seascorr: Seasonal (partial) correlation analysis

In treeclim: Numerical Calibration of Proxy-Climate Relationships

Seasonal (partial) correlation analysis

Description

Calculate seasonal correlation with primary and secondary climate variables and tree-ring data, similar to the seascorr function for MATLAB.

Usage

seascorr(
  chrono,
  climate,
  var_names = NULL,
  timespan = NULL,
  complete = 9,
  season_lengths = c(1, 3, 6),
  primary = 1,
  secondary = 2,
  ci = 0.05
)

Arguments

chrono	data.frame containing a tree-ring chronologies, e.g. as obtained by chron of package dplR.
climate	either a data.frame or matrix with climatic data in monthly resolution, with year, month and climate parameters in columns (all columns except year and month will be recognized as parameters for response or correlation functions), or a single data.frame or matrix in 13-column format (see below), or a (potentially named) list of one or several of the latter.
var_names	character vector with variable names. Defaults to corresponding column names of parameter climate if given as data.frame, or its names if given as list.
timespan	integer vector of length 2 specifying the time interval (in years) to be considered for analysis. Defaults to the maximum possible interval.
complete	integer scalar, month when tree-ring growth is expected to have finished.
season_lengths	numeric vector giving the lengths of the seasons for variable grouping
primary	position numeric or name character of primary climate variable
secondary	position numeric or name character of secondary climate variable
ci	numerical value to set the test level for significance test (values 0.01, 0.05 and 0.1 are allowed); the confidence intervals are adapted accordingly.

Details

This function mimicks the behaviour of the MATLAB function seascorr (Meko et al. 2011), which calculates partial correlations of tree-ring data with a primary and a secondary climatic variable for seasons of different lengths.

Input chronology data can be a data.frame such as produced by function chron of package dplR. It has to be a data.frame with at least one column containing the tree-ring indices, and the corresponding years as rownames.

For climatic input data, there are three possibilities: Firstly, input climatic data can be a data.frame or matrix consisting of at least 3 rows for years, months and at least one climate parameter in the given order. Secondly, input climatic data can be a single data.frame or matrix in the style of the original DENDROCLIM2002 input data, i.e. one parameter with 12 months in one row, where the first column represents the year. Or thirdly, input climatic data can be a list of several of the latter described data.frame or matrices. As an internal format dispatcher checks the format automatically, it is absolutely necessary that in all three cases, only complete years (months 1-12) are provided. It is not possible to mix different formats in one go.

The 'complete' parameter specifies the months of the current year in which tree-growth is assumed to finish. This month marks the last month of the first season, and starting from here, 14 different seasons are computed for each specified season length in one-month steps. E.g., for a starting value of 9 (current September) and season length of 3 months, the first season comprises current July to current September, the second season comprises current June to current August, and the last season comprises previous June to previous August. This results in 14 seasons for a given season length. An arbitrary number of season lengths can be specified.

The choice for primary vs. secondary variable can be made either via numeric selection (the integer value 1 stands for the first variable in the supplied climate data set), or by name ("temp", when one of the variables is named "temp"). The correlation of the primary variable with tree-growth is computed as the simple (Pearson) correlation coefficient, while the influence of the secondary variable on tree-growth is computed with the influence of the primary variable on tree-growth removed.

Like in the original seascorr program, the significance of each (partial) correlation is evaluated using exact bootstrapping by circulant embedding of the tree-ring data (Percival \& Constantine, 2006).

Value

'seascorr' returns an 'object' of class '"tc_seascorr"'.

The 'plot' function is used to obtain a plot of the results.

An object of class '"tc_seascorr"' is a list containing at least the following components:

call	the call made to 'seascorr'
seasons	a list of length n, where n is the number of season lengths provided; each list element consists of a data.frame with end month, correlation coefficient and significance flag
truncated	the input data truncated to the common timespan or the specified timespan
original	the original input data, with the climate data being recast into a single data.frame

Author(s)

Christian Zang; the procedure incl. exact bootstrapping was implemented first by Dave Meko in MATLAB

References

Meko DM, Touchan R, Anchukaitis KJ (2011) Seascorr: A MATLAB program for identifying the seasonal climate signal in an annual tree-ring time series. Computers & Geosciences, 37, 1234-1241.

Percival DB, Constantine WLB (2006) Exact simulation of Gaussian Time Series from Nonparametric Spectral Estimates with Application to Bootstrapping. Statistics and Computing 16:25-35

Examples

sc <- seascorr(muc_fake, muc_clim)
sc
plot(sc)

treeclim documentation built on March 18, 2022, 7:22 p.m.