Description of the PILPSC-1 experimentN. Viovy
February 19, 2002

1

Introduction

1.1

objective of PILPSC-1

The PILPS experiments which have been conduced up to now (Pitman et al. 1993, HendersonSellers et al. 1993, Henderson-Sellers et al. 1995) have clearly improved our knowledge of
evaporation driving the exchanges between atmosphere and land surface. This has lead tomany improvements in land surface schemes and has emphasized the importance of model
parameterization of soil moisture and soil water stress for instance.
In parallel but with no link to PILPS several intercomparison project have also beenaccomplished in the framework of the IGBP program to compare the simulation of biogeochemical processes taking place at the terrestrial surface (mainly the carbon cycle).
But it is now established that biogeochemical and biophysical processes are highly coupledat the land surface from the scale of seconds to centurys. For instance it as been demonstrated
that the feedback of atmospheric CO2 concentration on stomatal conductance induces change
in the hydrologic cycle with important consequence on the climate and vice-versa (Sellers etal. 1996, Betts et al. 1997)
The recent evolution of land surface representation both for the study of carbon cycle
or climate have lead to an improvement of the coupling between biophysical and biogeochemical processes. At the same time, since the middle of the 1990’s several sites have been
instrumented to continuously measuring both net CO2 flux and energy fluxes (latent andsensible).
Thus we are able now to go further in our understanding of coupling between CO2 andthe water cycle by comparing simulated CO2 and energy fluxes with in situ data.
The PILPSC-1 experiment proposed here is an initiative within the GEWEX/GLASS(Global Land-Atmosphere System Study) panel. The objective of this project is the comparison of models that simulate water cycle and model that simulate both CO2 and water
cycles with continuous “in situ” observations for 2 years.
Among the sciences questions to be addressed by PILPS-C1 are:• What is the ability of the models to correctly reproduce both biophysical and biogeochemical process ?
• Taking into account the long term history of the site, are the models able to reproduce
the observed sink of carbon ?
• Do models which include the carbon cycle out-perform those which do not for thesimulation of the energy and water budgets ?
Today, different types of models that are able to simulate the water and CO2 fluxes. On
the one hand we have SVATs that simulate only the water and energy cycles and which
in some cases have introduced a parameterization for the CO2 cycle mainly to improve thesimulation of physical processes. On the other hand we have biogeochemical models which
are oriented towards the carbon cycle (BGC and DGVM). More recently there has been amove towards ”complete” models that couple SVAT, BGC and DGVM modules. Likewise
there are 2D models for large scale applications and local models specific to one ecosystems.
1All these kinds of models have not been compared to evaluate the relative advantage ofeach one. Hence, one objective of this project, and one of its originality, is to compare very
differents kinds of surface models that have been designed for differents purposes For thisreason we encourage the participation of a large scientific community coming from traditional
land surface scheme but also from more carbon oriented models. In particular we would liketo attract the participation of global scale 2D models as well as 0D models. Likewise, because
available measurements include not only net CO2 fluxes but also latent and sensible heat
fluxes, we encourage participation of model that only simulate latent and heat fluxes.

1.2

Description of the site and instrumentation

The experiment will be carried out on the site of Loobos. This experiment site belongs to the“Euroflux” program network. The vegetation cover is a scots pine (Pinus sylvestris) forest
located in the center Netherlands (see figure 1 for a map of situation of Loobos and 2 for aview of the site). The soil is sandy and the climate is cool with a mean annual temperatureof 10 C and an annual precipitation around 800mm. The forest was planted on sand dunes inthe beginning of the 20th century and is now almost 100 years of age. Existing soil organic
matter was largely removed at this time. There is an understory of Deschampsia flexuosa, agrass that can reach a height of 50cm. The soil is sandy (humuspodzol) with a 10cm layerof organic matter.
The net CO2 fluxes, latent and sensible heat fluxes have been measured by eddy correlation method from January 1997 to December 1998 on a flux tower at a height of 27 meterswith a 30 minutes time step. The fetch is at least 1.5 kilometres in all directions and consistsof similar forests with the same species of similar age and height. For this site Elbers et al.
calculated that most of the flux originates from 500m around the tower, with maximum flux
contribution at 120m for neutral atmosperic conditions. An automated weather station was
put on the site and measured incoming and reflected shortwave and longwave radiation, soil
heat flux, windspeed, temperature and relative humidity. Rainfall was also measured above
the canopy and in the open field with automated tipping bucked rain gauges
Several ancillary data like leaf area index, albedo, surface roughness or soil characteristics
have also been measured. Main features of the site with regards to vegetation and soil
characteristics are summarized in table 1.2 The period considered for the experiment will
begin in January 1997 and finish in December 1998.
It should be noted that a meteorologic station which has measured major meteorological parameters since the beginning of the 20th century exists near the Loobos site. This
information combined with the fact that the forest was planted on a soil with almost no
carbon, gives us the unique oportunity to simulate the entire history of carbon in the soil
and vegetation at this location.

2

Site description
Latitude52 10’00” N
Longitude
05 44’38” E
Elevation [m a.s.l.]
52 mTopology
Flat
Vegetation and stand characteristics
Dominant species
Pinus Sylvestris
Understorey
Deschampsia flexuosa
Stand age (years)
80
Canopy height(m)14Stem density (ind/ha)
620
ClimateAnnual precipitation (mm)786Annual mean temperature (C)9.8
Nitrogen deposition (Kg N /ha/ y)40
soil
Parent material
sand dune
Soil texture
sandy
Soil depth (cm)50
measurement conditions
Tower Height (m)24Measurements Height (m)27Table 1: Main characteristics of Loobos site

3

Figure 1: Situation of the Loobos site

Figure 2: A photograph view of the Loobos site4

2

Experimental set-up

2.1

GeneralThe model forcing consist of 2 years starting from January 1, 1997 and ending December
31, 1998. The atmospheric forcing has a 30’ time step giving a total of 35040 values for each
forcing variable.
The atmospheric forcing data are to be linearly interpolated from the 30’ to the modeltime step if it is need at a higher time resolution. On the contrary fields must be averaged
or summed (depending on the variables) for models working with longuer time step.

2.2

Proposed model Intercomparison runs

A set of simulations will be proposed in the frame of this project, some more orientedof coupling between biophysical and biogeochemical process and some more oriented on
the carbon cycle. Because the models expected to participate to the project will be very
differents and because the objectives of the project are multiple, it is not expected that all
participants do all the simulations. Participants are invitated to choose the simulations thatthey will do. The minimum requirements is the first simulation described below.
2.2.1

short term simulations, free run

For this first simulation we propose that models simulate CO2 net fluxes, latent and sensible
heat fluxes with the half hourly time step for the years 1997 and 1998. For spin-up it isasked that participants run their models with the first year of data in loop until equilibriumof all the differents pools has been reached. Equilibrium mean that all state variables of themodel must changes of less than few percent from one year to another. From this equilibrium
state, we ask that the modelers simulate 2 more years and only report results for these last
two years. For this simulation called “free models”, we propose to use a minimum of input
parameters to the model (i.e climate, and soil forcing). The objective of this simulation
is to evaluate models ability to reproduce in situ data where they are driven only by theparameters that could be provided over large region (i.e they are run the same conditions as
in a GCM for instance). For models that compute vegetation distribution (e.g DGVM) it isasked to do one simulation with prescribed vegetation (if possible) and one simulation withcalculated vegetation.
2.2.2

short term simulations, constrained run

For the second simulation called “constrained models” we propose a protocol similar to theprevious one except that the models are forced by the available ancillary data ( mainly theLeaf Area index, the albedo, the roughness and soil carbon) that will be provided for the site.
The objective of this simulation is to separate the contribution of various processes in thediscrepancies between models and observations found in the previous experiment. Obviously
this simulation will be only possible for models that can prescibe these ancillary paremeters5to their models. Participants are requested to report the parameters which have been forced
and those which remained simulated.
2.2.3

long term simulation

The spin-up problem of models when considering carbon pools is complex. The time for
equilibrium between vegetation and soil carbon can take hundreds of years. In fact for mostof the sites where net CO2 flux have been measured, a net carbon sink is observed (i.e theannual net ecosystem exchange is negative) and no equilibrium has been reached. This is
the case for the Loobos site where a net carbon sink is observed for both 1997 and 1998.
An important question then, is to know if models that simulate the full carbon cycle (i.e invegetation and soil) are able to reproduce the observed sink at the site when the simulation
is not done using historical forcing data.
To reproduce what has been observed one needs in theory to know the history of the site
for several hundred of years. Fortunately in the Loobos case, the forest was planted eighty
years ago on a soil with almost no carbon. Moreover a long time series of meteorological data
is available from a station not too far away from the site. Thus a good approximation of realsite conditions can be simulated by models which explicitly simulate the soil decomposition.
In this simulation it is asked to run the models for 100 years (the age of the forest) beginningwith a zero biomass and no soil carbon content (The real initial soil carbon content was lessthan 0.4 % of soil weight). For living biomass a inital non zero value is allowed if needed topermit the start of the first vegetative cycle. For this simulation we will provided a hundredyear time series of climate. A synthetic 30’ time step data (constructed from the initial daily
measurements) will be provided to the participants. For this simulation we will also provide
the annual mean atmospheric CO2 concentration.
2.2.4

Summary of Experiments

In Summary, three runs are proposed in the frame of the PILPS-C1:
1. The “free with equilibrium spin-up” run (named F-E). This is the base simulation for
all participants with climate and soil data provided with an equilibrium spin-up. It
will be compared to observed latent and sensible heat fluxes and unbiased net CO2
fluxes.
2. The “Constrained with equilibrium spin-up” run ( named C-E). Simulation with ancillary forcing (LAI, z0 etc...) and equilibrium spin-up. For model that can prescribed
these parameters.
For these two first simulations, because spin-up will be done until equilibrium of allreservoirs is reached, the annual net coarbon exchange will be zero by definition. Thus,
to be comparable to observation, the mean net CO2 flux observed at Loobos will be
subtracted from the halfhourly net CO2 flux prior to comparison with simulations.
3. The “100 years simulation” run (named F-100). This simulation will be done onlywith climate and soil data and is limited to models that explicitly calculate all the6respiration terms. In this case, the simulations will be directly compared to observed
net CO2 flux.

3

Model forcings

All the data are provided using the NetCDF format and the ALMA conventions. Additional
information on the ALMA conventions and links for information on the netCDF format canbe found at : http://www.lmd.jussieu.fr/ALMA/

3.1

Surface forcings

The surface forcings will be provided at an half-hour time step for the period 1997-1998.
The forcings are summarized in table 2. The variables are :1. the wind speed (only the module of the wind is available)
2. Rainfall and snowfall. In the initial file only water precipitation was provided, theestimation of snowfall was done assuming snowfall when temperature is below 273.3
K.
3. air temperature
4. Specific humidity. In the original data, only the relative humidity of the air was given.
the specific humidity was estimated by the following formula:
Qair =

0.622hr ∗ Sv
P − hr ∗ Sv ∗ 0.378

Hr is relative humidity, P the Pression (in hPa) and Sv the saturation vapor pressure:
Sv = 611.0 exp17.269(T −273.16)/(T −35.86)
where T is the air temperature at 2m (K)5. Incident shortwave radiation
6. Incident long wave radiation
7. Surface pressure. This measurement was not available at the site. A constant standard
value of 1.013105 Pa was assumed.

3.2

Soil characteristics

The table 3 and 4 gives respectively the texture and hydraulic characteristics of the soil

7

Variable namedescription
UnitsTime step−1
Wind
surface module of wind
m.s
1800s
Rainf
Rainfall ratekg.m−2.s−1
1800s
Snowf
Snowfall ratekg.m−2.s−1
1800s
Tair
Near surface air temperature
K
1800s
−1
Qair
Near surface specific humidity
Kg.Kg
1800s
Psurf
Surface pressure
Pa
1800s
−2
SWdownSurface incident shortwave radiation
W.m
1800s
−2
LWdownSurface incident longwave radiation
W.m
1800s
Table 2: Atmospheric forcing for models forcing

Figure 3: daily mean air temperature at Loobos for 1997-1998 (K)8Figure 4: daily mean shortwave radiation at Loobos for 1997-1998 (W.m−2 )9Figure 5: daily mean longwave radiation at Loobos for 1997-1998 (W.m−2 )10Figure 6: daily total precipitation at Loobos for 1997-1998(mm/day))11

Loobosdepth(m)0-0.200.40-0.60

xsand
xclay
xorg
ρsample(m3 m−3 ) (m3 m−3 ) (m3 m−3 ) (m3 m−3 )
0.53
0.01
0.02
1.56
0.60
0.01
0.00
1.63

Table 3: Volume fractions of sand, clay and organic matter and the density.
Loobos
depth
θr
θs
α
N
m
l
ks
ks∗
−1
−1
(m)
(-)
(-) (cm )
(-)
(-)
(-)
(cm d ) (cm d−1 )0.10-0.18 0.01 0.44 0.0239 3.429 0.708
0.519.717268.0
0.50-0.58 0.01 0.38 0.0198 5.410 0.815
-0.9
9.334
178.0
∗
) saturated hydraulic conductivity determined seperatedly on a suturated
sample.
Table 4: The fitting parameters of the soil hydraulic characteristics as defined by van
Genuchten (1980). The last column shows the saturated hydraulic conductivity as determined seperatedly for a saturated sample.

3.3

additional forcing data for “constrained model” simulation

In addition to data provided in the previous section (3.1) the ancillary data provided for the“constrained” simulation will be the leaf area index, the roughness length, the displacement
height, the Vmax at leaf level and rooting depth.
This data will be provided after the results of the first simulation.

4

Model output data

The information to return for each participant are summarized in table 5. For the F-100
simulation we ask to report only the last two years of the run except for Total living biomass,
Total soil carbon, NPP and NEE that must be provided for the 100 years but with an annual
time step.

5

Proposed Analyses

As described above, the experiment will consist of two kind of simulations (with and without
constraint) with two different spin-up (“equilibrium” and “stand age run”). This section
summarizes the expected analyses to be made. This list may change and expand with time.
The analysis will be done on the parameters directly measured at the site. The primaryparameters that will be compared will be Latent Heat flux (Qle), Sensible heat flux (Qh),
Net shortwave radiation (SWnet), Net longwave radiation (LWnet) and Net ecosystem production (NEE). For the carbon cycle, an estimation of partition of the NEE measured at

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Variable namedescription
0.1 Energy balance components
SWnet
Net shortwave radiation
LWnet
Net longwave radiation
Qle
Latent heat flux
Qh
Sensible heat flux
0.2 General water balance components
Rainf
Rainfall rate
Evap
Total Evapotranspiration
0.4 Subsurface State Variables
SoilMoist
Average layer soil moisture
SoilTemp
Average layer soil temperature
SoilWetTotal soil wetness
0.5 Evaporation components
PotEvap
Potential Evapotranspiration
EcanopInterception evaporation
Tveg
vegetation transpiration
ESoil
Bare soil evaporation
Ewaterevaporation from surface water storage
ACond
Aerodynamic conductance
0.9 Carbon budget components
GPP
Gross Primary Production
NPP
Net Primary Production
NEE
Net Ecosystem Exchange
AutoResp
Autotrophic Respiration
HeteroResp
Heterotrophic Respiration
TotSoilCarb
Total soil carbon
TotLivBiom
Total living Biomass

UnitsTime step

W.m−2
W.m−2
W.m−2
W.m−2

3 hours
3 hours
3 hours
3 hoursKg.m−2 .s−1
Kg.m−2 .s−1

3 hours
3 hourskg.m−2K-

3 hours
3 hours
3 hoursKg.m−2 .s−1
Kg.m−2 .s−1
Kg.m−2 .s−1
Kg.m−2 .s−1
Kg.m−2 .s−1
m.s−1

3 hours
3 hours
3 hours
3 hours
3 hours
3 hoursKg.m−2 .s−1
Kg.m−2 .s−1
Kg.m−2 .s−1
Kg.m−2 .s−1
Kg.m−2 .s−1
Kg.m−2
Kg.m−2

3 hours
3 hours
3 hours
3 hours
3 hours
daily
daily

Table 5: Subset of ALMA output variables to be returned in the runs

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Loobos between gross ecosystem production and total respiration will be done to also compared components of the net flux. A first analysis will be done both on daily mean anddiurnal cycles:
For each of these parameters we will do the following comparison with data at the Loobos
site:• Comparison between simulated and observed monthly mean
• Comparison between simulated and observed daily mean
• Comparison between simulated and observed daily amplitude of the diurnal cycle• Comparison between simulated and observed daily phase of the diurnal cycle• Comparison between simulated and observed min and max of the diurnal cycle
This first analysis will allow to determine the ability of models to correctly reproduce theobserved variability of the differents parameters at differents time scale
Then a set of more complex statistical analysis will be done:• Analysis by type of weather: Several classes of weather will be determined from meteorological parameters. The mean differences between simulation and observationfor each weather class and each model will be determined. This will allow to findsystematic errors of models for particular weather conditions.
• Analysis of coefficient of correlation on time windows. This analysis will allowto determine at which period the variation of both simulated and observed signal are correlated.
• Analysis of the coefficient of determination. This coefficient RXY is defined as
Pj=i+λ/22j=i−λ/2 (Yi − Xi )RXY = 1 − Pj=i+λ/22
j=i−λ/2 (Xi − X̄)

where X is the observations and Y the simulation. This coefficient of determiniation,in complement of the coefficient of correlation take also into account for the absolutevalue of the signal weighted by the inverse of variance of the signal.
Complementary analysis can be done after the first analysis

6

Data protocols

All data handling and format requirements will follow the guidelines of ALMA, as describedon the ALMA web page (see http://www.lmd.jussieu.fr/p̃olcher/ALMA/dataex main.html.
Meteorological forcing data will be distributed and returned in netCDF format with the
GDT convention following the ALMA guidelines. Guidelines for utilizing netCDF directly,
or for transferring to and from ASCII files, can also be found on the ALMA web page.

14

6.1

Data distribution and Return

The forcing data and models output will be provided via FTP. Output variables that agiven model cannot provide or does not produce, should simply be omitted in the generated
netCDF file. the adresses where data should be taken and return as well as instructions
about these data (e.g naming convention) will be provided be E-Mail to each participant
when the data will be available.

6.2

Quality Control

ALMA has made available a screening program to check the correctness of the output netCDF
files prior to return. This program based on those define for PILPS 2e Experiment will apply
the annual water, energy and carbon balance criteria, as well as ensuring that all variables
are within reasonable ranges. The range requirements are not meant to comment on theapproprianess of model output, merely to verify unit correctness and sign. A number ofutilities are freely available for plotting netCDF files, as listed on the ALMA web site, and
we encourage you to use these as well. The screening program will be run after submission
prior to any analysis. Any data that fail the screening will not be considered. So we encourage
participants to run the program prior to submitting results.

6.3

Results Documentation

In addition to results of runs some informations will be asked to participants:• Short description of model and model structure ,(especially for model that have notparticipated to previous PILPS experiments)• General impressions and comments on the results obtained based on experiences with
one’s own model• Specific problems or concerns which were experienced• If a interpolation/average method have been use for application of forcing data, detailthe methodology used.
• For the “constrained run” Detail on the parameters that have been forced to the model

7

Proposed Time line

The anticipated timeline for the experiments is as follows:• February 2001: Submission of experimental protocol• May 2001: Distribution of forcing data to the participant• August 2002: Deadline for submission of results.
• November 2002: workshop for analysis of preliminary results.
15

References
Betts R. A., Cox P. M., Lee S. E., Woodward F. I. (1997) Contrasting physiological andstructural vegetation feedbacks in climate change simulations. Nature 387:796–799
Henderson-Sellers A., Pitman A., Love P., Irannejad P., Chen T. (1995) The Project forIntercomparison of Land Surface Parameterization Schemes (PILPS): Phases 2 and 3.
Bull. of the Amer. Met. Soc. 76:489–503
Henderson-Sellers A., Yang Z., Dickinson R. (1993) The Project for Intercomparison of Land
Surface Parameterization Schemes. Bull. of the Amer. Met. Soc. 74:1335–1349
Pitman A., Henderson-Sellers A., Abramopoulos F., Avissar R., Garratt J., Frech M., Hahmann A., Koster R., Kowalczyk E., Laval K., Lean J., Lee T., Lettenmaier D., LiangX., Mahfouf J.-F., Mahrt L., Milly P., Mitchell K., de Noblet N., Noilhan J., Pan H.,
Pielke R., Robock A., Rosenzweig G., Schlosser C., Scott R., Suarez M., Thompson
S., Verseghy D., Wetzel P., Wood E., Xue Y., Yang Z.-L., , Zhang L. (1993) Projectfor Intercomparison of Land-Surface Parameterization Schemes (PILPS). Results from
off-line control simulations (Phase 1A). Gewex report IPGO Publication
Sellers P., Bounoua L., Collatz G., Randall D., Dazlich D., andJ.A. Berry S. L., Fung I.,
Tucker C., Field C., Jensen T. (1996) Comparison of radiative and physiological effecs
of doubled atmospheric CO2 on climate. Science 271:1402–1406

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