Title:	Download and Visualize Essential Climate Change Data
Version:	0.8.5
Description:	Provides easy access to essential climate change datasets to non-climate experts. Users can download the latest raw data from authoritative sources and view it via pre-defined 'ggplot2' charts. Datasets include atmospheric CO2, methane, emissions, instrumental and proxy temperature records, sea levels, Arctic/Antarctic sea-ice, Hurricanes, and Paleoclimate data. Sources include: NOAA Mauna Loa Laboratory https://gml.noaa.gov/ccgg/trends/data.html, Global Carbon Project https://www.globalcarbonproject.org/carbonbudget/, NASA GISTEMP https://data.giss.nasa.gov/gistemp/, National Snow and Sea Ice Data Center https://nsidc.org/home, CSIRO https://research.csiro.au/slrwavescoast/sea-level/measurements-and-data/sea-level-data/, NOAA Laboratory for Satellite Altimetry https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/ and HURDAT Atlantic Hurricane Database https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html, Vostok Paleo carbon dioxide and temperature data: <doi:10.3334/CDIAC/ATG.009>.
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
URL:	https://cortinah.github.io/hockeystick/, https://github.com/cortinah/hockeystick
BugReports:	https://github.com/cortinah/hockeystick/issues
Imports:	ggplot2, lubridate, tools, readr, dplyr, tidyr, patchwork, scales, rvest, tibble, treemapify, RColorBrewer, jsonlite
Depends:	R (≥ 4.0.0)
Suggests:	rmarkdown, knitr, spelling, viridisLite
Language:	en-US
NeedsCompilation:	no
Packaged:	2024-11-25 01:25:43 UTC; hernandocortina
Author:	Hernando Cortina [aut, cre]
Maintainer:	Hernando Cortina <hch@alum.mit.edu>
Repository:	CRAN
Date/Publication:	2024-11-25 09:00:02 UTC

hockeystick: Download and Visualize Essential Climate Change Data

Description

Provides easy access to essential climate change datasets to non-climate experts. Users can download the latest raw data from authoritative sources and view it via pre-defined 'ggplot2' charts. Datasets include atmospheric CO2, methane, emissions, instrumental and proxy temperature records, sea levels, Arctic/Antarctic sea-ice, Hurricanes, and Paleoclimate data. Sources include: NOAA Mauna Loa Laboratory https://gml.noaa.gov/ccgg/trends/data.html, Global Carbon Project https://www.globalcarbonproject.org/carbonbudget/, NASA GISTEMP https://data.giss.nasa.gov/gistemp/, National Snow and Sea Ice Data Center https://nsidc.org/home, CSIRO https://research.csiro.au/slrwavescoast/sea-level/measurements-and-data/sea-level-data/, NOAA Laboratory for Satellite Altimetry https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/ and HURDAT Atlantic Hurricane Database https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html, Vostok Paleo carbon dioxide and temperature data: doi:10.3334/CDIAC/ATG.009.

Author(s)

Maintainer: Hernando Cortina hch@alum.mit.edu (ORCID)

See Also

Useful links:

• https://cortinah.github.io/hockeystick/

• https://github.com/cortinah/hockeystick

• Report bugs at https://github.com/cortinah/hockeystick/issues

Download and plot essential climate data

Description

Plots a 2x2 grid of carbon, temperature, sea ice, and sea level charts.

Usage

climate_grid(print = TRUE)

Arguments

Details

warming_stripes invisibly returns a ggplot2 object with 2x2 grid of carbon, temperature, sea ice, and sea level charts from get_carbon, get_temp, get_seaice, and get_sealevel. By default the chart is also displayed. Users may further modify the output ggplot2 chart.

Value

Invisibly returns a ggplot2 object with climate grid

Author(s)

Hernando Cortina, hch@alum.mit.edu

Examples

# Draw grid

grid <- climate_grid() 

Download and plot essential climate data

Description

Plots a treemap of cumulative co2 emissions by country since 1900.

Usage

emissions_map(
  dataset = get_emissions(),
  print = TRUE,
  since = 1900,
  number = "all",
  title = substitute(paste(since, "-", to, " Cumulative " * CO[2] *
    " Emissions by Country"), list(since = since, to =
    as.character(dataset[nrow(dataset), 2])))
)

Arguments

Details

emissions_map invisibly returns a ggplot2 object with cumulative emissions treemap by country since 1900 from get_emissions. By default the chart is also displayed. Users may further modify the output ggplot2 chart.

Value

Invisibly returns a ggplot2 object with emissions treemap

Author(s)

Hernando Cortina, hch@alum.mit.edu

Examples

# Draw treemap

co2map <- emissions_map()

co2map <- emissions_map(since=2000, number=20, title="Top 20 Cumulative Emitters Since 2000") 

Display cached file info

Description

Internal function

Usage

file_info_(x)

Arguments

Download and plot essential climate data

Description

Retrieves atmospheric carbon dioxide measurements from National Oceanic and Atmospheric Administration Earth System Research Laboratories monitoring laboratory in Mauna Loa, Hawaii. https://gml.noaa.gov/ccgg/trends/data.html

Usage

get_carbon(use_cache = TRUE, write_cache = getOption("hs_write_cache"))

Arguments

Details

get_carbon invisibly returns a tibble with NOAA's monthly average carbon dioxide measurement. The returned object includes date, year, month, average, trend, std dev, and uncertainty columns. Trend is NOAA's published trend. Please refer to above website for details.

Value

Invisibly returns a tibble with the monthly carbon dioxide series

Author(s)

Hernando Cortina, hch@alum.mit.edu

References

Dr. Pieter Tans, NOAA/GML https://gml.noaa.gov/ccgg/trends/ and Dr. Ralph Keeling, Scripps Institution of Oceanography https://scrippsco2.ucsd.edu/.

C.D. Keeling, R.B. Bacastow, A.E. Bainbridge, C.A. Ekdahl, P.R. Guenther, and L.S. Waterman, (1976), Atmospheric carbon dioxide variations at Mauna Loa Observatory, Hawaii, Tellus, vol. 28, 538-551

Examples

# Fetch from cache if available, otherwise download:
maunaloa <- get_carbon()
#
# Force fetch from source:
maunaloa <- get_carbon(use_cache=FALSE)
#
# Review cache contents and last update dates:
hockeystick_cache_details()
#
# Plot output using package's built-in ggplot2 settings
plot_carbon(maunaloa) 

Download and plot essential climate data

Description

Retrieves the daily air or sea-surface temperature data since 1940 from ClimateReanalyzer.org Source is University of Maine Climate Change Institute. https://climatereanalyzer.org/clim/t2_daily/

Usage

get_dailytemp(
  use_cache = TRUE,
  write_cache = getOption("hs_write_cache"),
  region = "W",
  mean_start = if (region %in% c("WS", "NS", "ws", "ns")) 1982 else 1979,
  mean_end = 2000
)

Arguments

Value

Invisibly returns a tibble with the daily 2-meter air or sea surface temperatures since 1940 as well as historic mean by day-of-year and current anomaly versus mean.

get_dailytemp invisibly returns a tibble with the daily temperatures since 1940 as well as mean by day-of-year and anomaly. Default to world data, but region can be selected among six options.

Region options include world air (default), Northern Hemisphere air, Southern Hemisphere air, Tropics air, Arctic air, Antarctic air, World sea surface and North Atlantic sea surface and is stored in attribute of output. The historic daily mean-by-day period defaults to 1979-2000. This range can be optionally modified.

Data are updated daily. For day-of-year mean removes observations from February 29 on leap years.

Author(s)

Hernando Cortina, hch@alum.mit.edu

References

• ClimateReanalyzer.org: https://climatereanalyzer.org/clim/t2_daily/

  Notes: daily mean surface air temperature (2-meter height) estimates from the ECMWF Reanalysis version 5 (ERA5) for the period January 1940 to present. ERA5 is a state-of-the-art numerical climate/weather modeling framework that ingests surface, radiosonde, and satellite observations to estimate the state of the atmosphere through time. ERA5 files have a horizontal grid resolution of 0.25° x 0.25° (about 31km x 31km at 45°N). Each daily temperature represents an average across all model gridcells within the defined latitude/longitude bounds for the selected domain. The means are area-weighted to account for the convergence of longitude lines at the poles

Examples

# Fetch temp anomaly from cache if available:
dailytemps <- get_dailytemp()
#
# Force cache refresh:
dailytemps <- get_dailytemp(use_cache=FALSE)
#
# Review cache contents and last update dates:
hockeystick_cache_details()
#
# Plot output using package's built-in ggplot2 settings
plot_dailytemp(dailytemps)

# Change region to Arctic
arctictemp <- get_dailytemp(region='AR', use_cache=FALSE)

Download and plot essential climate data

Description

Retrieves the daily air temperature since 1940 from the EU Copernicus Service https://cds.climate.copernicus.eu/#!/home

Usage

get_dailytempcop(
  use_cache = TRUE,
  write_cache = getOption("hs_write_cache"),
  region = "W"
)

Arguments

Value

Invisibly returns a tibble with the daily 2-meter air temperatures since 1940 as well as historic mean by day-of-year and current anomaly versus mean.

get_dailytempcop invisibly returns a tibble with the daily temperatures since 1940 as well as mean by day-of-year and anomaly.

Region options include world air (default). The historic daily mean-by-day period defaults to 1991-2020.

Data are updated daily.

Author(s)

Hernando Cortina, hch@alum.mit.edu

References

• Copernicus: https://cds.climate.copernicus.eu/#!/home

  Notes: daily mean surface air temperature (2-meter height) estimates from the ECMWF Reanalysis version 5 (ERA5) for the period January 1940 to present. ERA5 is a state-of-the-art numerical climate/weather modeling framework that ingests surface, radiosonde, and satellite observations to estimate the state of the atmosphere through time. ERA5 files have a horizontal grid resolution of 0.25° x 0.25° (about 31km x 31km at 45°N). Each daily temperature represents an average across all model gridcells within the defined latitude/longitude bounds for the selected domain. The means are area-weighted to account for the convergence of longitude lines at the poles

Examples

# Fetch temp anomaly from cache if available:
dailytemps <- get_dailytempcop()
#
# Force cache refresh:
dailytemps <- get_dailytempcop(use_cache=FALSE)
#
# Review cache contents and last update dates:
hockeystick_cache_details()
#
# Plot output using package's built-in ggplot2 settings
plot_dailytemp(dailytemps) 

Download and plot essential climate data

Description

Retrieves Global Carbon Project (GCP) annual global carbon dioxide emissions since 1750 from Our World In Data repository https://github.com/owid/co2-data

Usage

get_emissions(use_cache = TRUE, write_cache = getOption("hs_write_cache"))

Arguments

Details

get_emissions invisibly returns a tibble with GCP's annual carbon dioxide emissions from fossil fuels in aggregate and for every nation. The returned object includes ISO code, country, year, co2 emissions, growth rates, per capita, and decompositions by industry and gas type. Please refer to above website for details.

Value

Invisibly returns a tibble with annual carbon dioxide emissions

Author(s)

Hernando Cortina, hch@alum.mit.edu

References

https://www.globalcarbonproject.org/carbonbudget/

Friedlingstein, P. et al (2020), Global Carbon Budget 2020, Earth System Science Data, vol. 12, 3269-3340 doi:10.5194/essd-12-3269-2020

Examples

# Fetch from cache if available:
emissions <- get_emissions()
#
# Force cache refresh:
emissions <- get_emissions(use_cache=FALSE)
#
# Review cache contents and last update dates:
hockeystick_cache_details()
#
# Plot output using package's built-in ggplot2 settings
plot_emissions(emissions) 

Download and plot essential climate data

Description

Retrieves Atlantic basin hurricane data since 1851 from National Oceanic and Atmospheric Administration HURDAT Atlantic Hurricane Database Re-analysis Project. https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html

Usage

get_hurricanes(use_cache = TRUE, write_cache = getOption("hs_write_cache"))

Arguments

Details

get_hurricanes invisibly returns a tibble with NOAA's annual North Atlantic revised HURDAT hurricane data since 1851. The returned object includes Year, and number of named storms, hurricanes, major hurricanes (S-S scale 3-5), Accumulated Cyclone Energy (ACE), and U.S. hurricane strikes.

ACE is an index that combines the number of systems, how long they existed and how intense they became. It is calculated by squaring the maximum sustained surface wind in the system every six hours that the cyclone is a Named Storm and summing it up for the season. Please refer to above website for details.

Value

Invisibly returns a tibble with the annual HURDAT hurricane data since 1851

Author(s)

Hernando Cortina, hch@alum.mit.edu

References

HURDAT North Atlantic Hurricane Database Re-analysis Project, Hurricane Research Division, NOAA https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html.

Data from: https://www.aoml.noaa.gov/hrd/hurdat/comparison_table.html

https://en.wikipedia.org/wiki/Accumulated_cyclone_energy

Examples

# Fetch from cache if available:
hurricanes <- get_hurricanes()
#
# Force cache refresh:
hurricanes <- get_hurricanes(use_cache=FALSE)
#
# Review cache contents and last update dates:
hockeystick_cache_details()
#
# Plot output using package's built-in ggplot2 settings
plot_hurricanes(hurricanes) 

Download and plot essential climate data

Description

Retrieves Arctic or Antarctic monthly Sea Ice Index time series (in million square km). Source is the National Snow and Ice Data Center, defaults to Arctic (Northern Hemisphere) monthly sea ice extent since 1979. https://nsidc.org/data/explore-data

Usage

get_icecurves(
  pole = "N",
  measure = "extent",
  use_cache = TRUE,
  write_cache = getOption("hs_write_cache")
)

Arguments

Value

Invisibly returns a tibble with the series of monthly Sea Ice Index since 1979 (in million square km).

get_icecurves invisibly returns a tibble with time series of monthly Sea Ice Index since 1979 (in million square km).

User may select monthly Arctic or Antarctic sea ice extent or area (in millions of square kilometers). Defaults to Arctic sea ice extent. https://nsidc.org/sea-ice-today/about-data#area_extent

Author(s)

Hernando Cortina, hch@alum.mit.edu

References

• NSIDC Data Archive: https://nsidc.org/data/explore-data

• All About Sea Ice: https://nsidc.org/learn/parts-cryosphere/sea-ice

• Sea Ice terminology: https://nsidc.org/learn

Examples

# Fetch monthly sea ice history from cache if available:
icecurves <- get_icecurves()
#
# Force cache refresh:
icecurves <- get_icecurves(use_cache = FALSE)
# change region
south_icecurves <- get_icecurves(pole='S',  use_cache = FALSE)
#
# Review cache contents and last update dates:
hockeystick_cache_details()
#
# Plot output using package's built-in ggplot2 settings
plot_icecurves(icecurves) 

Download and plot essential climate data

Description

Retrieves globally averaged marine surface methane monthly mean data from National Oceanic and Atmospheric Administration. The Global Monitoring Division of NOAA’s Earth System Research Laboratory has measured methane since 1983 at a globally distributed network of air sampling sites. A global average is constructed by first smoothing the data for each site as a function of time, and then smoothed values for each site are plotted as a function of latitude. Global means are calculated from the latitude plot at each time step. https://gml.noaa.gov/ccgg/trends_ch4/ https://gml.noaa.gov/ccgg/about/global_means.html

Usage

get_methane(use_cache = TRUE, write_cache = getOption("hs_write_cache"))

Arguments

Details

get_methane invisibly returns a tibble with NOAA's monthly globally averaged methane measurement. The returned object includes year, month, date, average, average uncertainty, trend, and trend uncertainty columns. Trend is NOAA's published trend. Please refer to above website for details. CH4 expressed as a mole fraction in dry air, nanomol/mol, abbreviated as ppb.

Value

Invisibly returns a tibble with the monthly methane series

Author(s)

Hernando Cortina, hch@alum.mit.edu

References

Lan, X., K.W. Thoning, and E.J. Dlugokencky: Trends in globally-averaged CH4, N2O, and SF6 determined from NOAA Global Monitoring Laboratory measurements. Version 2022-11, doi:10.15138/P8XG-AA10

Examples

# Fetch from cache if available, otherwise download:
ch4 <- get_methane()
#
# Force fetch from source:
ch4 <- get_methane(use_cache=FALSE)
#
# Review cache contents and last update dates:
hockeystick_cache_details()
#
# Plot output using package's built-in ggplot2 settings
plot_methane(ch4) 

Download and plot essential climate data

Description

Retrieves historical carbon dioxide and temperature records from the Vostok ice core during the past 420,000 years. Source of data is the U.S. Department of Energy’s (DOE) Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE). https://ess-dive.lbl.gov/ and https://data.ess-dive.lbl.gov/datasets/doi:10.3334/CDIAC/CLI.006

Usage

get_paleo(use_cache = TRUE, write_cache = getOption("hs_write_cache"))

Arguments

Value

Invisibly returns a tibble with the age of the ice (years before C.E.), carbon dioxide (ppm) and temperature (degrees C).

get_paleo invisibly returns a tibble with Vostok ice core data: the age of the ice (years before C.E.), carbon dioxide (ppm) and temperature (degrees C).

Data are from: Barnola J; Raynaud D; Lorius C; Barkov N (2003): Historical Carbon Dioxide Record from the Vostok Ice Core (417,160 - 2,342 years BP) and Petit J R ; Raynaud D ; Lorius C ; Delaygue G ; Jouzel J ; Barkov N I ; Kotlyakov V M (2000): Historical Isotopic Temperature Record from the Vostok Ice Core. CDIAC.

Author(s)

Hernando Cortina, hch@alum.mit.edu

References

• Historical Carbon Dioxide Record from the Vostok Ice Core (US Dept of Energy): https://data.ess-dive.lbl.gov/datasets/doi:10.3334/CDIAC/CLI.006

• Petit J R ; Raynaud D ; Lorius C ; Delaygue G ; Jouzel J ; Barkov N I ; Kotlyakov V M (2000): Historical Isotopic Temperature Record from the Vostok Ice Core. CDIAC. doi:10.3334/CDIAC/CLI.006. https://data.ess-dive.lbl.gov/view/doi:10.3334/CDIAC/CLI.006

• Barnola J ; Raynaud D ; Lorius C ; Barkov N (2003): Historical Carbon Dioxide Record from the Vostok Ice Core (417,160 - 2,342 years BP). None. doi:10.3334/CDIAC/ATG.009 https://data.ess-dive.lbl.gov/view/doi:10.3334/CDIAC/ATG.009

Examples

# Fetch Vostok paleo carbon and temperature data from cache if available:
vostok <- get_paleo()
#
# Force cache refresh:
vostok <- get_paleo(use_cache=FALSE)
#
# Review cache contents and last update dates:
hockeystick_cache_details()
#
# Plot output using package's built-in ggplot2 settings
plot_paleo(vostok) 

Download and plot essential climate data

Description

Retrieves Arctic or Antarctic annual Sea Ice Index (in million square km). Source is the National Snow and Ice Data Center, defaults to Arctic (Northern Hemisphere) July sea ice extent. https://nsidc.org/data/explore-data

Usage

get_seaice(
  pole = "N",
  month = "07",
  measure = "extent",
  use_cache = TRUE,
  write_cache = getOption("hs_write_cache")
)

Arguments

Value

Invisibly returns a tibble with the annual series of monthly Sea Ice Index since 1979 (in million square km).

get_seaice invisibly returns a tibble with annual series of monthly Sea Ice Index since 1979 (in million square km).

User may select Arctic or Antarctic sea ice extent or area (in millions of square kilometers) by year for a given month, specified by argument month. Defaults to Arctic July sea ice extent. https://nsidc.org/sea-ice-today/about-data#area_extent

Author(s)

Hernando Cortina, hch@alum.mit.edu

References

• NSIDC Data Archive: https://nsidc.org/data/explore-data

• All About Sea Ice: https://nsidc.org/learn/parts-cryosphere/sea-ice

• Sea Ice terminology: https://nsidc.org/learn

Examples

# Fetch sea ice from cache if available:
seaice <- get_seaice()
#
# Force cache refresh:
seaice <- get_seaice(use_cache = FALSE)
# change region and month
seaice <- get_seaice(pole='S', month='09', use_cache = FALSE)
#
# Review cache contents and last update dates:
hockeystick_cache_details()
#
# Plot output using package's built-in ggplot2 settings
plot_seaice(seaice) 

Download and plot essential climate data

Description

Retrieves global mean sea level (GMSL) data from historic tide gauge and recent satellite altimeter observations (in mm). Source for tide gauge data is Commonwealth Scientific and Industrial Research Organisation (CSIRO) as described in Church and White (2011). https://research.csiro.au/slrwavescoast/sea-level/measurements-and-data/sea-level-data/

Source for satellite data is NOAA Laboratory for Satellite Altimetry: https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/

Usage

get_sealevel(use_cache = TRUE, write_cache = getOption("hs_write_cache"))

Arguments

Value

Invisibly returns a tibble with the global mean sea level time series (in mm) over 1880-2013 using tide gauges and since 1993 for satellite measurements.

get_sealevel invisibly returns a tibble with mean sea level in mm time series from tide gauges and satellite observations.

The satellite observations have been releveled so that their mean level in 1993 matches that of the tide gauges.

The tide gauge data are no longer updated and cover the period from 1880 to 2013, per Church, J. A. and N.J. White (2011) https://research.csiro.au/slrwavescoast/sea-level/measurements-and-data/sea-level-data/

Satellite data are updated monthly or more frequently from the NOAA Laboratory for Satellite Altimetry. TOPEX and Jason-1,-2,-3 satellites dataset, with seasonal signals removed. https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/

Author(s)

Hernando Cortina, hch@alum.mit.edu

References

• CSIRO reconstructed tide gauge GMSL for 1880 to 2009: https://research.csiro.au/slrwavescoast/sea-level/measurements-and-data/sea-level-data/

• Church, J. A. and N.J. White (2011), Sea-level rise from the late 19th to the early 21st Century. Surveys in Geophysics, doi:10.1007/s10712-011-9119-1. https://link.springer.com/article/10.1007/s10712-011-9119-1

• NOAA Laboratory for Satellite Altimetry https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/

Examples

# Fetch sea level from cache if available:
gmsl <- get_sealevel()
#
# Force cache refresh:
gmsl <- get_sealevel(use_cache=FALSE)
#
# Review cache contents and last update dates:
hockeystick_cache_details()
#
# Plot output using package's built-in ggplot2 settings
plot_sealevel(gmsl) 

Download and plot essential climate data

Description

Retrieves the combined global land- and sea-surface temperature anomaly (Land-Ocean Temperature Index, LOTI). Source is NASA/GISS Surface Temperature Analysis (GISTEMP v4), an estimate of global surface temperature change. https://data.giss.nasa.gov/gistemp/

Usage

get_temp(use_cache = TRUE, write_cache = getOption("hs_write_cache"))

Arguments

Value

Invisibly returns a tibble with the annual mean and monthly Combined Land-Surface Air and Sea-Surface Water Temperature Anomalies.

get_temp invisibly returns a tibble with the NASA/GISS annual mean and monthly global temperature anomaly. Data are global from 1880 to present, and represent the deviations from the 1951-1980 mean.

The returned object includes monthly and annual average anomalies, as well as seasonal anomalies. GISS Surface Temperature Analysis (GISTEMP v4) is an estimate of global surface temperature change.

Data are updated around the middle of every month using current data files from NOAA GHCN v4 (meteorological stations) and ERSST v5 (ocean areas). Station data are combined as described in Hansen et al. (2010) https://data.giss.nasa.gov/gistemp/references.html and Lenssen et al. (2019) https://pubs.giss.nasa.gov/abs/le05800h.html

Author(s)

Hernando Cortina, hch@alum.mit.edu

References

• GISS Surface Temperature Analysis (GISTEMP v4): https://data.giss.nasa.gov/gistemp/

• GISTEMP Team, 2020: GISS Surface Temperature Analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies.

• Lenssen, N., G. Schmidt, J. Hansen, M. Menne, A. Persin, R. Ruedy, and D. Zyss, 2019: Improvements in the GISTEMP uncertainty model. J. Geophys. Res. Atmos., 124, no. 12, 6307-6326, doi:10.1029/2018JD029522. https://pubs.giss.nasa.gov/abs/le05800h.html

Examples

# Fetch temp anomaly from cache if available:
anomaly <- get_temp()
#
# Force cache refresh:
anomaly <- get_temp(use_cache=FALSE)
#
# Review cache contents and last update dates:
hockeystick_cache_details()
#
# Plot output using package's built-in ggplot2 settings
plot_temp(anomaly) 

Download and plot essential climate data

Description

Retrieves the Common Era Global Surface Temperature Reconstructions. Source is PAGES2k Consortium and NOAA National Centers for Environmental Information. https://www.ncei.noaa.gov/access/paleo-search/study/26872

Usage

get_temp2k(use_cache = TRUE, write_cache = getOption("hs_write_cache"))

Arguments

Value

Invisibly returns a tibble with filtered and unfiltered temperature reconstructions and Cowtan & Way instrumental temperatures.

get_temp2k invisibly returns a tibble with the PAGES2k Consortium temperature reconstruction (years 1-2000 CE) and instrumental record (years 1850-2017 CE). Temperatures represent deviations from the 1961-1990 mean.

The returned object includes annual average temperature anomalies as well as filtered anomalies using a 31-year Butterworth filter. Reconstructions use seven different statistical methods that draw from a global collection of temperature-sensitive palaeoclimate records.

Methodology described in PAGES2k (2019) https://www.nature.com/articles/s41561-019-0400-0

Author(s)

Hernando Cortina, hch@alum.mit.edu

References

• PAGES2k Common Era Surface Temperature Reconstructions. https://www.ncei.noaa.gov/access/paleo-search/study/26872

• PAGES 2k Consortium., Neukom, R., Barboza, L.A. et al. Consistent multidecadal variability in global temperature reconstructions and simulations over the Common Era. Nat. Geosci. 12, 643–649 (2019). doi:10.1038/s41561-019-0400-0

Examples

# Fetch temp anomaly from cache if available:
anomaly <- get_temp2k()
#
# Force cache refresh:
anomaly <- get_temp2k(use_cache=FALSE)
#
# Review cache contents and last update dates:
hockeystick_cache_details()
#
# Plot output using package's built-in ggplot2 settings
plot_temp2k(anomaly) 

Get rounded size of file in kB

Description

Internal function

Usage

getsize(x)

Arguments

Manage cached datasets

Description

Manage cached datasets

Usage

hockeystick_cache_list()

hockeystick_cache_delete(files, force = TRUE)

hockeystick_cache_delete_all(force = TRUE)

hockeystick_cache_details(files = NULL)

hockeystick_update_all()

Arguments

Details

cache_delete only accepts 1 file name, while cache_delete_all doesn't accept any names, but deletes all files. For deleting many specific files, use cache_delete in a lapply() type call

We cache using tools::R_user_dir(), find your cache folder by executing tools::R_user_dir("hockeystick","cache")

Value

hockeystick_cache_list() returns a character vector of full path filenames in cache.

hockeystick_cache_delete() no return value, called for side effect.

hockeystick_cache_delete_all() no return value, called for side effect.

hockeystick_cache_details() returns list of filenames and sizes of cached files.

hockeystick_update_all() updates all datasets and caches them. No return value, called for side effect.

Functions

• hockeystick_cache_list() returns a character vector of full path file names in cache

• hockeystick_cache_delete() deletes one or more files, returns nothing

• hockeystick_cache_delete_all() delete all files, returns nothing

• hockeystick_cache_details() prints file name and file size of each file, supply with one or more files, or no files (and get details for all available)

• hockeystick_update_all() updates the cache with the latest co2, temperature, sea level, and sea ice data.

References

Caching data sets: ROpenSci guide to persistent config and data for R packages: https://blog.r-hub.io/2020/03/12/user-preferences/

Examples

# list files in cache
hockeystick_cache_list()

# List info for single files
hockeystick_cache_details(files = hockeystick_cache_list()[1])
hockeystick_cache_details(files = hockeystick_cache_list()[2])

# List info for all files
hockeystick_cache_details()

# Delete cached files by name
hockeystick_cache_delete(files = hockeystick_cache_list()[1])

# Update all hockeystick data and place in cache
hockeystick_update_all()

# Delete all cached data
hockeystick_cache_delete_all()

Return path of data cache directory

Description

Internal Function

Usage

hscache_path()

Value

Return path of data cache directory

Download and plot essential climate data

Description

Merge NOAA carbon and NASA temperature datasets on common dates.

Usage

merge_carbontemp(carbon = get_carbon(), temp = get_temp())

Arguments

Details

merge_carbontemp invisibly returns a tibble with the merged data from from get_carbon and get_temp functions. Tibble only includes data from dates when both datasets are available, essentially from 1960.

Value

Invisibly returns a tibble with merged datasets from get_carbon and get_temp functions.

Author(s)

Hernando Cortina, hch@alum.mit.edu

Examples

# Create merged tibble

mergedcarbontemp <- merge_carbontemp() 

Download and plot essential climate data

Description

Plots carbon dioxide data retrieved using get_carbon() with ggplot2. The output ggplot2 object may be modified.

Usage

plot_carbon(dataset = get_carbon(), print = TRUE, annot = TRUE)

Arguments

Details

plot_carbon invisibly returns a ggplot2 object with a pre-defined carbon dioxide chart using data from get_carbon. By default the chart is also displayed. Users may further modify the output ggplot2 chart.

Value

Invisibly returns a ggplot2 object with carbon dioxide chart

Author(s)

Hernando Cortina, hch@alum.mit.edu

Examples

# Fetch carbon dioxide data:
maunaloa <- get_carbon()
#
# Plot output using package's built-in ggplot2 defaults
plot_carbon(maunaloa)

# Or just call plot_carbon(), which defaults to get_carbon() dataset
plot_carbon()

p <- plot_carbon(maunaloa, print = FALSE)
# Modify plot such as: p + ggplot2::labs(title='The Keeling Curve') 

Download and plot essential climate data

Description

Plots the global monthly mean temperature anomaly vs atmospheric carbon with ggplot2. The output ggplot2 object may be further modified.

Usage

plot_carbontemp(dataset = merge_carbontemp(), print = TRUE)

Arguments

Details

plot_carbontemp invisibly returns a ggplot2 object with a pre-defined temperature anomaly vs carbon chart using data from merge_carbontemp. By default the chart is also displayed. Users may further modify the output ggplot2 chart.

Value

Invisibly returns a ggplot2 object with temperature anomaly vs carbon chart

Author(s)

Hernando Cortina, hch@alum.mit.edu

Examples

# Fetch temperature anomaly:
mergedtemp <- merge_carbontemp()
#
# Plot output using package's built-in ggplot2 defaults
plot_carbontemp(mergedtemp)

# Or just call plot_carbontemp(), which defaults to merge_carbontemp() dataset
plot_carbontemp()

p <- plot_carbontemp(mergedtemp, print = FALSE)
# Modify plot such as: p + ggplot2::labs(title='The Signature of Climate Change') 

Download and plot essential climate data

Description

Plots the daily temperatures since 1940 and current anomaly data retrieved using get_dailytempcop() with ggplot2. The output ggplot2 object may be further modified.

Usage

plot_dailytemp(
  dataset = get_dailytempcop(),
  print = TRUE,
  anomaly = FALSE,
  maxtemp = FALSE,
  current_year = as.numeric(substr(Sys.Date(), 1, 4)),
  title_lab = "Daily Average Air Temperature",
  cop = TRUE
)

Arguments

Details

plot_temp invisibly returns a ggplot2 object with a pre-defined daily temperature anomaly chart using data from get_dailytemp. By default the chart is also displayed. Plots one line per year, as well as mean and anomaly (which may be disabled). Users may further modify the output ggplot2 chart.

Value

Invisibly returns a ggplot2 object with daily temperature anomaly chart

Author(s)

Hernando Cortina, hch@alum.mit.edu

Examples

# Fetch temperature anomaly:
dailydata <- get_dailytempcop()
#
# Plot output using package's built-in ggplot2 defaults
plot_dailytemp(dailydata)

# Don't plot anomaly shading and highight specific year
plot_dailytemp(anomaly = FALSE, current_year = 2012)

# Or just call plot_temp(), which defaults to get_dailytempcop() dataset
plot_dailytemp()

p <- plot_dailytemp(dailydata, print = FALSE)
# Modify plot such as: p + ggplot2::labs(title='Record Temperatures in 2023') 

Download and plot essential climate data

Description

Plots carbon dioxide emissions retrieved using get_emissions() with ggplot2. The output ggplot2 object may be modified. Alternative columns from the dataset may also be plotted.

Usage

plot_emissions(
  dataset = get_emissions(),
  start_year = 1900,
  region = "World",
  field = "co2",
  print = TRUE,
  annot = TRUE,
  title_expression = expression("Fossil Combustion " * CO[2] * " Emissions"),
  yaxis_expression = expression("Gt " * CO[2] * " per year")
)

Arguments

Details

plot_emissions invisibly returns a ggplot2 object with a pre-defined carbon dioxide emissions chart using data from get_emissions. Use the field parameter to select alternative columns from the data set such as co2_per_capita. By default the chart is also displayed. Users may further modify the output ggplot2 chart.

Value

Invisibly returns a ggplot2 object with carbon dioxide emissions chart

Author(s)

Hernando Cortina, hch@alum.mit.edu

Examples

# Fetch carbon dioxide emissions:
emissions <- get_emissions()

# Plot output using package's built-in ggplot2 defaults
plot_emissions(emissions)

# Or just call plot_emissions(), which defaults to get_emissions() dataset
plot_emissions()

# You can also select a region by name and start year
plot_emissions(region='United States', start_year=1950)
p <- plot_emissions(emissions, print = FALSE)

# Modify plot such as: p + ggplot2::labs(title='Anthropogenic Carbon Emissions')

# Plot a different field from GCP dataset
plot_emissions(field='co2_per_capita', yaxis_expression=expression(CO[2]*' per capita' ))

Download and plot essential climate data

Description

Plots carbon dioxide emissions (including land use change) retrieved using get_emissions() with ggplot2. The output ggplot2 object may be modified. Alternative columns from the dataset may also be plotted.

Usage

plot_emissions_with_land(
  dataset = get_emissions(),
  start_year = 1900,
  region = "World",
  print = TRUE,
  annot = TRUE,
  title_expression = expression("Fossil + Land Use Change " * CO[2] * " Emissions"),
  yaxis_expression = expression("Gt " * CO[2] * " per year")
)

Arguments

Details

plot_emissions_with_land invisibly returns a ggplot2 object with a pre-defined carbon dioxide emissions (including land use change) chart using data from get_emissions. By default the chart is also displayed. Users may further modify the output ggplot2 chart.

Value

Invisibly returns a ggplot2 object with carbon dioxide emissions chart

Author(s)

Hernando Cortina, hch@alum.mit.edu

Examples

# Fetch carbon dioxide emissions:
emissions <- get_emissions()

# Plot output (including land use change) using package's built-in ggplot2 defaults
plot_emissions_with_land(emissions)

# Or just call plot_emissions_with_land(), which defaults to get_emissions() dataset
plot_emissions_with_land()

# You can also select a region by name and starting year
plot_emissions_with_land(region='United States', start_year=1950)
p <- plot_emissions_with_land(emissions, print = FALSE)

# Modify plot such as: p + ggplot2::labs(title='Anthropogenic Carbon Emissions')

Download and plot essential climate data

Description

Plots the hurricane energy data (ACE) retrieved using get_hurricanes() with ggplot2. The output ggplot2 object may be further modified.

Usage

plot_hurricane_nrg(dataset = get_hurricanes(), print = TRUE)

Arguments

Details

plot_hurricane_nrg invisibly returns a ggplot2 object with a pre-defined hurricane energy data chart using data from get_hurricanes. By default the chart is also displayed. Smooths using ggplot2's built-in loess smoother. Users may further modify the output ggplot2 chart.

ACE is an index that combines the number of systems, how long they existed and how intense they became. It is calculated by squaring the maximum sustained surface wind in the system every six hours that the cyclone is a Named Storm and summing it up for the season. Please refer to above website for details.

Value

Invisibly returns a ggplot2 object with hurricane energy chart

Author(s)

Hernando Cortina, hch@alum.mit.edu

Examples

# Fetch hurricane data:
hurricanes <- get_hurricanes()
#
# Plot output using package's built-in ggplot2 defaults
plot_hurricane_nrg(hurricanes)

# Or just call plot_hurricane_nrg(), which defaults to get_hurricanes() dataset
plot_hurricane_nrg()

p <- plot_hurricane_nrg(hurricanes, print = FALSE)
# Modify plot such as: p + ggplot2::labs(title='Accumulated Cyclone Energy') 

Download and plot essential climate data

Description

Plots the hurricane data retrieved using get_hurricanes() with ggplot2. The output ggplot2 object may be further modified.

Usage

plot_hurricanes(dataset = get_hurricanes(), cat = "major", print = TRUE)

Arguments

Details

plot_hurricanes invisibly returns a ggplot2 object with a pre-defined hurricane data chart using data from get_hurricanes. By default the chart is also displayed. Smooths using ggplot2's built-in loess smoother. Users may further modify the output ggplot2 chart. Categories may be "major" (category 3-5), "hurricane" (category 1-5), or "storm" (named storm).

Value

Invisibly returns a ggplot2 object with hurricanes chart

Author(s)

Hernando Cortina, hch@alum.mit.edu

Examples

# Fetch hurricane data:
hurricanes <- get_hurricanes()
#
# Plot output using package's built-in ggplot2 defaults
plot_hurricanes(hurricanes)

# Or just call plot_hurricanes(), which defaults to get_hurricanes() dataset
plot_hurricanes()

p <- plot_hurricanes(hurricanes, print = FALSE)
# Modify plot such as: p + ggplot2::labs(title='Growing number of North Atlantic named storms') 

Download and plot essential climate data

Description

Plots the monthly Sea Ice Index data retrieved using get_icecurves() with ggplot2. The output ggplot2 object may be further modified.

Usage

plot_icecurves(dataset = get_icecurves(), print = TRUE)

Arguments

Details

plot_icecurves invisibly returns a ggplot2 object with a pre-defined Sea Ice Index chart using data from get_icecurves. By default the chart is also displayed. Users may further modify the output ggplot2 chart. Chart consists of one line per year showing monthly sea ice from January through December. Current year is highlighted.

Value

Invisibly returns a ggplot2 object with Sea Ice Index chart

Author(s)

Hernando Cortina, hch@alum.mit.edu

Examples

# Fetch historic monthly sea ice data since 1979:
icecurves <- get_icecurves()
#
# Plot output using package's built-in ggplot2 defaults
plot_icecurves(icecurves)

# Or just call plot_icecurves(), which defaults to get_icecurves() dataset
plot_icecurves()

p <- plot_icecurves(icecurves, print = FALSE)
# Modify plot such as: p + ggplot2::labs(title='Shrinking Arctic Sea Ice') 

Download and plot essential climate data

Description

Plots atmospheric methane data retrieved using get_methane() with ggplot2. The output ggplot2 object may be modified.

Usage

plot_methane(dataset = get_methane(), print = TRUE, annot = TRUE)

Arguments

Details

plot_methane invisibly returns a ggplot2 object with a pre-defined methane chart using data from get_methane. By default the chart is also displayed. Users may further modify the output ggplot2 chart.

Value

Invisibly returns a ggplot2 object with methane chart

Author(s)

Hernando Cortina, hch@alum.mit.edu

Examples

# Fetch methane data:
ch4 <- get_methane()
#
# Plot output using package's built-in ggplot2 defaults
plot_methane(ch4)

# Or just call plot_methane(), which defaults to get_methane() dataset
plot_methane()

p <- plot_methane(ch4, print = FALSE)
# Modify plot such as: p + ggplot2::labs(title='Trend in Atmospheric Methane') 

Download and plot essential climate data

Description

Plots the Vostok ice core data retrieved using get_paleo() with ggplot2. The output ggplot2 object may be further modified.

Usage

plot_paleo(dataset = get_paleo(), print = TRUE)

Arguments

Details

plot_paleo invisibly returns a ggplot2 object with a pre-defined Vostok ice core chart using data from get_paleo. The returned chart stacks carbon dioxide concentration over temperature over 420,000 years. By default the chart is also displayed. Users may further modify the output ggplot2 chart.

Value

Invisibly returns a ggplot2 object with the Vostok chart

Author(s)

Hernando Cortina, hch@alum.mit.edu

References

Barnola, J.-M., D. Raynaud, C. Lorius, and N.I. Barkov. 2003. Historical CO2 record from the Vostok ice core. In Trends: A Compendium of Data on Global Change. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A

Examples

# Fetch Vostok data:
vostok <- get_paleo()
#
# Plot output using package's built-in ggplot2 defaults
plot_paleo(vostok)

# Or just call plot_paleo(), which defaults to get_paleo() dataset
plot_paleo()

p <- plot_paleo(vostok, print = FALSE)
# Modify plot such as: p + patchwork::plot_annotation(title='A Long History of Carbon') 

Download and plot essential climate data

Description

Plots the Sea Ice Index data retrieved using get_seaice() with ggplot2. The output ggplot2 object may be further modified.

Usage

plot_seaice(dataset = get_seaice(), title = "Arctic Sea Ice", print = TRUE)

Arguments

Details

plot_seaice invisibly returns a ggplot2 object with a pre-defined Sea Ice Index chart using data from get_seaice. By default the chart is also displayed. Users may further modify the output ggplot2 chart.

Value

Invisibly returns a ggplot2 object with Sea Ice Index chart

Author(s)

Hernando Cortina, hch@alum.mit.edu

Examples

# Fetch sea ice data:
seaice <- get_seaice()
#
# Plot output using package's built-in ggplot2 defaults
plot_seaice(seaice)

# Or just call plot_seaice(), which defaults to get_seaice() dataset
plot_seaice()

p <- plot_seaice(seaice, print = FALSE)
# Modify plot such as: p + ggplot2::labs(title='Shrinking Arctic Sea Ice') 

Download and plot essential climate data

Description

Plots the global mean sea level data retrieved using get_sealevel() with ggplot2. The output ggplot2 object may be further modified.

Usage

plot_sealevel(dataset = get_sealevel(), print = TRUE)

Arguments

Details

plot_sealevel invisibly returns a ggplot2 object with a pre-defined sealevel change chart using data from get_sealevel. By default the chart is also displayed. Users may further modify the output ggplot2 chart.

Value

Invisibly returns a ggplot2 object with sealevel chart

Author(s)

Hernando Cortina, hch@alum.mit.edu

Examples

# Fetch sealevel data:
gmsl <- get_sealevel()
#
# Plot output using package's built-in ggplot2 defaults
plot_sealevel(gmsl)

# Or just call plot_sealevel(), which defaults to get_sealevel() dataset
plot_sealevel()

p <- plot_sealevel(gmsl, print = FALSE)
# Modify plot such as: p + ggplot2::labs(title='Rising Waters') 

Download and plot essential climate data

Description

Plots the global annual mean temperature anomaly retrieved using get_temp() with ggplot2. The output ggplot2 object may be further modified.

Usage

plot_temp(dataset = get_temp(), print = TRUE)

Arguments

Details

plot_temp invisibly returns a ggplot2 object with a pre-defined temperature annual mean anomaly chart using data from get_temp. By default the chart is also displayed. Smooths using ggplot2's built-in loess smoother. Users may further modify the output ggplot2 chart.

Value

Invisibly returns a ggplot2 object with temperature anomaly chart

Author(s)

Hernando Cortina, hch@alum.mit.edu

Examples

# Fetch temperature anomaly:
anomaly <- get_temp()
#
# Plot output using package's built-in ggplot2 defaults
plot_temp(anomaly)

# Or just call plot_temp(), which defaults to get_temp() dataset
plot_temp()

p <- plot_temp(anomaly, print = FALSE)
# Modify plot such as: p + ggplot2::labs(title='The Signature of Climate Change') 

Download and plot essential climate data

Description

Plots the Common Era 2000-year global temperature anomaly retrieved using get_temp2k() with ggplot2. The output ggplot2 object may be further modified.

Usage

plot_temp2k(
  dataset = get_temp2k(),
  instrumental = TRUE,
  filtered = TRUE,
  print = TRUE
)

Arguments

Details

plot_temp2k invisibly returns a ggplot2 object with a pre-defined temperature anomaly chart using data from get_temp2k. By default the chart is also displayed. Users may further modify the output ggplot2 chart.

Value

Invisibly returns a ggplot2 object with temperature anomaly chart

Author(s)

Hernando Cortina, hch@alum.mit.edu

Examples

# Fetch temperature anomaly:
anomaly <- get_temp2k()
#
# Plot output using package's built-in ggplot2 defaults
plot_temp2k(anomaly)

# Or just call plot_temp2k(), which defaults to get_temp2k() dataset
plot_temp2k()

p <- plot_temp2k(anomaly, print = FALSE)
# Modify plot such as: p + ggplot2::labs(title='Temperature Increase in the Common Era') 

Download and plot essential climate data

Description

Plots the monthly mean temperature anomaly retrieved using get_temp() with ggplot2. The output ggplot2 object may be further modified.

Usage

plot_temp_monthly(dataset = get_temp(), print = TRUE)

Arguments

Details

plot_temp_monthly invisibly returns a ggplot2 object with a pre-defined temperature monthly mean anomaly chart using data from get_temp. By default the chart is also displayed. Smooths using ggplot2's built-in loess smoother. Users may further modify the output ggplot2 chart.

Value

Invisibly returns a ggplot2 object with temperature anomaly chart

Author(s)

Hernando Cortina, hch@alum.mit.edu

Examples

# Fetch temperature anomaly:
anomaly <- get_temp()
#
# Plot output using package's built-in ggplot2 defaults
plot_temp_monthly(anomaly)

# Or just call plot_temp_monthly(), which defaults to get_temp() dataset
plot_temp_monthly()

p <- plot_temp_monthly(anomaly, print = FALSE)
# Modify plot such as: p + ggplot2::labs(title='The Signature of Climate Change') 

Download and plot essential climate data

Description

Plots scatter of monthly temperature anomaly retrieved using get_temp() with ggplot2. The output ggplot2 object may be further modified.

Usage

plot_temp_scatter(
  dataset = get_temp(),
  print = TRUE,
  labelmax = FALSE,
  labellatest = TRUE
)

Arguments

Details

plot_temp_scatter invisibly returns a ggplot2 object with a pre-defined temperature monthly mean anomaly chart using data from get_temp. By default the chart is also displayed. Smooths using ggplot2's built-in loess smoother. Users may further modify the output ggplot2 chart.

Value

Invisibly returns a ggplot2 object with temperature anomaly chart

Author(s)

Hernando Cortina, hch@alum.mit.edu

Examples

# Fetch temperature anomaly:
anomaly <- get_temp()
#
# Plot output using package's built-in ggplot2 defaults
plot_temp_scatter(anomaly)

# Or just call plot_temp_scatter(), which defaults to get_temp() dataset
plot_temp_scatter()

p <- plot_temp_scatter(anomaly, print = FALSE)
# Modify plot such as: p + ggplot2::labs(title='The Signature of Climate Change') 

Display data cache info Shows filenames and cache file sizes

Description

Display data cache info Shows filenames and cache file sizes

Usage

## S3 method for class 'hockeystick_cache_info'
print(x, ...)

Arguments

Value

Display data cache info. No return value, called for side effect.

Download and plot essential climate data

Description

Plots global "warming stripes" graph in the style popularized by Ed Hawkins, based on temperature anomaly retrieved using get_temp(). Function can output stripes chart with legend or a minimal chart. The output ggplot2 object may be further modified.

Usage

warming_stripes(
  dataset = get_temp(),
  stripe_only = FALSE,
  col_strip = RColorBrewer::brewer.pal(11, "RdBu"),
  print = TRUE
)

Arguments

Details

warming_stripes invisibly returns a ggplot2 object with warming stripes chart using data from get_temp. By default the chart is also displayed. User may modify color palette or remove axes and legend. Users may further modify the output ggplot2 chart.

Value

Invisibly returns a ggplot2 object with warming stripes

Author(s)

Hernando Cortina, hch@alum.mit.edu

References

• Climate Lab. 2018. https://www.climate-lab-book.ac.uk/2018/warming-stripes/

• GISS Surface Temperature Analysis (GISTEMP v4): https://data.giss.nasa.gov/gistemp/

• GISTEMP Team, 2020: GISS Surface Temperature Analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies.

• Dr. Dominic Roye blog post "How to Create Warming Stripes in R": https://dominicroye.github.io/en/2018/how-to-create-warming-stripes-in-r/

Examples

# Draw with axes and legend
stripes <- warming_stripes()

# Draw stripes only
stripes <- warming_stripes(stripe_only = TRUE)

# Don't display, store for further modifications
stripes <- warming_stripes(print = FALSE)

# Change color palette
stripes <- warming_stripes(stripe_only = TRUE, col_strip = viridisLite::viridis(11))