Evaluation of global ocean-sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)

Hiroyuki Tsujino*, L. Shogo Urakawa, Stephen M. Griffies, Gokhan Danabasoglu, Alistair J. Adcroft, Arthur E. Amaral, Thomas Arsouze, Mats Bentsen, Raffaele Bernardello, Claus W. Böning, Alexandra Bozec, Eric P. Chassignet, Sergey Danilov, Raphael Dussin, Eleftheria Exarchou, Pier Giuseppe Fogli, Baylor Fox-Kemper, Chuncheng Guo, Mehmet Ilicak, Doroteaciro IovinoWho M. Kim, Nikolay Koldunov, Vladimir Lapin, Yiwen Li, Pengfei Lin, Keith Lindsay, Hailong Liu, Matthew C. Long, Yoshiki Komuro, Simon J. Marsland, Simona Masina, Aleksi Nummelin, Jan Klaus Rieck, Yohan Ruprich-Robert, Markus Scheinert, Valentina Sicardi, Dmitry Sidorenko, Tatsuo Suzuki, Hiroaki Tatebe, Qiang Wang, Stephen G. Yeager, Zipeng Yu

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: ???type-name???Makalebilirkişi

110 Atıf (Scopus)

Özet

We present a new framework for global ocean- sea-ice model simulations based on phase 2 of the Ocean Model Intercomparison Project (OMIP-2), making use of the surface dataset based on the Japanese 55-year atmospheric reanalysis for driving ocean-sea-ice models (JRA55-do).We motivate the use of OMIP-2 over the framework for the first phase of OMIP (OMIP-1), previously referred to as the Coordinated Ocean-ice Reference Experiments (COREs), via the evaluation of OMIP-1 and OMIP-2 simulations from 11 state-of-the-science global ocean-sea-ice models. In the present evaluation, multi-model ensemble means and spreads are calculated separately for the OMIP-1 and OMIP-2 simulations and overall performance is assessed considering metrics commonly used by ocean modelers. Both OMIP-1 and OMIP-2 multi-model ensemble ranges capture observations in more than 80% of the time and region for most metrics, with the multi-model ensemble spread greatly exceeding the difference between the means of the two datasets. Many features, including some climatologically relevant ocean circulation indices, are very similar between OMIP-1 and OMIP- 2 simulations, and yet we could also identify key qualitative improvements in transitioning from OMIP-1 to OMIP- 2. For example, the sea surface temperatures of the OMIP- 2 simulations reproduce the observed global warming during the 1980s and 1990s, as well as the warming slowdown in the 2000s and the more recent accelerated warming, which were absent in OMIP-1, noting that the last feature is part of the design of OMIP-2 because OMIP-1 forcing stopped in 2009. A negative bias in the sea-ice concentration in summer of both hemispheres in OMIP-1 is significantly reduced in OMIP-2. The overall reproducibility of both seasonal and interannual variations in sea surface temperature and sea surface height (dynamic sea level) is improved in OMIP-2. These improvements represent a new capability of the OMIP-2 framework for evaluating processlevel responses using simulation results. Regarding the sensitivity of individual models to the change in forcing, the models show well-ordered responses for the metrics that are directly forced, while they show less organized responses for those that require complex model adjustments. Many of the remaining common model biases may be attributed either to errors in representing important processes in ocean-sea-ice models, some of which are expected to be reduced by using finer horizontal and/or vertical resolutions, or to shared biases and limitations in the atmospheric forcing. In particular, further efforts are warranted to resolve remaining issues in OMIP-2 such as the warm bias in the upper layer, the mismatch between the observed and simulated variability of heat content and thermosteric sea level before 1990s, and the erroneous representation of deep and bottom water formations and circulations. We suggest that such problems can be resolved through collaboration between those developing models (including parameterizations) and forcing datasets. Overall, the present assessment justifies our recommendation that future model development and analysis studies use the OMIP-2 framework.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)3643-3708
Sayfa sayısı66
DergiGeoscientific Model Development
Hacim13
Basın numarası8
DOI'lar
Yayın durumuYayınlandı - 21 Ağu 2020

Bibliyografik not

Publisher Copyright:
© 2020 American Society of Civil Engineers (ASCE). All rights reserved.

Finansman

Financial support. This research has been supported by the The JMA-MRI contribution to this study was supported by Meteorological Research Institute. The MIROC-COCO4.9 and JMA-MRI contributions were partially supported by the Integrated Research Program for Advancing Climate Models (TOUGOU) grant no. JPMXD0717935457 and JPMXD0717935561, respec- tively, from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. The AWI contributors (Qiang Wang, Dmitry Sidorenko, Sergey Danilov, and Nikolay Koldunov) acknowledge funding from the projects S1 (Diagnosis and Metrics in Climate Models) and S2 (Improved parameterizations and numerics in climate models) of the Collaborative Research Centre TRR 181 “Energy Transfer in Atmosphere and Ocean” funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project no. 274762653, Helmholtz Climate Initiative REKLIM (Regional Climate Change), and the European Union’s Horizon 2020 Research & Innovation program through grant agreement no. 727862 APPLICATE. Yohan Ruprich Robert’s contribution was founded by the European Union’s Horizon 2020 Research and Innovation Programme in the framework of the Marie Skłodowska-Curie grant INADEC (grant agreement no. 800154). The NorESM-BLOM contribution was supported by the Research Council of Norway (projects EVA (229771) and INES (270061)) and the Centre for Climate Dynamics at the Bjerknes Centre for Climate Research, and simulations were performed on resources provided by UNINETT Sigma2 – the National Infrastructure for High Performance Computing and Data Storage in Norway. NCAR contribution was supported by the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office Climate Variability and Predictability Program. NCAR is a major facility sponsored by the US National Science Foundation (NSF) under co-operative agreement no. 1852977. Pengfei Lin, Hailong Liu, Zipeng Yu, and Yiwen Li are supported by the National Natural Science Foundation of China (grant nos. 41931183 and 41976026). Raphael Dussin’s research at the Geophysical Fluid Dynamics Laboratory is supported by NOAA’s Science Collaboration Program and administered by UCAR’s Cooperative Programs for the Advancement of Earth System Science (CPAESS) under awards NA16NWS4620043 and NA18NWS4620043B. Alistair Adcroft acknowledges support for his work at the Geophysical Fluid Dynamics Laboratory from award NA18OAR4320123 of NOAA.

FinansörlerFinansör numarası
Centre for Climate Dynamics
EVA
European Union’s Horizon 2020 Research & Innovation program
Helmholtz Climate Initiative REKLIM
Meteorological Research Institute
UNINETT
US National Science Foundation
National Science Foundation1852977
National Oceanic and Atmospheric Administration
University Corporation for Atmospheric ResearchNA18OAR4320123, NA16NWS4620043, NA18NWS4620043B
Horizon 2020 Framework Programme800154
Deutsche Forschungsgemeinschaft274762653
Ministry of Education, Culture, Sports, Science and TechnologyJPMXD0717935457, JPMXD0717935561
National Natural Science Foundation of China41931183, 41976026
Norges Forskningsråd229771
Instituto Nacional de Ciência e Tecnologia para Engenharia de Software270061
Horizon 2020727862

    Parmak izi

    Evaluation of global ocean-sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)' araştırma başlıklarına git. Birlikte benzersiz bir parmak izi oluştururlar.

    Alıntı Yap