question of s_vcad in paramfile in CLM5.0

[syang]

Dear all,

I successfully run the case I2000CLm50BgcCruGs with my own forcing data and surface data for a single-point case. However, the NEE from the outputs is higher than the flux tower data. So, I tended to modify the parameters to match the data. I changed the default s_vcad values in file "clm5_params.c200624.nc" in paramdata directory. However, the results did not change at all for the new outputs. I would like to ask why does this happen and how can I modify the parameters. THANKS!!!

 

[oleson]

I don't think s_vcad has any effect if LUNA is on (default is on in CLM5), unless it's a C4 plant or a crop.

 

[syang]

I don't think s_vcad has any effect if LUNA is on (default is on in CLM5), unless it's a C4 plant or a crop.

Hi Oleson,
Thank you for your reply. So I would like to ask which parameter(s) should I modify to increase/decrease NEE values? I also tried "i_vcad" and it does not work. Thanks!

 

[oleson]

i_vcad is in the same category as s_vcad.
Based on an ongoing global parameter perturbation study we are conducting, you could try leafcn (leaf carbon to nitrogen ratio). The model GPP at least is quite sensitive to changes of +-30% in that parameter.

 

[liyongkang]

i_vcad is in the same category as s_vcad.
Based on an ongoing global parameter perturbation study we are conducting, you could try leafcn (leaf carbon to nitrogen ratio). The model GPP at least is quite sensitive to changes of +-30% in that parameter.

The leafcn in paramdata is a 1D array and include 79 numbers.I want to know the meaning of each index in the 76 numbers. Which and where document can I found ? Thanks.

 

[liyongkang]

i_vcad is in the same category as s_vcad.
Based on an ongoing global parameter perturbation study we are conducting, you could try leafcn (leaf carbon to nitrogen ratio). The model GPP at least is quite sensitive to changes of +-30% in that parameter.

if the 76 is the different layer based on depth? Thanks!

 

[oleson]

Dimensioned by pft (plant functional type).
Do an ncdump -v pftname on the parameter file:

data:

pftname =
"not_vegetated ",
"needleleaf_evergreen_temperate_tree ",
"needleleaf_evergreen_boreal_tree ",
"needleleaf_deciduous_boreal_tree ",
"broadleaf_evergreen_tropical_tree ",
"broadleaf_evergreen_temperate_tree ",
"broadleaf_deciduous_tropical_tree ",
"broadleaf_deciduous_temperate_tree ",
"broadleaf_deciduous_boreal_tree ",
"broadleaf_evergreen_shrub ",
"broadleaf_deciduous_temperate_shrub ",
"broadleaf_deciduous_boreal_shrub ",
"c3_arctic_grass ",
"c3_non-arctic_grass ",
"c4_grass ",
"c3_crop ",
"c3_irrigated ",
"temperate_corn ",
"irrigated_temperate_corn ",
"spring_wheat ",
"irrigated_spring_wheat ",
"winter_wheat ",
"irrigated_winter_wheat ",
"temperate_soybean ",
"irrigated_temperate_soybean ",
"barley ",
"irrigated_barley ",
"winter_barley ",
"irrigated_winter_barley ",
"rye ",
"irrigated_rye ",
"winter_rye ",
"irrigated_winter_rye ",
"cassava ",
"irrigated_cassava ",
"citrus ",
"irrigated_citrus ",
"cocoa ",
"irrigated_cocoa ",
"coffee ",
"irrigated_coffee ",
"cotton ",
"irrigated_cotton ",
"datepalm ",
"irrigated_datepalm ",
"foddergrass ",
"irrigated_foddergrass ",
"grapes ",
"irrigated_grapes ",
"groundnuts ",
"irrigated_groundnuts ",
"millet ",
"irrigated_millet ",
"oilpalm ",
"irrigated_oilpalm ",
"potatoes ",
"irrigated_potatoes ",
"pulses ",
"irrigated_pulses ",
"rapeseed ",
"irrigated_rapeseed ",
"rice ",
"irrigated_rice ",
"sorghum ",
"irrigated_sorghum ",
"sugarbeet ",
"irrigated_sugarbeet ",
"sugarcane ",
"irrigated_sugarcane ",
"sunflower ",
"irrigated_sunflower ",
"miscanthus ",
"irrigated_miscanthus ",
"switchgrass ",
"irrigated_switchgrass ",
"tropical_corn ",
"irrigated_tropical_corn ",
"tropical_soybean ",
"irrigated_tropical_soybean " ;

 

[liyongkang]

Dimensioned by pft (plant functional type).
Do an ncdump -v pftname on the parameter file:

data:

pftname =
"not_vegetated ",
"needleleaf_evergreen_temperate_tree ",
"needleleaf_evergreen_boreal_tree ",
"needleleaf_deciduous_boreal_tree ",
"broadleaf_evergreen_tropical_tree ",
"broadleaf_evergreen_temperate_tree ",
"broadleaf_deciduous_tropical_tree ",
"broadleaf_deciduous_temperate_tree ",
"broadleaf_deciduous_boreal_tree ",
"broadleaf_evergreen_shrub ",
"broadleaf_deciduous_temperate_shrub ",
"broadleaf_deciduous_boreal_shrub ",
"c3_arctic_grass ",
"c3_non-arctic_grass ",
"c4_grass ",
"c3_crop ",
"c3_irrigated ",
"temperate_corn ",
"irrigated_temperate_corn ",
"spring_wheat ",
"irrigated_spring_wheat ",
"winter_wheat ",
"irrigated_winter_wheat ",
"temperate_soybean ",
"irrigated_temperate_soybean ",
"barley ",
"irrigated_barley ",
"winter_barley ",
"irrigated_winter_barley ",
"rye ",
"irrigated_rye ",
"winter_rye ",
"irrigated_winter_rye ",
"cassava ",
"irrigated_cassava ",
"citrus ",
"irrigated_citrus ",
"cocoa ",
"irrigated_cocoa ",
"coffee ",
"irrigated_coffee ",
"cotton ",
"irrigated_cotton ",
"datepalm ",
"irrigated_datepalm ",
"foddergrass ",
"irrigated_foddergrass ",
"grapes ",
"irrigated_grapes ",
"groundnuts ",
"irrigated_groundnuts ",
"millet ",
"irrigated_millet ",
"oilpalm ",
"irrigated_oilpalm ",
"potatoes ",
"irrigated_potatoes ",
"pulses ",
"irrigated_pulses ",
"rapeseed ",
"irrigated_rapeseed ",
"rice ",
"irrigated_rice ",
"sorghum ",
"irrigated_sorghum ",
"sugarbeet ",
"irrigated_sugarbeet ",
"sugarcane ",
"irrigated_sugarcane ",
"sunflower ",
"irrigated_sunflower ",
"miscanthus ",
"irrigated_miscanthus ",
"switchgrass ",
"irrigated_switchgrass ",
"tropical_corn ",
"irrigated_tropical_corn ",
"tropical_soybean ",
"irrigated_tropical_soybean " ;

Thanks with your patient reply!

 

[qian yang]

dear oleson
I conducted a local sensitivity experiment on this parameter LeafCN, and the results showed that NPP was sensitive to this parameter, but GPP was not sensitive to this parameter. I would like to ask if this is reasonable?

 

[oleson]

Without knowing the specifics of your simulations and your sensitivity metrics I can't really answer that question. Based on our own global perturbed parameter ensemble, we found that the model global GPP is quite sensitive to changes of +-30% in that parameter.

 

[qian yang]

Without knowing the specifics of your simulations and your sensitivity metrics I can't really answer that question. Based on our own global perturbed parameter ensemble, we found that the model global GPP is quite sensitive to changes of +-30% in that parameter.

thank you for your reply .I did the experiment based on grassland vegetation type.I changed this parameter by + 10%，-10% + 20%，-20%，+50%，-50% respectively, and then compared the simulation results. This is the big result of my experiment. The figure shows that NPP changed dramatically in 12 months, but GPP changed slightly.

I first calculated the difference between the output result after the change and the output result of the default value, and then calculated the ratio with the output result of the default value, and found that the index corresponding to NPP is large, while the index corresponding to GPP is small.

 

[oleson]

I might expect GPP to show more sensitivity based on recent results from our CLM Perturbed Parameter Ensemble (PPE). There are sensitivity plots organized by biome here:

CLM5PPE/figs/rankings/biome at master · djk2120/CLM5PPE

place for some of the preparatory work towards generating the CLM5 parameter perturbation experiment - djk2120/CLM5PPE

github.com

In particular, for the temperature grassland/desert biome, leafcn is the 8th most sensitive parameter for GPP (among a couple of hundred perturbed parameters) and doesn't show up for NPP (although it is probably near the top 10). But these are from global simulations and your single-point simulation may not necessarily produce similar results.
For more information about parameter sensitivity in CLM5 in an earlier study, see:

Dagon, K., B.M. Sanderson, R.A. Fisher, D.M. Lawrence (2020), A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5. Advances in Statistical Climatology, Meteorology and Oceanography, 6, 223-244, doi:10.5194/ascmo-6-223-2020.

 

[qian yang]

Thank you for your reply. I don't quite understand whether this situation is explainable or not, and I am worried that I have misunderstood something wrong and made a wrong analysis. I'll analyze the cause of this situation.