Boreal Forest Surface Parameterization in the ECMWF Model—1D Test … · 2014-12-10 · JANUARY...

JANUARY 2003 95G U S T A F S S O N E T A L .

q 2003 American Meteorological Society

Boreal Forest Surface Parameterization in the ECMWF Model—1D Test withNOPEX Long-Term Data

D. GUSTAFSSON,* E. LEWAN,1 B. J. J. M. VAN DEN HURK,# P. VITERBO,@ A. GRELLE,& A. LINDROTH,**E. CIENCIALA,11 M. MOLDER,** S. HALLDIN,## AND L.-C. LUNDIN##

*Department of Land and Water Resources Engineering, Royal Institute of Technology, Stockholm, Sweden1Division of Environmental Physics, Department of Soil Sciences, SLU, Uppsala, Sweden

#KNMI, De Bilt, Netherlands@European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, Berkshire, United Kingdom

&Department for Production Ecology, SLU, Uppsala, Sweden**Department of Physical Geography, Lund University, Lund, Sweden

11Institute of Forest Ecosystem Research, Jilove u Prahy, Czech Republic##Department of Hydrology, Uppsala University, Uppsala, Sweden

(Manuscript received 24 January 2002, in final form 26 June 2002)

ABSTRACT

The objective of the present study was to assess the performance and recent improvements of the land surfacescheme used operationally in the European Centre for Medium-Range Weather Forecasts (ECMWF) in a Scan-dinavian boreal forest climate/ecosystem. The previous (the 1999 scheme of P. Viterbo and A. K. Betts) andthe new (Tiled ECMWF Surface Scheme for Exchange Processes over Land, TESSEL) surface schemes werevalidated by single-column runs against data from NOPEX (Northern Hemisphere Climate-Processes Land-Surface Experiment). Driving and validation datasets were prepared for a 3-yr period (1994–96). The new surfacescheme, with separate surface energy balances for subgrid fractions (tiling), improved predictions of seasonalas well as diurnal variation in surface energy fluxes in comparison with the old scheme. Simulated wintertimeevaporation improved significantly as a consequence of the introduced additional aerodynamic resistance forevaporation from snow lying under high vegetation. Simulated springtime evaporation also improved becausethe limitation of transpiration in frozen soils was now accounted for. However, downward sensible heat fluxwas still underestimated during winter, especially at nighttime, whereas soil temperatures were underestimatedin winter and overestimated in summer. The new scheme also underestimated evaporation during dry periodsin summer, whereas soil moisture was overestimated. Sensitivity tests showed that further improvements ofsimulated surface heat fluxes and soil temperatures could be obtained by calibration of parameters governingthe coupling between the surface and the atmosphere and the ground heat flux, and parameters governing thewater uptake by the vegetation. Model performance also improved when the seasonal variation in vegetationproperties was included.

1. Introduction

Land surface energy fluxes constitute the lowerboundary for the physical processes in the atmosphere.Adequate formulations and parameterization of landsurface processes are thus essential for significant im-provements of operational models for climate andweather predictions. Thorough testing of the surfaceschemes in offline mode (i.e., decoupled from the com-plex interactions in the full 3D model) has been stressed,as well as the need for tests with respect to seasonaltimescales (Viterbo and Beljaars 1995; Henderson-Sell-ers 1996; Chen et al. 1997; Verseghy 2000). Verseghy(2000) also emphasized the importance of addressing

Corresponding author address: Elisabet Lewan, Department ofSoil Sciences, Swedish University of Agricultural Sciences, Ulls vag17, Ultuna, Box 7014, S–750 07 Uppsala, Sweden.E-mail: [email protected]

the ability to reproduce fluxes from homogeneous sur-faces prior to tests for heterogeneous surfaces at regionaland global scales. The evaluation of land surfaceschemes for different ecosystems has still been ratherlimited, because of the lack of suitable datasets.

The boreal forests at high latitudes have a great in-fluence on the annual and seasonal climatology of theNorthern Hemisphere by reducing the otherwise highalbedo of snow-covered areas during winter and by af-fecting the partitioning of net radiation between sensibleand latent heat fluxes at all times of the year. Severalexperiments with global circulation models (e.g., Bonanet al. 1992; Thomas and Rowntree 1992; Douville andRoyer 1997; Viterbo and Betts 1999) have shown thatthe boreal forests increase surface air temperature, latentheat flux, and atmospheric moisture over the wholeNorthern Hemisphere when compared with a deforestedsurface. The impact of these effects is largest in spring

96 VOLUME 42J O U R N A L O F A P P L I E D M E T E O R O L O G Y

in the Arctic and subarctic regions, but extends in timethroughout the year and spatially toward the Tropics.The boreal forests also have an important influence onthe long-term budgets of atmospheric carbon dioxideand methane. Bonan et al. (1995) showed that thesebiochemical interactions are more important for the cli-mate at long timescales, when redistribution of vege-tation due to climate change is taken into account.

The surface scheme introduced operationally in Au-gust 1993 at the European Centre for Medium-RangeWeather Forecasts (ECMWF) was presented by Viterboand Beljaars (1995). This scheme was subsequentlymodified in September 1996 to account for soil freezing(Viterbo et al. 1999) and in December 1999 to accountfor the albedo of boreal forest (Viterbo and Betts1999)—and is hereinafter referred to as VB95. It isbased on the heat and water budgets for four active soillayers plus an additional surface layer with four subgridsurface fractions: bare soil, vegetation, snow, and in-terception layer, with one surface temperature in com-mon. The scheme was evaluated within the Project forIntercomparison of Land-Surface ParameterizationSchemes (Henderson-Sellers et al. 1993), and was testedwith data from the First ISLSCP (International LandSurface Satellite Climatology Project) Field Experi-ment, tall grass prairie, United States (Betts et al.1998a), Cabauw (grassland, Netherlands) (Bosveld etal. 1999), Amazonial Regional Meteorological Experi-ment, rainforest, Brazil (Viterbo and Beljaars 1995), andrecently also the BOREAS (Boreal Ecosystem–Atmo-sphere Study, Canada) experiments (Betts et al. 1998a).These studies revealed a number of shortcomings in thescheme and emphasized the need for more realistic de-scriptions of different vegetation types (Douville et al.1998; van den Hurk et al. 2000).

Consequently the land surface parameterizationscheme was redesigned and a new scheme was put intooperation in June 2000, hereinafter referred to as theTiled ECMWF Surface Scheme for Exchange Processesover Land (TESSEL). A description of the scheme andthe most important changes can be found in van denHurk et al. (2000). The new scheme includes six landtiles: bare soil, high vegetation, low vegetation, highvegetation with snow beneath, snow on low vegetation,and an interception layer. The energy balance is solvedseparately for each tile, giving individual surface tem-peratures for different surface fractions. The VB95 andthe TESSEL schemes were evaluated in offline modeagainst data from seven land surface experiments rep-resenting different climate regions and vegetation types,including a long-term dataset from the BOREAS ex-periment (van den Hurk et al. 2000).

Betts et al. (1998b) and van den Hurk et al. (2000)concluded that evaporation rates over boreal forestswere overestimated by the VB95 scheme in winter,spring, and summer in comparison with data from BO-REAS. The discrepancies were mainly attributed to anoverestimation of evaporation from snow and the ne-glect of the influence of frozen soil and vapor pressuredeficit on transpiration (Betts et al. 1999).

Comparison of the VB95 and TESSEL schemes withlong-term flux data from the BOREAS Northern StudyArea (BOREAS NSA) showed significant improve-ments of simulated heat fluxes (van den Hurk et al.2000). These improvements were ascribed to the intro-duction of a separate energy budget for the snow layer,an additional aerodynamic resistance within the canopyabove the snow layer, the limitation of water extractionin frozen soil, and the inclusion of a vapor pressuredeficit response function for transpiration.

The objective of the present study is to make an in-dependent assessment of the performance and recentimprovements of the ECMWF land surface scheme withrespect to boreal forest using long-term flux data fromthe NOPEX (Northern Hemisphere Climate-ProcessesLand-Surface Experiment; Halldin et al. 1998, 1999).The climate of the NOPEX study area is warmer whencompared with the more continental climate of the BO-REAS area referred to above. The annual mean air tem-perature is 5.58C (Uppsala, 1961–90; Lundin et al. 1999)as compared with 23.48C in BOREAS NSA (Thomp-son, Manitoba, Canada, 1968–89; Lafleur et al. 1997).The largest difference in air temperature occurs duringwinter: the mean daily air temperatures in January are23.68 and 2258C for NOPEX and BOREAS, respec-tively. However, the mean annual precipitation is rathersimilar for the two areas (527 mm in Uppsala and 536mm in Thompson), of which the largest proportion oc-curs in summer and autumn. On average, summers aresomewhat drier in the NOPEX area, where 59% of theannual precipitation occurs in May–October as com-pared with 73% for BOREAS.

The VB95 and the TESSEL surface schemes wereevaluated based on single-column runs with data fromthe NOPEX Central Tower site. The models were pa-rameterized based on the standard parameter values gen-erally used within the operational 3D versions of themodels. Forcing and validation datasets were preparedfor a 3-yr period (1994–96) and the models were runfor the whole period with a 30-min time step. We fo-cused on comparisons with measured latent and sensibleheat fluxes that represent aggregated areal fluxes (Grelle1997). However, comparisons with the observed tem-poral variation in more local-scale data (soil water con-tent, soil temperature, ground heat flux, and stand tran-spiration) are also presented, since these contribute toan understanding of improvements and limitations inthe present land surface scheme. In order to identifyperiods during which the dynamic representation of thesurface was of major importance for model perfor-mance, the seasonal variation in evapotranspiration ofthe new and the old scheme was compared with thatobtained with a simple Penman–Monteith formulation.

2. Material and methodsa. Model description

A brief description of the present (TESSEL) and pre-vious versions (VB95) of the land-surface scheme op-


TABLE 1. Major differences between VB95 and the TESSEL surface schemes in the ECMWF model.

Concept VB95 TESSEL

Subgrid fractions over land (tiles) 4 (bare soil, vegetation, snow, intercepted water) 6 (bare soil, low vegetation, highvegetation, snow on low vegeta-tion, high vegetation with snowbeneath, intercepted water)

Surface temperature Common for all tiles 1 for each tileVegetation characteristics 1 global parameter set Dependent on vegetation typeTranspiration, controlled by: Total soil moisture and radiation Unfrozen soil moisture, radiation

and vapor pressure deficitVegetation albedo Fixed Seasonal courseSnow layer Thermally mixed with the upper soil layer Independent thermal contentSnow albedo Fixed Varies with snow age and tileBare soil evaporation Relative humidity concept, a scheme* Surface resistance concept, b

scheme*

* See, for example, Mahfouf and Noilhan (1991).

erational in the ECMWF will be given here. Furtherdetails can be found in Viterbo and Beljaars (1995) andvan den Hurk et al. (2000).

Both versions simulate the water and heat budgetsfor a vertical soil column discretized into four layers(7, 21, 72, and 189 cm in thickness), based on twocoupled differential equations: the Richards equationand Fourier law of diffusion. In offline mode bothschemes are forced by near-surface observed weathervariables (wind speed, air temperature, air humidity, in-coming shortwave and longwave radiation, and precip-itation). A ‘‘skin layer’’ (on top of the soil column) withzero heat capacity is in instantaneous equilibrium withits forcing; that is, the skin temperature is calculated bysolving the surface energy balance equation:

R 5 H 1 LE 1 G,n (1)

where Rn is the net radiation (W m22), H and LE arethe turbulent heat fluxes of sensible and latent heat (Wm22), and G is the ground heat flux (W m22). Here Rn

and G are defined as positive downward and H and LEas positive upward. The ground heat flux between theskin layer and the topsoil layer is governed by an em-pirical parameter, the skin layer heat conductivity, whichhas values corresponding to about 10%–20% of the soilthermal conductivity at field capacity. Bottom boundaryconditions are zero heat flux and free drainage. Theaerodynamic resistances for the turbulent exchange arebased on an iterative transformation of Richardson’s num-ber into the Monin–Obukhov stability parameter (z/L)(Beljaars and Holtslag 1991). Total surface energy flux-es are the sum of fluxes from the different subgrid sur-face fractions (tiles), weighted by their areal fractions.

The major changes between the VB95 and the TES-SEL scheme concern the treatment and concepts fordifferent tiles—especially with respect to snow and veg-etation. The most important differences are summarizedin Table 1 and in Fig. 1 and are briefly commented uponbelow.

The VB95 scheme had four tiles (Table 1) for whicha common skin layer temperature was calculated by

solving the grid surface energy balance [Eq. (1)] (Vi-terbo and Beljaars 1995). In TESSEL the water and heatfluxes are calculated separately for each tile, giving in-dividual surface temperatures for each subgrid fraction.The tiles for vegetation and snow have been differen-tiated into four new tiles (Table 1). The skin layer con-ductivity is now tile dependent (Fig. 1), and the aero-dynamic resistance and the vapor pressure at the surfaceare based on the skin temperature for each tile. Evap-oration from high vegetation with snow beneath is cal-culated from both the canopy and the snow component,where evaporation from the snowpack is based on anadditional aerodynamic resistance (Fig. 1). Bare soilevaporation has been changed from a relative humidityconcept to a surface resistance approach.

In TESSEL, the snowpack (previously merged intothe top soil layer) is treated as an additional layer ontop of the soil column, with a specific snow temperature.The heat flux from the snow to the topsoil layer is cal-culated with a resistance approach. The snow albedo(which is fixed in VB95) varies with snow age (0.85–0.50), except for tiles with high vegetation with snowbeneath for which it is fixed to 0.20. The snow-freealbedo in VB95 is time-invariant, while in TESSEL itfollows a prescribed climatological cycle. In VB95 allradiation is absorbed in the skin layer, whereas in TES-SEL 3%–5% of the net shortwave radiation is trans-mitted through the canopy (high or low) to the topsoilor the snow layer. In addition, the energy absorbed/released by melting/freezing of soil water has been in-cluded in the soil thermal budget.

The VB95 scheme had only one set of vegetationparameters, thus representing a single type of vegeta-tion. TESSEL has a coverage map of 15 different veg-etation types (e.g., short grass, tall grass, annual crop,deciduous broad leaf trees, evergreen shrubs, evergreenneedle-leaf trees). Vegetation specific values are chosenfor leaf area index (LAI), fractional vegetation coverage,minimum canopy resistance, sensitivity coefficient forvapor pressure deficit, and root distribution. In contrastto the previous version, transpiration is also governed


FIG. 1. Resistance scheme for three types of coupling between the surface and the atmosphere(from van den Hurk et al. 2000). The list below each scheme refers to the subgrid fraction types(tiles) in the VB95 (black dots) and the TESSEL (circles) models.

by vapor pressure deficit and is limited in frozen soil(Table 1).

b. Field site

The central NOPEX experimental site (60.58N,17.298E, altitude 45 m) on Norunda Common, about 30km north of Uppsala, Sweden, is located in the southernpart of the boreal forest zone (Halldin et al. 1998, 1999).The surrounding area consists of 70–100-year-old forestdominated by Norway spruce (Picea abies) and Scotspine (Pinus sylvestris) of rather uniform height (25–30m). The canopy has a leaf area index between 3 and 5.The soil is a deep boulder-rich sandy till of glacial or-igin. The region is flat and homogeneous with a max-imum fetch of 6 km extending to the southwest and aminimum fetch determined by a clearing and a smalllake at 1 km distance from the central mast toward thenorth–northwest. The mean air temperature is 5.58C(Uppsala, 1961–90), the mean annual precipitation is527 mm, and the mean Penman open-water evaporationis 454 mm (Lundin et al. 1999).

c. Forcing data

The continuous climate-monitoring program at thissite has been running since the beginning of June 1994.Atmospheric fluxes of heat and moisture, shortwave andlongwave radiation, and meteorological variables weremeasured in the 102-m mast, at the center of the site.Soil moisture and temperature, soil heat flux, and stem

sap flow were measured in the 70- and 100-year-oldforest stands as described by Lundin et al. (1999).

Air temperature, wind speed, and air humidity wereobtained at 35-m height. Missing data were replacedwith observations from Uppsala Airport, about 30 kmsouth of Norunda, after corrections based on linear re-gression between the stations. Downward shortwave ra-diation was measured at 102-m height and longwaveradiation at 68-m height (Molder et al. 1999b). For pe-riods of missing data, longwave radiation was estimatedas a function of air temperature, vapor pressure, andcloudiness (Konzelmann et al. 1994). Missing short-wave radiation was estimated with Angstrom’s formula(Angstrom 1924), using cloudiness from Uppsala Air-port as input. Precipitation was measured at Norunda ina small clearing with a specially designed rain gaugethat minimizes the systematic errors due to wind andwetting losses (Seibert and Moren 1999). However, pre-cipitation was only measured during summer. Winterprecipitation was therefore estimated from observationsat Uppsala Airport made with conventional rain gauges.The precipitation was assumed to consist of snow at airtemperatures below 228C, as rain above 28C, and as amixture (linear proportions) of rain and snow at air tem-peratures between 228 and 28C. Winter precipitationwas systematically corrected by 115% for snowfall and17% for rainfall (Eriksson 1983). In general, the me-teorological forcing data are of high quality, especiallyduring summer periods (May–October). During winter,measured air humidity was occasionally higher than


FIG. 2. Measured net radiation and sum of measured sensible,latent, and ground heat flux (10-day averages).

TABLE 2. Variables used for the evaluation of the ECMWF surface models. Periods are displayed in year (19XX),month, day format (yymmdd).

Variable Location PeriodCapturefraction* Comment

Latent heat flux (W m22) 35 m 940611–961031 95%Sensible heat flux (W m22) 35 m 940611–961031 95%Net radiation (W m22) 68 m (96%)

98 m (4%)940611–961031 95%

Soil heat flux (W m22) 6-cm depth 940611–961031 95% Average of four plates in twolocations

Tree sap flow (mm day21) Measurements on trees within 30 mfrom the central tower, scaled torepresent stand transpiration

950704–950831940617–941031950422–951028960423–961024

100%85%

100%83%

Within-day valuesDaily averagesDaily averagesDaily averages

Soil temperature (8C) 6 levels0–100 cm

940605–961231 96% Average of 14 soil profiles,integrated over the modellayer depths

Soil moisture (m3 m23) 6 levels0–100 cm

940610–961231 95% Cf. soil temperature

* Percentage of time coverage within the measuring period.

could be expected from the corresponding saturated va-por pressure. For such cases, the saturated humidity val-ue was used instead. The data, functions, and instru-ments used for the meteorological forcing are summa-rized in the appendix.

d. Validation data

Sensible and latent heat fluxes were obtained fromeddy-correlation measurements at 35-m height (Grelleand Lindroth 1996; Grelle 1997) using a sonic ane-mometer, a fast-response platinum thermometer, and aclosed-path gas analyzer. The sonic anemometer wascalibrated to correct for flow distortion and the turbulentfluxes were corrected for sensor inclination, signal timelag caused by the length of the sampling tube, frequencyloss caused by both tube attenuation and sensor-responsetime, and air-density fluctuations because of sensibleheat fluxes (Grelle and Lindroth 1996). Net radiation

was obtained from net radiometers at 68- (96%) and98- (4%) m height (Molder et al. 1999b) and soil heatflux from measurements with heat flux plates at 6-cmdepth at two locations (Kellner et al. 1999). The sumof turbulent heat fluxes and the soil heat flux constituted86% of the observed net radiation, when averaged overthe total available dataset (Fig. 2). During summer pe-riods the accumulated energy balance closure was 96%(Grelle and Lindroth 1996). It should be emphasizedthat a full closure of the energy balance could not beexpected since the observed components represent dif-ferent footprint areas. The measured net radiation rep-resents a rather limited area around the mast, whereasthe eddy-correlation fluxes represent a larger footprintfrom different areas depending on wind direction andatmospheric stability, especially during winter (Grelle1997). A source area analysis following Schmid (1994)indicates that the daytime fluxes originate from an areabetween 50 m and 1 km from the mast, whereas thesource area of the nighttime fluxes is spread up to tensof kilometers from the mast (Grelle 1997).

Liquid soil water content and soil temperature weremeasured with time-domain reflectometry and ther-mocouples in 14 profiles at 5–6 depths down to 100 cm(Kellner et al. 1999). The profile measurements wereaveraged and integrated over the depths of the modellayers to generate comparable variables. The variationbetween soil profiles was high, especially for the soilwater content.

Transpiration data were obtained from sap flow mea-surements on individual trees scaled to represent theaverage stand transpiration (Cienciala et al. 1999). Dailyaverage values of sap flow measurements are a goodestimate of daily transpiration. However, at shorter (di-urnal) timescales, sap flow measurements show a sig-nificant time lag, due to the storage of water in the tree.

The validation data are summarized in Table 2 andthe instruments are given in the appendix.


TABLE 3. Model parameter values for the old and the new version of the ECMWF surface scheme, used in the simulations for theNOPEX boreal forest. Figures in boldface represent values based on site-specific observations.

Parameter VB95

TESSEL

High vegetation Low vegetation

Vegetation coverageLAIMinimum canopy resistance (s m21)Sensitivity coef to VPD (hPa21)Root distribution (four layers) (%)

0.90460—33, 33, 33, 0

0.90 3 0.98651000.0326, 39, 29, 6

0.90 3 0.014360024, 41, 31, 4

Skin conductivity (W m22 K21)

Skin conductivity—bare soilSkin conductivity—intercepted waterRoughness (momentum) (m)

15

15151.75

20 (unstable)9.5 (stable)15101.75

10

1.75Roughness (heat) (m)Albedo (vegetation)Albedo (snow)

Height (z) over displacement height (m)

0.175*0.080.20 (vegetation)0.70 (other)13.9

0.175*0.0815–0.09650.20 (high vegetation)0.50–0.85 (other)13.9

0.175*

Interception capacity (m LAI 21)Interception efficiencySoil porosity (m3 m23)Water content at field capacity (m3 m23)

0.00020.547.232.3

0.00020.547.232.3

Water content at wilting point (m3 m23)Matric potential at saturation (m)Hydraulic conductivity at saturation (m s21)

17.120.3384.57 3 1026

17.120.3384.57 3 1026

* ECMWF praxis.

e. Model parameterization

Model parameters were set according to values gen-erally used within the operational 3D version of theECMWF model corresponding, in the case of geograph-ically dependent parameters, to the value at the closestgrid point. For the new scheme, the relative area of highand low vegetation was 0.986 and 0.014, respectively.The total fractional vegetation cover was set to 0.90 inboth schemes. However, vegetation albedo, vegetationroughness length for momentum, and displacementheight were based on measurements from the NOPEXarea (Molder and Lindroth 1999; Molder et al. 1999a,b).The values chosen for each of the surface schemes aresummarized in Table 3.

The surface schemes were run in offline mode for a3-yr period (1 January 1994–19 December 1996), at a30-min time step. Initial soil moisture and temperatureprofiles were obtained by running the model three timesin sequence for the first year (1994). The first of theseruns used uniform soil profiles as initial conditions (soilmoisture content 5 0.323 m3 m23 and soil temperature5 58C). The final values of the soil state variables ob-tained from these runs were used as initial conditionsfor the 1994–96 simulations presented in the following.No calibration was done.

f. Model evaluation

Simulated fluxes were compared with observed sur-face energy balance components, net radiation, sensibleheat flux, latent heat flux, and ground heat flux [Eq.

(1)]. The comparisons focused on long-term seasonalvariations. Discrepancies between simulated and mea-sured fluxes were also examined with respect to tem-poral dynamics in soil temperature, soil water content,stand transpiration and the degree of atmospheric and/or surface control. The results are discussed in relationto the major recent changes of the ECMWF surfacescheme and its parameterization.

The effect of a dynamic representation of the landsurface is most significant when the surface control islarge in comparison with the atmospheric forcing—forexample, during droughts. Such periods were identifiedby comparing the observed and simulated evapotrans-piration with that obtained from a simple Penman for-mulation (Penman 1953) as modified by Monteith(1965), with a constant surface resistance:

deD(R 2 G) 1 rcn p raLE 5 , (2)

D 1 g*

where de is the vapor pressure deficit (Pa) at the ob-servation height, D is the slope of saturation pressurevs temperature (Pa K21), r is the air density (kg m23),cp is the specific heat of air (J kg21 K21), and

g(r 1 r )a sg* 5 , (3)ra

where g is the psychrometric constant 66 (Pa K21), ra

(s m21) is the aerodynamic resistance, and rs (s m21)is a surface resistance.

Latent heat flux was calculated at daily resolution


TABLE 4. Mean air temperature and precipitation during thesimulation period.

YearMean air temperature

(8C)Precipitation

(mm)

199419951996

5.95.44.8

537550528

FIG. 4. Simulated and measured latent heat flux (10-day averages).

FIG. 3. Weather conditions during the 3 yr of simulation: (a) airtemperature, (b) vapor pressure deficit, (c) global radiation, and (d)precipitation (daily average values).

FIG. 5. Simulated and measured sensible heat flux(10-day averages).

using climate data from the Norunda site, a constantsurface resistance, and assuming the ground heat fluxto be negligible. The aerodynamic resistance was basedon the same roughness values as used in the model runs,assuming neutral atmospheric stability. The surface re-sistance (195 s m21) was inferred from calibrating themodel with the observed latent heat flux (1994–96), torepresent the average surface control on evaporation.Periods during which the Penman–Monteith (P–M)evapotranspiration was significantly higher than that ob-tained from the simulations and measurements dem-onstrate when evapotranspiration was mainly controlledby soil moisture conditions.

An additional sensitivity test was performed with theTESSEL model in order to better understand the causesof the differences between the two models and the re-maining discrepancies between the TESSEL simulationsand the observations. Some key parameters were varied(skin layer conductivity, roughness length for heat, min-imum canopy resistance, and leaf area index), based onthe findings of the original simulations.

3. Results

a. Climatic conditions

The period examined included both dry and wet sum-mers as well as mild and cold winters. Annual precip-itation (Table 4) was similar but the within-year distri-bution differed between years (Fig. 3). Summer 1994

was warm and dry; summer 1995 was initially warmand humid but changed to drier conditions at the end,whereas summer 1996 was rather wet. The 1994/95 win-ter was mild and humid, in contrast to the 1995/96 win-ter, which was cold and dry.

b. Simulated and measured surface fluxes—A quickoverview

Seasonal dynamics in surface energy fluxes comparedsignificantly better to observed values when simulatedwith the new scheme (TESSEL) as opposed to the oldone (VB95). Latent heat flux was systematically over-estimated in winter, spring, and early summer by theold scheme (Fig. 4). The new scheme significantly im-proved the prediction of latent heat flux and, as a con-sequence, the seasonal variation in sensible heat fluxwas also improved (Fig. 5). However, the new schemestill revealed some systematic discrepancies. The sen-sible heat flux from the air to the surface was under-estimated in winter. This discrepancy was linked to acompensating overestimation of the heat flux from the


FIG. 6. Simulated and measured ground heat flux (10-dayaverages). FIG. 7. Discrepancy between simulated (TESSEL) and measured

net shortwave radiation, net longwave radiation, and net radiation(10-day averages).

ground to the surface during the same period (Fig. 6).Simulated and measured net radiation agreed well ingeneral (Fig. 7). Discrepancies could be related to smalloverestimations of surface temperature in summer andalbedo in winter.

The significance of different changes of the land sur-face scheme for predicted surface energy fluxes is dis-cussed below.

c. Latent heat flux—The role of snow evaporation

The VB95 scheme largely overestimated latent heatflux from the snow cover, which was the reason behindthe general overestimation of latent heat flux in winterand early spring (Figs. 4 and 8). The overestimation wasworse in 1994/95, probably because of warm and humidweather with a higher frequency of neutral conditionsand thus generally higher values of the exchange co-efficients in comparison with the colder and more stableconditions in 1995/96 (Figs. 8c,d). The introduction ofa separate energy balance over snow-covered areas andan additional aerodynamic resistance for evaporationfrom snow under the tree canopy (TESSEL) signifi-cantly improved the predictions of latent heat flux dur-ing these periods (Fig. 4).

d. Sensible heat flux and ground heat flux

The sensible heat flux during spring and summer wassignificantly better reproduced by the new surfacescheme in comparison with the previous scheme. Thiswas mainly because the predicted latent heat flux hadbeen improved and compensating errors in sensible heatflux were thus eliminated. In contrast, simulated andmeasured sensible heat flux showed some discrepanciesduring autumn and winter (Fig. 5), especially in Sep-tember–December 1995. During this period the sensibleheat flux from the atmosphere to the surface was sig-nificantly underestimated by both model schemes. Thiswas most likely related to an underestimation of fluxes

during stable conditions—especially during nighttime(Fig. 9), which was compensated for by an overesti-mated upward ground heat flux. The large differencebetween simulated and observed ground heat flux isrelated to the fact that the observations represent theheat flux at the soil surface whereas the simulationsrepresent the heat flux at the top of the canopy. In fact,the diurnal amplitude of the simulated ground heat fluxagreed well with the residual of observed energy balancecomponents (Fig. 9d) and the systematic discrepancycorresponds to the underestimation of sensible heat flux.Too much heat was taken from the soil instead of fromthe air to balance the negative radiation budget duringcold winter nights. This suggests that the aerodynamiccoupling between the surface and the atmosphere wastoo weak and/or the thermal skin layer conductivity wastoo high. A sensitivity test in which the values for skinlayer conductivity and roughness length for heat werevaried showed that simulation of both ground heat fluxand soil temperatures could be significantly improved.The best result was obtained by increasing the roughnesslength for heat by a factor of 10 and reducing the skinlayer conductivity to 10% of its original value, in thesame run (Table 5). The slope of the linear regressionbetween simulated and observed sensible heat flux thenimproved from 0.73 to 0.88, while for ground heat fluxthe corresponding value changed from 2.4 to 1.4. Thelarger value of roughness length for heat is supportedby results from Molder and Lindroth (1999), whichshow that for the NOPEX area the roughness length forheat is in the same range as that for momentum. Thelack of closure in the observed energy balance (Fig. 9b)may be explained by heat storage within the canopy,which is not accounted for in the measurements.

e. Soil temperatures

The prediction of soil temperatures during winter wasslightly improved with the new scheme (TESSEL), most


FIG. 8. (a),(b) Simulated and measured (Uppsala Airport) snow depth, and (c),(d) simulated snow evaporation for (left) winter 1994–95and (right) winter 1995–96.

likely because of changed routines for heat flux betweensnow and soil. Simulated soil temperatures were gen-erally overestimated during the vegetation period andunderestimated during winter by both models (Fig. 10).This could have been caused by too-large values for thesoil thermal conductivity and/or the skin layer conduc-tivity. Sensitivity tests showed substantially improvedprediction of soil temperature when the skin layer con-ductivity was decreased (Table 5). The slope of the lin-ear regression between simulated and observed soil tem-peratures in the uppermost soil layer (0–7 cm) changedfrom 1.43 to 1.03 when the skin layer conductivity wasdecreased to 10% of its original value. However, thistest also showed that the parameter should be kept atits original value for the snow tiles (high vegetation withsnow beneath and snow on low vegetation) in order toaccurately simulate snowmelt. Another source of errorcould be that the assumption of zero heat flux at thelower boundary of the soil profile (2.89 m) was notappropriate for the seasonal timescale. A test simulationwas run in which the lower boundary zero heat flux wasreplaced by an annual heat flux sine wave, estimatedfrom the measured ground heat flux. However, this did

not significantly improve the simulated soil tempera-tures.

f. Latent heat flux—The role of transpiration

The TESSEL scheme improved simulated latent heatflux just after snowmelt, because 1) the reduction oftranspiration in the presence of frozen soils was nowtaken into account and 2) the predicted duration of snowcover had improved (Figs. 8a,b). However, in spring1996, the soil temperature and moisture measurementsindicate that the TESSEL scheme overestimated the du-ration of soil frost by some weeks, at 28–100-cm depth(Figs. 10 and 11). Grelle et al. (1999) suggested thattranspiration in spring and summer 1996 was reducedbecause of frost-damage of roots and tissues in the fo-liage. The new scheme indeed correctly reduced thetranspiration, but possibly partly for the wrong reason.

The VB95 scheme overestimated latent heat flux aswell as transpiration in early summer (Figs. 4 and 12).Results from the TESSEL scheme agreed well with ob-served values during the same period, most likely as a


FIG. 9. Surface heat fluxes (turbulent fluxes of sensible and latent heat are positive upward, net radiation and ground heat flux arepositive downward), average diurnal course Oct–Dec 1995.

TABLE 5. Linear regression for TESSEL simulations vs NOPEX observations—intercept (a0), slope (a1), coefficient of determination(r2)—and root-mean-square error (rmse), based on daily average values.

ID*

Net radiation (W m22)

r2 a0 a1 Rmse

Sensible heat flux (W m22)

r2 a0 a1 Rmse

Latent heat flux (W m22)

r2 a0 a1 Rmse

Ground heat flux (W m22)

r2 a0 a1 Rmse

(a)(b)(c)(d)(e)(f )

0.9110.9070.9070.9110.9110.907

5.0018.4528.5295.1035.1398.585

0.9040.9210.9350.9060.9080.925

22.723.623.822.722.723.7

0.8030.8380.8340.7960.7980.839

11.4614.2114.49.448.61

11.30

0.7270.8180.8770.6870.6690.760

24.623.824.724.724.722.6

0.8040.8070.7730.8070.8110.812

4.6684.7726.0854.6284.1314.247

0.8010.7980.7490.8920.9450.944

13.313.314.413.513.913.9

0.3720.4790.4760.3730.3710.479

1.6911.9621.9681.6931.6831.958

2.3921.3931.3962.3912.3801.389

15.27.07.0

15.215.1

6.9

Transpiration (mm day21)

r2 a0 a1 Rmse

Temperature 0–7 cm (8C)

r2 a0 a1 Rmse


r2 a0 a1 Rmse


r2 a0 a1 Rmse

(a)(b)(c)(d)(e)(f )

0.8570.8500.8510.8450.8600.852

0.2150.2230.2190.2610.2600.270

0.7120.7190.6570.8320.9000.906

0.390.390.430.380.390.40

0.9580.9710.9710.9580.9580.971

22.270.0860.027

22.2822.28

0.068

1.4231.0381.0311.4201.4171.034

2.811.041.002.782.771.02

0.9670.9710.9700.9670.9670.971

22.4120.1620.2122.4222.4220.18

1.4331.0621.0551.4291.4261.058

2.520.990.972.502.490.97

0.9830.9300.9270.9820.9830.929

22.2320.2920.3322.2322.2220.30

1.4391.0871.0771.4331.4301.080

1.991.281.271.961.941.27

* Simulation identification: (a) default simulation, (b) the skin layer conductivity set to 10% of the original value for all tiles except thesnow tiles, (c) as (a) but with the roughness length for heat set equal to the roughness length for momentum, (d) the minimum canopyresistances set to 75% of the original values, (e) as (d) but with a seasonal variation of leaf area index (20% around the original value), and(f ) a combination of (b) and (e).


FIG. 10. Simulated and measured soil temperature in model layers at (a) 0–7, (b) 7–28, and (c) 28–100 cm (10-day averages).Measurements from 15 soil profiles were averaged and integrated over the model layer depths to match the simulations.

consequence of the increased value for minimum can-opy resistance (Table 3).

The VB95 and TESSEL models reproduced obser-vations well in June–August 1994, when the P–M equa-tion failed (Fig. 4). On the other hand, both land surfaceschemes, especially TESSEL, underestimated the latentheat flux during the same period in 1995. The resultssuggest that the control of transpiration by soil moistureon the canopy resistance was efficient in 1994 whereasit was overestimated in 1995. Simulations as well asobservations indicated a significant soil water deficit inboth 1994 and 1995. The simulated degree of stressrelated to soil moisture was of similar size in both years.The difference in performance between years may haveseveral explanations:

1) In the ECMWF surface scheme, the water uptake isdistributed in the profile according to a fixed rootdistribution, which will strongly affect the degree ofstress. However, the stand average root distributionmay deviate from the one assumed and may alsovary between years.

2) Some soil–vegetation–atmosphere transfer schemesaccount for ‘‘compensatory uptake of water,’’ whichmeans that root water demand in stressed parts of asoil profile may be compensated for by higher uptakefrom unstressed parts of the root zone. Jansson et al.(1999) showed that a certain degree of reallocation

of the water uptake from stressed parts of the rootzone to nonstressed parts was needed to explain ob-served evaporation, transpiration, and soil water con-tent at the NOPEX Central Tower site in 1994.

Compensatory uptake of water and/or a different rootdistribution may thus have limited the degree of stressin 1995, which was not accounted for in either of theECMWF models. The lack of seasonal development ofLAI in the model schemes may also contribute to thegeneral pattern of closer agreement between simulatedand observed values in spring and early summer fol-lowed by underestimation later in the season (Fig. 12).For a forest of mixed pine and spruce, seasonal LAImay vary by 10%–20% (E. Cienciala 2000, personalcommunication), which would correspond to about 10W m22 in summertime latent heat flux for the range ofthe data presented in Fig. 4.

In contrast to VB95, TESSEL accounts for the impactof diurnal variation in vapor pressure deficit on tran-spiration and thus on latent heat flux. The results in-dicate that the average diurnal course of latent heat fluxsimulated by TESSEL was slightly better during thesummer period than that simulated with the VB95scheme (Fig. 13). The new scheme reproduced betterthe rate of change of latent heat flux in morning andevening.


FIG. 11. Simulated and measured unfrozen soil moisture in model layers at (a) 0–7, (b) 7–28, and (c) 28–100 cm (10-day averages).Measurements from 15 soil profiles were averaged and integrated over the model layer depths to match the simulations.

FIG. 12. Simulated transpiration and measured sap flow (10-dayaverages).

During the period of concern, the relative increase ofcanopy resistance due to soil moisture stress in the VB95model was about twice as high as in the TESSEL model(see f 2, Fig. 14). The stress with respect to radiation( f 1) during midday was similar in both models, althoughthe response function had been modified. The stressfunction due to vapor pressure deficit ( f 3) increased thecanopy resistance by about 50% in the TESSEL scheme.

Soil moisture stress had thus partly been replaced bystress related to vapor pressure deficit, which has a dif-ferent temporal pattern. The total stress in the VB95model exceeded that in the TESSEL model (Fig. 14).However, the net uptake of water was still higher in theVB95 model, because the ‘‘unstressed transpiration’’governed by the minimum canopy resistance value washigher than in the TESSEL model.

A sensitivity test with the TESSEL scheme showedthat a reduction of the minimum canopy resistance by25% reduced the discrepancies between simulated andobserved fluxes, especially in summer 1995 and summer1996. The slope of the linear regression between sim-ulated and observed values increased from 0.80 to 0.89and from 0.71 to 0.83 for latent heat flux and transpi-ration, respectively (Table 5). Further improvementswere obtained when, in addition, a seasonal variationin LAI (20% around the mean, with a minimum in Feb-ruary and a maximum in August) was introduced. Theregression slopes then improved to 0.945 and 0.9 forlatent heat flux and transpiration, respectively (Table 5).

g. Evaporation components

Total evaporation over the period of concern de-creased from 1429 mm in VB95 to 1057 mm in theTESSEL simulations (1056 days). This could mainly be


FIG. 13. (a) Simulated and measured latent heat flux and (b) simulated transpiration and observed sap flow, average diurnal course 4 Jul–31 Aug 1995.

FIG. 14. (a) Relative increase of canopy resistance in the VB95 and TESSEL simulations, and (b) their components representing stressdue to solar radiation (f1), soil moisture stress (f2), and vapor pressure deficit (f3), average diurnal course 4 Jul–31 Aug 1995.


FIG. 15. (left) Total evaporation (E ) and transpiration simulated by the VB95 and TESSEL models and observed with eddy-correlation(E-total) and sap flow (E-transpiration), based on 435 days May–Oct and 358 days Nov–Apr 1994, 1995, and 1996. (right) Evaporation fromdifferent components simulated by the VB95 and TESSEL models, based on 552 days May–Oct and 543 days Nov–Apr 1994, 1995, and1996.

attributed to decreased evaporation from snow (2233mm) and transpiration (2179 mm) and thus to thechanged routines governing these components (Fig. 15).In contrast, evaporation from bare soil increased sig-nificantly (179 mm), as a result of the changed for-mulation for bare soil evaporation. Comparison withavailable observations indicates that the TESSEL sim-ulation reduced evaporation and especially transpirationtoo much in May–October (Fig. 15). However, becauseof the increased bare soil evaporation the proportion oftranspiration decreased from 71% (VB95) to 61% (TES-SEL), which compared better to the relation betweenobserved transpiration (sap flow) and total evaporation(eddy-correlation) during this period (62%). Total evap-oration simulated by the TESSEL model agreed wellwith observations in November–April. Evaporationfrom intercepted water did not change much, but in-creased when expressed as a proportion of total evap-oration because of the general reduction of evaporation,especially in winter.

4. ConclusionsSingle-column runs showed that measured seasonal

variations in surface energy fluxes for a Scandinavian

boreal forest were significantly better reproduced by thenew ECMWF surface scheme (TESSEL) in comparisonwith the old version. The major reasons for this are thenew parameterizations of winter processes and transpi-ration control, which improved the simulated partition-ing of net radiation into sensible and latent heat flux.

Evaporation during winter was greatly overestimatedby the old scheme because of an overestimated evap-oration from snow. The significant improvement in thesimulated winter evaporation in the TESSEL model con-firms the results obtained by van den Hurk et al. (2000)that a more realistic representation of evaporation fromsnow lying under vegetation was achieved when theextinction of turbulent exchange under the canopy wastaken into account. The introduction of a function thatlimits transpiration in frozen soils also improved thesimulated evaporation in spring. However, on some oc-casions an overestimation of the duration of soil frostmight have compensated for other soil frost–inducedprocesses affecting water uptake by roots.

Despite the improved simulation of evaporation inwinter, downward sensible heat flux was still somewhatunderestimated during winter, especially at night. This


was compensated for by an overestimated upwardground heat flux. The results indicate that the aerody-namic coupling between the surface and the atmospherewas too weak and/or the skin layer conductivity wastoo high to represent the NOPEX site. This was alsoconfirmed by sensitivity tests.

The introduction of a higher canopy resistance rep-resenting typical boreal forest reduced the total simu-lated evaporation during summer to more realistic val-ues. The TESSEL scheme also simulated diurnal vari-ation in evapotranspiration with better accuracy, be-cause of the inclusion of a canopy resistance responseto vapor pressure deficit. The proportion of transpirationto total evaporation during summer was in much betteragreement with the observations, even though the totalamount of evaporation was somewhat underestimated.Transpiration was underestimated in late summer, prob-ably because seasonal development of LAI and com-pensatory uptake of water were not accounted for in themodel scheme. Even though total evaporation was re-duced, the change from a relative humidity approach toa surface resistance approach in TESSEL considerablyincreased evaporation from bare soil.

Sensitivity tests showed that the ECMWF surfacescheme could be further improved with respect to borealforest areas by introducing seasonal variations in veg-etation properties (Betts et al. 1998b) and by changing

the minimum canopy resistance. Model performancewould also be improved by modification of the stabilitycorrection for stable conditions and by calibration of theparameter for skin layer conductivity.

Acknowledgments. Thanks are due to Dr. J. Seibertand Dr. E. Kellner (Uppsala University) for making dataavailable on precipitation and soil moisture/temperature.Meteorological data from Uppsala Airport were ob-tained from the Swedish Meteorological and Hydrolog-ical Institute (SMHI). We also kindly thank Prof. P-E.Jansson (KTH, Stockholm), Prof. N. Jarvis (SLU, Upp-sala), and Prof. A. Beljaars (ECMWF, Reading) for valu-able discussions and comments. This project was fundedby the Swedish Natural Science Research Council.

APPENDIX

Instruments and Data-Processing Functions

All variables and data-processing functions used forthe preparation of the meteorological forcing are sum-marized in Table A1. Linear regression functions usedfor the transformation of climatic variables at Uppsalaairport to the Norunda site are listed in Table A2. Ad-ditional functions used are listed in Table A3, and theinstruments are listed in Table A4. Instruments used forthe validation data are summarized in Table A5.


TABLE A1. Variables used for the meteorological forcing.

Forcing variables

Variable

Source variables

Name LocationCapturefractiond Data processing

Air temperature (K) TCTS (8C) CTSa 35 m 83 (97)% 1) Weighted average of two sensors at 31.7and 36.9 m, 2) T 5 TCTS 1 273.159CTS

TUA (8C) UAb 1.5 m 17 (3)% 1) Scaled by linear regression between TCTS

and TUA (cf. Table A2), 2) T 5 TUA 19UA

273.15Wind speed (m s21) UCTS (m s21) CTS 35 m 76 (88)%

UUA (m s21) UA 10 m 24 (12)% Scaled by linear regression between UCTS

and UUA (cf. Table A2)Specific air humidity

(kg kg21)qCTS,1 (g kg21) CTS 35 m 28 (32)% 1) q 5 qCTS,1 3 1023, 2) larger drifts in9CTS,1

the calibration were scaled by comparisonof moving averages (96 h) between q9CTS,1

and qUA (cf. Table A2)mrCTS (g kg21) CTS 35 m 53 (61)% 1) qCTS,2 5 (mrCTS 3 1023)/(1 1 mrCTS 3 1023)

2) average of two sensors at 31.7 and36.9 m

eUA (Pa) UA 1.5 m 17 (6)% 1) qUA 5 (0.622eUA)/[(PUA 2 eUA) 1 0.622eUA]2) scaled by linear regression betweenqCTS,2 and qUA (cf. Table A2)

qsat (kg kg21) Estimated 2 (1)% Monteith and Unsworth (1990), cf. Table A3Atmospheric pressure (Pa) PaCTS (Pa) CTS 2 m 83 (97)% 5 PaCTS 2 (33 m 3 8 Pa m21)Pa9CTS

PaUA (Pa) UA 1.5 m 17 (3)% Scaled by linear regression between Pa9CTS

and PaUA (cf. Table A2)Downward shortwave radi-

ation (W m22)RIS, CTS (W m22) CTS 102 m 83 (97)%

RIS, EST (W m22) Estimate 17 (3)% Angstrom (1924), cf. Table A3Downward longwave radia-

tion (W m22)RIL, CTS 68 (W m22) CTS 68 m 79 (94)%

RIL, EST (W m22) Estimate 21 (6)% Konzelmann et al. (1994), cf. Table A3Precipitation (mm h21) PCTS [mm (10 min)21] CTS 1.5 m 37 (42)% Accumulation within hours

PMMO [mm (10 min)21] MMOc 1.5 m 6 (7)% 1) Accumulation within hours, 2) multipliedby a factor of 1.1 based on comparison ofaccumulated values of PCTS and PMMO

from common time periodsPUA [mm (12 h)21] UA 1.5 m 57 (51)% 1) Equally divided between hours with ob-

served precipitation events, 2) correctionof systematic errors following Eriksson(1983), cf. Table A3

a CTS 5 NOPEX Central Tower Station.b UA 5 Uppsala Airport.c MMO 5 Marsta Meteorological Observatory.d Percentage of time in the final dataset during Jan 1994–Dec 1996 (validation period, Jun 1994–Dec 1996).

TABLE A2. Transformations of climatic variables from Uppsala Airport to Norunda.

Variable (unit) Function r2 N

Air temperature (8C)Wind speed (m s21)Atmospheric pressure (Pa)Air humidity (kg kg21)

TCTS 5 0.0317 1 0.947TUA

UCTS 5 1.356 1 0.290UUA

Pa 5 257.5 1 0.991PaUA9CTS

q 5 1.96 3 1024 1 0.900qUA9CTS,2

0.9610.4330.9920.951

21 88619 95621 88713 964


TABLE A3. Functions used in the preparation of the meteorological forcing data.

Variable Function

Saturated vapor pressure (Pa)(12.555322667)/TCTS10 T # 273.15 KCTS

e 5 (Monteith and Unsworth 1990)s (11.405122353)/T5 CTS10 T . 273.15 KCTS

Downward longwave radiation (W m22)1/8eCTS 4 4 4R 5 0.23 1 0.484 (1 2 N ) 1 0.952N sTIL,EST UA UA CTS5 1 2 6[ ]TCTS

(Konzelmann et al. 1994)*

Downward shortwave radiation (W m22)Sz1) R 5 1360 , S 5 sinF cosL, S 5 cosF cosL,IS,POT x y2 2 21 2S 1 S 1 Sx y z

S 5 sinLz

where the azimuth angle F and the elevation angle L of the sun are given by thelatitude, the longitude, and the time and day of year (Monteith and Unsworth1990)

˚2) R 5 R (0.72 2 0.5N ) (Angstrom 1924)*IS,EST IS,POT UA

Precipitation correction for systematic errors1.15 T # 228CUA

P9 5 P 3 1.11 2 0.02T 228C , T , 28C (Eriksson 1983)UA UA UA UA1.07 T $ 28C UA

* NUA 5 Cloud cover observed at UA.

TABLE A4. Instruments used for the meteorological forcing data.

Variable Name Instrument

Air temperature

Wind speed

TCTS

TUA

UCTS

UUA

Copper–constantan thermocouple (radiation-shielded and ventilated;In Situ, Ockelbo, Sweden)

SMHI*Sonic anemometer (Gill Instruments Solent Basic, Lymington, United Kingdom)SMHI*

Specific air humidity

Atmospheric pressure

qCTS,1

mrCTS

eUA

PaCTS

PaUA

Gas analyzer (LI-COR 6262, Lincoln, NE)Gas analyzer (LI-COR 6262, Lincoln, NE)SMHI*Indoors pressure sensor (Vaisala PTA 427, Helsinki, Finland)SMHI*

Downward shortwave radiationDownward longwave radiationPrecipitation

RIS, CTS

RIL, CTS 68

PCTS

PMMO

PUA

Pyranometer (Kipp and Zonen CM21, Delft, Netherlands)Net radiometer (Dr. Bruno Lange LXV055, Berlin, Germany)Rain gauge (In Situ IS200 W, Ockelbo, Sweden)Rain gauge (In Situ IS200 W, Ockelbo, Sweden)Rain gauge, SMHI*

* Weather observations maintained by the Swedish Meteorological and Hydrological Institute (SMHI), Norrkoping, Sweden.

TABLE A5. Instruments used for the validation data.

Variable Instrument

Latent heat flux (W m22) Sonic anemometer (Gill InstrumentsSolent Basic, Lymington, UnitedKingdom), and gas analyzer (LI-COR 6262, Lincoln, NE)

Sensible heat flux (W m22) Sonic anemometer (as above)Net radiation (W m22) Net radiometer (Dr. Bruno Lange

LXV055, Berlin, Germany)Soil heat flux (W m22) Heat-flux plates (REBS HFT-1, Seat-

tle, WA)Tree sap flow (mm day21) Tissue-heat-balance sap-flow meter

(Environmental Measuring SystemsP609.2, Brno, Czech Republic)

Soil temperature (8) Thermocouple (In Situ, Ockelbo,Sweden)

Soil moisture (m3 m23) TDR (Tektronix 1502B, Pittsfield, MA)


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Boreal Forest Surface Parameterization in the ECMWF Model—1D Test … · 2014-12-10 · JANUARY...

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