CIGAR is a state-of-the-art ocean reanalysis system, specifically built at CNR ISMAR for multi-decadal climate applications
Two streams of the reanalyses are planned:
A "contemporary stream" (CS), forced by a state-of-the-art atmospheric reanalysis (ECMWF ERA5), with full observing network ingested. CIGAR-CS has been produced and covers the period 1961-2022 (updated in delayed time mode)
An "historical stream" (HS), forced by a centennial atmospheric reanalysis with reduced set of observations assimilated to ensure the temporal consistency of the dataset (NOAA-CIRES-DOE 20CRv3). CIGAR-HS is being produced and will span the 1871-2015 period, with extension into present foreseen later
The two systems (CIGAR-CS and CIGAR-HS) share the same ocean modelling, data assimilation, and ensemble generation formulation, while differ in the input datasets (observations and boundary forcing)
A reanalysis system is a numerical model-based reconstruction of the climate of the past, where the model is constrained by observations by means of a modern data assimilation system
Reanalyses need to minimize sharp changes in accuracy, and spurious variability, caused by the intermittent observing networks. This is accomplished by relying on the same numerical model and data assimilation configurations, using input datasets as stable as possible during the period, and implementing bias- and drift- correction schemes. Additionally, an ensemble generation strategy that includes perturbation of all input datasets, together with stochastic modulation of model physics and observations, enables a probabilistic representation of the ocean climate of the past, allowing the characterization of the reanalysis uncertainty and the assessment of the confidence levels of all long-term metrics
The CIGAR reanalysis system includes:
The NEMO ocean model (v4.0.7) at about 1/3°-1° of horizontal resolution and 75 vertical depth levels
A variational data assimilation with time-varying background-error covariances and a variational quality control scheme
A deep-ocean large-scale bias correction scheme
An ensemble member-dependent ingestion of sea surface temperature data, in-situ profiles and atmospheric forcing
Ensemble-generation relying on stochastic physics and assimilation schemes
DATASET
CIGAR streams are available to the scientific community and any interested user. Produced streams are as follows:
CIGAR-CS is a 32-member contemporary reanalysis forced by ERA5 and covering the period 1961-2022, with near-real-time extension.
Ocean Heat Content data, together with analysis increments, are available as a Zenodo dataset at this link Selected ensemble mean and ensemble standard deviation fields and other variables are planned for release later and can be obtained through FTP by filling the contact form below, upon reasonable request
CIGAR-HS is a 24-member historical reanalysis forced by 20CRv3 and under production, planned for release at the end of 2024
PUBLICATIONS
CIGAR-CS reference article is
Storto, A., Yang, C. Acceleration of the ocean warming from 1961 to 2022 unveiled by large-ensemble reanalyses. Nat Commun 15, 545 (2024). https://doi.org/10.1038/s41467-024-44749-7
The reanalysis system takes advantage of several previous developments and studies, such as, for example:
Storto A and Yang C (2023) Stochastic schemes for the perturbation of the atmospheric boundary conditions in ocean general circulation models. Front. Mar. Sci. 10:1155803. https://doi.org/10.3389/fmars.2023.1155803
Storto, A, Andriopoulos, P. A new stochastic ocean physics package and its application to hybrid-covariance data assimilation. Q J R Meteorol Soc. 2021; 1691–1725. https://doi.org/10.1002/qj.3990
Yang, C., Storto, A. & Masina, S. Quantifying the effects of observational constraints and uncertainty in atmospheric forcing on historical ocean reanalyses. Clim Dyn 52, 3321–3342 (2019). https://doi.org/10.1007/s00382-018-4331-z
Storto A, Alvera-Azcárate A, Balmaseda MA, Barth A, Chevallier M, Counillon F, Domingues CM, Drevillon M, Drillet Y, Forget G, Garric G, Haines K, Hernandez F, Iovino D, Jackson LC, Lellouche J-M, Masina S, Mayer M, Oke PR, Penny SG, Peterson KA, Yang C and Zuo H (2019) Ocean Reanalyses: Recent Advances and Unsolved Challenges. Front. Mar. Sci. 6:418. https://doi.org/10.3389/fmars.2019.00418
Storto A., Oddo P., Cipollone A., Mirouze I., Lemieux B. (2018). Extending an oceanographic variational scheme to allow for affordable hybrid and four-dimensional data assimilation. Ocean Modelling. 128, 67–86. https://doi.org/10.1016/j.ocemod.2018.06.005
Yang, C., Masina, S. and Storto, A. (2017), Historical ocean reanalyses (1900–2010) using different data assimilation strategies. Q.J.R. Meteorol. Soc., 143: 479-493. https://doi.org/10.1002/qj.2936
