Sentences with phrase «of the linear trends from»

A comparison of the linear trends from these two series indicates that about 69 % of the increase in ocean heat content during 1955 to 1998 (the period when estimates from both time series are available) occurred in the upper 700 m of the World Ocean.

The continuation of the linear trend from August 1975 to July 1997 (green dashed), would have predicted a temperature anomaly in August 2012 of 0.524 ºC.

A comparison of the linear trends from these two series indicates that about 69 % of the increase in ocean heat content during 1955 to 1998 (the period when estimates from both time series are available) occurred in the upper 700 m of the World Ocean.

Not exact matches

The number of stair - related injuries decreased 11.6 % during the study period, with a significant linear trend from 101335 cases in 1999 to 89619 cases in 2008 (m = − 1103, P =.011).

(Bottom) Patterns of linear global temperature trends from 1979 to 2005 estimated at the surface (left), and for the troposphere (right) from the surface to about 10 km altitude, from satellite records.

The increases in frequency and duration metrics translate to 30 additional marine heatwave days per year by the end of the 35 - year period (p < 0.01; based on a linear trend) from a baseline level of about 25 days in the 1980s (Fig. 2).

Because of the stratospheric warming episodes following major volcanic eruptions, the trends are far from being linear.

The trend is not linear, and the warming from the first 50 years of instrumental record (1850 — 1899) to the last 5 years (2001 — 2005) is 0.76 °C ± 0.19 °C.

Figure 2: The data (green) are the average of the NASA GISS, NOAA NCDC, and HadCRUT4 monthly global surface temperature anomaly datasets from January 1970 through November 2012, with linear trends for the short time periods Jan 1970 to Oct 1977, Apr 1977 to Dec 1986, Sep 1987 to Nov 1996, Jun 1997 to Dec 2002, and Nov 2002 to Nov 2012 (blue), and also showing the far more reliable linear trend for the full time period (red).

They fail to mention it also removes any linear trend, which is obvious from just a few steps of basic arithmetic.

Figure 5.5 shows the linear trends (based on pentadal anomaly fields) of zonally averaged salinity in the upper 500 m of the World Ocean and individual ocean basins (Boyer et al., 2005) from 1955 to 1998.

While using a percent growth rate for free cash flows might be conventional, mathematically convenient and easier to convey to others, it is not as accurate or conservative as using an absolute rate of change from a linear trend model.

But even though Call of Duty: WWII continues the typical trend of linear heroic storytelling mixed with incredible and explosive action set - pieces, there are a few small changes that makes the whole experience differ slightly from previous campaigns.

The standard deviation of the residuals from a linear regression to annual averages 1975 - 2007 is 0.472, so we expect a range of variation of roughly + / - 0.94 deg.C from the long - term trend.

BPL: A trend is present if the linear regression of a time series against elapsed time is significantly different from zero.

It is arguably impossible to accurately detangle a multidecadal oscillation from a long - term (probably not linear) forced trend in 100 years of data.

As a result of this evaluation our conservative estimates of the uncertainty of the linear ice volume trend from 1979 - present is about 30 %.

[Response: At the time (1988), there were no suggestions that climate should be following a linear trend (though if you know of some prediction along those lines from the 1980s, please let me know — the earliest I can find is from 1992, and the prediction was for 0.1 degC / dec).

One baseline model is a simple linear trend from the start of the century.

The DSLPA index computed from HadSLP2 shows a much more «trend - like» reduction than the datasets shown in the manuscript, in which the 1970s shift plays a less pivotal role; though the amplitude of slope of the linear trend is consistent with the model and observations.

Internal variability as estimated from observations can't explain sea - ice loss Superposition of a linear trend and internal variability explains sea - ice loss Observational sea - ice record shows no signs of self - acceleration

I think it's just another way of saying that the volume series has an accelerating downward trend, while the other measures (extent, area) are not easily distinguishable from a linear trend — at least over the (unspecified) period you're using.

I went to the trouble of fitting a linear trend line to the A2 model input line from 2002 - 2009 and obtained a correlation coefficient (R2) of 0.99967.

Pielke apparently did not understand why the temperatures before 1910 hardly affect this conclusion (in fact increasing the probability from 78 % to 80 %), and that the linear trend from 1880 or 1910 is not a useful predictor for this probability of breaking a record.

Also, about 2/3 of the individual ensemble - members (46 out of 68) from all the model runs have linear trends that indicate at least a nominal weakening — this is significantly different from what one would be expected from a Binomial distribution with a 50 % probability.

# 242 Jim Eager in # 229 showed graphs of the linear trend of monthly data from January 1999 through December 2008.

To conclude, a projection from 1981 for rising temperatures in a major science journal, at a time that the temperature rise was not yet obvious in the observations, has been found to agree well with the observations since then, underestimating the observed trend by about 30 %, and easily beating naive predictions of no - change or a linear continuation of trends.

Piece-wise joined or step - like constructs of linear trends (figs 1 and 3) would always suffer from the arbitrariness of the breakpoints.

Of course, as they point out «because rainfall is such a variable element, trend values are highly dependent on the start and end dates of the analysis» and the fact they are simply using linear interpolation it is very difficult to derive anything meaningful in terms of climate change from just one maOf course, as they point out «because rainfall is such a variable element, trend values are highly dependent on the start and end dates of the analysis» and the fact they are simply using linear interpolation it is very difficult to derive anything meaningful in terms of climate change from just one maof the analysis» and the fact they are simply using linear interpolation it is very difficult to derive anything meaningful in terms of climate change from just one maof climate change from just one map.

In «The Evolution of ENSO and Global Atmospheric Temperatures», Trenberth et al identify the linear trend in global temperatures that result from ENSO events: «For 1950 - 98, ENSO linearly accounts for 0.06 deg C of global warming.»

12 - month running averages are shown as well as linear trend lines, and compared to the scenarios of the IPCC (blue range and lines from the 2001 report, green from the 2007 report).

Note the sizeable departure of the data from the linear - plus - cyclic trend over the last several decades.

Our reconstruction of his prediction takes the natural variability of ENSO, the sun, and volcanic eruptions from Foster and Rahmstorf (2011)(with a 12 - month running average) and adds a 0.02 °C per decade linear warming trend.

You downplay the fact that «homogenization» of the Hobart RO record has increased the fitted linear trend from 1893 - 1992 by a factor of ~ 1.6 by appealing to mysterious «confidence intervals.»

It's stated in the text, but understanding that the absolute deviation of the Earth's LOD from its long — term trend refers to the absolute value of the deviation from a linear trend fit, whatever the sign of the deviation.

Eg (from Georgia Tech blurb): «The linear trend was removed from all indices to focus only the multi-decadal component of natural variability.»

The linear trend of global mean SLR from 2004 to 2015 amounts to 3.38 ± 0.10 millimeters per year, and the σ of the detrended global mean is 3.90 millimeters (Table 1).

The «noise level,» that is, the amplitude of internal variability, approximated here by the standard deviation (σ) of the OHC time series after the linear trend is removed, amounts to 0.77 × 1022 J from 2004 to 2015 (Table 1).

Figure 2: The data (green) are the average of the NASA GISS, NOAA NCDC, and HadCRUT4 monthly global surface temperature anomaly datasets from January 1970 through November 2012, with linear trends for the short time periods Jan 1970 to Oct 1977, Apr 1977 to Dec 1986, Sep 1987 to Nov 1996, Jun 1997 to Dec 2002, and Nov 2002 to Nov 2012 (blue), and also showing the far more reliable linear trend for the full time period (red).

So perhaps Mr. House can try to learn a little science rather than expatiating with malevolent ignorance on everything from the least - squares linear - regression trend on monthly temperature anomaly datasets to the arcana of United Kingdom peerage law.

And that is not going to happen any time soon even if you take the totally unjustified and unscientific step of fitting a linear «trend» to 35 years of data taken from a system with a sizeable 60y periodicity and project it 85 years into the future.

These are included in the HadCRUT4 ensemble, and when computing linear trends in global temperatures from August 1997 to August 2012 these give a trend of 0.034 ± 0.011 °C per decade (95 % confidence interval) for the observed portion of the earth.»

Guest post by Clive Best The UK Met Office seem determined to stand by their claim made in response to the David Rose article in the Mail on Sunday: «The linear trend from August 1997 (in the middle of an exceptionally strong...

the totally unjustified and unscientific step of fitting a linear «trend» to 35 years of data taken from a system with a sizeable 60y periodicity and project it 85 years into the future.

Muller et al., 2011 found that the linear trends of the Unadjusted records for stations with Ratings 1, 2 or 3 were comparable to those of stations with Ratings 4 or 5, and that there was not much difference between estimates constructed from the Ratings 1 - 3 and Ratings 4 - 5 subsets of the USHCN.

Chart # 1 had 1919 - 1943 anomaly plot adjusted to start at same anomaly point as 1991 - 2015 period; chart # 2 linear trends are based off plots of chart # 1; chart # 3 uses 5 - year averages calculated from each period's anomaly dataset and then the 1919 - 1943 5 yr average was adjusted (i.e. offset) to start at same anomaly point as 1991 - 2015 5 yr average; chart # 4 cumulative differences calculation: the December 31, 1943 anomaly minus the December 31, 1918 anomaly and the December 31, 2015 anomaly minus the December 31, 1990 anomaly (both calculations covering a full 300 months).

If the DATA were falling off that extension of the linear trend line from 1975 to 1997, then that might be evidence that the warming was really slowing down.

The data is annual data from 1955 to 1995, with a mean of 23.025 C, a Standard Deviation of 0.2981 C, and a trend of 0.05 + / - 0.08 C / 10 years, as determined by simple linear regression.

http://www.skepticalscience.com/graphics.php?g=47 The data (green) are the average of the NASA GISS, NOAA NCDC, and HadCRUT4 monthly global surface temperature anomaly datasets from January 1970 through November 2012, with linear trends for the short time periods Jan 1970 to Oct 1977, Apr 1977 to Dec 1986, Sep 1987 to Nov 1996, Jun 1997 to Dec 2002, and Nov 2002 to Nov 2012 (blue), and also showing the far more reliable linear trend for the full time period (red

1) A 0.2 °C per decade global warming trend 2) Two «natural cycles» (cosine functions) both with 0.15 °C amplitude and periods of 10 and 20 years, respectively 3) Random noise with 0.07 °C amplitude 4) The sum of the warming trend, cycles, and noise 5) The sum fit with a step function with three steps: linear trends from 1950 to 1963, 1967 to 1986, and 1987 to 2003 (light blue) 6) The sum with a linear trend fit from 1950 to 2010.