%0 Journal Article %1 Slevin2016Global %A Slevin, Darren %A Tett, Simon F. B. %A Exbrayat, Jean-Francois %A Bloom, Anthony A. A. %A Williams, Mathew %D 2016 %J Geoscientific Model Development Discussions %K model carboncycle landsurface %P 1--31 %R 10.5194/gmd-2016-214 %T Global Evaluation of Gross Primary Productivity in the JULES Land Surface Model %U http://dx.doi.org/10.5194/gmd-2016-214 %X This study evaluates the ability of the JULES Land Surface Model (LSM) to simulate Gross Primary Productivity (GPP) at regional and global scales for 2001–2010. Model simulations, performed at various spatial resolutions and driven with a variety of meteorological datasets (WFDEI-GPCC, WFDEI-CRU and PRINCETON), were compared to the MODIS GPP product, spatially gridded estimates of upscaled GPP from the FLUXNET network (FLUXNET-MTE) and the CARDAMOM terrestrial carbon cycle analysis. Firstly, JULES was found to simulate interannual variability (IAV) at global scales. When JULES was driven with the WFDEI-GPCC dataset (at 0.5º × 0.5º spatial resolution), it was found that the annual average global GPP simulated by JULES for 2001–2010 was higher than the observation-based estimates (MODIS and FLUXNET-MTE), by 25 \% and 8 \%, respectively, and CARDAMOM estimates by 23 \%. Secondly, GPP fluxes simulated by JULES for various biomes (forests, grasslands and shrubs) at global and regional scales were compared. It was found that differences between JULES, FLUXNET-MTE, MODIS and CARDAMOM at global scales were mostly due to differences in the tropics with CARDAMOM performing better than JULES in this region. Thirdly, it was shown that spatial resolution (0.5º × 0.5º, 1º × 1º and 2º × 2º) had no impact on simulated GPP on these large scales. Finally, the sensitivity of JULES to meteorological driving data, a major source of model uncertainty, was examined. Estimates of annual average global GPP were higher when JULES was driven with the PRINCETON meteorological dataset than when driven with the WFDEI-GPCC dataset by 4 PgC year−1. At regional scales, differences between two were observed with the WFDEI-GPCC driven model simulations estimating higher GPP in the tropics (at 5º N–5º S) and the PRINCETON driven model simulations estimating higher GPP in the extratropics (at 30º N–60º N).

@article{Slevin2016Global, abstract = {This study evaluates the ability of the JULES Land Surface Model (LSM) to simulate Gross Primary Productivity (GPP) at regional and global scales for 2001–2010. Model simulations, performed at various spatial resolutions and driven with a variety of meteorological datasets (WFDEI-GPCC, WFDEI-CRU and PRINCETON), were compared to the MODIS GPP product, spatially gridded estimates of upscaled GPP from the FLUXNET network (FLUXNET-MTE) and the CARDAMOM terrestrial carbon cycle analysis. Firstly, JULES was found to simulate interannual variability (IAV) at global scales. When JULES was driven with the WFDEI-GPCC dataset (at 0.5º × 0.5º spatial resolution), it was found that the annual average global GPP simulated by JULES for 2001–2010 was higher than the observation-based estimates (MODIS and FLUXNET-MTE), by 25 \% and 8 \%, respectively, and CARDAMOM estimates by 23 \%. Secondly, GPP fluxes simulated by JULES for various biomes (forests, grasslands and shrubs) at global and regional scales were compared. It was found that differences between JULES, FLUXNET-MTE, MODIS and CARDAMOM at global scales were mostly due to differences in the tropics with CARDAMOM performing better than JULES in this region. Thirdly, it was shown that spatial resolution (0.5º × 0.5º, 1º × 1º and 2º × 2º) had no impact on simulated GPP on these large scales. Finally, the sensitivity of JULES to meteorological driving data, a major source of model uncertainty, was examined. Estimates of annual average global GPP were higher when JULES was driven with the PRINCETON meteorological dataset than when driven with the WFDEI-GPCC dataset by 4 PgC year−1. At regional scales, differences between two were observed with the WFDEI-GPCC driven model simulations estimating higher GPP in the tropics (at 5º N–5º S) and the PRINCETON driven model simulations estimating higher GPP in the extratropics (at 30º N–60º N).}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Slevin, Darren and Tett, Simon F. B. and Exbrayat, Jean-Fran\c{c}ois and Bloom, Anthony A. A. and Williams, Mathew}, biburl = {https://www.bibsonomy.org/bibtex/2fb10a95dddb0050f86f771fa930a8d2d/pbett}, citeulike-article-id = {14144268}, citeulike-linkout-0 = {http://dx.doi.org/10.5194/gmd-2016-214}, day = 21, doi = {10.5194/gmd-2016-214}, interhash = {458ec85711d9d402f4f5e1a99665cc04}, intrahash = {fb10a95dddb0050f86f771fa930a8d2d}, issn = {1991-962X}, journal = {Geoscientific Model Development Discussions}, keywords = {model carboncycle landsurface}, month = sep, pages = {1--31}, posted-at = {2016-09-22 11:18:39}, priority = {2}, timestamp = {2018-06-22T18:36:38.000+0200}, title = {Global Evaluation of Gross Primary Productivity in the JULES Land Surface Model}, url = {http://dx.doi.org/10.5194/gmd-2016-214}, year = 2016 }