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NCL: Climatology

There are numerous climatological functions that compute daily and monthly climatologies; calculate anomalies from the climatologies; remove monthly and daily annual cycles, and, calculate interannual variabilities. For historical reasons, some of these function names end in TLL, TLLL, LLT, LLLT. They refer to expected input array ordering (nominally): (time,lat,lon), (time,lev,lat,lon), (lat,lon,time), (lev,lat,lon,time), respectively.

This page illustrates some simple applications of these functions.

climo_0.ncl: Compute monthly climatologies and the monthly interannual variabilities using contributed functions clmMonTLL and clmStdTLL. Built-in functions used: cd_calendar, ind

Only the January and July climaytologies are displayed.

climo_1.ncl: Compute decadal means and standard deviation for SLP for two different decades, compute the t-statistic, and plot the 5% level as stippling.

Built-in functions used: ttest, ind.

Contributed functions used: clmMonTLL, stdMonTLL, copy_VarCoords. runave_n_Wrap.

Shea_util functions used: ShadeLtContour.

climo_2.ncl: Calculates monthly climatologies and then conducts an eof analysis.

Built-in functions used: runave, dimsizes.

Contributed functions used: clmMonLLT, stdMonLLT, eofcov_ts_Wrap.

climo_3.ncl: Demonstrates the use of clmMonLLT and stdMonTLL to derive climatology and the interannual variability. Though this example derives the climatology based on the entire time period, a subset may be used by using either conventional subscripting or coordinate dimensions.

To get the climatology for Jan 1980 through Dec 1989 for this dataset:

prcClm = clmMonTLL (prc(12:131,:,:)), using conventional subscripts.

or prcClm = clmMonTLL (prc({198001:198912},:,:)), using coordinate subscripting.

climo_4.ncl: Demonstrates the use of clmMonLLT to derive a zonally averaged annual cycle.

climo_5.ncl: Calculate the daily mean annual cycle and daily anomalies from the mean annual cycle. For illustration: (a) compute raw and smoothed annual cycles; (b) create a netCDF file of the daily anomalies; (c) plot results.

This example only uses 5-years of data. Hence, there is considerable day-to-day variability in this example.

Which is the proper daily annual cycle to use: raw or smoothed? It depends on your usage. The smoothed annual cycle can be thought of as the values that would be obtained if there was an infinite ensemble of data under the same forcing conditions.

climo_6.ncl: (a) Read files containing year-month data, (b) create climatologies spanning user specified years (c) plot November-April and May-October climatologies over a user specified region