuration of atmospheric circulation anomalies provides favorable conditions for the SAT variations over Eurasia.
... As Arctic sea ice extent continues to decline in concert with increases in carbon dioxide (CO 2 ) emissions (Notz and Stroeve, 2016), remote sensing observations are becoming even more vital for the monitoring and understanding of Arctic sea ice. Recently, the sea ice community has entered a new era of synthetic aperture radar (SAR) satellites operating at C-band (wavelength, λ = 5.5 cm) with the launch of Sentinel-1A in 2014, Sentinel-1B in 2016 (S1; Torres et al., 2012), and the RADARSAT Constellation Mission (RCM) in 2019 (Thompson, 2015). ...
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As Arctic sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of sea ice. Recently, the sea ice community has entered a new era of synthetic aperture radar (SAR) satellites operating at C-band with the launch of Sentinel-1A in 2014 and Sentinel-1B (S1) in 2016 and the RADARSAT Constellation Mission (RCM) in 2019. These missions represent five spaceborne SAR sensors that together routinely cover the pan-Arctic sea ice domain. Here, we describe, apply, and validate the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS) that routinely generates large-scale sea ice motion (SIM) over the pan-Arctic domain using SAR images from S1 and RCM. We applied the ECCC-ASITS to the incoming image streams of S1 and RCM from March 2020 to October 2021 using a total of 135 471 SAR images and generated new SIM datasets (7 d 25 km and 3 d 6.25 km) by combining the image stream outputs of S1 and RCM (S1 + RCM). Results indicate that S1 + RCM SIM provides more coverage in the Hudson Bay, Davis Strait, Beaufort Sea, Bering Sea, and directly over the North Pole compared to SIM from S1 alone. Based on the resolvable S1 + RCM SIM grid cells, the 7 d 25 km spatiotemporal scale is able to provide the most complete picture of SIM across the pan-Arctic from SAR imagery alone, but considerable spatiotemporal coverage is also available from 3 d 6.25 SIM products. S1 + RCM SIM is resolved within the narrow channels and inlets of the Canadian Arctic Archipelago, filling a major gap from coarser-resolution sensors. Validating the ECCC-ASITS using S1 and RCM imagery against buoys indicates a root-mean-square error (RMSE) of 2.78 km for dry ice conditions and 3.43 km for melt season conditions. Higher speeds are more apparent with S1 + RCM SIM as comparison with the National Snow and Ice Data Center (NSIDC) SIM product and the Ocean and Sea Ice Satellite Application Facility (OSI SAF) SIM product indicated an RMSE of u=4.6 km d−1 and v=4.7 km d−1 for the NSIDC and u=3.9 km d−1 and v=3.9 km d−1 for OSI SAF. Overall, our results demonstrate the robustness of the ECCC-ASITS for routinely generating large-scale SIM entirely from SAR imagery across the pan-Arctic domain.
... Among the Arctic regions, exceptional sea ice melt occurs in the Western Arctic over the Chukchi Sea-Beaufort Sea regions [3][4][5]. Many observational and climate modeling studies claim anthropogenic drivers as a reason for the drastic decline of the Arctic sea ice, e.g., [6][7][8][9][10]. However, recent studies [11][12][13][14][15] indicate that the internal variability of the climate system also contributes to the observed trend in the September pan-Arctic sea ice extent decline. ...
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Abstract: Significant changes in the Arctic climate, particularly a rapid decline of September Arctic sea ice has occurred over the past few decades. Though the exact reason for such drastic changes is still unknown, studies suggest anthropogenic drivers, natural variability of the climate system, and a combination of both as reasons. The present study focus on the influence of one of the natural variabilities of the climate system, the teleconnections associated with the Indian Summer Monsoon (ISM), and its relationship to September Arctic sea ice. Using 50 years (1951-2000) of NCEP/NCAR reanalysis data, APHRODITE precipitation data, Gridded Monthly Sea Ice Extent and Concentration, 1850 Onward, V2, and HadISST sea-ice concentration data, it is shown that during many strong (weak) ISM years, the Arctic sea ice increased (decreased) predominantly over the Chukchi and Beaufort Seas. The ISM plays a significant role in causing a positive (negative) North Atlantic Oscillation (NAO) during strong (weak) ISM years through the monsoon-desert mechanism associated with monsoonal heating. Simultaneously, the NAO during a strong (weak) ISM causes weakening (strengthening) of the Beaufort Sea High (BSH). The strength of the BSH modulates the Arctic atmospheric circulation, advecting cold air and the direction of the transpolar drift stream, both leading to the generation of more (less) sea ice over the Chukchi-Beaufort Sea region during strong (weak) ISM years. The study illustrates a new atmospheric teleconnection between the tropics and the Arctic. Keywords: Polar climate variability. Arctic Sea ice; Teleconnections; Indian Monsoon; North Atlantic Oscillation; Beaufort Sea High
... It is noteworthy that sea ice extent trends outside late summer/early fall are well outside the range of simulated CESM1 trends and are not improved by nudging to observed wind anomalies. Whereas summer Arctic sea ice trends have received considerable attention in recent literature (e.g., Notz & Stroeve, 2016;Schröder et al., 2014;SIMIP, 2020;Stroeve et al., 2007), our results highlight the importance of studying the mechanisms that drive winter and spring sea ice trends to better understand and predict changes in the warming Arctic. (Meier et al., 2021), available at https://nsidc. ...
... A first estimate of the lateral flux caused by this disturbance in the surrounding ice has been made and its contribution compared to that due to vertical fluxes. En région Arctique, l'augmentation de la température de l'air est plus de deux fois plus élevée que l'augmentation moyenne au cours des deux dernières décennies (Notz et Stroeve, 2016 ;Richter-Menge et al., 2017) : on parle d'amplification polaire ( Figure 2). Ce phénomène est dû à plusieurs boucles de rétroactions positives (Hall, 2004;Pithan et Mauritsen, 2014;Stuecker et al., 2018). ...
Thesis
En Arctique, les conséquences du réchauffement climatique sont plus fortes que partout ailleurs sur le globe : ainsi, l’augmentation de la température de l’air depuis deux décennies y est plus de deux fois plus élevée que l’augmentation moyenne, selon le dernier rapport du GIEC. La banquise témoigne de ces changements de façon privilégiée. On observe une diminution importante de la couverture de glace de mer, associée à une perte de volume entoute saison. La glace de mer devient plus jeune, fragile et mobile. Cette évolution de la banquise a fait entrer l’Arctique dans un nouvel état où les interactions air/neige/glace/océan sont modifiées et difficiles à appréhender. Mieux comprendre et prévoir ces changements nécessite des observations et des modèles numériques performants pour simuler correctement les interactions entre la glace de mer et les autres composantes qui commandent le climat de l’Arctique. Dans ce contexte de changement climatique, l’objectif de cette thèse est d’analyser des séries d’observations collectées principalement en hiver par des bouées dérivantes IAOOS -Ice Atmosphere Arctic Ocean Observing System- (équipées de SIMBAs -SAMS Ice MassBalance for the Arctic-) pour comprendre quels processus déterminent l’évolution récente de la glace de mer en Arctique. En plus d’une analyse des observations “per se”, des simulations numériques sont réalisées à partir du modèle unidimensionnel de glace et de neige LIM1D (Louvain-la-Neuve sea Ice Model).
... However, the ocean is suffering from the severe impacts of mixed factors such as climate change and anthropopressure. Furthermore, excessive interference poses a serious threat to the function of marine ecosystem [5,6]. As a macro-observation method, remote sensing has incomparable advantages over traditional observation in many aspects [7][8][9]. ...
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The ocean is of great significance in the climate system, global resources and strategic decision making. With the continuous improvement in remote sensing technology, ocean remote sensing research has increasingly become an important topic for resource development and environmental protection. This paper uses bibliometric analysis method and VOSviewer visual software to conduct analysis. The analysis focuses on the period from 1990 to 2020. The analysis results show that articles have been steadily increasing over the past two decades. Scholars and researchers form the United States, China and Europe (mainly Western European countries), as well as NASA, Chinese Academy of Sciences and NOAA have bigger influence in this field to some extent. Among them, the United States and NASA holds the core leading position. Moreover, global cooperation in this field presents certain characteristics of geographical distribution. This study also reveals journals that include the most publications and subject categories that are highly relevant to related fields. Cluster analysis shows that remote sensing, ocean color, MODIS (or Moderate Resolution Imaging Spectroradiometer), chlorophy, sea ice and climate change are main research hotspots. In addition, in the context of climate warming, researchers have improved monitoring technology for remote sensing to warn and protect ocean ecosystems in hotspots (the Arctic and Antarctica). The valuable results obtained from this study will help academic professionals keep informed of the latest developments and identify future research directions in the field related to ocean remote sensing.
... In addition to the contribution of anthropogenic forcing, internal variability dominating the Arctic summer circulation trend is also important cause of declining in September Arctic sea ice since 1979 (Ding et al., 2017). The rate of Arctic sea ice decline is unprecedented over at least the past 1,500 years according to reconstructed sea ice historical data (Kinnard et al., 2011), and an ice-free state is expected in the middle of twenty-first century according to climate model predictions under the high emission of the greenhouse gases (GHGs) scenarios (Wang and Overland, 2012;Notz and Stroeve, 2016;Guarino et al., 2020;Notz et al., 2020). ...
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The satellite-derived sea ice concentration (SIC) and thickness (SIT) observation over the Arctic region are assimilated by implementing the Ensemble Optimal Interpolation (EnOI) into the Community Ice CodE version 5.1.2 (CICE5) model. The assimilated observations are derived from Special Sensor Microwave Imager/Sounder (SSMIS) for the SIC, European Space Agency's (ESA) Soil Moisture and Ocean Salinity mission (SMOS) for the SIT of the thin ice, and ESA's CryoSat-2 satellite for the SIT of the thick ice. The SIC, and SIT observations are assimilated during 2000–2019, and 2011–2019, respectively. The quality of the reanalysis is evaluated by comparing with observation and modeled data. Three data assimilation experiments are conducted: noDA without data assimilation, Ver1 with SIC assimilation, and Ver2 with SIC and SIT assimilation. The climatological bias of the SIC in noDA was reduced in Ver1 by 29% in marginal ice zones during boreal winter, and 82% in pan-Arctic ocean during boreal summer. The quality of simulating the interannual variation of sea ice extent (SIE) is improved in all months particularly during boreal summer. The root-mean-square errors (RMSEs) of SIE anomaly in August are significantly reduced compared to noDA. However, the interannual variations of SIT is unrealistic in Ver1 which requires the additional assimilation of the SIT observation. The climatological bias of SIT over the Arctic was further reduced in Ver2 by 28% during boreal winter compared to that in Ver1. The interannual variability of SIT anomalies is realistically simulated in Ver2 by reducing the RMSEs of SIT anomalies by 33% in February, and 28% in August by compared to that in Ver1. The dominant interannual variation extracted by empirical orthogonal function (EOF) of SIT anomalies in Ver2 is better simulated than Ver1. The additional assimilation of SIT improves not only SIT, but also SIC. The climatological bias of SIE and the errors in leading EOF of SIC anomalies in Ver2 is further reduced compared to those in Ver1 during boreal winter. However, improvements led by assimilating additional SIT observation is not clear during boreal summer, possible due to the lack of available SIT observation during this season.
... In particular, September sea ice extent has shrunk by about 50 % since the beginning of the satellite era (Onarheim et al., 2018). The loss of sea ice, which is largely attributed to the accumulation of greenhouse gases in the atmosphere following anthropogenic emissions (Notz and Stroeve, 2016;Screen et al., 2018) but also to internal climate variability (Ding et al., 2017), has been proposed as a key driver of "Arctic amplification" (AA) through changes in albedo (Manabe and Stouffer, 1994;Screen and Simmonds, 2010) and other temperature-related feedbacks (Pithan and Mauritsen, 2014). ...
Article
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The retreat of Arctic sea ice is frequently considered to be a possible driver of changes in climate extremes in the Arctic and possibly down to mid-latitudes. However, it remains unclear how the atmosphere will respond to a near-total retreat of summer Arctic sea ice, a reality that might occur in the foreseeable future. This study explores this question by conducting sensitivity experiments with two global coupled climate models run at two different horizontal resolutions to investigate the change in temperature and precipitation extremes during summer over peripheral Arctic regions following a sudden reduction in summer Arctic sea ice cover. An increase in frequency and persistence of maximum surface air temperature is found in all peripheral Arctic regions during the summer, when sea ice loss occurs. For each 1×106 km2 of Arctic sea ice extent reduction, the absolute frequency of days exceeding the surface air temperature of the climatological 90th percentile increases by ∼ 4 % over the Svalbard area, and the duration of warm spells increases by ∼ 1 d per month over the same region. Furthermore, we find that the 10th percentile of surface daily air temperature increases more than the 90th percentile, leading to a weakened diurnal cycle of surface air temperature. Finally, an increase in extreme precipitation, which is less robust than the increase in extreme temperatures, is found in all regions in summer. These findings suggest that a sudden retreat of summer Arctic sea ice clearly impacts the extremes in maximum surface air temperature and precipitation over the peripheral Arctic regions with the largest influence over inhabited islands such as Svalbard or northern Canada. Nonetheless, even with a large sea ice reduction in regions close to the North Pole, the local precipitation response is relatively small compared to internal climate variability.
... However, environmental NO 2 − isotope datasets are still rare and have not been applied to wider ecosystems. The response of the Arctic Ocean to climate change has been rapid and dramatic, resulting in warmer (Huang et al., 2017;Miller et al., 2013), desalinated (Notz & Stroeve, 2016;Peterson et al., 2002) and more stratified waters (McLaughlin & Carmack, 2010;Toole et al., 2010). However, current studies on the formation of PNM are limited to mid-and low-latitude oceans Chen et al., 2021;Liu et al., 2020;Newell et al., 2013;Santoro et al., 2013;Ward et al., 1982;Zakem et al., 2018), and studies on the Arctic Ocean are lacking. ...
Article
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Plain Language Summary Bioactive nitrite is a key intermediate in the microbial‐driven nitrogen cycle and has important effects on marine carbon and nitrogen cycles. However, the mechanism of nitrite accumulation in aerobic environments has been controversial. As a typical feature in the Arctic waters, it is of great scientific value to study the formation of PNM and the nitrite cycle. Accordingly, we applied stable isotope and geochemical model of nitrite to elucidated the formation of PNM in Arctic and subarctic waters. We found that ammonia oxidation dominates the formation of Arctic and subarctic PNMs, and nitrite oxidation plays an important role in the consumption of nitrite in PNMs. More importantly, an active nitrite cycle was demonstrated in the upper Arctic ecosystem. These findings not only fill a gap in our understanding of the Arctic nitrogen cycle, but also help to elucidate possible changes in nitrogen cycle driven by climate change.
... Min et al., 2008). Notz and Stroeve (2016) found a linear relationship between observed sea-ice loss and cumulative CO 2 emissions. The Arctic LFWC changes significantly on seasonal to decadal time scales in response to wind variability (Cornish et al., 2020;Dukhovskoy et al., 2004;Proshutinsky et al., 2002), but the recent changes in Arctic liquid freshwater budget might already contain signals of anthropogenic climate change (Jahn & Laiho, 2020). ...
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In this study we assessed the representation of the sea surface salinity (SSS) and liquid freshwater content (LFWC) of the Arctic Ocean in the historical simulation of 31 CMIP6 models with comparison to 39 Coupled Model Intercomparison Project phase 5 (CMIP5) models, and investigated the projected changes in Arctic liquid and solid freshwater content and freshwater budget in scenarios with two different shared socioeconomic pathways (SSP2‐4.5 and SSP5‐8.5). No significant improvement was found in the SSS and LFWC simulation from CMIP5 to CMIP6, given the large model spreads in both CMIP phases. The overestimation of LFWC continues to be a common bias in CMIP6. In the historical simulation, the multi‐model mean river runoff, net precipitation, Bering Strait and Barents Sea Opening (BSO) freshwater transports are 2,928 ± 1,068, 1,839 ± 3,424, 2,538 ± 1,009, and −636 ± 553 km³/year, respectively. In the last decade of the 21st century, CMIP6 MMM projects these budget terms to rise to 4,346 ± 1,484 km³/year (3,678 ± 1,255 km³/year), 3,866 ± 2,935 km³/year (3,145 ± 2,651 km³/year), 2,631 ± 1,119 km³/year (2,649 ± 1,141 km³/year) and 1,033 ± 1,496 km³/year (449 ± 1,222 km³/year) under SSP5‐8.5 (SSP2‐4.5). Arctic sea ice is expected to continue declining in the future, and sea ice meltwater flux is likely to decrease to about zero in the mid‐21st century under both SSP2‐4.5 and SSP5‐8.5 scenarios. Liquid freshwater exiting Fram and Davis straits will be higher in the future, and the Fram Strait export will remain larger. The Arctic Ocean is projected to hold a total of 160,300 ± 62,330 km³ (141,590 ± 50,310 km³) liquid freshwater under SSP5‐8.5 (SSP2‐4.5) by 2100, about 60% (40%) more than its historical climatology.
... In the context of amplified Arctic warming, summer Arctic sea ice has decreased in extent and thinned rapidly in recent decades. While it is recognized that the disappearing sea ice directly follows the anthropogenic global warming [1], the processes that control the interannual variations of Arctic sea ice are still not well understood. ...
Article
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The Greenland high (GL-high) coincides with a local center of action of the summer North Atlantic Oscillation and is known to have significant influence on Greenland ice sheet melting and summer Arctic sea ice. However, the mechanism behind the influence on regional Arctic sea ice is not yet clear. In this study, using reanalysis datasets and satellite observations, the influence of the GL-high in early summer on Arctic sea ice variability, and the mechanism behind it, are investigated. In response to an intensified GL-high, sea ice over the Beaufort Sea shows significant decline in both concentration and thickness from June through September. This decline in sea ice is primarily due to thermodynamic and mechanical redistribution processes. Firstly, the intensified GL-high increases subsidence over the Canadian Basin, leading to an increase in surface air temperature by adiabatic heating, and a substantial decrease in cloud cover and thus increased downward shortwave radiation. Secondly, the intensified GL-high increases easterly wind frequency and wind speed over the Beaufort Sea, pushing sea ice over the Canadian Basin away from the coastlines. Both processes contribute to an increase in open water areas, amplifying ice-albedo feedback and leading to sea ice decline. The mechanism identified here differs from previous studies that focused on northward moisture and heat transport and the associated increase in downward longwave radiation over the Arctic. The impact of the GL-high on the regional sea ice (also Arctic sea ice extent) can persist from June into fall, providing an important source for seasonal prediction of Arctic sea ice. The GL-high has an upward trend and reached a record high in 2012 that coincided with a record minimum summer Arctic sea ice extent, and has strong implications for summer Arctic sea ice changes.
... In this study, after tracing the core sediment source during the Holocene by rare earth elements, we show the sand sediment fraction (>63 μm, %), known as the ice-rafted debris (IRD) proxy [33][34][35] , and the sedimentation rate to reconstruct the Holocene variations in Arctic sea ice and regional river heat discharge, respectively (Methods, Supplementary Figs. [2][3][4][5][6][7][8]. Based on high-resolution seismic profiles in the ESAS region ( Supplementary Fig. 2), two gravity cores, LV77-36-1 (155.66°E, ...
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Arctic sea ice retreat is linked to extrapolar thermal energy import, while the potential impact of pan-Arctic river heat discharge on sea-ice loss has been unresolved. We reconstructed the Holocene history of Arctic sea ice and Russian pan-Arctic river heat discharge, combining ice-rafted debris records and sedimentation rates from the East Siberian Arctic Shelf with a compilation of published paleoclimate and observational data. In the mid-Holocene, the early summer (June–July) solar insolation was higher than that during the late Holocene, which led to a larger heat discharge of the Russian pan-Arctic rivers and contributed to more Arctic sea ice retreat. This intensified decline of early-summer sea ice accelerated the melting of sea ice throughout the summertime by lowering regional albedos. Our findings highlight the important impact of the larger heat discharge of pan-Arctic rivers, which can reinforce Arctic sea-ice loss in the summer in the context of global warming.
... Based on this assessment, the nearly 30 million cruise passengers in 2019 would, on the whole, emit nearly 24.6 million tons (or 22,316,744 metric tons) of CO 2 . If 1 metric ton of emitted CO 2 can cause the loss of 3 m 2 of Arctic sea ice (Notz and Stroeve, 2016), then the cruise passengers, together, would have contributed to a loss of Artic sea ice roughly 10 times the size of Gibraltar-an area loss of more than 66 million m 2 . ...
Article
Bunkerization, a term often associated with military fortifications on 20th-century battlefields or the fallout shelters of the Cold War, can now refer to the building, buying and selling of artificial environments designed to provide protective and defensive responses to the ecological, military, and political threats of the Anthropocene. As places of elite retreat, however, these are not spartan spaces. This article documents how—for some—forms of bunkerization have emerged as privileged reactions or responses to contemporary environmental crises, such as climate change, by considering the case of last-chance tourism and luxury cruising. In 2020, both climate change and COVID-19 became intertwined as global crises emerging from humans’ troubling relationships with nature. To examine bunkerization as an individualistic reaction to these converging crises, we first outline the challenges presented by COVID-19 and its connections with human exploitation of animals and the environment. We then turn to the particular uses of the environment—in this case, the oceans—as locations of leisure and retreat, and offer an analysis of the image, operations and impact of the luxury cruise industry. In light of our current path of crisis accumulation, we conclude with an urgent call to adopt a more holistic view of planetary public health—one that includes not only humans but also other species and the natural environment.
... Arctic climate is subject to human-made forcing by atmospheric greenhouse gases (Dai et al 2019;Notz and Stroeve 2016) and anthropogenic aerosols (Booth et al 2012;Chylek et al 2016), as well as to natural forcing (mainly changes in solar irradiance and volcanic activity). Unforced climate variability involves atmospheric and oceanic variability (Bengtsson et al 2004;Chylek et al 2014;Ding et al 2019) and naturally occurring changes in atmospheric aerosols and carbon dioxide. ...
Article
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While the annual mean Arctic Amplification (AA) index varied between two and three during the 1970–2000 period, it reached values exceeding four during the first two decades of the 21st century. The AA did not change in a continuous fashion but rather in two sharp increases around 1986 and 1999. During those steps the mean global surface air temperature trend remained almost constant, while the Arctic trend increased. Although the “best” CMIP6 models reproduce the increasing trend of the AA in 1980s they do not capture the sharply increasing trend of the AA after 1999 including its rapid step‐like increase. We propose that the first sharp AA increase around 1986 is due to external forcing, while the second step close to 1999 is due to internal climate variability, which models cannot reproduce in the observed time.
... The temperature in the HL and HA has increased fast. The rate of temperature rise in HL is twice the global average (Notz and Stroeve, 2016;Richter-Menge and Druckenmiller, 2020) while that in HA is 0.1°C higher than the global average in the past several decades (IPCC, 2018). Precipitation in HL has increased by 50-60% over the 21st century (Bintanja, 2018), whereas that in HA remains unchanged (Rogora et al., 2004;Hu et al., 2016). ...
Article
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Cold biome ecosystems, extensively distributed on our planet, are highly sensitive to global changes. Fluctuations caused by climate change would inevitably affect the ecosystems’ structure and functions. However, the linkage between cold biome ecosystems and global changes demonstrates high spatial heterogeneity, especially between high-latitude ecosystems (HL) and high-altitude alpine ecosystems (HA). A comparative analysis of their response patterns would provide deeper insight into the underlying mechanisms at play. We used meta-analysis to synthesize ecosystems’ response to warming and altered precipitation performed in HL and HA. Warming and enhanced precipitation increases ecosystem biomass and carbon fluxes in HL and HA. Warming significantly stimulates aboveground biomass (AGB), root biomass (RB), total biomass (TB), aboveground net primary productivity, gross ecosystem productivity (GEP), soil respiration (SR), and net ecosystem productivity (NEP) in HL and HA. Similarly, AGB, GEP, and NEP increase significantly with enhanced precipitation. Respondent of ecosystem carbon storage and fluxes in HL and HA showed diverse results to warming treatment. Warming increases AGB and RB in HA while RB remains unaltered in HL. GEP and ER exhibit a positive response to warming in HL but an insignificant response in HA. In general, HL is sensitive to warming, and HA is sensitive to precipitation. The differential responses of HL and HA to climate change imply specific ecosystem traits and particular environmental constraining factors. Future cold biome ecosystem studies should further consider specific conditions like microtopography, soil moisture, and local climate unique to high-latitude and high-altitude ecosystem
... Perhaps the most famous example of this intimate relationship is the large-scale oxygenation of Earth's atmosphere following the emergence of photosynthesis. 1 This dramatic change in the composition of the atmosphere is believed to have caused a massive extinction, as the biosphere was not adapted to an oxygenated atmosphere. [2][3][4] Over the past 10,000 years, humans have likewise transformed the planet, directly affecting the rise and fall of ecosystems, 5-13 the pH and surface temperature of the oceans, 14,15 the composition of terrestrial biological and human-made mass, 16,17 the planetary albedo and ice cover, [18][19][20][21][22][23][24][25][26][27] and the chemistry of the atmosphere, [28][29][30][31][32][33] to name just a few examples. The breadth of human impacts on the planet is so diverse that it touches on nearly every facet of the Earth system and every scientific discipline. ...
Article
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The Human Impacts Database (www.anthroponumbers.org) is a curated, searchable resource housing quantitative data relating to the diverse anthropogenic impacts on our planet, with topics ranging from sea-level rise to livestock populations, greenhouse gas emissions, fertilizer use, and beyond. Each entry in the database reports a quantitative value (or a time series of values) along with clear referencing of the primary source, the method of measurement or estimation, an assessment of uncertainty, and links to the underlying data, as well as a permanent identifier called a Human Impacts ID (HuID). While there are other databases that house some of these values, they are typically focused on a single topic area, like energy usage or greenhouse gas emissions. The Human Impacts Database facilitates access to carefully curated data, acting as a quantitative resource pertaining to the myriad ways in which humans have an impact on the Earth, for practicing scientists, the general public, and those involved in education for sustainable development alike. We outline the structure of the database, describe our curation procedures, and use this database to generate a graphical summary of the current state of human impacts on the Earth, illustrating both their numerical values and their intimate interconnections.
... By contrast, sea-ice decline in the Arctic is greatest in the summer (Notz & Stroeve, 2016;Serreze et al., 2007). These findings demonstrated that the changes in sea-ice and temperature in the Arctic are roughly out-of-phase across seasons. ...
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The temperature of a well‐mixed ice‐water mixture stays constant until the ice melts due to external heat. Whether the temperature over the Arctic Ocean exhibits an analogous stagewise evolution to reach an ice‐free point remains unclear. Therefore, this study explored the characteristics of extratropical climate change before and after a period during which the Arctic Ocean was ice‐free in summer using multimodel simulations. Here, we show that the seasonality of Arctic warming varies between the two periods separated by an ice‐free summer. The warming maximum in the cold season delayed for a month after becoming ice‐free than before. In addition, the warming maximum lagged behind the sea‐ice decline maximum before becoming ice‐free, whereas the maximums of the two became coordinated after becoming ice‐free. The closed cross‐season energy cycle demonstrated that the capacitor effect of the Arctic Ocean with delayed release of the energy taken up in spring and summer due to sea‐ice decline and seawater absorption is crucial for the seasonality observed in Arctic climate change. Moreover, we found that although Arctic amplification induced general weakening in high‐frequency weather variability in the mid‐high latitudes via decreased meridional temperature gradients, significant weakening was induced only after becoming ice‐free under high emission. Our findings suggest that the two stages of Arctic sea‐ice decline should be taken into consideration when dealing with global warming.
... However, when it comes to modeling individual breakup events, and accurately reproducing the spatial distribution of leads, there have been few successful attempts (Ólason, Rampal, & Dansereau, 2021;Wang et al., 2016), and breakup events are not well captured in current sea ice and climate models (Spreen et al., 2017). This presents a critical gap in our understanding of atmosphere-ocean-ice interaction processes and limits the credibility of future projections of the climate in polar and subpolar regions (Notz & Stroeve, 2016). This paper is the first step toward filling this gap by presenting simulations using the next-generation sea ice model-neXtSIM (Ólason, Rampal, & Dansereau, 2021;Rampal et al., 2016Rampal et al., , 2019Samaké et al., 2017)focusing on a large breakup event that occurred in the Beaufort Sea during February and March 2013. ...
Article
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Plain Language Summary The loss of thick multiyear sea ice in the Arctic leads to weaker sea ice that is more easily broken up by strong winds. As a consequence, extreme sea ice breakup events may become more frequent, even during the middle of winter when the sea ice cover is frozen solid. This can lead to an earlier onset of the melt season and potentially accelerate Arctic sea ice loss. Such extreme breakup events are generally not captured by climate models, potentially limiting our confidence in projections of Arctic sea ice. We investigated the driving forces behind sea ice breakup events during winter and how they change in a future climate. Our sea ice model is the first to reproduce such breakup events and reveals that the combination of strong winds and thin sea ice is a key factor for these breakups. We found that winter breakups have a large effect on local heat and moisture transfer and cause enhanced sea ice production, but also increase the overall movement of the sea ice cover, making it more vulnerable. Finally, we show that if the Arctic sea ice continues to thin, these extreme breakup events could become even more frequent.
... While there have been improvements in climate models to realistically represent the evolution of Arctic climate 30,31 and sea ice 32 under global warming, most models in the latest generation of Coupled Model Intercomparison Project phase 6 (CMIP6) still fail to simulate plausible sensitivity of Arctic sea-ice loss to the rise of global temperatures 33 . In earlier studies, the discrepancy between observed and simulated sea ice trends have been attributed to a lower sensitivity of modelled Arctic sea ice trends to global warming 34 or anthropogenic CO 2 emissions 35 . However, Swart et al. 36 argued that the observed and simulated September Arctic sea-ice trends over 1979-2013 are not inconsistent when accounting properly for the internal climate variability. ...
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In recent decades, the warming in the Arctic has been much faster than in the rest of the world, a phenomenon known as Arctic amplification. Numerous studies report that the Arctic is warming either twice, more than twice, or even three times as fast as the globe on average. Here we show, by using several observational datasets which cover the Arctic region, that during the last 43 years the Arctic has been warming nearly four times faster than the globe, which is a higher ratio than generally reported in literature. We compared the observed Arctic amplification ratio with the ratio simulated by state-of-the-art climate models, and found that the observed four-fold warming ratio over 1979–2021 is an extremely rare occasion in the climate model simulations. The observed and simulated amplification ratios are more consistent with each other if calculated over a longer period; however the comparison is obscured by observational uncertainties before 1979. Our results indicate that the recent four-fold Arctic warming ratio is either an extremely unlikely event, or the climate models systematically tend to underestimate the amplification. Over the past four decades, Arctic Amplification - the ratio of Arctic to global warming - has been much stronger than thought, and is probably underestimated in climate models, suggest analyses of observations and the CMIP5 and CMIP6 simulations.
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Sea ice cover plays an important role in modulating local temperature through heat and moisture fluxes. The influence of thin ice, lead and polynya has been well investigated, however, the effect of perennial ice (also called multiyear ice, MYI) has not. This study is motivated by it and investigated a hypothesis that changes in MYI concentration in winter triggers changes in short-term local 2-m air temperature. The hypothesis was tested using time series analysis of the two parameters and correlation between them. Data from the winters of 2004‒2009 were used for the examination at three spatial scales. The hypothesis is found to be potentially accepted when MYI exists in a consolidated ice regime with negligible thin ice or open water in the surroundings, and the air temperature is low enough. Conditions for the acceptance of the hypothesis were quantitatively identified. The qualifications entail that the ice cell must experience daily change of MYI concentration meanwhile satisfy the criteria of total ice concentration (TIC), young ice concentration (YIC) and air temperature (Tair) in the surrounding area, which are TIC > 88.3%, YIC < 9.5% and Tair<−19.0°C. Inverse relationships between changes in MYI concentration and the corresponding changes in air temperature were developed retroactively using data that satisfied the acceptance conditions. The relationships varied with years, depending on ice conditions such as ice type distributions and snow cover. This study offers a first attempt to assess the effect of MYI on the same-day local surface air temperature using satellite observations, and provides evidence of this effect under quantitatively quanlifying conditions.
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Estimates of the anthropogenic effective radiative forcing (ERF) trend have increased by 50% since 2000 (+0.4W/m ² /decade in 2000-2009 to +0.6W/m ² /decade in 2010-2019), the majority of which is driven by changes in the aerosol ERF trend, due to aerosol emissions reductions. Here we study the extent to which observations of the climate system agree with these ERF assumptions. We use a large ERF ensemble from IPCC’s Sixth Assessment Report (AR6) to attribute the anthropogenic contributions to global mean surface temperature (GMST), top-of-atmosphere radiative flux, and aerosol optical depth observations. The GMST trend has increased from +0.18°C/decade in 2000-2009 to +0.35°C/decade in 2010-2019, coinciding with the anthropogenic warming trend rising from +0.19°C/decade in 2000-2009 to +0.24°C/decade in 2010-2019. This, and observed trends in top-of-atmosphere radiative fluxes and aerosol optical depths support the claim of an aerosol-induced temporary acceleration in the rate of warming. However, all three observation datasets additionally suggest smaller aerosol ERF trend changes are compatible with observations since 2000, since radiative flux and GMST trends are significantly influenced by internal variability over this period. A zero-trend-change aerosol ERF scenario results in a much smaller anthropogenic warming acceleration since 2000, but is poorly represented in AR6’s ERF ensemble. Short-term ERF trends are difficult to verify using observations, so caution is required in predictions or policy judgments that depend on them, such as estimates of current anthropogenic warming trend, and the time remaining to, or the outstanding carbon budget consistent with, 1.5°C warming. Further systematic research focused on quantifying trends and early identification of acceleration or deceleration is required.
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The fast decline of Arctic sea ice necessitates a stronger focus on understanding the Arctic sea ice predictability and developing advanced forecast methods for all seasons and for pan-Arctic and regional scales. In this study, the operational forecasting system combining an advanced eddy-permitting ocean–sea ice ensemble reanalysis ORAS5 and state-of-the-art seasonal model-based forecasting system SEAS5 is used to investigate effects of sea ice dynamics and thermodynamics on seasonal (growth-to-melt) Arctic sea ice predictability in 1993–2020. We demonstrate that thermodynamics (growth/melt) dominates the seasonal evolution of mean sea ice thickness at pan-Arctic and regional scales. The thermodynamics also dominates the seasonal predictability of sea ice thickness at pan-Arctic scale; however, at regional scales, the predictability is dominated by dynamics (advection), although the contribution from ice growth/melt remains perceptible. We show competing influences of sea ice dynamics and thermodynamics on the temporal change of ice thickness predictability from 1993–2006 to 2007–20. Over these decades, there was increasing predictability due to growth/melt, attributed to increased winter ocean heat flux in both Eurasian and Amerasian basins, and decreasing predictability due to advection. Our results demonstrate an increasing impact of advection on seasonal sea ice predictability as the region of interest becomes smaller, implying that correct modeling of sea ice drift is crucial for developing reliable regional sea ice predictions. This study delivers important information about sea ice predictability in the “new Arctic” conditions. It increases awareness regarding sea ice state and implementation of sea ice forecasts for various scientific and practical needs that depend on accurate seasonal sea ice forecasts.
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Increased surface temperatures (0.7°C per decade) in the Arctic affects polar ecosystems by reducing the extent and duration of annual snow cover. Monitoring of these important ecosystems needs detailed information on snow cover properties at resolutions (< 100 m) that influence ecological habitats and permafrost thaw. A machine learning method using topographic parameters with the Random Forest (RF) algorithm previously developed in alpine environments was applied over an arctic landscape for the first time. The topographic parameters used in the RF algorithm were Topographic Position Index (TPI) and up‐wind slope index (Sx), which were estimated from the freely available Arctic DEM at 2 m resolution. Addition of an ecotype parameter (proxy for vegetation height) showed minimal predictive improvement. Using RF, snow depth distributions were predicted from topographic parameters with a root mean square error = 8 cm (23%) (R2 = 0.79) at 10 m resolution for an arctic watershed (1500 km2) in western Nunavut, Canada. This article is protected by copyright. All rights reserved.
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Alpine and arctic environments are predicted to be strongly influenced by climate change because their cold-adapted species may be sensitive to rapid warming. Genetic diversity, phenotypic plasticity, and dispersal ability of seeds might be crucial for species to persist and/or migrate in these habitats. We reviewed the literature to synthetize current knowledge on seed-trait responses to direct and indirect effects of climate warming. Most experimental and observational studies we reviewed have focused on the effects of warming on seed germination, while other seed functions have received less attention. Overall, there is compelling evidence that increasing temperatures and water stress decrease the number, size, and germination of seeds, suggesting that the net effect of warming will depend mostly on changes in water availability. These responses to climate change have been evaluated mainly in alpine temperate and arctic life zones, while alpine-tropical mountains have been largely neglected.
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A cage‐based metal‐organic framework (Ni‐NKU‐101) with biphenyl‐3,3’,5,5’‐tetracarboxylic acid was synthesized via solvothermal method. Ni‐NKU‐101 contains two types of cages based on trinuclear and octanuclear nickel‐clusters that are connected with each other by the 4‐connected bptc 4‐ ligands, to form a 3D framework with a new topology. A mixed‐metal strategy was used to synthesize isostructural bimetallic MOFs of M x Ni 1‐x ‐NKU‐101 (M = Mn, Co, Cu, Zn). The electrochemical studies showed that the hydrogen evolution reaction (HER) activity of Cu x Ni 1‐x ‐NKU‐101 is much higher than that of other M x Ni 1‐x ‐NKU‐101 catalysts in acidic aqueous solution, owing to the synergistic effect of the bimetallic centers. The optimized Cu 0.19 Ni 0.81 ‐NKU‐101 has an overpotential of 324 mV at 10 mA cm ‐2 and a Tafel slope of 131 mV dec ‐1 . The mechanism of HER activity over these bimetallic MOF‐based electrocatalysts are discussed in detail.
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Arctic sea ice in summer shows both interannual and long-term variations, and atmospheric circulation anomalies are known to play an important role. This study compares the summertime large-scale circulation anomalies associated with Arctic sea ice on interannual and decadal timescales. The results indicate that the circulation anomalies associated with decreased sea ice on interannual timescale are characterized by a barotropic anticyclonic anomaly in the central Arctic, and the thermodynamic process is important for the circulation–sea ice coupling. On one hand, the descending adiabatic warming in low levels associated with the central Arctic anticyclonic anomaly leads to decreased sea ice by enhancing the downwelling longwave radiation. On the other hand, the anticyclonic anomaly also induces more moisture in low levels. The enhanced moisture and temperature (coupled with each other) further favor the reduction of sea ice by emitting more downwelling longwave radiation. By contrast, associated with the decadal sea ice decline, there is an anticyclonic anomaly over Greenland and a cyclonic anomaly over northern Siberia, and the wind-driven sea ice drift dominates the sea ice decline. The transpolar circulation anomalies between the anticyclonic and cyclonic anomalies promote transport of the ice away from the coasts of Siberia toward the North Pole, and drive the ice out of the Arctic Ocean to the North Atlantic. These circulation anomalies also induce sea ice decline through thermodynamic process, but it is not as significant as that on interannual timescale.
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Arctic sea ice characteristics have been changing rapidly and significantly in the last few decades. Using a long-term time series of sea ice products from satellite observations - the extended AVHRR Polar Pathfinder (APP-x), trends in sea ice concentration, ice extent, ice thickness, and ice volume in the Arctic from 1982 to 2020 are investigated. Results show that the Arctic has become less ice-covered in all seasons, especially in summer and autumn. Arctic sea ice thickness has been decreasing at the rate of -3.24 cm per year, resulting in about a 52% reduction in thickness from 2.35 m in 1982 to 1.13 m in 2020. Arctic sea ice volume has been decreasing at the rate of -467.7 km3 per year, resulting in about a 63% reduction in volume, from 27590.4 km3 in 1982 to 10305.5 km3 in 2020. These trends are further examined from a new perspective, where the Arctic Ocean is classified into open water, perennial, and seasonal sea ice-covered areas based on the sea ice persistence. The loss of the perennial sea ice-covered area is the major factor in the total sea ice loss in all seasons. If the current rates of sea ice changes in extent, concentration, and thickness continue, the Arctic is expected to have ice-free summer by the early 2060s.
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Arctic sea ice has decreased substantially and is projected to reach a seasonally ice-free state in the coming decades. Little is known about whether dwindling Arctic sea ice is capable of influencing the occurrence of strong El Niño, a prominent mode of climate variability with global impacts. Based on time slice coupled model experiments, here we show that no significant change in the occurrence of strong El Niño is found in response to moderate Arctic sea-ice loss that is consistent with satellite observations to date. However, as the ice loss continues and the Arctic becomes seasonally ice-free, the frequency of strong El Niño events increases by more than one third, as defined by gradient-based indices that remove mean tropical Pacific warming induced by the seasonally ice-free Arctic. By comparing our time slice experiments with greenhouse warming experiments, we conclude that at least 37–48% of the increase of strong El Niño near the end of the 21st century is associated specifically with Arctic sea-ice loss. Further separation of Arctic sea-ice loss and greenhouse gas forcing only experiments implies that the seasonally ice-free Arctic might play a key role in driving significantly more frequent strong El Niño events.
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Significant changes in the Arctic climate, particularly a rapid decline of September Arctic sea ice has occurred over the past few decades. Though the exact reason for such drastic changes is still unknown, studies suggest anthropogenic drivers, natural variability of the climate system, and a combination of both as reasons. The present study focus on the influence of one of the natural variabilities of the climate system, the teleconnections associated with the Indian Summer Monsoon (ISM), and its relationship to September Arctic sea ice. Using 50 years (1951–2000) of National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) NCEP/NCAR reanalysis data, APHRODITE precipitation data, Gridded Monthly Sea Ice Extent and Concentration, 1850 Onward, V2, and HadISST sea-ice concentration data, it is shown that during many strong (weak) ISM years, the Arctic sea ice increased (decreased) predominantly over the Chukchi and Beaufort Seas. The ISM plays a significant role in causing a positive (negative) North Atlantic Oscillation (NAO) during strong (weak) ISM years through the monsoon-desert mechanism associated with monsoonal heating. Simultaneously, the NAO during a strong (weak) ISM causes weakening (strengthening) of the Beaufort Sea High (BSH). The strength of the BSH modulates the Arctic atmospheric circulation, advecting cold air and the direction of the transpolar drift stream, both leading to the generation of more (less) sea ice over the Chukchi-Beaufort Sea region during strong (weak) ISM years. The study illustrates a new atmospheric teleconnection between the tropics and the Arctic.
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Electrochemical conversion of carbon dioxide into fuel and chemicals with added value represents an appealing approach to reduce the greenhouse effect and realize a carbon-neutral cycle, which has great potential in mitigating global warming and effectively storing renewable energy. The electrochemical CO 2 reduction reaction (CO 2 RR) usually involves multiproton coupling and multielectron transfer in aqueous electrolytes to form multicarbon products (C 2+ products), but it competes with the hydrogen evolution reaction (HER), which results in intrinsically sluggish kinetics and a complex reaction mechanism and places higher requirements on the design of catalysts. In this review, the advantages of electrochemical CO 2 reduction are briefly introduced, and then, different categories of Cu-based catalysts, including monometallic Cu catalysts, bimetallic catalysts, metal-organic frameworks (MOFs) along with MOF-derived catalysts and other catalysts, are summarized in terms of their synthesis method and conversion of CO 2 to C 2+ products in aqueous solution. The catalytic mechanisms of these catalysts are subsequently discussed for rational design of more efficient catalysts. In response to the mechanisms, several material strategies to enhance the catalytic behaviors are proposed, including surface facet engineering, interface engineering, utilization of strong metal-support interactions and surface modification. Based on the above strategies, challenges and prospects are proposed for the future development of CO 2 RR catalysts for industrial applications. Graphical Abstract
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A novel method for constructing a g‐C 3 N 4 /SnO 2 heterostructure for CO 2 reduction reaction (CO2RR) is presented. In the heterostructure, the introduction of g‐C 3 N 4 makes for restraining the recombination of photo‐induced electrons and holes while the existence of SnO 2 plays a vital role in selective catalytic reduction of CO 2 into HCOOH, thus their cooperative interaction makes the photoelectric property of g‐C 3 N 4 /SnO 2 towards CO2RR improved greatly. The so‐fabricated g‐C 3 N 4 /SnO 2 shows an exclusive HCOOH selectivity and a high faradic efficiency of 58%, which is about 8 times higher than that of single SnO 2 . The photocurrent reaches 7.5 μA cm ‐2 under 0.5 V. Besides, the overpotential is as low as 300 mV (vs. RHE). Moreover, the PL emission decreases obviously after the introduction of g‐C 3 N 4 . Given these performance advantages of g‐C 3 N 4 /SnO 2 , the solution‐processed strategy with post‐annealing treatment offers a facile, efficient, yet scalable approach reference to construct CO2RR heterostructure catalysts, which shows higher potential in CO2RR electrocatalysis.
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Cloud feedbacks play an important role in Arctic warming. Cloud morphology, for example, cloud size and spatial distributions, is among key factors that directly impact their radiative effects. In this work, we use two cases observed during the Cold‐air Outbreak (CAO) in the Marine Boundary Layer Experiment (COMBLE) to study the evolution of cloud size distributions as an air mass is advected from the Arctic over a comparatively warm ocean and cloud mesoscale organization changes from rolls to cells. Cloud objects are identified from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance images through an object segmentation procedure and roll breakup is identified by homogeneities in cloud water path (CWP). Roll breakup is found to be accompanied by a local minimum in wind shear and local maxima in cloud size and marine cold air outbreak index. The mean cloud horizontal aspect ratio has weak fetch dependency and is around 2 in roll, transition, and cell regimes. Regardless of distance from the ice edge, smaller clouds (<10 km²) dominate the population number but not cloud cover. Cloud size distributions show bimodality in transition and cell regimes. For clouds with comparable sizes, mean nearest neighbor distances normalized by equivalent cloud radius converge to a single value for all regimes and for all but the smallest clouds, suggesting that clouds of comparable sizes in CAOs are separated by distance proportional to their sizes. The presented statistical results pave the way to evaluating model simulated cloud organizations during CAO events.
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Arctic sea ice is diminishing with climate warming1 at a rate unmatched for at least 1,000 years2. As the receding ice pack raises commercial interest in the Arctic3, it has become more variable and mobile4, which increases safety risks to maritime users5. Satellite observations of sea-ice thickness are currently unavailable during the crucial melt period from May to September, when they would be most valuable for applications such as seasonal forecasting6, owing to major challenges in the processing of altimetry data7. Here we use deep learning and numerical simulations of the CryoSat-2 radar altimeter response to overcome these challenges and generate a pan-Arctic sea-ice thickness dataset for the Arctic melt period. CryoSat-2 observations capture the spatial and the temporal patterns of ice melting rates recorded by independent sensors and match the time series of sea-ice volume modelled by the Pan-Arctic Ice Ocean Modelling and Assimilation System reanalysis8. Between 2011 and 2020, Arctic sea-ice thickness was 1.87 ± 0.10 m at the start of the melting season in May and 0.82 ± 0.11 m by the end of the melting season in August. Our year-round sea-ice thickness record unlocks opportunities for understanding Arctic climate feedbacks on different timescales. For instance, sea-ice volume observations from the early summer may extend the lead time of skilful August–October sea-ice forecasts by several months, at the peak of the Arctic shipping season. Deep learning and numerical simulations of CryoSat-2 radar altimeter data are used to generate a pan-Arctic sea-ice thickness dataset for the Arctic melt period.
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Arctic sea ice characteristics have been changing rapidly and significantly in the last few decades. Using a long-term time series of sea ice products from satellite observations - the extended AVHRR Polar Pathfinder (APP-x), trends in sea ice concentration, ice extent, ice thickness, and ice volume in the Arctic from 1982 to 2020 are investigated. Results show that the Arctic has warmed and become less ice covered in all seasons, especially in summer and autumn. Arctic sea ice thickness has been decreasing at the rate of -3.24 cm per year, resulting in about a 52% reduction in thickness from 1982 to 2020. Arctic sea ice volume has been decreasing at the rate of -467.7 km3 per year, resulting in a volume of 10305.5 km3 in 2020 compared to 27590.4 km3 in 1982. These trends are further examined from a new perspective. The Arctic Ocean is classified into open water, and perennial and seasonal sea ice-covered areas based on the sea ice persistence. The loss of the perennial sea ice covered area is the major factor in the total sea ice loss in all seasons. If the current rates of sea ice changes continue, the Arctic is expected to have ice-free summers by the mid-2060s.
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Climate models have continued to be developed and improved since the AR4, and many models have been extended into Earth System models by including the representation of biogeochemical cycles important to climate change. These models allow for policy-relevant calculations such as the carbon dioxide (CO2) emissions compatible with a specified climate stabilization target. In addition, the range of climate variables and processes that have been evaluated has greatly expanded, and differences between models and observations are increasingly quantified using ‘performance metrics’. In this chapter, model evaluation covers simulation of the mean climate, of historical climate change, of variability on multiple time scales and of regional modes of variability. This evaluation is based on recent internationally coordinated model experiments, including simulations of historic and paleo climate, specialized experiments designed to provide insight into key climate processes and feedbacks and regional climate downscaling. Figure 9.44 provides an overview of model capabilities as assessed in this chapter, including improvements, or lack thereof, relative to models assessed in the AR4. The chapter concludes with an assessment of recent work connecting model performance to the detection and attribution of climate change as well as to future projections.
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It is well accepted that increasing atmospheric CO<sub>2</sub> results in global warming, leading to a decline in polar sea ice area. Here, the specific question of whether there is a tipping point in the sea ice cover is investigated. The global climate model HadCM3 is used to map the trajectory of sea ice area under idealised scenarios. The atmospheric CO<sub>2</sub> is first ramped up to four times pre-industrial levels (4 × CO<sub>2</sub>), then ramped down to pre-industrial levels. We also examine the impact of stabilising climate at 4 × CO<sub>2</sub> prior to ramping CO<sub>2</sub> down to pre-industrial levels. Against global mean temperature, Arctic sea ice area is reversible, while the Antarctic sea ice shows some asymmetric behaviour – its rate of change slower, with falling temperatures, than its rate of change with rising temperatures. However, we show that the asymmetric behaviour is driven by hemispherical differences in temperature change between transient and stabilisation periods. We find no irreversible behaviour in the sea ice cover.
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Climate Model simulations give a large range of over 100 years for predictions of when the Arctic could first become ice-free in the summer, and many studies have attempted to narrow this uncertainty range. However, given the chaotic nature of the climate system, what amount of spread in the prediction of an ice-free summer Arctic is inevitable? Based on results from large ensemble simulations with the Community Earth System Model, we show that internal variability alone leads to a prediction uncertainty of about two decades, while scenario uncertainty between the strong (RCP8.5) and medium (RCP4.5) forcing scenarios adds at least another 5 years. Common metrics of the past and present mean sea ice state (such as ice extent, volume, and thickness) as well as global mean temperatures do not allow a reduction of the prediction uncertainty from internal variability.
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The usefulness of a climate-model simulation cannot be inferred solely from its degree of agreement with observations. Instead, one has to consider additional factors such as internal variability, the tuning of the model, observational uncertainty, the temporal change in dominant processes or the uncertainty in the forcing. In any model-evaluation study, the impact of these limiting factors on the suitability of specific metrics must hence be examined. This can only meaningfully be done relative to a given purpose for using a model. I here generally discuss these points and substantiate their impact on model evaluation using the example of sea ice. For this example, I find that many standard metrics such as sea-ice area or volume only permit limited inferences about the shortcomings of individual models.
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Recent studies have identified an approximately proportional relationship between global warming and cumulative carbon emissions, yet the robustness of this relationship has not been tested over a broad range of cumulative emissions and emission rates. This study explores the path dependence of the climate and carbon cycle response using an Earth system model of intermediate complexity forced with 24 idealized emissions scenarios across five cumulative emission groups (1275–5275 Gt C) with varying rates of emission. We find the century-scale climate and carbon cycle response after cessation of emissions to be approximately independent of emission pathway for all cumulative emission levels considered. The ratio of global mean temperature change to cumulative emissions – referred to as the transient climate response to cumulative carbon emissions (TCRE) – is found to be constant for cumulative emissions lower than � 1500 GtC but to decline with higher cumulative emissions. The TCRE is also found to decrease with increasing emission rate. The response of Arctic sea ice is found to be approximately proportional to cumulative emissions, while the response of the Atlantic Meridional Overturning Circulation does not scale linearly with cumulative emissions, as its peak response is strongly dependent on emission rate. Ocean carbon uptake weakens with increasing cumulative emissions, while land carbon uptake displays non-monotonic behavior, increasing up to a cumulative emission threshold of � 2000 GtC and then declining.
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Arctic sea ice is a keystone indicator of greenhouse-gas induced global climate change, which is expected to be amplified in the Arctic. Here we directly compare observed variations in arctic sea-ice extent and CO2 since the beginning of the 20th century, identifying a strengthening linkage, such that in recent decades the rate of sea-ice decrease mirrors the increase in CO2, with r² ∼ 0.95 over the last four decades, thereby indicating that 90% (r² ∼ 0.90) of the decreasing sea-ice extent is empirically “accounted for” by the increasing CO2 in the atmosphere. The author presents an empirical relation between annual sea-ice extent and global atmospheric CO2 concentrations, in which sea-ice reductions are linearly, inversely proportional to the magnitude of increase of CO2 over the last few decades. This approximates sea-ice changes during the most recent four decades, with a proportionality constant of 0.030 million km² per ppmv CO2. When applied to future emission scenarios of the Intergovernmental Panel on Climate Change (IPCC), this relationship results in substantially faster ice decreases up to 2050 than predicted by IPCC models. However, departures from this projection may arise from non-linear feedback effects and/or temporary natural variations on interannual timescales, such as the record minimum of sea-ice extent observed in September 2007.
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Climate models projected stronger warming over the past 15 years than has been seen in observations. Conspiring factors of errors in volcanic and solar inputs, representations of aerosols, and El Niño evolution, may explain most of the discrepancy.
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Despite continued growth in atmospheric levels of greenhouse gases, global mean surface and tropospheric temperatures have shown slower warming since 1998 than previously. Possible explanations for the slow-down include internal climate variability, external cooling influences and observational errors. Several recent modelling studies have examined the contribution of early twenty-first-century volcanic eruptions to the muted surface warming. Here we present a detailed analysis of the impact of recent volcanic forcing on tropospheric temperature, based on observations as well as climate model simulations. We identify statistically significant correlations between observations of stratospheric aerosol optical depth and satellite-based estimates of both tropospheric temperature and short-wave fluxes at the top of the atmosphere. We show that climate model simulations without the effects of early twenty-first-century volcanic eruptions overestimate the tropospheric warming observed since 1998. In two simulations with more realistic volcanic influences following the 1991 Pinatubo eruption, differences between simulated and observed tropospheric temperature trends over the period 1998 to 2012 are up to 15% smaller, with large uncertainties in the magnitude of the effect. To reduce these uncertainties, better observations of eruption-specific properties of volcanic aerosols are needed, as well as improved representation of these eruption-specific properties in climate model simulations.
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The areal extent, concentration and thickness of sea ice in the Arctic Ocean and adjacent seas have strongly decreased during the recent decades, but cold, snow-rich winters have been common over mid-latitude land areas since 2005. A review is presented on studies addressing the local and remote effects of the sea ice decline on weather and climate. It is evident that the reduction in sea ice cover has increased the heat flux from the ocean to atmosphere in autumn and early winter. This has locally increased air temperature, moisture, and cloud cover and reduced the static stability in the lower troposphere. Several studies based on observations, atmospheric reanalyses, and model experiments suggest that the sea ice decline, together with increased snow cover in Eurasia, favours circulation patterns resembling the negative phase of the North Atlantic Oscillation and Arctic Oscillation. The suggested large-scale pressure patterns include a high over Eurasia, which favours cold winters in Europe and northeastern Eurasia. A high over the western and a low over the eastern North America have also been suggested, favouring advection of Arctic air masses to North America. Mid-latitude winter weather is, however, affected by several other factors, which generate a large inter-annual variability and often mask the effects of sea ice decline. In addition, the small sample of years with a large sea ice loss makes it difficult to distinguish the effects directly attributable to sea ice conditions. Several studies suggest that, with advancing global warming, cold winters in mid-latitude continents will no longer be common during the second half of the twenty-first century. Recent studies have also suggested causal links between the sea ice decline and summer precipitation in Europe, the Mediterranean, and East Asia.
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Sea ice in the Arctic is one of the most rapidly changing components of the global climate system. Over the past few decades, summer areal extent has declined over 30%, and all months show statistically significant declining trends. New satellite missions and techniques have greatly expanded information on sea ice thickness, but many uncertainties remain in the satellite data and long-term records are sparse. However, thickness observations and other satellite-derived data indicate a 40% decline in thickness, due in large part to the loss of thicker, older ice cover. The changes in sea ice are happening faster than models have projected. With continued increasing temperatures, summer ice-free conditions are likely sometime in the coming decades, though there are substantial uncertainties in the exact timing and high interannual variability will remain as sea ice decreases. The changes in Arctic sea ice are already having an impact on flora and fauna in the Arctic. Some species will face increasing challenges in the future, while new habitat will open up for other species. The changes are also affecting peoples living and working in the Arctic. Native communities are facing challenges to their traditional ways of life, while new opportunities open for shipping, fishing, and natural resource extraction. Significant progress has been made in recent years in understanding of Arctic sea ice and its role in climate, the ecosystem, and human activities. However, significant challenges remain in furthering the knowledge of the processes, impacts, and future evolution of the system.
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A class of mean annual, zonally averaged energy-balance climate models of the Budyko-Sellers type are studied by a spectral (expansion in Legendre polynomials) method. Models with constant thermal diffusion coefficient can be solved exactly, The solution is approached by a rapidly converging sequence with each succeeding approximant taking into account information from ever smaller space and time scales. The first two modes represent a good approximation to the exact solution as well as to the present climate. The two-mode approximation to a number of more general models are shown to be either formally or approximately equivalent to the same truncation in the constant diffusion case. In particular, the transport parameterization used by Budyko is precisely equivalent to the two-mode truncation of thermal diffusion. Details of the dynamics do not influence the first two modes which fortunately seem adequate for the study of global climate change. Estimated ice age temperatures and ice line latitude agree well with the model if the solar constant is reduced by 1.3%.
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Climate change is governed by changes to the global energy balance. At the top of the atmosphere, this balance is monitored globally by satellite sensors that provide measurements of energy flowing to and from Earth. By contrast, observations at the surface are limited mostly to land areas. As a result, the global balance of energy fluxes within the atmosphere or at Earth's surface cannot be derived directly from measured fluxes, and is therefore uncertain. This lack of precise knowledge of surface energy fluxes profoundly affects our ability to understand how Earth's climate responds to increasing concentrations of greenhouse gases. In light of compilations of up-to-date surface and satellite data, the surface energy balance needs to be revised. Specifically, the longwave radiation received at the surface is estimated to be significantly larger, by between 10 and 17 Wm−2, than earlier model-based estimates. Moreover, the latest satellite observations of global precipitation indicate that more precipitation is generated than previously thought. This additional precipitation is sustained by more energy leaving the surface by evaporation — that is, in the form of latent heat flux — and thereby offsets much of the increase in longwave flux to the surface.
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Climate change is amplified in the Arctic region. Arctic amplification has been found in past warm and glacial periods, as well as in historical observations and climate model experiments. Feedback effects associated with tem- perature, water vapour and clouds have been suggested to contribute to amplified warming in the Arctic, but the surface albedo feedback—the increase in surface absorption of solar radiation when snow and ice retreat—is often cited as the main contributor. However, Arctic amplification is also found in models without changes in snow and ice cover. Here we analyse climate model simulations from the Coupled Model Intercomparison Project Phase 5 archive to quantify the contributions of the various feedbacks. We find that in the simulations, the largest contribution to Arctic amplification comes from a temperature feedbacks: as the surface warms, more energy is radiated back to space in low latitudes, compared with the Arctic. This effect can be attributed to both the different vertical structure of the warming in high and low latitudes, and a smaller increase in emitted blackbody radiation per unit warming at colder temperatures. We find that the surface albedo feedback is the second main contributor to Arctic amplification and that other contributions are substantially smaller or even oppose Arctic amplification.
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The rapid retreat and thinning of the Arctic sea ice cover over the past several decades is one of the most striking manifestations of global climate change. Previous research revealed that the observed downward trend in September ice extent exceeded simulated trends from most models participating in the World Climate Research Programme Coupled Model Intercomparison Project Phase 3 (CMIP3). We show here that as a group, simulated trends from the models contributing to CMIP5 are more consistent with observations over the satellite era (1979-2011). Trends from most ensemble members and models nevertheless remain smaller than the observed value. Pointing to strong impacts of internal climate variability, 16% of the ensemble member trends over the satellite era are statistically indistinguishable from zero. Results from the CMIP5 models do not appear to have appreciably reduced uncertainty as to when a seasonally ice-free Arctic Ocean will be realized.
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We discuss the current understanding of past and future sea-ice evolution as inferred from combining model simulations and observations. In such combined analysis, the models allow us to enhance our understanding behind the observed evolution of sea ice, while the observations allow us to assess how realistically the models represent the processes that govern sea-ice evolution in the real world. Combined, observations and models thus provide robust insights into the functioning of sea ice in the Earth's climate system, and can inform policy decisions related to the future evolution of the ice cover. We find that models and observations agree well on the sensitivity of Arctic sea ice to global warming and on the main drivers for the observed retreat. In contrast, a robust reduction of the uncertainty range of future sea-ice evolution remains difficult, in particular since the observational record is often too short to robustly examine the impact of internal variability on model biases. Process-based model evaluation and model evaluation based on seasonal-prediction systems provide promising ways to overcome these limitations.
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The ratio of warming to cumulative emissions of carbon dioxide has been shown to be approximately independent of time and emissions scenarios and directly relates emissions to temperature. It is therefore a potentially important tool for climate mitigation policy. The transient climate response to cumulative carbon emissions (TCRE), defined as the ratio of global-mean warming to cumulative emissions at CO2 doubling in a 1% yr(-1) CO2 increase experiment, ranges from 0.8 to 2.4 K EgC(-1) in 15 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5)-a somewhat broader range than that found in a previous generation of carbon-climate models. Using newly available simulations and a new observational temperature dataset to 2010, TCRE is estimated from observations by dividing an observationally constrained estimate of CO2-attributable warming by an estimate of cumulative carbon emissions to date, yielding an observationally constrained 5%-95% range of 0.7-2.0 K EgC(-1).
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The sensitivity of Northern Hemisphere sea ice cover to global temperature change is examined in a group of climate models and in the satellite-era observations. The models are found to have well-defined, distinguishable sensitivities in climate change experiments. The satellite-era observations show a larger sensitivity-a larger decline per degree of warming-than any of the models. To evaluate the role of natural variability in this discrepancy, the sensitivity probability density function is constructed based upon the observed trends and natural variability of multidecadal ice cover and global temperature trends in a long control run of the GFDL Climate Model, version 2.1 (CM2.1). This comparison shows that the model sensitivities range from about 1 to more than 2 pseudostandard deviations of the variability smaller than observations indicate. The impact of natural Atlantic multidecadal temperature trends (as simulated by the GFDL model) on the sensitivity distribution is examined and found to be minimal.
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The HadCM3 AOGCM has been used to undertake an ensemble of four integrations from 1860 to 1999 with forcings due to all major anthropogenic and natural climate factors. The simulated decreasing trend in average Arctic sea ice extent for 1970-1999 (-2.5% per decade) is very similar to observations. HadCM3 indicates that internal variability and natural forcings (solar and volcanic) of the climate system are very unlikely by themselves to have caused a trend of this size. The simulated decreasing trend in Arctic sea ice volume (-3.4% per decade for 1961-1998) is less than some recent observationally based estimates. Extending the integrations into the 21st century, Arctic sea ice area and volume continue to decline. Area decreases linearly as global-average temperature rises (by 13% per K), and volume diminishes more rapidly than area. By the end of the century, in some scenarios, the Arctic is ice-free in late summer.