Articles | Volume 8, issue 10
https://doi.org/10.5194/gmd-8-3071-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-3071-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| Highlight paper
| 
06 Oct 2015
Model description paper | Highlight paper |
| 06 Oct 2015
ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation
G. Forget
CORRESPONDING AUTHOR
Dept. of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
J.-M. Campin
Dept. of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
P. Heimbach
Dept. of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
Jackson School of Geosciences, The University of Texas at Austin, Austin, TX 78712, USA
C. N. Hill
Dept. of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
R. M. Ponte
Atmospheric and Environmental Research, Inc., Lexington, MA 02421, USA
C. Wunsch
Dept. of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02139, USA
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climateincludes all sub-elements (ocean, atmosphere, ice, etc.). A common form of combination arises from sequential estimation theory, a methodology susceptible to a variety of errors that can accumulate through time for long records. Using two simple analogs, examples of these errors are identified and discussed, along with suggestions for accommodating them.
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Rachael N. C. Sanders, Daniel C. Jones, Simon A. Josey, Bablu Sinha, and Gael Forget
Ocean Sci., 18, 953–978, https://doi.org/10.5194/os-18-953-2022, https://doi.org/10.5194/os-18-953-2022, 2022
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In 2015, record low temperatures were observed in the North Atlantic. Using an ocean model, we show that surface heat loss in December 2013 caused 75 % of the initial cooling before this "cold blob" was trapped below the surface. The following summer, the cold blob re-emerged due to a strong temperature difference between the surface ocean and below, driving vertical diffusion of heat. Lower than average surface warming then led to the coldest temperature anomalies in August 2015.
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Geosci. Model Dev., 15, 2309–2324, https://doi.org/10.5194/gmd-15-2309-2022, https://doi.org/10.5194/gmd-15-2309-2022, 2022
Short summary
Short summary
The Green's functions methodology offers a systematic, easy-to-implement, computationally cheap, scalable, and extendable method to tune uncertain parameters in models accounting for the dependent response of the model to a change in various parameters. Herein, we successfully show for the first time that long-term errors in earth system models can be considerably reduced using Green's functions methodology. The method can be easily applied to any model containing uncertain parameters.
Carl Wunsch
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Preprint withdrawn
Short summary
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Combinations of observations with dynamical, chemical, etc, models are essential tools for understanding of climate change. By "climate" is meant all of the sub-elements including ocean, atmosphere, ice, et al. A common form of combination arises from sequential estimation theory, a methodology susceptible to a variety of errors that can accumulate through time for long records. Using two simple analogues, many of these errors are identified here, with suggestions for accommodating them.
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Short summary
The ECCO v4 non-linear inverse modeling framework and its reference solution are made publicly available. The inverse estimate of ocean physics and atmospheric forcing yields a dynamically consistent and global state estimate without unidentified sources of heat and salt that closely fits in situ and satellite data. Any user can reproduce it accurately. Parametric and external model uncertainties are of comparable magnitudes and generally exceed structural model uncertainties.
The ECCO v4 non-linear inverse modeling framework and its reference solution are made publicly...
