TUTORIAL-07: GLUE Analysis Laura Dobor, Péter Ittzés, Dóra Ittzés, Ferenc Horváth & Zoltán Barcza Training WS for Ecosystem Modelling studies Budapest, of May, 2014

BOX WROTE THAT „Essentially, all models are wrong, but some are useful"

MODULE X: Understanding basics of GLUE Analysis 1) Purpose and main features of GLUE = Generalized Likelihood Uncertainty Estimation AIM: to estimate parameter values NEEDS: based on observation data & Monte Carlo Experiment METHOD: compare the obs to different model results (based on different parameter sets)  Find out which parameter set gives the best results to our observations? How to measure the goodness of the outputs? misfit calculation (root mean square error)  likelihood calculation Greater LH value refers to better parameter set

2) Randomized parameters, output settings (MCE) + + Observational dataset >>> GLUE options GLUE needs careful preparation! A) Monte Carlo Experiment: -- randomize the parameters which you want to calibrate -- ask for the outputs wherefor you have observation b) Observation data: -- match your observation to one of the MuSo outputs (look up for the variable short name and code at the output variables) -- prepare your file in csv format and upload it to the database Run GLUE workflow at the portal!

MODULE X: Guess a riddle! -- We defined 3 different epc files with differences in a few parameters  give invented names to them (HARMONY, LEAFAREA, NITROGEN) -- We run carbon simulations and get the outputs -- We defined these unreal-data as observation datasets in the project database  give invented names to them (HUMUS, AIR, NINE) The question is: Which observation comes from which ecophysiological parametrization? KEY: Use the GLUE analysis! Group work: Creat 3 groups, each select one obs dataset

Canopy average specific leaf area Leaf N in Rubisco HARMONY NITROGEN LEAFAREA Differencies in the defined epc-s

Already exist Monte Carlo Experiments based on the 3 different epc…

The question is: Which observation comes from which ecophysiological parametrization? Exercise: 1) Choose one TEST DEMO Daily Observation dataset (i.e. NINE) 2) Run a GLUE workflow at the portal compare your obs data to the different MCE results (i.e. NINE X LEAFAREA; NINE X HARMONY …) 3) Download (from database) and check the results in Excel! 4) Draw GLUE plots and try to answer the question!

How to run GLUE? GO TO PORTAL: LOG IN! GO TO ECOSYSTEM MODELING!

SELECT GLUE WORKFLOW TO RUN…

GIVE A NAME…START RUN… …WAIT FOR THE INTERACTION PAGE…

SET YOUR RUN ON THE INTERACTION PAGE… GIVE A NAME TO THE RUN… Biome-BGC MuSo 2.2 TEST DEMO HHS (HU) [855]

SET YOUR RUN… AIR, NINE OR HUMUS HARMONY, NITROGEN OR LEAFAREA TEST DEMO HHS MCE FIVEPARAMS ….HARMONY [1145] TEST DEMO Daily observation data – AIR [1164]

CHECK THE STATUS OF YOUR RUN AT PROJECT DATABASE …GET THE RESULTS

GLUE RESULTS: 2 FILES LOOK THE randinputs_likelihoods.csv Every line refers to one parameter set. Last column is the likelihood value.  plot likelihood vs parameters one-by-one

EPC files HARMONY NITROGEN LEAFAREA Observations AIR HUMUS NINE  ? Canopy average specific leaf area Leaf N in Rubisco HARMONY NITROGEN LEAFAREA Differencies in the defined epc-s Match the pairs!

Answers…

HUMUS observation compared to MCE Canopy average specific leaf areaLeaf N in Rubisco Find max Likelihood… Canopy average specific leaf area Leaf N in Rubisco HARMONY Likelihood

AIR observation compared to MCE Canopy average specific leaf areaLeaf N in Rubisco Find max Likelihood… Canopy average specific leaf area Leaf N in Rubisco LEAFAREA Likelihood

NINE observation compared to MCE Canopy average specific leaf areaLeaf N in Rubisco Find max Likelihood… Canopy average specific leaf area Leaf N in Rubisco NITROGEN Likelihood

EPC files HARMONY NITROGEN LEAFAREA Observations AIR HUMUS NINE Canopy average specific leaf area Leaf N in Rubisco HARMONY NITROGEN LEAFAREA Differencies in the defined epc-s Match the pairs!

Thank you for your attention!