In this paper we present one year of meteorological and flux measurements obtained near NyÅlesund, Spitsbergen. Fluxes are derived by the eddy covariance method and by a hydrodynamic model approach (HMA) as well. Both methods are compared and analyzed with respect to season and mean wind direction. Concerning the wind field we find a clear distinction between 3 prevailing regimes (which have influence on the flux behavior) mainly caused by the topography at the measurement site. Concerning the fluxes we find a good agreement between the HMA and the eddy covariance method in cases of turbulent mixing in summer but deviations at stable conditions, when the HMA almost always shows negative fluxes. Part of the deviation is based on a dependence of HMA fluxes on friction velocity and the influence of the molecular boundary layer. Moreover, the flagging system of the eddy covariance software package TK3 is briefly revised. A new quality criterion for the use of fluxes obtained by the eddy covariance method, which is based on integral turbulence characteristics, is proposed.
Climate in the Arctic is known to show stronger variability in surface temperature than elsewhere in the Northern hemisphere, a phenomenon which is called “Arctic amplification” [
Due to the complexity of feedback mechanisms within the Arctic atmosphere, especially within the Arctic atmospheric boundary layer, climate models show the largest deviations there [
All measurements relating to the results in this paper took place at NyÅlesund, Svalbard (N78°55.287′, E011°54.851′), which is since 1968 a centre for different polar research institutions, also for the Alfred Wegener Institute AWI (AWIPEV research station). NyÅlesund is located in the roughly westeast orientated Kongsfjord on the west coast of Svalbard and, apart from the fjord entrance, surrounded by glaciers and mountains up to 800 m (see Figures
(a) Map of Svalbard (
The climatology of this measurement site is well described in, for example, [
The eddy covariance method is nowadays a well known, detailed described, and popular method to determine the near surface turbulent vertical fluxes [
The data postprocessing was made with the internationally compared eddy covariance software TK3 [
TK3 also includes a quality flag scheme (detailed described in [
The overall flag scheme combines then the steadystate test and the results of the ITCtest of the two time series, of which the covariance is calculated. If there is no agreement in the two ITCtests, the higher flag is used for the overall classification (Table
Overall flag system after Foken [
Steady state flag  ITC flag  Final flag 

1  12 

2  12 

12  34 

34  12 

1–4  3–5 

5  ≤5 

≤6  ≤6 

≤8  ≤8 

9  9 

The authors of this scheme suggest using only data with flags 1–3 for fundamental research. Classes 4–6 can be used for continuously running systems (e.g., in networks), 7 and 8 for orientation. Data of class 9 should be always excluded.
Another way to calculate the sensible heat flux, not often used but quite sophisticated, is a hydrodynamic threelayer model approach, developed originally for flux measurements above seawater by Foken [
The friction velocity
The integral between the surface and the top of the molecular boundary layer depends on friction velocity, the kinematic viscosity of air, and the thickness of the molecular temperature boundary layer. For this hydrodynamic model approach a fundamental change of the behaviour of the molecular boundary layer at a friction velocity of 0.23 m s^{−1} was found [
For the sensible heat flux
If the sensible heat flux and the air temperature in a given height are known, formula (
To get started with the results, Figures
30 min averages of the kinematic sensible heat flux (K m s^{−1}) for the period July 2011 to June 2012, plotted against the air temperature in measurement height (eddy covariance: black dots, HMA: yellow dots): (a) sensible heat fluxes for friction velocities ≤0.23 m s^{−1}, (b) sensible heat fluxes for friction velocities >0.23 m s^{−1}.
On the other hand the eddy covariance results in both figures reproduce positive sensible heat fluxes in negative temperature ranges, but care should be taken here for the interpretation: Jocher et al. [
Two major flux regimes are obvious in the course of the year. On the one hand the short summer period in which convection is possible (called “convective” in the following), on the other hand the rest of the year with mostly stable or neutral conditions. The transition between the two periods is quite fast; when the snow melt is over and the surface has dried, the flux regime can rapidly change to convective conditions from one hour to the next. Values of two summers are combined here. By investigating the general behaviour of the sensible heat flux we proved in detail if this is justified and came to the conclusion that there is no reason against combining the periods 1.7.2011–31.8.2011 and 16.6.2012–30.6.2012 to one “convective” period. Outside this period the incoming shortwave radiation is too weak to generate convection (or the surface is still covered with snow or melting water which inhibits a clear warming of the surface), the incoming shortwave radiation is the main driving parameter for the flux regime in summer. Figures
30 min averages of the kinematic sensible heat flux (K m s^{−1}), eddy covariance versus HMA. Additionally shown is the correlation coefficient between the two data series and the linear regression (line and equation) for data from the convective period: (a) for the time window 16.6. to 31.8. and friction velocities ≤0.23 m s^{−1}, (b) for the time window 16.6. to 31.8. and friction velocities >0.23 m s^{−1}, (c) Figure
The rest of the year behaves completely different to the short summer period; in Figures
This section shall highlight the connection between the nearsurface turbulent flux behaviour and the wind direction at the measurement site. Figure
Wind rose for the investigation period July 2011 to June 2012, built with data of the sonic anemometer at the eddy covariance site (compare Figure
Continuing the earlier introduced data separation in two friction velocity classes, Figure
Kinematic sensible heat flux eddy covariance versus sensible heat flux HMA (30 min averages, K m s^{−1}), separated for the three wind sectors “normal” (20°–150°, marked with black dots), “disturbed” (150°–270°, marked with pink dots), and “synchronisation” (270°–20°, marked with blue dots). Additionally shown are the linear regression equations for the different sectors (colourcoded) for data from the convective period: (a) for the time window 16.6. to 31.8. and friction velocities ≤0.23 m s^{−1}, (b) for the time window 16.6. to 31.8. and friction velocities >0.23 m s^{−1}, (c) for the time window 1.9. to 15.6. and friction velocities ≤0.23 m s^{−1}, and (d) for the time window 1.9. to 15.6. and friction velocities >0.23 m s^{−1}.
Figure
Back to the topic “synchronisation”: the investigations in the course of this paper led to the knowledge that we can find a direct connection between the synoptic situation and the cases of wind blowing from the “synchronisation” sector into the Kongsfjord, and herewith a direct connection between the synoptic situation and the flux behaviour in these cases. For the other introduced wind sectors this connection is much more complicated due to the surrounding orography, channeling effects, and the influence of glacier outflows, so we start at this point with the description of the major synopticscale/smallscale connection for the “synchronisation” sector: in most of the cases of synchronisation a correlation with a low pressure system in the northeast of Svalbard occurs. This is the predominant synoptic situation for a westerly flow in the roughly westeast orientated Kongsfjord and herewith a synchronisation of the nearsurface processes in the fjord. To investigate the topic “synchronisation” we proceeded at this point as following: as synchronisation event we classified a time period of at least 4 hours wind blowing from the sector 300°–360° (to be sure to investigate a “real” synchronisation event the sector was chosen a bit narrower than at the definition for the 3 wind direction classes earlier in this paper). Filtering the sonic anemometer data (1 min averages) at the eddy covariance measurement site by these criteria for the whole year data which is presented in this paper (July 2011 to June 2012) and looking at the large scale synoptic situation for the in this way obtained synchronisation periods led to following statistical distribution: the 73 events which were found for the whole year correlate in 55 cases with the already above mentioned low pressure system in the northeast of Svalbard, the remaining 18 cases cannot be brought in a connection with a specific synoptic situation. Furthermore the 55 cases with a low pressure system in the northeast of Svalbard can be roughly divided in cases where the rotation of the low pressure system causes the wind field in the Kongsfjord (46 cases) and in cases where most probably a geostrophic flow causes the wind field in Kongsfjord (9 cases; the low pressure system in the northeast of Svalbard and a corresponding high pressure system in the southwest of Svalbard are quite far away from each other). For statements to the synoptic situation we used in all cases 6 hour averaged ERAInterim Reanalysis data, which are provided by the ECMWF (European centre for mediumrange weather forecasts). Investigated were the surface pressure (hPa), the wind speed (m s^{−1}) in 850 hPa, the wind vector in 850 hPa, and the geopotential height in 850 hPa. 850 hPa was chosen to have the “pure” synoptic circumstances without influence of the earth’s surface. To make the statements visible, Figure
ERAinterim reanalysis data for 28.5.2012, 12 UTC. The wind velocity in 850 hPa is shown in different colours, the wind vector in 850 hPa with arrows, and the geopotential height in 850 hPa with lines. Additionally, Svalbard is marked with a white cross.
The investigations concerning the correlation between surface processes and the synoptic situation are still ongoing, also for the other wind sectors which seem to behave much more complicated.
In this section we will discuss three important issues which became obvious in the course of this paper a bit more in detail. Adaptations of (
Directly following out of these statements is Section
In Section
Thinking back to Section
Over land obviously an adaptation is necessary: we set in a kind of case study the values for (
(a) 30 min averages of the kinematic sensible heat flux (K m s^{−1}), eddy covariance versus HMA (with adapted (
Because there is still a deviation in Figures
In any case it is highly recommended to investigate these issues on site, variations for different measurement sites in different climatic conditions are most probable.
Outside the convective period the correlation between the eddy covariance method and the hydrodynamic model approach is very low (compare Figures
The simple and reliable HMA provides some possible further applications. First of all the HMA can be easily applied for all eddy covariance systems and provide flux values that are not such dependent from the existence of fully developed turbulence like the eddy covariance method (but note that the layer structure of the lowermost meters over the surface can vary for different measurement sites, compare Section
Moreover, the conditional equation for the sensible heat flux can be rewritten to obtain the surface temperature according to (
As mentioned in Section
In the following the topic of the temperature ITC’s shall be discussed further by presenting corresponding data of the investigation period which is shown in this paper. Figure
Absolute values of the temperature ITC’s (calculated following formulas (
Several things are obvious in Figure
Next it is well to see that in the not convective period a quite huge amount of negative values of
Furthermore it is obvious that the lower limit of the temperature ITC’s is rising with the stability values and the more stable the stratification is the more are the temperature ITC’s scattering. For
Taking the temperature ITC’s in the convective period as a reference (in Section
A few more words to the motivation of this criterion: seeking an exclusion criterion for the sensible heat fluxes at the shown Arctic tundra site (just the decision: accept or refuse) which is applicable for an as wide as possible stability range the correlation in (
In difference to already existing criteria (like the one by Foken et al. [
In the previous sections the nearsurface sensible heat flux behaviour (obtained by the eddy covariance method and a hydrodynamic model approach (HMA)) in the course of the year was presented for an Arctic measurement site near NyÅlesund, Spitsbergen. It became clear that the behaviour of the nearsurface sensible heat flux strongly depends on season, wind direction, and friction velocity. Taking all shown results into account, especially the results in the “disturbed” sector (wind direction 150°–270°) should be handled carefully, in this sector mountains and glaciers in the direct vicinity of the eddy covariance measurement complex lead to many disturbances. The recommendation at this point is to exclude the flux values which were obtained during flow from the mentioned sector, if possible. If not possible, filter techniques (like wavelet for example; compare, e.g., [
Main range of the kinematic sensible heat flux (eddy covariance, K m s^{−1}) in the course of the year depending on season, wind direction and friction velocity.
Normal (dir 20°–150°)  Synchronisation (dir 270°–20°)  Disturbed (dir 150°–270°)  Additional remarks  








Summer (16.6.–31.8.)  −0.025–0.15 K m s^{−1}  −0.05–0.15 K m s^{−1}  −0.025–0.12 K m s^{−1}  −0.025–0.12 K m s^{−1}  −0.025–0.05 K m s^{−1}  −0.025–0.08 K m s^{−1}  Good agreement between eddy and ( 


Outside summer (1.9.–15.6.)  −0.06–0.03 K m s^{−1}  −0.08–0.03 K m s^{−1}  −0.03–0.03 K m s^{−1}  −0.04–0.03 K m s^{−1}  −0.06–0.1 K m s^{−1}  −0.04–0.02 K m s^{−1}  Bad agreement between eddy and HMA (HMA limited in the positive range); fictitious positive fluxes due to gravity waves in the disturbed sector at low friction velocities; synchronisation sector more maritime, normal sector more continental 
Some thoughts to the use of the eddy covariance method on the one side and gradient approaches (including the introduced hydrodynamic model approach as a special case) on the other side regarding their application at Arctic measurement sites shall be inserted at this point: the eddy covariance method determines the turbulent fluxes, gradient approaches turbulent and laminar exchange due to their use of the temperature gradient between a defined measurement height and the surface. Both methods fit quite well together if we can assume the turbulent exchange as dominant process (we saw that in this paper for the convective period). Indeed, the performance of the two methods differs clearly during polar night conditions if turbulence is damped and/or only intermittent. Quite large negative fluxes using gradient approaches (which represent turbulent and molecular exchange) are then standing against fluxes mainly fluctuating around zero using the eddy covariance method (which represent only the turbulent flux), the gradient approaches are “too stable” regarding the turbulent exchange. This difference is fostered by clear sky conditions and low wind velocities, which means a strong longwave radiative loss (and therefore big temperature differences between measurement height and surface) and damped mixing due to low friction. Cloudy conditions and/or higher friction velocities limit the formation of a strong temperature gradient between measurement height and surface and therefore the laminar exchange. What could now be a potential application of this knowledge? It should be possible to make severe statements about the development of turbulence in the course of the whole year by comparing results of the eddy covariance method (determines turbulent flux) and results of the hydrodynamic model approach (determines turbulent flux and laminar flux). By doing this a kind of critical value of turbulence development could be defined up to which it makes sense to compare eddy covariance results and hydrodynamic model results in special (but also model results in general) as equal methods for turbulent flux determination. Precondition for that: the knowledge of the structure of the undermost meters of the atmosphere. Investigations about the thickness of the molecular boundary layer should be done at each measurement site before providing further investigations.
To verify this idea, further work is needed and ongoing. Indeed, the hydrodynamic model approach used in this paper would be an ideal tool for such investigations due to its possibility to resolve the layer structure and the corresponding processes in the lowermost meters of the atmosphere exactly and physically wellfounded, a clear advantage of the HMA comparing to other gradient approaches.
The authors declare that there is no conflict of interests regarding the publication of this paper.
Many thanks go to Jürgen Graeser, AWI Potsdam, for technical support regarding the installation of the instruments mentioned in this paper and the AWIPEV Crew for the routine checks and maintenance work to keep the instruments running on site.