Update paper citation

Author: tomvothecoder

docs/papers/paper.bib

@@ -141,7 +141,7 @@ @article{Zhang:2022
    doi        = {10.5194/gmd-15-9031-2022}
 }
 
-@manual{Dask Development Team:2016,
+@manual{dask:2016,
    title      = {Dask: Library for dynamic task scheduling},
    author     = {Dask Development Team},
    year       = {2016},

docs/papers/paper.md

@@ -32,7 +32,7 @@ bibliography: paper.bib
 
 # 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 and weather data by exposing common climate and weather 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 (Williams et al., 2009; Williams, 2014; Doutriaux et al., 2019) and leverage powerful packages in the [Xarray](https://docs.xarray.dev/en/stable/) ecosystem including [xESMF](https://github.com/pangeo-data/xESMF) (Zhuang et al., 2023), [xgcm](https://xgcm.readthedocs.io/en/latest/) (Abernathey et al., 2022), and [CF xarray](https://cf-xarray.readthedocs.io/en/latest/) (Cherian et al., 2023). To ensure general compatibility across various climate models, xCDAT operates on datasets compliant with the Climate and Forecast (CF) metadata conventions (Hassell et al, 2017).
+xCDAT (Xarray Climate Data Analysis Tools) is an open-source Python package that extends Xarray [@Hoyer:2017] for climate data analysis on structured grids. xCDAT streamlines analysis of climate and weather data by exposing common climate and weather 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 [@Williams:2009] [@Williams:2017] [@Doutriaux:2017] and leverage powerful packages in the [Xarray](https://docs.xarray.dev/en/stable/) ecosystem including [xESMF](https://github.com/pangeo-data/xESMF) [@xesmf], [xgcm](https://xgcm.readthedocs.io/en/latest/) [@xgcm], and [CF xarray](https://cf-xarray.readthedocs.io/en/latest/) [@cf-xarray]. To ensure general compatibility across various climate models, xCDAT operates on datasets compliant with the Climate and Forecast (CF) metadata conventions [@Hassell:2017].
 
 # Statement of need
 
@@ -50,7 +50,7 @@ xCDAT's intentional design emphasizes software sustainability and reproducible s
 
 **Figure 2:** 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 simulation over the historical period (1850 – 2014).
 
-Performance is another fundamental driver in how xCDAT is designed, especially with large datasets. xCDAT conveniently inherits Xarray's support for parallel computing with Dask (Team, D. D.). [Parallel computing with Dask](https://docs.xarray.dev/en/stable/user-guide/dask.html) 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 3 shows xCDAT's significant performance advantage over CDAT for spatial averaging on datasets of varying sizes.
+Performance is another fundamental driver in how xCDAT is designed, especially with large datasets. xCDAT conveniently inherits Xarray's support for parallel computing with Dask [@dask:2016]. [Parallel computing with Dask](https://docs.xarray.dev/en/stable/user-guide/dask.html) 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 3 shows xCDAT's significant performance advantage over CDAT for spatial averaging on datasets of varying sizes.
 
 ![Figure 3](figures/fig3.png)
 
@@ -102,7 +102,7 @@ xCDAT is available for Linux, MacOS, and Windows via the conda-forge channel on
 
 # Projects using xCDAT
 
-xCDAT is actively being integrated as a core component of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (Lee et al. 2023) and the Energy Exascale Earth System Model (E3SM) Diagnostics Package (Zhang et al. 2022; 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).
+xCDAT is actively being integrated as a core component of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package [@pcmdi-metrics] [@Lee:2023] and the Energy Exascale Earth System Model (E3SM) Diagnostics Package [@Zhang:2022] [@e3sm-diags]. xCDAT is also included in the E3SM Unified Anaconda Environment [@e3sm-unified] 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:2022].
 
 # Acknowledgements