Reliability ensemble averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties

Exbrayat, Jean-François; Bloom, A. Anthony; Falloon, Pete; Ito, Akihiko; Smallman, T. Luke; Williams, Mathew

doi:https://doi.org/10.5194/esd-9-153-2018

Articles | Volume 9, issue 1

https://doi.org/10.5194/esd-9-153-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/esd-9-153-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 9, issue 1

Research article

|

21 Feb 2018

Research article |

| 21 Feb 2018

Reliability ensemble averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties

Jean-François Exbrayat, A. Anthony Bloom, Pete Falloon, Akihiko Ito, T. Luke Smallman, and Mathew Williams

Supplement

https://doi.org/10.5194/esd-9-153-2018-supplement

Data sets

CARDAMOM 2001–2010 global carbon Model-Data Fusion (MDF) analysis  A. A. Bloom and M. Williams https://doi.org/10.7488/ds/316

FLUXCOM (RS+METEO) Global Land Carbon Fluxes using CRUNCEP climate data M. Jung https://doi.org/10.17871/ FLUXCOM_RS_METEO_CRUNCEPv6_1980_2013_v1

Reliability Ensemble Averaging of ISIMIP NPP projections for 2095–2099 under RCP8.5 J.-F. Exbrayat and M. Williams https://doi.org/10.7488/ds/2304

Drought-induced reduction in global (http://ﬁles.ntsg.umt.edu/data/NTSG_Products/MOD17/GeoTIFF/MOD17A3/) M. Zhao and S. W. Running https://doi.org/10.1126/science.1192666

Improvements of the MODIS terrestrial gross and net primary production global data set (http://ﬁles.ntsg.umt.edu/data/NTSG_Products/MOD17/GeoTIFF/MOD17A3/) M. Zhao, F. A. Heinsch, R. R. Nemani, and S. W. Running https://doi.org/10.1016/j.rse.2004.12.011

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Short summary

We use global observations of current terrestrial net primary productivity (NPP) to constrain the uncertainty in large ensemble 21st century projections of NPP under a "business as usual" scenario using a skill-based multi-model averaging technique. Our results show that this procedure helps greatly reduce the uncertainty in global projections of NPP. We also identify regions where uncertainties in models and observations remain too large to confidently conclude a sign of the change of NPP.