Temperature Trend Analysis

The Coming Climate July 4 Update

Posted on by Ron Clutz

Update July 4 below

When you see a graph like that below, it is obvious that an unusually strong El Nino just happened in our climate system. It resulted in higher global temperatures the last two years and so far in 2016. But that event is over now, and naturally we wonder what to expect in the months and years ahead.

cdas_v2_hemisphere_2016june2
For example some comments from a recent thread at WUWT (here) were intriguing:

It will be interesting to see what comes next. The major difference between the 1998 el nino and this one is that in 1998 the sun was increasing in solar activity, while this one solar activity is decreasing. (rishrac)

Nino3,4 and UAH LT dC Anomalies, and UAH LT Scaled *3 and Lagged 4 Months h/t Allan MacRae

And richard takes the long view of the situation:

While we all stare at the short-term ups and downs of the global temperatures, pay a little thought to the fact that the Earth’s orbit around the Sun causes snow in the winter and warmth during the summer, so it may be important?

Perihelion presently occurs around January 3, (Northern hemisphere winter, Southern summer) while aphelion is around July 4. Therefore, the southern hemisphere receives more solar radiation and is therefore warmer in summer and colder in winter (aphelion). The Northern hemisphere has cooler summers and milder winter (solar radiation-wise).

Also the northern hemispheres autumn and winter are slightly shorter than spring and summer, because the Earth is moving faster around the Sun in winter slower in summer.

This alone could account for “Global Warming” attributed to CO2, (which no doubt plays some part in it).

Over the next 10,000 years, northern hemisphere winters will become gradually longer and summers will become shorter, due to the change in the Earth’s Orbital Eccentricity.

Couple this with changes in the Earth’s tilt, which varies from 22.1 degrees to 24.5 degrees, (currently at 23.4 degrees). More tilt means more solar radiation gets to the poles (global warming) and less tilt means less radiation gets to the poles (global cooling). The last maximum tilt occurred in 8700 BC (Holocene maximum) and the next minimum tilt will happen in 11,800 AD (the advance of the ice sheets), precisely at the time of longer northern winters and shorter summers.

Orbital Climate Factors: E for eccentricity, T for tilt, and P for precession

Predicting the Future is Tough

Chiefio (E.M. Smith) has a good post (here) reminding us that statistical projections do not help us much in this case. Temperature series produced by our climate system have special qualities. The patterns are auto-correlated, meaning that tomorrow’s weather will be similar to today’s; the occurrence is not totally independent, like the flip of coin. IOW there is momentum in the climate characteristics, which can and do fluctuate over seasons, decades, centuries and more. Our attempts to use linear regressions to forecast are thwarted by temperature time series that do not follow a normal gaussian distribution, and are semi-chaotic and non-stationary.

Four Possibilities Forward From Today

From past experience, the next few years could logically follow one of four temperature scenarios:
1. The Plateau since 1998 continues.
2. The Warming prior to 1998 resumes.
3. A new Plateau begins with 2016 at a higher (step up) level.
4. A Cooling begins comparable to the years after 1940.

All of these have analogues in our recent climate observations. If this now finished El Nino triggers a regime change comparable to the 1998 event, then a step-up plateau can result. If warmists are right, and there is a release of pent-up heat in the system, then a warming trend would resume.

If this El Nino is not strong enough to shift the regime, then the Plateau could continue at the same level. Finally, it could be that several factors align to reverse the warming since the 1970’s, and bring a return to cooler 1950’s weather.

Those who see a quasi-60 year cycle in weather patterns note that it is about time for the PDO in the Pacific and the AMO in the Atlantic to be in cooler phases, along with a quiet sun, which went spotless last week. There are also those attending to orbital climate patterns, which gave us the Modern Warming Period and will eventually take it away.

 

Changes in climate due to earth’s orbit around the sun

Update July 4

In the previous thread is a chart from J. Martin displaying the effects of the changing tilt of earth’s axis.  As shown, the long term pattern is toward cooling.

J. Martin: Tilt (obliquity) has a 41k yr cycle and prior to the arrival of 100,000 year glaciations, the 41k year world dominated with short sharp glaciations every 41 thousand years. The 41k influence us still there. Javier has done a graph which overlays the obliquity cycle on top of a graph of the holocene which produces a perfect match. So it would seem that interstitial temperatures are largely governed by obliquity, but glaciation temperatures are governed by eccentricity with occasional disturbances from precession .

In addition, ren provides interesting links to studies showing SA (Sunspot numbers) correlating to Middle Ages Warm period and LIA, and a 2012 study forecasting the next 2 cycles.

shepherd etalfig1

Figure 1. Bottom plot: the summary component of the two PCs (solid curve) and the decaying component (dashed curves) for the “historical” data (cycles 21–23) and predicted data (cycles 24–26). The cycle lengths (about 11 yr) are marked with different colors.

Abstract: We can conclude with a sufficient degree of confidence that the solar activity in cycles 24–26 will be systematically decreasing because of the increasing phase shift between the two magnetic waves of the poloidal field leading to their full separation into opposite hemispheres in cycles 25 and 26. This separation is expected to result in the lack of their subsequent interaction in any of the hemispheres, possibly leading to a lackof noticeable sunspot activity on the solar surface lasting for a decade or two, similar to those recorded in the medieval period.

Again, to the extent that SSNs are a proxy for changes in heat content within the earth’s climate system, the graph is also indicating future cooling.

For an approach to quantifying climate effects from Solar Activity, as well as oceanic cycles, see:
Quantifying Natural Climate Change

Quantifying Natural Climate Change

Posted on by Ron Clutz

 

Natural climate change

Recent posts have stressed the complexity of climates and their component variables. However, global warming was invented on the back of a single metric: rising global mean temperatures the last decades of last century. That was de-emphasized during the “pause” but re-emerged lately with the El-Nino-induced warming. So this post is focusing on that narrow aspect of climate change.

There are several papers on this blog referring to a quasi-60 year oscillation of surface temperatures due to oceanic circulations. I have also noted the attempts by many to make the link between solar activity (SA) and earth climate patterns.

Dan Pangburn is a professional engineer who has synthesized the solar and oceanic factors into a mathematical model that correlates with Average Global Temperature (AGT). On his blog is posted a monograph (here) Cause of Global Climate Change explaining clearly his thinking and the maths.  I am providing some excerpts and graphs as a synopsis of his analysis, in hopes others will also access and appreciate his work on this issue.

Introduction

The basis for assessment of AGT is the first law of thermodynamics, conservation of energy, applied to the entire planet as a single entity. Much of the available data are forcings or proxies for forcings which must be integrated (mathematically as in calculus, i.e. accumulated over time) to compute energy change. Energy change divided by effective thermal capacitance is temperature change. Temperature change is expressed as anomalies which are the differences between annual averages of measured temperatures and some baseline reference temperature; usually the average over a previous multiple year time period. (Monthly anomalies, which are not used here, are referenced to previous average for the same month to account for seasonal norms.)

At this point, it appears reasonable to consider two temperature anomaly data sets extending through 2015.  These are co-plotted on Figure 8.

Slide8lrg

1) The set used previously [12] through 2012 with extension 2013-2015 set at the average 2002-2012 (when the trend was flat) at 0.4864 K above the reference temperature. 2) Current (5/27/16) HadCRUT4 data set [13] through 2012 with 2013-2015 set at the average 2002-2012 at 0.4863 K above the reference temperature.

Accuracy of the model is determined using the Coefficient of Determination, R 2, to compare calculated AGT with measured AGT.

Oceanic Climate Impacts

Approximation of the sea surface temperature anomaly oscillation can be described as varying linearly from –A/2 K in 1909 to approximately +A/2 K in 1941 and linearly back to the 1909 value in 1973. This cycle repeats before and after with a period of 64 years.

Slide1

Figure 1: Ocean surface temperature oscillations (α-trend) do not significantly affect the bulk energy of the planet.

Comparison with PDO, ENSO and AMO

Ocean cycles are perceived to contribute to AGT in two ways: The first is the direct measurement of sea surface temperature (SST). The second is warmer SST increases atmospheric water vapor which acts as a forcing and therefore has a time-integral effect on temperature. The approximation, (A,y), accounts for both ways.

Successful accounting for oscillations is achieved for PDO and ENSO when considering these as forcings (with appropriate proxy factors) instead of direct measurements. As forcings, their influence accumulates with time. The proxy factors must be determined separately for each forcing.

Slide2

Figure 2: Comparison of idealized approximation of ocean cycle effect and the calculated effect from PDO and ENSO.

The AMO index [9] is formed from area-weighted and de-trended SST data. It is shown with two different amounts of smoothing in Figure 3 along with the saw-tooth approximation for the entire planet per Equation (2) with A = 0.36.

Slide3

The high coefficients of determination in Table 1 and the comparisons in Figures 2 and 3 corroborate the assumption that the saw-tooth profile with a period of 64 years provides adequate approximation of the net effect of all named and unnamed ocean cycles in the calculated AGT anomalies.

Solar-Climate Connection

An assessment of this is that sunspots are somehow related to the net energy retained by the planet, as indicated by changes to average global temperature. Fewer sunspots are associated with cooling, and more sunspots are associated with warming. Thus the hypothesis is made that SSN are proxies for the rate at which the planet accumulates (or loses) radiant energy over time. Therefore the time-integral of the SSN anomalies is a proxy for the amount of energy retained by the planet above or below breakeven.

Also, a lower solar cycle over a longer period might result in the same increase in energy retained by the planet as a higher solar cycle over a shorter period. Both magnitude and time are accounted for by taking the time-integral of the SSN anomalies, which is simply the sum of annual mean SSN (each minus Savg) over the period of study.

The values for Savg are subject to two constraints. Initially they are determined as that which results in derived coefficients and maximum R2. However, calculated values must also result in rational values for calculated AGT at the depths of the Little Ice Age. The necessity to calculate a rational LIA AGT is a somewhat more sensitive constraint. The selected values for Savg result in calculated LIA AGT of approximately 1 K less than the recent trend which appears rational and is consistent with most LIA AGT assessments.

The sunspot number anomaly time-integral is a proxy for a primary driver of the temperature anomaly β-trend. By definition, energy change divided by effective thermal capacitance is temperature change.

Slide10

Figure 10: 5-year running average of measured temperatures with calculated prior and future trends (Data Set 1) using 34 as the average daily sunspot number and with V1 SSN. R2 = 0.978887

Projections until 2020 use the expected sunspot number trend for the remainder of solar cycle 24 as provided [6] by NASA. After 2020 the ‘limiting cases’ are either assuming sunspots like from 1924 to 1940 or for the case of no sunspots which is similar to the Maunder Minimum.

Some noteworthy volcanoes and the year they occurred are also shown on Figure 9. No consistent AGT response is observed to be associated with these. Any global temperature perturbation that might have been caused by volcanoes of this size is lost in the natural fluctuation of measured temperatures.

Although the connection between AGT and the sunspot number anomaly time-integral is demonstrated, the mechanism by which this takes place remains somewhat speculative.

Various papers have been written that indicate how the solar magnetic field associated with sunspots can influence climate on earth. These papers posit that decreased sunspots are associated with decreased solar magnetic field which decreases the deflection of and therefore increases the flow of galactic cosmic rays on earth.

These papers [14,15] associated the increased low-altitude clouds with increased albedo leading to lower temperatures. Increased low altitude clouds would also result in lower average cloud altitude and therefore higher average cloud temperature. Although clouds are commonly acknowledged to increase albedo, they also radiate energy to space so increasing their temperature increases S-B radiation to space which would cause the planet to cool. Increased albedo reduces the energy received by the planet and increased radiation to space reduces the energy of the planet. Thus the two effects work together to change the AGT of the planet.

Summary

Simple analyses [17] indicate that either an increase of approximately 186 meters in average cloud altitude or a decrease of average albedo from 0.3 to the very slightly reduced value of 0.2928 would account for all of the 20th century increase in AGT of 0.74 K. Because the cloud effects work together and part of the temperature change is due to ocean oscillation (low in 1901, 0.2114 higher in 2000), substantially less cloud change would suffice.

All of this leaves little warming left to attribute to rising CO2. Pangburn estimates CO2 forcing could be at most 18.6% or 0.23C added since 1895. Given uncertainties in proxies from the past, the estimate could be as low as 0.05C, and the correlation with natural factors would still be .97 R2.

However, all is not lost for CO2. It is still an important player in the atmosphere, despite its impotence as a warming agent.

 

The Coming Climate

Posted on by Ron Clutz

Update July 4 below

When you see a graph like that below, it is obvious that an unusually strong El Nino just happened in our climate system. It resulted in higher global temperatures the last two years and so far in 2016. But that event is over now, and naturally we wonder what to expect in the months and years ahead.

cdas_v2_hemisphere_2016june2
For example some comments from a recent thread at WUWT (here) were intriguing:

It will be interesting to see what comes next. The major difference between the 1998 el nino and this one is that in 1998 the sun was increasing in solar activity, while this one solar activity is decreasing. (rishrac)

Nino3,4 and UAH LT dC Anomalies, and UAH LT Scaled *3 and Lagged 4 Months h/t Allan MacRae

And richard takes the long view of the situation:

While we all stare at the short-term ups and downs of the global temperatures, pay a little thought to the fact that the Earth’s orbit around the Sun causes snow in the winter and warmth during the summer, so it may be important?

Perihelion presently occurs around January 3, (Northern hemisphere winter, Southern summer) while aphelion is around July 4. Therefore, the southern hemisphere receives more solar radiation and is therefore warmer in summer and colder in winter (aphelion). The Northern hemisphere has cooler summers and milder winter (solar radiation-wise).

Also the northern hemispheres autumn and winter are slightly shorter than spring and summer, because the Earth is moving faster around the Sun in winter slower in summer.

This alone could account for “Global Warming” attributed to CO2, (which no doubt plays some part in it).

Over the next 10,000 years, northern hemisphere winters will become gradually longer and summers will become shorter, due to the change in the Earth’s Orbital Eccentricity.

Couple this with changes in the Earth’s tilt, which varies from 22.1 degrees to 24.5 degrees, (currently at 23.4 degrees). More tilt means more solar radiation gets to the poles (global warming) and less tilt means less radiation gets to the poles (global cooling). The last maximum tilt occurred in 8700 BC (Holocene maximum) and the next minimum tilt will happen in 11,800 AD (the advance of the ice sheets), precisely at the time of longer northern winters and shorter summers.

Orbital Climate Factors: E for eccentricity, T for tilt, and P for precession

Predicting the Future is Tough

Chiefio (E.M. Smith) has a good post (here) reminding us that statistical projections do not help us much in this case. Temperature series produced by our climate system have special qualities. The patterns are auto-correlated, meaning that tomorrow’s weather will be similar to today’s; the occurrence is not totally independent, like the flip of coin. IOW there is momentum in the climate characteristics, which can and do fluctuate over seasons, decades, centuries and more. Our attempts to use linear regressions to forecast are thwarted by temperature time series that do not follow a normal gaussian distribution, and are semi-chaotic and non-stationary.

Four Possibilities Forward From Today

From past experience, the next few years could logically follow one of four temperature scenarios:
1. The Plateau since 1998 continues.
2. The Warming prior to 1998 resumes.
3. A new Plateau begins with 2016 at a higher (step up) level.
4. A Cooling begins comparable to the years after 1940.

All of these have analogues in our recent climate observations. If this now finished El Nino triggers a regime change comparable to the 1998 event, then a step-up plateau can result. If warmists are right, and there is a release of pent-up heat in the system, then a warming trend would resume.

If this El Nino is not strong enough to shift the regime, then the Plateau could continue at the same level. Finally, it could be that several factors align to reverse the warming since the 1970’s, and bring a return to cooler 1950’s weather.

Those who see a quasi-60 year cycle in weather patterns note that it is about time for the PDO in the Pacific and the AMO in the Atlantic to be in cooler phases, along with a quiet sun, which went spotless last week. There are also those attending to orbital climate patterns, which gave us the Modern Warming Period and will eventually take it away.

 

Changes in climate due to earth’s orbit around the sun

Update July 4

In the thread below is a chart from J Martin displaying the effects of the changing tilt of earth’s axis.  As shown, the long term pattern is toward cooling.

In addition, ren provides interesting links to studies showing SA (Sunspot numbers) correlating to Middle Ages Warm period and LIA, and a 2012 study forecasting the next 2 cycles.

Figure 1. Bottom plot: the summary component of the two PCs (solid curve) and the decaying component (dashed curves) for the “historical” data (cycles 21–23) and predicted data (cycles 24–26). The cycle lengths (about 11 yr) are marked with different colors.
shepherd etalfig1

Again, to the extent that SSNs are a proxy for changes in heat content within the earth’s climate system, the graph is also indicating future cooling.

For quantification of climate effects from Solar Activity, see:
Quantifying Natural Climate Change

Rise and Fall of El Nino (illustrated)

Posted on by Ron Clutz

cdas_v2_hemisphere_2016june2Here is a great view of how the 2015-16 El Nino caused higher surface temperatures last year and this, displayed in 2-meter temp anomalies (weather station height). The satellites’ data show the uptick began in earnest October 2015 and returned to neutral in May 2016. SSTs are now firmly in neutral.
h/t Joe Bastardi

Summary

The temperature variations portrayed above were 100% Natural, no additives were involved.   Keep your popcorn handy as we await temperature measurements for the second half of 2016.

Source: Weatherbell

Arctic Warming Unalarming

Posted on by Ron Clutz

Locations of arctic stations examined in this study

Locations of arctic stations examined in this study

An recent extensive analysis of Northern surface temperature records gives no support for Arctic “amplification” fears.

The Arctic has warmed at the same rate as Europe over the past two centuries. Heretofore, it has been supposed that any global warming would be amplified in the Arctic. This may still be true if urban heat island effects are responsible for part of the observed temperature increase at European stations. However, European and Arctic temperatures have remained closely synchronized for over 200 years during the rapid growth of urban centres.

And the warming pattern in Europe and the Arctic is familiar and unalarming.

Arctic temperatures have increased during the period 1820– 2014. The warming has been larger in January than in July. Siberia, Alaska and Western Canada appear to have warmed slightly more than Eastern Canada, Greenland, Iceland and Northern Europe. The warming has not occurred at a steady rate. Much of the warming trends found during 1820 to 2014 occurred in the late 1990s, and the data show temperatures levelled off after 2000. The July temperature trend is even slightly negative for the period 1820–1990. The time series exhibit multidecadal temperature fluctuations which have also been found by other temperature reconstructions.

The paper is:

Arctic temperature trends from the early nineteenth century to the present W. A. van Wijngaarden, Theoretical & Applied Climatology (2015) here

Temperatures were examined at 118 stations located in the Arctic and compared to observations at 50 European stations whose records averaged 200 years and in a few cases extend to the early 1700s.

Fig. 3 Temperature change for a January, b July and c annual relative to the temperature during 1961 to 1990 for Arctic stations. The red curve is the moving 5-year average while the blue curve is the number of stations

Fig. 3 Temperature change for a January, b July and c annual relative to the temperature during 1961 to 1990 for Arctic stations. The red curve is the moving 5-year average while the blue curve is the number of stations

Summary

The data and results for all stations are provided in detail, and the findings are inescapable.

The Arctic has warmed at the same rate as Europe over the past two centuries. . . The warming has not occurred at a steady rate. . .During the 1900s, all four (Arctic) regions experienced increasing temperatures until about 1940. Temperatures then decreased by about 1 °C over the next 50 years until rising in the 1990s.

For the period 1820–2014, the trends for the January, July and annual temperatures are 1.0, 0.0 and 0.7 °C per century, respectively. . . Much of the warming trends found during 1820 to 2014 occurred in the late 1990s, and the data show temperatures levelled off after 2000.

Once again conclusions based on observations are ignored while projections from models are broadcast and circulated like gossip. The only amplification going on is the promotion of global warming alarms.

megaphone

Footnote: I did a study last year of 25 World Class surface temperature records (all European) and found the same patterns (here).

Revisiting “Pause Deniers Busted”

Posted on by Ron Clutz

 

The original post and updates were done in October 2015.  Now Radford Neal has done a complete deconstruction of the published paper in his post (here): Critique of ‘Debunking the climate hiatus’, by Rajaratnam, Romano, Tsiang, and Diffenbaugh .  Neal says:

Climatic Change appears to be a reputable refereed journal, which is published by Springer, and which is cited in the latest IPCC report. The paper was touted in popular accounts as showing that the whole hiatus thing was mistaken — for instance, by Stanford University itself.

You might therefore be surprised that, as I will discuss below, this paper is completely wrong. Nothing in it is correct. It fails in every imaginable respect.

Original post and updates October 3 and 30 below

With Paris COP drawing near, the lack of warming this century is inconvenient and undermines the cause.

As Dr. Judith Curry said, “I have been expecting to start seeing papers on the ‘hiatus is over.’ Instead I am seeing papers on ‘the hiatus never happened.’”

One that was trumpeted came out of my Alma Mater, Stanford.  They garnered the expected headlines from the usual places:

Global Warming “Hiatus” Never Happened: Eos

There never was any global warming “pause.”:  Washington Post

The text is here: Debunking the climate hiatus

http://download.springer.com/static/pdf/969/art%253A10.1007%252Fs10584-015-1495-y.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Farticle%2F10.1007

The write up has statistical razzle-dazzle and lots of opaque sentences, but let’s not get lost in the weeds.

Let’s not talk about the multiple tamperings to the land records they chose to study.  Let’s even overlook their including the bogus upward adjustments to the SSTs by Karl et al.  Bob Tisdale dissected that here: https://bobtisdale.wordpress.com/2015/06/04/noaancdcs-new-pause-buster-paper-a-laughable-attempt-to-create-warming-by-adjusting-past-data/

We don’t need to get into the technicalities of why they stopped with 2013 data, the suitability of the tests applied or their interpretations of the results.

Here’s what you need to know about this study:

They ignored the satellite records (RSS and UAH), the gold standard of temperature measurements, because the absence of warming there is undeniable.

For the land and ocean datasets they analyzed, they ignored the huge divergence between observations and the predictions (projections) from climate models.

Conclusion:

Natural variability in the climate system has neutralized any warming from increased CO2 this century, and also offset most, if not all of the secular rise in temperature since the Little Ice Age.  The models did not forecast this; they can only project warming, and do so at rates several times higher than observations.  The models fail for three reasons:  high sensitivity to CO2; positive feedback from water vapor; and lack of thermal inertia by the oceans.

For more on climate models and temperature projections:
https://rclutz.wordpress.com/2015/03/24/temperatures-according-to-climate-models/

The Stanford football team was impressive beating highly-rated Southern Cal on their home field last Saturday.  The work of the research team, however, looks like pandering rather than science.  They need to up their game: No cookies.

Update October 3

I found the time to look into the details of this paper and the statistical trick comes to light.

They took as the null hypothesis: “Temperatures are not rising.”  After applying several statistical tests, they conclude that the statement is not supported by the data, so we cannot say with certainty temperatures are not rising.

And what about the other null hypothesis: “Temperatures are rising.”  Silence.

I suspect they didn’t want to admit that the same statistical tests would also disprove that statement.

A reasonable person concludes: When you can not say for sure that temperatures are not rising, or that they are rising, that would surely indicate a plateau in temperatures.

Update October 30–Another classic from Josh

The Climate Story (Illustrated)

Posted on by Ron Clutz

The captions and most comments below come from Mike van Biezen in his recent essay published at the Daily Wire (here). To illustrate his points, I added images collected from various internet addresses. Michael van Biezen teaches physics and earth sciences at Loyola Marymount University, Los Angeles, and his many lectures are available on Youtube at his website (here).

Temperature records from around the world do not support the assumption that today’s temperatures are unusual.

giss-annual-temps4

Global Mean Temperature from land and ocean expressed in absolute degrees F.

Satellite temperature data do not support the assumption that temperatures are rising rapidly.

john-christy-climate-change-chart-0a201a1637955761

The world experienced a significant cooling trend between 1940 and 1980. CO2 levels do not correlate consistently with temperatures.

Urban heat island effect skews the temperature data of a significant number of weather stations.

There is a natural inverse relationship between global temperatures and atmospheric CO2 levels.

Higher temperatures increase atmospheric CO2 levels and lower temperatures decrease atmospheric CO2 levels, not the other way around.

The CO2 cannot, from a scientific perspective, be the cause of significant global temperature changes

The H2O molecule which is much more prevalent in the Earth’s atmosphere, and which is a bend molecule, has many more vibrational modes, and absorbs many more frequencies emitted by the Earth, including to some extent the radiation absorbed by CO2. It turns out that between water vapor and CO2, nearly all of the radiation that can be absorbed by CO2 is already being absorbed.

Many periods during our recent history show that a warmer climate was prevalent long before the industrial revolution.

Glaciers have been melting for more than 150 years.

“Data adjustment” is used to continue the perception of global warming.

The Moral of The Climate Story:

Global warming alarm is not supported by temperature data.

Pause Deniers Busted

Posted on by Ron Clutz

Updates October 3 and 30 below

With Paris COP drawing near, the lack of warming this century is inconvenient and undermines the cause.

As Dr. Judith Curry said, “I have been expecting to start seeing papers on the ‘hiatus is over.’ Instead I am seeing papers on ‘the hiatus never happened.’”

One that was trumpeted came out of my Alma Mater, Stanford.  They garnered the expected headlines from the usual places:

Global Warming “Hiatus” Never Happened: Eos

There never was any global warming “pause.”:  Washington Post

The text is here: Debunking the climate hiatus

http://download.springer.com/static/pdf/969/art%253A10.1007%252Fs10584-015-1495-y.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Farticle%2F10.1007

The write up has statistical razzle-dazzle and lots of opaque sentences, but let’s not get lost in the weeds.

Let’s not talk about the multiple tamperings to the land records they chose to study.  Let’s even overlook their including the bogus upward adjustments to the SSTs by Karl et al.  Bob Tisdale dissected that here: https://bobtisdale.wordpress.com/2015/06/04/noaancdcs-new-pause-buster-paper-a-laughable-attempt-to-create-warming-by-adjusting-past-data/

We don’t need to get into the technicalities of why they stopped with 2013 data, the suitability of the tests applied or their interpretations of the results.

Here’s what you need to know about this study:

They ignored the satellite records (RSS and UAH), the gold standard of temperature measurements, because the absence of warming there is undeniable.

For the land and ocean datasets they analyzed, they ignored the huge divergence between observations and the predictions (projections) from climate models.

Conclusion:

Natural variability in the climate system has neutralized any warming from increased CO2 this century, and also offset most, if not all of the secular rise in temperature since the Little Ice Age.  The models did not forecast this; they can only project warming, and do so at rates several times higher than observations.  The models fail for three reasons:  high sensitivity to CO2; positive feedback from water vapor; and lack of thermal inertia by the oceans.

For more on climate models and temperature projections:
https://rclutz.wordpress.com/2015/03/24/temperatures-according-to-climate-models/

The Stanford football team was impressive beating highly-rated Southern Cal on their home field last Saturday.  The work of the research team, however, looks like pandering rather than science.  They need to up their game: No cookies.

Update October 3

I found the time to look into the details of this paper and the statistical trick comes to light.

They took as the null hypothesis: “Temperatures are not rising.”  After applying several statistical tests, they conclude that the statement is not supported by the data, so we cannot say with certainty temperatures are not rising.

And what about the other null hypothesis: “Temperatures are rising.”  Silence.

I suspect they didn’t want to admit that the same statistical tests would also disprove that statement.

A reasonable person concludes: When you can not say for sure that temperatures are not rising, or that they are rising, that would surely indicate a plateau in temperatures.

Update October 30–Another classic from Josh

When is it Warming?–The Real Reason for the Pause

Posted on by Ron Clutz

June 21 E.M. Smith made an intriguing comment on the occasion of Summer Solstice (NH) and Winter Solstice (SH):

“This is the time when the sun stops the apparent drift in the sky toward one pole, reverses, and heads toward the other. For about 2 more months, temperatures lag this change of trend. That is the total heat storage capacity of the planet. Heat is not stored beyond that point and there can not be any persistent warming as long as winter brings a return to cold.

I’d actually assert that there are only two measurements needed to show the existence or absence of global warming. Highs in the hottest month must get hotter and lows in the coldest month must get warmer. BOTH must happen, and no other months matter as they are just transitional.

I’m also pretty sure that the comparison of dates of peaks between locations could also be interesting. If one hemisphere is having a drift to, say, longer springs while the other is having longer falls, that’s more orbital mechanics than CO2 driven and ought to be reflected in different temperature trends / rates of drift.”

https://chiefio.wordpress.com/2015/06/21/summer-solstice-is-here/

Notice that the global temperature tracks with the seasons of the NH. The reason for this is simple. The NH has twice as much land as the Southern Hemisphere (SH). Oceans have greater heat capacity and do not change temperatures as much as land does. So every year when there is almost a 4 °C swing in the temperature of the Earth, it follows the seasons of the NH. This is especially interesting because the Earth gets the most energy from the sun in January right now. That is because of the orbit of the Earth. The perihelion is when the Earth is closest to the sun and that currently takes place in January.

http://theinconvenientskeptic.com/2010/10/how-the-northern-hemisphere-drives-the-modern-climate/

Observations and Analysis:

My curiosity piqued by Chiefio’s comment, I went looking for data to analyze to test his proposition. As it happens, Berkeley Earth provides data tables for monthly Tmax and Tmin by hemisphere (NH and SH), from land station records. Setting aside any concerns about adjustments or infilling I did the analysis taking the BEST data tables at face value. Since land surface temperatures are more variable than sea surface temps, it seems like a reasonable dataset to analyze for the mentioned patterns.

Tmax Records

NH and SH long-term trends are the same 0.07C/decade, and in both there was cooling before 1979 and above average warming since. However, since 1950 NH warmed more strongly, and mostly prior to 1998, while SH has warmed strongly since 1998. (Trends below are in C/yr.)

 Tmax Trends NH Tmax SH Tmax
All years 0.007 0.007
1998-2013 0.018 0.030
1979-1998 0.029 0.017
1950-1979 -0.003 -0.003
1950-2013 0.020 0.014

Summer Comparisons:

NH summer months are June, July, August, (6-8) and SH summer is December, January, February (12-2). The trends for each of those months were computed and the annual trends subtracted to show if summer months were warming more than the rest of the year (Trends below are in C/yr.).

Month less Annual NH
Tmax		 NH Tmax NH Tmax SH Tmax SH Tmax SH Tmax
Summer Trends

6

 7 8 12 1

2
All years -0.002 -0.004 -0.004 0.000 0.003 0.002
1998-2013 0.026 0.002 0.006 0.022 0.004 -0.029
1979-1998 0.003 -0.004 -0.003 -0.014 -0.029 0.001
1950-1979 -0.002 -0.002 -0.005 0.004 0.005 -0.005
1950-2013 -0.002 -0.003 -0.002 -0.002 -0.002 -0.002

NH summer months are cooler than average overall and since 1950. Warming does appear since 1998 with a large anomaly in June and also warming in August.SH shows no strong pattern of Tmax warming in summer months. A hot December trend since 1998 is offset by a cold February. Overall SH summers are just above average, and since 1950 have been slightly cooler.

Tmin Records

Both NH and SH show Tmin rising 0.12C/decade, much more strongly warming than Tmax. SH show that average warming persisting throughout the record, slightly higher prior to 1979. NH Tmin is more variable, showing a large jump 1979-1998, a rate of 0.25 C/decade (Trends below are in C/yr.).

 Trends NH Tmin SH Tmin
All years 0.012 0.012
1998-2013 0.010 0.010
1979-1998 0.025 0.011
1950-1979 0.006 0.014
1950-2013 0.022 0.014

Winter Comparisons:

SH winter months are June, July, August, (6-8) and NH winter is December, January, February (12-2). The trends for each of those months were computed and the annual trends subtracted to show if winter months were warming more than the rest of the year (Trends below are in C/yr.).

Month less Annual NH Tmin NH Tmin NH Tmin SH Tmin SH Tmin SH Tmin
Winter Trends

12

 1 2 6 7

8
All years 0.007 0.008 0.007 0.005 0.003 0.004
1998-2013 -0.045 -0.035 -0.076 -0.043 -0.024 -0.019
1979-1998 -0.018 -0.005 0.024 0.034 0.008 -0.008
1950-1979 0.008 0.005 0.007 0.008 0.012 0.013
1950-2013 0.001 0.007 0.008 -0.001 -0.002 0.002

NH winter Tmin warming is stronger than SH Tmin trends, but shows quite strong cooling since 1998. An anomalously warm February is the exception in the period 1979-1998.Both NH and SH show higher Tmin warming in winter months, with some irregularities. Most of the SH Tmin warming was before 1979, with strong cooling since 1998. June was anomalously warming in the period 1979 to 1998.

Summary

Tmin did trend higher in winter months but not consistently. Mostly winter Tmin warmed 1950 to 1979, and was much cooler than other months since 1998.

Tmax has not warmed in summer more than in other months, with the exception of two anomalous months since 1998: NH June and SH December.

Conclusion:

I find no convincing pattern of summer Tmax warming carrying over into winter Tmin warming. In other words, summers are not adding warming more than other seasons. There is no support for concerns over summer heat waves increasing as a pattern.

It is interesting to note that the plateau in temperatures since the 1998 El Nino is matched by winter months cooler than average during that period, leading to my discovering the real reason for lack of warming recently.

The Real Reason for the Pause in Global Warming

These data suggest warming trends are coming from less cold overnight temperatures as measured at land weather stations. Since stations exposed to urban heat sources typically show higher minimums overnight and in winter months, this pattern is likely an artifact of human settlement activity rather than CO2 from fossil fuels.

Thus the Pause (more correctly the Plateau) in global warming is caused by end of the century completion of urbanization around most surface stations. With no additional warming from additional urban heat sources, temperatures have remained flat for more than 15 years.

 

Data is here:
http://berkeleyearth.lbl.gov/regions/northern-hemisphere
http://berkeleyearth.lbl.gov/regions/southern-hemisphere

Temperature Data Review Project–My Submission

Posted on by Ron Clutz

An International Temperature Data Review Project has been announced, along with a call for analyses of surface temperature records to be submitted. The project is described here: http://www.tempdatareview.org/

Below is my submission.

Update April 27:  Notice was received today that this submission has gone to the Panel.

Overview

I did a study of 2013 records from the CRN top rated US surface stations. It was published Aug. 20, 2014 at No Tricks Zone. Most remarkable about these records is the extensive local climate diversity that appears when station sites are relatively free of urban heat sources. 35% (8 of 23) of the stations reported cooling over the century. Indeed, if we remove the 8 warmest records, the average rate flips from +0.16°C to -0.14°C. In order to respect the intrinsic quality of temperatures, I calculated monthly slopes for each station, and averaged them for station trends.

Recently I updated that study with 2014 data and compared adjusted to unadjusted records. The analysis shows the effect of GHCN adjustments on each of the 23 stations in the sample. The average station was warmed by +0.58 C/Century, from +.18 to +.76, comparing adjusted to unadjusted records. 19 station records were warmed, 6 of them by more than +1 C/century. 4 stations were cooled, most of the total cooling coming at one station, Tallahassee. So for this set of stations, the chance of adjustments producing warming is 19/23 or 83%.

Adjustments Multiply Warming at US CRN1 Stations

A study of US CRN1 stations, top-rated for their siting quality, shows that GHCN adjusted data produces warming trends several times larger than unadjusted data.

The unadjusted files from ghcn.v3.qcu have been scrutinized for outlier values, and for step changes indicative of non-climatic biases. In no case was the normal variability pattern interrupted by step changes. Coverages were strong, the typical history exceeding 95%, and some achieved 100%.(Measured by the % of months with a reported Tavg value out of the total months in the station’s lifetime.)

The adjusted files are another story. Typically, years of data are deleted, often several years in a row. Entire rows are erased including the year identifier, so finding the missing years is a tedious manual process looking for gaps in the sequence of years. All stations except one lost years of data through adjustments, often in recent years. At one station, four years of data from 2007 to 2010 were deleted; in another case, 5 years of data from 2002 to 2006 went missing. Strikingly, 9 stations that show no 2014 data in the adjusted file have fully reported 2014 in the unadjusted file.

It is instructive to see the effect of adjustments upon individual stations. A prime example is 350412 Baker City, Oregon.

Over 125 years GHCN v.3 unadjusted shows a trend of -0.0051 C/century. The adjusted data shows +1.48C/century. How does the difference arise? The coverage is about the same, though 7 years of data are dropped in the adjusted file. However, the values are systematically lowered in the adjusted version: Average annual temperature is +6C +/-2C for the adjusted file; +9.4C +/-1.7C unadjusted.

Baker City GHCHM NOAA

How then is a warming trend produced? In the distant past, prior to 1911, adjusted temperatures decade by decade are cooler by more than -2C each month. That adjustment changes to -1.8C 1912-1935, then changes to -2.2 for 1936 to 1943. The rate ranges from -1.2 to -1.5C 1944-1988, then changes to -1C. From 2002 onward, adjusted values are more than 1C higher than the unadjusted record.

Some apologists for the adjustments have stated that cooling is done as much as warming. Here it is demonstrated that by cooling selectively in the past, a warming trend can be created, even though the adjusted record ends up cooler on average over the 20th Century.

San Antonio GHCHM NOAA

A different kind of example is provided by 417945 San Antonio, Texas. Here the unadjusted record had a complete 100% coverage, and the adjustments deleted 262 months of data, reducing the coverage to 83%. In addition, the past was cooled, adjustments ranging from -1.2C per month in 1885 gradually coming to -0.2C by 1970. These cooling adjustments were minor, only reducing the average annual temperature by 0.16C. Temperatures since 1997 were increased by about 0.5C each year.  Due to deleted years of data along with recent increases, San Antonio went from an unadjusted trend of +0.30C/century to an adjusted trend of +0.92C/century, tripling the warming at that location.

The overall comparison for the set of CRN1 stations:

Area FIRST CLASS US STATIONS
History 1874 to 2014
Stations 23
Dataset Unadjusted Adjusted
Average Trend 0.18 0.76 °C/Century
Std. Deviation 0.66 0.54 °C/Century
Max Trend 1.18 1.91 °C/Century
Min Trend -2.00 -0.48 °C/Century
Ave. Length 119 Years

These stations are sited away from urban heat sources, and the unadjusted records reveal a diversity of local climates, as shown by the deviation and contrasting Max and Min results. Seven stations showed negative trends over their lifetimes through 2014.

Adjusted data reduces the diversity and shifts the results toward warming. The average trend is 4 times warmer, only 2 stations show any cooling, and at smaller rates. Many stations had warming rates increased by multiples from the unadjusted rates. Whereas 4 months had negative trends in the unadjusted dataset, no months show cooling after adjustments.
Periodic Rates from US CRN1 Stations

°C/Century °C/Century
Start End Unadjusted Adjusted
1915 1944 1.22 1.51
1944 1976 -1.48 -0.92
1976 1998 3.12 4.35
1998 2014 -1.67 -1.84
1915 2014 0.005 0.68

Looking at periodic trends within the series, it is clear that adjustments at these stations increased the trend over the last 100 years from flat to +0.68 C/Century. This was achieved by reducing the cooling mid-century and accelerating the warming prior to 1998.

Methodology

Surfacestations.org provides a list of 23 stations that have the CRN#1 Rating for the quality of the sites. I obtained the records from the latest GHCNv3 monthly qcu report, did my own data quality review and built a Temperature Trend Analysis workbook. I made a companion workbook using the GHCNv3 qca report. Both datasets are available here:
ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v3/

As it happens, the stations are spread out across the continental US (CONUS): NW: Oregon, North Dakota, Montana; SW: California, Nevada, Colorado, Texas; MW: Indiana, Missouri, Arkansas, Louisiana; NE: New York, Rhode Island, Pennsylvania; SE: Georgia, Alabama, Mississippi, Florida.

The method involves creating for each station a spreadsheet with monthly average temperatures imported into a 2D array, a row for each year, a column for each month. The sheet calculates a trend for each month for all of the years recorded at that station. Then the monthly trends are averaged together for a lifetime trend for that station. To be comparable to others, the station trend is presented as degrees per 100 years. A summary sheet collects all the trends from all the sheets to provide trend analysis for the set of stations and the geographical area of interest. Thus the temperatures themselves are not compared, but rather the change derivative expressed as a slope.

I have built Excel workbooks to do this analysis, and have attached two workbooks: USHCN1 Adjusted and Unadjusted.

Conclusion

These 23 US stations comprise a random sample for studying the effects of adjustments upon historical records. Included are all USHCN stations inspected by surfacestations.org that, in their judgment, met the CRN standard for #1 rating. The sample was formed on a physical criterion, siting quality, independent of the content of the temperature records. The only bias in the selection is the expectation that the measured temperatures should be uncontaminated by urban heat sources.

It is startling to see how distorted and degraded are the adjusted records compared to the records submitted by weather authorities. No theory is offered here as to how or why this has happened, only to disclose the records themselves and make the comparisons.

In conclusion, it is not only a matter of concern that individual station histories are altered by adjustments. But also the adjusted dataset is the one used as input into programs computing global anomalies and averages. This much diminished dataset does not inspire confidence in the temperature reconstruction products built upon it.

Thank you for undertaking this project. Hopefully my analyses are useful in your work.

Sincerely, Ron Clutz

US CRN1 Unadjusted TTA2 2014       US CRN1 Adjusted TTA 2014