Constraining uncertainty in projected gross primary production (GPP) with machine learning#
Warning
Not all datasets necessary to run these recipes are available on ESGF. The following datasets are missing:
Dataset: co2, Amon, CMIP5, HadGEM2-ES, esmHistorical, r1i1p1
Dataset: gpp, Lmon, CMIP5, MIROC-ESM, esmFixClim1, r1i1p1
Supplementary: sftlf, fx, CMIP5, MIROC-ESM, esmFixClim1, r0i0p0
Overview#
These recipes reproduce the analysis of Schlund et al., JGR: Biogeosciences (2020). In this paper, a machine learning regression (MLR) approach (using the MLR algorithm Gradient Boosted Regression Trees, GBRT) is proposed to constrain uncertainties in projected gross primary production (GPP) in the RCP 8.5 scenario using observations of process-based diagnostics.
Available recipes and diagnostics#
Recipes are stored in recipes/
schlund20jgr/recipe_schlund20jgr_gpp_abs_rcp85.yml
schlund20jgr/recipe_schlund20jgr_gpp_change_1pct.yml
schlund20jgr/recipe_schlund20jgr_gpp_change_rcp85.yml
Diagnostics are stored in diag_scripts/
General information (including an example and more details) on machine learning regression (MLR) diagnostics is given here. The API documentation is available here.
Variables#
co2s (atmos, monthly, longitude, latitude, time)
gpp (land, monthly, longitude, latitude, time)
gppStderr (land, monthly, longitude, latitude, time)
lai (land, monthly, longitude, latitude, time)
pr (atmos, monthly, longitude, latitude, time)
rsds (atmos, monthly, longitude, latitude, time)
tas (atmos, monthly, longitude, latitude, time)
Observations and reformat scripts#
CRU (pr, tas)
ERA-Interim (rsds)
LAI3g (lai)
MTE (gpp, gppStderr)
Scripps-CO2-KUM (co2s)
References#
Schlund, M., Eyring, V., Camps‐Valls, G., Friedlingstein, P., Gentine, P., & Reichstein, M. (2020). Constraining uncertainty in projected gross primary production with machine learning. Journal of Geophysical Research: Biogeosciences, 125, e2019JG005619, https://doi.org/10.1029/2019JG005619.