A cooling trend is observed in the raw and USHCN V2 records for the past 12 years... In both the short and longer term cases the USHCN V2 adjusted
data yielded trends that were roughly 1ºC per century higher than those found in the raw temperature records.»
Not exact matches
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The
yield curve, which normally slopes upward, has extended its tightening
trend as lackluster economic
data have pushed down long - dated
yields even as senior Fed officials» backing for a gradual - but - sustained hiking trajectory have lifted long - dated
yields.
Our model indicates that going forward, long - term
yields will likely be subject to three upward pressures: (1) Our forecasted increase in inflation will boost nominal GDP growth; (2) As forward guidance is replaced by a
data - dependent monetary tightening, volatility in short rates will increase; and (3) As the impact of QE on the Treasury market fades, long - term
yields will
trend back to their historical link with nominal GDP growth.
Although computer models of archaeological sites are ideal software tools for managing spatially referenced
data and commonly used to
yield insights which contribute to the protection of heritage materials, some scientists question their credibility, calling for these long - term
trends be «ground truthed» in order to ensure that calculated rates of change reflect observed phenomena «in the field».
Other, more stable
data sets, such as satellite, radiosonde and ocean temperatures
yield smaller warming
trends.
Computing technologies like
data analytics determine
trends, patterns and associations between different
data to
yield meaningful information.
With bond
yields trending higher, on days when market - moving economic
data is released, bond investors react and the
yield curve adjusts, helping to dampen the impact on risk - sensitive assets.
The price action is being driven by rising U.S. Treasury
yields, a slew of U.S. economic
data and new earnings results.Daily June E-mini S&P 500 IndexDaily Swing Chart Technical AnalysisThe main
trend is up
Which
yields a better fit to the
data (i.e., R ^ 2) a linear
trend or a non-linear
trend?
From the perspective proposed in this post, that Mennian slicing methods applied to GHCN
data yield a slightly warmer
trend than CRU and NOAA using unsliced
data should not be viewed as an unexpected result.
The
trend line exceeds the 5 - year average of the
data, which shows the
trend has slowed — see http://www.woodfortrees.org/plot/hadcrut4gl/from:1970/plot/hadcrut4gl/from:1970/to:2001/
trend/plot/hadcrut4gl/from:1970/
trend/plot/hadcrut4gl/from:1970/mean:60 Calculating the
trend from the average
data yields: 1972.5 to 1997.0: 0.017 C / year 1997.0 to 2010.08: 0.008 C / year
I suspect that other conventional global temperature
data sets, when controlled for ENSO in a similar way, will
yield a similarly significant recent temperature
trend.
Other, more stable
data sets, such as satellite, radiosonde and ocean temperatures
yield smaller warming
trends.
The ISPM also states: «Adjusting T2
data to remove an estimated contribution from the stratosphere
yields tropospheric
trend coefficients ranging from about 0.12 oC to 0.19 oC per decade, depending on the method.»
If you refer to Section 3.7.4 you will see that it is not just Church & White that are being cited and that in Fig 3.14 Church & White
data yields the lowest SLR through this 1920 - 50 period, hitting a momentary peak of just 2.3 mm / yr from 18 - year lnear
trend calculations.
These guys (e.g. Lobell) try to explain the level and
trend of crop
yields (often on a grid scale covering the globe, given you have sufficient
data) using statistical models that include all kinds of independent variables (soils, input use, varieties, pressure by pests, management etc.).
This adjustment of tide gauge
data to
yield a rising sea level
trend where none exists is not occasional or episodic.
While a recent report tells us current droughts in the western USA hardly make the top ten, we have this from Stanford University, a claim about drought related crop insurance claims that doesn't seem to match
data on national
yields and
trend.
Statistically significant
trends obtained from records longer than 40 years
yielded sea - level - rise estimates between 1.06 — 1.75 mm / yrear - 1, with a regional average of 1.29 mm yr - 1, when corrected for global isostatic adjustment (GIA) using model
data, with a regional average of 1.29 mm - 1..
It is visually apparent in Fig. 4B that removing the oscillatory AMO from the raw
data organizes the
data points into a monotonic band and
yields a more stable linear
trend, converging to the 50 - y
trend of 0.08 °C / decade.
Maybe we should just throw out all the temperature
data, because it is
yielding trends much lower than the models calculate.
The
data record that NCDC currently provides, GHCNv3, initiated in 2011,
yields a slightly larger global warming
trend (0.75 °C for 1900 — 2010, while GHCNv2
yields 0.72 °C), but the changes are too small to affect the conclusions of our present study.
One would think that the two sources of
data would
yield quite similar
trends and have important similarities since they attempt to measure air temperatures so close to each other.
Interpret
data on price,
yield, stability, future investment - risk
trends, economic influences, and other factors affecting investment programs.