DOI: 10.5281/zenodo.7303807
title: IPCC Climate Zones (from the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories)

description: <strong>Description</strong> These data (re)create spatial data for the 2019 IPCC Climate Zones, shown in <em>Figure 3A.5.1</em> of Chapter 3: Consistent Representation of Lands in Volume 4: Agriculture, Forestry and Other Land Use of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. I recreated these data because I could not readily identify the data in a spatial format online, a problem which has previously been noted by ESDAC, who produced a spatial version of <em>Figure 3A.5.1</em> from the original 2006 guidelines. Resolution: 0.5 arc degree CRS: lon/lat WGS 84 <strong>If you use these data please ensure you also cite the IPCC</strong> - Calvo Buendia, E et al. (2019). 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. IPCC, Switzerland. <strong>Methods</strong> The data were derived using the classification scheme shown in <em>Figure 3A.5.2</em> based on the gridded Climate Research Unit (CRU) Time Series (TS) monthly climate data (Harris et al., 2014) for the period from 1985 to 2015 following the methods described in <em>Annex 3A.5 Default climate and soil classifications </em>of the above Chapter. All data were processed in <em>R</em> version 4.2.1, with the packages <em>elevatr</em> (v0.4.2), <em>lubridate</em> (v1.8.0), <em>magrittr</em> (v2.0.3), and <em>terra</em> (v1.6-7)<em> </em>attached. The full session info is included as a <em>.txt</em> file. As these methods are not exhaustively described in the Annex, the following assumptions were made: CRU TS3.25 was used as the most recently published data (published on 2017-09-22) that could have been incorporated into the Refinement. Other possibilities include CRU TS3.24 (which are the first data to include 2015), or CRU TS4.00 or CRU TS4.01 (both of which were published in parallel to 3.24 and 3.25). These data were all investigated, and CRU TS3.25 produced results that were the most visually similar to the published <em>Figure 3A.5.1</em> (though non-identical). As the methods did not mention a preferred elevation data source, the <em>elevatr</em> R package was used to obtain data at zoom level 2 (approx resolution of 0.15 arc degree), that was then resampled to match the 0.5-degree resolution of the CRU data. These data originally come from the ETOPO1 global relief model. <strong>Known discrepancies</strong> The distribution of Tropical Wet and Tropical Moist in South America does not exactly match the original data. There are small discrepancies in Tropical Montane classifications (likely arising from the use of a different elevation layer). These are most noticeable in, but not restricted to, Africa. The classification of Boreal Dry, Polar Dry, and Polar Moist in northern Russia and (to a lesser extent) in northern Canada does not exactly match the original data. There are a small number of Cool Temperate Dry pixels in the UK, and Warm Temperate Dry pixels around Brittany which do not occur in the original data. <strong>Disclaimer</strong> <strong>I am not affiliated with the IPCC in any way</strong>, I just needed spatial data of the Climate Zones, and could not readily identify any online. This is a problem which has previously been noted by ESDAC, who produced a spatial version of <em>Figure 3A.5.1</em> from the original 2006 guidelines. <strong>File description</strong> <em>README.html</em> - ~this description file. <em>IPCC_Climate_Zones_ts_3.25.tif</em> - the output Climate Zones map at 0.5-arc degree resolution based on the CRU TS3.25 data. <em>IPCC_Climate_Zones_colour_map.clr </em>- a colour map file to render the output map with the same colours as in the IPCC 2019 Refinement figure. <em>IPCC_Climate_Zones_ts_3.25.png</em> - an image file of the output Climate Zones map. <em>ipcc_climate_zones_2019.R</em> - the script used to produce these data. <em>session_info.txt</em> - the R session info.
description: {"references": ["Calvo Buendia, E et al. (2019). 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. IPCC, Switzerland.", "University of East Anglia Climatic Research Unit; Harris, IC; Jones, PD (2017): CRU TS3.25: Climatic Research Unit (CRU) Time-Series (TS) Version 3.25 of High-Resolution Gridded Data of Month-by-month Variation in Climate (Jan. 1901- Dec. 2016). Centre for Environmental Data Analysis, 05 December 2017. doi:10.5285/c311c7948e8a47b299f8f9c7ae6cb9af. http://dx.doi.org/10.5285/c311c7948e8a47b299f8f9c7ae6cb9af", "Harris, I., Jones, P., Osborn, T. and Lister, D. (2014), Updated high-resolution grids of monthly climatic observations \u2013 the CRU TS3.10 Dataset. Int. J. Climatol., 34: 623-642. https://doi.org/10.1002/joc.3711", "NOAA National Geophysical Data Center (2009). ETOPO1 1 Arc-Minute Global Relief Model. NOAA National Centers for Environmental Information. Accessed 2022-11-07.", "R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.", "Bache S, Wickham H (2022). magrittr: A Forward-Pipe Operator for R. R package version 2.0.3, https://CRAN.R-project.org/package=magrittr.", "Grolemund G, Wickham H (2011). Dates and Times Made Easy with lubridate. Journal of Statistical Software, 40(3), 1-25. https://www.jstatsoft.org/v40/i03/.", "Hijmans R (2022). terra: Spatial Data Analysis. R package version 1.6-7, https://CRAN.R-project.org/package=terra.", "Hollister, JW (2021). elevatr: Access Elevation Data from Various APIs. R package version 0.4.1. https://CRAN.R-project.org/package=elevatr."]}