To facilitate the construction of a satellite-derived 2 m air temperature
(T2m) product for the snow- and ice-covered regions in the
Arctic, observations from weather stations are used to quantify the
relationship between the T2m and skin temperature
(Tskin). Multiyear data records of simultaneous Tskin
and T2m from 29 different in situ sites have been analysed for
five regions, covering the lower and upper ablation zone and the accumulation
zone of the Greenland Ice Sheet (GrIS), sea ice in the Arctic Ocean, and
seasonal snow-covered land in northern Alaska. The diurnal and seasonal
temperature variabilities and the impacts from clouds and wind on the
T2m–Tskin differences are quantified.
Tskin is often (85 % of the time, all sites weighted equally)
lower than T2m, with the largest differences occurring when the
temperatures are well below 0 ∘C or when the surface is melting.
Considering all regions, T2m is on average
0.65–2.65 ∘C higher than Tskin, with the largest
differences for the lower ablation area and smallest differences for
the seasonal snow-covered sites. A negative net surface radiation balance generally cools
the surface with respect to the atmosphere, resulting in a surface-driven
surface air temperature inversion. However, Tskin and
T2m are often highly correlated, and the two temperatures can
be almost identical (<0.5∘C difference), with the smallest
T2–Tskin differences around noon and early afternoon during
spring, autumn and summer during non-melting conditions. In general, the
inversion strength increases with decreasing wind speeds, but for the sites
on the GrIS the maximum inversion occurs at wind speeds of about
5 m s-1 due to the katabatic winds. Clouds tend to reduce the vertical
temperature gradient, by warming the surface, resulting in a mean overcast
T2m–Tskin difference ranging from -0.08 to
1.63 ∘C, with the largest differences for the sites in the
low-ablation zone and the smallest differences for the seasonal snow-covered
sites. To assess the effect of using cloud-limited infrared satellite
observations, the influence of clouds on temporally averaged
Tskin has been studied by comparing averaged clear-sky
Tskin with averaged all-sky Tskin. To this end, we
test three different temporal averaging windows: 24 h, 72 h and 1 month.
The largest clear-sky biases are generally found when 1-month averages are
used and the smallest clear-sky biases are found for 24 h. In most cases,
all-sky averages are warmer than clear-sky averages, with the smallest bias
during summer when the Tskin range is smallest.
Introduction
The Arctic region is warming about twice as much as the global
average because of Arctic amplification (Graversen et al., 2008). Greenland
meteorological data show that the last decade (2000s) is the warmest since
meteorological measurements of surface air temperatures started in the 1780s
(Cappelen, 2016; Masson-Delmotte et al., 2012) and the period 1996–2014
yields an above-average warming trend compared to the past 6 decades
(Abermann et al., 2017). The reason for the Arctic amplification is a number
of positive feedback mechanisms, e.g. the lapse rate feedback, which is
positive in high latitudes (Manabe and Wetherald, 1975) and the ice–albedo
feedback (e.g. Arrhenius, 1896; Curry et al., 1995), which is driven by the
retreat of Arctic sea ice, glaciers and terrestrial snow cover. The warming
leads to a declining mass balance of the Greenland Ice Sheet (GrIS),
contributing to global sea level rise. The increased mass loss of the GrIS
partly comes from increased calving rates, while the other part is a result
of increased surface melt (Rignot, 2006), which is driven by changes in the
surface energy balance. Several studies have focussed on the assessment of
current albedo trends and their possible further enhancement of the impact of
atmospheric warming on the GrIS (e.g. Box et al., 2012; Stroeve et al., 2013;
Tedesco et al., 2011), but recent studies have shown that uncorrected sensor
degradation in MODIS Collection 5 data was contributing falsely to the albedo
decline in the dry snow areas, while the decline in wet snow and ice areas is
confirmed but at a lower magnitude than initially estimated (Casey et
al., 2017). Future projections of the GrIS mass balance show that the surface
melt is exponentially increasing as a function of the increase in projected
surface air temperature (Franco et al., 2013). Further, the Arctic warming
may contribute to mid-latitude weather events through its effects on the
configuration of the jet stream (Cohen et al., 2014; Overland et al., 2015;
Vihma, 2014; Walsh, 2014). It is therefore important to monitor the
temperature of the Arctic to understand and predict the local as well as
global effects of climate change. Current global surface temperature products
are fundamental for the assessment of climate change (Stocker et al., 2014),
but in the Arctic these data traditionally include only near-surface air
temperatures from buoys and automatic weather stations (AWSs; Hansen et
al., 2010; Jones et al., 2012; Rayner, 2003). However, in situ observations
are rare and the available time series have gaps and/or limited duration. In
particular, the Arctic land ice and sea ice regions are sparsely covered with
in situ measurements due to the extreme weather conditions and low
population density (Reeves Eyre and Zeng, 2017). The global surface
temperature products are thus based on a limited number of observations in
this very sensitive region. Consequently, crucial climatic signals and trends
could be missed in the assessment of the Arctic climate changes.
Satellite observations in the thermal infrared (IR) have a large potential
for improving the surface temperature products in the Arctic due to good
spatial and temporal coverage. However, the variable retrieved from IR
satellite observations is the clear-sky surface skin temperature
(Tskin), whereas current global surface temperature products
estimate the all-sky 2 m air temperature (T2m; Hansen et
al., 2010; Jones et al., 2012). An important step towards integrating the
satellite observations and near-surface air temperature products is thus to
assess the relationships between Tskin and T2m and
the role of clouds in this relationship as we do here.
A surface-based air temperature inversion is a common feature of the Arctic
(Serreze et al., 1992; Zhang et al., 2011). The inversion exists because of a
negative net radiation balance, leading to a cooling of the surface relative
to the air above it, which mostly occurs when the absorbed incoming solar
radiation is small (during winter and night). A few studies have investigated
the temperature inversion in the ice regions for the lowest 2 m of the
atmosphere, focusing on limited time periods and single locations, such as
Summit, Greenland (Adolph et al., 2018; Hall et al., 2008), the South Pole
(Hudson and Brandt, 2005) and the Arctic sea ice (Vihma and Pirazzini, 2005).
Previously, work has been carried out to characterize the relationship between
T2m and land surface temperatures observed from satellites and
identified land cover, vegetation fraction, and elevation as the dominating
factors impacting this relationship (Good et al., 2017). Until now, no
systematic studies had yet been made for the high-latitude ice sheets and
over sea ice.
The difference between T2m and Tskin is very
important in validation studies of remotely sensed temperatures. Several
studies have used T2m observations for validating satellite
Tskin products on the GrIS (Dybkjær et al., 2012; Hall et
al., 2008; Koenig and Hall, 2010; Shuman et al., 2014) and over the Arctic
sea ice (Dybkjær et al., 2012) and found that a significant part of the
satellite versus in situ differences could be attributed to the difference
between Tskin and T2m. Conversely, Rasmussen et
al. (2018) used satellite Tskin observations in a simple way to
correct T2m, which was used to force a coupled ocean and
sea ice model, and obtained an improved snow cover.
In order to facilitate the integrated use of Tskin and
T2m from in situ observations, satellite observations and
models, there is a need for a better understanding and characterization of
the observed relationship. The aim of this paper is to bring further insight
into this relationship, using in situ observations. This study extends the
previous analyses to include multiyear observational records from 29
different sites located on the GrIS, on Arctic sea ice and in the coastal
region of northern Alaska. The aim is to identify the key parameters
influencing the temperature difference between the surface and 2 m height
and to assess under which conditions Tskin is, or is not, a good
proxy for T2m and to quantify the differences. The findings
are intended to aid the users of satellite data and to support the derivation
of T2m using satellite Tskin observations. An
effort has therefore also been made to estimate a clear-sky bias of
Tskin based on in situ observations. The paper is structured such
that Sect. 2 describes the in situ data. Section 3 gives an introduction to
the near-surface boundary conditions. The results are presented in Sect. 4
and conclusions are given in Sect. 5.
Data
In situ observations have been collected from various sources and campaigns
covering ice and snow surfaces in the Arctic. The focus has been on
collecting in situ data with simultaneous observations of Tskin,
derived from IR radiometers and T2m measured with a shielded
and ventilated thermometer about 2 m above the surface. Table 1 gives an
overview of the data and the abbreviations used in this paper. The data have
been divided into five different categories based on surface characteristics
and location: accumulation area (ACC), upper–middle ablation zone (UAB) and
lower ablation zone (LAB) of the GrIS, seasonal snow-covered (SSC) sites in
northern Alaska, and Arctic sea ice (SICE) sites. All time series which cover
multiple full years have been cut to cover an integer number of years (within
5 days), in order to avoid seasonal biases (see Table 1 for start date and
end date for each site). The geographical distribution and elevations of all
sites are shown in Fig. 1, while Fig. 2 shows the temporal data coverage.
Observations from the sites in Table 1 include T2m, wind speed,
and shortwave- and longwave radiation. Measurement heights vary depending on
the site and snow depth, but for this paper near-surface air temperatures are
referred to as 2 m air temperature despite these variations. The impact of
these height variations is discussed in Sect. 4.1. For all sites,
Tskin has been derived from the longwave radiation measurements
and the data have afterwards been filtered to exclude observations with
Tskin>0∘C. Further details are provided for each data
source in Sect. 2.1–2.6.
Spatial coverage and elevation for each site included in
this study. Each surface type group has been labelled with a different
colour: ACC sites are purple, UAB sites are blue, LAB sites are red, SSC are
black and SICE sites are green. The colour bar is elevation above sea level
in metres.
Temporal coverage for each observation site included in this study.
Observation sites used in this study covering the following surface
types: accumulation zone (ACC), upper–middle ablation zone (UAB), lower
ablation zone (LAB), seasonal snow cover (SSC) and sea ice (SICE).
ProjectSiteStationSurfaceLatitudeLongitudeElevationStart dateEnd datetype(∘ N)(∘ W)(m)PROMICEEast GripEGPACC75.6235.9726601 May 201630 Apr 2018PROMICEKangerlussuaqKAN_UACC67.0047.0318404 Apr 20093 Apr 2018PROMICECrown Prince Christian LandKPC_UACC79.8325.1787017 Jul 200816 Jul 2018PROMICEKangerlussuaqKAN_MUAB67.0748.8412702 Sep 20081 Sep 2018PROMICENuukNUK_NUAB64.9549.8992025 Jul 201024 Jul 2014PROMICENuukNUK_UUAB64.5149.27112020 Aug 200719 Aug 2018PROMICEQassimiutQAS_AUAB61.2446.73100020 Aug 201219 Aug 2015PROMICEQassimiutQAS_MUAB61.1046.8363011 Aug 201610 Aug 2018PROMICEQassimiutQAS_UUAB61.1846.829007 Aug 20086 Aug 2018PROMICEScoresbysundSCO_UUAB72.3927.2397021 Jul 200820 Jul 2018PROMICETasiilaqTAS_AUAB65.7838.9089028 Aug 201327 Aug 2018PROMICETasiilaqTAS_UUAB65.6738.8757011 Mar 200810 Mar 2015PROMICEThuleTHU_UUAB76.4268.157609 Aug 20108 Aug 2018PROMICEUpernavikUPE_UUAB72.8953.5894018 Aug 200917 Aug 2018PROMICEKangerlussuaqKAN_LLAB67.1049.956701 Sep 200831 Aug 2018PROMICECrown Prince Christian LandKPC_LLAB79.9124.0837017 Jul 200816 Jul 2018PROMICENuukNUK_LLAB64.4849.5453020 Aug 200719 Aug 2018PROMICEQassimiutQAS_LLAB61.0346.8528024 Aug 200723 Aug 2018PROMICEScoresbysundSCO_LLAB72.2226.8246022 Jul 200821 Jul 2018PROMICETasiilaqTAS_LLAB65.6438.9025023 Aug 200722 Aug 2018PROMICEThuleTHU_LLAB76.4068.275709 Aug 20108 Aug 2018PROMICEUpernavikUPE_LLAB72.9054.3022017 Aug 200916 Aug 2018ARMAtqasukATQSSC70.47149.8927 Nov 20036 Nov 2010ARMUtqiagvik (formerly Barrow)BARSSC71.32156.62831 Oct 200328 Oct 2018ARMOliktok PointOLISSC70.50157.412018 Oct 201313 Oct 2018ICEARCQaanaaqDMI_QSICE77.4369.14Sea level31 Jan 20158 Jun 2017FRAM 2014/15Arctic OceanFRAMSICE82.22–89.35-180.00–180.00Sea level5 Sep 20143 Jul 2015SHEBAArctic OceanSHEBASICE74.62–80.37143.92–168.15Sea level1 Nov 199726 Sep 1998TARAArctic OceanTARASICE71.41–88.540.01–148.28Sea level1 Apr 200720 Sep 2007PROMICE
Data have been obtained from the Programme for Monitoring of the Greenland
Ice Sheet (PROMICE) provided by the Geological Survey of Denmark and
Greenland (GEUS). PROMICE was initiated in 2007 by the Danish Ministry of
Climate and Energy and operated by GEUS in collaboration with the National
Space Institute at the Technical University of Denmark and Asiaq (Greenland
Survey; e.g. Ahlstrøm et al., 2008). PROMICE collects in situ observations
from a number of AWSs mostly located along the margin of the GrIS (Fig. 1).
Each observational site has one or more stations, typically one located in
the lower ablation zone close to the ice sheet margin and one or two located
in the middle–upper ablation zone near the equilibrium line altitude.
Exceptions are KAN_U and KPC_U located in the lower accumulation area
and EGP, which is located in the upper accumulation area. All 22 PROMICE AWSs
located on the GrIS have been used in this study. PROMICE Tskin
has been calculated from upwelling longwave radiation, measured with a Kipp &
Zonen CNR1 or CNR4 radiometer, assuming a surface longwave emissivity of 0.97
(van As, 2011). The air temperature is measured by a thermometer at a height
of 2.7 m, while the wind speed is measured at about 3.1 m in height, if no
snow is present. Snow accumulation during winter reduces the measurement
height. Data where the surface albedo is less than 0.3 indicate that the snow
and ice have disappeared and these data have been excluded to ensure that we
only consider snow-/ice-covered surfaces. In this study, we use hourly
averages of the data, provided by PROMICE.
ARM
The Atmospheric Radiation Measurement (ARM) program (Ackerman and Stokes,
2003; Stamnes et al., 1999) was established in 1989 and it provides data on
the cloud and radiative processes at high latitudes. Three ARM sites from the
North Slope of Alaska (NSA) are used in this study: Atqasuk (ATQ), Utqiagvik
(formerly Barrow)
(BAR) and Oliktok Point (OLI). The stations provide surface snow IR
temperature measured using a Heitronics KT19.85 IR radiation pyrometer
(Moris, 2006) and air temperature measured at 2 m in height. Wind speed is
measured at 10 m in height. All measurements are provided with a sampling
interval of 1 min. The ARM stations have seasonal snow coverage; i.e. the
snow melts away in summer. As for the PROMICE stations, data with a surface
albedo of less than 0.3 have been excluded. The data used here are thus biased
towards autumn, winter and spring with 92 % of all observations being
measured during the months of September–May (all three SSC sites weighted
equally).
ICEARC
We use the ICEARC sea ice temperature and radiation data set from the Danish
Meteorological Institute (DMI) field campaign in Qaanaaq. The DMI AWS is
deployed on first-year sea ice in Qaanaaq and is funded by the European
climate research project, ICE-ARC. The AWS was deployed for the first time in
late January 2015 at the north side of the fjord Inglefield Bredning and
recovered in early June before breakup of the fjord ice. The campaign has
been repeated every year since then and the data used in this study are
procured by fieldwork performed in the period of
January–June 2015–2017. The AWS is equipped to measure snow
surface IR temperature and air temperature at 1 and 2 m heights. In this
study, the 1 m air temperature is used instead of the 2 m air temperature,
as careful analysis of the 2 m air observations revealed anomalies that
could arise from a systematic temperature-dependent error. Using the 1 m
instead of 2 m air temperature observations will have an impact on the
strength of the relationship with the Tskin observations, but the
observations are included here as the dependency with other parameters, such
as cloud cover and wind, is still important to assess. The data used here are
snapshot measurements every 10 min (Høyer et al., 2017) and are
referenced as DMI_Q in this paper.
SHEBA
The Surface Heat Budget of the Arctic (SHEBA) experiment was a multi-agency
program led by the National Science Foundation and the Office of Naval
Research. The data used in this study originate from deployment of a
Canadian icebreaker, Des Groseilliers, in the Arctic ice pack 570 km
northeast of Prudhoe Bay, Alaska, in 1997 (Uttal et al., 2002). During its
year-long deployment, SHEBA provided atmospheric and sea ice measurements
from the icebreaker and the surrounding frozen ice floe. The data used here
contain hourly averaged data collected by the SHEBA Atmospheric Surface Flux
Group (ASFG) and James C. Liljegren from
the ARM project. The SHEBA ASFG installed a 20 m tall tower, which was used
to obtain measurements of the surface energy budget, focusing on the
turbulent heat fluxes and the near-surface boundary layer structure
(Bretherton et al., 2000; Persson, 2002). The mast contains five different
levels, varying in height from 2.2 to 18.2 m, on which temperature and humidity
probes and a sonic anemometer are mounted. The air temperature and wind data
used here originate from the lowest mounted instruments (2.2 m), which vary
in height from 1.9 to 3 m depending on snow accumulation and snowmelt.
Three different methods to measure surface temperature were deployed: a
General Eastern thermometer, an Eppley radiometer and a Barnes radiometer,
for which data are available over the period from April to September 2007.
According to ASFG, the Eppley radiometer is the most reliable, though there
are periods when the other two are also reasonable and one period (May)
when the Eppley data may be slightly off (Persson, 2002). They provide an
estimate of Tskin, which is based on slight corrections to the
Eppley temperatures and the Barnes temperatures when Eppley was known to be
wrong (Persson, 2002). We use the processed data from the SHEBA ASFG
(Persson, 2002).
FRAM 2014/15
The scientific program of the FRAM 2014/15 expedition is carried out by the
Nansen Center (NERSC) in co-operation with the Alfred Wegener Institute;
Helmholtz Centre for Polar and Marine Research, Germany, University of
Bergen; Bjerknes Center for Climate Research and Norwegian Meteorological
Institute. FRAM 2014/15 is a Norwegian ice drift station deployed near the
North Pole in August 2014 using a hovercraft as the logistic and scientific
platform (Kristoffersen and Hall, 2014). This type of mission allows
exploration of the Arctic Ocean not accessible to icebreakers and enables
scientific field experiments, which require physical presence. By the end of
March 2015 they had drifted 1450 km. During the drift with sea ice they obtained Tskin
measurements using a Campbell Scientific IR120 (later corrected for sky
temperature and surface emissivity) mounted on the hovercraft and near-surface air temperature measurements, with a sampling interval of 1 min.
TARA
Tara is a French polar schooner that was built to withstand the forces
of Arctic sea ice. In late August 2006 Tara sailed to the Arctic Ocean,
where she drifted for 15 months frozen into the sea ice. The TARA
multidisciplinary experiment was part of the international polar year
DAMOCLES (Developing Arctic Modelling and Observing Capabilities for
Long-term Environmental Studies) program (Gascard et al., 2008; Vihma et
al., 2008). Air temperature and wind speed were measured from a 10 m tall
Aanderaa weather mast at heights of 1, 2, 5, and 10 m and wind direction
was measured at 10 m in height. We use the air temperatures and wind speed
measured at 2 m in height. They also deployed an Eppley broadband radiation
mast with two sensors for longwave fluxes and two sensors for shortwave
fluxes (upward and downward looking). The downward-looking IR sensor also
provided Tskin from April to September 2007. The data used in
this study are 10 min averages.
Radiometric observations of Tskin
The Tskin observations used in this study are all derived from
radiometric observations, but with spectral characteristics that range from
the Heitronics KT19.85 with a spectral response function of
9.5–11.5 µm to the Campbell Scientific IR120 with a
8–14 µm spectral window to broadband longwave observations from
∼4–40 µm. The emissivity of the ice surface varies for the
different spectral windows for the radiometers and this will lead to a
difference in observed Tskin as radiation from surfaces with
emissivities <1 will include (one emissivity) reflected radiation from the
sky. The radiation emitted from a cold sky during cloud-free conditions
will thus result in a colder Tskin observation for surfaces with
lower emissivities, compared to high-emissivity surfaces, and this may
introduce a Tskin difference among radiometers with different
spectral windows. However, ice and snow surfaces generally have very high
emissivities, which reduce the effects from the reflected sky radiation. In
Høyer et al. (2017), the difference in emissivity between the KT15.85 and
the IR120 was modelled using an IR snow emissivity model with the spectral
response functions for the two types of instruments (e.g. Dozier and Warren,
1982). This resulted in averaged emissivities of 0.998 for the KT15.85 and
0.996 for the IR120 spectral windows for a typical snow surface and an
incidence angle of 25∘. Using the same approach for a broadband
4–40 µm spectrum resulted in an emissivity of 0.997. The high
emissivities for all three instruments mean that the contributions from the
sky are small. For realistic conditions in the Arctic, this introduces
an average difference of 0.06 ∘C between the IR120 and the KT15.85
radiometer (which has a similar spectral response function as the KT19.85),
with the IR120 being colder than the KT15.85 (Høyer et al., 2017). It is
thus clear that the KT15.85 is closest to the true Tskin due to
the high emissivity but also that these Tskin variations due to
different spectral windows can be neglected.
Several of the stations (ATQ, BAR, OLI, DMI_Q, SHEBA and FRAM) used here
observed both narrowband and wideband IR observations of the ice surface.
The two types of Tskin have been calculated and compared for each
of the stations. Figure 3 shows an example of a comparison of the two
Tskin estimates from DMI_Q, showing a correlation of 0.99 and
a bias of 0.69 ∘C when comparing the two Tskin
estimates. There is a good relation between the two observations for the full
range of temperatures, meaning that there are no temperature dependencies in
the comparison. Considering all sites, a good agreement is found with a small
mean difference between the two Tskin types of 0.06 ∘C
and a mean root-mean-squared value of 0.96 ∘C. In the following we
use the narrowband Tskin observations when available and the
broadband at the other stations, and we assume that all the Tskin-derived observations have the same characteristics.
Scatter plot
of Tskin estimated from narrowband IR observations versus
Tskin estimated from broadband IR observations for DMI_Q.
Longwave-equivalent cloud cover fraction
For all observation pairs, the longwave-equivalent cloud cover fraction (CCF)
has been estimated based on the relationship between T2m and
downwelling longwave radiation (LWd), following the cloud cover
estimation already included in the PROMICE data sets (van As, 2011; van As et
al., 2005). It is based on the work of Swinbank (1963), who developed a simple approach
for estimation of clear-sky (CCF =0) atmospheric longwave radiation as a
function of T2m:
LWd_clear=9.365×10-6⋅T2m2⋅σ⋅T2m4,
where σ is the Stefan–Boltzmann constant. Overcast conditions (CCF =1) are assumed to occur when the observed LWd exceeds the
blackbody radiation emitted from the surface, which is calculated using
T2m. The CCF for any observed T2m and
LWd pair from all individual observation sites is then calculated
by linear interpolation of the observed LWd, between the
theoretical clear-sky (from Eq. 1) and the overcast estimates. See van As
(2011) for more details on the CCF calculation.
Introduction to the near-surface boundary conditions
To perform an analysis of the Tskin and T2m
relationship and interpret the following results, it is important to consider
the surface energy balance and the specific surface characteristics that
apply in the Arctic. The surface temperature and surface melt are driven by
the surface energy balance. The surface energy balance is the sum of the
energy fluxes between the atmosphere and the snow–ice surface and the
sub-surface land, snow–ice or ocean. The surface energy balance can be
written as
SWd-SWu+LWd-LWu+SH+LH+G=M,
where M is the net energy flux at the surface and SWd,
SWu, LWd, LWu, SH, LH, and G represent
the downwelling and reflected (at the surface) shortwave radiation, down- and
upwelling longwave radiation, sensible and latent heat flux, and subsurface
conductive heat flux, respectively. The energy fluxes have the unit
watts per square metre. All fluxes are defined positive when energy is added to the
surface. The surface is a skin layer, which is an infinitesimal thin layer
without heat capacity, and there is an instantaneous balance among the
different fluxes. This means that the elements in the surface energy balance
are balanced and M equals 0 if there is no phase change (melt or refreeze).
The warming or cooling of the medium below the surface affects the surface
temperature through G and LH
release when refreezing occurs. This affects the temperature of the medium
and with that the temperature gradient close to the surface and thus G at the surface. The radiative budget of sea ice is
dominated by net longwave radiation flux during much of the year. Even during
summer the net shortwave radiation flux is on the same order of magnitude as
the net longwave radiation flux because of extensive cloud cover, especially
during late summer, and the high surface albedo of the snow (Maykut, 1986).
However, SWd is the dominating source for ice melt in Greenland
(van den Broeke et al., 2008; Box et al., 2012; van As et al., 2012), even though turbulent energy fluxes can
dominate during shorter periods (Fausto et al., 2016).
The latter is related to the fact that on average, the turbulent fluxes are
an order of magnitude smaller than the radiation fluxes, and since the net
radiation flux is small compared to the individual radiation fluxes, the
variations in SH and LH fluxes are important for the total surface energy
balance and thus the surface temperature. The turbulent mixing of the lower
atmosphere increases as a function of wind speed (van As et al., 2005).
During clear-sky conditions, when SWd is negligible,
LWu is higher than LWd. This results in a negative
radiative balance cooling the surface and this drives a positive sensible
heat flux. When the heat conduction flux from below the surface is limited on
thick sea ice and on continental ice sheets, the negative radiation balance at
the surface makes the surface temperature colder than the surface air
temperature, resulting in a surface-based temperature inversion (Maykut,
1986). At low to moderate wind speeds, when turbulent mixing is limited, this
creates a very stable stratification of the lower atmosphere. On a sloping
surface, the surface air starts to flow downslope, driven by the existence of
a horizontal temperature gradient and gravity. The generated winds are called
inversion or katabatic winds and are characterised by stronger winds at more
negative surface net radiation and a strong correlation between slope and
wind direction (Lettau and Schwerdtfeger, 1967). In this paper, these winds
will be referred to as katabatic winds. Clouds play a complex role in the
Arctic surface energy budget. For example, they reflect SWd, leading to a
cloud shortwave cooling effect, and absorb LWu and emit
LWd, which tends to have a warming effect. In the Arctic, clouds
have a predominantly warming effect on the surface (Intrieri, 2002; Walsh and
Chapman, 1998) as the dry atmosphere, with lower emissivity and with
absorptivity to LW radiation, enhances the cloud longwave warming effect,
while the high surface albedo and the high solar zenith angles reduce the
impact of the cloud shortwave cooling effect (Curry et al., 1996; Curry and
Herman, 1985; Zygmuntowska et al., 2012).
ResultsDiurnal and seasonal temperature variability
The local air and surface temperature conditions in the Arctic are to a large
extent influenced by the length of the day or night, with extreme variations
depending on latitude and time of the year. In this study we will focus on
the diurnal and seasonal temperature variations, as these are key temporal
scales of variability and therefore important to understand when the aim is
to derive T2m from satellite observations. As an example of the
large seasonal variations, Fig. 4 shows the 2014 monthly mean diurnal
temperature variation in Tskin and T2m at the upper
PROMICE site in Kangerlussuaq, Greenland (KAN_U), during January, April,
July and October. The seasonal variability in the diurnal temperature at
KAN_U is representative of the conditions at the other stations, except
for the general temperature level at each station, which changes with
latitude and altitude. At KAN_U both Tskin and
T2m reach a maximum in July, while the coldest month is
December (not shown) during 2014. During winter and polar night, Fig. 4 shows
no clear diurnal cycle in T2m or Tskin, and
T2m is higher than Tskin. However, during spring
there is a strong diurnal cycle, with Tskin lower than
T2m at night and small T2m–Tskin
differences during daytime. The shadings indicate the standard deviations in
T2m and Tskin. The largest
variability is found in spring and winter as a result of more frequent and
rapid passages of cold and warm air masses in contrast to the summer months
(Steffen, 1995). The summer temperature variability is moreover limited by
the upper limit of 0 ∘C on Tskin during surface melt.
Considering all months individually, there is high correlation between
Tskin and T2m, ranging from an average value of 0.92
in January to an average of 0.99 in July considering the entire time series
of KAN_U, 2008–2018. The high correlations arise from hourly variability
and daily cycles in temperatures that are seen in both temperature records.
The correlation decreases for stations which have occasional surface melt,
where Tskin is constrained to the freezing point of water. The
presence of a lower Tskin compared to T2m is a
general phenomenon found for all stations. Tskin is thus lower
than T2m 85 % of the time, when all sites are weighted
equally, whereas the opposite is true for only 13.7 % of the observation
times.
Mean diurnal variability of 2 m air temperature (T2m)
and skin temperature (Tskin) at KAN_U during the months:
January, April, July and October 2014. The orange lines are the temperature
difference T2m–Tskin. The shadings indicate the
standard deviations, which represent the variability in the monthly mean.
The large seasonal variations in Fig. 4 and the relationship between
T2m and Tskin are typical for all sites. Figure 5a
shows the monthly mean Tskin for all sites and all years. EGP is
by far the coldest site due to its high elevation, with a monthly mean
Tskin of -42∘C in January and a maximum of
-11∘C in July. All sites reach a maximum in Tskin in
July, regardless of latitude. July is also the month with least variation in
temperature among sites, where melt at most stations (exceptions are the ACC
sites) constrains Tskin, while the winter months show a larger
variance in Tskin among sites since local conditions
dominate Tskin. The AWS data from the GrIS show the effect of
altitude and latitude on Tskin, with the high-altitude sites
being the coldest (EGP, KAN_U and KAN_M) together with the most
northern sites (THU_U and KPC_U). The southern (e.g. QAS_A and
QAS_U) and low-altitude sites (most LAB sites, TAS_U and TAS_A) are
the warmest. The SICE sites are comparable in temperature with the coldest
sites on the GrIS (except EGP) but are slightly warmer in summer and autumn.
Monthly mean Tskin(a), daily range in
Tskin(b) and T2m–Tskin
difference (c) for all sites. Each surface type has its own line
style or line width. See Table 1 for station locations and types.
Figure 5b shows the mean daily range (daily max–daily min difference) of
Tskin as a function of month for all sites and all years. Again,
the observations show a similar pattern across the diverse geographical
locations. During summer, the high-elevation sites tend to have the largest
daily range in Tskin, while the observations from LAB and SICE
sites show the smallest daily range. This is mostly an effect of the warmer
temperatures and the Tskin upper temperature limit at
0 ∘C, the melting point for ice. This constraint is seen during
summer in almost all data records included in this study (exceptions are the
ACC sites). Figure 5c shows the monthly mean difference between
T2m and Tskin for all observation sites as a
function of time of year. The T2m–Tskin
differences observed in Fig. 5c have been averaged for each surface type
category in Table 2, divided into summer months (June–August), winter months
(December–February) and all available months. Note that DMI_Q is withheld
from the averaging for the SICE sites to avoid systematic impacts from the
1 m height observations used from DMI_Q. In general, the ACC, SSC and
SICE sites show the weakest inversion, while the UAB and LAB sites show the
strongest inversion. For the ACC sites the weakest inversion is found during
summer, while the UAB and LAB sites have the strongest inversion during
summer. This is explained by the UAB and LAB sites having surface melt in
contrast to the high-elevation ACC sites, where the surface warms but does
not reach the upper limit at the melting point.
Overall 2 m air temperature and skin temperature differences
(T2m–Tskin, ∘C) for each surface type for
different seasons and sky conditions. All months refer to the full time
series as given in Table 1. The square brackets are the ranges of the
T2m–Tskin, differences for the stations included
in each surface type category. The DMI_Q site is excluded from the SICE
averages.
The SSC sites also experience melt, but the snow melts away in summer, which
limits the time when Tskin is constrained to the melting point.
It is difficult to interpret the seasonal dependencies for the SICE sites, as
none of the individual sites cover an entire year. Figure 5 indicates both
seasonal and daily variations in the observed Tskin and
T2m relationship. Figure 6a and b illustrate the mean diurnal
and seasonal T2m–Tskin differences for the ACC and
LAB sites, respectively. The SSC and SICE sites have not been included as
none of the individual sites have a continuous data record throughout the
year. Figure 6a and b indicate that the winter months have very little
diurnal variability in the T2m–Tskin difference
(as is also evident in Fig. 4), with an approximately constant difference of
about 1.5–2.5 ∘C for the LAB sites and 0.5–1.5 ∘C for
the ACC sites. During spring and summer the differences decrease at the ACC
sites and the weakest vertical stratification is found around noon or early
afternoon, where Tskin may even exceed T2m
slightly, resulting in an unstable stratification of the surface air column.
For the LAB sites, the weakest stratification is found in spring and autumn,
around noon and early afternoon. The summer months show large
T2m–Tskin differences due to the constrain of
Tskin for melting surfaces, which is common to all LAB sites. At
night the net radiation is typically negative, thus cooling the surface and
resulting in a surface-based inversion for both surface types. The
T2m–Tskin differences are higher (especially in
summer) at the LAB sites compared to the ACC sites, and the UAB sites have
temperature differences in between. The reason for the higher temperature
difference at the lower-altitude sites is the longer time periods with
surface melt, which is due to higher temperatures.
Mean difference between 2 m air temperatures (T2m)
and skin temperatures (Tskin) for (a) ACC and
(b) LAB sites as a function of time of year (with a bin size of 15
days) and local time of the day. The dotted lines indicate the maximum number
of sunlight hours each month. All sites in each surface type category are
weighted equally.
As mentioned in Sect. 2, the measurement height changes with snowfall and
snowmelt and with the strength of the inversion measured. The PROMICE
data include a height of the sensor boom, which can be used to determine the
impact of using different measurement heights on our results. We reproduced
the numbers in Table 2, based upon observations measured at a height of
1.9–2.1 m only and found over all all-sky, all-month differences less than
0.22 ∘C for all the different PROMICE regions. In addition, the
screening did not change the conclusions regarding the impact of clouds and
the seasonal behaviour of the T2m–Tskin
differences. Data from the other sites do not all include such information on
the measurement height. For consistency, we therefore chose not to screen the
PROMICE data. In addition, we chose not to perform an adjustment of the
observations, as we estimate the uncertainty of such an adjustment to be
equal to or larger than the uncertainty in the results obtained here.
Impact by wind
The surface wind speed is an important component in the near-surface thermal
stratification since the turbulent mixing increases as a function of wind
speed (Monin and Obukhov, 1954). Figure 7 shows how the wind regimes differ
among the observation sites used in this study. In general, winds on the GrIS
are strongest in winter and reach a minimum around July (see also Steffen and
Box, 2001). The surface radiative cooling and the terrain play the primary
role in the generation of the surface winds. The direction and strength of
the prevailing surface winds are closely related to the direction and
steepness of the slope and the strength of the inversion. Surface winds at
the PROMICE sites generally have a high directional persistence (see Fig. 4
in van As et al., 2014), commonly blowing from inland, which is an indication
that local winds are often of katabatic origin. High-elevation sites
experience stronger winds due to the larger radiative cooling of the surface
(provided a comparable surface slope is present; Fig. 7; van As et al., 2014).
The SSC and SICE sites show less variability in wind speed on an annual
basis. At these sites the wind is determined by large-scale synoptic
conditions combined with local topography.
Monthly mean wind speed (m s-1) for all sites. Each surface
type has its own line style or line width. See Table 1 for station locations
and types.
The 2 m air temperature (T2m) and skin temperature
(Tskin) difference as a function of binned wind speed for
(a) DMI_Q (SICE site) and (b) THU_U (UAB site). The
wind speed bin size is 0.5 m s-1, the
T2m–Tskin bin size is 1 ∘C and only bins
with more than 50 members are included. The upper plots show the standard
deviation (dashed lines) and mean difference (solid lines). The middle plots
show the number of members in each bin while the bottom plots show the
number of members (blue lines) and the cumulative percentage of members (red
lines) in each wind speed bin.
The expectation is that stronger inversions can develop in low wind speed
conditions because of reduced turbulent mixing. Figure 8a and b show the
T2m–Tskin difference as a function of wind speed
for selected sites. The top plots show the mean (solid lines) and standard
deviation (dashed lines) of the T2m–Tskin
difference as a function of wind speed. Figure 8a shows data from the
DMI_Q AWS on sea ice. As expected, the strongest temperature inversion
occurs at low wind speeds, and larger wind speeds have larger turbulent mixing
and thus smaller vertical temperature differences between Tskin
and T2m. However, data from THU_U (Fig. 8b) show that this
relationship is more complex. The maximum inversion is reached at wind speeds
from 3 to 5 m s-1, whereas the mean and standard deviation decrease for
calm winds (<2.5 m s-1).
The wind dependencies shown in Fig. 8 are representative for all the stations
in this paper, for which the SICE and the SSC sites resemble Fig. 8a and all
the PROMICE stations have a wind dependency similar to Fig. 8b. The pattern
of the PROMICE stations is explained by the combination of inversion and a
surface slope that results in a flow, which reduces the strength of the
inversion (its own forcing). For large wind speeds the inversion will be
destroyed and calm winds can only occur when the inversion is close to zero
(as the presence of inversion on sloping surfaces forces a wind). As a result
there is an optimum in inversion strength and wind speed, which in this case
is at wind speeds of 3–5 m s-1. This behaviour is also found by
Adolph et al. (2018) at the Summit station on the GrIS. Miller et al. (2013)
also found that the surface-based inversion intensity peaks at wind speeds
ranging from 3 to 10 m s-1 at Summit based on
microwave-radiometer-retrieved profiles. Furthermore, Hudson and Brandt
(2005) show that at the South Pole the maximum inversion strength occurs at
wind speeds of 3–5 m s-1. They investigated this using the model by
Mahrt and Schwerdtfeger (1970) and their results supported the idea that the
inversion forces an air flow, which can explain the “unexpected” location
of the maximum in inversion strength. The nature of the surface winds and the
directional constancy are highly comparable between the sloping surfaces of
Antarctica and Greenland (van den Broeke et al., 1994; King and Turner, 1997)
and in both cases the maximum inversion occurs at non-zero wind speeds.
Impact by clouds
The difference in LWd radiation between clear-sky and overcast
conditions can result in large differences in both T2m and
Tskin due to the cloud effect on the surface radiation budget. As
IR satellite Tskin can only be retrieved during clear-sky
conditions, the assessment of the cloud effect on the average conditions is
essential to facilitate the combination of satellite and in situ
observations. In this section, we therefore assess the inversion strength as
a function of the cloud cover and in the next section the clear-sky bias is
estimated for all sites.
Clear-sky conditions are defined to be cases in which CCF <0.3, while
overcast conditions are defined to have CCF >0.7. The frequency of
clear-sky (overcast) observations is defined as the number of clear-sky
(overcast) observations compared to the total number of observations.
Figure 9 shows the frequency of clear-sky and overcast observations for each
of the observation sites used in this study. The SSC and SICE sites and EGP
all show a much larger frequency of overcast conditions compared to the
frequency of clear-sky conditions. Also, the TAS_U, TAS_A and TAS_L
sites located in the high-accumulation area (Ohmura and Reeh, 1991) of the
southeastern part of the GrIS tend to have more overcast observations
compared to clear-sky observations. There is a general tendency with more
frequent overcast observations for increasing altitudes for the PROMICE
sites. The ACC sites have a strong seasonal dependence with more clear-sky
observations during summer and more overcast conditions during winter (not
shown). A similar but much weaker seasonal cycle is seen for UAB. The LAB and
SSC sites show limited seasonal variability, while the SICE sites have almost
no clear-sky observations during the months from August to March (not shown).
Frequency of clear-sky and overcast observations as percentages of all
observations for each site.
The relation between the inversion strength and CCF is shown in Fig. 10 for
all sites. As expected, the inversion strength decreases for larger cloud
cover fractions due to increasing LWd radiation. For each surface
type category the average slope has been calculated based on linear fits to
the graphs in Fig. 10: ACC =-0.011±0.0037∘C %-1,
UAB =-0.019±0.0012∘C %-1, LAB =-0.021±0.0016∘C %-1, SSC =-0.016±0.0026∘C %-1 and SICE =-0.017±0.0048∘C %-1, for which the uncertainties are given as
95 % confidence intervals on the slope values. The average r2 fit
values for each surface type category are 0.25 (ACC sites), 0.76 (UAB sites),
0.83 (LAB sites), 0.55 (SSC sites) and 0.40 (SICE sites). Excluding ATQ and
EGP (with very low r2 values of 0.013 and 0.0014, respectively)
increases the average r2 to 0.83 and 0.38 for SSC and ACC sites,
respectively. These results indicate that a linear approximation is a good
assumption for UAB, LAB and SSC (excluding ATQ), whereas the ACC and SICE
dependencies are further away from linear.
The 2 m air temperature and skin temperature differences for all sites
as a function of binned cloud cover fraction (CCF). The CCF bin size is 0.05,
the T2m–Tskin bin size is 1 ∘C and only
bins with more than 50 members are considered. Each surface type has its own
line style or line width.
Figure 11a and b show how the temperature differences at the ACC sites vary
as a function of season and local time for clear-sky and overcast conditions,
respectively. Clear-sky conditions show the largest stratification with
temperature differences up to 2–3 ∘C during winter and night-time.
Overcast conditions reduce the temperature gradient at all times, with the
maximum temperature differences of about 1 ∘C. During summer around
noon, overcast conditions usually lead to an unstable stratification of the
order of -1∘C. An unstable stratification may also occur during
clear-sky conditions and large solar insolation. This behaviour is common for
all sites included in this study, but the strength of the inversion varies
among the different sites. Table 2 also summarizes the impact of clouds on
the T2m–Tskin differences for each surface type
category. For all surface types and for all times of the year, cloud cover
tends to decrease the inversion strength.
To assess the impact of the different spectral characteristics of the used
radiometers (broadband versus narrowband, as discussed in Sect. 2.7) on the
observed Tskin, the T2m–Tskin
differences were calculated as a function of CCF for both narrow- and
broadband Tskin for the sites containing both instruments (ATQ,
BAR, OLI, DMI_Q, SHEBA and FRAM). The average slope for the above sites
was estimated in both cases and resulted in a small difference in the slope
from -0.017 to -0.020∘C %-1 for narrowband and broadband
Tskin estimates, respectively.
Mean difference between 2 m air temperatures (T2m)
and skin temperatures (Tskin) for ACC sites in cases of
(a) clear-sky and (b) overcast conditions. The dotted lines
indicate the maximum number of sunlight hours each month. All sites in each
surface type category are weighted equally.
Clear-sky bias
The most accurate surface temperature satellite observations are thermal IR
observations that can only be utilized during clear-sky conditions. As the
satellite IR observations thus have gaps resulting from cloud cover, the
satellite Tskin products are often averages of the available
satellite observations over a 1–3-day period (see e.g. Rasmussen et
al., 2018). However, these satellite averages will differ from the all-sky
average temperature since the Tskin is typically lower during
clear-sky conditions compared to cloudy conditions. This difference is
referred to as clear-sky bias. When using the averaged Tskin
observations from satellites for monitoring or in combination with ocean, sea
ice or atmospheric models, it is thus important to assess the impact off the
different temporal averaging windows on the clear-sky bias. Hall et
al. (2012) show monthly temperature maps from MODIS and discuss the fact that
the monthly average temperatures (from satellites) are likely lower than the
all-sky monthly average temperatures. Here, we use the in situ observations
to estimate the clear-sky effects that satellite observations would
introduce. We use a cloud mask derived from the longwave-equivalent cloud
cover fraction and assume that it is equivalent to the cloud masks used for
IR satellite processing. The clear-sky bias is assessed by comparing all
available clear-sky Tskin observations (where clear sky has been
defined as a CCF <0.3) with all available all-sky Tskin
observations, averaged for different time windows: 24 h, 72 h and 1 month,
for all sites. The three averaging windows were chosen to examine the
clear-sky effect for previously used averaging windows in Rasmussen et
al. (2018) (72 h) and when calculating monthly climatological values. The
results are shown in Fig. 12. For most stations all-sky observations are
warmer than clear-sky observations for all time windows and the difference
tends to increase with increasing length of temporal averaging window. The
larger clear-sky biases for longer temporal averaging windows arise from
persistent cloud cover lasting for days. A clear-sky bias cannot be computed
when using temporal averaging windows of shorter length than the duration of
overcast conditions due to missing clear-sky observations. If however, a
longer temporal averaging window is used, the Tskin observations
during the overcast conditions (which tend to be warmer than during
clear sky) will be included in the all-sky average. The result is a warmer
all-sky Tskin for longer temporal averaging windows and thus a
larger clear-sky bias. There is large variability among the stations, and at a
few stations, such as EGP, KPC_U, ATQ, OLI and DMI_Q, the all-sky
observations are colder than clear-sky observations using one or more of the
temporal averaging windows. These positive clear-sky biases are very likely
a result of seasonal differences in cloud cover.
Observed clear-sky biases
(Tskin.clearsky–Tskin.allsky) averaged for different
time intervals, for all sites (∘C).
Figure 13a and b show the monthly mean difference in the 24 h averaged
clear-sky and all-sky Tskin for the ACC stations (a) and the LAB
stations (b), together with the average number of hours with clear sky per
day. For both groups of stations it is found that the 24 h averaged
clear-sky bias is closest to zero during summer, which can partly be
explained by the smaller daily Tskin range in summer (Fig. 5b).
The UAB sites (not shown) look very similar to the LAB sites but with a
slightly more pronounced seasonal cycle in the clear-sky bias. The figures
have not been produced for the SSC and SICE sites as none of the individual
sites included in these categories cover an entire season. Figure 13 also
shows more hours with clear skies for LAB stations compared to ACC stations
except for the period of May–July, when both surface groups on average have
about 12 h with clear sky per day. For the ACC sites the number of hours
with clear sky decreases to about 4 h per day during September–March. It is
found that EGP has no clear-sky observations in December–February and at
DMI_Q there are no clear-sky observations available for January–March,
which means that the results in Fig. 12 are biased towards the months when a
zero or positive clear-sky bias is observed. This very likely explains the
positive clear-sky biases observed (in Fig. 12) for these stations. The 72 h
and 1-month averaged clear-sky biases show the same seasonal variation as in
Fig. 13, with the smallest biases in summer and largest biases in winter (not
shown).
Monthly mean differences between 24 h averaged clear-sky and
all-sky skin temperatures for (a) ACC stations and (b) LAB
stations. The orange lines show the 24 h average number of hours with
clear sky (CCF <0.3) per day for each month. The grey bands show the
monthly average of the daily standard deviations. All sites in each surface
type category are weighted equally.
The observed clear-sky bias explains part of the cold bias observed in IR
satellite retrievals of skin surface temperature compared to in situ skin
surface temperatures as seen in Høyer et al. (2017) and Rasmussen et
al. (2018). Another contribution to a satellite versus in situ cold bias is
related to the fact that the satellite skin observations are compared to in
situ observations measured at typically 2 m in height (Shuman et al., 2014).
Temperature inversions in the lowest 2 m of the atmosphere will thus result
in the satellite retrievals of surface temperature being colder than the in
situ measurements at 2 m in height.
Relationship between Tskin and T2m
Section 4.3 showed how clouds impact the T2m and
Tskin relationship, and Sect. 4.4 revealed how satellite
Tskin is affected by clouds. With the aim of deriving
T2m based upon satellite Tskin observations, it is
important to examine how the T2m–Tskin difference
is related to the skin temperature itself. The relationship with
Tskin is shown in Fig. 14 in which the strength of the surface-based
inversion is shown as a function of Tskin. All PROMICE sites show
an almost linear trend towards weaker inversion strength for higher skin
temperatures with the steepest slope of the curve for low-elevation sites.
The average slopes of the linear fits of the graphs in Fig. 14 for all
categories are found to ACC =-0.030±0.003, UAB =-0.066±0.004,
LAB =-0.101±0.004, SSC =-0.044±0.005 and SICE =-0.043±0.007, for which the uncertainty estimates are given as 95 % confidence
intervals on the slopes. The average r2 fit values for each surface type
category are 0.76 (ACC sites), 0.77 (UAB sites), 0.86 (LAB sites), 0.54 (SSC
sites) and 0.51 (SICE sites). The numbers demonstrate that the linear
relationship is a better assumption when using Tskin compared to
cloud cover fraction. The results of this section show that the slopes are
similar within each region but tend to vary from region to region. This
indicates that Tskin and T2m relationship models can
be derived on a regional level using Tskin for situations in
which
the cloud cover and longwave radiation are not available, such as the case
with satellite observations.
Mean 2 m air temperature and skin temperature differences
(T2m–Tskin) for all sites as a function of binned
skin temperature (Tskin). The Tskin bin size is
1 ∘C, the T2m–Tskin bin size is
1 ∘C and only bins with more than 50 members are considered. Each
surface type has its own line style or line width.
As in Sect. 4.3, the impact of the different spectral characteristics of the
radiometers on the above results has been assessed. The
T2m–Tskin differences were calculated for both
types of radiometers as a function of Tskin for the sites
containing both instruments (ATQ, BAR, OLI, DMI_Q, SHEBA and FRAM).
Again, the difference in the average slope was small, from -0.046 to
-0.055 for narrow- and broadband Tskin estimates, respectively.
Conclusions
Coincident in situ skin temperature (Tskin) and 2 m
air temperatures (T2m) from 29 sites in the Arctic region have
been analysed to assess the variability and the factors controlling the
Tskin and T2m variations. The aim is to facilitate
the combined use of satellite-observed Tskin and traditional
observations of T2m. The extensive data set used in this study
represents a wide range of conditions including all-year observations from
Arctic sea ice, land ice in northern Alaska, and low- and high-altitude
land ice covering the lower, middle and upper ablation zones and the
accumulation region of the Greenland Ice Sheet (GrIS). It has been found that
for each region there is a good correspondence between the Tskin
and T2m and that the main factors influencing the relationship
between Tskin and T2m are seasonal variations, wind
speed and cloud cover.
Considering all surface type categories, the mean
T2m–Tskin difference is on average
0.65–2.65 ∘C, with the strongest inversion at the sites located in
the lower ablation zone and the weakest inversion at the sea ice sites.
Inversions are predominantly found during winter (low-sun and polar night
periods), which allows for a strong radiative cooling at the surface. Smaller
T2m–Tskin differences dominate around noon
and early afternoon in spring and summer, when the sun is warming the surface
but no melting occurs. This is in agreement with Adolph et al. (2018), who
found large T2m–Tskin differences during night-time and small differences during the peak solar irradiance at Summit, GrIS
(see Fig. 5 in Adolph et al., 2018). During local noon in spring, autumn and
summer (during non-melting conditions), satellite-observed skin temperatures
will therefore have the best agreement with the T2m.
Increasing wind speeds are expected to decrease the inversion strength
through increased turbulence and mixing of warmer air towards the surface.
This is seen at the ARM sites and the Arctic sea ice sites, where the
strongest inversion occurs at calm winds. Conversely, the inversion strength
decreases with increasing wind speed. The relationship is more complicated
over a sloping terrain with the maximum inversion strength at winds of
3–5 m s-1 for all the GrIS sites. This feature has previously been
identified by others for Antarctica (Hudson and Brandt, 2005) and at Summit,
GrIS (Adolph et al., 2018; Miller et al., 2013), and can be explained by the
presence of a katabatic wind driven by the surface temperature inversion over
a sloping terrain. The katabatic wind reduces the inversion strength, and as a
result there is an optimum in inversion strength and wind speed.
The analysis of the impact of clouds showed an almost linear relationship
between cloud cover fraction (CCF) and the
T2m–Tskin difference, with a trend towards zero
with increasing CCF, for most sites (Fig. 10). Considering all surface type
categories, the T2m–Tskin difference decreases
from an all-sky mean value ranging from 0.65 to 2.65 ∘C to a
difference ranging from -0.08 to 1.63 ∘C for observations with a CCF
above 0.7. Conversely, the T2m–Tskin
difference increases to the range of 1.05–3.44 ∘C by only
considering observations with CCFs below 0.3. The smaller inversion strength
under cloudy conditions is explained by the fact that clouds have a
predominantly warming effect on the surface in the Arctic (Intrieri, 2002;
Walsh and Chapman, 1998). In situations in which the cloud cover and longwave
radiation are not available, the T2m–Tskin
relationship can be quantified by using the Tskin. We have found
an almost linear relationship between the inversion strength and the skin
temperatures, with weaker inversions for higher Tskin. This is in
agreement with Adolph et al. (2018), who found larger
T2m–Tskin differences at lower temperatures at the
Summit station during summer.
In order to facilitate the construction of a satellite-derived
T2m product, the influence of clouds on temporally averaged
Tskin has been assessed. This has been performed by comparing
clear-sky Tskin observations with all-sky Tskin
observations averaged over different time intervals: 24 h, 72 h and
1 month. In general, the clear-sky average is colder than the all-sky average
with increasing bias with the length of the averaging time interval. The
clear-sky bias is smaller during summer than winter for all averaging
windows. This is also reported by Comiso (2000), who finds a monthly mean
clear-sky bias of about -0.3∘C during summer (January) and
-0.5∘C during winter (July) at Antarctic stations. The seasonal
variation in clear-sky bias in combination with differences in frequency and
timing of clear-sky observations lead to differences among the stations. The
average positive clear-sky bias at EGP, for example, is thus a result of persistent
cloud cover during winter months and predominantly clear sky in summer
months, when the clear-sky bias is small or positive.
The assessment of the T2m–Tskin differences and
the identification of the main variables that control the variability are
important findings when developing a statistical model to estimate the
T2m from satellite Tskin observations. In addition,
the findings in the diurnal and seasonal variations in the
T2m–Tskin differences are valuable when validating
the satellite Tskin against T2m observations. All
the identified parameters can be derived from either the satellite retrievals
themselves or from numerical weather prediction (NWP) analysis. The
generation of a daily satellite-derived T2m product for the
polar regions using a statistical model is thus facilitated with these
results, which is the focus of current developments. Such a satellite-derived
product can be independent of other existing surface temperature products and
NWP reanalysis and can therefore contribute significantly to improvements in
Arctic climate change monitoring and assessment.
Data availability
The PROMICE data can be accessed through
http://www.promice.dk (last access: 16 November 2018). The ARM data are
available at https://www.archive.arm.gov/discovery/\#v/results/s/s::co
(last access: 21 December 2018). The SHEBA data are available from
https://atmos.uw.edu/~roode/SHEBA.nc.readme.html (last access: 28
November 2017), while the DMI ICEARC data are available through the
10.6084/m9.figshare.7831526 (Høyer et al., 2019). The Tara data are
provided through personal communication with Timo Palo from the TARA
expedition. Similarly, data from the FRAM 2014/15 expedition can be obtained
through personal communication with Steinar Eastwood from the Norwegian
Meteorological Institute.
Author contributions
PNE, KSM, RT, GD and EA compiled the in situ data. PNE, JLH
and KSM designed the experiments and PNE carried them out. PNE prepared the
paper with contributions from all authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This study was carried out as a part of the European Union Surface
Temperatures for All Corners of Earth (EUSTACE), which is financed by the
European Union's Horizon 2020 Programme for Research and Innovation, under
grant agreement no. 640171. The aim of EUSTACE is to provide a spatially
complete daily field of air temperatures since 1850 by combining satellite
and in situ observations. The author would like to thank the data providers.
Data were provided by PROMICE, which is funded by the Danish Ministry of
Climate, Energy and Building, operated by the Geological Survey of Denmark
and Greenland and conducted in collaboration with the National Space
Institute (DTU Space) and Asiaq (Greenland Survey). Data were also obtained
from the Atmospheric Radiation Measurement (ARM) Climate Research Facility, a
U.S. Department of Energy Office of Science user facility sponsored by the
Office of Biological and Environmental Research. We thank our colleagues in
the SHEBA Atmospheric Surface Flux Group, Ed Andreas, Chris Fairall, Peter
Guest and Ola Persson for help collecting and processing the data. The
National Science Foundation supported this research with grants to the U.S.
Army Cold Regions Research and Engineering Laboratory, NOAA's Environmental
Technology Laboratory, and the Naval Postgraduate School. Data were also
provided by Timo Palo from the TARA expedition, supported by the European
Commission 6th Framework Integrated Project DAMOCLES and in part by the
Academy of Finland through the CACSI project. We thank Steinar Eastwood from
the Norwegian Meteorological Institute for providing us with data from the
FRAM 2014/15 expedition. Finally, we would like to thank the anonymous
reviewers for their careful reading and their insightful suggestions and
comments, which substantially improved this paper.
Edited by: John Yackel
Reviewed by: three anonymous referees
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