Inconsistent strategies to spin up models in CMIP5: Implications for ocean biogeochemical model performance assessment
This is the final version of the article. Available from EGU via the DOI in this record. ; During the fifth phase of the Coupled Model Intercomparison Project (CMIP5) substantial efforts were made to systematically assess the skill of Earth system models. One goal was to check how realistically representative marine biogeochemical tracer distributions could be reproduced by models. In routine assessments model historical hindcasts were compared with available modern biogeochemical observations. However, these assessments considered neither how close modeled biogeochemical reservoirs were to equilibrium nor the sensitivity of model performance to initial conditions or to the spin-up protocols. Here, we explore how the large diversity in spin-up protocols used for marine biogeochemistry in CMIP5 Earth system models (ESMs) contributes to model-to-model differences in the simulated fields. We take advantage of a 500-year spin-up simulation of IPSL-CM5A-LR to quantify the influence of the spin-up protocol on model ability to reproduce relevant data fields. Amplification of biases in selected biogeochemical fields (O2, NO3, Alk-DIC) is assessed as a function of spin-up duration. We demonstrate that a relationship between spin-up duration and assessment metrics emerges from our model results and holds when confronted with a larger ensemble of CMIP5 models. This shows that drift has implications for performance assessment in addition to possibly aliasing estimates of climate change impact. Our study suggests that differences in spin-up protocols could explain a substantial part of model disparities, constituting a source of model-to-model uncertainty. This requires more attention in future model intercomparison exercises in order to provide quantitatively more correct ESM results on marine biogeochemistry and carbon cycle feedbacks. ; We sincerely thank I. Kriest, F. Joos, the anonymous reviewer and A. Yool for their useful comments on this paper. This work was supported by H2020 project CRESCENDO "Coordinated Research in Earth Systems and Climate: Experiments, kNowledge, Dissemination and Outreach", which received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 641816 and by the EU FP7 project CARBOCHANGE "Changes in carbon uptake and emissions by oceans in a changing climate" which received funding from the European community's Seventh Framework Programme under grant agreement no. 264879. Supercomputing time was provided by GENCI (Grand Equipement National de Calcul Intensif) at CCRT (Centre de Calcul Recherche et Technologie), allocation 016178. Finally, we are grateful to the ESGF project which makes data available for all the community. Roland Séférian is grateful to Aurélien Ribes for his kind advices on statistics. Jerry Tjiputra acknowledges ORGANIC project (239965/F20) funded by the Research Council of Norway. Christoph Heinze and Jerry Tjiputra are grateful for support through project EVA – Earth system modelling of climate variations in the Anthropocene (229771/E10) funded by the Research Council of Norway, as well as CPU-time and mass storage provided through NOTUR project NN2345K as well as NorStore project NS2345K. Keith Lindsay and Scott C. Doney acknowledge support from the National Science Foundation.