Introduction to the timemachine app

This app is intended for educational purposes. The interactive plots should instill a better appreciation of long geological timescales and our current understanding of past, present and future climate. The current rate of climate change is unprecedented when compared to what we know of past climate and extreme climate events, such as the Paleocene-Eocene Thermal Maximum (PETM; ~56 Ma).

I am still developing the app.

Data sources and compilation

The surface temperature record spanning the Cretaceous up to the Holocene is based on the corrected $\delta$ ¹⁸O dataset of Westerhold et al. (2020), and following the same corrections for the presence of ice as described in Hansen et al. (2013). The Holocene temperatures are based on the global temperature anomaly (Standard_5x5Grid) stack of Marcott et al. (2013) spliced on top of a 1961–1990 mean temperature of 14 $\,$ °C (Hansen et al. 2013). The instrumental HadCrut4 sea surface anomaly dataset was downloaded from the Climatic Research Unit (University of East Anglia) and Met Office website. This record was spliced on top of the mean temperature of the Marcott et al. (2013) record for the interval between 1961 and 1990. Future extrapolations (scenarios or Representative Concentration Pathways) are based on the General Circulation Model data generated by the BCC_CM1 model, which was downloaded from the Climate4impact website. The composite dataset of the app can be recompiled by running the scripts contained in the data-raw directory. The function read_instrum_data() will generate a global average value for the instrumental HadCrut4 data, see also the website above for the same code. The script reduce_clim_model.R can be used to flatten the array of time-incremented model data spanning up 2100.

Basic usage of the R package

Installation

You can install the released version of timemachine and run the app from your local console.

# Install timemachine from GitHub:
# install.packages("devtools")
devtools::install_github("MartinSchobben/timemachine")

Usage

Load point with library.

library(timemachine)

Run the app

Start the app by running.

timemachine_app()

Credits

The timemachine app is created with shiny(???) in the R language(R Core Team 2020). The package and app rely on a set of external packages from the tidyverse universe, including: dplyr (???), tidyr (???), tibble (???), ggplot2 (???), rlang (???). Package development is aided by; devtools (???), roxygen2 (???), testthat (???). This README file is generated with knitr (??? ; ???), rmarkdown (???; ???). The graphics for the chronostratigraphic plots use the packages; gridExtra (???), gtable (???), and Cairo (???).

The book: Mastering Shiny, by Wickham (2020) greatly helped development of the app.

References

Hansen, James, Makiko Sato, Gary Russell, and Pushker Kharecha. 2013. “Climate sensitivity, sea level and atmospheric carbon dioxide.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371 (2001). https://doi.org/10.1098/rsta.2012.0294.

Marcott, Shaun a., Jeremy D. Shakun, Peter U. Clark, and Alan C. Mix. 2013. “A Reconstruction of Regional.” Science (New York, N.Y.) 339 (6124): 1198–1201. http://www.ncbi.nlm.nih.gov/pubmed/23471405.

R Core Team. 2020. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Westerhold, Thomas, Norbert Marwan, Anna Joy Drury, Diederik Liebrand, Claudia Agnini, Eleni Anagnostou, James S. K. Barnet, et al. 2020. “An astronomically dated record of Earth’s climate and its predictability over the last 66 million years.” Science 369 (6509): 1383–8. https://doi.org/10.1126/SCIENCE.ABA6853.

Wickham, Hadley. 2020. Mastering shiny; Build interactive apps, reports & Dashboards.