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https://joss.theoj.org/papers/10.21105/joss.06762 + + + https://joss.theoj.org/papers/10.21105/joss.06762.pdf + + + + + + Rapid ocean acidification and protracted +Earth system recovery followed the end-Cretaceous Chicxulub +impact + Henehan + Proceedings of the National Academy of +Sciences + 45 + 116 + 10.1073/pnas.1905989116 + 0027-8424 + 2019 + Henehan, M. 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+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6762 +10.21105/joss.06762 + +cgeniepy: A Python package for analysing cGENIE Earth +System Model output + + + +https://orcid.org/0000-0001-5811-2388 + +Ying +Rui + +rui.ying@bristol.ac.uk + +* + + + +School of Earth Sciences, University of Bristol, +UK + + + + +* E-mail: rui.ying@bristol.ac.uk + + +19 +9 +2024 + +9 +101 +6762 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +cGENIE +Earth System Model +Ocean +Climate + + + + + + Summary +

cGENIE is a numerical model that simulates the Earth system (e.g., + atmosphere, ocean, land, biosphere, ice sheet, and their interactions) + in different geological ages + (Ridgwell + et al., 2007). It has been widely used in studying and + reconstructing the past ocean and climate states. Here, I provide a + Python package cgeniepy for reading, analysing, and + visualising the cGENIE model output, and performing the model-data + comparison. The package is designed to facilitate the post-simulation + analysis for all cGENIE users, as used in my recent studies + (Ying + et al., 2023a; + Ying + et al., 2023b). The package is designed as object-oriented, + thus many features are standalone and can be used without cGENIE + background.

+
+ + Statement of need +

Earth System Models are the essential tool used to study the + mechanisms regulating the complex climate and their impacts. cGENIE is + such a model with intermediate model complexity that strengthens its + application in paleoceanography studies. For instance, Henehan et al. + (2019) + used cGENIE to study the impact of an extreme climatic event, the + Cretaceous-Paleogene massive extinction in 66 Million years ago. Pohl + et al. + (2022) used + cGENIE to study the long-term evolution of ocean oxygen over the past + 550 million years. The application of this model has promoted our + understanding of climate change in the geological past.

+

Despite the power of cGENIE, the analysis of its model output has + relied on a collection of MATLAB scripts developed by the cGENIE + maintainer (https://github.com/derpycode/muffinplot). A systematic + package has been long missing. Such gap might hamper the efficiency of + the research, in particular for users who are not familiar with MATLAB + or need to perform customised analysis (e.g., model ensemble based + analysis).

+

Python is a popular open-source programming language that has a + built-in package management system. Therefore, relative to MATLAB, + Python packages can be easier to install, use, and demonstrate across + platforms based on Jupyter notebooks and Quarto. Many climate and + ocean models have their own Python package support (e.g., Caneill + (2023) + for the NEMO model and Forget + (2023) for + the MITgcm model). As such, it is useful to develop a similar one for + the growing cGENIE community.

+
+ + Package Design +

This package first provides a class model to + read the cGENIE model output + ([fig:0]). Then the + accessed data will in be stored in corresponding data structure class + (GriddedData or + ScatterData). The two data structure classes + are based on the xarray.DataArray and + pandas.DataFrame respectively, which are common + data structures used in the Python community. The two data structures + can also be converted to each other easily using built-in methods.

+

Once the data classes are initialised (read from GENIE model or + not), the users can perform basic operations from + xarray.DataArray and + pandas.DataFrame. However, additional features + are also provided such as publication-ready visualisations in the + GriddedDataVis and + ScatterDataVis classes, which both contain + various options to customise plots based on the + matplotlib and cartopy + packages.

+

Another common demand for Earth system model users is model-data + comparisons. Thus, I provided a skill module to + conduct the skill score calculation including the correlation + coefficient, root mean square error, and the Taylor diagram (see the + Examples section).

+

For the cGENIE model specifically, its coarse model output can be + interpolated using the Interpolator class + ([fig:0]). This is a + wrapper of the scipy.interpolate subpackage and + its purpose is to help increase the grid resolution and create + prettier figures. However, a long-term goal is to incorporate more + advanced interpolation methods (e.g., the DIVA method) to make cGENIE + model output more comparable to the high-resolution + model/observational results.

+ +

A schematic figure showing the structure of the + cgeniepy package and its functionalities. + cgeniepy helps users to access the model + output and operate the visualisation and analysis, including + interpolation and model-data comparison. +

+ +
+
+ + Examples +

In this section, I provide two examples to show the core + functionalities of cgeniepy. More examples + however can be found in the package documentation website + (https://cgeniepy.readthedocs.io/en/latest/).

+ + Access, analyse and visualise the cGENIE model output +

The following code example demonstrates using + cgeniepy in a common use case for cGENIE + users. It initialises the cGENIE model instance, reads the sea + surface temperature data, and plots the last time slice as a map. + The data is adapted from Ying et al. + (2023b) + and Gutjahr et al. + (2017) + ([fig:1]). The users can + easily change the variable name to access other model outputs.

+ ## import the package +from cgeniepy.model import GenieModel + +## initialise a model instance bu providing the path to the model output +model = GenieModel("/Users/foo/model_experiment_id") + +## get the time-slice variable +ocn_sst = model.get_var("ocn_surf_temp") + +## plot the last time slice +ocn_sst.isel(time=-1).plot() + +

The simulated sea surface temperature in the Modern + (left) and Paleogene-Eocene Thermal Maximum event (right) in + cGENIE and visualised by cgeniepy. The data + is adapted from Ying et al. + (2023b) + and Gutjahr et al. + (2017) + respectively. +

+ +
+
+ + Model-data comparison +

In this example, I demonstrate how to conduct a model-data + comparison using cgeniepy. I compare the + ocean carbon isotope in sediment cores (i.e., observation) + (Peterson + et al., 2014) with cGENIE model ouptputs + (Ying + et al., 2023b) in the Last Glacial Maximum (21 ka). First, + both observations and model results are read by the + cgeniepy package. Then I search the nearest + cGENIE model grid boxes for each sediment core and append the + matched results to the existing dataframe. Finally, I visualise the + model-data comparison by plotting the scatter plot with multiple + metrics calculated + ([fig:2]).

+ from cgeniepy.model import GenieModel +from cgeniepy.table import ScatterData + +## The example model and data are archived in +## https://zenodo.org/records/13786013 and +## https://zenodo.org/records/8189647 + +## initialise a model instance +lgm_model = GenieModel("path/to/the/model/") +## get the variable and select the last time slice of the spin-up model +lgm_d13C = lgm_model.get_var("ocn_DIC_13C").isel(time=-1) + +## read in the proxy data and construct ScatterData object +proxy_d13C = ScatterData("path/to/the/data") +proxy_d13C.set_index(["Lat", "Lon", "Depth"]) + +## find the model data for each core location +model_data = [] +for i in proxy_d13C.data.index: + lat, lon, depth = i + pos = (depth, lat, lon) + data = lgm_d13C.search_point(pos, ignore_na=True) + model_data.append(data) + +## add the model data to the dataframe +proxy_d13C.data["GENIE_d13C"] = model_data + +## rename the column for y axis label +proxy_d13C.data.rename(columns={"LGM":"Observational d13C"}, inplace=True) + +## conduct the model-data comparison and plot the 1:1 lineplot +## by default, model data is in the col, and observational col is in the second +proxy_d13C.compare("GENIE_d13C","LGM").plot() + +

A cgeniepy example of + model-data comparison for the Last Glacial Maximum carbon isotope + data. The model output is adapted from Ying et al. + (2023b) + and the data is from Peterson et al. + (2014). +

+ +
+
+
+ + Acknowledgements +

R.Y. acknowledges financial support from China Scholarship Council + (202006380070). R.Y. also thanks Shao Jun for his suggestions on the + package.

+
+ + + + + + + + HenehanMichael J. + RidgwellAndy + ThomasEllen + ZhangShuang + AlegretLaia + SchmidtDaniela N. + RaeJames W. B. + WittsJames D. + LandmanNeil H. + GreeneSarah E. + HuberBrian T. + SuperJames R. + PlanavskyNoah J. + HullPincelli M. + + Rapid ocean acidification and protracted Earth system recovery followed the end-Cretaceous Chicxulub impact + Proceedings of the National Academy of Sciences + National Academy of Sciences + 201911 + 20201207 + 116 + 45 + 0027-8424 + 10.1073/pnas.1905989116 + 31636204 + 22500 + 22504 + + + + + + PohlAlexandre + RidgwellAndy + StockeyRichard G. + ThomazoChristophe + KeaneAndrew + VenninEmmanuelle + ScoteseChristopher R. + + Continental configuration controls ocean oxygenation during the Phanerozoic + Nature + Nature Publishing Group + 202208 + 20230716 + 608 + 7923 + 1476-4687 + 10.1038/s41586-022-05018-z + 523 + 527 + + + + + + CaneillRomain + + Interface to open NEMO ocean global circulation model output dataset and create a xgcm grid + GitHub repository + GitHub + 2023 + https://github.com/rcaneill/xnemogcm + 10.5281/zenodo.5724577 + + + + + + ForgetGaël + + Tools to interact with MITgcm (setup, run, output, plot, etc) + GitHub repository + GitHub + 2023 + https://github.com/gaelforget/MITgcmTools.jl + 10.5281/zenodo.13766391 + + + + + + RidgwellA. + HargreavesJ. C. + EdwardsN. R. + AnnanJ. D. + LentonT. M. + MarshR. + YoolA. + WatsonA. + + Marine geochemical data assimilation in an efficient Earth System Model of global biogeochemical cycling + Biogeosciences + 200701 + 20201006 + 4 + 1 + 1726-4189 + 10.5194/bg-4-87-2007 + 87 + 104 + + + + + + YingRui + MonteiroFanny M. + WilsonJamie D. + SchmidtDaniela N. + + ForamEcoGEnIE 2.0: Incorporating symbiosis and spine traits into a trait-based global planktic foraminiferal model + Geoscientific Model Development + Copernicus GmbH + 202302 + 20230202 + 16 + 3 + 1991-959X + 10.5194/gmd-16-813-2023 + 813 + 832 + + + + + + YingRui + MonteiroFanny + WilsonJamie + SchmidtDaniela + + Marine zooplankton acclimated to geological warming while facing limits by the next century + Climate + 202308 + 20230807 + 10.31223/X5D10G + + + + + + GutjahrMarcus + RidgwellAndy + SextonPhilip F. + AnagnostouEleni + PearsonPaul N. + PälikeHeiko + NorrisRichard D. + ThomasEllen + FosterGavin L. + + Very large release of mostly volcanic carbon during the Palaeocene–Eocene Thermal Maximum + Nature + Nature Publishing Group + 201708 + 20210131 + 548 + 7669, 7669 + 1476-4687 + https://www.nature.com/articles/nature23646 + 10.1038/nature23646 + 573 + 577 + + + + + + PetersonCarlye D. + LisieckiLorraine E. + SternJoseph V. + + Deglacial whole-ocean δ13C change estimated from 480 benthic foraminiferal records + Paleoceanography + 2014 + 20220613 + 29 + 6 + 1944-9186 + https://onlinelibrary.wiley.com/doi/abs/10.1002/2013PA002552 + 10.1002/2013PA002552 + 549 + 563 + + + + +