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Melt onset over Arctic sea ice controlled by atmospheric moisture transport: Melt controlled by atmospheric moisture



The timing of melt onset affects the surface energy uptake throughout the melt season. Yet the processes triggering melt and causing its large interannual variability are not well understood. Here, we show that melt onset over Arctic sea ice is initiated by positive anomalies of water vapor, clouds, and air temperatures that increase the downwelling longwave radiation (LWD) to the surface. The earlier melt onset occurs, the stronger are these anomalies. Downwelling shortwave radiation (SWD) is smaller than usual at melt onset, indicating that melt is not triggered by SWD. When melt occurs early, an anomalously opaque atmosphere with positive LWD anomalies preconditions the surface for weeks preceding melt. In contrast, when melt begins late, clearer than usual conditions are evident prior to melt. Hence, atmospheric processes are imperative for melt onset. It is also found that spring LWD increased during recent decades, consistent with trends towards an earlier melt onset.
Melt onset over Arctic sea ice controlled
by atmospheric moisture transport
Jonas Mortin
, Gunilla Svensson
, Rune G. Graversen
, Marie-Luise Kapsch
, Julienne C. Stroeve
and Linette N. Boisvert
Department of Meteorology and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden,
Department of Physics and Technology, UiT Arctic University of Norway, Tromsø, Norway,
Max-Planck-Institute for
Meteorology, Hamburg, Germany,
National Snow and Ice Data Center, Cooperative Institute for Research in Environmental
Sciences, University of Colorado Boulder, Boulder, Colorado, USA,
Centre for Polar Observation and Modelling, Earth
Sciences/Department of Space and Climate Physics (MSSL), University College London, London, UK,
Earth System Science
Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
Abstract The timing of melt onset affects the surface energy uptake throughout the melt season. Yet the
processes triggering melt and causing its large interannual variability are not well understood. Here we
show that melt onset over Arctic sea ice is initiated by positive anomalies of water vapor, clouds, and air
temperatures that increase the downwelling longwave radiation (LWD) to the surface. The earlier melt onset
occurs; the stronger are these anomalies. Downwelling shortwave radiation (SWD) is smaller than usual
at melt onset, indicating that melt is not triggered by SWD. When melt occurs early, an anomalously
opaque atmosphere with positive LWD anomalies preconditions the surface for weeks preceding melt. In
contrast, when melt begins late, clearer than usual conditions are evident prior to melt. Hence, atmospheric
processes are imperative for melt onset. It is also found that spring LWD increased during recent decades,
consistent with trends toward an earlier melt onset.
1. Introduction
The seasonal transition from winter to summer plays an important role for the Arctic climate. The timing of
sea ice melt onset affects the energy absorbed by the surface throughout the summer melt season, because
after melt begins, the albedo continues to decrease until either the sea ice is completely melted and
disappears or freeze-up has begun [Perovich and Polashenski, 2012]. Over multiyear ice, for any day melt
begins earlier, and additional energy sufcient to melt 3 cm of sea ice during the melt season is absorbed
[Perovich et al., 2007]. Since melt onset has been occurring successively earlier over the last few decades,
the energy uptake over the Arctic Ocean in summer has increased by an amount large enough to melt about
1 m of ice over a recent 5 year period [Stroeve et al., 2014]. This additional energy warms the ocean during
summer, leading to a substantially later fall freeze-up [Stroeve et al., 2014] and a warmer lower atmosphere
in the fall [Kurtz et al., 2011; Serreze et al., 2008; Screen and Simmons, 2010]. Hereby the atmospheric circula-
tion, both within and outside of the Arctic region, may be altered [Cohen et al., 2014]. Furthermore, the timing
of the melt onset may have predictable skills regarding the following September sea ice minimum [Kapsch
et al., 2014; Schröder et al., 2014].
Although the timing of melt onset is of large importance for the climate in the Arctic and beyond, the pro-
cesses controlling the melt onset and its large interannual variability are understood only to a limited extent.
The Arctic-wide melt onset varies by up to 3 weeks between years [Mortin et al., 2014; Stroeve et al., 2014],
corresponding to at least 0.5 m of ice melt over the melt season [Perovich et al., 2007], but locally and region-
ally, the interannual variability can be much larger [Mortin et al., 2014; Stroeve et al., 2014]. At a specic site
and a certain year, the melt onset was found to be triggered by moist, warm air masses associated with
synoptic-scale weather systems that augmented the atmospheric energy uxes to the surface [Persson,
2012]. Part of the melt variability in the Arctic is explained by anomalies in downwelling longwave radiation
at the surface (LWD) [Maksimovich and Vihma, 2012; Barber and Thomas, 1998], and studies based on a few
years have shown that the Arctic melt onset is weakly linked to two atmospheric circulation indicators, the
Arctic oscillation [Belchansky et al., 2004] and the 500 hPa height [Drobot and Anderson, 2001]. However,
no studies have succeeded in drawing general conclusions concerning the processes important for melt
onset. So the question remains: what processes control the timing of melt onset and its large variability
Geophysical Research Letters
Key Points:
Humid air masses trigger Arctic sea ice
melt by means of longwave radiation
Melt preconditioning of the sea ice
surface prior to melt onset
Trends toward earlier melt onset
linked to positive trends of longwave
radiation in spring
Supporting Information:
Supporting Information S1
Correspondence to:
G. Svensson,
Mortin, J., G. Svensson, R. G. Graversen,
M.-L. Kapsch, J. C. Stroeve, and
L. N. Boisvert (2016), Melt onset over
Arctic sea ice controlled by atmospheric
moisture transport, Geophys. Res. Lett.,
43, 66366642, doi:10.1002/
Received 25 APR 2016
Accepted 1 JUN 2016
Accepted article online 3 JUN 2016
Published online 28 JUN 2016
©2016. American Geophysical Union.
All Rights Reserved.
and trends on an Arctic-wide scale and over long time periods? To approach this question, we analyze
35 years of ERA-Interim data [Dee et al., 2011] and independent melt onset data [Markus et al., 2009], in addi-
tion to 10 years of water vapor elds retrieved from the satellite-borne Atmospheric Infrared Sounder (AIRS)
[Chahine et al., 2006].
2. Data and Methods
Melt onset dates are retrieved from the microwave radiometers Scanning Multichannel Microwave Radiometer,
Special Sensor Microwave/Imager, and Special Sensor Microwave Imager and Sounder [Markus et al., 2009;
Stroeve et al., 2014]. One advantage of using melt retrievals from microwave radiometers is that microwave
emissions are directly related to the melt signature of ice and snow [Markus et al., 2009]. As ice and snow begin
to melt, water forms on the surface and within the snowpack, altering the surfaces dielectric properties and
thereby its emissivity; the emissivity increases signicantly as snow and ice become wetter. Therefore, melt
initiation can be measured by the increase in liquid water. More specically, the method of Markus et al.
[2009] detects m elt onset based on the temporal variability o f brightness tempe ratures at 19 GHz and 37 GHz
in different combinations, which are dominated by variations of the amount of liquid water at the surface
[Markus et al., 2009]. The data set contains two occurrences of melt onset: early melt onset (EMO), representing
the rst time melt isdetected, and continuous melt onset. Weuse the EMO parameter onlyhere referred to as
melt onsetbecause we found this to be more closely connected to the atmospheric processes initiating melt.
The melt data are utilized at a 25 km resolution for the period 19792013.
The atmospheric variables used in this study are from ERA-Interim reanalysis, provided by the European
Centre for Medium Range Forecasts (ECMWF) [Dee et al., 2011]. ERA-Interim has been evaluated extensively
for the Arctic region showing good performance for large-scale variables when compared with the limited
available observations [Jakobson et al., 2012; Lindsay et al., 2014; Serreze et al., 2012; Zygmuntowska et al.,
2012]. See Text S1 in the supporting information for a brief discussion on our condence in different
ERA-Interim elds in the context of this paper. The radiative uxes and cloud water are 24 h forecasts initiated
at 00 UTC; other elds are daily means of 6-hourly analyses. ERA-Interim data elds were obtained at a
0.75° × 0.75° resolution for the period 19792013.
The total integrated column water vapor burden from AIRS and the Advanced Microwave Sounding Unit
(AMSU-A) instruments on board the polar-orbiting Aqua platform [Chahine et al., 2006] are utilized to
gain further condence in the results and as an alternative to ERA-Interim (see Text S1). We employ level 3
(version 6) daily data provided at a spatial resolution of 1° × 1° for the period 20032013 [Susskind et al.,
2014]. This is considered a mature product [e.g., Devasthale et al., 2013].
ERA-Interim data are interpolated to a 1.5° ×6° latitude-longitude grid, and AIRS data are interpolated to a
1° × 4° grid. At 75°N, the interpolated ERA-Interim grid cells cover about 170 km, both meridionally and zon-
ally. We have checked that the analysis is insensitive to these resolutions, as long as the resolution is equal
to or higher than 3° by 12°. To analyze atmospheric data only for locations where melt occurs and to ensure
data consistency between surface and atmospheric variables, we include only grid cells with at least ve
detected melt onset dates (obtained from the 25 × 25 km EMO data) during at least 5 years. Further, we
exclude grid cells that contain more than 50% land. The anomaly analysis in Figure 1 is insensitive to these
thresholds. The analysis includes 579 grid cells in total (1.5° × 6°), spanning 46.5°N to 87.75°N (Figure 1a).
The anomalies of ERA-Interim and AIRS data are computed as deviations from the daily climatologies of
thefulltimeperiods,whicharedifferentforERA-Interim(19792013) and AIRS (20032013). Time series
of atmospheric anomalies relative to the melt dates are obtained and aggregated over each grid cell using
an area-weighted mean. The Arctic sea ice domain is then divided into 13 regions [Stroeve et al., 2014]
(Figure S1), and for each of these regions, ve melt-timing quintiles (20th percentiles) are computed and
used for a categorization of the melt dates (Figure 1b). The lowest quintile of the melt dates represents
an anomalous early melt onset, while the highest quintile an anomalous late melt onset. The results are
insensitive to the exact distribution and size of these regions. This division into regions is applied in order
to take into account the large spatiotemporal variability and the meridional gradient of melt onset [Markus
et al., 2009; Mortin et al., 2014]. Using the 13 regions is a trade-off between two extreme cases: using
quintiles of the full sea ice cover would bias the late melt and early melt categories toward northerly
and southerly located grid cells, respectively, while using quintiles of each grid cell would assume that all
Geophysical Research Letters 10.1002/2016GL069330
categories of melt timing are represented in each grid cell, which may not be the case. Note that the date of
melt onset for an early melt category can be later than the date for the subsequent category with a later melt
date and vice versa (Figure 1b). This is a result of the large spatiotemporal variability of melt. The ve categories
are based on melt dates forthe entire time period dened for each region individually. Averaging the values for
each category and year over the 13 regions can result in a temporal variability of the mean dates of the different
melt categories, primarily as melt onset dates are not normally distributed.
Statistical signicance is determined using a two-tailed Studentsttest on the aggregated time series for the
null hypothesis that the distribution mean is equal to zero (Figures 1 and S1). Daily trends, discussed in
section 5, are computed with an area-weighted least squares regression using all anomaly data of ERA-
Interim (19792013) for a given year day.
3. Atmospheric State Around Melt Onset
Persson [2012] found that increased downwelling longwave radiation, due to advection of moist and warm air
masses, triggered sea ice melt onset during one specic year and over a particular site. Here we use a similar
Figure 1. Study region, annual mean melt onset dates, and anomalies of atmospheric elds relative to the local melt onset date, presented as composites of the ve
melt onset categories introduced in section 2. (a) The grid and the study region. (b) Annual mean melt dates for each of the ve categories of melt data (see
Figures 1c and 1d for legend). Anomalies of atmospheric parameters from ERA-Interim (except in Figure 1e) for each melt category as a function of time lag relative
to the local melt onset for (c) surface downwelling longwave radiation, (d) total column water vapor, (e) total integrated column water vapor burden retrieved
from satellite (AIRS), (f) temperature at the 850 hPa pressure surface, (g) total column cloud water (liquid + ice), and (h) surface downwelling shortwave radiation.
Dots in Figures 1c1h indicate statistical signicance (p0.01). For completeness, Figure S1 shows additional parameters.
Geophysical Research Letters 10.1002/2016GL069330
approach but instead analyze atmospheric parameters around the time of the melt onset on a pan-Arctic-
wide area and long-term time scale. The Arctic melt onset dates are divided into ve categories based on
melt timing, from very early to very late melt (Figures 1 and S1). At melt onset, LWD at the surface exhibits
signicant positive anomalies, as a result of atmospheric water vapor, cloud water, and atmospheric tempera-
ture being larger than the long-term mean (Figures 1c1g). This pattern is clear for all melt categories, for
both ERA-Interim and AIRS, and positive anomalies are evident in a deep atmospheric column (Figure S2).
AIRS exhibits weaker anomalies than ERA-Interim, most likely because of the shorter and later data period
that constitutes the AIRS climatology (Figures 1d and 1e). The exception to the anomaly pattern is for the very
late melt category, which instead exhibits negative anomalies prior to melt onset and normal conditions
when melt begins (Figure 1). The increased cloudiness leads to a reduction of downwelling shortwave radia-
tion at the surface (SWD; Figures 1g, 1h, and S2). These ndings imply that the enhanced greenhouse effect
associated with more moisture and clouds in the atmosphere is crucial for the timing of the melt onset over
sea ice. Further, SWD in itself seems of minor importance for triggering melt. After melt is initiated, however,
the importance of the SWD increases as the albedo of the sea ice surface decreases and more solar radiation
is absorbed by the surface. Over sea ice, the albedo typically decreases from approximately 0.8 to about 0.5
after melt onset over sea ice [e.g., Persson, 2012; Karlsson and Svensson, 2013]. The atmospheric water vapor
associated with the moisture and cloud anomalies is provided by atmospheric transport from remote areas
rather than by local sources because the moisture and heat uxes from the surface to the atmosphere are
small over the sea ice covered ocean, especially prior to a large-scale melt event [Boisvert et al., 2013;
Persson, 2012]. In addition, the moisture-transport convergence increases before and at melt onset (Figure
S1). Hence, surface melt is triggered by increased LWD from transport of moisture into the Arctic.
The earlier the melt onset occurs, the stronger the atmospheric anomalies are (Figures 1, S1, and S2).
Moreover, preconditioning of the surface is important for an early melt onset. For a very early melt onset (ear-
liest 20%) LWD, water vapor, cloud water, and the atmospheric temperature all show signicantly positive
anomalies during a whole month preceding melt (Figures 1c, 1d, 1f, and 1g). During this month, positive
anomalies are most likely a result of several smaller atmospheric events that individually are of insufcient
strength to initiate melt, but their integrated enhanced greenhouse effect acts to raise the temperature of
the snow-ice surface and the near-surface air temperatures, thereby preconditioning the surface for melt
(Figure S1). This is consistent with in situ observations [Persson, 2012]. Again, once melt is initiated, the albedo
decreases, resulting in the shortwave radiation becoming a strong amplifying factor to surface warming and
melting of the snow-ice surface [Persson, 2012]. This effect seems particularly important for the early melt
category, as indicated by positive anomalies of net shortwave radiation after melt onset (Figure S1c).
On the other hand, anomalously clear-sky conditions and negative anomalies of water vapor during spring
lead coincide with a late melt onset (Figure 1). For the very late melt onset (latest 20%), atmospheric condi-
tions during the month prior to melt are characterized by negative anomalies of water vapor and cloud water
and colder-than-usual atmospheric and surface conditions (Figures 1d1g, S1, and S2). Negative anomalies of
water vapor and clouds reduce the greenhouse effect and lead to radiative cooling of the surface and delay
the melt onset. The anomalously clear atmosphere also results in positive SWD anomalies during the month
prior to melt onset (Figure 1h), yet the extra energy provided by SWD is insufcient to initiate surface melt
since a large fraction of the SWD is reected by the still snow-covered sea ice.
4. Spatial Variability of Atmospheric Anomalies and Surface Melt
The spatiotemporal variability of melt onset is consistent with the spatiotemporal variability of atmospheric
moisture anomalies (Figure 2). These moisture anomalies exhibit a strong spatial variability (Figures 2a2c are
typical), are present in a deep atmospheric column (Figure S2), and are often a result of an interplay between
midlatitude cyclones and atmospheric blockings [e.g., Doyle et al., 2011; Woods et al., 2013]. Retarded by the
blockings, these cyclones either deect poleward with their associated moisture or remain at midlatitudes,
while creating sustained, narrow injections of moisture into the Arctic [Woods et al., 2013]. The narrow
moisture structures may trigger melt over small areas, while the cyclones traversing the Arctic can trigger
a spatially more extensive melt with their associated frontal systems and deep-layer moisture transport
[Persson, 2012; Stramler et al., 2011]. Clouds associated with these humid air masses are important since they
generate particularly strong positive anomalies of LWD [Doyle et al., 2011; Persson, 2012], especially clouds
Geophysical Research Letters 10.1002/2016GL069330
containing liquid water [Shupe and Intrieri, 2004]. If the LWD anomalies from the cloudy and humid air masses
are of insufcient strength to trigger melt for the given surface conditions in spring, they nevertheless act to
increase the surface temperature [Persson, 2012], thereby preconditioning the surface for melt. Typically, the
melt onset timing is similar over areas of at least 300600 km (Figure 2d). Between these areas, strong
gradients of melt timing are evidentcommonly 3 week differences over short distances (Figure 2d)
mainly as a result of the strong spatial variability and transient behavior of the moisture and cloud water
anomalies. The size of the areas of similar melt onset differs between years, following the spatial scale of
the moisture anomalies (not shown). Note that the thickness distribution of snow, and to some extent
sea ice, contributes to the large spatial variability of melt onset, due to the insulating properties of snow
the melting temperature at the surface is reached earlier for a thin snow layer than for a thick layer under
similar atmospheric conditions.
Figure 3. Histogram of melt onset timing and trends of pan-Arctic atmospheric parameters as a function of the day of the year. (a) Histogram of the pan-Arctic melt
onset timing during 19792013. (bf) Trends of (b), 2 m temperature, (c) downwelling longwave radiation at the surface, (d) total column water vapor, (e) atmospheric
temperature at the 850 hPa pressure surface, (f) total column cloud water (liquid + ice) given in unit per decade. Annotations on right axis mark the day of the year.
See section 2 for details on how the trends are computed.
Figure 2. Example of melt onset and water vapor anomalies. (ac) Snapshots of 3 day mean total column water vapor anomalies with the melt timing superimposed
in the year 1991. Black dots mark areas where melt begins within the 3 day period, and green dots mark earlier initiated melt. (d) Melt timing. (e) Melt-timing
anomalies (relative to the local climatology). Figure S3 shows all frames for 17 May through 12 June 1991.
Geophysical Research Letters 10.1002/2016GL069330
5. Trends in Atmospheric Fields
Moisture anomalies increasing in intensity or frequency, or a combination of the two, during early spring
would trigger an earlier melt onset. Positive pan-Arctic trends of LWD (about 6 W m
/decade) and near-
surface temperature (about 0.8 K/decade) are found in spring, especially in late May (around day of the year
145; Figure 3a). These positive trends early in the season coincide with the observed tendency toward an
earlier melt onset during recent decades [Stroeve et al., 2014]. The trends of the melt onset are mostly nega-
tive, indicating that early melt onset tends to occur earlierearly melt onset begins more than 2 days/decade
earlier considering the whole Arctic (Table S1). Locally, early melt onset trends are larger and can reach up
to 8 days/decade, e.g., in the Barents, East Greenland, and Kara Seas, as well as in the Bafn and Hudson
Bay. The LWD trend is a result of increasing water vapor, air temperatureand to some extentcloud water
(Figures 3d3f). This is consistent with previous ndings of increasing trends of low-level clouds, moisture,
atmospheric temperature, and thus LWD and near-surface temperature during recent decades [Eastman
and Warren, 2010; Francis and Hunter, 2007; Serreze et al., 2012; Boisvert and Stroeve, 2015].
6. Conclusions
In conclusion, both for a late and an early melt onset, transport of humid, warm air over the Arctic sea ice area
and the associated augmentation of the greenhouse effect appear to be the dominating processes for the
initiation of surface melt. For an early melt onset, signicant positive anomalies of total column water vapor,
atmospheric temperature, and cloud water act in concert to induce the melt through enhanced LWD,
whereas for late melt onset, it is rather a change from negative anomalies of these parameters to normal
conditions that triggers the melt onset. These ndings are in line with previous studies, showing that LWD
explains a large fraction of melt onset variability in the Arctic [Maksimovich and Vihma, 2012; Persson,
2012]. We further discuss mechanisms that explain variations in LWD, and hereby the timing of melt onset.
Our results suggest that in order to model the Arctic climate system properly, the representation of the atmo-
spheric variability in spring, before and at melt onset, is the key. Finally, in light of continued warming of the
Arctic region, we expect melt onset to continue to occur earlier in spring due to positive trends in LWD in the
weeks prior to the present day melt onset. This shift toward an earlier melt has consequences for the climate
of the Arctic and beyond.
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The authors would like to thank Michael
Tjernström for fruitful discussions on
this subject. We further would like to
thank the, in section 2, mentioned data
centers and individuals collecting,
computing, and supplying accessible
high-quality data. ERA-Interim reanaly-
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website ( and AIRS and
AMSU-A satellite retrievals from the Jet
Propulsion Laboratory website (airs.jpl. Melt data were obtained
from the NASA Cryosphere Science
Research Portal (
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Woods, C., R. Caballero, and G. Svensson (2013), Large-scale circulation associated with moisture intrusions into the Arctic during winter,
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... Indeed, recent studies have shown that Arctic Eady growth rates (rapid increases in baroclinic disturbance) have significantly increased in winter and spring over the past four decades as low-level static stability in the atmosphere has decreased with retreating sea ice 8 . Interactions between the ice and atmosphere during the spring can be influential in determining sea ice survivability during the melt season 26,27 . While global models from the Climate Model Intercomparison Project (CMIP6) still exhibit large biases and a spread of projected Arctic change under different climate scenarios, there is strong agreement that Arctic sea ice loss and Arctic amplification will continue through this century 28 . ...
... ref. 32). Cyclone winds can influence sea ice motion [33][34][35] and the thermodynamic characteristics can alter the surface energy budget, triggering sea ice growth or melt 27,36,37 . The interactions between cyclones, sea ice, and the local climate are complex because they vary with a given cyclone's characteristics, location, duration, and season (e.g. ...
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In recent decades, the Arctic has experienced rapid atmospheric warming and sea ice loss, with an ice-free Arctic projected by the end of this century. Cyclones are synoptic weather events that transport heat and moisture into the Arctic, and have complex impacts on sea ice, and the local and global climate. However, the effect of a changing climate on Arctic cyclone behavior remains poorly understood. This study uses high resolution (4 km), regional modeling techniques and downscaled global climate reconstructions and projections to examine how recent and future climatic changes alter cyclone behavior. Results suggest that recent climate change has not yet had an appreciable effect on Arctic cyclone characteristics. However, future sea ice loss and increasing surface temperatures drive large increases in the near- surface temperature gradient, sensible and latent heat fluxes, and convection during cyclones. The future climate can alter cyclone trajectories and increase and prolong intensity with greatly augmented wind speeds, temperatures, and precipitation. Such changes in cyclone characteristics could exacerbate sea ice loss and Arctic warming through positive feedbacks. The increasing extreme nature of these weather events has implications for local ecosystems, com- munities, and socio-economic activities.
... The imagery is scarce between 18 and 22 June due to widespread cloud coverage. This weather system likely enhanced the melt (Mortin et al., 2016), and when it passed, the MPF was high, averaging 15.2% between 24 and 29 June. The ...
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We investigate sea ice conditions during the 2020 melt season, when warm air temperature anomalies in Spring led to early melt onset, an extended melt season and the second-lowest September minimum Arctic ice extent observed. We focus on the region of the most persistent ice cover and examine melt pond depth retrieved from ICESat-2 using two distinct algorithms in concert with a time series of melt pond fraction and ice concentration derived from Sentinel-2 imagery to obtain insights about the melting ice surface in three dimensions. We find melt pond fraction derived from Sentinel-2 in the study region increased rapidly in June, with the mean melt pond fraction peaking at 16 % +/- 6 % on 24 June 2020, followed by a slow decrease to 8 % +/- 6 % by 3 July, and remained below 10 % for the remainder of the season through 15 September. Sea ice concentration was consistently high (>95 %) at the beginning of the melt season until 4 July, and as floes disintegrated, decreased to a minimum of 70 % on July 30, then became more variable ranging from 75 % to 90 % for the remainder of the melt season. Pond depth increased steadily from a median depth of 0.40 m +/- 0.17 m in early June, peaked at 0.97 m +/- 0.51 m on 16 July, even as melt pond fraction had already started to decrease. Our results demonstrate that by combining high-resolution passive and active remote sensing we now have the ability to track evolving melt conditions and observe changes in the sea ice cover throughout the summer season.
... The timing of melt onset and freeze up throughout the melt season is important for sea ice survivability throughout the year and in understanding the Arctic climate system 18,32,78,82,83 . Throughout the summer melt season, air temperatures tend to oscillate around the freezing point and there are multiple melt and refreezing events that occur at the surface 84 . ...
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Since the early 2000s, sea ice has experienced an increased rate of decline in thickness, extent and age. This new regime, coined the ‘New Arctic’, is accompanied by a reshuffling of energy flows at the surface. Understanding of the magnitude and nature of this reshuffling and the feedbacks therein remains limited. A novel database is presented that combines satellite observations, model output, and reanalysis data with sea ice parcel drift tracks in a Lagrangian framework. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states, including remotely sensed surface energy budget terms. Additionally, flags indicate when sea ice parcels travel within cyclones, recording cyclone intensity and distance from the cyclone center. The quality of the ice parcel database was evaluated by comparison with sea ice mass balance buoys and correlations are high, which highlights the reliability of this database in capturing the seasonal changes and evolution of sea ice. This database has multiple applications for the scientific community; it can be used to study the processes that influence individual sea ice parcel time series, or to explore generalized summary statistics and trends across the Arctic.
... As warm air-mass intrusions arrive in the Arctic at varying altitudes, boundary layer characteristics and cloud properties are affected, albeit not uniformly [11][12][13] . A number of studies have associated melting processes on Greenland's ice sheet and changes in sea ice concentration to intense warming events introduced into the Arctic during springtime 6,12,14,15 , highlighting the importance of such events. ...
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Frequency and intensity of warm and moist air-mass intrusions into the Arctic have increased over the past decades and have been related to sea ice melt. During our year-long expedition in the remote central Arctic Ocean, a record-breaking increase in temperature, moisture and downwelling-longwave radiation was observed in mid-April 2020, during an air-mass intrusion carrying air pollutants from northern Eurasia. The two-day intrusion, caused drastic changes in the aerosol size distribution, chemical composition and particle hygroscopicity. Here we show how the intrusion transformed the Arctic from a remote low-particle environment to an area comparable to a central-European urban setting. Additionally, the intrusion resulted in an explosive increase in cloud condensation nuclei, which can have direct effects on Arctic clouds’ radiation, their precipitation patterns, and their lifetime. Thus, unless prompt actions to significantly reduce emissions in the source regions are taken, such intrusion events are expected to continue to affect the Arctic climate.
... The latent heat-transport convergence showed significant positive anomalies in May and June, which is conducive to moisture increase (Graversen, Mauritsen, Drijfhout, Tjernström, and Mårtensson, 2011) ( Figure 5B). The cloud water and water vapor, which are higher than average, will increase the opacity of the atmosphere, thereby causing the greenhouse effect, which leads to an increase in LWN (Kapsch, Graversen, Tjernström, and Bintanja, 2016;Mortin, Svensson, Graversen, Kapsch, Stroeve, and Boisvert, 2016;Lee, Kwon, Yeh, Kwon, Park, Park et al., 2017;Gimeno, Vázquez, Eiras-Barca, Sorí, Algarra, and Nieto, 2019;He, Hu, Chen, Wang, Huang, and Stamnes, 2019). The reason for the abnormally low LWN in May is mainly related to the abnormally low cloud water, and has nothing to do with water vapor and the latent heat-transport convergence ( Figures 5A,B). ...
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Arctic sea ice is a key factor in high–latitude air–sea–ocean interactions. In recent decades, its extent has been decreasing in all seasons with large interannual variability, especially for the Northwind Ridge. After removing the trend in the changes during July 1979 to 2020, 2019 had an abnormally low value, while the following year, 2020, had an abnormally high value. The underlying processes driving this variability in July near the southern Northwind Ridge, which is one of the areas with the most drastic changes in Arctic, are not well understood. There, we demonstrated that the shortwave radiation anomaly in July is the direct reason for the sea ice anomaly in July 2019 and July 2020. Importantly, the total energy surplus in the spring of 2019 (enough to melt ∼18 cm of sea ice) and 2020 (potentially melting ∼11 cm of sea ice) indirectly influenced the sea ice. The abnormal change in moisture and its convergence mainly caused by atmospheric circulation were the main reasons for the longwave radiation and latent flux anomalies. Cloud water mainly affected shortwave radiation, including the positive net shortwave radiation anomaly in May 2019.
... Les nuages et la glace de mer sont couplés de manière étroite en Arctique. Certaines études [Mortin et al. 2016] suggèrent que les nuages enclenchent le processus de fonte de la neige à la surface de la glace de mer au printemps. La fonte de la glace va à son tour jouer un rôle sur la formation des nuages. ...
L’étude de l’atmosphère arctique présente un intérêt scientifique grandissant, la température de sa surface augmentant deux à trois plus fois rapidement que dans le reste du monde. Par la modulation qu’ils exercent sur le rayonnement, les nuages apparaissent comme un élément crucial du bilan d’énergie du système océan-glace-atmosphère en Arctique. Pourtant, la formation et la persistance de ces nuages sont toujours mal représentées dans les modèles atmosphériques, de même que la couche limite où ils se forment et résident. Dans le cadre du projet innovant IAOOS, un système d’observation intégré à bord de bouées dérivant dans l’océan arctique a permis de collecter simultanément et en temps réel des informations relatives à l’état des couches supérieures de l’océan, de la basse atmosphère et de la glace de mer arctique. Une partie de ces observations a été effectué lors de la campagne de terrain N-ICE au nord du Svalbard en 2015. Les travaux menés dans le cadre de cette thèse visent à mieux quantifier les différents termes du bilan d’énergie à la surface dans des conditions environnementales et de surface variées et d’améliorer la représentation dans le modèle régional Polar-WRF des nuages dans la couche limite arctique.
... Snowmelt variabilities are jointly affected by the underlying surface conditions (Panday et al. 2011;Ganey et al. 2017;Zhou et al. 2019) and the large-scale atmospheric fields (Mortin et al. 2016;Scott et al. 2019;Zheng et al. 2020a). Relationships between melt conditions in the cryosphere and the large-scale atmospheric conditions were diagnosed in both hemispheres (Tedesco et al. 2009;Tedesco and Monaghan 2009;Wang et al. 2013;Nicolas et al. 2017). ...
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Snowmelt is a critical component in the cryosphere and has a direct impact on Earth’s energy and water budget. Here, a 40-yr integrated melt onset (MO) dataset over sea ice, ice sheets, and terrestrial snow is compiled from spaceborne microwave radiometers and ERA5, allowing an overall assessment of the cryosphere. Results suggest that MO in both hemispheres shows latitudinal and vertical zonalities. The global cryosphere presented a trend toward earlier MO (−2 days decade−1) with hotpots distributed at the Northern Hemisphere high latitudes where the warming rate is much higher than that at lower latitudes. Overall, variations in MO showed a similar pattern to that in near-surface temperature. The advance of MO has been slowing down since the 1990s and no significant trend was observed during the so-called warming hiatus period (1998–2012). Regionally, climatic linkage analyses suggest the local MO variations were associated with different climate indices. MO in the pan-Arctic region is related with the Arctic Oscillation and North Atlantic Oscillation, while that in the pan-Antarctic region is associated with El Niño–Southern Oscillation and the southern annular mode. Occasionally, abnormal MO occurs as a result of extreme weather conditions. In February 2018, abnormal early melt events that occurred in the Arctic Ocean are found to be linked with the warm southerly flow due to sudden stratospheric warming. These findings suggest the satellite-based MO allows examining the dynamics and extremes in the climate system, both regionally and globally.
The Arctic Ocean is one of the most rapidly changing regions on the planet. Its warming climate has driven reductions in the region's sea ice cover which are likely unprecedented in recent history, with many of the environmental impacts being mediated by the overlying snow cover. As well as impacting energetic and material fluxes, the snow cover also obscures the underlying ice from direct satellite observation. While the radar waves emitted from satellite-mounted altimeters have some ability to penetrate snow cover, an understanding of snow geophysical properties remains critical to remote sensing of sea ice thickness. The paucity of Arctic Ocean snow observations was recently identified as a key knowledge gap and uncertainty by the Intergovernmental Panel on Climate Change's Special Report on Oceans and Cryosphere in a Changing Climate. This thesis aims to address that knowledge gap. Between 1937 and 1991 the Soviet Union operated a series of 31 crewed stations which drifted around the Arctic Ocean. During their operation, scientists took detailed observations of the atmospheric conditions, the physical oceanography, and the snow cover on the sea ice. This thesis contains four projects that feature these observations. The first two consider a well known snow depth and density climatology that was compiled from observations at the stations between 1954 & 1991. Specifically, Chapter two considers the role of seasonally evolving snow density in sea ice thickness retrievals, and Chapter three considers the impact of the climatological treatment itself on satellite estimates of sea ice thickness variability and trends. Chapter four presents a statistical model for the sub-kilometre distribution of snow depth on Arctic sea ice through analysis of snow depth transect data. Chapter five then compares the characteristics of snow melt onset at the stations with satellite observations and results from a recently developed model.
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The energy budget of Arctic sea ice is strongly affected by the snow cover. Intensive sampling of snow properties was conducted near Qikiqtarjuaq in Baffin Bay on typical landfast sea ice during two melt seasons in 2015 and 2016. The sampling included stratigraphy, vertical profiles of snow specific surface area (SSA), density and irradiance, and spectral albedo (300–1100 nm). Both years featured four main phases: (I) dry snow cover, (II) surface melting, (III) ripe snowpack, and (IV) melt pond formation. Each phase was characterized by distinctive physical and optical properties. A high SSA value of 49.3 m2 kg−1 was measured during phase I on surface wind slabs together with a corresponding broadband albedo (300–3000 nm) of 0.87. Phase II was marked by alternating episodes of surface melting, which dramatically decreased the SSA below 3 m2 kg−1, and episodes of snowfall re-establishing pre-melt conditions. Albedo was highly time-variable, with minimum broadband values around 0.70. In phase III, continued melting led to a fully ripe snowpack composed of clustered rounded grains. Albedo began to decrease in the visible as snow thickness decreased but remained steady at longer wavelengths. Moreover, significant spatial variability appeared for the first time following snow depth heterogeneity. Spectral albedo was simulated by radiative transfer using measured SSA and density vertical profiles and estimated impurity contents based on limited measurements. Simulations were most of the time within 1 % of measurements in the visible and within 2 % in the infrared. Simulations allowed the calculations of albedo and of the spectral flux at the snow–ice interface. These showed that photosynthetically active radiation fluxes at the bottom of the snowpack durably exceeded 5 W m−2 (∼9.2 µmol m−2 s−1) only when the snowpack thickness started to decrease at the end of phase II.
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Precipitation is an important part of the atmospheric circulation in the Arctic and is of great significance to the energy budget and hydrological characteristics of the Arctic region. The distribution of precipitation affects the exchange of energy, which then affects the Arctic sea ice indirectly. Arctic precipitation impacts the sea surface albedo, which leads to changes in the sea ice concentration (SIC) and the energy exchange between the sea, ice, and air. In this study, GPM IMERG precipitation data, which have a spatial resolution of 0.1°, were used to analyze the characteristics of precipitation in the Northeast Passage (NEP) from May to December during the period 2011–2020. This analysis of the amount of precipitation and its distribution were performed for the Barents Sea, Kara Sea, Laptev Sea, and East Siberian Sea. The relationship between precipitation and sea ice was also explored. The results show that, during the study period, the average precipitation over the Barents Sea from May to December was 57–561 mm/year and that this area had the highest precipitation in the NEP. For the Kara Sea, the average precipitation for May to December was 50–386 mm/year and for the East Siberian Sea and the Laptev Sea it was 48–303 mm/year and 53–177 mm/year, respectively. For the NEP as a whole, September was found to be the month with the highest average precipitation. An analysis of the correlation between the precipitation and the SIC gave a correlation coefficient of −0.792 for the study period and showed that there is a 15-day delay between the precipitation increase and the decrease in SIC. The analysis of the precipitation data in these areas thus showed that precipitation is related to SIC and is of great importance to understanding and predicting the navigable capacity of the NEP.
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The Arctic region has warmed more than twice as fast as the global average - a phenomenon known as Arctic amplification. The rapid Arctic warming has contributed to dramatic melting of Arctic sea ice and spring snow cover, at a pace greater than that simulated by climate models. These profound changes to the Arctic system have coincided with a period of ostensibly more frequent extreme weather events across the Northern Hemisphere mid-latitudes, including severe winters. The possibility of a link between Arctic change and mid-latitude weather has spurred research activities that reveal three potential dynamical pathways linking Arctic amplification to mid-latitude weather: changes in storm tracks, the jet stream, and planetary waves and their associated energy propagation. Through changes in these key atmospheric features, it is possible, in principle, for sea ice and snow cover to jointly influence mid-latitude weather. However, because of incomplete knowledge of how high-latitude climate change influences these phenomena, combined with sparse and short data records, and imperfect models, large uncertainties regarding the magnitude of such an influence remain. We conclude that improved process understanding, sustained and additional Arctic observations, and better coordinated modelling studies will be needed to advance our understanding of the influences on mid-latitude weather and extreme events.
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The Arctic cryosphere is an integral part of Earth's climate sys-tem and has undergone unprecedented changes within the past few decades. Rapid warming and sea-ice loss has had significant impacts locally, particularly in late summer and early autumn. September sea ice has declined at a rate of 12.4% per dec-ade since 1979 (ref.1), so that by summer 2012, nearly half of the areal coverage had disappeared. This decrease in ice extent has been accompanied by an approximately 1.8 m (40%) decrease in mean winter ice thickness since 1980 (ref.2) and a 75–80% loss in volume 3 . Though sea-ice loss has received most of the research and media attention, snow cover in spring and summer has decreased at an even greater rate than sea ice. June snow cover alone has decreased at nearly double the rate of September sea ice 4 . The decrease in spring snow cover has contributed to both the rise in warm season surface temperatures over the Northern Hemisphere extratropical landmasses and the decrease in summer Arctic sea ice 5 . The com-bined rapid loss of sea ice and snow cover in the spring and sum-mer has played a role in amplifying Arctic warming. However, snow cover and sea-ice trends diverge in the autumn and winter with sea ice decreasing in all months while snow cover has exhibited a neutral to positive trend in autumn and winter 6
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Recent studies have shown that atmospheric processes in spring play an important role for the initiation of the summer ice melt and therefore may strongly influence the September sea-ice concentration (SSIC). Here a simple statistical regression modelbased on only atmospheric spring parameters is applied in order to predict the SSIC over the major part of the Arctic Ocean. By using spring anomalies of downwelling longwave radiation or atmospheric water vapor as predictor variables, correlation coefficients between observed and predicted SSIC of up to 0.5 are found. These skills of seasonal SSIC predictions are similar to those obtained using more complex dynamical forecast systems, despite the fact that the simple model applied here takes neitherinformation of the sea-ice state, oceanic conditions nor feedback mechanisms during summer into account. The results indicate that a realistic representation of spring atmospheric conditions in the prediction system plays an important role for the predictive skills of a model system.
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Atmospheric reanalyses depend on a mix of observations and model forecasts. In data-sparse regions such as the Arctic, the reanalysis solution is more dependent on the model structure, assumptions, and data assimilation methods than in data-rich regions. Applications such as the forcing of ice-ocean models are sensitive to the errors in reanalyses. Seven reanalysis datasets for the Arctic region are compared over the 30-yr period 1981-2010: National Centers for Environmental Prediction (NCEP)-National Center for Atmospheric Research Reanalysis 1 (NCEP-R1) and NCEP-U.S. Department of Energy Reanalysis 2 (NCEP-R2), Climate Forecast System Reanalysis (CFSR), Twentieth-Century Reanalysis (20CR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), ECMWF Interim Re-Analysis (ERA-Interim), and Japanese 25-year Reanalysis Project (JRA-25). Emphasis is placed on variables not observed directly including surface fluxes and precipitation and their trends. The monthly averaged surface temperatures, radiative fluxes, precipitation, and wind speed are compared to observed values to assess how well the reanalysis data solutions capture the seasonal cycles. Three models stand out as being more consistent with independent observations: CFSR, MERRA, and ERA-Interim. A coupled ice-ocean model is forced with four of the datasets to determine how estimates of the ice thickness compare to observed values for each forcing and how the total ice volume differs among the simulations. Significant differences in the correlation of the simulated ice thickness with submarine measurements were found, with the MERRA products giving the best correlation (R = 0.82). The trend in the total ice volume in September is greatest with MERRA (-4.1 x 10(3) km(3) decade(-1)) and least with CFSR (-2.7 x 10(3) km(3) decade(-1)).
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The area of Arctic September sea ice has diminished from about 7 million km2 in the 1990s to less than 5 million km2 in five of the past seven years, with a record minimum of 3.6 million km2 in 2012 (ref. 1). The strength of this decrease is greater than expected by the scientific community, the reasons for this are not fully understood, and its simulation is an on-going challenge for existing climate models2, 3. With growing Arctic marine activity there is an urgent demand for forecasting Arctic summer sea ice4. Previous attempts at seasonal forecasts of ice extent were of limited skill5, 6, 7, 8, 9. However, here we show that the Arctic sea-ice minimum can be accurately forecasted from melt-pond area in spring. We find a strong correlation between the spring pond fraction and September sea-ice extent. This is explained by a positive feedback mechanism: more ponds reduce the albedo; a lower albedo causes more melting; more melting increases pond fraction. Our results help explain the acceleration of Arctic sea-ice decrease during the past decade. The inclusion of our new melt-pond model10 promises to improve the skill of future forecast and climate models in Arctic regions and beyond.
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We examine the poleward transport of water vapor across 70°N during boreal winter in the ERA-Interim reanalysis product, focusing on intense moisture intrusion events. We analyze the large-scale circulation patterns associated with these intrusions and the impacts they have at the surface. A total of 298 events are identified between 1990 and 2010, an average of 14 per season, accounting for 28% of the total poleward transport of moisture across 70°N. They are concentrated over the main ocean basins at that latitude in the Labrador Sea, North Atlantic, Barents/Kara Sea, and Pacific. Composites of sea level pressure and potential temperature on the 2 potential vorticity unit surface during intrusions show a large-scale blocking pattern to the east of each basin, deflecting midlatitude cyclones and their associated moisture poleward. The interannual variability of intrusions is strongly correlated with variability in winter-mean surface downward longwave radiation and skin temperature averaged over the Arctic.
Over the past decade, the Arctic has seen unprecedented declines in the summer sea ice area, leading to larger and longer exposed open water areas. The Atmospheric Infrared Sounder is a useful yet underutilized tool to study corresponding atmospheric changes and their feedbacks between 2003 and 2013. Most pronounced warming occurs between November and April, with skin and air temperatures increasing on average 2.5 K and 1.5 K over the Arctic Ocean. In response to sea ice loss, evaporation rates (i.e., moisture flux) increased between August and October by 1.5 × 10−3 g m−2 s−1 (3.8 W m−2 latent heat flux energy), increasing the water vapor feedback and cloud cover. Although most trends are positive over the Arctic Ocean, there is considerable interannual variability. Increasing specific humidity in May and corresponding downward moisture fluxes cause earlier melt onset; warming skin temperatures and radiative responses to increased water vapor and cloud cover in autumn delay freeze-up.
The atmospheric infrared sounder (AIRS) science team version-6 AIRS/advanced microwave sounding unit (AMSU) retrieval algorithm is now operational at the Goddard Data and Information Services Center (DISC). AIRS version-6 level-2 products are generated near real time at the Goddard DISC and all level-2 and level-3 products are available starting from September 2002. Some of the significant improvements in retrieval methodology contained in the version-6 retrieval algorithm compared to that previously used in version-5 are described. In particular, the AIRS science team made major improvements with regard to the algorithms used to (1) derive surface skin temperature and surface spectral emissivity; (2) generate the initial state used to start the cloud clearing and retrieval procedures; and (3) derive error estimates and use them for quality control. Significant improvements have also been made in the generation of cloud parameters. In addition to the basic AIRS/AMSU mode, version-6 also operates in an AIRS only (AO) mode, which produces results almost as good as those of the full AIRS/AMSU mode. The improvements of some AIRS version-6 and version-6 AO products compared to those obtained using version-5 are also demonstrated.
The Arctic sea ice acts as a barrier between the ocean and lower atmosphere, reducing the exchange of heat and moisture. In recent years the ice pack has undergone many changes, in particular a rapid reduction in sea ice extent and compactness in summer and autumn. This, along with modeling studies, would cause one to believe that the moisture flux would be increasing. We estimate the daily moisture flux from 2003 to 2011 using geophysical data from multiple sensors onboard NASA’s Aqua satellite, taking advantage of observations being collected at the same time and along the same track. Our findings show the moisture flux, averaged over the entire Arctic, has had large interannual variations, with smallest fluxes in 2010, 2003, and 2004, and largest ones in 2007, 2008, and 2005. Increases in air specific humidity tend to reduce the moisture flux, whereas the decrease in sea ice cover tends to increase the flux. Statistically significant seasonal decreasing trends are seen in December, January, and February because of the dominating effect of increase in 2 m air specific humidity increasing, reducing the surface-air specific humidity difference by �0.0547 kg/kg in the Kara/Barents Seas, E. Greenland Sea, and Baffin Bay regions where there is some open water year round. Our results also show that the contribution of the sea ice zone to the total moisture flux (from the open ocean and sea ice zone) has increased by 3.6% because the amount of open water within the sea ice zone has increased by 4.3%.