Moving towards a global biogeophysical parameter optimization for CLM5

Katie Dagon Land Model Working GroupNCAR ASP Postdoc June 19, 2018

With input and assistance from: Gordon Bonan, Rosie Fisher, David John Gagne, Daniel Kennedy, Dave Lawrence, Danica Lombardozzi, Ben Sanderson, Bill Sacks, and Sean Swenson

What role do parameter choices play in overall land model uncertainty?

Bonan and Doney (2018)

Sources of Model Uncertainty

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• Initial conditions

• Model forcing

• Model structure

• Parameters

CLM Biogeophysical Processes

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Image: CLM5 Tech Note

Research Questions

1. What are the highly sensitive CLM biogeophysicalparameters?

2. Given a set of sensitive parameters and using existing observational datasets, what are the optimal values?

3. How are the results of global and regional climate modeling studies impacted by parameter uncertainty?

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CLM5 Parameter Ensemble• CLM5SP, 4°x5° resolution, 20 year runs (sample last 5

years)

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CLM5 Parameter Ensemble• CLM5SP, 4°x5° resolution, 20 year runs (sample last 5

years)• One-at-a-time min/max perturbations: 34 parameters

• 10 PFT-dependent parameters (params file)• 3 namelist parameters (user_nl_clm)• 21 hard-coded parameters (SourceMods)

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CLM5 Parameter Ensemble• CLM5SP, 4°x5° resolution, 20 year runs (sample last 5

years)• One-at-a-time min/max perturbations: 34 parameters

• 10 PFT-dependent parameters (params file)• 3 namelist parameters (user_nl_clm)• 21 hard-coded parameters (SourceMods)

• 7 Outputs to assess sensitivity1. Gross Primary Productivity (GPP)2. Evapotranspiration (ET)3. Transpiration Fraction = Transpiration/ET4. Sensible Heat Flux (SH)5. 10cm Soil Moisture6. Total Column Soil Moisture7. Water Table Depth

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34 parameters top 6• Based on sensitivity metric, pattern correlations,

availability of relevant observational data

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Name Description

medlynslope Medlyn slope of conductance-photosynthesis relationship

kmax Plant segment max conductance (PHS)

fff Surface runoff parameter; decay factor for fractional saturated area

dint Fraction of saturated soil for moisture value at which dry surface layer initiates

dleaf Characteristic dimension of leaves in the direction of wind flow (leaf boundary layer resistance)

baseflow_scalar Scalar multiplier for base flow rate

Sensitivity Metric

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Parameter Effect (PE) = |Xmax – Xmin| (then global mean, annual mean)

Sensitivity to GPP for final 6 parameters(annual mean, last 5 years)

𝜇𝜇mol CO2 m-2 s-1

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Sensitivity MetricParameter Effect (PE) = |Xmax – Xmin| (then global mean, annual mean)

Sensitivity to GPP for final 6 parameters(annual mean, last 5 years)

Global mean values:

𝜇𝜇mol CO2 m-2 s-1

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Name GPP PEdleaf 0.07kmax 0.82medlynslope 0.36baseflow_scalar 0.01fff 0.34dint 0.10

Sensitivity MetricParameter Effect (PE) = |Xmax – Xmin| (then global mean, annual mean)

Pattern Correlations• What are the spatial correlations of the PE between these

parameters?• All output combinations for each parameter reveals overlapping

outputs (e.g., ET and SH)• All pairwise parameter combinations for each output reveals

overlapping parameters

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Pattern Correlations• What are the spatial correlations of the PE between these

parameters?• All output combinations for each parameter reveals overlapping

outputs (e.g., ET and SH)• All pairwise parameter combinations for each output reveals

overlapping parameters

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dleaf kmax medlynslope baseflow_scalar fff dintdleaf 1 0.34 0.70 0.08 0.62 0.69kmax 0.34 1 0.36 0.09 0.18 0.003

medlynslope 0.70 0.36 1 0.19 0.67 0.65

baseflow_scalar 0.08 0.09 0.19 1 0.13 0.16fff 0.62 0.18 0.67 0.13 1 0.70dint 0.69 0.003 0.65 0.16 0.70 1

CLM5 Optimization Ensemble

• Use Latin Hypercube sampling to generate 100 random parameter sets for top 6 parameters• Including unique ranges for each PFT as applicable

• Run 100 simulations with CLM5SP, 4°x5° resolution• Build and train a neural network to emulate model output

given parameter values

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Training the Neural Network

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Model Output (global mean GPP)

Model Input (parameter values)

100 simulations

100 parameter sets for 6 parameters

P1 P2 P3 P4 P5 P6S1 x1,1 x1,2 x1,3 x1,4 x1,5 x1,6

S2 x2,1 x2,2 x2,3 x2,4 x2,5 x2,6

S3 x3,1 x3,2 x3,3 x3,4 x3,5 x3,6

… … … … … … …S100 x100,1 x100,2 x100,3 x100,4 x100,5 x100,6

Training the Neural Network

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Observations(GPP, ET, etc.)

Model Input (parameter values)

1000s of parameter sets for 6 parameters

P1 P2 P3 P4 P5 P6S1 x1,1 x1,2 x1,3 x1,4 x1,5 x1,6

S2 x2,1 x2,2 x2,3 x2,4 x2,5 x2,6

S3 x3,1 x3,2 x3,3 x3,4 x3,5 x3,6

… … … … … … …… … … … … … …… … … … … … …S1000+ … … … … … …

Which parameter set(s) produces optimal value for given observation(s)?

Summary and Future Work

• Narrowed the CLM5 biogeophysical parameter space through one-at-a-time parameter sensitivity simulations

• Built a simple neural network and trained model output (GPP) against model input (parameter values)

Up next:• Optimize parameter sets using observational datasets

with trained neural network• Apply results to climate change simulations

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Contact: kdagon@ucar.edu

Backup

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CLM Hydrologic Processes

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Image: LMWG

Previous Work: CLM4.5 Parameter Sensitivity Study

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Focused on land surface hydrology: evapotranspiration and soil moisture

Testing equilibrium time: 30 years of CLM5SP spin-up under default parameter values; the same 5 years of GSWP forcing data repeated

Is 15 years of spin-up enough?

Soil moisture trends:0.98 kgm-2/yr (30 years)-0.08 kgm-2/yr (last 15 years)

-- Latent Heat-- Sensible Heat

Heat flux trends:-0.006 Wm-2/yr (LH)0.014 Wm-2/yr (SH)0.011 Wm-2/yr (SH, last 15 yrs)

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Improving PFT parameter ranges based on observational data

• dleaf: characteristic dimension of leaves in the direction of wind flow (previously constant for all PFTs)• 1 dataset from the TRY database with concurrent measurements of

leaf width and PFT information (leaf type, phenology, growth form)• Enough to get reasonable leaf width ranges for each PFT• dleaf = f*(leaf_width), where f = leaf shape-dependent factor

Campbell and Norman (1998)Parkhurst et al. (1968)

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• medlynslope: slope of stomatal conductance-photosynthesis relationship• Genus or species-based linear regressions to obtain slope values• Min and max values from set of slopes for each PFT

y = 3.1887xR² = 0.1865

0

0.05

0.1

0.15

0.2

0.25

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08

Acer

Improving PFT parameter ranges based on observational data

medlynslope for genus Acer contributes to range of values for broadleaf deciduous PFT

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Parameter Name PE rank PC rank Type CLM5 default value Min value Max value Units

medlynslope 1 33 P ranges [1.62, 5.79] ranges [0.53, 3.46] ranges [4.03, 7.70]𝜇𝜇mol H2O/𝜇𝜇mol CO2

kmax 2 24 P 2.00E-08 2.00E-09 3.80E-08 s-1

fff 3 19 HC 0.5 0.02 5 m-1

dint 4 16 HC 0.8 0.5 1 --

dleaf 10 23 P 0.04ranges [0.000144,

0.0081]ranges [0.00108,

0.243] m

baseflow_scalar 17 2 N 0.001 0.0005 0.1 --

Summarizing the top 6 parameters

Parameter Effect (PE) = |Xmax – Xmin| (then global mean, annual mean)Ranked sensitivity to 7 outputsAverage rank across outputs to generate most sensitive parameters (larger PE implies higher rank)

Pattern Correlation (PC) = spatial correlation of PE between all pairwise combinations of parameters Summed correlations for each pair across 7 outputs Average across parameters; compute rank (smaller PC implies higher rank)

Type denotes PFT-dependent (P), namelist (N), or hard-coded (HC)Move HC parameters into the namelistTackle PFT and namelist parameters separately

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