Estimates and removes the least squares linear trend of the rightmost dimension from all grid points (missing values allowed).
function dtrend_msg ( x [*] : numeric, y : numeric, remove_mean  : logical, return_info  : logical ) return_val [dimsizes(y)] : numeric
One-dimensional array containing the coordinate of the rightmost dimension of y. [eg: time].y
A multi-dimensional array or scalar value equal to the data to be detrended. The dimension from which the trend is calculated needs to be the rightmost dimension. This is usually time.remove_mean
A logical scalar indicating whether or not the mean is removed from return_val. True = remove mean, False = do not remove mean.return_info
A logical scalar controlling whether attributes corresponding to the y-intercept and slope are attached to return_val. True = attributes returned. False = no attributes returned.
An array of the same size as y. Double if y is double, float otherwise.
Two attributes (slope and y_intercept) may be attached to return_val if return_info = True. These attributes will be one-dimensional arrays if y is one-dimensional. If y is multi-dimensional, the attributes will be the same size as y minus the rightmost dimension but in the form of a one-dimensional array. e.g. if y is 45 x 34, then the attributes will be a one-dimensional array of size 45*34. This occurs because attributes cannot be multi-dimensional. Double if return_val is double, float otherwise.
You access the attributes through the @ operator:
Estimates and removes the least squares linear trend of the rightmost dimension from all grid points. The mean is optionally removed. Missing values are allowed. Optionally returns the slope (i.e., linear trend per unit time interval) and y-intercept for graphical purposes.
Use dtrend_msg_n if the dimension to do the calculation on is not the rightmost dimension and reordering is not desired. This function can be significantly faster than dtrend_msg.
Let x be one-dimensional with dimension time and y be three-dimensional with dimensions lat,lon, and time. The return_val will be three-dimensional with dimensions lat,lon, time. The mean is removed.
yDtrend = dtrend_msg (x,y,True,False)Example 2
Same as example 1 but with the optional attributes. Let y be temperatures in units of K and the time dimension have units of months.
yDtrend = dtrend_msg (x,y,True,True) ; yDtrend@slope is a one-dimensional array of size nlat x nlon elements.
Since attributes cannot be returned as two-dimensional arrays, the user should use onedtond to create a two-dimensional array for plotting purposes:
yDtrend = dtrend_msg (x,y,False,True) slope2D = onedtond(yDtrend@slope, (/nlat,mlon/) ) delete (yDtrend@slope) slope2D = slope2D*120 ; would give [K/decade] yInt2D = onedtond(yDtrend@y_intercept, (/nlat,mlon/) ) delete (yDtrend@y_intercept)Example 3
Let y be a three-dimensional array with dimensions time, lat, lon. Reorder y so that time is the rightmost dimension.
yDtrend = dtrend_msg(y&time,y(lat|:,lon|:,time|:),True,False) ; yDtrend will be three-dimensional with dimensions lat, lon, time. ; In V5.2.0 or later, you can use dtrend_msg_n to avoid reordering: ; yDtrend = dtrend_msg_n(y&time,y,True,False,0) ; yDtrend will be three-dimensional with dimensions time, lat, lon.Example 4
This example shows how to calculate the significance of trends by evaluating the incomplete beta function using betainc. Let z be a three-dimensional array with dimensions named lat, lon, time.
dimz = dimsizes(z) ; retrieve dimension sizes of z zDtrend = dtrend_msg(ispan(0,dimz(2)-1,1),z,True,True) tval = new((/dimz(0),dimz(1)/),"float") ; preallocate tval as a float array and df = new((/dimz(0),dimz(1)/),"integer") ; df as an integer array for use in regcoef rc = regcoef(ispan(0,dimz(2)-1,1),z,tval,df) ; regress z against a straight line to ; return the tval and degrees of freedom df = equiv_sample_size(z,0.05,0) ; If your data may be significantly autocorrelated ; it is best to take that into account, and one can ; do that by using equiv_sample_size. Note that ; in this example df (output from regcoef) is ; overwritten with the output from equiv_sample_size. ; If your data is not significantly autocorrelated one ; can skip using equiv_sample_size. df = df-2 ; regcoef/equiv_sample_size return N, need N-2 beta_b = new((/dimz(0),dimz(1)/),"float") ; preallocate space for beta_b beta_b = 0.5 ; set entire beta_b array to 0.5, the suggested value of beta_b ; according to betainc documentation z_signif = (1.-betainc(df/(df+tval^2), df/2.0, beta_b))*100. ; significance of trends ; expressed from 0 to 100%