ICOS-NEON

Part 5. Assimilation of fluxnet data: single vs multiple sites

In a separate study we have used the variational (gradient-descent) method to optimize different PFTs of the ORCHIDEE model using FluxNet data (Figure 1). The results shown here are for the optimization of the temperate broadleaved deciduous forest PFT in ORCHIDEE using data from 12 sites across the globe (Table 1). These results are described in Kuppel et al., (2012). In a further study (Kuppel et al., 2014, submitted), between 2 and 24 sites have been assimilated per PFT from stations across the globe depending on the data availability.

Both NEE and LE daily fluxes were assimilated both at each site (single-site — SS) and for all sites for the same PFT (multi-site — MS — optimization). Many studies have used FluxNet data to optimize ecosystem/land surface models in the past, but none have used all the sites together in the same optimization for such a range of PFTs.

A MS optimization is important, because for global simulations we need to have one parameter vector per PFT. If we just optimize at each site, we may find a range of posterior parameter values for each parameter for the same PFT. In order to do global simulations therefore, should the average be taken? Or is it better to include all sites in the same optimization? The aim of this study is therefore to see if the MS optimization performs as well as the SS optimization and/or the average of the SS optimisations.

The number of years assimilated is different for each site (see Table 1). The parameters that were optimized are described in Table 2. It was assumed that there was no correlations between parameters. The errors on the observations were given as the prior RMSE between the model and the observation, and were assumed to be independent (i.e. no correlation).

Table 1. Sites used in this study, with their name code made from the country (first two letters) and site name (last three letters) (Kuppel et al., 2014)

Site Location Main tree species Stand age LAI Period References
DE-Hai 51.079° N, 10.452° E Beech, ash, maple 1-250 5 2000-2006 Knohl et al. (2003)
DK-Sor 55.487° N, 11.646° E European beech 84 4.8 2004-2006 Pilegaard et al. (2001)
FR-Fon 48.476° N, 2.78° E Oak 100-150 5.1 2006 Prevost-Boure et al. (2010)
FR-Hes 48.674° N, 7.064° E European beech 35 4.8-7.6 2001-2003 Granier et al. (2008)
JP-Tak 36.146° N, 137.423° E Oak, birch 50 1999-2004 Ito et al. (2006)
UK-Ham 51.121° N, 0.861° W Oak 70 2004-2005 http://www.forestry.gov.uk
US-Bar 44.065° N, 71.288° W American beech, sugar, yellow birch 73 3.6-4.5 2004-2005 Jenkins et al. (2007)
US-Hal 42.538° N, 72.172° W Red oak, red maple 75-110 2003-2006 Urbanski et al. (2007)
US-LPH 42.542° N, 72.185° W Red oak 45-110 4-5 2003-2004 Hadley et al. (2008)
US-MOz 38.744° N, 92.2° W White & black oaks, shagbark hickory,
sugar maple, eastern red cedar
2005-2006 Gu et al. (2012)
US-UMB 45.56° N, 84.714° W Bigtooth aspen, trembling aspen 90 3.7 1999-2003 Curtis et al. (2002)
US-WCr 45.806° N, 90.08° W Sugar maple, basswood, green ash 60-80 5.3 1999-2004 Cook et al. (2004)

Table 2. ORCHIDEE parameters optimized in this study (Kuppel et al., 2012)

Parameter Description Prior value Prior range σprior
Photosynthesis
Vcmax Maximum carboxylation rate (μmol m–2s–1) 55 27-110 33.2
Gs,slope Ball-Berry slope 9 3-15 4.8
Topt Optimal photosynthesis temperature (°C) 26 6-46 16
Tmin Minimal photosynthesis temperature (°C) –2 (–7)–3 4
SLA Specific leaf area (LAI per dry matter content, m2g–1) 0.026 0.013-0.05 0.0148
LAImax Maximum LAI per PFT (m2m–2) 5 3-7 1.6
Klai,happy LAI threshold to stop carbohydrate use 0.5 0.35-0.7 0.14
Phenology
Kpheno,crit Multiplicative factor for growing season start threshold 1 0.5-2 0.6
Tsenses Temperature threshold for senescence (°C) 12 2-22 8
Lagecrit Average critical age for leaves (days) 180 80-280 80
Soil water availability
Humcste Root profile (m–1) 0.8 0.2-3 1.12
Dpucste Total depth of soil water pool (m) 2 0.1-6 2.36
Respiration
Q10 Temperature dependence of heterotrophic respiration 1.99372 1-3 0.8
KsoilC Multiplicative factor of initial carbon pools 1 0.1-2 0.76
HRH,b First-degree coefficient of the function for moisture control factor of heterotrophic respiration 2.4 2.1-2.7 0.24
HRH,c Offset of the function for moisture control factor of heterotrophic respiration –0.29 (–0.59)–0.01 0.24
MRa Slope of the affine relationship between temperature and maintenance respiration 0.16 0.08-0.24 0.064
MRb Offset of the affine relationship between temperature and maintenance respiration 1 0.1-2 0.76
GRfrac Fraction of biomass available for growth respiration 0.28 0.2-0.36 0.064
Energy balance
Z0overheight Characteristic rugosity length (m) 0.0625 0.02-0.1 0.032
Kalbedo,veg Multiplying factor for surface albedo 1 0.8-1.2 0.16

SECTION A

Examine the time series below from two temperate broadleaved deciduous forest (TempDBF) sites in France and the US when optimizing with NEE and LE data and answer the following questions.


Figure. Caption

  1. Does the optimization result in a better fit to the observations in all cases?
  2. Does the multi-site optimization work as well as the single-site? What are the reasons for the differences?
  3. What do you think is causing any remaining discrepancies between the model and data after optimisation?

SECTION B

Now take a look at the following figure:


Figure. Caption

Compare the RMSD using the parameters derived from the optimization at each site (Local SS parameters) to the RMSD from parameters derived in the MS optimization (MS parameters), and to the parameters derived from single-site optimization from other sites (Foreign SS parameters) and to the mean of the SS parameters. Answer the questions below:

  1. If you use parameters derived from a single site optimisation at another site (yellow), does it result in a similar RMSD between the model and observations?
  2. Why is there such a variety in the model-observation RMSD when parameters derived from single-site optimisations at other sites are used? What are the differences between sites?
  3. Comment on the MS (blue) performances versus the SS (red) optimization at each site. What could be the cause of a better fit to the data with the MS optimisation than with the SS optimization?
  4. What are the differences between the mean of all the single-site optimisations versus the simulations using parameters derived in the MS optimisation? Which do you think would be better for global simulations?

SECTION C

Now take a look at the posterior parameter values in the figure below (grey = prior, black = MS value, colours = each SS optimization):


Figure. Caption

SECTION A

  1. Does the optimization result in a better fit to the observations in all cases?
  2. Does the multi-site optimization work as well as the single-site? What are the reasons for the differences?
  3. What do you think is causing any remaining discrepancies between the model and data after optimisation?

SECTION B

  1. If you use parameters derived from a single site optimisation at another site (yellow), does it result in a similar RMSD between the model and observations?
  2. Why is there such a variety in the model-observation RMSD when parameters derived from single-site optimisations at other sites are used? What are the differences between sites?
  3. Comment on the MS (blue) performances versus the SS (red) optimization at each site. What could be the cause of a better fit to the data with the MS optimisation than with the SS optimization?
  4. What are the differences between the mean of all the single-site optimisations versus the simulations using parameters derived in the MS optimisation? Which do you think would be better for global simulations?

SECTION C

  1. Which processes do we constrain the most (refer to Table 2)?
  2. Why do you think there is such variation for some parameters between the sites?
  3. Why do you think the MS parameter values do not approximate the mean of the SS parameters?