GCM_compareR is a web application developed to assist ecologists,
conservationists and policy makers at understanding climate change
scenarios and differences between General Circulation Models (GCMs), and
at assisting the triage of subsets of models in an objective and
informed manner. GCM compareR is written in R and uses the web app
development package Shiny. This repository contains the development version of the software and web application.
General Circulation Models (GCMs) are commonly used for exploring scenarios of climate change. Currently, scientists can chose from a large number of GCMs, as meteorological research centers worldwide have contributed more than 35 different GCMs for four distinct climate change scenarios as part of the Coupled Model Intercomparison Project Phase 5 (CMIP5; (Taylor, Stouffer, and Meehl 2012)). Projections of future climate from all these models tell a common story, but the spread among them is also significant (Zappa and Shepherd 2017), which is an indicator of the irreducible uncertainty concerning any unverifiable future projection. For this reason, studies have shown that the choice of GCMs in modeling studies is an important source of variability in model outputs (Thuiller et al. 2019). The situation demands for workflows to help researchers exploring climate change scenarios to increase objectivity and repeatability in research and assure a well judged treatment of uncertainty (Shepherd et al. 2018).
GCM_compareR has been developed to play this role in helping researchers
approaching GCMs in climate change studies and assist the selection of
climate models. The app offers quick access to preloaded CMIP5
downscaled GCMs for the four RCPs (Vuuren et al. 2011) and allows users
to compare their projections for future years. Comparison results are
provided as scatterplots and maps where users may learn what makes
different any GCM, identify groups of GCMs with similar characteristics
(e.g. “colder” or “warmer” in their projection of temperature increase)
and define storylines about the future climate (Zappa and Shepherd
2017).
- New publication usable for citation (Jan 2020)
- Release of
GCM compareR(Sep 26, 2018)
GCM_compareR compareR contains tabs that might be used from left to right to
define a comparison scenario, retrieve results and generate a report
with them.
- The Intro tab includes all the information needed to use the app. Move to the Workflow section to find full details about how to use the app, and go to About to find information about developers.
- In the Scenario tab you will be able to set up a comparison scenario by making all choices: select the GCMs you would like to compare, pick a climate change scenario (year of projection, RCP…) and set the geographic extent of your analysis. Use the Analyse button on this tab to trigger the start of the analyses.
- The tabs Explore selected GCMs, Variation from present and Variation among futures will display the results after the calculation is completed. Finally, Report will download a report with all the figures produced and some explanatory text.
Please, if you use GCM_compareR as part of your research, cite the app
as:
Fajardo, J, Corcoran, D., Roehrdanz, P, Hannah, P, Marquet, P (in press) GCM compareR: A web application to assess differences and assist in the selection of General Circulation Models for climate change research. Methods in Ecology and Evolution.
You need to be connected online to use the app. When an analysis is run in the app, many climatic layers in raster format are loaded and analysed in the background, and these layers encompass several Gb of hard drive space. However, if you prefer to use the app offline, you may download it (including climatic layers) from the following location: (~4.3 Gb) http://bit.ly/GCM_compareR_offline (copy and paste the link in your browser)
To run the app locally you need to have
R and
RStudio
Desktop installed. After unzipping the files, locate and open the
GCM_compareR.Rproj file. RStudio will open. While any of the ui.R
or the server.R files are opened, you should see a run button on the
top right corner of the script quadrant. Clic run to start the app on
your default browser. Alternatively, you can run the code
shiny::runApp instead of pressing run.
Please, email derek.corcoran.barrios@gmail.com with any question of create an issue on github.
GCM_compareR has been developed by Javier Fajardo, Derek Corcoran,
Patrick Roehrdanz, Lee Hannah and Pablo Marquet in
Marquet Lab in
Pontificia Universidad Católica de Chile, in Santiago de Chile. It was
built as part of the
Spatial
Planning for Protected Areas in Response to Climate Change initiative
(SPARC) project, a GEF initiative leaded by Conservation
International (CI), and with the support of
Instituto de Ecología
y Biodiversidad (IEB) in
Chile.
Javier
Fajardo
Derek
Corcoran
Patrick Roehrdanz
Lee Hannah
Pablo Marquet
This application uses downscaled climate data published by CGIAR-CCAFS
(Research Program on Climate Change, Agriculture and Food Security)
under CC 4.0 license. All the raster data used by GCM_compareR is
available from their
data portal
and their R package (Chamberlain 2017).
Chamberlain, Scott. 2017. Ccafs: Client for ’Ccafs’ ’Gcm’ Data. https://CRAN.R-project.org/package=ccafs.
Shepherd, Theodore G, Emily Boyd, Raphael A Calel, Sandra C Chapman, Suraje Dessai, Ioana M Dima-west, Hayley J Fowler, and Rachel James. 2018. “Storylines: an alternative approach to representing uncertainty in physical aspects of climate change.” Climatic Change 151. Climat ic Change: 555–71. https://doi.org/10.1007/s10584-018-2317-9.
Taylor, Karl E., Ronald J. Stouffer, and Gerald A. Meehl. 2012. “An overview of CMIP5 and the experiment design.” Bulletin of the American Meteorological Society 93 (4): 485–98. https://doi.org/10.1175/BAMS-D-11-00094.1.
Thuiller, Wilfried, Maya Guéguen, Julien Renaud, Dirk N Karger, and Niklaus E Zimmermann. 2019. “Uncertainty in ensembles of global biodiversity scenarios.” Nature Communications 10 (1446). Springer US: 1–9. https://doi.org/10.1038/s41467-019-09519-w.
Vuuren, Detlef P. van, Jae Edmonds, Mikiko Kainuma, Keywan Riahi, Allison Thomson, Kathy Hibbard, George C. Hurtt, et al. 2011. “The representative concentration pathways: An overview.” Climati c Change 109 (1): 5–3 1. https://doi.org/10.1007/s10584-011-0148-z.
Zappa, Giuseppe, and Theodore G. Shepherd. 2017. “Storylines of Atmospheric Circulation Change for European Regional Climate Impact Assessment.” Journal of Climate 30: 6561–77. https://doi.org/10.1175/JCLI-D-16-0807.1.