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README.Rmd
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README.Rmd
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---
output: github_document
bibliography: Rmd/biblio.bib
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
#### GCM compareR
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. The code of this app can be find in the project's github, <a href = "https://github.com/marquetlab/GCM_compareR" target = "_blank">https://github.com/marquetlab/GCM_compareR</a>.
General Circulation Models (GCMs) are key to climate change research. 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; [@Taylor2012a]). Projections of future climate from all these models tell a common story, but the spread among them is also significant [@Zappa2017], 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 [@Thuiller2019]. 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 [@Shepherd2018].
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 [@VanVuuren2011] 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 [@Zappa2017].
***
#### Lastest news
> - New publication usable for citation (Jan 2020)
> - Release of GCM compareR (Sep 26, 2018)
***
#### Use of the App
GCM 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.
***
#### Citation
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.
***
#### Use offline
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 here: (~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 <a href = "https://cran.r-project.org/" target = "_blank">R</a> and <a href = "https://rstudio.com/products/rstudio/download/" target = "_blank">RStudio Desktop</a> 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*.
***
#### Contact us
Please, email derek.corcoran.barrios@gmail.com with any question of create an issue on <a href = "https://github.com/marquetlab/GCM_compareR" target = "_blank">github</a>.
***
#### Development
GCM compareR has been developed by Javier Fajardo, Derek Corcoran, Patrick Roehrdanz, Lee Hannah and Pablo Marquet in <a href = "http://marquet.cl/" target = "_blank">Marquet Lab</a> in Pontificia Universidad Católica de Chile, in Santiago de Chile. It was built as part of the <a href = "http://www.sparc-website.org/" target = "_blank">Spatial Planning for Protected Areas in Response to Climate Change initiative (SPARC)</a> project, a GEF initiative leaded by Conservation International (CI), and with the support of <a href = "http://ieb-chile.cl/" target = "_blank">Instituto de Ecología y Biodiversidad (IEB)</a> in Chile.
***
#### Authors
<a href = "https://scholar.google.es/citations?user=L30b63UAAAAJ&hl=es&oi=ao" target = "_blank">Javier Fajardo
<a href = "https://derek-corcoran-barrios.github.io/" target = "_blank">Derek Corcoran</a>
Patrick Roehrdanz
Lee Hannah
<a href = "http://marquet.cl/" target = "_blank">Pablo Marquet</a>
***
#### Climatic data
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 <a href = "http://ccafs-climate.org/" target = "_blank">data portal</a> and their R package [@Chamberlain2017].
***
#### References