.. _recipes_schlund20jgr: 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. .. _`Gradient Boosted Regression Trees, GBRT`: https://scikit-learn.org/stable/modules/ensemble.html#gradient-tree-boosting .. _`Schlund et al., JGR: Biogeosciences (2020)`: https://doi.org/10.1029/2019JG005619 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/ * :ref:`mlr/evaluate_residuals.py` * :ref:`mlr/main.py` * :ref:`mlr/mmm.py` * :ref:`mlr/plot.py` * :ref:`mlr/postprocess.py` * :ref:`mlr/preprocess.py` * :ref:`mlr/rescale_with_emergent_constraint.py` General information (including an example and more details) on machine learning regression (MLR) diagnostics is given :ref:`here`. The API documentation is available :ref:`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*) .. _CRU: https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.02/cruts.1811131722.v4.02/ .. _ERA-Interim: http://apps.ecmwf.int/datasets/data/interim-full-moda/ .. _LAI3g: http://cliveg.bu.edu/modismisr/lai3g-fpar3g.html .. _MTE: http://www.bgc-jena.mpg.de/geodb/BGI/Home .. _Scripps-CO2-KUM: https://scrippsco2.ucsd.edu/data/atmospheric_co2/kum.html 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, ``_. Example plots ------------- .. _fig_schlund20jgr_1: .. figure:: /recipes/figures/schlund20jgr/map_prediction_output___GBRT_change.png :align: center :width: 50% GBRT-based prediction of the fractional GPP change over the 21st century (= GPP(2091-2100) / GPP(1991-2000)). .. _fig_schlund20jgr_2: .. figure:: /recipes/figures/schlund20jgr/map_prediction_output_error___GBRT_change.png :align: center :width: 50% Corresponding error of the GBRT-based prediction of the fractional GPP change over the 21st century (considering errors in the MLR model and errors in the predictors). .. _fig_schlund20jgr_3: .. figure:: /recipes/figures/schlund20jgr/map_prediction_output___GBRT_abs.png :align: center :width: 50% GBRT-based prediction of the absolute GPP at the end of the 21st century (2091-2100). .. _fig_schlund20jgr_4: .. figure:: /recipes/figures/schlund20jgr/map_prediction_output_error___GBRT_abs.png :align: center :width: 50% Corresponding error of the GBRT-based prediction of the absolute GPP at the end of the 21st century (considering errors in the MLR model and errors in the predictors). .. _fig_schlund20jgr_5: .. figure:: /recipes/figures/schlund20jgr/rmse_plot.png :align: center :width: 50% Boxplot of the root mean square error of prediction (RMSEP) distributions for six different statistical models used to predict future absolute GPP (2091-2100) using a leave-one-model-out cross-validation approach. The distribution for each statistical model contains seven points (black dots, one for each climate model used as truth) and is represented in the following way: the lower and upper limit of the blue boxes correspond to the 25% and 75% quantiles, respectively. The central line in the box shows the median, the black "x" the mean of the distribution. The whiskers outside the box represent the range of the distribution .. _fig_schlund20jgr_6: .. figure:: /recipes/figures/schlund20jgr/feature_importance.png :align: center :width: 50% Global feature importance of the GBRT model for prediction of the absolute GPP at the end of the 21st century (2091-2100). .. _fig_schlund20jgr_7: .. figure:: /recipes/figures/schlund20jgr/residuals_distribution.png :align: center :width: 50% Distribution of the residuals of the GBRT model for the prediction of absolute GPP at the end of the 21st century (2091-2100) for the training data (blue) and test data excluded from training (green). .. _fig_schlund20jgr_8: .. figure:: /recipes/figures/schlund20jgr/training_progress.png :align: center :width: 50% Training progress of the GBRT model for the prediction of absolute GPP at the end of the 21st century (2091-2100) evaluated as normalized root mean square error on the training data (blue) and test data excluded from training (green).