Analysis of future projections
projections.RdAutomatically process climate model projections and compute useful statistics
Usage
projections(
data,
bias.correction = F,
uppert = NULL,
lowert = NULL,
season,
consecutive = F,
frequency = F,
n.sessions = 1,
duration = "max",
method = "eqm",
window = "monthly"
)Arguments
- data
output of load_data
- bias.correction
logical
- uppert
numeric of length=1, upper threshold
- lowert
numeric of length=1, lower threshold
- season
list, containing seasons to select. For example, list(1:6, 7:12)
- consecutive
logical, to use in conjunction with lowert or uppert
- 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.
- n.sessions
numeric, number of sessions to use, default is one. Parallelization can be useful when multiple scenarios are used (RCPS, SSPs). However, note that parallelizing will increase RAM usage
- 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.
- 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.
- 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