Introduction
Arctic ecosystems are undergoing very rapid
changes as a result of warming climate and
their response to climatic change has important implications, not only on
local to regional scales but also on a
global scale . Thawing permafrost has the
potential to release large quantities of carbon dioxide and methane that are
currently trapped in frozen soil. Microbes may also produce increasing
amounts of carbon dioxide and methane as more organic material becomes
available due to thawing. The Arctic is likely to be affected by changes to
the timing of snowmelt, to the length of the growing season, to the
vegetation, and to precipitation regimes. The regional energy budget of
Arctic ecosystems can be changed, both directly or indirectly, through a
lower albedo as a result of reduced snow cover or a higher albedo due to the changes in vegetation
. provided evidence that fire-induced
changes in the surface energy budget also contribute to regional cooling at
high latitudes through an increase in surface albedo during spring and
summer. The sensible heat flux (H) and latent heat flux (λE), which
together form a major part of the surface energy budget, therefore have a
marked effect on climatic variability and associated feedbacks.
Surface energy partitioning is an important physical process that has a
strong influence on the ground heat flux and hence on the thermal condition
of Arctic ecosystems. Direct measurements of surface fluxes are usually made
using eddy-covariance (EC) flux towers . Energy fluxes
have been previously investigated in different polar regions using a variety
of techniques. analyzed surface fluxes and the energy
budget of a tussock tundra ecosystem in Alaska; they reported a strong
correlation between daily fluctuations in evapotranspiration and daily
fluctuations in net radiation, as well as a predominance of biological
limitations to evapotranspiration over meteorological limitations during the
measurement period. and
used independent measurements of radiation and heat flux and documented the
annual cycle of the surface energy budget on Svalbard and Samoylov Island in
the Lena River Delta; both of these sites are high Arctic permafrost sites.
The relative importance of different budget components over a full year was
also investigated. The ratio of H to λE, which is known as the Bowen
ratio, was found to vary between 0.25 and 2, depending on the water content
of the uppermost soil layer .
investigated surface energy fluxes measured at Council, on the Seward
Peninsula of Alaska, at five sites representing the major vegetation types in
the transition zone from Arctic tundra to forest, these being tundra, low
shrub, tall shrub, woodland (treeline), and boreal forest sites. Changes in
vegetation structure that increased sensible heat flux were shown to enhance
warming in northern high latitudes. evaluated changes in
regional surface energy fluxes due to fire and spring warming in Alaska
between 2000 and 2011, based on an upscaling of EC tower measurements, and
highlighted the importance of these processes in amplifying or reducing
Arctic warming over decadal timescales.
EC tower measurements may, however, only be representative of small areas
immediately surrounding the tower locations . Moreover, due to the lack of infrastructure, EC towers are
scarce and unevenly distributed over high-latitude permafrost wetlands, which
makes it difficult to use EC tower measurements for accurate model upscaling
from regional to global flux contributions from the Arctic. Airborne
measurements can be used as an alternative way to investigate surface
exchange at regional scales . used
airborne flux measurements and developed a procedure to estimate the sensible
heat and latent heat fluxes for different land covers in a heterogeneous
landscape. This method extracts environmental response functions (ERFs),
which establish a relationship between spatially or temporally resolved flux
observations and environmental drivers. analyzed airborne
data in the space and time domains using the flux fragment method (FFM) and
compared the theory behind the FFM with that behind the wavelet method. An
improved random-error estimate was proposed that takes into account the
serial correlation of the time–space series and the heterogeneity of the
signal. used the FFM method to analyze regional-scale
drivers of the heterogeneity and variability of methane fluxes measured by a
small, low-flying aircraft over the North Slope of Alaska. Airborne flux
measurements can be also used to detect strong emissions from geologic
methane sources below ground or to investigate
waterbodies as a source of the methane in the Arctic permafrost zone
.
Since changes in climate-related parameters such as evaporation,
precipitation, and land cover can have a significant effect on the regional
surface energy budget, a good understanding of how energy fluxes in the
Arctic will respond to climatic changes is crucial. In this study we aimed to
upscale airborne flux measurements and to develop spatially extensive, high-resolution flux maps that could be used to provide new insights into surface
exchange processes and to validate coupled atmospheric–land-surface models.
Particular emphasis was placed on a detailed analysis of airborne EC
measurements and the spatial patterns of surface energy exchange across the
North Slope of Alaska. In this paper we attempt to answer the following
questions: (i) Which surface properties are the main drivers for
energy fluxes in permafrost areas? (ii) Is it possible to use relationships
extracted across heterogeneous Arctic landscapes to create high-resolution
surface flux maps and to directly upscale observational data with minimal
assumptions? (iii) How large are land-cover-specific energy fluxes under
particular meteorological conditions and what are the energy partitioning
patterns in northern ecosystems? Lastly, airborne and modeled fluxes are
compared with EC tower measurements and the factors leading to discrepancies
are discussed.
The rest of this paper is organized as follows. The study area and climate
are first described (Sect. ). The experimental setup and
the state-of-the-art processing of airborne EC measurements are then
presented in Sect. . Section provides a
summary of the model configuration and model data used for the flux
upscaling. Section explains how a nonparametric machine
learning technique was used to upscale direct flux measurements across the
North Slope of Alaska. The potential of the extracted relationships between
flux observations and surface properties are evaluated in
Sect. . The ERFs of the energy surface fluxes are first
presented in Sect. . The variability of energy fluxes
between different northern ecosystems and energy partitioning within northern
ecosystems are discussed in Sect.
and . The airborne flux measurements are compared
with the modeled fluxes in Sect. . The final
section (Sect. ) presents our conclusions and discusses
possible improvements and applications of the presented methods.
Material and methods
Study area
The following analysis focuses on the North Slope of Alaska, a large
terrestrial area at latitudes greater than 69∘ N, bordered to the
north by the Arctic Ocean (the Chukchi Sea to the northwest and the Beaufort
Sea to the northeast) and to the south by the Brooks Range. The investigated
area covers 87 160 km2, extending 330 km in an east–west direction and
275 km north–south; it consists mainly of coastal plains to the north and
foothills to the south, which differ in their climate and topography as well
as in their vegetation (both structure and composition).
According to , the North Slope of Alaska can be divided into
three main climate zones which they referred to as the Arctic foothills,
Arctic inland, and Arctic coastal zones. The climate is strongly influenced
by both continental and marine environments. Cloud cover, fog, and
northeasterly winds are common over the coastal zone between June and
August, while the inland area experiences higher average air temperatures,
more variable wind directions, and more frequent clear sky conditions.
The mean monthly temperatures over the North Slope of Alaska are below
10 ∘C. Only between June and August are average air
temperatures above the freezing point and the annual mean temperature is
below -10 ∘C. Precipitation in the coastal zone is of
the order of 150 mm, increasing towards the south, and the tundra is covered
with snow for about 9 months of the year. The mean annual wind speed is about
6 m s-1. The active layer above the permafrost is about 300
to 400 mm thick . The predominant forms of vascular
vegetation on the North Slope are tundra shrubs and graminoids
.
Airborne eddy-covariance measurements
An airborne survey to measure methane fluxes was carried out across the North
Slope of Alaska from 28 June to 2 July 2012 (AIRMETH-2012: airborne
measurement of methane fluxes), based out of Utqiaġvik (formerly Barrow),
Alaska (71∘18′ N,
156∘46′ W). The research aircraft Polar 5
belonging to the Alfred Wegener Institute (AWI)
Helmholtz Centre for Polar and Marine Sciences flew at low altitudes,
measuring fluxes along horizontal transects totaling more than 3115 line
kilometers (about 41 flight hours) over the North Slope of Alaska. Forty
vertical profiles were also obtained to estimate the height of the planetary
boundary layer. The results presented in the following analysis are
representative for the period from 10:00 local time (LT = UTC-8 h) to
14:00 LT, which we refer to as the “reference period”. Flight lines
are shown in Fig. and the four time
intervals used in our analysis are summarized in Table .
These time intervals are characterized by air temperatures between 5 and
11 ∘C and a light breeze blowing from the northwest
or from the northeast, east, or southeast.
Flight lines from the 2012 airborne survey over the North Slope of
Alaska that were used in the analysis. The dark blue flight lines were more
frequently surveyed than the light blue lines. The insert shows the location
of the EC tower in Atqasuk that was used for the comparison in
Sect. . Map data: Google, DigitalGlobe.
Details of the Polar 5 survey flights carried out in 2012 over the
North Slope of Alaska, the time intervals used in the analysis, and median
values for meteorological parameters averaged over these time intervals.
Flight
Start time
End time
Time used for
Median in situ
Median horizontal
Median wind
date
(LT)
(LT)
the analysis (LT)
temperature (∘C)
wind speed (m s-1)
direction (∘)
28 June 2012
13:43
18:02
13:43–14:05
5
2.1
306
29 June 2012
09:22
16:39
09:52–13:54
6
4.8
79
30 June 2012
10:59
14:19
10:59–14:03
9
2.6
150
02 July 2012
13:21
16:58
13:21–13:43
11
4.6
53
The Polar 5 aircraft was equipped with a nose boom carrying a Rosemount
five-hole probe to measure the 3-D wind vector. A PT100 sensor was installed in
an unheated Rosemount housing at the tip of the nose boom to measure the air
temperature. A HMT-330 sensor (Vaisala, Helsinki, Finland) to measure the
humidity of the air was also mounted in a Rosemount housing. Data were
recorded at 100 Hz. A CR2 chilled mirror hygrometer (Buck Research
Instruments LLC, Aurora, Colorado, USA) providing highly accurate (but slow)
absolute values was used to validate humidity measurements. The aircraft
movements and attitude were acquired by a Laseref V Inertial Navigation
System (Honeywell International Inc., Morristown, New Jersey, USA), with the
position derived using a global positioning system (NovAtel Inc., Calgary,
Alberta, USA). The aircraft was also equipped with a KRA 405B radar altimeter
(Honeywell International Inc., Morristown, New Jersey, USA), an LD90/RIEGL
laser altimeter (Laser Measurements Systems GmbH, Horn, Austria), and a CMP22
pyranometer (Kipp & Zonen B.V., Delft, the Netherlands). The median altitude
for the survey flights was 38 m a.g.l. and the median true
airspeed was 69 m s-1.
Airborne flux measurements are often a trade-off between priority of flights
with near-constant height above the ground and minimizing pilot adjustments due
to flight safety. Minimizing control pressures increases the accuracy of the
flux measurements by minimizing the flow distortion. However, it usually
requires flights at higher altitude and the advantage of cleaner sampling of
heterogeneous surface fluxes is reduced. But neither of these were really
enforced during the measuring campaign. Occasionally pilot action was strong,
but for most of the survey flights the measuring height was nearly constant.
Additionally, the Alaskan North Slope is relatively flat and the median
terrain height and its median absolute deviation along the flight lines was
21 m ± 13 m and allowed us to measure at the median height 38 m with
median absolute deviation ± 7 m. The horizontal heterogeneity is usually
hundreds of meters and well suited for the study of the natural systems.
To estimate the energy fluxes between the earth's surface and the atmosphere
we followed and used a modified version of their
time-frequency-resolved EC method in an early version of the
edd4R EC data processing software . The
spikes were first removed from the raw turbulence data and the sampling
frequency reduced from the 100 Hz of the original data to a 20 Hz resolution,
using block averaging. Computations were made using a continuous wavelet
transform to enable a 100 m spatial discretization of the flux measurements.
This was achieved by integrating the wavelet cross scalograms in frequency
over transport scales up to 20 km and in space using a 1000 m moving window
along the flight paths, in 100 m steps. This allowed the calculation of
spatially resolved turbulence statistics and of sensible heat and latent heat
fluxes for overlapping subintervals of 1000 m length, with a 100 m
resolution. However, because of the deep overlap this method can lead to
strong autocorrelation, implying fewer degrees of freedom than there are
fluxes in the sample. This reduction has to be taken into account and can be
estimated by determining the decorrelation length. The flux data were
subjected to quality assurance and quality control measures, which included a
steady state test to detect non-steady state
conditions during the selected perturbation timescale and an ITC (Integral
Turbulence Characteristics) test to compare the measured
integral turbulence characteristics with the modeled characteristics. Data
with quality flags from 1 to 6 were retained for subsequent analysis. The
subintervals were centered above each cell of the remote sensing data
overflown by the Polar 5 aircraft. Footprint-weighted surface properties,
which preserve the continuous nature of the information content, were
subsequently determined for a total of 21 529 sensible heat flux observations
and 25 608 latent heat flux observations. The footprint model used was the
2-D version of the 1-D model.
Configuration and evaluation of the WRF model
The Weather Research and Forecasting (WRF) model was used to simulate the
potential temperature, the dry mole fraction of water vapor, the shortwave
down-welling radiation, and the height of the planetary boundary layer. These
atmospheric drivers were used to project the surface–atmosphere exchange of
sensible heat and latent heat throughout the North Slope of Alaska. The WRF
model is a numerical weather prediction model designed for use on a regional
scale that can be used for operational forecasting and
atmospheric research. It is, however, adaptable to a higher resolution (1 km or less)
by using a nested domains technique and zooming in on the area of
interest. For our analysis we used the WRF-ARW (Advanced Research WRF core)
version 3.2.1; the configuration of the WRF model is given in
Table . The WRF model was initialized using two nested
domains, D1 and D2, with spatial resolutions of 3 and 1 km and temporal
resolutions of 3 h and 30 min, respectively (Fig. ). The
meteorological input data were obtained from the final global gridded
analysis archive of the , which had a 1∘ × 1∘
spatial resolution and 6 h temporal resolution. Sea surface
temperatures with a 0.5∘ spatial resolution were provided by the
National Centers for Environmental Prediction (NCEP;
, ).
Configuration of the WRF model domains and physical parameterizations.
Domains and physical parameterizations
dx, dy (m)
3000 (D1); 1000 (D2)
Microphysics
Lin (Purdue) scheme
Longwave radiation
Rapid Radiative Transfer Model
Shortwave radiation
Goddard shortwave scheme
Surface layer
MM5 similarity theory surface layer scheme
Land surface
Noah Land Surface Model
Planetary boundary layer
Yonsei University scheme
Cumulus parameterization
Kain–Fritsch scheme
Location of the D1 and D2 nested domains.
Figure shows weather conditions during the
reference period. The synoptic situation was characterized by air
temperatures close to 0∘ over the Arctic Ocean, rising
to ≈ 20∘ in the southern part of the study
area. Close to the coast the wind blew mainly from the northeast, changing
to blow from the south or southeast close to the Arctic foothills;
northwesterly winds were observed over the Utqiaġvik area on 28 June.
The wind speed was between 1 and 4 m s-1, indicating light
breezes.
Air temperature at 2 m above the ground and wind speed at 10 m above
the ground, simulated by the WRF model for 28 June at 14:00 h LT (a), for
29 June at 12:00 h LT (b), and for 30 June at 12:00 h LT (c). Black
lines represent Polar 5 flight lines.
Estimation of environmental response functions
A boosted regression tree (BRT) technique was
used to estimate ERFs between spatially
and temporally resolved flux observations and the corresponding biophysical
and meteorological drivers. The BRT technique is a nonparametric machine
learning technique that attempts to learn a response by observing inputs and
their associated responses, finding dominant patterns (regression trees),
establishing a response function according to the coherencies in the training
data, and then adaptively combining large numbers of relatively simple tree
models to optimize the predictive performance. An example of the BRT method
is shown in Fig. .
Example of boosted regression trees (BRT) learning a response of
sensible heat flux (H) to observations of the downward shortwave solar
radiation (S↓), the enhanced vegetation index (EVI), the mixing
ratio (r), and the land-surface albedo (α).
To train the model we used remote sensing data, meteorological state
variables from WRF modeling, and airborne measurements. The remote sensing
data came from the Moderate Resolution Imaging Spectroradiometer (MODIS),
post-processed by the National Research Council (NRC) of Canada
. We used bilinear interpolation to increase
the spatial resolution to 100 m and linear interpolation in time to obtain a
separate map for each flight day. The flux footprints were subsequently used
to link surface properties with the corresponding measured energy fluxes. In
order to take into account the altitude dependency of surface fluxes, the
ratio of the measurement height (zm) to the height of the
planetary boundary layer (zABL), estimated by the WRF model,
was used as a training parameter. Using WRF data allowed us to mitigate the
assumption of horizontally homogeneous meteorological states
, which is clearly violated in our study area, as shown in
Fig. . The temporal variations in the surface
fluxes were taken into account by using the time of observation as a training
parameter. The mid-point time for each flight line was used as the time for
the projection. A full list of the drivers tested is provided in
Table .
Biophysical and meteorological drivers used for estimating
environmental response functions, as well as the corresponding data sources.
Data source
Parameter
Response
Projection
Enhanced vegetation index EVI
MODIS MOD13Q1
MODIS MOD13Q1
Land-surface albedo α
NRC SW BB Albedo
NRC SW BB Albedo
Downward shortwave solar radiation S↓
Polar 5
WRF
Potential temperature θ
Polar 5
WRF
Mixing ratio r
Polar 5
WRF
Daytime
Observation time
Projection time
Ratio of measurement height zm to the height
Polar 5, WRF
5 % of zABL
of the planetary boundary layer zABL
Results and discussion
Environmental response functions of energy fluxes
BRTs can provide deep insights into ecologically complex interactions. These
can be visualized using fitted ERFs that show the effect on surface fluxes of
a specific state variable over its entire range, while all other state
variables are held at their means. The ERFs for sensible heat flux are shown
in Fig. and for latent heat flux in
Fig. . The most important factors affecting surface heat
fluxes are S↓, enhanced
vegetation index (EVI), and α, all of which yield almost linear
responses within the 10–90 % range of the data distribution, and θ and
r, which yield nonlinear responses.
Environmental mean response functions for the sensible heat flux.
The functions show the responses to changes in the downward shortwave solar
radiation (S↓), potential temperature (θ), enhanced
vegetation index (EVI), mixing ratio (r), and land-surface albedo (α).
The black line shows the variable response of the BRT and the red line is an
equidistantly smoothed representation of the black line. Rug plots along the
top margins of the plots show the distribution of the variables in deciles.
Environmental mean response functions for the latent heat flux. The
functions show the responses to changes in the downward shortwave solar
radiation (S↓), potential temperature (θ), enhanced
vegetation index (EVI), mixing ratio (r), and land-surface albedo (α).
The black line shows the variable response of the BRT and the red line is an
equidistantly smoothed representation of the black line. Rug plots along the
top margins of the plots show the distribution of the variables in deciles.
Figure shows a scatterplot with
hexagonal binning of the measured airborne values and BRT predicted values
for sensible heat (a) and latent heat (b) fluxes. Both the observed H and
λE are in a good agreement with the BRT fitted values for fluxes up
to 100 W m-2, with a slight underestimation by the BRT
technique for values greater than 100 W m-2. The median
absolute deviations in the residuals for the sensible heat and latent heat
fluxes are less than 8 and 3 %, respectively, and the coefficient of
determination (R2) is greater than 0.99 in both cases. It
has to be mentioned that only a small fraction of the data are located in the
range of the gray point cloud. For the sensible heat flux only 10 % of the
data are less than -5 W m-2 or more than
80 W m-2 and located outside of the black cloud. For the
latent heat flux only 6 % of the data are less than 0 W m-2
or more than 110 W m-2. We interpret these as a spurious yet
systematic process that the machine learning technique cannot yet describe
with the selected drivers and small sample size alone.
showed that underestimations mostly occur along short sections of the flight
lines that have highly intermittent solar irradiance. Finally, the resulting
ERFs were used to extrapolate the sensible heat
and water vapor exchange over spatiotemporally explicit grids of the Alaskan
North Slope, using the remote sensing and model output data as biophysical
and meteorological drivers. In order to match the remote sensing data, the WRF
gridded data were downscaled from the finest domain to a 100 m spatial
resolution using bivariate interpolation, and a bias adjustment was made of
WRF atmospheric variables to match the in situ airborne survey data.
Scatterplot with hexagonal binning of the measured (airborne
survey) and BRT predicted sensible heat (a) and latent heat (b) fluxes.
Altogether 21 529 data points were used for the sensible heat flux scatterplot and 25 608 for the latent heat flux.
Variability of energy fluxes between northern ecosystems
The BRT technique was used to extrapolate sensible
heat and latent heat fluxes across the North Slope of Alaska. Separate flux
maps for each flight line were created using a trained BRT model, together
with meteorological data for corresponding times from the WRF model and
remote sensing data. Median values were calculated from the individual maps
and used to produce the ensemble maps in Fig. ,
which illustrate the spatial variability of energy fluxes across the North
Slope of Alaska, well captured by ERFs. The latent heat flux varies
considerably and shows a strong gradient from 160–180 W m-2
in the south to 10–20 W m-2 in the north, whereas the
sensible heat flux has a less pronounced south–north gradient, with maximum
values of 60–80 W m-2 in the southwestern part of the study
area and 10–60 W m-2 elsewhere. The airborne measurements
obtained by along the
148∘55′ W line of longitude between
68∘55′ N and
70∘30′ N also indicated a decreasing
trend in sensible heat and latent heat fluxes from south to north.
Median sensible heat (a) and latent heat (b) fluxes over the North
Slope of Alaska, averaged over the reference period. Only those fluxes with a
standard error of the median value <30 % are shown. The insert shows the
location of the EC tower in Atqasuk that provided the measurements used for
the comparison in Sect. . Black lines represent
Polar 5 flight lines.
The upscaled latent heat fluxes are comparable to those reported in previous
publications. Latent heat fluxes measured by were of the
order of 100 W m-2 in the southern part of the survey area
and close to 50 W m-2 in the northern part of the area. The
averaged sensible heat flux measured by was of the same
order as the average latent heat flux, whereas the sensible heat fluxes
derived in our study along the same path surveyed by have
less variability and only range between 10 and 40 W m-2.
This discrepancy may be due to the different times of day and dates of the
measurements, to cloudiness, to variations in the EVI (as a proxy for soil
moisture), and also to the different altitudes of the two aircraft during the
flux measurements. The median altitude in the Polar 5 survey was 38 m while
the measurements obtained by were from an altitude of
10–20 m. Possible reasons of flux inconsistencies will be discussed
in Sect. .
Specific energy fluxes for different land cover classes
(Table ) were derived by combining high-resolution
surface flux maps (Fig. ) with the National Land
Cover Database (NLCD) data from 2011 shown in
Fig. . The averaged latent heat flux was 2–3 times
greater than the averaged sensible heat fluxes for all land cover classes. A
high latent heat flux of 112–113 W m-2 was found over
vegetation types located in the southern part of the North Slope, such as
dwarf shrubs (i.e., shrubs less than 20 cm high, with the shrub
canopy typically comprising more than 20 % of the total vegetation) and
shrubs or scrub (i.e., shrubs less than 5 m high, with the shrub canopy again
typically comprising more than 20 % of the total vegetation). Moderate fluxes
(57–83 W m-2) were projected over herbaceous sedge (sedges
and forbs, generally comprising more than 80 % of the total vegetation),
barren areas (bedrock scarps, talus, glacial debris, strip mines, and gravel
pits, where vegetation generally accounts for less than 15 % of the total
cover), and emergent herbaceous wetlands (where perennial herbaceous
vegetation comprises more than 80 % of the vegetative cover and the soil or
substrate is continuously saturated or covered with water). The lowest latent
heat fluxes (30 and 46 W m-2) were
projected over open water (areas with less than 25 % vegetation or soil
cover) and perennial ice/snow (perennial cover of ice and/or snow, generally
comprising more than 25 % of the total cover), respectively. The relative
proportions of each land cover class therefore need to be taken into account
when considering flux uncertainty. Less representative land cover classes
appear only rarely in flux footprints and were therefore less frequently used
for model training than the more representative classes. The spatial pattern
of the projected latent heat flux (Fig. b)
closely matches the spatial pattern of the land cover map and air temperature
(Figs. and ,
respectively), indicating a strong influence of these parameters on the
latent heat flux. Sensible heat flux showed less variability over different
land cover classes but was found to be highest over dwarf shrub vegetation,
with moderate fluxes projected over herbaceous sedge, shrubs or scrub, emergent
herbaceous wetlands, and barren land, and only low fluxes projected over open
water and perennial ice/snow. The spatial pattern of the projected sensible
heat flux (Fig. a) is more complicated than that
of the projected latent heat flux indicating that there are additional
processes influencing the sensible heat flux.
Land cover classes according to the National Land Cover Database,
2011. Black lines represent Polar 5 flight lines.
Relative coverage for each land cover class and median, maximum,
25 % percentile, and 75 % percentile of energy fluxes for different NLCD land
cover classes, calculated from the ensemble flux maps shown in
Fig. .
Wetland class
Coverage
Sensible heat flux (W m-2)
Latent heat flux (W m-2)
(%)
25 %
Median
75 %
Maximum
25 %
Median
75 %
Maximum
Emergent herbaceous wetlands
9.4
25
34
42
107
41
57
76
207
Herbaceous sedge
42.5
28
37
45
111
60
83
101
216
Shrub, scrub
4.0
31
36
42
96
100
112
122
219
Dwarf shrub
34.5
35
41
51
117
101
113
122
221
Barren land
1.6
20
30
38
96
47
68
88
200
Perennial ice, snow
1.3
9
18
29
100
18
30
48
180
Open water
6.7
14
23
33
100
29
46
68
211
analyzed available results from long-term (one or more
years) and short-term surveys and summarized the summer surface energy budget
for a range of Arctic tundra and boreal ecosystems. Their mean fluxes for
July were selected where the data time series were long enough and used for
comparison. The lowest sensible heat and latent heat fluxes reported
by were measured over the large, deep Toolik Lake
(10 and 13 W m-2, respectively),
whereas our ERF projected energy fluxes for open water ecosystems were
13 and 33 W m-2 higher, respectively,
because they are representative of different types of lakes, including small,
shallow lakes. The sensible heat and latent heat fluxes measured by the
EC tower over a sedge ecosystem near Happy Valley (22 and 80 W m-2, respectively) differ from the ERF projected
fluxes for the herbaceous sedge ecosystem by 15 and
3 W m-2, respectively. The sensible heat and latent heat
fluxes in that were measured by multiple EC towers over
shrub ecosystems ranged from 25 to 63 W m-2 and 33 to
93 W m-2, respectively. The ERF projected sensible heat
fluxes lie within the same range but the ERF projected latent heat flux for
shrub ecosystems is higher. This could be due to higher evapotranspiration
rates as a result of the warm air temperatures observed during the reference
period over the southern part of the North Slope of Alaska, where dwarf
shrubs and scrub are more common.
Energy partitioning in northern ecosystems
The Bowen ratio (β) can be used as an
indicator of an ecosystem's energy contributions to the regional climate.
Figure shows the spatial variability of β derived
from the median projected surface energy fluxes shown in
Fig. . All data with latent heat flux values
above the uncertainty of 10 W m-2 have been plotted. The
maximum value of β was found to be 4.03. Figure
indicates that evapotranspiration is the dominant process in the surface
energy exchange over most of the area and β varies from values close to
0 up to 1; i.e., this is a freely evaporating area. Only close to the
coast does the sensible heat exchange predominate and β exceeds 1.3.
showed that variations in β are closely related
to the water content of the surface soil layer. In this area
evapotranspiration from the coastal wetlands is restricted by cold surface
temperatures and the amount of moisture available is limited by the thinness
of the active layer overlying the permafrost . Similar
observations have previously been reported by . Under the
cold and humid meteorological situation influenced by the Arctic Ocean, the
latitudinal temperature gradient over high-latitude ecosystems increases and
leads to a high sensible heat exchange at the coast. Therefore β
increased by more than 1.5. In contrast, warm, dry atmospheric conditions
increase evapotranspiration and β therefore decreased to values of less
than 1.
Median Bowen ratio (β) over the North Slope of Alaska,
averaged over the reference period. Black lines represent Polar 5 flight
lines.
Superimposing the β map (Fig. ) on the NLCD 2011
land cover map (Fig. ) allowed us to derive β
for the reference periods that were specific to particular types of land
cover. The β values were between 0.33 and 0.62 (see
Table ), which is within the range found in published
literature. For example, summarized typical ranges of
β for different Arctic ecosystems. The β values of Arctic
wetlands, low Arctic shrub tundra, and low Arctic coastal tundra were found
to range from 0.2 to 0.7, 0.3 to 5, and 0.6 to 2.1, respectively. This is
in agreement with the β values for emergent herbaceous wetlands and
dwarf shrubs presented in this study. The spatial variations in β in
response to different meteorological conditions also lie within these ranges.
For areas of emergent herbaceous wetlands, which are continuously saturated
or covered with water, β is close to that for perennial ice/snow or
open water. For areas of herbaceous sedge and dwarf shrubs, which can be
periodically or seasonally wet and/or saturated, β was found to be
lower then the ratio for emergent herbaceous wetlands, but higher than that
for shrubs or scrub. The low β values and small median deviations
estimated for shrubs, dwarf shrubs, and scrub, which cover 38.5 % of the
investigated area, indicate that these ecosystems are important regulators of
water loss to the atmosphere.
Median Bowen ratio (β) values and median absolute deviation
(MAD) of β for different NLCD land cover classes, estimated from
β map (Fig. ).
Bowen ratio
Land cover class
Median
MAD
Emergent herbaceous wetlands
0.58
0.15
Herbaceous sedge
0.48
0.22
Shrub, scrub
0.33
0.07
Dwarf shrub
0.37
0.10
Barren land
0.43
0.20
Perennial ice, snow
0.62
0.25
Open water
0.53
0.25
Throughout the entire study area
0.42
0.18
Comparison of surface energy fluxes derived from airborne survey,
WRF modeling, and EC tower measurements
Realistic modeling of surface exchanges
requires accurate representation of surface–atmosphere interactions, which
means that the turbulent fluxes of energy and matter exchange must be
accurately reproduced. Precise modeling of surface fluxes requires accurate
simulation of the planetary boundary layer and fluxes need to be calculated
using appropriate model parameterization. The modeled and measured
meteorological parameters of the planetary boundary layer and turbulent
energy fluxes were compared in order to test the performance of the WRF
model. Data from the EC tower at Atqasuk
(70∘28′ 10.6′ ′ N,
157∘24′ 32.2′ ′ W),
100 km south of Utqiaġvik, were available for the period of the airborne
survey . Surface fluxes derived from the WRF model were
compared with those derived from the Polar 5 airborne survey and from the
EC tower measurements. The modeled data were averaged over nine grid cells
(300 m × 300 m) around the tower. The Polar 5 aircraft traverses between
about 4 and 7 km to the east of the tower and we averaged those fluxes that were
measured not more than 7 km from the tower and had less than 10 min time
difference.
The measuring site represents wetland complexes that consist primarily of
fens, dominated by moist non-tussock sedges, prostrate dwarf shrubs, and
mosses, which are usually present in the slightly elevated hummocks and rims
of low-centered ice-wedge polygons . Measurements were made
at a tower height of 2.25 m. Wind velocity and sonic temperature were also
measured using a Solent R3 sonic anemometer (Gill Instruments Ltd.,
Lymington, UK) at a height of 2.28 m. To measure water vapor a LI-7200 gas
analyzer (LI-COR Biogeosciences, Nebraska, US) was used.
Figure shows the measured and modeled surface
fluxes, together with boundary-layer meteorological parameters. As can be
seen in Fig. a and b, on 28 June, 1 and
2 July 2012 the sky at Atqasuk was almost cloud-free, shortwave radiation
was up to 700 W m-2, and the maximum air temperatures were
about 12 or 13 ∘C. The synoptic situation on 29 and
30 June 2012 was cloudy with a maximum air temperature of about 11
or 12 ∘C. The airborne radiation measurements are in
agreement with those from the tower. The relative humidity reached a maximum
of 90–95 % at night, dropping to 65–70 % at around midday or later. These
trends in temperature and relative humidity were also observed by the Polar 5
aircraft but the WRF model overestimated the shortwave radiation on 29 and
30 June 2012 and the sensible heat flux is therefore highly overestimated
by the model on these particular days (Fig. d).
The sensible heat fluxes measured by Polar 5 are lower (median absolute
deviation 81 W m-2) and the latent heat fluxes slightly
higher (median absolute deviation 26 W m-2) than those
measured by the EC tower (Fig. d, e).
Left frame: shortwave radiation (a), air temperature (b), and
relative humidity (c). Right frame: sensible heat flux (d), latent heat flux
(e), and sum of the sensible heat and latent heat fluxes (f) based on
measurements from the EC tower at Atqasuk (blue), the Polar 5 airborne survey
(red), or output from the WRF model (black). Red error bars indicate mean
absolute deviations of the averaged Polar 5 data.
Many previous investigations have also reported lower airborne sensible heat
fluxes and higher airborne latent heat fluxes than those derived from
EC tower measurements . showed that sensible heat flux
derived from EC tower measurements was generally higher than that measured by
all airborne surveys, but latent heat flux showed a temporally more
variable trend, with the EC tower fluxes being higher during June surveys and
slightly lower during August surveys.
A summary of possible reasons for the discrepancy between fluxes measured by
airborne surveys and those derived from EC towers can be found
in . The airborne and tower data are collected from
different levels and the storage and advection can lead to height dependency
in turbulent fluxes. As described above, we addressed this discrepancy by
introducing the ratio of aircraft measuring height to boundary-layer height
as a parameter in the ERF projected maps.
As reported by , surface energy budgets measured from aircraft
seem to be more accurate when mesoscale fluxes are included, because the scale
of horizontal flux increases with altitude and significant flux may occur
where turbulence occurs on a scale greater than 2 km. The wavelet
decomposition used in our data processing yields a high spatial resolution
for the flux observations and takes into account significant flux
contributions from large eddies (2–4 km across), which are “invisible” for
tower-based systems due to insufficient sampling of large-scale atmospheric
movements. showed that exchange processes on the larger
scales of a heterogeneous landscape have a significant influence on the
energy balance closure. By including these fluxes, the energy balance can be
approximately closed.
The footprint of tower measurements is smaller than that of airborne flux
measurements. Aircraft measure turbulent fluxes over different surfaces from
an EC tower due to land-surface heterogeneity. The footprint of the Polar 5
survey in the vicinity of the EC tower had a width of between 800 m and
3.6 km, and it therefore “sees” a more averaged flux that is representative
of the landscape as a whole, whereas the tower only “sees” a relatively
small area. Sensible heat flux figures derived from the EC tower measurements
were noticeably higher than those from the Polar 5 survey under conditions of
high incoming radiation. This can be explained by the larger proportion of
wet surfaces within the Polar 5 footprint area and the fact that dry surfaces
heat up more rapidly and to a higher level than wet surfaces, resulting in
increased sensible heat flux. This can also be confirmed by considering the
sum of both energy fluxes (Fig. f), which tends to
be in agreement with flux data derived from EC tower measurements when the
incoming shortwave radiation is high.
During the AIRMETH 2012 survey some lakes were partly covered by ice; the
surface water temperature was therefore close to 0∘ and
12 ∘C lower than the air temperature at the time of
high sensible heat fluxes. On the one hand, turbulent fluxes over water
surfaces can be suppressed due to lack of both mechanical and buoyant
generation. On the other hand, due to the stable layer over the water
surfaces, turbulent fluxes can be directed to the surface, whereas over dry
surfaces they are directed upwards. This leads to low averaged airborne
fluxes, but high locally measured turbulent fluxes. A similar compensation of
fluxes on a regional scale and the discrepancy between those fluxes and
fluxes derived from EC tower measurements were also noted during the SHEBA
experiment and reported by .
Conclusions
Projection of regional-scale flux measurements into
regional or continental flux inventories is a useful way to improve our
understanding of regional and global climatic changes. In this study we used
Polar 5 airborne turbulence measurements to upscale sensible heat and latent
heat EC fluxes over the North Slope of Alaska, using a machine learning, in
this case the boosted regression tree technique. We have shown that this
method can be used to isolate and quantify significant surface properties and
to extend airborne flux observations to a regional scale, thus producing
high-resolution surface flux maps.
The downward shortwave solar radiation, potential temperature, enhanced
vegetation index, mixing ratio, and land-surface albedo were found to be the
most important parameters driving energy exchange processes between the land
surface and the atmosphere in permafrost areas. The resulting environmental
mean response functions indicate linear responses of surface heat fluxes to
changes in the downward shortwave solar radiation, the enhanced vegetation
index, and the land-surface albedo, and nonlinear responses to changes in
the potential temperature and the mixing ratio. The comparison of measured
fluxes with predicted fluxes indicated the potential for using ERFs to extend airborne flux measurements to a regional scale,
and quantitatively linking flux observations in the atmospheric surface layer
to meteorological and biophysical drivers in the flux footprints reveals a
good agreement with median absolute deviations in the residuals of less than
8 and 3 % for the sensible heat and latent heat fluxes, respectively. The
coefficient of determination (R2) was greater than 0.99 in
both cases.
To overcome the disadvantage of the method presented in ,
which used the median meteorological state variables during each flight
pattern to upscale airborne flux measurements, we utilized the WRF model simulations of the driving meteorological
parameters. This improved the ability of their method to capture the spatial
variability of energy fluxes across the North Slope of Alaska. The maps of
energy fluxes were projected with a high spatial resolution of 100 m × 100 m.
Marked regional differences were detected showing the nonuniform
distribution of surface fluxes. High-resolution flux maps allow
land-cover-specific energy fluxes to be estimated, which can be used to
validate coupled atmospheric–land-surface models. Our results show a strong
south–north gradient in the latent heat exchange if cold weather conditions
prevail in the north and warm conditions in the south, with winds blowing
from the Arctic Ocean. Sensible heat exchange is lower and has a less
pronounced south–north gradient.
Energy partitioning information and the Bowen ratio are critical components
of micrometeorological, climatic, and hydrological models and are widely used
for comparing the surface energy balances of different climate zones and
vegetation types. Our investigations into energy partitioning in northern
ecosystems confirmed that, under the meteorological conditions of the
measuring period, evapotranspiration was one of the main process in the
surface energy exchange over almost the whole of the North Slope. Only close
to the coast was the evapotranspiration restricted and sensible heat exchange
prevalent. The low Bowen ratio values derived for shrub, dwarf shrub, and
scrub ecosystems indicate that they are important regulators of moisture loss
to the atmosphere. The higher evapotranspiration capacity associated with
such ecosystems results in a predominance of latent heat exchange over
sensible heat exchange.
The spatial representativeness of flux tower measurements was checked and
these data compared with the modeled and airborne fluxes. The airborne
sensible heat fluxes were found to be lower than those measured by the tower,
and small differences were observed in the latent heat fluxes. These
discrepancies can be explained by the different heights at which the data
were
collected, where storage and advection can lead to height dependency, and the
fact that the footprint of airborne flux measurements is more representative
for the landscape as a whole. However, more measurements are needed covering
different meteorological situations in order to improve the machine learning,
verify our results, and validate the model data.
The results obtained provide a valuable contribution to the advanced, scale-dependent quantification of surface energy fluxes over extensive areas of
terrestrial permafrost and reveal the potential of the upscaling method. The
presented data set is unique in its spatial extent for heterogeneous Arctic
landscapes due to the extensive use of airborne data, which are more
representative on a regional scale than EC tower measurements. High-resolution flux maps for Arctic areas, such as those presented herein, are
scarce: they can be used to validate modeling results and improve our
understanding of physical processes related to permafrost–atmosphere
interactions in Arctic landscapes.