How to Read Nc File From Internet Into R
Climate model output files are available for CMIP5 in the netCDF file format. This vignette explains how files in this format can be opened and worked with in R to generate the required delimited format to apply with the futureheatwaves package. Information technology also provides details on CMIP5 climate model output files in general.
Downloading netCDF files for CMIP5
The Coupled Model Intercomparison Projection is a fundamental source of climate model output data for researchers studying the potential impact of climate change. This project is currently in its fifth phase (CMIP5). You lot can download netCDF files from CMIP5 from the Earth Climate Enquiry Programme CMIP5 website at https://pcmdi.llnl.gov/search/cmip5/, hosted past the Department of Energy'south Lawrence Livermore National Laboratory. The data is stored and distributed across multiple data nodes at different institutions, but this and other information portals let access to all available data through ane website. You volition demand to register to download climate model output data.
There are a number of parameters that can be used to search files, including:
- "Institute": The climate modeling group that created the climate model. A list with the full names and IDs for each of these is available at http://cmip-pcmdi.llnl.gov/cmip5/docs/CMIP5_modeling_groups.pdf. Output from a few groups is restricted to non-commercial utilise. Several institutes have multiple models.
- "Climate model": The name of the climate model used to produce the output. Chapter nine of the Intergovernmental Panel on Climatic change (IPCC)'s Fifth Assessment Study provides an evaluation of climate models (available at https://www.ipcc.ch/pdf/assessment-study/ar5/wg1/WG1AR5_Chapter09_FINAL.pdf; Flato et al. 2013).
- "Experiment": The scenario of external, fourth dimension-varying values (e.g., greenhouse gas concentrations) that are input to the model. Standard experiments are "historical" (uses observed or reconstructed data typically from 1850 to 2005 as model inputs), "piControl" (pre-Industrial control), "rcp45" (uses concentrations based on a scenario of forcing agents from 2006 to 2100), and "rcp85" (same period equally "rcp45", just with a different scenario of forcing agents). For more information on the CMIP5 experiment design, see Taylor et al. (2012) and http://cmip-pcmdi.llnl.gov/cmip5/experiment_design.html.
- "Time frequency": The time pace of model output (east.g., day, month, yr)
- "Ensemble": The ensemble member. Many climate models are run with multiple ensemble members, to allow exploration of dubiety related to internal climate variability. For different ensemble member run for a climate model and experiment, the initial weather are changed by a small amount, just the model and time-varying inputs are identical. The most common ensemble fellow member is "ri1ip1".
- "Variable": The variable output past the model. Examples available at the near-surface include "tas" (nigh-surface air temperature), "tasmin" (daily minimum near-surface air temperature), "tasmax" (daily maximum near-surface air temperature), "hurs" (about-surface relative humidity), "pr" (atmospheric precipitation). Explanations of variable abbreviations are available at http://cmip-pcmdi.llnl.gov/cmip5/docs/standard_output.pdf.
You tin download files from https://pcmdi.llnl.gov/search/cmip5/ using a indicate-and-click interface, but you can also utilize their ESGF Search RESTful API to create a wget script that tin be used to download many files at once and to later update files by only downloading those that have changed since the final download. These climate model output files can exist very large, and so be certain you have adequate deejay infinite before downloading.
Working with netCDF files in R
To try out the examples in this section, you lot will need to download the following climate model output file to your own figurer from https://pcmdi.llnl.gov/search/cmip5/ and save it in a "tmp" subdirectory of your habitation directory:
- tas_day_GFDL-ESM2G_rcp85_r1i1p1_20710101-20751231.nc
As a notation, the CMIP5 file names encode information almost the climate model output given in the file, including the variable (here, "tas", the virtually-surface air temperature), the experiment ("historical"), the dates covered past the output (January. 1, 1986 to Dec. 31, 1990), the climate model ("GFDL-ESM2G", from the NOAA Geophysical Fluid Dynamics Laboratory), the fourth dimension step of the output ("day"), and the ensemble member ("r1i1p1").
3 R packages— ncdf4, ncdf4.helpers, and PCICt—provide useful tools for working with netCDF climate model output files.
library(ncdf4) library(ncdf4.helpers) library(PCICt) Some of the standard tidyverse packages are too helpful and should be loaded to run the example code in this vignette:
library(readr) library(dplyr) library(tidyr) library(ggplot2) You can read netCDF data in R using the ncdf4 package. You lot can utilize the nc_open part from the ncdf4 parcel to open up a connection to a netCDF file. The version of netCDF changed from version 3 to version four in 2008, and some R packages for netCDF files volition only work with the older version 3, but the ncdf4 package can work with either.
climate_filepath <- paste0("~/tmp/tas_day_GFDL-ESM2G_rcp85", "_r1i1p1_20710101-20751231.nc") climate_output <- nc_open(climate_filepath) The nc_open function opens a connection to the netCDF file, but does not read all the data in the file into memory. Instead, this function reads into memory some information nigh the file, and it opens a connexion that can exist used in conjunction with other commands to read in specific parts of the data. Once you no longer need the file connexion, you lot should close the connection with nc_close.
If yous print the object created past nc_open, you will become some metadata on the file, including the variables that are bachelor in the file, which are used for dimensions, and what units each are in. For example, hither is the output returned for the "tas_day_GFDL-ESM2G_rcp85_r1i1p1_20710101-20751231.nc" from CMIP5:
File ~/tmp/tas_day_GFDL-ESM2G_rcp85_r1i1p1_20710101-20751231.nc (NC_FORMAT_64BIT): eight variables (excluding dimension variables): double average_DT[fourth dimension] long_name: Length of average period units: days double average_T1[time] long_name: Outset time for average flow units: days since 2006-01-01 00:00:00 double average_T2[time] long_name: Cease time for boilerplate menses units: days since 2006-01-01 00:00:00 float tas[lon,lat,time] long_name: Near-Surface Air Temperature units: K valid_range: 100 valid_range: 400 cell_methods: time: mean coordinates: peak missing_value: 1.00000002004088e+twenty _FillValue: ane.00000002004088e+20 standard_name: air_temperature original_units: deg_k original_name: t_ref cell_measures: surface area: areacella associated_files: baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation areacella: areacella_fx_GFDL-ESM2G_rcp85_r0i0p0.nc double time_bnds[bnds,fourth dimension] long_name: time axis boundaries units: days since 2006-01-01 00:00:00 double height[] units: m positive: up long_name: meridian standard_name: acme axis: Z double lat_bnds[bnds,lat] double lon_bnds[bnds,lon] iv dimensions: time Size:1825 *** is unlimited *** long_name: time units: days since 2006-01-01 00:00:00 cartesian_axis: T calendar_type: noleap calendar: noleap premises: time_bnds standard_name: time axis: T lat Size:90 long_name: breadth units: degrees_north standard_name: latitude axis: Y bounds: lat_bnds lon Size:144 long_name: longitude units: degrees_east standard_name: longitude axis: 10 bounds: lon_bnds bnds Size:2 long_name: vertex number cartesian_axis: Due north 27 global attributes: championship: NOAA GFDL GFDL-ESM2G, RCP8.5 (run 1) experiment output for CMIP5 AR5 institute_id: NOAA GFDL source: GFDL-ESM2G 2010 bounding main: TOPAZ (TOPAZ1p2,Tripolar360x210L63); atmosphere: AM2 (AM2p14,M45L24); sea water ice: SIS (SISp2,Tripolar360x210L63); land: LM3 (LM3p7_cESM,M45) contact: gfdl.climate.model.info@noaa.gov project_id: CMIP5 table_id: Table day (31 Jan 2011) experiment_id: rcp85 realization: 1 modeling_realm: atmos tracking_id: 547a3ae9-d62c-4ef5-91f3-3846156ca0ce Conventions: CF-1.4 references: The GFDL Data Portal (http://nomads.gfdl.noaa.gov/) provides access to NOAA/GFDL's publicly available model input and output data sets. From this web site one tin can view and download data sets and documentation, including those related to the GFDL coupled models experiments run for the IPCC'southward 5th Assessment Report and the United states of america CCSP. comment: GFDL experiment name = ESM2G-HC2_2006-2100_all_rcp85_ZC2. PCMDI experiment proper noun = RCP8.5 (run1). Initial weather for this experiment were taken from 1 Jan 2006 of the parent experiment, ESM2G-C2_all_historical_HC2 (historical). Several forcing agents varied during the 95 year duration of the RCP8.five experiment based upon the Message integrated assessment model for the 21st century. The time-varying forcing agents include the well-mixed greenhouse gases (CO2, CH4, N2O, halons), tropospheric and stratospheric O3, model-derived aerosol concentrations (sulfate, black and organic carbon, sea table salt and dust), and land use transitions. Volcanic aerosols were zero and solar irradiance varied seasonally based upon tardily 20th century averages just with no interannual variation. The direct effect of tropospheric aerosols is calculated by the model, but not the indirect effects. gfdl_experiment_name: ESM2G-HC2_2006-2100_all_rcp85_ZC2 creation_date: 2011-11-21T22:35:45Z model_id: GFDL-ESM2G branch_time: 52925 experiment: RCP8.5 forcing: GHG,SD,Oz,LU,SS,BC,Physician,OC (GHG includes CO2, CH4, N2O, CFC11, CFC12, HCFC22, CFC113) frequency: day initialization_method: 1 parent_experiment_id: historical physics_version: 1 product: output1 establishment: NOAA GFDL(201 Forrestal Rd, Princeton, NJ, 08540) history: File was processed by fremetar (GFDL analog of CMOR). TripleID: [exper_id_4Uu6vwuRdV,realiz_id_guLAFs4zpC,run_id_19LHaF9KF9] parent_experiment_rip: r1i1p1 Once you know the names of variables in the file, y'all can use the ncvar_get function to read all values of that variable into an R object. For example, to get all the values of longitude and latitude in the example file, which ascertain two of the dimensions of the assortment for the temperature values reported in the file, you lot can run:
lon <- ncvar_get(climate_output, varid = "lon") lat <- ncvar_get(climate_output, varid = "lat") summary(lon) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## one.25 ninety.63 180.00 180.00 269.40 358.80 ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## -89.49 -45.00 0.00 0.00 45.00 89.49 In add-on to the longitude and latitude dimensions, the climate model output files likewise include a time dimension. The metadata for the file includes information on how this fourth dimension is reported, which is typically in days since some origin date for climate model output. These origin dates can vary across climate models. This information is included in the $dim$time$units chemical element of the R object returned with nc_open:
climate_output$dim$time$units ## [1] "days since 2006-01-01 00:00:00" While the R function equally.Date allows the user to specify an origin to employ to compute a date, this role typically will not work correctly with climate model output files, as climate models often provide output using calendars other than the standard Gregorian agenda. Climate model output tin can be tied to a number of different calendars, including a "no-leap" calendar, which omits leap days from the agenda, a 360-twenty-four hour period calendar, which uses 30-day months for all 12 months, and a number of other calendars. You tin determine the calendar used for a netCDF file from the $dim$fourth dimension$calendar element of the R object returns with nc_open:
climate_output$dim$time$agenda ## [1] "noleap" This utilize of non-standard calendars messes upwards the calculations of every bit.Engagement, then y'all must use a more specialized part to read in the fourth dimension variable of the climate model output and map information technology to standard calendar dates. Yous can practise this with the nc.become.time.series role from the ncdf4.helpers bundle (y'all should also load the PCICt package to utilize this office):
tas_time <- nc.go.time.series(climate_output, 5 = "tas", fourth dimension.dim.name = "time") tas_time[c(1:3, length(tas_time) - ii:0)] ## [one] "2071-01-01 12:00:00" "2071-01-02 12:00:00" "2071-01-03 12:00:00" ## [4] "2075-12-29 12:00:00" "2075-12-thirty 12:00:00" "2075-12-31 12:00:00" The longitude, latitude, and fourth dimension variables ascertain the dimensions for climate model output for variables given at a single depth (e.g., near-surface variables; other variables like ocean temperature may as well take a depth or pressure dimension). For the example file, this is the near-surface air temperature at a specific filigree point (longitude, latitude) and twenty-four hour period (time). You lot can read in this main variable using ncvar_get:
tas <- ncvar_get(climate_output, "tas") This variable is in a three-dimensional array, with dimensions ordered as showtime longitude, so latitude, and so time:
## [1] 144 90 1825 ## [1] 144 ## [1] 90 ## [1] 1825 You can use indexing to pull the modeled temperature at a certain location and time step. For example, Beijing, China, is at latitude 39.9 degrees north and 116.4 degrees eastward. To get the value of the modeled temperature on July fifteen, 2075, at the model grid indicate closest to Beijing, you can run (the units on the output are Kelvin):
lon_index <- which.min(abs(lon - 116.iv)) lat_index <- which.min(abs(lat - 39.9)) time_index <- which(format(tas_time, "%Y-%yard-%d") == "2075-07-xv") tas[lon_index, lat_index, time_index] ## [1] 293.3458 If you but want to read in a sure department of the data from the netCDF, y'all can do that using the nc.become.var.subset.past.axes function from the package ncdf4.helpers. For example, to become and plot the full time series of modeled temperature at the grid signal closest to Beijing, you can run:
lon_index <- which.min(abs(lon - 116.four)) lat_index <- which.min(abs(lat - 39.nine)) tas <- nc.become.var.subset.by.axes(climate_output, "tas", axis.indices = list(X = lon_index, Y = lat_index)) data_frame(time = tas_time, tas = as.vector(tas)) %>% mutate(time = as.Engagement(format(time, "%Y-%m-%d"))) %>% ggplot(aes(x = time, y = tas)) + geom_line() + xlab("Date") + ylab("Temperature (One thousand)") + ggtitle("Daily modeled almost-surface air temprature, 2071-2075", subtitle = "At model grid point nearest Beijing, China") + theme_classic()
Note that this code uses format and every bit.Date to convert the PCICt object to a appointment object, to permit use of a date axis when plotting with ggplot2.
To become the modeled temperature on July 15, 2075, at all grid betoken locations, you tin run:
july_15_2075 <- nc.get.var.subset.by.axes(climate_output, "tas", centrality.indices = list(T = time_index)) july_15_2075[1:3, 1:3, ] ## [,1] [,ii] [,3] ## [ane,] 218.8687 214.4591 219.0996 ## [2,] 218.8718 214.2570 218.6634 ## [3,] 218.8741 213.7723 217.6466 This modeled temperature data for a unmarried day tin exist mapped using ggmap and ggplot2 functions (the viridis package is also used here for the color scale and the weathermetrics packet to convert the temperature from Kelvin to Celsius). Because the data is a iii-dimensional assortment, where the starting time element is specific to longitude and the second to latitude. To tidy this data to go far easier to map with ggplot2, you can use expand.grid to create a dataframe with every combination of longitude and breadth, and then use as.vector with the temperature values for July 15, 2075, to add temperature values at each of these longitudes and latitudes. Considering the longitude dimension precedes the latitude dimension in the tas array, it should be put commencement in the expand.grid so that longitude and latitude values align correctly with the unlisted temperature data. If longitude is expressed between 0 and 360 degrees, it should be mutated to have values betwixt -180 and 180 to work better with ggplot2 mapping.
library(ggmap) library(viridis) library(weathermetrics) time_index <- which(format(tas_time, "%Y-%m-%d") == "2075-07-15") tas <- nc.become.var.subset.by.axes(climate_output, "tas", centrality.indices = list(T = time_index)) aggrandize.grid(lon, lat) %>% rename(lon = Var1, lat = Var2) %>% mutate(lon = ifelse(lon > 180, -(360 - lon), lon), tas = as.vector(tas)) %>% mutate(tas = convert_temperature(tas, "k", "c")) %>% ggplot() + geom_point(aes(x = lon, y = lat, color = tas), size = 0.8) + borders("world", colour= "black", fill= NA) + scale_color_viridis(name = "Temperature (C)") + theme_void() + coord_quickmap() + ggtitle("Modeled temperature on a day in July 2075", subtitle = "GFDL-ESM2G model, RCP8.5 experiment, r1i1p1 ensemble fellow member")
In one case you have read all variables into R objects, you lot tin can close the file using:
Working with climate model output information with other R packages
The RCMIP5 bundle
The RCMIP5 packet (Todd-Brown and Bond-Lamberty 2015) tin besides exist used to work with climate model output data. Some functions in this package only work with monthly or less frequent data (checkTimePeriod, cmip5data, functions that piece of work with cmip5data objects like filterDimensions and getProjectionMatrix). The package'southward getFileInfo function, however, will piece of work with CMIP5 files of any time footstep; this function identifies all CMIP5 files in a directory and creates a dataframe that parses the information contained in the file name. For example, if you download the CMIP5 files required for the adjacent section of the vignette in the "tmp" subdirectory of your home directory, you can parse their filenames to get information almost the files by running:
library(RCMIP5) getFileInfo("~/tmp") ## path ## one /Users/_gbanders/tmp ## 2 /Users/_gbanders/tmp ## 3 /Users/_gbanders/tmp ## 4 /Users/_gbanders/tmp ## filename variable domain ## ane tas_day_GFDL-ESM2G_historical_r1i1p1_19860101-19901231 tas day ## 2 tas_day_GFDL-ESM2G_historical_r1i1p1_19910101-19951231 tas day ## iii tas_day_GFDL-ESM2G_rcp85_r1i1p1_20710101-20751231 tas day ## four tas_day_GFDL-ESM2G_rcp85_r1i1p1_20760101-20801231 tas day ## model experiment ensemble time size ## 1 GFDL-ESM2G historical r1i1p1 19860101-19901231 92487K ## two GFDL-ESM2G historical r1i1p1 19910101-19951231 92487K ## 3 GFDL-ESM2G rcp85 r1i1p1 20710101-20751231 92487K ## 4 GFDL-ESM2G rcp85 r1i1p1 20760101-20801231 92487K While the loadCMIP5 office does successfully load the daily information as a cmip5data object, it seems that most of the methods for this object blazon do not do anything meaningful for information at this fourth dimension resolution.
The wux packet
The wux package (Mendlik, Heinrich, and Leuprecht 2016) includes functions that allow the user to download CMIP5 monthly-aggregated output directly from within R with the CMIP5fromESGF function. However, this role does not let downloading of climate model output with finer time steps, like daily data.
This package uses the models2wux role to read in climate model output netCDF files and convert to a WUX dataframe that can be used past other functions in the package. While this office can input climate model output with daily time steps, if the element "what.timesteps" of the modelinput list input is ready to "daily", the role aggregates this data to a monthly or less frequent (e.g., seasonal) assemblage when creating the WUX dataframe. Therefore, while this package provides very useful functionality for working with averaged output of daily climate model output data, information technology cannot hands be used to identify and characterize multi-day extreme events like heat waves.
Processing climate model output information to use with futureheatwaves
To try out the examples in this department, you volition demand to download the following climate model output files to your own computer from https://pcmdi.llnl.gov/search/cmip5/ and save them in a "tmp" subdirectory of your abode directory:
- tas_day_GFDL-ESM2G_historical_r1i1p1_19860101-19901231.nc
- tas_day_GFDL-ESM2G_historical_r1i1p1_19910101-19951231.nc
- tas_day_GFDL-ESM2G_rcp85_r1i1p1_20710101-20751231.nc
- tas_day_GFDL-ESM2G_rcp85_r1i1p1_20760101-20801231.nc
The futureheatwaves parcel requires that climate output files are in a certain output to be candy. The data in the climate model output netCDF must be saved in iii files: one for time, one for grid point location (breadth and longitude), and one for the climate model output variable (eastward.g., temperature in the example file used in this case).
The following function can be used to correctly format and save the data from the climate model output netCDF file in a way that will allow processing by the futureheatwaves package. Note that, when the data for a single ensemble member of one climate model and experiment is split across multiple files, the information from after files should be appended to the delimited file written out with data from the first file. Further, if you are but studying a certain region, you tin can subset the climate model output to just the latitude-longitude ranges of that region before writing out delimited files, which volition salve infinite for saving those files. In this example, we accept subset data to latitude-longitude ranges appropriate for People's republic of china (70 to 140 degrees east for longitude and xv to sixty degrees northward for latitude).
# Load required libraries library(ncdf4) library(ncdf4.helpers) library(PCICt) library(readr) library(dplyr) library(tidyr) #' Role to procedure CMIP5 netCDF files to format required by #' `futureheatwaves` packet #' #' filepath: A character vector giving the path to the netCDF #' climate model output file #' output_dir: A character vector giving the path to the directory #' where output should be saved #' var: The climate model variable ("tas" is nigh-surface air temperature) #' suspend: Logical specifying whether results should be appended to #' existing files in the output directory #' lon_range: If not NULL, a length two numerical vector giving the range #' of longitudes to be saved. Values should exist in degrees east in the #' 0 to 360 degree range. #' lat_range: If not Nil, a length two numerical vector giving the range #' of latitudes to exist saved. Values should be in degrees north in the # -90 to 90 range. process_cmip5_file <- function(filepath, output_dir, var = "tas", append = FALSE, lon_range = NULL, lat_range = NULL){ # Open netCDF connection, read in dimensions and climate variable climate_output <- ncdf4::nc_open(filepath) lon <- ncdf4::ncvar_get(climate_output, "lon") lat <- ncdf4::ncvar_get(climate_output, "lat") tas_time <- ncdf4.helpers::nc.get.fourth dimension.serial(climate_output, v = var, fourth dimension.dim.name = "time") tas <- ncdf4::ncvar_get(climate_output, var) ncdf4::nc_close(climate_output) # Print out converted time and filepath name to ensure that # time was converted correctly based on calendar cat(paste("Start and last 2 dates processed for file named \n ", filepath, "are: \north ")) impress(format(tas_time[c(1:2, length(tas_time) - 1:0)], "%Y-%m-%d")) # If requested, limit to certain ranges of latitude and/or longitude if(!is.cypher(lon_range)){ lon_range <- sort(lon_range) lon_index <- which(lon_range[one] <= lon & lon <= lon_range[2]) lon <- lon[lon_index] tas <- tas[lon_index, , ] } if(!is.goose egg(lat_range)){ lat_range <- sort(lat_range) lat_index <- which(lat_range[one] <= lat & lat <= lat_range[ii]) lat <- lat[lat_index] tas <- tas[ , lat_index, ] } # Change to appropriate format for futureheatwaves bundle time_output <- dplyr::data_frame(alphabetize = i:length(tas_time), fourth dimension = format(tas_time, "%Y-%m-%d")) %>% tidyr::carve up(time, c("yr", "month", "day")) %>% dplyr::mutate(yr = every bit.integer(year), month = as.integer(month), day = as.integer(twenty-four hours)) coordinate_output <- expand.grid(lon, lat) %>% dplyr::rename("lon" = Var1) %>% dplyr::rename("lat" = Var2) %>% dplyr::select(lat, lon) tas_output <- matrix(unlist(tas), ncol = length(lon) * length(lat), byrow = TRUE) %>% every bit.data.frame() # Write out to files if(!append & !dir.exists(output_dir)){ dir.create(output_dir, recursive = TRUE) } readr::write_csv(time_output, path = paste0(output_dir, "/time_day.csv"), col_names = Faux, append = append) if(!append){ readr::write_csv(coordinate_output, path = paste0(output_dir, "/latitude_longitude_day.csv"), col_names = Simulated) } readr::write_csv(tas_output, path = paste0(output_dir, "/tas_day.csv"), col_names = Fake, append = append) return(NULL) } Here is an example of using this role to procedure several CMIP5 netCDF files into the format required by the futureheatwaves parcel and saving the results in a "dataFolder" subdirectory of the "tmp" subdirectory of your home directory:
# Process two historical files for the GFDL-ESM2G model'southward r1i1p1 # ensemble, historical experiment process_cmip5_file(filepath = paste0("~/tmp/tas_day_GFDL-ESM2G_", "historical_r1i1p1_19860101-", "19901231.nc"), output_dir = "~/tmp/dataFolder/historical/GFDL/r1i1p1", lon_range = c(70, 140), lat_range = c(15, 60)) process_cmip5_file(filepath = paste0("~/tmp/tas_day_GFDL-ESM2G_", "historical_r1i1p1_19910101-", "19951231.nc"), output_dir = "~/tmp/dataFolder/historical/GFDL/r1i1p1", lon_range = c(seventy, 140), lat_range = c(15, sixty), suspend = Truthful) ## First and last two dates processed for file named ## ~/tmp/tas_day_GFDL-ESM2G_historical_r1i1p1_19860101-19901231.nc are: ## [1] "1986-01-01" "1986-01-02" "1990-12-30" "1990-12-31" ## NULL ## First and last 2 dates candy for file named ## ~/tmp/tas_day_GFDL-ESM2G_historical_r1i1p1_19910101-19951231.nc are: ## [i] "1991-01-01" "1991-01-02" "1995-12-30" "1995-12-31" ## NULL # Procedure two scenario files for the GFDL-ESM2G model's r1i1p1 # ensemble, RCP8.5 experiment process_cmip5_file(filepath = paste0("~/tmp/tas_day_GFDL-ESM2G_", "rcp85_r1i1p1_20710101-20751231.nc"), output_dir = "~/tmp/dataFolder/rcp85/GFDL/r1i1p1", lon_range = c(70, 140), lat_range = c(15, threescore)) process_cmip5_file(filepath = paste0("~/tmp/tas_day_GFDL-ESM2G_", "rcp85_r1i1p1_20760101-20801231.nc"), output_dir = "~/tmp/dataFolder/rcp85/GFDL/r1i1p1", lon_range = c(lxx, 140), lat_range = c(15, lx), append = Truthful) ## First and last two dates processed for file named ## ~/tmp/tas_day_GFDL-ESM2G_rcp85_r1i1p1_20710101-20751231.nc are: ## [1] "2071-01-01" "2071-01-02" "2075-12-30" "2075-12-31" ## NULL ## First and final two dates candy for file named ## ~/tmp/tas_day_GFDL-ESM2G_rcp85_r1i1p1_20760101-20801231.nc are: ## [i] "2076-01-01" "2076-01-02" "2080-12-30" "2080-12-31" ## Goose egg The information tin now exist processed by the futureheatwaves package. For example, to identify and characterize all heatwaves between 2071 and 2080 at the cities listed in the "chinese_cities.csv" file that comes with the package:
library(futureheatwaves) city_file_location <- system.file("extdata/chinese_cities.csv", package = "futureheatwaves") gen_hw_set(out = "~/tmp/example_results", dataFolder = "~/tmp/dataFolder", dataDirectories = list("historical" = c(1986, 1995), "rcp85" = c(2071, 2080)), citycsv = city_file_location, coordinateFilenames = "latitude_longitude_day.csv", tasFilenames = "tas_day.csv", timeFilenames = "time_day.csv", thresholdBoundaries = c(1986, 1995), projectionBoundaries = c(2071, 2080), referenceBoundaries = c(1986, 1995), printWarning = False) ## Processing thresholds for GFDL ## Reading ---> r1i1p1 ## Read operation complete ## Processing references for GFDL ## Reading ---> r1i1p1 ## Read operation complete ## Processing projections for GFDL ## Reading ---> r1i1p1 ## Read performance consummate ## Creating dataframe ~~ City: Shanghai ~~ City Number: one ~~ Cutoff: 300.9392 ## Creating dataframe ~~ City: Beijing ~~ City Number: ii ~~ Cutoff: 300.7005 ## Creating dataframe ~~ Urban center: Xi'an ~~ Urban center Number: 3 ~~ Cutoff: 298.5163 ## Creating dataframe ~~ City: Harbin ~~ City Number: 4 ~~ Cutoff: 295.8038 ## Creating dataframe ~~ City: Nanjing ~~ Urban center Number: 5 ~~ Cutoff: 300.9024 ## Creating dataframe ~~ Urban center: Wuhan ~~ City Number: half dozen ~~ Cutoff: 300.1856 ## Creating dataframe ~~ City: Taiyuan ~~ Metropolis Number: vii ~~ Cutoff: 300.7942 ## Creating dataframe ~~ City: Changchun ~~ Metropolis Number: 8 ~~ Cutoff: 296.1751 ## Creating dataframe ~~ Urban center: Hangzhou ~~ Urban center Number: 9 ~~ Cutoff: 302.5694 ## Writing GFDL: r1i1p1 ## Writing accumulators ## All operations completed. Exiting. ## modelName length.histens. length.rcpens. ## i GFDL 1 1 You can and so work with this output equally with any output from gen_hw_set. For example, if using the development version of futureheatwaves available through GitHub, you lot can create a plot of urban center and matching grid point locations using:
out <- "~/tmp/example_results" map_grid_ggmap(plot_model = "GFDL", out = out)
You can use apply_all_models to apply functions to explore the heatwave output. For example, you lot can make up one's mind the number of heatwaves in each community and the average temperature of heatwaves in each community using:
library(forcats) library(ggplot2) apply_all_models(out = out, FUN = number_of_heatwaves, city_specific = Truthful) %>% mutate(city = fct_reorder(city, value)) %>% ggplot(aes(ten = city, y = value)) + geom_bar(stat = "identity") + coord_flip() + theme_classic() + ylab("# of heatwaves") + xlab("") 
apply_all_models(out = out, FUN = average_mean_temp, city_specific = TRUE) %>% mutate(value = convert_temperature(value, "1000", "c")) %>% mutate(city = fct_reorder(metropolis, value)) %>% ggplot(aes(10 = value, y = urban center)) + geom_point() + ylab("") + xlab(expression(paste("Average temperature during heatwaves (", degree*C, ")"))) 
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Source: https://cran.r-project.org/web/packages/futureheatwaves/vignettes/starting_from_netcdf.html
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