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Geosci. Model Dev., 11, 195-212, 2018
https://doi.org/10.5194/gmd-11-195-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
17 Jan 2018
On the predictability of land surface fluxes from meteorological variables
Ned Haughton et al.
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Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version      Supplement - Supplement
 
SC1: 'Code Accessibility', Lutz Gross, 05 Aug 2017 Printer-friendly Version 
AC1: 'Code accessibility and DOI', Ned Haughton, 10 Oct 2017 Printer-friendly Version 
 
RC1: 'Review', Anonymous Referee #1, 24 Aug 2017 Printer-friendly Version Supplement 
AC2: 'Response to Reviewer 1', Ned Haughton, 10 Oct 2017 Printer-friendly Version Supplement 
 
RC2: 'Review of 'On the Predictability of Land Surface Fluxes from Meteorological Variables'', Anonymous Referee #2, 27 Aug 2017 Printer-friendly Version 
AC3: 'Response to Reviewer 2', Ned Haughton, 10 Oct 2017 Printer-friendly Version Supplement 
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision
AR by Ned Haughton on behalf of the Authors (10 Oct 2017)  Author's response  Manuscript
ED: Referee Nomination & Report Request started (17 Oct 2017) by Chiel van Heerwaarden
RR by Anonymous Referee #1 (14 Nov 2017)  
ED: Publish subject to minor revisions (review by editor) (14 Nov 2017) by Chiel van Heerwaarden  
AR by Ned Haughton on behalf of the Authors (24 Nov 2017)  Author's response  Manuscript
ED: Publish as is (24 Nov 2017) by Chiel van Heerwaarden
CC BY 4.0
Publications Copernicus
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Short summary
Previous studies indicate that fluxes of heat, water, and carbon between the land surface and atmosphere are substantially more predictable than the performance of the current crop of land surface models would indicate. This study uses simple empirical models to estimate the amount of useful information in meteorological forcings that is available for predicting land surface fluxes. These models can be used as benchmarks for land surface models and may help identify areas ripe for improvement.
Previous studies indicate that fluxes of heat, water, and carbon between the land surface and...
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