references.bib

@misc{AppliedSpatialData,
  title = {Applied {{Spatial Data Analysis}} with {{R}}},
  journal = {Applied Spatial Data Analysis with R},
  urldate = {2023-01-18},
  abstract = {Web site with book resources (data, scripts)},
  howpublished = {https://asdar-book.org/},
  langid = {american},
  file = {/home/phanaur/Zotero/storage/QTA4DP5E/asdar-book.org.html}
}

@misc{ar5,
  title = {{{AR5 Regions}}},
  shorttitle = {{{AR5}}},
  author = {AR5, ipcc},
  journal = {Data Distribution Centre: AR5 Reference Regions},
  urldate = {2022-07-28},
  howpublished = {https://www.ipcc-data.org/guidelines/pages/ar5\_regions.html},
  langid = {english},
  file = {/home/phanaur/Zotero/storage/V67SCNDE/ar5_regions.html}
}

@misc{ar6,
  title = {{{AR6 Regions}}},
  journal = {Scientific Regions: AR6 Regions},
  urldate = {2022-07-28},
  howpublished = {https://regionmask.readthedocs.io/en/stable/defined\_scientific.html},
  langid = {english}
}

@misc{atlas-github,
  title = {Repository Supporting the Implementation of {{FAIR}} Principles in the {{IPCC-WGI Atlas}}},
  year = {2022},
  month = nov,
  urldate = {2022-11-10},
  abstract = {Repository supporting the implementation of FAIR principles in the IPCC-WGI Atlas},
  howpublished = {IPCC-WG1}
}

@incollection{atlas-ipcc,
  title = {Atlas},
  booktitle = {Climate {{Change}} 2021: {{The Physical Science Basis}}. {{Contribution}} of {{Working Group I}} to the {{Sixth Assessment Report}} of the {{Intergovernmental Panel}} on {{Climate Change}}},
  author = {Guti{\'e}rrez, Jos{\'e} Manuel and Jones, Richard G. and Narisma, Gemma Teresa and Muniz Alves, Lincoln and Amjad, Muhammad and Gorodetskaya, Irina V. and Grose, Michael and Klutse, Nana Ama Browne and Krakovska, Svitlana and Li, Jian and {Mart{\'i}nez-Castro}, Daniel and Mearns, Linda O. and Mernild, Sebastian H. and {Ngo-Duc}, Thanh and {van den Hurk}, Bart and Yoon, Jin-Ho},
  editor = {{Masson-Delmotte}, Val{\'e}rie and Zhai, Panmao and Pirani, Anna and Connors, Sarah L. and P{\'e}an, Clotilde and Berger, Sophie and Caud, Nada and Chen, Yang and Goldfarb, Leah and Gomis, Melissa I. and Huang, Mengtian and Leitzell, Katherine and Lonnoy, Elisabeth and Matthews, J. B. Robin and Maycock, Thomas K. and Waterfield, Tim and Yelek{\c c}i, {\"O}zge and Yu, Rong and Zhou, Botao},
  year = {2021},
  pages = {1927--2058},
  publisher = {{Cambridge University Press}},
  address = {{Cambridge, United Kingdom and New York, NY, USA}},
  doi = {10.1017/9781009157896.001},
  urldate = {2022-08-24}
}

@misc{atlas-ipcc-web,
  title = {{{IPCC AR6-WGI Atlas}}},
  shorttitle = {{{WGI Atlas}}},
  journal = {IPCC WGI Interactive Atlas},
  urldate = {2022-07-28},
  abstract = {IPCC Assestment Report 6 Atlas},
  howpublished = {https://interactive-atlas.ipcc.ch/atlas},
  langid = {english},
  file = {/home/phanaur/Zotero/storage/758J6G6N/interactive-atlas.ipcc.ch.html}
}

@misc{ATLASDatasets,
  title = {{{ATLAS Datasets}}},
  urldate = {2022-11-03},
  howpublished = {https://raw.githack.com/IPCC-WG1/Atlas/devel/data-sources/Dataset\_DOIs.html},
  file = {/home/phanaur/Zotero/storage/NAEKXWSB/Dataset_DOIs.html}
}

@misc{AtlasGlobalRegional,
  title = {Atlas of {{Global}} and {{Regional Climate Projections}} \textemdash{} {{IPCC}}},
  urldate = {2022-11-03},
  howpublished = {https://www.ipcc.ch/report/ar5/wg1/atlas-of-global-and-regional-climate-projections/}
}

@misc{AtlasReferenceregionsMain,
  title = {Atlas/Reference-Regions at Main {$\cdot$} {{IPCC-WG1}}/{{Atlas}}},
  urldate = {2022-11-03},
  howpublished = {https://github.com/IPCC-WG1/Atlas/tree/main/reference-regions}
}

@misc{binder-web,
  title = {Binder},
  urldate = {2022-08-24},
  howpublished = {https://mybinder.org/},
  file = {/home/phanaur/Zotero/storage/RJLEB64A/mybinder.org.html}
}

@article{CDOguide,
  title = {{{CDO User Guide}}},
  author = {Schulzweida, Uwe},
  year = {2022},
  month = oct,
  publisher = {{Zenodo}},
  doi = {10.5281/zenodo.7112925},
  urldate = {2022-11-10},
  abstract = {The Climate Data Operator (CDO) software is a collection of operators for standard processing of climate and forecast model data. The operators include simple statistical and arithmetic functions, data selection, subsampling tools, and spatial interpolation.},
  langid = {english},
  keywords = {analysis,CDO,Climate Data Operator,GRIB,interpolation,NetCDF,processing,remapping}
}

@misc{christensenCORDEXArchiveDesign,
  title = {{{CORDEX Archive Design}}},
  author = {Christensen, O.B. and Gutowski, W.J. and Nikulin, G. and Legutke, S.}
}

@article{christensenSummaryPRUDENCEModel2007,
  title = {A Summary of the {{PRUDENCE}} Model Projections of Changes in {{European}} Climate by the End of This Century},
  author = {Christensen, Jens Hesselbjerg and Christensen, Ole B{\o}ssing},
  year = {2007},
  month = may,
  journal = {Climatic Change},
  volume = {81},
  number = {S1},
  pages = {7--30},
  issn = {0165-0009, 1573-1480},
  doi = {10.1007/s10584-006-9210-7},
  urldate = {2022-07-28},
  langid = {english}
}

@misc{CIMPPhaseCIMP5,
  title = {{{CIMP Phase}} 5 ({{CIMP5}})},
  urldate = {2022-07-28},
  howpublished = {https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip5},
  langid = {english}
}

@misc{climate4r,
  title = {{{SantanderMetGroup}}/{{climate4R}}},
  year = {2022},
  month = jul,
  urldate = {2022-07-28},
  abstract = {An R Framework for Climate Data Access and Post-processing},
  howpublished = {https://github.com/SantanderMetGroup/climate4R}
}

@misc{Climate4RUDG2022,
  title = {{{climate4R}}.{{UDG}}},
  year = {2022},
  month = jun,
  urldate = {2022-11-10},
  abstract = {Harmonized data access via UDG},
  copyright = {GPL-3.0},
  howpublished = {Santander Meteorology Group (UC-CSIC)}
}

@misc{CMIP,
  title = {{{CMIP}}},
  urldate = {2022-11-03},
  howpublished = {https://www.wcrp-climate.org/wgcm-cmip},
  file = {/home/phanaur/Zotero/storage/9BQC3ZGS/wgcm-cmip.html}
}

@misc{cmip5,
  title = {{{CMIP5}}},
  urldate = {2022-08-24},
  howpublished = {https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip5},
  file = {/home/phanaur/Zotero/storage/X9YWC5NU/wgcm-cmip5.html}
}

@misc{cmip6,
  title = {{{CMIP6}}},
  urldate = {2022-08-24},
  howpublished = {https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6},
  file = {/home/phanaur/Zotero/storage/6CAUC8F7/wgcm-cmip6.html}
}

@misc{colorb,
  title = {R {{Color Brewer}}'s Palettes},
  author = {Holtz, Yan},
  urldate = {2023-01-18},
  abstract = {The RColorBrewer package offers several color palette for R. This post displays all of them to help you pick the right one.},
  howpublished = {https://www.r-graph-gallery.com/38-rcolorbrewers-palettes.html},
  langid = {english},
  file = {/home/phanaur/Zotero/storage/EJQQSWH5/38-rcolorbrewers-palettes.html}
}

@misc{conda,
  title = {Conda \textemdash{} {{Conda}} Documentation},
  journal = {Conda},
  urldate = {2022-07-28},
  howpublished = {https://docs.conda.io/en/latest/},
  langid = {english},
  file = {/home/phanaur/Zotero/storage/UNUZZRY3/latest.html}
}

@misc{cordex,
  title = {Cordex \textendash{} {{Coordinated Regional Climate Downscaling Experiment}}},
  urldate = {2022-07-28},
  howpublished = {https://cordex.org/},
  file = {/home/phanaur/Zotero/storage/YGM3HBHY/cordex.org.html}
}

@misc{CRANPackageRgdal,
  title = {{{CRAN}} - {{Package}} Rgdal},
  urldate = {2022-09-27},
  howpublished = {https://cran.r-project.org/web/packages/rgdal/index.html}
}

@article{cucchiWFDE5Bias,
  title = {{{WFDE5}}: Bias-Adjusted {{ERA5}} Reanalysis Data for Impact Studies},
  shorttitle = {{{WFDE5}}},
  author = {Cucchi, Marco and Weedon, Graham P. and Amici, Alessandro and Bellouin, Nicolas and Lange, Stefan and M{\"u}ller Schmied, Hannes and Hersbach, Hans and Buontempo, Carlo},
  year = {2020},
  month = sep,
  journal = {Earth System Science Data},
  volume = {12},
  number = {3},
  pages = {2097--2120},
  issn = {1866-3516},
  doi = {10.5194/essd-12-2097-2020},
  urldate = {2023-01-18},
  abstract = {Abstract. The WFDE5 dataset has been generated using the WATCH Forcing Data (WFD) methodology applied to surface meteorological variables from the ERA5 reanalysis. The WFDEI dataset had previously been generated by applying the WFD methodology to ERA-Interim. The WFDE5 is provided at 0.5{$\circ$} spatial resolution but has higher temporal resolution (hourly) compared to WFDEI (3-hourly). It also has higher spatial variability since it was generated by aggregation of the higher-resolution ERA5 rather than by interpolation of the lower-resolution ERA-Interim data. Evaluation against meteorological observations at 13 globally distributed FLUXNET2015 sites shows that, on average, WFDE5 has lower mean absolute error and higher correlation than WFDEI for all variables. Bias-adjusted monthly precipitation totals of WFDE5 result in more plausible global hydrological water balance components when analysed in an uncalibrated hydrological model (WaterGAP) than with the use of raw ERA5 data for model forcing. The dataset, which can be downloaded from https://doi.org/10.24381/cds.20d54e34 (C3S,~2020b), is distributed by the Copernicus Climate Change Service (C3S) through its Climate Data Store (CDS,~C3S,~2020a) and currently spans from the start of January 1979 to the end of 2018. The dataset has been produced using a number of CDS Toolbox applications, whose source code is available with the data \textendash{} allowing users to regenerate part of the dataset or apply the same approach to other data. Future updates are expected spanning from 1950 to the most recent year. A sample of the complete dataset, which covers the whole of the year 2016, is accessible without registration to the CDS at https://doi.org/10.21957/935p-cj60 (Cucchi et al.,~2020).},
  langid = {english}
}

@misc{DevelopingMapWorld,
  title = {Developing a Map of the World's Mountain Forests.},
  urldate = {2022-11-03},
  howpublished = {https://www.cabdirect.org/cabdirect/abstract/20000613977}
}

@misc{ERA5HourlyData,
  title = {{{ERA5}} Hourly Data on Single Levels from 1959 to Present},
  urldate = {2022-11-03},
  howpublished = {https://cds.climate.copernicus.eu/cdsapp\#!/dataset/reanalysis-era5-single-levels?tab=overview}
}

@misc{ESDOCErrataSearch,
  title = {{{ES-DOC}} - {{Errata}} - {{Search}}},
  urldate = {2022-11-03},
  howpublished = {https://errata.es-doc.org/static/index.html},
  file = {/home/phanaur/Zotero/storage/VLHFM2IZ/index.html}
}

@misc{ESSDAnthropogenicLand,
  title = {{{ESSD}} - {{Anthropogenic}} Land Use Estimates for the {{Holocene}} \textendash{} {{HYDE}} 3.2},
  urldate = {2022-11-03},
  howpublished = {https://essd.copernicus.org/articles/9/927/2017/}
}

@misc{ESSDWFDE5Biasadjusted,
  title = {{{ESSD}} - {{WFDE5}}: Bias-Adjusted {{ERA5}} Reanalysis Data for Impact Studies},
  urldate = {2022-09-27},
  howpublished = {https://essd.copernicus.org/articles/12/2097/2020/}
}

@article{eyringOverviewCoupledModel2016,
  title = {Overview of the {{Coupled Model Intercomparison Project Phase}} 6 ({{CMIP6}}) Experimental Design and Organization},
  author = {Eyring, Veronika and Bony, Sandrine and Meehl, Gerald A. and Senior, Catherine A. and Stevens, Bjorn and Stouffer, Ronald J. and Taylor, Karl E.},
  year = {2016},
  month = may,
  journal = {Geoscientific Model Development},
  volume = {9},
  number = {5},
  pages = {1937--1958},
  issn = {1991-9603},
  doi = {10.5194/gmd-9-1937-2016},
  urldate = {2022-08-24},
  abstract = {Abstract. By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1)~a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850\textendash near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2)~common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3)~an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions:  \textendash{} How does the Earth system respond to forcing? \textendash{} What are the origins and consequences of systematic model biases?  \textendash{} How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21~CMIP6-Endorsed MIPs.},
  langid = {english},
  file = {/home/phanaur/Zotero/storage/HWY237F9/Eyring et al. - 2016 - Overview of the Coupled Model Intercomparison Proj.pdf}
}

@article{fayGlobalOpenoceanBiomes2014,
  title = {Global Open-Ocean Biomes: Mean and Temporal Variability},
  shorttitle = {Global Open-Ocean Biomes},
  author = {Fay, A. R. and McKinley, G. A.},
  year = {2014},
  month = aug,
  journal = {Earth System Science Data},
  volume = {6},
  number = {2},
  pages = {273--284},
  publisher = {{Copernicus GmbH}},
  issn = {1866-3508},
  doi = {10.5194/essd-6-273-2014},
  urldate = {2022-11-03},
  abstract = {{$<$}p{$><$}strong class="journal-contentHeaderColor"{$>$}Abstract.{$<$}/strong{$>$} Large-scale studies of ocean biogeochemistry and carbon cycling have often partitioned the ocean into regions along lines of latitude and longitude despite the fact that spatially more complex boundaries would be closer to the true biogeography of the ocean. Herein, we define 17 open-ocean biomes classified from four observational data sets: sea surface temperature (SST), spring/summer chlorophyll \emph{a} concentrations (Chl \emph{a}), ice fraction, and maximum mixed layer depth (maxMLD) on a 1\textdegree{} \texttimes{} 1\textdegree{} grid (available at {$<$}a href="http://dx.doi.org/10.1594/PANGAEA.828650"{$>$}doi:10.1594/PANGAEA.828650{$<$}/a{$>$}). By considering interannual variability for each input, we create dynamic ocean biome boundaries that shift annually between 1998 and 2010. Additionally we create a core biome map, which includes only the grid cells that do not change biome assignment across the 13 years of the time-varying biomes. These biomes can be used in future studies to distinguish large-scale ocean regions based on biogeochemical function.{$<$}/p{$>$}},
  langid = {english},
  file = {/home/phanaur/Zotero/storage/J3MQ2ZQC/2014.html}
}

@misc{giorgiUncertaintiesRegionalClimate,
  title = {Uncertainties in Regional Climate Change Prediction: A Regional Analysis of Ensemble Simulations with the {{HADCM2}} Coupled {{AOGCM}} | {{SpringerLink}}},
  author = {Giorgi, F. and Francisco, R},
  urldate = {2022-11-03},
  howpublished = {https://link.springer.com/article/10.1007/PL00013733}
}

@misc{GMDRequirementsGlobal,
  title = {{{GMD}} - {{Requirements}} for a Global Data Infrastructure in Support of {{CMIP6}}},
  urldate = {2022-11-03},
  howpublished = {https://gmd.copernicus.org/articles/11/3659/2018/},
  file = {/home/phanaur/Zotero/storage/RY49TRI7/2018.html}
}

@misc{GMDStatisticalDownscaling,
  title = {{{GMD}} - {{Statistical}} Downscaling with the {{downscaleR}} Package (v3.1.0): Contribution to the {{VALUE}} Intercomparison Experiment},
  urldate = {2022-09-27},
  howpublished = {https://doi.org/10.5194/gmd-13-1711-2020}
}

@misc{GMDTrendpreservingBias,
  title = {{{GMD}} - {{Trend-preserving}} Bias Adjustment and Statistical Downscaling with {{ISIMIP3BASD}} (v1.0)},
  urldate = {2022-09-27},
  howpublished = {https://doi.org/10.5194/gmd-12-3055-2019}
}

@article{gregorComparativeAssessmentUncertainties2019,
  title = {A Comparative Assessment of the Uncertainties of Global Surface Ocean {{CO}}{\textsubscript{2}} Estimates Using a Machine-Learning Ensemble ({{CSIR-ML6}} Version 2019a) \textendash{} Have We Hit the Wall?},
  author = {Gregor, Luke and Lebehot, Alice D. and Kok, Schalk and Scheel Monteiro, Pedro M.},
  year = {2019},
  month = dec,
  journal = {Geoscientific Model Development},
  volume = {12},
  number = {12},
  pages = {5113--5136},
  publisher = {{Copernicus GmbH}},
  issn = {1991-959X},
  doi = {10.5194/gmd-12-5113-2019},
  urldate = {2022-11-03},
  abstract = {{$<$}p{$><$}strong class="journal-contentHeaderColor"{$>$}Abstract.{$<$}/strong{$>$} Over the last decade, advanced statistical inference and machine learning have been used to fill the gaps in sparse surface ocean CO\textsubscript{2} measurements (R\"odenbeck et al., 2015). The estimates from these methods have been used to constrain seasonal, interannual and decadal variability in sea\textendash air CO\textsubscript{2} fluxes and the drivers of these changes (Landsch\"utzer et al., 2015, 2016; Gregor et al., 2018). However, it is also becoming clear that these methods are converging towards a common bias and root mean square error (RMSE) boundary: ``the wall'', which suggests that \emph{p}CO\textsubscript{2} estimates are now limited by both data gaps and scale-sensitive observations. Here, we analyse this problem by introducing a new gap-filling method, an ensemble average of six machine-learning models (CSIR-ML6 version 2019a, Council for Scientific and Industrial Research \textendash{} Machine Learning ensemble with Six members), where each model is constructed with a two-step clustering-regression approach. The ensemble average is then statistically compared to well-established methods. The ensemble average, CSIR-ML6, has an RMSE of 17.16\&thinsp;\textmu atm and bias of 0.89\&thinsp;\textmu atm when compared to a test dataset kept separate from training procedures. However, when validating our estimates with independent datasets, we find that our method improves only incrementally on other gap-filling methods. We investigate the differences between the methods to understand the extent of the limitations of gap-filling estimates of \emph{p}CO\textsubscript{2}. We show that disagreement between methods in the South Atlantic, southeastern Pacific and parts of the Southern Ocean is too large to interpret the interannual variability with confidence. We conclude that improvements in surface ocean \emph{p}CO\textsubscript{2} estimates will likely be incremental with the optimisation of gap-filling methods by (1) the inclusion of additional clustering and regression variables (e.g. eddy kinetic energy), (2) increasing the sampling resolution and (3) successfully incorporating \emph{p}CO\textsubscript{2} estimates from alternate platforms (e.g. floats, gliders) into existing machine-learning approaches.{$<$}/p{$>$}},
  langid = {english},
  file = {/home/phanaur/Zotero/storage/EA6HBQSZ/2019.html}
}

@misc{hauserRegionmaskRegionmaskVersion2022,
  title = {Regionmask/Regionmask: Version 0.9.0},
  shorttitle = {Regionmask/Regionmask},
  author = {Hauser, Mathias and Spring, Aaron and Busecke, Julius and van Driel, Martin and Lorenz, Ruth and {readthedocs-assistant}},
  year = {2022},
  month = mar,
  doi = {10.5281/zenodo.6324240},
  urldate = {2022-11-10},
  abstract = {Version 0.9.0 contains some exiting improvements: overlapping regions and unstructured grids can now be masked correctly. Further, :py:class:Regions can now be round-tripped to geopandas.GeoDataFrame objects. The new version also adds PRUDENCE regions and a more stable handling of naturalearth regions. Many thanks to the contributors to the v0.9.0 release: Aaron Spring, Mathias Hauser, and Ruth Lorenz!},
  howpublished = {Zenodo},
  file = {/home/phanaur/Zotero/storage/LZQ27PWF/6324240.html}
}

@article{hoyerXarrayNDLabeled2017,
  title = {Xarray: {{N-D}} Labeled {{Arrays}} and {{Datasets}} in {{Python}}},
  shorttitle = {Xarray},
  author = {Hoyer, Stephan and Hamman, Joe},
  year = {2017},
  month = apr,
  journal = {Journal of Open Research Software},
  volume = {5},
  number = {1},
  pages = {10},
  publisher = {{Ubiquity Press}},
  issn = {2049-9647},
  doi = {10.5334/jors.148},
  urldate = {2022-11-10},
  abstract = {Article: xarray: N-D labeled Arrays and Datasets in Python},
  copyright = {Authors who publish with this journal agree to the following terms:    Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a  Creative Commons Attribution License  that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.  Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.  Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See  The Effect of Open Access ).  All third-party images reproduced on this journal are shared under Educational Fair Use. For more information on  Educational Fair Use , please see  this useful checklist prepared by Columbia University Libraries .   All copyright  of third-party content posted here for research purposes belongs to its original owners.  Unless otherwise stated all references to characters and comic art presented on this journal are \textcopyright, \textregistered{} or \texttrademark{} of their respective owners. No challenge to any owner's rights is intended or should be inferred.},
  langid = {english},
  file = {/home/phanaur/Zotero/storage/WYQCGDS2/jors.148.html}
}

@misc{InicioServicioClimatico,
  title = {Inicio | {{Servicio Clim\'atico}} de {{Datos}}},
  urldate = {2022-11-10},
  howpublished = {https://www.scds.es/es/}
}

@misc{InputDataSet,
  title = {Input Data Set: {{Historical}}, Gridded Population},
  urldate = {2022-11-03},
  howpublished = {https://www.isimip.org/gettingstarted/details/31/}
}

@misc{IPCCWG1PMIPAR6,
  title = {{{IPCC-WG1}}/{{PMIP}}\_for\_{{AR6}}\_{{Interactive}}\_{{Atlas}}},
  urldate = {2022-11-03},
  howpublished = {https://github.com/IPCC-WG1/PMIP\_for\_AR6\_Interactive\_Atlas}
}

@misc{IPCCWGIInteractive,
  title = {{{IPCC WGI Interactive Atlas}}},
  urldate = {2022-11-03},
  howpublished = {https://interactive-atlas.ipcc.ch/regional-information\#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}
}

@misc{irkernel,
  title = {Installation {$\cdot$} {{IRkernel}}},
  journal = {IRkernel},
  urldate = {2022-07-28},
  howpublished = {https://irkernel.github.io/installation/},
  langid = {english},
  file = {/home/phanaur/Zotero/storage/PUL5UXFP/installation.html}
}

@article{iturbide_update_2020,
  title = {An Update of {{IPCC}} Climate Reference Regions for Subcontinental Analysis of Climate Model Data: Definition and Aggregated Datasets},
  shorttitle = {An Update of {{IPCC}} Climate Reference Regions for Subcontinental Analysis of Climate Model Data},
  author = {Iturbide, Maialen and Guti{\'e}rrez, Jos{\'e} M. and Alves, Lincoln M. and Bedia, Joaqu{\'i}n and {Cerezo-Mota}, Ruth and Cimadevilla, Ezequiel and Cofi{\~n}o, Antonio S. and Di Luca, Alejandro and Faria, Sergio Henrique and Gorodetskaya, Irina V. and Hauser, Mathias and Herrera, Sixto and Hennessy, Kevin and Hewitt, Helene T. and Jones, Richard G. and Krakovska, Svitlana and Manzanas, Rodrigo and {Mart{\'i}nez-Castro}, Daniel and Narisma, Gemma T. and Nurhati, Intan S. and Pinto, Izidine and Seneviratne, Sonia I. and {van den Hurk}, Bart and Vera, Carolina S.},
  year = {2020},
  month = nov,
  journal = {Earth System Science Data},
  volume = {12},
  number = {4},
  pages = {2959--2970},
  publisher = {{Copernicus GmbH}},
  issn = {1866-3508},
  doi = {10.5194/essd-12-2959-2020},
  urldate = {2022-07-28},
  abstract = {{$<$}p{$><$}strong class="journal-contentHeaderColor"{$>$}Abstract.{$<$}/strong{$>$} Several sets of reference regions have been used in the literature for the regional synthesis of observed and modelled climate and climate change information. A popular example is the series of reference regions used in the Intergovernmental Panel on Climate Change (IPCC) Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Adaptation (SREX). The SREX regions were slightly modified for the Fifth Assessment Report of the IPCC and used for reporting subcontinental observed and projected changes over a reduced number (33) of climatologically consistent regions encompassing a representative number of grid boxes. These regions are intended to allow analysis of atmospheric data over broad land or ocean regions and have been used as the basis for several popular spatially aggregated datasets, such as the Seasonal Mean Temperature and Precipitation in IPCC Regions for CMIP5 dataset.{$<$}/p{$>$} {$<$}p{$>$}We present an updated version of the reference regions for the analysis of new observed and simulated datasets (including CMIP6) which offer an opportunity for refinement due to the higher atmospheric model resolution. As a result, the number of land and ocean regions is increased to 46 and 15, respectively, better representing consistent regional climate features. The paper describes the rationale for the definition of the new regions and analyses their homogeneity. The regions are defined as polygons and are provided as coordinates and a shapefile together with companion R and Python notebooks to illustrate their use in practical problems (e.g. calculating regional averages). We also describe the generation of a new dataset with monthly temperature and precipitation, spatially aggregated in the new regions, currently for CMIP5 and CMIP6, to be extended to other datasets in the future (including observations). The use of these reference regions, dataset and code is illustrated through a worked example using scatter plots to offer guidance on the likely range of future climate change at the scale of the reference regions. The regions, datasets and code (R and Python notebooks) are freely available at the ATLAS GitHub repository: https://github.com/SantanderMetGroup/ATLAS (last access: 24 August 2020), {$<$}a href="https://doi.org/10.5281/zenodo.3998463"{$>$}https://doi.org/10.5281/zenodo.3998463{$<$}/a{$>$} (Iturbide et al., 2020).{$<$}/p{$>$}},
  langid = {english},
  file = {/home/phanaur/Zotero/storage/64LP6UCH/2020.html}
}

@article{jonesSpatiallyExplicitGlobal2016,
  title = {Spatially Explicit Global Population Scenarios Consistent with the {{Shared Socioeconomic Pathways}}},
  author = {Jones, B. and O'Neill, B. C.},
  year = {2016},
  month = jul,
  journal = {Environmental Research Letters},
  volume = {11},
  number = {8},
  pages = {084003},
  publisher = {{IOP Publishing}},
  issn = {1748-9326},
  doi = {10.1088/1748-9326/11/8/084003},
  urldate = {2022-11-03},
  abstract = {The projected size and spatial distribution of the future population are important drivers of global change and key determinants of exposure and vulnerability to hazards. Spatial demographic projections are widely used as inputs to spatial projections of land use, energy use, and emissions, as well as to assessments of the impacts of extreme events, sea level rise, and other climate-related outcomes. To date, however, there are very few global-scale, spatially explicit population projections, and those that do exist are often based on simple scaling or trend extrapolation. Here we present a new set of global, spatially explicit population scenarios that are consistent with the new Shared Socioeconomic Pathways (SSPs) developed to facilitate global change research. We use a parameterized gravity-based downscaling model to produce projections of spatial population change that are quantitatively consistent with national population and urbanization projections for the SSPs and qualitatively consistent with assumptions in the SSP narratives regarding spatial development patterns. We show that the five SSPs lead to substantially different spatial population outcomes at the continental, national, and sub-national scale. In general, grid cell-level outcomes are most influenced by national-level population change, second by urbanization rate, and third by assumptions about the spatial style of development. However, the relative importance of these factors is a function of the magnitude of the projected change in total population and urbanization for each country and across SSPs. We also demonstrate variation in outcomes considering the example of population existing in a low-elevation coastal zone under alternative scenarios.},
  langid = {english}
}

@misc{jupyter,
  title = {Project {{Jupyter}}},
  shorttitle = {Jupyter},
  journal = {Jupyter},
  urldate = {2022-07-28},
  abstract = {The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media.},
  howpublished = {https://jupyter.org},
  langid = {english},
  file = {/home/phanaur/Zotero/storage/2AEX78KS/jupyter.org.html}
}

@misc{jupyter-lab,
  title = {The {{JupyterLab Interface}} \textemdash{} {{JupyterLab}} 3.4.4 Documentation},
  urldate = {2022-07-28},
  howpublished = {https://jupyterlab.readthedocs.io/en/stable/user/interface.html},
  langid = {english},
  file = {/home/phanaur/Zotero/storage/SVDRBL25/interface.html}
}

@misc{MajorRiverBasins,
  title = {Major {{River Basins}} of the {{World}} | {{Data Catalog}}},
  urldate = {2022-11-03},
  howpublished = {https://datacatalog.worldbank.org/search/dataset/0041426}
}

@misc{MathauseCmipWarming,
  title = {Mathause/Cmip\_warming\_levels: Year When a Certain Warming Level Was Reached in Cmip5 and Cmip6 Data},
  urldate = {2022-11-03},
  howpublished = {https://github.com/mathause/cmip\_warming\_levels},
  file = {/home/phanaur/Zotero/storage/TMF5PD5F/cmip_warming_levels.html}
}

@misc{metaclip,
  title = {{{METACLIP}}: {{Metadata}} for Climate Products},
  shorttitle = {{{METACLIP}}},
  author = {Bedia, Joaqu{\'i}n and Guti{\'e}rrez, Jos{\'e} Manuel and Herrera, Sixto and Iturbide, Maialen and San Mart{\'i}n, Daniel},
  journal = {METACLIP: Metadata for climate products},
  urldate = {2022-07-28},
  howpublished = {http://www.metaclip.org/},
  langid = {english},
  file = {/home/phanaur/Zotero/storage/8LL78VFL/www.metaclip.org.html}
}

@misc{metaclip-atlas,
  title = {Metaclip/Metaclipcc: {{METACLIP}} for the {{IPCC-AR6 Interactive Atlas}} Version 1.1.1 from {{GitHub}}},
  shorttitle = {Metaclip/Metaclipcc},
  urldate = {2022-07-28},
  abstract = {Extend the METACLIP Provenance Framework for the products to be delivered by the IPCC-AR6 Interactive Atlas.},
  howpublished = {https://rdrr.io/github/metaclip/metaclipcc/},
  langid = {english},
  file = {/home/phanaur/Zotero/storage/65GIYSC3/metaclipcc.html}
}

@misc{MetaclipccDatasetTable,
  title = {Metaclipcc/Dataset\_table.Csv at Master {$\cdot$} Metaclip/Metaclipcc},
  urldate = {2022-11-03},
  howpublished = {https://github.com/metaclip/metaclipcc/blob/master/inst/dataset\_table.csv}
}

@misc{NaturalEarthFree,
  title = {Natural {{Earth}} - {{Free}} Vector and Raster Map Data at 1:10m, 1:50m, and 1:110m Scales},
  shorttitle = {Natural {{Earth}} - {{Free}} Vector and Raster Map Data at 1},
  urldate = {2022-11-10},
  langid = {american},
  file = {/home/phanaur/Zotero/storage/C9QYMM4R/www.naturalearthdata.com.html}
}

@misc{neuwirthRColorBrewerColorBrewerPalettes2022,
  title = {{{RColorBrewer}}: {{ColorBrewer Palettes}}},
  shorttitle = {{{RColorBrewer}}},
  author = {Neuwirth, Erich},
  year = {2022},
  month = apr,
  urldate = {2023-01-18},
  abstract = {Provides color schemes for maps (and other graphics) designed by Cynthia Brewer as described at http://colorbrewer2.org.},
  copyright = {Apache License 2.0},
  keywords = {Spatial}
}

@article{nikulinEffectsDegreesGlobal2018,
  title = {The Effects of 1.5 and 2 Degrees of Global Warming on {{Africa}} in the {{CORDEX}} Ensemble},
  author = {Nikulin, Grigory and Lennard, Chris and Dosio, Alessandro and Kjellstr{\"o}m, Erik and Chen, Youmin and H{\"a}nsler, Andreas and Kupiainen, Marco and Laprise, Ren{\'e} and Mariotti, Laura and Maule, Cathrine Fox and van Meijgaard, Erik and Panitz, Hans-J{\"u}rgen and Scinocca, John F. and Somot, Samuel},
  year = {2018},
  month = may,
  journal = {Environmental Research Letters},
  volume = {13},
  number = {6},
  pages = {065003},
  publisher = {{IOP Publishing}},
  issn = {1748-9326},
  doi = {10.1088/1748-9326/aab1b1},
  urldate = {2022-11-03},
  langid = {english},
  file = {/home/phanaur/Zotero/storage/Y9KRKWIT/aab1b1.html}
}

@misc{PackageVisualizeCommunicate,
  title = {An {{R}} Package to Visualize and Communicate Uncertainty in Seasonal Climate Prediction - {{ScienceDirect}}},
  urldate = {2022-09-27},
  howpublished = {https://doi.org/10.1016/j.envsoft.2017.09.008}
}

@misc{pebesmaRnews2005,
  title = {Rnews 2005},
  author = {Pebesma and Bivand}
}

@misc{pebesmaSpClassesMethods2022,
  title = {Sp: {{Classes}} and {{Methods}} for {{Spatial Data}}},
  shorttitle = {Sp},
  author = {Pebesma, Edzer and Bivand, Roger and Rowlingson, Barry and {Gomez-Rubio}, Virgilio and Hijmans, Robert and Sumner, Michael and MacQueen, Don and Lemon, Jim and Lindgren, Finn and O'Brien, Josh and O'Rourke, Joseph},
  year = {2022},
  month = nov,
  urldate = {2023-01-18},
  abstract = {Classes and methods for spatial data; the classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided, e.g. for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc.},
  copyright = {GPL-2 | GPL-3 [expanded from: GPL ({$\geq$} 2)]},
  keywords = {Spatial,SpatioTemporal}
}

@misc{R,
  title = {R: {{The R Project}} for {{Statistical Computing}}},
  shorttitle = {R},
  author = {{The R foundation}},
  journal = {The R Project for Statistical Computing},
  urldate = {2022-07-28},
  howpublished = {https://www.r-project.org/},
  langid = {english},
  file = {/home/phanaur/Zotero/storage/BTW354LT/www.r-project.org.html}
}

@misc{RbasedClimate4ROpen,
  title = {The {{R-based climate4R}} Open Framework for Reproducible Climate Data Access and Post-Processing - {{ScienceDirect}}},
  urldate = {2022-09-27},
  howpublished = {https://doi.org/10.1016/j.envsoft.2018.09.009}
}

@misc{regionmask,
  title = {Create Masks of Geographical Regions \textemdash{} Regionmask 0.9.0 Documentation},
  urldate = {2022-11-10},
  howpublished = {https://regionmask.readthedocs.io/en/stable/}
}

@misc{RmgscCrUsgs,
  title = {Rmgsc.Cr.Usgs.Gov - /Outgoing/Ecosystems/{{Global}}/},
  urldate = {2022-11-03},
  howpublished = {https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/}
}

@misc{SantanderMeteorologyGroup,
  title = {Santander {{Meteorology Group}} ({{UC-CSIC}})},
  journal = {GitHub},
  urldate = {2022-09-27},
  abstract = {Santander Meteorology Group (UC-CSIC) has 71 repositories available. Follow their code on GitHub.},
  howpublished = {https://github.com/SantanderMetGroup},
  langid = {english},
  file = {/home/phanaur/Zotero/storage/D2ESDQ8E/SantanderMetGroup.html}
}

@incollection{seneviratneChangesClimateExtremes2012,
  title = {Changes in {{Climate Extremes}} and Their {{Impacts}} on the {{Natural Physical Environment}}},
  booktitle = {Managing the {{Risks}} of {{Extreme Events}} and {{Disasters}} to {{Advance Climate Change Adaptation}}},
  author = {Seneviratne, Sonia I. and Nicholls, Neville and Easterling, David and Goodess, Clare M. and Kanae, Shinjiro and Kossin, James and Luo, Yali and Marengo, Jose and McInnes, Kathleen and Rahimi, Mohammad and Reichstein, Markus and Sorteberg, Asgeir and Vera, Carolina and Zhang, Xuebin and Rusticucci, Matilde and Semenov, Vladimir and Alexander, Lisa V. and Allen, Simon and Benito, Gerardo and Cavazos, Tereza and Clague, John and Conway, Declan and {Della-Marta}, Paul M. and Gerber, Markus and Gong, Sunling and Goswami, B. N. and Hemer, Mark and Huggel, Christian and {van den Hurk}, Bart and Kharin, Viatcheslav V. and Kitoh, Akio and Tank, Albert M.G. Klein and Li, Guilong and Mason, Simon and McGuire, William and {van Oldenborgh}, Geert Jan and Orlowsky, Boris and Smith, Sharon and Thiaw, Wassila and Velegrakis, Adonis and Yiou, Pascal and Zhang, Tingjun and Zhou, Tianjun and Zwiers, Francis W.},
  editor = {Field, Christopher B. and Barros, Vicente and Stocker, Thomas F. and Dahe, Qin},
  year = {2012},
  month = may,
  edition = {First},
  pages = {109--230},
  publisher = {{Cambridge University Press}},
  doi = {10.1017/CBO9781139177245.006},
  urldate = {2022-11-03},
  isbn = {978-1-107-02506-6 978-1-107-60780-4 978-1-139-17724-5},
  langid = {english}
}

@misc{SurfaceMeteorologicalVariables,
  title = {Near Surface Meteorological Variables from 1979 to 2019 Derived from Bias-Corrected Reanalysis},
  urldate = {2022-11-03},
  howpublished = {https://cds.climate.copernicus.eu/cdsapp\#!/dataset/10.24381/cds.20d54e34?tab=overview}
}

@misc{TestingBiasAdjustment,
  title = {Testing Bias Adjustment Methods for Regional Climate Change Applications under Observational Uncertainty and Resolution Mismatch - {{Casanueva}} - 2020 - {{Atmospheric Science Letters}} - {{Wiley Online Library}}},
  urldate = {2022-09-27},
  howpublished = {https://doi.org/10.1002/asl.978},
  file = {/home/phanaur/Zotero/storage/IV37PTBN/asl.html}
}

@misc{UDGTAPHome,
  title = {{{UDG-TAP Home}}},
  urldate = {2022-09-27},
  howpublished = {http://meteo.unican.es/udg-tap/home}
}

@article{van_vuuren_representative_2011,
  title = {The Representative Concentration Pathways: An Overview},
  shorttitle = {The Representative Concentration Pathways},
  author = {{van Vuuren}, Detlef P. and Edmonds, Jae and Kainuma, Mikiko and Riahi, Keywan and Thomson, Allison and Hibbard, Kathy and Hurtt, George C. and Kram, Tom and Krey, Volker and Lamarque, Jean-Francois and Masui, Toshihiko and Meinshausen, Malte and Nakicenovic, Nebojsa and Smith, Steven J. and Rose, Steven K.},
  year = {2011},
  month = nov,
  journal = {Climatic Change},
  volume = {109},
  number = {1-2},
  pages = {5--31},
  issn = {0165-0009, 1573-1480},
  doi = {10.1007/s10584-011-0148-z},
  urldate = {2022-07-28},
  langid = {english}
}

@misc{VistazoCDOProject,
  title = {Vistazo - {{CDO}} - {{Project Management Service}}},
  urldate = {2022-11-10},
  howpublished = {https://code.mpimet.mpg.de/projects/cdo/}
}

@misc{WFDE5,
  title = {{{WFDE5}} over Land Merged with {{ERA5}} over the Ocean ({{W5E5}})},
  urldate = {2022-11-03},
  howpublished = {https://dataservices.gfz-potsdam.de/pik/showshort.php?id=escidoc:4855898},
  file = {/home/phanaur/Zotero/storage/MCZUYT9B/showshort.html}
}

@misc{WGS84EPSG,
  title = {{{WGS}} 84: {{EPSG Projection}} -- {{Spatial Reference}}},
  urldate = {2022-11-03},
  howpublished = {https://spatialreference.org/ref/epsg/wgs-84/}
}

@misc{WhatLoadeR2022,
  title = {What Is {{loadeR}}?},
  year = {2022},
  month = jul,
  urldate = {2022-11-10},
  abstract = {A climate4R package for data access},
  copyright = {GPL-3.0},
  howpublished = {Santander Meteorology Group (UC-CSIC)}
}

@misc{XarrayDocumentation,
  title = {Xarray Documentation},
  urldate = {2022-11-10},
  abstract = {Xarray makes working with labelled multi-dimensional arrays in Python simple, efficient, and fun! Useful links: Home| Code Repository| Issues| Discussions| Releases| Stack Overflow| Mailing List| B...},
  howpublished = {https://docs.xarray.dev/en/latest/index.html},
  langid = {english},
  file = {/home/phanaur/Zotero/storage/E5Z88JZF/stable.html}
}

@misc{zotero-77,
  urldate = {2022-07-28},
  howpublished = {https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6},
  langid = {english}
}