Spring Snow Cover Extent Research Articles

Increased impact of the Tibetan Plateau Spring Snow Cover to the Meiyu Rainfall over the Yangtze River Valley after 1990s

AbstractThis study investigated the increased impact of the spring (March-April-May) snow cover extent (SCE) over the western Tibetan Plateau (TP) (SSTP) to the Meiyu rainfall (June-July, JJ) over the Yangtze River valley (YRV) (MRYRV) after 1990s. The correlation between the MRYRV and SSTP is significantly increased from 1970-1992 (P1) to 1993-2015 (P2). In P1, the MRYRV-related SSTP anomalies locate southwest TP which cause a perturbation near the SWJ core and favor an eastward propagation in the form of a wave train. The wave train results in a southward shift of the SWJ over the ocean south of Japan in JJ and exerts a limited effect on the MRYRV. Differently, in P2, the MRYRV-related anomalous SSTP causes an anomalous cooling temperature and upper-level cyclonic system centered over the northwestern TP. The cyclonic system develops and extends eastward to the downstream region with time and reaches coastal East Asia in JJ. The anomalous westerly winds along its south flank cause an enhanced subtropical westerly jet (SWJ) which is accompanied by an anomalous lower-level air convergence and ascent motion near the YRV region, favoring enhanced MRYRV. In addition, the forecast experiments performed with empirical regression models illustrate that the prediction skill of the MRYRV variation is clearly increased in P2 with the additional forecast factor of the SSTP.

Changes in the Relationship Between the Variation in Spring Eurasian Snow and the Surface Temperature Over the Northern Hemisphere Around the Late 1980s

AbstractThe spring snow cover extent (SCE) over central‐eastern Eurasia (SCE_CEE) shows an obvious decrease around 1988. The period 1979–2019 is thus divided into two subperiods spanning 1967–1988 (P1) and 1989–2019 (P2) to investigate the interdecadal changes in the characteristics of the SCE_CEE and its relationship with surface air temperature (SAT) in the Northern Hemisphere. Positive SCE_CEE anomalies are related to a cooler Eurasia–warmer North America (CEWN) spring SAT pattern, which obviously intensifies from P1 to P2. The possible mechanisms responsible for the interdecadal changes in this snow–SAT relationship are investigated. A local energy budget analysis shows that the impact of the SCE_CEE on the overlying atmosphere is weak and the atmospheric circulation anomalies associated with the SCE_CEE variation are confined mainly to eastern Eurasia and the North Pacific in P1. In contrast, positive SCE_CEE anomalies have a significant cooling effect on the atmospheric column through anomalous heat fluxes and wind‐related cold advection in P2. In P2, more SCE_CEE is associated with an anomalous cyclone in the upper atmosphere, which imposes a significant vorticity perturbation near the East Asian westerly jet core and induces eastward propagation of perturbations. The sea surface temperature anomalies in the North Pacific and the North Atlantic as well as the mean flow over the Eurasian continent help to maintain the zonal propagation of this perturbation, thus forming a hemispheric atmospheric wave pattern that contributes to the enhanced snow–CEWN SAT relationship in P2.

Quantifying the Anthropogenic Greenhouse Gas Contribution to the Observed Spring Snow-Cover Decline Using the CMIP6 Multimodel Ensemble

AbstractThis study conducts a detection and attribution analysis of the observed changes in boreal spring snow-cover extent (SCE) for an extended period of 1925–2019 for early spring (March and April) and 1970–2019 for late spring (May and June) using updated observations and multimodel simulations from phase 6 of the Coupled Model Intercomparison Project (CMIP6). The observed and simulated SCE changes over the Northern Hemisphere (NH), Eurasia, and North America are compared using an optimal fingerprinting technique. Detection results indicate that anthropogenic influences are robustly detected in the observed SCE decrease over NH and the continental regions, in separation from natural forcing influences. In contrast to previous studies, anthropogenic response in the early spring SCE shows a consistent magnitude with observations, due to an extension of the time period to 2019. It is demonstrated for the first time that the greenhouse gas (GHG) influence is robustly detected in separation from anthropogenic aerosol and natural forcing influences, and that most of the observed spring SCE decrease is attributable to GHG influences. The observed SCE decline is also found to be closely associated with the surface warming over the corresponding extratropical lands. Our first quantification of GHG contribution to the observed SCE changes has important implications for reliable future projections of the SCE changes and its hydrological and ecological impacts.

Observed earlier start of the growing season from middle to high latitudes across the Northern Hemisphere snow-covered landmass for the period 2001–2014

Vegetation phenology in spring has received much attention for its importance to terrestrial ecosystem carbon exchange and climate–biosphere interactions studies. Through control on surface water and heat balance, snow cover largely impacts on spring vegetation phenology. However, under the background of global warming and rapid reduction of spring snow cover extent across the Northern Hemisphere (NH), the responses of spring vegetation phenology have not been well documented. Using two satellite-derived land cover dynamic datasets and 420 in situ vegetation phenology observations from five filed datasets, this study evaluated the accuracy of satellite-derived vegetation phenology datasets and explored the changes of start of the growing season (SOS) across the NH snow-covered landmass for the period 2001–2014. Compared with MEaSUREs VIPPHEN, the MODIS SOS maps displayed higher accuracy in capture the real SOS climatology by validating with in situ observations (R2 = 0.67, bias = −3.99 d). Moreover, evidences from MODIS SOS maps pointed out that the SOS advanced by approximately 2.36 d in NH middle to high latitudes (43.5°N–70.0°N), but delayed by about 0.53 d in lower latitudes (33.0°N–43.5°N) from 2001 to 2014. The contrast SOS anomalies across the NH snow-covered landmass were further proved by changes in spring NDVI derived from GIMMS in the corresponding period. In addition, the observed changes in SOS were consistent with the spatiotemporal pattern of spring snow end date found in previous studies, indicating vegetation phenology changes should be taken into account in estimating the impacts of snow in climate–biosphere interactions studies.

Key indicators of Arctic climate change: 1971–2017

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.

Arctic Climate Changes Based on Historical Simulations (1900‒2013) with the CAMS-CSM

The Chinese Academy of Meteorological Sciences Climate System Model (CAMS-CSM) is a newly developed global climate model that will participate in the Coupled Model Intercomparison Project phase 6. Based on historical simulations (1900‒2013), we evaluate the model performance in simulating the observed characteristics of the Arctic climate system, which includes air temperature, precipitation, the Arctic Oscillation (AO), ocean temperature/salinity, the Atlantic meridional overturning circulation (AMOC), snow cover, and sea ice. The model‒data comparisons indicate that the CAMS-CSM reproduces spatial patterns of climatological mean air temperature over the Arctic (60°‒90°N) and a rapid warming trend from 1979 to 2013. However, the warming trend is overestimated south of the Arctic Circle, implying a subdued Arctic amplification. The distribution of climatological precipitation in the Arctic is broadly captured in the model, whereas it shows limited skills in depicting the overall increasing trend. The AO can be reproduced by the CAMS-CSM in terms of reasonable patterns and variability. Regarding the ocean simulation, the model underestimates the AMOC and zonally averaged ocean temperatures and salinity above a depth of 500 m, and it fails to reproduce the observed increasing trend in the upper ocean heat content in the Arctic. The large-scale distribution of the snow cover extent (SCE) in the Northern Hemisphere and the overall decreasing trend in the spring SCE are captured by the CAMS-CSM, while the biased magnitudes exist. Due to the underestimation of the AMOC and the poor quantification of air–sea interaction, the CAMS-CSM overestimates regional sea ice and underestimates the observed decreasing trend in Arctic sea–ice area in September. Overall, the CAMS-CSM reproduces a climatological distribution of the Arctic climate system and general trends from 1979 to 2013 compared with the observations, but it shows limited skills in modeling local trends and interannual variability.

Quantifying the Uncertainty in Historical and Future Simulations of Northern Hemisphere Spring Snow Cover

AbstractProjections of twenty-first-century Northern Hemisphere (NH) spring snow cover extent (SCE) from two climate model ensembles are analyzed to characterize their uncertainty. Phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble exhibits variability resulting from both model differences and internal climate variability, whereas spread generated from a Canadian Earth System Model–Large Ensemble (CanESM-LE) experiment is solely a result of internal variability. The analysis shows that simulated 1981–2010 spring SCE trends are slightly weaker than observed (using an ensemble of snow products). Spring SCE is projected to decrease by −3.7% ± 1.1% decade−1 within the CMIP5 ensemble over the twenty-first century. SCE loss is projected to accelerate for all spring months over the twenty-first century, with the exception of June (because most snow in this month has melted by the latter half of the twenty-first century). For 30-yr spring SCE trends over the twenty-first century, internal variability estimated from CanESM-LE is substantial, but smaller than intermodel spread from CMIP5. Additionally, internal variability in NH extratropical land warming trends can affect SCE trends in the near future (R2 = 0.45), while variability in winter precipitation can also have a significant (but lesser) impact on SCE trends. On the other hand, a majority of the intermodel spread is driven by differences in simulated warming (dominant in March–May) and snow cover available for melt (dominant in June). The strong temperature–SCE linkage suggests that model uncertainty in projections of SCE could be potentially reduced through improved simulation of spring season warming over land.

Eurasian snow cover variability in relation to warming trend and Arctic Oscillation

Two distinct modes of snow cover variability over Eurasia are investigated using cyclostationary empirical orthogonal function (CSEOF) analysis. The first mode of Eurasian snow cover extent (SCE) represents a seasonally asymmetric trend between spring and fall. The spring SCE shows a decreasing trend, while the fall SCE particularly in October exhibits a clear increasing trend. This seasonally asymmetric trend of SCE is closely linked to Arctic sea ice decline accompanied by warming in the northern Eurasia. The decreased SCE during spring is primarily attributed to the warm air temperature anomalies, while the increased SCE in October results from the loss of sea ice and the ensuing moisture transport to the atmosphere, which is realized as increased snow in October. The second mode of Eurasian SCE, on the other hand, is closely related to Arctic Oscillation (AO), which is a dominant mode of Northern Hemisphere atmospheric variability. The snow cover variability over Europe during winter is largely affected by AO variability, rather than the warming signal represented by the first CSEOF mode. Detailed descriptions of the two distinct modes of Eurasian SCE and their interactions with oceanic and atmospheric variables are presented along with possible implications for future climate.

Attribution of the spring snow cover extent decline in the Northern Hemisphere, Eurasia and North America to anthropogenic influence

While it is generally accepted that the observed reduction of the Northern Hemisphere spring snow cover extent (SCE) is linked to warming of the climate system caused by human induced greenhouse gas emissions, it has been difficult to robustly quantify the anthropogenic contribution to the observed change. This study addresses the challenge by undertaking a formal detection and attribution analysis of SCE changes based on several observational datasets with different structural characteristics, in order to account for the substantial observational uncertainty. The datasets considered include a blended in situ-satellite dataset extending from 1923 to 2012 (Brown), the National Oceanic and Atmospheric Administration (NOAA) snow chart Climate Data Record for 1968–2012, the Global Land Data Assimilation System version 2.0 (GLDAS-2 Noah) reanalysis for 1951–2010, and the NOAA 20th-century reanalysis, version 2 (20CR2) covering 1948–2012. We analyse observed early spring (March-April) and late spring (May-June) NH SCE extent changes in these datasets using climate simulations of the responses to anthropogenic and natural forcings combined (ALL) and to natural forcings alone (NAT) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The ALL-forcing response is detected in all of the observed records, indicating that observed changes are inconsistent with internal variability. The analysis also shows that the ALL-forcing simulations substantially underestimate the observed changes as recorded in the Brown and NOAA datasets, but that they are more consistent with changes seen in the GLDAS and 20CR2 reanalyses. A two-signal analysis of the GLDAS data is able to detect the influence of the anthropogenic component of the observed SCE changes separately from the effect of natural forcing. Despite dataset and modelling uncertainty, these results, together with the understanding of the causes of observed warming over the past century, provide substantial evidence of a human contribution to the observed decline in Northern Hemisphere spring snow cover extent.

Interdecadal change of Eurasian snow, surface temperature, and atmospheric circulation in the late 1980s

AbstractBoreal winter and spring snow cover extent and snow water equivalent over Eurasia experienced an obvious decrease in late 1980s. A concurrent surface warming is observed over the North Atlantic and Eurasia. The present study documents the relationship among the interdecadal changes in snow, surface air temperature, and wind and the plausible reason for this change. Analysis shows that the snow decrease contributes to the temperature increase only in some limited regions and that the surface warming is largely related to the change in atmospheric circulation. The effect of atmospheric circulation change on surface warming is manifested in wind‐induced heat advection and cloud‐radiation changes. The wind changes over the North Atlantic and Eurasia feature a wave train with an anomalous anticyclone over Europe and anomalous southerly winds over midlatitude east Europe and Asia. The anomalous southerly winds contribute to surface warming through advection of warmer air from lower latitudes. The anomalous anticyclone contributes to surface warming over Europe through anomalous warm advection by bringing warmer air from ocean and enhanced downward shortwave radiation by suppressing upward motion. The wave pattern appears to be connected to equatorial Atlantic warming. Warmer sea surface temperature (SST) in the equatorial Atlantic Ocean enhances convection and induces a lower level anomalous cyclone to the northwest of the SST warming. The role of equatorial North Atlantic SST increase in the formation of the wave train over Eurasia is supported by numerical experiments with an atmospheric general circulation model.

Spring snow cover deficit controlled by intraseasonal variability of the surface energy fluxes

Spring snow cover extent (SCE) in the Northern Hemisphere has decreased in the last four decades but with significant interannual variability. Investigations of the mechanisms that control SCE variations were almost exclusively focused on the year-to-year variability of forcing variables and SCE integrated over a certain period of the year (e.g. season). Here, we use state-of-the-art climate reanalysis dataset to analyze the contribution of different surface energy fluxes to the inception and development of below-normal spring SCE from an intraseasonal perspective. During years identified with lower-than-average SCE by the end of spring, higher-than-average net longwave radiation and sensible heat that is greater than the decrease of net shortwave radiation in the early spring snowmelt season induces the initial SCE deficit. This can be mainly explained by the finding that the increase of downwelling longwave radiation because of increased water vapor significantly exceeds the attenuation of downwelling short-wave radiation due to increased cloudiness. When a SCE deficit has been incepted in early spring, net shortwave radiation in late spring gradually becomes higher than average through snow albedo feedback, which further accelerates snowmelt. This suggests that short-wave radiation is not responsible for the initiation of negative SCE anomaly by the end of spring but acts as an amplifying feedback once the snow melt is started.

Recent changes in pan‐Arctic melt onset from satellite passive microwave measurements

A new satellite passive microwave (PMW) melt onset retrieval algorithm based on temporal variations in the differences of the brightness temperature between 19 and 37 GHz is shown to be as effective as radar (e.g., QuikScat) measurements. The PMW technique shows improved melt estimates that are more closely linked to observed snow‐off dates than previous studies. An integrated pan‐Arctic (north of 50°N) melt onset date (MOD) dataset is produced by combining estimates on land and sea ice for the entire satellite PMW record. During the 1979–2011 period, significant trends of 2~3 days (decade)−1 to earlier MOD are mainly concentrated over the Eurasian land sector of the Arctic, consistent with changes in spring snow cover extent observed with visible satellite data. The variability and change in melt onset are largely driven by spring surface air temperature, with insignificant influence from low‐frequency modes of atmospheric circulation.

A retrospective analysis of pan Arctic permafrost using the JULES land surface model

There is mounting evidence that permafrost degradation has occurred over the past century. However, the amount of permafrost lost is uncertain because permafrost is not readily observable over long time periods and large scales. This paper uses JULES, the land surface component of the Hadley Centre global climate model, driven by different realisations of twentieth century meteorology to estimate the pan-arctic changes in near-surface permafrost. Model simulations of permafrost are strongly dependent on the amount of snow both in the driving meteorology and the way it is treated once it reaches the ground. The multi-layer snow scheme recently adopted by JULES significantly improves its estimates of soil temperatures and permafrost extent. Therefore JULES, despite still having a small cold bias in soil temperatures, can now simulate a near-surface permafrost extent which is comparable to that observed. Changes in snow cover have been shown to contribute to changes in permafrost and JULES simulates a significant decrease in late twentieth century pan-Arctic spring snow cover extent. In addition, large-scale modelled changes in the active layer are comparable with those observed over northern Russia. Simulations over the period 1967–2000 show a significant loss of near-surface permafrost—between 0.55 and 0.81 million km2 per decade with this spread caused by differences in the driving meteorology. These runs also show that, for the grid cells where the active layer has increased significantly, the mean increase is ~10 cm per decade. The permafrost degradation discussed here is mainly caused by an increase in the active layer thickness driven by changes in the large scale atmospheric forcing. However, other processes such as thermokarst development and river and coastal erosion may also occur enhancing permafrost loss.

An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models

Abstract. The 20th century seasonal Northern Hemisphere (NH) land snow cover as simulated by available CMIP5 model output is compared to observations. On average, the models reproduce the observed snow cover extent very well, but the significant trend towards a reduced spring snow cover extent over the 1979–2005 period is underestimated (observed: (−3.4 ± 1.1)% per decade; simulated: (−1.0 ± 0.3)% per decade). We show that this is linked to the simulated Northern Hemisphere extratropical spring land warming trend over the same period, which is also underestimated, although the models, on average, correctly capture the observed global warming trend. There is a good linear correlation between the extent of hemispheric seasonal spring snow cover and boreal large-scale spring surface air temperature in the models, supported by available observations. This relationship also persists in the future and is independent of the particular anthropogenic climate forcing scenario. Similarly, the simulated linear relationship between the hemispheric seasonal spring snow cover extent and global mean annual mean surface air temperature is stable in time. However, the slope of this relationship is underestimated at present (observed: (−11.8 ± 2.7)% °C−1; simulated: (−5.1 ± 3.0)% °C−1) because the trend towards lower snow cover extent is underestimated, while the recent global warming trend is correctly represented.

Spring snow cover loss exceeds model predictions

Spring snow cover extent in the Northern Hemisphere has declined to record lows in recent years, Derksen and Brown report. They analyzed spring (April–June) snow cover extent from the National Oceanic and Atmospheric Administration snow chart climate data record. They found that recent years show statistically significant reductions in snow cover compared to the historic record. In Eurasia, new records for the lowest spring snow cover extent have been set every year since 2008, and in North America, record lows have been set in 3 of the past 5 years.

Spring snow cover extent reductions in the 2008–2012 period exceeding climate model projections

Analysis of Northern Hemisphere spring terrestrial snow cover extent (SCE) from the NOAA snow chart Climate Data Record (CDR) for the April to June period (when snow cover is mainly located over the Arctic) has revealed statistically significant reductions in May and June SCE. Successive records for the lowest June SCE have been set each year for Eurasia since 2008, and in 3 of the past 5 years for North America. The rate of loss of June snow cover extent between 1979 and 2011 (−17.8% decade−1) is greater than the loss of September sea ice extent (−10.6% decade−1) over the same period. Analysis of Coupled Model Intercomparison Project Phase 5 (CMIP5) model output shows the marked reductions in June SCE observed since 2005 fall below the zone of model consensus defined by +/−1 standard deviation from the multi‐model ensemble mean.

Contribution of late spring Eurasian snow cover extent to Canadian winter temperatures

AbstractThis study examines intercontinental linkages between late spring and early summer Eurasian snow cover extent (SCEss) anomalies and the following winter temperature anomalies over Canada for the 1972–2006 period. The structure of the second interannual mode of Canadian winter temperatures variability captures the SCEss related modulation. The North Atlantic winter atmospheric circulation changes associated with the SCEss, resembling the negative phase of the North Atlantic Oscillation (NAO), suggest a possible pathway for the SCEss influences on the Canadian winter temperatures.Regression and composite analyses show that the SCEss relate robustly to the Canadian winter climate. Larger‐than‐normal SCEss is associated with below normal winter temperatures in south‐central Canada and above normal temperatures over northeastern Canada. Predictive skill of Canadian winter temperatures based on a cross‐validated regression model shows that the SCEss offers the predictive potential over regions of Canada where El Niño‐Southern Oscillation (ENSO) related skill is weak or nonexistent. Analysis of winter extreme minimum temperatures, by a non‐stationary generalized extreme value model, with the SCEss as a covariate, exhibits statistically significant changes over Canada resembling a pattern similar to that of winter mean temperatures. Wavelet analysis shows significant coherence between the SCEss and the second mode of winter temperature variability in the 8–12‐year band. Copyright © 2011 Crown in the right of Canada. Published by John Wiley & Sons, Ltd

Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty

Abstract. An update is provided of Northern Hemisphere (NH) spring (March, April) snow cover extent (SCE) over the 1922–2010 period incorporating the new climate data record (CDR) version of the NOAA weekly SCE dataset, with annual 95% confidence intervals estimated from regression analysis and intercomparison of multiple datasets. The uncertainty analysis indicates a 95% confidence interval in NH spring SCE of ±5–10% over the pre-satellite period and ±3–5% over the satellite era. The multi-dataset analysis shows larger uncertainties monitoring spring SCE over Eurasia (EUR) than North America (NA) due to the more complex regional character of the snow cover variability and larger between-dataset variability over northern Europe and north-central Russia. Trend analysis of the updated SCE series provides evidence that NH spring snow cover extent has undergone significant reductions over the past ~90 yr and that the rate of decrease has accelerated over the past 40 yr. The rate of decrease in March and April NH SCE over the 1970–2010 period is ~0.8 million km2 per decade corresponding to a 7% and 11% decrease in NH March and April SCE respectively from pre-1970 values. In March, most of the change is being driven by Eurasia (NA trends are not significant) but both continents exhibit significant SCE reductions in April. The observed trends in SCE are being mainly driven by warmer air temperatures, with NH mid-latitude air temperatures explaining ~50% of the variance in NH spring snow cover over the 89-yr period analyzed. However, there is also evidence that changes in atmospheric circulation around 1980 involving the North Atlantic Oscillation and Scandinavian pattern have contributed to reductions in March SCE over Eurasia.

A multi‐data set analysis of variability and change in Arctic spring snow cover extent, 1967–2008

A new multi‐data set estimate of Arctic monthly snow cover extent (SCE) in the May–June melt period is derived from 10 data sources covering different time periods from 1967 to 2008. The data sources include visible and microwave satellite observations, objective analyses of surface snow depth observations, reconstructed snow cover from daily temperature and precipitation, and proxy information derived from thaw dates. The new estimates show a more linear reduction in spring SCE than previously characterized by the National Oceanic and Atmospheric Administration weekly snow chart data set, with air temperature explaining 49% of the variability in Arctic SCE in May and 56% of the variability in June. The Arctic Oscillation is only significantly linked to Arctic SCE in May where it explains 25% of the variance in Eurasian sector SCE. Trend analysis of the multi‐data set series (including an annually varying estimate of error) reveals that May and June SCE have decreased 14% and 46%, respectively, over the pan‐Arctic region over the 1967–2008 period in response to earlier snow melt. These results are confirmed with in situ data from Canada, Alaska and Russia that show significant reductions in spring snow cover duration over the last 30 years. The spring snow cover temperature sensitivity over the pan‐Arctic region during this period is estimated to be in the range −0.8 to −1.00 × 106 km2 °C−1. The observed reductions in June SCE over the 1979–2008 period are found to be of the same magnitude as reductions in June sea ice extent with both series significantly correlated to air temperature changes over the Arctic region and to each other. This result underscores the close relationship between the cryosphere and surface air temperatures over the Arctic region in June when albedo feedback potential is at a maximum.

Reconstructing Century-long Snow Regimes Using Estimates of High Arctic Salix arctica Radial Growth

Using a combination of microscopic examination and electronic scanning and printing, we analyzed the impacts of early spring snow cover extent and temperature during the growing season on the annual radial growth in arctic willow (Salix arctica Pallas) in the Zackenberg valley, High Arctic Northeast Greenland. So far, only little dendroclimatological research has been conducted on Salix arctica, and the species constitutes a yet-untapped resource for climate reconstruction in the Arctic. We obtained reliable annual radial growth measurements from a total of 43 Salix arctica stem samples and analyzed these in a mixed model to determine the limiting climatic factors. We found that early spring snow cover extent impacted the annual growth significantly, whereas variable temperature regimes seemed unimportant. Following the building of a site chronology for the Zackenberg valley, the early spring snow cover extent during the last century was reconstructed.