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Hanqin Tian, Jia Yang, Chaoqun Lu, Rongting Xu, Josep G. Canadell, Robert B. Jackson, Almut Arneth, Jinfeng Chang, Guangsheng Chen, Philippe Ciais, Stefan Gerber, Akihiko Ito, Yuanyuan Huang, Fortunat Joos, Sebastian Lienert, Palmira Messina, Stefan Olin, Shufen Pan, Changhui Peng, Eri Saikawa, Rona L. Thompson, Nicolas Vuichard, Wilfried Winiwarter, Sönke Zaehle, Bowen Zhang, Kerou Zhang, and Qiuan Zhu


Nitrous oxide (N2O) is an important greenhouse gas and also an ozone-depleting substance that has both natural and anthropogenic sources. Large estimation uncertainty remains on the magnitude and spatiotemporal patterns of N2O fluxes and the key drivers of N2O production in the terrestrial biosphere. Some terrestrial biosphere models have been evolved to account for nitrogen processes and to show the capability to simulate N2O emissions from land ecosystems at the global scale, but large discrepancies exist among their estimates primarily because of inconsistent input datasets, simulation protocol, and model structure and parameterization schemes. Based on the consistent model input data and simulation protocol, the global N2O Model Intercomparison Project (NMIP) was initialized with 10 state-of-the-art terrestrial biosphere models that include nitrogen (N) cycling. Specific objectives of NMIP are to 1) unravel the major N cycling processes controlling N2O fluxes in each model and identify the uncertainty sources from model structure, input data, and parameters; 2) quantify the magnitude and spatial and temporal patterns of global and regional N2O fluxes from the preindustrial period (1860) to present and attribute the relative contributions of multiple environmental factors to N2O dynamics; and 3) provide a benchmarking estimate of N2O fluxes through synthesizing the multimodel simulation results and existing estimates from ground-based observations, inventories, and statistical and empirical extrapolations. This study provides detailed descriptions for the NMIP protocol, input data, model structure, and key parameters, along with preliminary simulation results. The global and regional N2O estimation derived from the NMIP is a key component of the global N2O budget synthesis activity jointly led by the Global Carbon Project and the International Nitrogen Initiative.

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Ned Haughton, Gab Abramowitz, Andy J. Pitman, Dani Or, Martin J. Best, Helen R. Johnson, Gianpaolo Balsamo, Aaron Boone, Matthias Cuntz, Bertrand Decharme, Paul A. Dirmeyer, Jairui Dong, Michael Ek, Zichang Guo, Vanessa Haverd, Bart J. J. van den Hurk, Grey S. Nearing, Bernard Pak, Joe A. Santanello Jr., Lauren E. Stevens, and Nicolas Vuichard


The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) illustrated the value of prescribing a priori performance targets in model intercomparisons. It showed that the performance of turbulent energy flux predictions from different land surface models, at a broad range of flux tower sites using common evaluation metrics, was on average worse than relatively simple empirical models. For sensible heat fluxes, all land surface models were outperformed by a linear regression against downward shortwave radiation. For latent heat flux, all land surface models were outperformed by a regression against downward shortwave radiation, surface air temperature, and relative humidity. These results are explored here in greater detail and possible causes are investigated. It is examined whether particular metrics or sites unduly influence the collated results, whether results change according to time-scale aggregation, and whether a lack of energy conservation in flux tower data gives the empirical models an unfair advantage in the intercomparison. It is demonstrated that energy conservation in the observational data is not responsible for these results. It is also shown that the partitioning between sensible and latent heat fluxes in LSMs, rather than the calculation of available energy, is the cause of the original findings. Finally, evidence is presented that suggests that the nature of this partitioning problem is likely shared among all contributing LSMs. While a single candidate explanation for why land surface models perform poorly relative to empirical benchmarks in PLUMBER could not be found, multiple possible explanations are excluded and guidance is provided on where future research should focus.

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Roberto Buizza, Stefan Brönnimann, Leopold Haimberger, Patrick Laloyaux, Matthew J. Martin, Manuel Fuentes, Magdalena Alonso-Balmaseda, Andreas Becker, Michael Blaschek, Per Dahlgren, Eric de Boisseson, Dick Dee, Marie Doutriaux-Boucher, Xiangbo Feng, Viju O. John, Keith Haines, Sylvie Jourdain, Yuki Kosaka, Daniel Lea, Florian Lemarié, Michael Mayer, Palmira Messina, Coralie Perruche, Philippe Peylin, Jounie Pullainen, Nick Rayner, Elke Rustemeier, Dinand Schepers, Roger Saunders, Jörg Schulz, Alexander Sterin, Sebastian Stichelberger, Andrea Storto, Charles-Emmanuel Testut, Maria-Antóonia Valente, Arthur Vidard, Nicolas Vuichard, Anthony Weaver, James While, and Markus Ziese


The European Reanalysis of Global Climate Observations 2 (ERA-CLIM2) is a European Union Seventh Framework Project started in January 2014 and due to be completed in December 2017. It aims to produce coupled reanalyses, which are physically consistent datasets describing the evolution of the global atmosphere, ocean, land surface, cryosphere, and the carbon cycle. ERA-CLIM2 has contributed to advancing the capacity for producing state-of-the-art climate reanalyses that extend back to the early twentieth century. ERA-CLIM2 has led to the generation of the first European ensemble of coupled ocean, sea ice, land, and atmosphere reanalyses of the twentieth century. The project has funded work to rescue and prepare observations and to advance the data-assimilation systems required to generate operational reanalyses, such as the ones planned by the European Union Copernicus Climate Change Service. This paper summarizes the main goals of the project, discusses some of its main areas of activities, and presents some of its key results.

Open access