Caili Liu

Ocean University of China

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Co-Authors

Sergey Danilov

Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung

Vasco Müller

Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung

Qiang Wang

Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung

Dmitry Sidorenko

Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung

Xinyue Li

Qingdao Agricultural University

Nikolay Koldunov

Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung

Thomas Jung

Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung

Shaoqing Zhang

Ocean University of China

Kacper Nowak

Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung

Nikolay Koldonov

Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung

Cooperative Institutions

Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung

Rutgers, The State University of New Jersey

Australian Wool Innovation (Australia)

University of Bremen

Rutgers Sexual and Reproductive Health and Rights

Lamont-Doherty Earth Observatory

Columbia University

Planetary Science Institute

University of Massachusetts Dartmouth

University of Massachusetts Amherst

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Implementation of implicit filter for spatial spectra extraction

arXiv (Cornell University) (2024)

Kacper Nowak Sergey Danilov Vasco Müller Caili Liu

Scale analysis based on coarse-graining has been proposed recently as an alternative to Fourier analysis. It is now broadly used to analyze energy spectra and energy transfers in eddy-resolving ocean simulations. However, for data from unstructured-mesh models it requires interpolation to a regular grid. We present a high-performance Python implementation of an alternative coarse-graining method which relies on implicit filters using discrete Laplacians. This method can work on arbitrary (structured or unstructured) meshes and is applicable to the direct output of unstructured-mesh ocean circulation atmosphere models. The computation is split into two phases: preparation and solving. The first one is specific only to the mesh. This allows for auxiliary arrays that are then computed to be reused, significantly reducing the computation time. The second part consists of sparse matrix algebra and solving linear system. Our implementation is accelerated by GPUs to achieve unmatched performance and scalability. This results in processing data based on meshes with more than 10M surface vertices in a matter of seconds. As an illustration, the method is applied to compute spatial spectra of ocean currents from high-resolution FESOM2 simulations.

Python

Granularity

Unstructured grid

Interpolation

10.48550/arxiv.2404.07398

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Eddy activity in the Arctic Ocean projected to surge in a warming world

Zenodo (CERN European Organization for Nuclear Research) (2023)

Xinyue Li Qiang Wang Sergey Danilov Nikolay Koldunov Caili Liu

High resolution FESOM2 results used for Arctic Ocean eddy activity study, with Figure 1-5 in the article and Extended Data Figures included in the supplementary material.

10.5281/zenodo.10020009

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Implementation of implicit filter for spatial spectra extraction

Kacper Nowak Sergey Danilov Vasco Müller Caili Liu

Abstract. Scale analysis based on coarse-graining has been proposed recently as an alternative to Fourier analysis. It is now broadly used to analyze energy spectra and energy transfers in eddy-resolving ocean simulations. However, for data from unstructured-mesh models it requires interpolation to a regular grid. We present a high-performance Python implementation of an alternative coarse-graining method which relies on implicit filters using discrete Laplacians. This method can work on arbitrary (structured or unstructured) meshes and is applicable to the direct output of unstructured-mesh ocean circulation atmosphere models. The computation is split into two phases: preparation and solving. The first one is specific only to the mesh. This allows for auxiliary arrays that are then computed to be reused, significantly reducing the computation time. The second part consists of sparse matrix algebra and solving linear system. Our implementation is accelerated by GPUs to achieve unmatched performance and scalability. This results in processing data based on meshes with more than 10M surface vertices in a matter of seconds. As an illustration, the method is applied to compute spatial spectra of ocean currents from high-resolution FESOM2 simulations.

Python

Granularity

Unstructured grid

Interpolation

10.5194/egusphere-2024-1119

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Spatial Scales of Kinetic Energy in the Arctic Ocean

Journal of Geophysical Research Oceans (2024)

Caili Liu Qiang Wang Sergey Danilov Nikolay Koldunov Vasco Müller

Abstract Despite the importance of the Arctic Ocean for the large‐scale circulation and climate, there is still a knowledge gap in our understanding of the spatial characteristics of the Arctic Ocean circulation, especially for the mesoscale. This paper investigates the spatial characteristics of the Arctic Ocean circulation using a simulation with 1 km horizontal resolution. We revealed that there are two peaks in the kinetic energy (KE) spectral density at the 400 m depth, one at the gyre scale of the Arctic Circumpolar Boundary Current (centered at 1,700–2,000 km), and the other associated with the mesoscale (at about 60 km). However, at the 70 m depth, the boundary currents tend to mask the spectrum peak associated with the mesoscale. The KE spectrum exhibits a power‐law scaling typical for ocean eddies. We found that about 80% (50%) of the KE is on scales smaller than 100 km (30 km). The maximum KE content is in the 10–20 km scale band in most of the eddy‐rich regions of the abyssal ocean. The seasonality of the KE spectrum and KE content inside the Arctic Ocean follow the seasonality of eddy activity and baroclinicity, with low values in spring and maxima in late summer to autumn, and the seasonal variation is stronger at the 70 m depth than the 400 m depth. The strong concentration of KE on the very small spatial scales warrants future studies on energy transfer between scales in the Arctic Ocean.

10.1029/2023jc020013

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Author Correction: Eddy activity in the Arctic Ocean projected to surge in a warming world

Nature Climate Change (2024)

Xinyue Li Qiang Wang Sergey Danilov Nikolay Koldunov Caili Liu

10.1038/s41558-024-01931-5

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Arctic Eddy activity as revealed by FESOM2

Zenodo (CERN European Organization for Nuclear Research) (2023)

Xinyue Li Qiang Wang Sergey Danilov Nikolay Koldunov Caili Liu

High resolution FESOM2 results used for Arctic Ocean eddy activity study, with Figure 1-5 featured in the article and Figure S1-10 included in the supplementary material.

10.5281/zenodo.8108247

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Eddy activity in the Arctic Ocean projected to surge in a warming world

Nature Climate Change (2024)

Xinyue Li Qiang Wang Sergey Danilov Nikolay Koldunov Caili Liu

Abstract Ocean eddies play a critical role in climate and marine life. In the rapidly warming Arctic, little is known about how ocean eddy activity will change because existing climate models cannot resolve Arctic Ocean mesoscale eddies. Here, by employing a next-generation global sea ice–ocean model with kilometre-scale horizontal resolution in the Arctic, we find a surge of eddy kinetic energy in the upper Arctic Ocean, tripling on average in a four-degree-warmer world. The driving mechanism behind this surge is an increase in eddy generation due to enhanced baroclinic instability. Despite the decline of sea ice, eddy killing (a process in which eddies are dampened by sea ice and winds) will not weaken in its annual mean effect in the considered warming scenario. Our study suggests the importance of adequately representing Arctic eddy activity in climate models for understanding the impacts of its increase on climate and ecosystems.

Eddy

10.1038/s41558-023-01908-w

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supplementary to "Spatial scales of kinetic energy in the Arctic Ocean", dataset from Caili Liu

Zenodo (CERN European Organization for Nuclear Research) (2023)

Caili Liu Qiang Wang Sergey Danilov Nikolay Koldonov Vasco Müller

## "Spatial scales of kinetic energy in the Arctic Ocean" Available dataset for each figure (1~9) in the main text, including Jupyter notebook scripts (Fig1, Fig2, Fig5) and Matlab scripts (Fig3, Fig4, Fig6, Fig7, Fig8, Fig9). ## Description This dataset is as the supplementary to the manuscript "Spatial scales of kinetic energy in the Arctic Ocean", including jupyter notebook scripts and matlab scripts of visualization directly for figures1~9. 1) Jupyter notebook scripts for visualization
the MESH and BG are used for visualization, and *.mat are the dataset for Fig1/2/5. The load path in the script should be changed to your files accordingly. 2) Matlab scripts for plots
All figures/panels are directly produced, but it is composed of panels for Fig7/8/9 additionally.

10.5281/zenodo.7895620

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Spatial scales of kinetic energy and its transfer across large-meso scales in the Arctic Ocean

Caili Liu Qiang Wang Sergey Danilov Nikolay Koldunov Vasco Mueller

Despite the importance of the Arctic Ocean for the large-scale circulation and climate, there is still a knowledge gap in our understanding of the spatial characteristics of the Arctic Ocean circulation, especially for the mesoscale. This research diagoses the kinetic energy (KE) and its transfer features of a rich-eddy Arctic Ocean from eddy-resolved sea-ice model FESOM (Finite-Element/volumE Sea ice-Ocean Model)  at about 1km in horizontal resolution, revealing the KE spectra at spatial scales and the seasonality. There are two peaks in the kinetic energy spectral density, one at the gyre scale of the Arctic boundary currents (centered at 1700-2000km), and the other associated with the mesoscale (centered at 60km), both of which exhibit a power-law scaling typical for large circulation and ocean eddies, respectively. Energy transfer diagnosis shows that there is inverse-cascade KE pathway at a hundred of kilometers to several killometers, of which the KE transfer value from larger scales to smaller scales is negative. The seasonality of the eddy KE spectrum and energy transfer in upper ocean is found to be highly consistent to sea ice loss trend, while eddy KE in deep ocean shows not significant seasonal variations. 

10.5194/egusphere-egu24-431

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Paleocene benthic foraminiferal biofacies and sequence stratigraphy, Island Beach borehole, New Jersey

Caili Liu James V. Browning Kenneth G. Miller Richard K. Olsson

Benthic foraminiferal biofacies and integrated stratigraphic studies of the sediments recovered from the Island Beach borehole in New Jersey reveal that the Paleocene strata penetrated in the borehole can be divided into three sequences, which we designate pa1, pa2, and pa3 in stratigraphically ascending order.Sequence boundaries are recognized by gamma-ray log excursions, hiatuses, and unconformities.Depositional paleoenvironments for each sequence are recognized by lithology and benthic foraminiferal biofacies.Strata in these sequences exhibit upward shallowing from outer neritic to middle neritic environments.Maximum flooding surfaces are associated with the greatest benthic foraminiferal biofacies changes, shell beds, and secondary gamma-ray kicks.Biofacies changes across sequence boundaries are less severe; we attribute this to the effects of a longer term decrease in water depth superimposed on these shorter-term transgressive/regressive cycles.

Sequence Stratigraphy

Sequence (biology)

10.2973/odp.proc.sr.150x.316.1997

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Citations (18)