Calculating the running mean temperature — over periods of 12, 60 and 132 months — provides a way to see long - term trends behind variability.
Not exact matches
Within each sample, we
calculated Na: K from duplicate
runs (
mean of [sodium1 / potassium1], [sodium2 / potassium2]-RRB-.
Set in rural Georgia in the 1960s, BREAKING TWIG is a coming - of - age novel about Becky (Twig) Cooper, a young woman trying to survive the physical and emotional abuse of her mother, Helen, a beautiful,
calculating woman who can, with a mere look, send the
meanest cur in Sugardale, Georgia
running for its life.
Here the adjustment is determined by (1)
calculating the collocated ship - buoy SST difference over the global ocean from 1982 - 2012, (2)
calculating the global areal weighted average of ship - buoy SST difference, (3) applying a 12 - month
running filter to the global averaged ship - buoy SST difference, and (4) evaluating the
mean difference and its STD of ship - buoy SSTs based on the data from 1990 to 2012 (the data are noisy before 1990 due to sparse buoy observations).
Since the values given are stated to be «
mean and 95 % confidence intervals», I can not see any justification for the efficacy uncertainty ranges actually being 95 % confidence intervals for a single
run, centered on the
mean efficacy
calculated over all
runs.
If you
calculate a 12 month
running mean of 850hPa temperature centered on July you will find that it pretty much follows August temperature.
Then take the
running window deviation method to
calculate the deviation of all the weighted proxies
runs from the
mean and compare it to the one from the reference temperature series.
It is basically a measure of the expected deviation of all the
runs used against the reconstructed average: Take a
running «window» average over a «straight» portion of the temperature reference, so that the local deviation against the local
mean is
calculated (for our temperature record one could simplify by detrending the record from 1940's to 2000's and
calculating a deviation).
Future global vegetation carbon change
calculated by seven global vegetation models using climate outputs and associated increasing CO2 from five GCMs
run with four RCPs, expressed as the change from the 1971 — 1999
mean relative to change in global
mean land temperature.
Oceanic Niño Index: 3 month
running mean of ERSST.v4 SST anomalies in the Niño 3.4 region (5 ° N - 5 ° S, 120 ° -170 ° W)
Calculated from the ERSST V5 (at NOAA / CPC).
Correlation (color) and regression maps (contour) of SST (left) and SLP (right) associated with the first EOF modes of annual precipitation (a, b), low - frequency precipitation (c, d), and total water storage (e, f), which are
calculated using annual
mean data for the first EOF mode of annual precipitation, 10 - year
running mean for precipitation, and 10 - year
running mean leading with 5 - year for total water storage.
Correlation coefficients are
calculated using annual
mean data for the first EOF mode of annual precipitation, 10 - year
running mean data for the low - frequency precipitation, and 10 - year
running mean data leading with 5 - year for the total water storage.
To further elucidate at what soil depth multi-year predictability emerges, we
calculated, for different depth ranges, the anomaly correlation skill between the CTL
run and the ensemble
mean for the two geographic index regions.
If over a long period of time that portion of the ocean accumulates heat, then the index (a statistic
calculated from temperatures and pressures)
running mean will change over that time span.
(Note that even though he has
calculated the results using individual
run, he has not reported them in his papers which only include the ensemble
mean results and where he bizarrely claims they have the same spatial characteristics as individual
runs — they do not).
To highlight the long term trend more clearly, below the same figure with in addition the 11 year
running mean (which stops 5 years short of each endpoint for lack of data to
calculate the
mean):