Load data and apply function climate_change_signal in spatial chunks

Automatically load and process climate models in a memory efficient way. Useful for analysing large areas

Usage

load_data_and_climate_change_signal(
  variable,
  country = NULL,
  years.hist = NULL,
  years.proj,
  path.to.data,
  path.to.obs = NULL,
  xlim,
  ylim,
  aggr.m = "none",
  chunk.size,
  overlap = 0.25,
  season,
  lowert = NULL,
  uppert = NULL,
  consecutive = F,
  duration = "max",
  frequency = F,
  bias.correction = F,
  domain = NULL,
  threshold = 0.6,
  n.sessions = 6,
  method = "eqm",
  percentage = F,
  window = "monthly"
)

Arguments

variable

character, indicating variable name

country

character, in English, indicating the country of interest or an object of class sf. Country will be used to crop and mask the data but you still need to specify the xlim and ylim arguments

years.hist

Numerical range, years to select for historical simulations and observations

years.proj

Numerical range, years to select for projections

path.to.data

character (CORDEX-CORE or path to local data) or NULL. If path to local data, specify path to the directory containing the RCP/SSPs folders and historical simulations (optional). For example, home/user/data/. data would contain subfolders with the climate/impact models. Historical simulations have to be contained in a folder called historical. If path.to.data is set as CORDEX-CORE, CORDEX-CORE simulations will be downloaded

path.to.obs

Default to NULL, if not, indicate the absolute path to the directory containing a reanalysis dataset, for example ERA5. To automatically load W5E5. specify W5E5

xlim

numeric of length = 2, with minimum and maximum longitude coordinates, in decimal degrees, of the bounding box selected.

ylim

same as xlim, but for the selection of the latitudinal range.

aggr.m

character, monthly aggregation. One of none, mean or sum

chunk.size

numeric, indicating the number of chunks. The smaller the better when working with limited RAM

overlap

numeric, amount of overlap needed to create the composite. Default 0.25

season

list, containing seasons to select. For example, list(1:6, 7:12)

lowert

numeric of length=1, lower threshold

uppert

numeric of length=1, upper threshold

consecutive

logical, to use in conjunction with lowert or uppert

duration

A parameter that can be set to either "max" or a specific number. It is relevant only when 'consecutive' is set to TRUE. For instance, to calculate the count of consecutive days with Tmax (maximum temperature) above 35°C, lasting for more than 3 days, you can set 'uppert' to 35, 'consecutive' to TRUE, and 'duration' to 3.

frequency

logical value. This parameter is relevant only when 'consecutive' is set to TRUE and 'duration' is not set to "max". For instance, if you want to determine the count of heatwaves, defined as the number of days with Tmax (maximum temperature) exceeding 35°C for a minimum of 3 consecutive days, set 'uppert' to 35, 'consecutive' to TRUE, 'duration' to 3, and 'frequency' to TRUE.

bias.correction

logical

domain

specify the CORDEX-CORE domain (e.g AFR-22, EAS-22). Used with path.to.data = CORDEX-CORE. Default is NULL

threshold

numerical value with range 0-1. It indicates the threshold for assigning model agreement. For example, 0.6 indicates that model agreement is assigned when 60 percent of the models agree in the sign of the change

n.sessions

numeric, number of sessions to use in parallel processing for loading the data. Default to 6. Increasing the number of sessions will not necessarily results in better performances. Leave as default unless necessary

method

character, bias-correction method to use. One of eqm (Empirical Quantile Mapping), qdm (Quantile Delta Mapping) or scaling. Default to eqm. When using the scaling method, the multiplicative approach is automatically applied only when the variable is precipitation.

percentage

logical, whether the climate change signal is to be calculated as relative changes (in percentage). Default to FALSE

window

character, one of none or monthly. Whether bias correction should be applied on a monthly or annual basis. Monthly is the preferred option when performing bias-correction using daily data

Value

list with SpatRaster. To explore the output run attributes(output)