Focus on Indicators of Arctic Environmental Variability and Change

This study applies an indicators framework to investigate climate drivers of tundra vegetation trends and variability over the 1982–2019 period. Previously known indicators relevant for tundra productivity (summer warmth index (SWI), coastal spring sea-ice (SI) area, coastal summer open-water (OW)) and three additional indicators (continentality, summer precipitation, and the Arctic Dipole (AD): second mode of sea level pressure variability) are analyzed with maximum annual Normalized Difference Vegetation Index (MaxNDVI) and the sum of summer bi-weekly (time-integrated) NDVI (TI-NDVI) from the Advanced Very High Resolution Radiometer time-series. Climatological mean, trends, and correlations between variables are presented. Changes in SI continue to drive variations in the other indicators. As spring SI has decreased, summer OW, summer warmth, MaxNDVI, and TI-NDVI have increased. However, the initial very strong upward trends in previous studies for MaxNDVI and TI-NDVI are weakening and becoming spatially and temporally more variable as the ice retreats from the coastal areas. TI-NDVI has declined over the last decade particularly over High Arctic regions and southwest Alaska. The continentality index (CI) (maximum minus minimum monthly temperatures) is decreasing across the tundra, more so over North America than Eurasia. The relationship has weakened between SI and SWI and TI-NDVI, as the maritime influence of OW has increased along with total precipitation. The winter AD is correlated in Eurasia with spring SI, summer OW, MaxNDVI, TI-NDVI, the CI and total summer precipitation. This winter connection to tundra emphasizes the role of SI in driving the summer indicators. The winter (DJF) AD drives SI variations which in turn shape summer OW, the atmospheric SWI and NDVI anomalies. The winter and spring indicators represent potential predictors of tundra vegetation productivity a season or two in advance of the growing season.

In this study, we aimed to reconstruct spring (April–June) sea ice changes in the western Arctic Ocean over recent centuries (ca. the last 250 years) by measuring biomarker distributions in a multicore (ARA01B-03MUC) retrieved from the Chukchi Shelf region and to evaluate outcomes against known or modelled estimates of sea ice conditions. Specifically, we analyzed for the Arctic sea ice proxy IP₂₅ and assessed the suitability of a further highly branched isoprenoid (HBI) lipid (HBI III), epi-brassicasterol, and dinosterol as complementary biomarkers for use with the so-called phytoplankton marker-IP₂₅ index (PIP₂₅; P_IIIIP₂₅, P_BIP₂₅, and P_DIP₂₅, respectively). The presence of IP₂₅ throughout core ARA01B-03MUC confirms the occurrence of seasonal sea ice at the study site over recent centuries. From a semi-quantitative perspective, all three PIP₂₅ indices gave different trends, with some dependence on the balance factor c, a term used in the calculation of the PIP₂₅ index. P_IIIIP₂₅-derived spring sea ice concentration (SpSIC) estimates using a c value of 0.63, determined previously from analysis of Barents Sea surface sediments, were likely most reliable, since SpSIC values were high throughout the record (SpSIC > 78%), consistent with the modern context for the Chukchi Sea and the mean SpSIC record of the 41 CMIP5 climate models over recent centuries. P_BIP₂₅-based SpSIC estimates were also high (SpSIC 108%−127%), albeit somewhat over-estimated, when using a c value of 0.023 obtained from a pan-Arctic distribution of surface sediments. In contrast, P_DIP₂₅ values using a pan-Arctic c value of 0.11, and PIP₂₅ data based on the mean biomarker concentrations from ARA01B-03MUC, largely underestimated sea ice conditions (SpSIC as low as 13%), and exhibited poor agreement with instrumental records or model outputs. On the other hand, P_BIP₂₅ values using a c factor based on mean IP₂₅ and epi-brassicasterol concentrations exhibited a decline towards the core top, which resembled recent decreasing changes in summer sea ice conditions for the Chukchi Sea; however, further work is needed to test the broader spatial generality of this observation.

Marine mammals respond to, and thereby reflect, changes in Arctic ecosystems that are important both to practitioners of conventional science (CS) and to holders of indigenous knowledge (IK). Although often seen as contrasting approaches to tracking ecosystem variability, when CS and IK are combined they can provide complementary and synergistic information. Despite exceptions, ecosystem-focused CS is often spatially broad and time shallow (1000 s km, decades) while IK is comparatively narrow spatially and time deep (10 s km, centuries). In addition, differences in how information is gathered, stored, applied and communicated can confound information integration from these two knowledge systems. Over the past four decades, research partnerships between CS practitioners and IK holders have provided novel insights to an Alaskan Arctic marine ecosystem in rapid transition. We identify insights from some of those projects, as they relate to changes in sea ice, oceanography, and more broadly to marine mammal ecology and health. From those insights and the protocols of existing community-based programs, we suggest that the strong seasonal cycle of Arctic environmental events should be leveraged as a shared framework to provide common ground for communication when developing projects related to marine mammal health and ecology. Adopting a shared temporal framework would foster joint CS–IK thinking and support the development of novel and nonlinear approaches to shared questions and concerns regarding marine mammals. The overarching goal is to extend the range and depth of a common understanding of marine mammal health and ecology during a period of rapid ecosystem alteration. The current focus on CS–IK co-production of knowledge and recent inclusion of marine mammals as essential variables in global ocean observatories makes this an opportune time to find common ground for understanding and adapting to the rapid changes now underway in Arctic marine ecosystems.

An increase in retrogressive thaw slump (RTS) activity has been observed in the Arctic in recent decades. However, a gap exists between observations in high Arctic polar desert regions where mean annual ground temperatures are as cold as −16.5 °C and vegetation coverage is sparse. In this study, we present a ∼30 year record of annual RTS observations (frequency and distribution) from 1989 to 2018 within the Eureka Sound Lowlands, Ellesmere and Axel Heiberg Islands. Record summer warmth in 2011 and 2012 promoted rapid RTS initialization, increasing active slumps from 100 in a given year or less to over 200 regionally and promoting RTS initiation in previously unaffected terrain. Differential GPS and remote sensing observations of 12 RTSs initiated during this period (2011–2018) provided a mean headwall retreat rate for all RTSs of 6.2 m yr⁻¹ and for specific RTSs up to 26.7 m yr⁻¹. To better understand the dynamics of climate and terrain factors controlling RTS headwall retreat rates we explored RTS interactions by correlating headwall retreat with climate factors (thawing degree days, annual rainfall and annual snowfall) and terrain factors (aspect and slope). Our findings indicate a sensitivity of cold permafrost in the high Arctic to climate-driven thermokarst initiation, but the decoupling of RTS dynamics from climate appears to occur over time for individual RTS as terrain factors take on a greater role controlling headwall retreat. Detailed observations of thermokarst development in a high Arctic polar desert permafrost setting are important as it demonstrates the sensitivity of this system to changes in summer temperatures and highlight differences to changes occurring in other Arctic permafrost regions.

Key observational indicators of climate change in the Arctic, most spanning a 47 year period (1971–2017) demonstrate fundamental changes among nine key elements of the Arctic system. We find that, coherent with increasing air temperature, there is an intensification of the hydrological cycle, evident from increases in humidity, precipitation, river discharge, glacier equilibrium line altitude and land ice wastage. Downward trends continue in sea ice thickness (and extent) and spring snow cover extent and duration, while near-surface permafrost continues to warm. Several of the climate indicators exhibit a significant statistical correlation with air temperature or precipitation, reinforcing the notion that increasing air temperatures and precipitation are drivers of major changes in various components of the Arctic system. To progress beyond a presentation of the Arctic physical climate changes, we find a correspondence between air temperature and biophysical indicators such as tundra biomass and identify numerous biophysical disruptions with cascading effects throughout the trophic levels. These include: increased delivery of organic matter and nutrients to Arctic near‐coastal zones; condensed flowering and pollination plant species periods; timing mismatch between plant flowering and pollinators; increased plant vulnerability to insect disturbance; increased shrub biomass; increased ignition of wildfires; increased growing season CO₂ uptake, with counterbalancing increases in shoulder season and winter CO₂ emissions; increased carbon cycling, regulated by local hydrology and permafrost thaw; conversion between terrestrial and aquatic ecosystems; and shifting animal distribution and demographics. The Arctic biophysical system is now clearly trending away from its 20th Century state and into an unprecedented state, with implications not only within but beyond the Arctic. The indicator time series of this study are freely downloadable at AMAP.no.

While Arctic sea ice is changing, new observation methods are developed and process understanding improves, whereas gaps in observations and understanding evolve. Some previous gaps are filled, while others remain, or come up new. Knowing about the status of observation and knowledge gaps is important for interpreting observation and research results, interpretation and use of key climate indicators, and for research and observation planning. This paper deals with identifying some of the important current gaps connected to Arctic sea ice and related climate indicators, including their role and functions in the sea ice and climate systems. Subtopics that are discussed here include Arctic sea-ice extent, concentration, and thickness, sea-ice thermodynamics, age and dynamic processes, and biological implications of changing sea ice. Among crucial gaps are few in situ observations during the winter season, limited observational data on snow and ice thickness from the Arctic Basin, and wide gaps in biological rate measurements in or under sea ice. There is a need to develop or improve analyzes and products of remote sensing, especially for new sensors and technology such as remotely operated vehicles. Potential gaps in observations are inevitably associated with interruptions in long-term observational time series due to sensor failure or cuts in observation programmes.

The seasonal evolution of Arctic sea ice can be described by the timing of key dates of sea ice concentration (SIC) change during its annual retreat and advance cycle. Here, we use SICs from a satellite passive microwave climate data record to identify the sea ice dates of opening (DOO), retreat (DOR), advance (DOA), and closing (DOC) and the periods of time between these events. Regional variability in these key dates, periods, and sea ice melt onset and freeze-up dates for 12 Arctic regions during the melt seasons of 1979–2016 is investigated. We find statistically significant positive trends in the length of the melt season (outer ice-free period) for most of the eastern Arctic, the Bering Sea, and Hudson and Baffin Bays with trends as large as 11.9 d decade⁻¹ observed in the Kara Sea. Trends in the DOR and DOA contribute to statistically significant increases in the length of the open water period for all regions within the Arctic Ocean ranging from 3.9 to 13.8 d decade⁻¹. The length of the ice retreat period (DOR−DOO) ranges from 17.1 d in the Sea of Okhotsk to 41 d in the Greenland Sea. The length of the ice advance period (DOC−DOA) is generally much shorter and ranges from 17.9 to 25.3 d in the Sea of Okhotsk and Greenland Sea, respectively. Additionally, we derive the extent of the seasonal ice zone (SIZ) and find statistically significant negative trends (SIZ is shrinking) in the Sea of Okhotsk, Baffin Bay, Greenland Sea, and Barents Sea regions, which are geographically open to the oceans and influenced by reduced winter sea ice extent. Within regions of the Arctic Ocean, statistically significant positive trends indicate that the extent of the SIZ is expanding as Arctic summer sea ice declines.

We investigate climatic changes that have occurred in the Arctic over the period 1982–2017 through examination of ten observational cryospheric time series, and develop a new quantitative composite Arctic climate change index (ACCI). Using Factor Analysis highlights joint trends of winter temperature increases and sea ice loss, tundra shifts, and secondarily summer sea ice loss, spring snow loss, and Greenland land ice loss. An Arctic-wide atmospheric circulation index (Arctic Oscillation) was not selected as a joint contributor. Distinct Arctic change began in 1990 and the trend increases after 2005 to the end of the series. That most variables of the collection project onto a single pattern of change suggests that the Arctic is responding as a coherent system over the previous three decades. However, no single index exclusively tracks change in the Arctic, a conclusion that emerges from a multivariate analysis. A composite quantitative index (ACCI) is useful to document the covariability of systematic Arctic change.

The uncertainties in sea ice extent (total area covered by sea ice with concentration >15%) derived from passive microwave sensors are assessed in two ways. Absolute uncertainty (accuracy) is evaluated based on the comparison of the extent between several products. There are clear biases between the extent from the different products that are of the order of 500 000 to 1 × 10⁶ km² depending on the season and hemisphere. These biases are due to differences in the algorithm sensitivity to ice edge conditions and the spatial resolution of different sensors. Relative uncertainty is assessed by examining extents from the National Snow and Ice Data Center Sea Ice Index product. The largest source of uncertainty, ∼100 000 km², is between near-real-time and final products due to different input source data and different processing and quality control. For consistent processing, the uncertainty is assessed using different input source data and by varying concentration algorithm parameters. This yields a relative uncertainty of 30 000–70 000 km². The Arctic minimum extent uncertainty is ∼40 000 km². Uncertainties in comparing with earlier parts of the record may be higher due to sensor transitions. For the first time, this study provides a quantitative estimate of sea ice extent uncertainty.

Climate models have been making significant progress encompassing an increasing number of complex feedback mechanisms from natural ecosystems. Permafrost thaw and subsequent induced greenhouse gas emissions, however, remain a challenge for climate models at large. Deducing permafrost conditions and associated greenhouse gas emissions from parameters that are simulated in climate models would be a helpful step towards estimating emission budgets from permafrost regions. Here we use a regional climate model with a 5 km horizontal resolution to assess future potential methane (CH₄) emissions over presently unglaciated areas in Greenland under an RCP8.5 scenario. A simple frost index is applied to estimate permafrost conditions from the model output. CH₄ flux measurements from two stations in Greenland; Nuuk representing sub-Arctic and Zackenberg high-Arctic climate, are used to establish a relationship between emissions and near surface air temperature. Permafrost conditions in Greenland change drastically by the end of the 21st century in an RCP8.5 climate. Continuous permafrost remains stable only in North Greenland, the north-west coast, the northern tip of Disko Island, and Nuussuaq. Southern Greenland conditions only sustain sporadic permafrost conditions and largely at high elevations, whereas former permafrost in other regions thaws. The increasing thawed soil leads to increasing CH₄ emissions. Especially the area surrounding Kangerlussuaq, Scoresby Land, and the southern coast of Greenland exhibit potentially high emissions during the longer growing season. The constructed maps and budgets combining modelled permafrost conditions with observed CH₄ fluxes from CH₄ promoting sites represent a useful tool to identify areas in need of additional monitoring as they highlight potential CH₄ hot spots.

Trends and variations in the amount of cold airmass in the Arctic and the Northern Hemisphere are evaluated for the 60 year period, 1959–2018. The two indicators are (1) polar cold air mass (PCAM), which is the amount of air below a potential temperature threshold, and (2) negative heat content (NHC), which includes a weighting by coldness. Because the metrics of coldness are based on multiple layers in the atmosphere, they provide a more comprehensive framework for assessment of warming than is provided by surface air temperatures alone. The negative trends of PCAM and NHC are stronger (as a % per decade) when the threshold is 245 K rather than 280 K, indicating that the loss of extremely cold air is happening at a faster rate than the loss of moderately cold air. The loss of cold air has accelerated, as the most rapid loss of NHC has occurred in recent decades (1989–2018). The spatial patterns of the trends of PCAM and NHC provide another manifestation of Arctic amplification. Of the various teleconnection indices, the Atlantic Multidecadal Oscillation shows the strongest correlations with the spatially integrated metrics of moderate coldness. Several Pacific indices also correlate significantly with these indicators. However, the amount of extremely cold air mass does not correlate significantly with the indices of internal variability used here.

Arctic winters have become increasingly warmer and rainier. Where permafrost prevails, winter rain (or rain-on-snow) is known to occasionally cause extensive ice layers at the snow/ground interface, i.e. 'basal ice' or 'ground ice', with potentially large ecological and socio-economic implications. However, an overall lack of field data has so far restricted our predictive understanding of the environmental conditions shaping spatiotemporal variation in basal ice. Here, we use time-series of spatially replicated snowpack measurements from coastal (Ny-Ålesund area; 2000–2017) and central Spitsbergen (Nordenskiöld Land; 2010–2017), Svalbard, to analyze spatiotemporal patterns in basal ice and how they are linked with topography, weather, snowpack and climate change. As expected, both the spatial occurrence and thickness of basal ice increased strongly with the annual amount of winter rain. This effect was modified by accumulated snowfall; a deeper snowpack restricts ice formation following a minor rain event, but enhances ice formation following heavy rain due to an increased contribution of snowmelt. Accordingly, inter-annual variation in snow depth was negatively related to basal ice thickness. Annual fluctuations in basal ice thickness were strongly correlated in space (average correlation ρ = 0.40; 0–142 km distance between plots) due to strong spatial correlation in winter rain (ρ = 0.62; 14–410 km distance between meteorological stations). Models of basal ice based on meteorological time-series (1957–2017) suggested that ice-free winters (i.e. mean basal ice <0.1 cm) had virtually not occurred since 1998, whereas such winters previously (1957–1998) occurred every three–four years on average. This detected cryosphere regime shift was linked to a parallel climate regime shift with increased winter rain amounts. Svalbard is regarded a bellwether for Arctic winter climate change. Our empirical study may therefore provide an early warning of future changes in high-arctic snowpacks.

The Arctic Monitoring and Assessment Program (AMAP 2017) report identifies the Arctic as the largest regional source of land ice to global sea-level rise in the 2003–2014 period. Yet, this contextualization ignores the longer perspective from in situ records of glacier mass balance. Here, using 17 (>55 °N latitude) glacier and ice cap mass balance series in the 1971–2017 period, we develop a semi-empirical estimate of annual sea-level contribution from seven Arctic regions by scaling the in situ records to GRACE averages. We contend that our estimate represents the most accurate Arctic land ice mass balance assessment so far available before the 1992 start of satellite altimetry. We estimate the 1971–2017 eustatic sea-level contribution from land ice north of ∼55 °N to be 23.0 ± 12.3 mm sea-level equivalent (SLE). In all regions, the cumulative sea-level rise curves exhibit an acceleration, starting especially after 1988. Greenland is the source of 46% of the Arctic sea-level rise contribution (10.6 ± 7.3 mm), followed by Alaska (5.7 ± 2.2 mm), Arctic Canada (3.2 ± 0.7 mm) and the Russian High Arctic (1.5 ± 0.4 mm). Our annual results exhibit co-variability over a 43 year overlap (1971–2013) with the alternative dataset of Marzeion et al (2015 Cryosphere 9 2399–404) (M15). However, we find a 1.36× lower sea-level contribution, in agreement with satellite gravimetry. The IPCC Fifth Assessment report identified constraining the pre-satellite era sea-level budget as a topic of low scientific understanding that we address and specify sea-level contributions coinciding with IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) 'present day' (2005–2015) and 'recent past' (1986–2005) reference periods. We assess an Arctic land ice loss of 8.3 mm SLE during the recent past and 12.4 mm SLE during the present day. The seven regional sea-level rise contribution time series of this study are available from AMAP.no.

In this study, we propose a new Arctic climate change indicator based on the strength of the Arctic halocline, a porous barrier between the cold and fresh upper ocean and ice and the warm intermediate Atlantic Water of the Arctic Ocean. This indicator provides a measure of the vulnerability of sea ice to upward heat fluxes from the ocean interior, as well as the efficiency of mixing affecting carbon and nutrient exchanges. It utilizes the well-accepted calculation of available potential energy (APE), which integrates anomalies of potential density from the surface downwards through the surface mixed layer to the base of the halocline. Regional APE contrasts are striking and show a strengthening of stratification in the Amerasian Basin (AB) and an overall weakening in the Eurasian Basin (EB). In contrast, Arctic-wide time series of APE is not reflective of these inter-basin contrasts. The use of two time series of APE—AB and EB—as an indicator of Arctic Ocean climate change provides a powerful tool for detecting and monitoring transition of the Arctic Ocean towards a seasonally ice-free Arctic Ocean. This new, straightforward climate indicator can be used to inform both the scientific community and the broader public about changes occurring in the Arctic Ocean interior and their potential impacts on the state of the ice cover, the productivity of marine ecosystems and mid-latitude weather.

Strong winter warming has dominated recent patterns of climate change along the Arctic Coastal Plain (ACP) of northern Alaska. The full impact of arctic winters may be best manifest by freshwater ice growth and the extent to which abundant shallow ACP lakes freeze solid with bedfast ice by the end of winter. For example, winter conditions of 2016–17 produced record low extents of bedfast ice across the ACP. In addition to high air temperatures, the causes varied from deep snow accumulation on the Barrow Peninsula to high late season rainfall and lake levels farther east on the ACP. In contrast, the previous winter of 2015–16 was also warm, but low snowpack and high winds caused relatively thick lake ice to develop and corresponding high extents of bedfast ice on the ACP. This recent comparison of extreme variation in lake ice responses between two adjacent regions and years in the context of long-term climate and ice records highlights the complexity associated with weather conditions and climate change in the Arctic. Recent observations of maximum ice thickness (MIT) compared to simulated MIT from Weather Research and Forcing (Polar-WRF) model output show greater departure toward thinner ice than predicted by models, underscoring this uncertainty and the need for sustained observations. Lake ice thickness and the extent of bedfast ice not only indicate the impact of arctic winters, but also directly affect sublake permafrost, winter water supply for industry, and overwinter habitat availability. Therefore, tracking freshwater ice responses provides a comprehensive picture of winter, as well as summer, weather conditions and climate change with implications to broader landscape, ecosystem, and resource responses in the Arctic.

Arctic societies, like Arctic environments, exhibit variability and rapid change. Social and environmental changes are sometimes interconnected, but Arctic societies also are buffeted by socioeconomic forces which can create problems or drive changes that eclipse those with environmental roots. Social indicators research offers a general approach for describing change and understanding causality through the use of numerical indices of population, health, education, and other key dimensions that can be compared across places and times. Here we illustrate such work with new analyses of demographic indicators, particularly involving migration, for contemporary communities of three predominantly Inuit Arctic regions: northern Alaska, Greenland, and Nunavut. Many places exhibit persistent outmigration, affecting population growth. Net migration and growth rates are not significantly different, however, comparing northern Alaska communities that are or are not threatened by climate-linked erosion. Stepping back to compare the three regions highlights their contrasting birth rates (high in northern Alaska and Nunavut) and outmigration (high in Alaska and Greenland)—yielding divergent population trajectories of gradual decline in Greenland, erratic but slow growth in Alaska, and rapid growth in Nunavut. Evidence for one consequential pattern observed in northern Alaska and Greenland, disproportionate outmigration by locally-born women, appears weak or absent in Nunavut.

Eroding permafrost coasts are likely indicators and integrators of changes in the Arctic System as they are susceptible to the combined effects of declining sea ice extent, increases in open water duration, more frequent and impactful storms, sea-level rise, and warming permafrost. However, few observation sites in the Arctic have yet to link decadal-scale erosion rates with changing environmental conditions due to temporal data gaps. This study increases the temporal fidelity of coastal permafrost bluff observations using near-annual high spatial resolution (<1 m) satellite imagery acquired between 2008–2017 for a 9 km segment of coastline at Drew Point, Beaufort Sea coast, Alaska. Our results show that mean annual erosion for the 2007–2016 decade was 17.2 m yr⁻¹, which is 2.5 times faster than historic rates, indicating that bluff erosion at this site is likely responding to changes in the Arctic System. In spite of a sustained increase in decadal-scale mean annual erosion rates, mean open water season erosion varied from 6.7 m yr⁻¹ in 2010 to more than 22.0 m yr⁻¹ in 2007, 2012, and 2016. This variability provided a range of coastal responses through which we explored the different roles of potential environmental drivers. The lack of significant correlations between mean open water season erosion and the environmental variables compiled in this study indicates that we may not be adequately capturing the environmental forcing factors, that the system is conditioned by long-term transient effects or extreme weather events rather than annual variability, or that other not yet considered factors may be responsible for the increased erosion occurring at Drew Point. Our results highlight an increase in erosion at Drew Point in the 21st century as well as the complexities associated with unraveling the factors responsible for changing coastal permafrost bluffs in the Arctic.

Large-scale changes in Arctic sea ice thickness, volume and multiyear sea ice (MYI) coverage with available measurements from submarine sonars, satellite altimeters (ICESat and CryoSat-2), and satellite scatterometers are summarized. The submarine record spans the period between 1958 and 2000, the satellite altimeter records between 2003 and 2018, and the scatterometer records between 1999 and 2017. Regional changes in ice thickness (since 1958) and within the data release area of the Arctic Ocean, previously reported by Kwok and Rothrock (2009 Geophys. Res. Lett. 36 L15501), have been updated to include the 8 years of CryoSat-2 (CS-2) retrievals. Between the pre-1990 submarine period (1958–1976) and the CS-2 period (2011–2018) the average thickness near the end of the melt season, in six regions, decreased by 2.0 m or some 66% over six decades. Within the data release area (∼38% of the Arctic Ocean) of submarine ice draft, the thinning of ∼1.75 m in winter since 1980 (maximum thickness of 3.64 m in the regression analysis) has not changed significantly; the mean thickness over the CS-2 period is ∼2 m. The 15 year satellite record depicts losses in sea ice volume at 2870 km³/decade and 5130 km³/decade in winter (February–March) and fall (October–November), respectively: more moderate trends compared to the sharp decreases over the ICESat period, where the losses were weighted by record-setting melt in 2007. Over the scatterometer record (1999–2017), the Arctic has lost more than 2 × 10⁶ km² of MYI—a decrease of more than 50%; MYI now covers less than one-third of the Arctic Ocean. Independent MYI coverage and volume records co-vary in time, the MYI area anomalies explain ∼85% of the variance in the anomalies in Arctic sea ice volume. If losses of MYI continue, Arctic thickness/volume will be controlled by seasonal ice, suggesting that the thickness/volume trends will be more moderate (as seen here) but more sensitive to climate forcing.

Extreme winter events that damage vegetation are considered an important climatic cause of arctic browning—a reversal of the greening trend of the region—and possibly reduce the carbon uptake of northern ecosystems. Confirmation of a reduction in CO₂ uptake due to winter damage, however, remains elusive due to a lack of flux measurements from affected ecosystems. In this study, we report eddy covariance fluxes of CO₂ from a peatland in northern Norway and show that vegetation CO₂ uptake was delayed and reduced in the summer of 2014 following an extreme winter event earlier that year. Strong frost in the absence of a protective snow cover—its combined intensity unprecedented in the local climate record—caused severe dieback of the dwarf shrub species Calluna vulgaris and Empetrum nigrum. Similar vegetation damage was reported at the time along ~1000 km of coastal Norway, showing the widespread impact of this event. Our results indicate that gross primary production (GPP) exhibited a delayed response to temperature following snowmelt. From snowmelt up to the peak of summer, this reduced carbon uptake by 14 (0–24) g C m⁻² (~12% of GPP in that period)—similar to the effect of interannual variations in summer weather. Concurrently, remotely-sensed NDVI dropped to the lowest level in more than a decade. However, bulk photosynthesis was eventually stimulated by the warm and sunny summer, raising total GPP. Species other than the vulnerable shrubs were probably resilient to the extreme winter event. The warm summer also increased ecosystem respiration, which limited net carbon uptake. This study shows that damage from a single extreme winter event can have an ecosystem-wide impact on CO₂ uptake, and highlights the importance of including winter-induced shrub damage in terrestrial ecosystem models to accurately predict trends in vegetation productivity and carbon sequestration in the Arctic and sub-Arctic.