variability in Tgav and using Hector to drive trained ESM emulators

JGCRI / fldgen

Given a global mean temperature pathway, generate random global climate fields consistent with it and with spatial and temporal correlation derived from an ESM

https://jgcri.github.io/fldgen/

GNU General Public License v2.0

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variability in Tgav and using Hector to drive trained ESM emulators #45

Open abigailsnyder opened 4 years ago

abigailsnyder commented 4 years ago

Documenting points here:

fldgen successfully deals with variability about the ESM_Tloc-ESM_Tgav regression.
ESM Tgav trajectories have interannual and decadal variability. However, Hector's Tgav trajectories are significantly smoother, even when calibrated to a specific ESM.
Work linking Hector and fldgen will need to take that into consideration and quantify or correct for that discrepancy

claudiatebaldi commented 4 years ago

Probably not a concern for the estimate of the intercept/slope but for the characteristics of the residuals. First thing to check, average globally the synthetic fields obtained using Hector and check that variability looks the same as original/real global average time series from ESM. If the inter-annual and multidecadal variability have lower amplitudes then we have a problem.