diff --git a/joss.06426/10.21105.joss.06426.crossref.xml b/joss.06426/10.21105.joss.06426.crossref.xml new file mode 100644 index 0000000000..8c3e8425af --- /dev/null +++ b/joss.06426/10.21105.joss.06426.crossref.xml @@ -0,0 +1,336 @@ + + + + 20240629144538-bcf99736c334c47c45cfd5ff5eeee532da4e6f08 + 20240629144538 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 06 + 2024 + + + 9 + + 98 + + + + xCDAT: A Python Package for Simple and Robust Analysis +of Climate Data + + + + Tom + Vo + https://orcid.org/0000-0002-2461-0191 + + + Stephen + Po-Chedley + https://orcid.org/0000-0002-0390-238X + + + Jason + Boutte + https://orcid.org/0009-0009-3996-3772 + + + Jiwoo + Lee + https://orcid.org/0000-0002-0016-7199 + + + Chengzhu + Zhang + https://orcid.org/0000-0002-9632-0716 + + + + 06 + 29 + 2024 + + + 6426 + + + 10.21105/joss.06426 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.12522560 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/6426 + + + + 10.21105/joss.06426 + https://joss.theoj.org/papers/10.21105/joss.06426 + + + https://joss.theoj.org/papers/10.21105/joss.06426.pdf + + + + + + The DOE E3SM model version 2: Overview of the +physical model and initial model evaluation + Golaz + Journal of Advances in Modeling Earth +Systems + 12 + 14 + 10.1029/2022MS003156 + 2022 + Golaz, J.-C., Van Roekel, L. P., +Zheng, X., Roberts, A. F., Wolfe, J. D., Lin, W., Bradley, A. M., Tang, +Q., Maltrud, M. E., Forsyth, R. M., Zhang, C., Zhou, T., Zhang, K., +Zender, C. S., Wu, M., Wang, H., Turner, A. K., Singh, B., Richter, J. +H., … Bader, D. C. (2022). The DOE E3SM model version 2: Overview of the +physical model and initial model evaluation. Journal of Advances in +Modeling Earth Systems, 14(12), e2022MS003156. +https://doi.org/10.1029/2022MS003156 + + + A data model of the climate and forecast +metadata conventions (CF-1.6) with a software implementation (cf-python +v2.1) + Hassell + Geoscientific Model +Development + 12 + 10 + 10.5194/gmd-10-4619-2017 + 2017 + Hassell, D., Gregory, J., Blower, J., +Lawrence, B. N., & Taylor, K. E. (2017). A data model of the climate +and forecast metadata conventions (CF-1.6) with a software +implementation (cf-python v2.1). Geoscientific Model Development, +10(12), 4619–4646. +https://doi.org/10.5194/gmd-10-4619-2017 + + + Xarray: N-D labeled Arrays and Datasets in +Python + Hoyer + Journal of Open Research +Software + 5 + 10.5334/jors.148 + 2049-9647 + 2017 + Hoyer, S., & Hamman, J. J. +(2017). Xarray: N-D labeled Arrays and Datasets in Python. Journal of +Open Research Software, 5, 10. +https://doi.org/10.5334/jors.148 + + + Objective evaluation of earth system models: +PCMDI metrics package (PMP) version 3 + Lee + EGUsphere + 2023 + 10.5194/egusphere-2023-2720 + 2023 + Lee, J., Gleckler, P. J., Ahn, M.-S., +Ordonez, A., Ullrich, P. A., Sperber, K. R., Taylor, K. E., Planton, Y. +Y., Guilyardi, E., Durack, P., Bonfils, C., Zelinka, M. D., Chao, L.-W., +Dong, B., Doutriaux, C., Zhang, C., Vo, T., Boutte, J., Wehner, M. F., … +Krasting, J. (2023). Objective evaluation of earth system models: PCMDI +metrics package (PMP) version 3. EGUsphere, 2023, 1–48. +https://doi.org/10.5194/egusphere-2023-2720 + + + Internal variability and forcing influence +model–satellite differences in the rate of tropical tropospheric +warming + Po-Chedley + Proceedings of the National Academy of +Sciences + 47 + 119 + 10.1073/pnas.2209431119 + 2022 + Po-Chedley, S., Fasullo, J. T., +Siler, N., Labe, Z. M., Barnes, E. A., Bonfils, C. J. W., & Santer, +B. D. (2022). Internal variability and forcing influence model–satellite +differences in the rate of tropical tropospheric warming. Proceedings of +the National Academy of Sciences, 119(47), e2209431119. +https://doi.org/10.1073/pnas.2209431119 + + + The flexible climate data analysis tools +(CDAT) for multi-model climate simulation data + Williams + 10.1109/ICDMW.2009.64 + 2009 + Williams, D. N., Doutriaux, C. M., +Drach, R. S., & Mccoy, R. B. (2009). The flexible climate data +analysis tools (CDAT) for multi-model climate simulation data. 254–261. +https://doi.org/10.1109/ICDMW.2009.64 + + + Visualization and analysis tools for +ultrascale climate data + Williams + Advanced Earth and Space +Sciences + 42 + 95 + 10.1002/2014EO420002 + 2014 + Williams, D. N. (2014). Visualization +and analysis tools for ultrascale climate data. Advanced Earth and Space +Sciences, 95(42), 377–378. +https://doi.org/10.1002/2014EO420002 + + + The E3SM diagnostics package (E3SM diags +v2.7): A python-based diagnostics package for earth system model +evaluation + Zhang + Geoscientific Model +Development + 24 + 15 + 10.5194/gmd-15-9031-2022 + 2022 + Zhang, C., Golaz, J.-C., Forsyth, R., +Vo, T., Xie, S., Shaheen, Z., Potter, G. L., Asay-Davis, X. S., Zender, +C. S., Lin, W., Chen, C.-C., Terai, C. R., Mahajan, S., Zhou, T., +Balaguru, K., Tang, Q., Tao, C., Zhang, Y., Emmenegger, T., … Ullrich, +P. A. (2022). The E3SM diagnostics package (E3SM diags v2.7): A +python-based diagnostics package for earth system model evaluation. +Geoscientific Model Development, 15(24), 9031–9056. +https://doi.org/10.5194/gmd-15-9031-2022 + + + Dask: Library for dynamic task +scheduling + Dask-Development-Team + 2016 + Dask-Development-Team. (2016). Dask: +Library for dynamic task scheduling. +https://dask.org + + + CDAT/cdat: CDAT 8.1 + Doutriaux + 10.5281/zenodo.2586088 + 2019 + Doutriaux, C., Nadeau, D., +Wittenburg, S., Lipsa, D., Muryanto, L., Chaudhary, A., & Williams, +D. N. (2019). CDAT/cdat: CDAT 8.1 (Version v8.1). Zenodo. +https://doi.org/10.5281/zenodo.2586088 + + + Cf_xarray + Cherian + 10.5281/zenodo.10038784 + 2023 + Cherian, D., Almansi, M., Bourgault, +P., Thyng, K., Thielen, J., Magin, J., Aoun, A., Buntemeyer, L., +Caneill, R., Davis, L., Fernandes, F., Hauser, M., Heerdegen, A., Kent, +J., Mankoff, K., Müller, S., Schupfner, M., Vo, T., & Haëck, C. +(2023). Cf_xarray (Version v0.8.5). Zenodo. +https://doi.org/10.5281/zenodo.10038784 + + + E3SM-project/e3sm_diags: +v2.9.0 + Zhang + 10.5281/zenodo.8339034 + 2023 + Zhang, J. C., Shaheen, Z., Vo, T., +Forsyth, R., Golaz, Asay-Davis, X., Mahfouz, N., Bradley, A. M., & +Doutriaux, C. (2023). E3SM-project/e3sm_diags: v2.9.0 (Version v2.9.0). +Zenodo. https://doi.org/10.5281/zenodo.8339034 + + + E3SM-unified: A metapackage for a unified +anaconda environment for analyzing results from the energy exascale +earth system model (E3SM) + Asay-Davis + 2023 + Asay-Davis, X. (2023). E3SM-unified: +A metapackage for a unified anaconda environment for analyzing results +from the energy exascale earth system model (E3SM) (Version v1.9.1). +GitHub. +https://github.com/E3SM-Project/e3sm-unified + + + PCMDI/pcmdi_metrics: PMP version +3.1.2 + Lee + 10.5281/zenodo.10236521 + 2023 + Lee, J., Gleckler, P., Ordonez, A., +Ahn, M.-S., Ullrich, P., Vo, T., Boutte, J., Doutriaux, C., Durack, P., +Shaheen, Z., Muryanto, L., Painter, J., & Krasting, J. (2023). +PCMDI/pcmdi_metrics: PMP version 3.1.2 (Version v3.1.2). Zenodo. +https://doi.org/10.5281/zenodo.10236521 + + + Pangeo-data/xESMF: v0.8.2 + Zhuang + 10.5281/zenodo.8356796 + 2023 + Zhuang, J., Dussin, R., Huard, D., +Bourgault, P., Banihirwe, A., Raynaud, S., Malevich, B., Schupfner, M., +Filipe, Levang, S., Gauthier, C., Jüling, A., Almansi, M., +Richardscottoz, Rondeaug, Rasp, S., Smith, T. J., Stachelek, J., Plough, +M., … Li, X. (2023). Pangeo-data/xESMF: v0.8.2 (Version v0.8.2). Zenodo. +https://doi.org/10.5281/zenodo.8356796 + + + Xgcm + Abernathey + 10.5281/zenodo.7348619 + 2022 + Abernathey, R. P., Busecke, J. J. M., +Smith, T. A., Deauna, J. D., Banihirwe, A., Nicholas, T., Fernandes, F., +James, B., Dussin, R., Cherian, D. A., Caneill, R., Sinha, A., Uieda, +L., Rath, W., Balwada, D., Constantinou, N. C., Ponte, A., Zhou, Y., +Uchida, T., & Thielen, J. (2022). Xgcm (Version v0.8.1). Zenodo. +https://doi.org/10.5281/zenodo.7348619 + + + + + + diff --git a/joss.06426/10.21105.joss.06426.pdf b/joss.06426/10.21105.joss.06426.pdf new file mode 100644 index 0000000000..68c216f8bc Binary files /dev/null and b/joss.06426/10.21105.joss.06426.pdf differ diff --git a/joss.06426/paper.jats/10.21105.joss.06426.jats b/joss.06426/paper.jats/10.21105.joss.06426.jats new file mode 100644 index 0000000000..7994901f54 --- /dev/null +++ b/joss.06426/paper.jats/10.21105.joss.06426.jats @@ -0,0 +1,863 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6426 +10.21105/joss.06426 + +xCDAT: A Python Package for Simple and Robust Analysis of +Climate Data + + + +https://orcid.org/0000-0002-2461-0191 + +Vo +Tom + + + + +https://orcid.org/0000-0002-0390-238X + +Po-Chedley +Stephen + + + + +https://orcid.org/0009-0009-3996-3772 + +Boutte +Jason + + + + +https://orcid.org/0000-0002-0016-7199 + +Lee +Jiwoo + + + + +https://orcid.org/0000-0002-9632-0716 + +Zhang +Chengzhu + + + + + +Lawrence Livermore National Lab, Livermore, +USA + + + + +24 +6 +2024 + +9 +98 +6426 + +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 +python +xarray +climate science +climate research +climate data +climate data analysis + + + + + + Summary +

xCDAT (Xarray Climate Data Analysis Tools) is an open-source Python + package that extends Xarray + (Hoyer + & Hamman, 2017) for climate data analysis on structured + grids. xCDAT streamlines analysis of climate data by exposing common + climate analysis operations through a set of straightforward APIs. + Some of xCDAT’s key features include spatial averaging, temporal + averaging, and regridding. These features are inspired by the + Community Data Analysis Tools + (CDAT) + library + (Dean + N. Williams et al., 2009) + (D. + N. Williams, 2014) + (Doutriaux + et al., 2019) and leverage powerful packages in the + Xarray + ecosystem including + xESMF + (Zhuang + et al., 2023), + xgcm + (Abernathey + et al., 2022), and + CF + xarray + (Cherian + et al., 2023). To ensure general compatibility across various + climate models, xCDAT operates on datasets that are compliant with the + Climate and Forecast (CF) metadata conventions + (Hassell + et al., 2017).

+ + + Statement of Need +

Analysis of climate data frequently requires a number of core + operations, including reading and writing of netCDF files, horizontal + and vertical regridding, and spatial and temporal averaging. While + many individual software packages address these needs in a variety of + computational languages, CDAT stands out because it provides these + essential operations via open-source, interoperable Python packages. + Since CDAT’s inception, the volume of climate data has grown + substantially as a result of both a larger pool of data products and + increasing spatiotemporal resolution of model and observational data. + Larger data stores are important for advancing geoscientific + understanding, but also require increasingly performant software and + hardware. These factors have sparked a need for new analysis software + that offers core geospatial analysis functionalities capable of + efficiently handling large datasets while using modern technologies + and standardized software engineering principles.

+

xCDAT addresses this need by combining the power of Xarray with + meticulously developed geospatial analysis features inspired by CDAT. + Xarray is the foundation of xCDAT because of its widespread adoption, + technological maturity, and ability to handle large datasets with + parallel computing via Dask. Xarray is also interoperable with the + scientific Python ecosystem (e.g., + NumPy, + pandas, + Matplotlib), + which greatly benefits users who need to use additional analysis + tools. Since Xarray is designed as a general-purpose library, xCDAT + fills in domain specific gaps by providing features to serve the + climate science community (refer to + Key + Features).

+

Performance is one fundamental driver in how xCDAT is designed, + especially with large datasets. xCDAT conveniently inherits Xarray’s + support for parallel computing with Dask + (Dask-Development-Team, + 2016). Parallel computing with Dask enables users to take + advantage of compute resources through multithreading or + multiprocessing. To use Dask’s default multithreading scheduler, users + only need to open and chunk datasets in Xarray before calling xCDAT + APIs. xCDAT’s seamless support for parallel computing enables users to + run large-scale computations with minimal effort. If users require + more resources, they can also configure and use a local Dask cluster + to meet resource-intensive computational needs. Figure 1 shows xCDAT’s + significant performance advantage over CDAT for global spatial + averaging on datasets of varying sizes.

+ +

A performance benchmark for global spatial averaging + computations using CDAT (serial only) and xCDAT (serial and parallel + with Dask distributed scheduler). xCDAT outperforms CDAT by a wide + margin for the 7 GB and 12 GB datasets. Runtimes could not be + captured for CDAT with datasets >= 22 GB and xCDAT serial for the + 105 GB dataset due to memory allocation errors. The performance + benchmark setup and scripts are available in the + xcdat-validation + repo. Disclaimer: Performance will vary depending + on hardware, dataset shapes/sizes, and how Dask and chunking schemes + are configured. There are also some cases where selecting a regional + averaging domain (e.g., Niño 3.4) can lead to CDAT outperforming + xCDAT. +

+ + +

xCDAT’s intentional design emphasizes software sustainability and + reproducible science. It aims to make analysis code reusable, + readable, and less-error prone by abstracting common Xarray + boilerplate logic into simple and configurable APIs. xCDAT extends + Xarray by using + accessor + classes that operate directly on Xarray Dataset objects. + xCDAT is rigorously tested using real-world datasets and maintains + 100% unit test coverage (at the time this paper was written). To + demonstrate the value in xCDAT’s API design, Figure 2 compares code to + calculate annual averages for global climatological anomalies using + Xarray against xCDAT. xCDAT requires fewer lines of code and supports + further user options (e.g., regional or seasonal averages, not shown). + Figure 2 shows the plots for the results produced by xCDAT.

+ +

A comparison of the code to calculate annual averages + for global climatological anomalies in A) Xarray and B) xCDAT. xCDAT + abstracts most of the Xarray boilerplate logic for calculating + weights and grouping data by specific time frequencies, leading to + code that is more readable, maintainable, and flexible. The results + from both sets of code are within machine precision. +

+ + + +

A) Monthly surface skin temperature anomalies for + September 1850. B) Monthly (gray) and annual (black) global mean + surface skin temperature anomaly values. Temperature data is from an + E3SMv2 climate model + (Golaz + et al., 2022) simulation over the historical period (1850 – + 2014). +

+ + +

xCDAT’s mission is to provide a maintainable and extensible package + that serves the needs of the climate community in the long-term. xCDAT + is a community-driven project and the development team encourages all + who are interested to get involved through the + GitHub + repository.

+ + + Key Features + + Extension of <monospace>xarray.open_dataset()</monospace> and + <monospace>xarray.open_mfdataset()</monospace> with post-processing + options +

xCDAT extends xarray.open_dataset() and + xarray.open_mfdataset() with additional + post-processing operations to support climate data analysis. These + APIs can generate missing coordinate bounds for the X, Y, T, and/or + Z axes and lazily decode time coordinates represented by + cftime + (more + info). Other functionality includes re-centering time + coordinates between time bounds and converting the longitudinal axis + orientation between [0, 360) and [-180, 180).

+ + + Robust interpretation of CF metadata +

xCDAT uses + CF + xarray to interpret CF metadata present in datasets, + enabling xCDAT to operate generally across model and observational + datasets that are CF-compliant. This feature enables xCDAT to + generate missing coordinate bounds, recognize the coordinates and + coordinate bounds to use for computational operations, and lazily + decode time coordinates based on the CF calendar attribute.

+ + + Temporal averaging +

xCDAT’s temporal averaging API utilizes Xarray and Pandas. It + includes features for calculating time series averages + (single-snapshot), grouped time series averages (e.g., seasonal or + annual averages), climatologies, and departures. Averages can be + weighted (default) or unweighted. There are optional configurations + for seasonal grouping including how to group the month of December + (DJF or JFD) and defining custom seasons to group by.

+ + + Geospatial weighted averaging +

xCDAT’s geospatial weighted averaging supports rectilinear grids + with an option to compute averages over a regional domain (e.g., + tropical region, Niño 3.4 region).

+ + + Horizontal structured regridding +

xCDAT makes use of + xESMF + for horizontal regridding capabilities. It simplifies and extends + the xESMF horizontal regridding API by generating missing bounds and + ensuring bounds and metadata are preserved in the output dataset. + xCDAT also offers a Python implementation of + regrid2 + for handling cartesian latitude by longitude grids.

+ + + Vertical structured regridding +

xCDAT makes use of + xgcm + for vertical regridding capabilities. It simplifies and extends the + xgcm vertical regridding API by automatically attempting to + determine the grid point position relative to the bounds, + transposing the output data to match the dimensional order of the + input data, and ensuring bounds and metadata are preserved in the + output dataset.

+ + + + Documentation & Case Studies +

The xCDAT + documentation + includes the + public + API list and a Jupyter Notebook + Gallery + that demonstrates real-world applications of xCDAT:

+ + +

A + Gentle Introduction to xCDAT (Xarray Climate Data Analysis + Tools)

+ + +

General + Dataset Utilities

+ + +

Calculate + Geospatial Weighted Averages from Monthly Time + Series

+ + +

Calculate + Time Averages from Time Series Data

+ + +

Calculating + Climatology and Departures from Time Series Data

+ + +

Horizontal + Regridding

+ + +

Vertical + Regridding

+ + + + + Distribution +

xCDAT is available for Linux, MacOS, and Windows via the + conda-forge channel on + Anaconda. + The + GitHub + Repository is where we host all development activity. xCDAT + is released under the Apache 2-0 license.

+ + + Projects using xCDAT +

xCDAT is actively being integrated as a core component of the + Program + for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics + Package + (Jiwoo + Lee et al., 2023) + (J. + Lee et al., 2023) and the + Energy + Exascale Earth System Model (E3SM) Diagnostics Package + (C. + Zhang et al., 2022) + (J. + C. Zhang et al., 2023). xCDAT is also included in the + E3SM + Unified Anaconda Environment + (Asay-Davis, + 2023) deployed on numerous U.S Department of Energy + supercomputers to run E3SM software tools. Members of the development + team are also active users of xCDAT and apply the software to advance + their own climate research + (Po-Chedley + et al., 2022).

+ + + Acknowledgements +

xCDAT is jointly developed by scientists and developers at Lawrence + Livermore National Laboratory + (LLNL) + from the Energy Exascale Earth System Model + (E3SM) + Project and Program for Climate Model Diagnosis and Intercomparison + (PCMDI). + The work is performed for the E3SM project, which is sponsored by + Earth System Model Development + (ESMD) + program, and the Simplifying ESM Analysis Through Standards + (SEATS) + project, which is sponsored by the Regional and Global Model Analysis + (RGMA) + program. ESMD and RGMA are programs for the Earth and Environmental + Systems Sciences Division + (EESSD) + in the Office of Biological and Environmental Research + (BER) + within the + Department + of Energy’s + Office + of Science. This work is performed under the auspices of + the U.S. Department of Energy by LLNL under Contract + No. DE-AC52-07NA27344.

+

Thank you to all of the xCDAT contributors and users including Rob + Jacob, Ana Ordonez, Mark Zelinka, Christopher Terai, Min-Seop Ahn, + Celine Bonfils, Jean-Yves Peterschmitt, Olivier Marti, Andrew + Manaster, and Andrew Friedman. We also give a special thanks to Karl + Taylor, Peter Gleckler, Paul Durack, and Chris Golaz who all have + provided valuable knowledge and guidance throughout the course of this + project.

+ + + + + + + + + GolazJean-Christophe + Van RoekelLuke P. + ZhengXue + RobertsAndrew F. + WolfeJonathan D. + LinWuyin + BradleyAndrew M. + TangQi + MaltrudMathew E. + ForsythRyan M. + ZhangChengzhu + ZhouTian + ZhangKai + ZenderCharles S. + WuMingxuan + WangHailong + TurnerAdrian K. + SinghBalwinder + RichterJadwiga H. + QinYi + PetersenMark R. + MametjanovAzamat + MaPo-Lun + LarsonVincent E. + KrishnaJayesh + KeenNoel D. + JefferyNicole + HunkeElizabeth C. + HannahWalter M. + GubaOksana + GriffinBrian M. + FengYan + EngwirdaDarren + Di VittorioAlan V. + DangCheng + ConlonLeAnn M. + ChenChih-Chieh-Jack + BrunkeMichael A. + BishtGautam + BenedictJames J. + Asay-DavisXylar S. + ZhangYuying + ZhangMeng + ZengXubin + XieShaocheng + WolframPhillip J. + VoTom + VenezianiMilena + TesfaTeklu K. + SreepathiSarat + SalingerAndrew G. + Reeves EyreJ. E. Jack + PratherMichael J. + MahajanSalil + LiQing + JonesPhilip W. + JacobRobert L. + HueblerGunther W. + HuangXianglei + HillmanBenjamin R. + HarropBryce E. + FoucarJames G. + FangYilin + ComeauDarin S. + CaldwellPeter M. + BartolettiTony + BalaguruKarthik + TaylorMark A. + McCoyRenata B. + LeungL. Ruby + BaderDavid C. + + The DOE E3SM model version 2: Overview of the physical model and initial model evaluation + Journal of Advances in Modeling Earth Systems + 2022 + 14 + 12 + https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022MS003156 + 10.1029/2022MS003156 + e2022MS003156 + + + + + + + HassellD. + GregoryJ. + BlowerJ. + LawrenceB. N. + TaylorK. E. + + A data model of the climate and forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1) + Geoscientific Model Development + 2017 + 10 + 12 + https://gmd.copernicus.org/articles/10/4619/2017/ + 10.5194/gmd-10-4619-2017 + 4619 + 4646 + + + + + + HoyerStephan + HammanJoseph J. + + Xarray: N-D labeled Arrays and Datasets in Python + Journal of Open Research Software + 201704 + 20190702 + 5 + 2049-9647 + http://openresearchsoftware.metajnl.com/articles/10.5334/jors.148/ + 10.5334/jors.148 + 10 + + + + + + + LeeJ. + GlecklerP. J. + AhnM.-S. + OrdonezA. + UllrichP. A. + SperberK. R. + TaylorK. E. + PlantonY. Y. + GuilyardiE. + DurackP. + BonfilsC. + ZelinkaM. D. + ChaoL.-W. + DongB. + DoutriauxC. + ZhangC. + VoT. + BoutteJ. + WehnerM. F. + PendergrassA. G. + KimD. + XueZ. + WittenbergA. 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