Evidence that global warming IS happening

[Politico]

Shame no one is denying the climate is changing or you would have a point.

 

[SSDD]

I don't want to believe that scientist don't want to advance science anymore. Destroying the temperature record by altering would be just that.

You have seen ample evidence of just that. Like it or not, some climate scientists are willing to alter the record in order to gain money, fame, and political power.

You have shown nothing but that adjustments were made to the data - and explanations were given for those adjustments. You have NOT shown that those adjustments weren't called for and you have NOT provided any evidence that those making the adustments had ulterior motives of any sort. You see the adjustments and simply assume that they were done for deceptive and malicious purposes when you have no evidence indicating that at all. They call that PREJUDICE. In severe cases, they call it BIGOTRY. In either case, it is the result of the application of ignorance.

Really? Let's hear a rational scientifically sound reason for altering temperatures prior to 1960.

 

[Abraham3]

You are the one with the burden of proof.

However, since you will very likely make no effort in that regard, being satisfied with your prejudices:

Time of Observation Bias Adjustments

Next, monthly temperature values were adjusted for the time-of-observation bias (Karl, et al. 1986; Vose et al., 2003). The Time of Observation Bias (TOB) arises when the 24-hour daily summary period at a station begins and ends at an hour other than local midnight. When the summary period ends at an hour other than midnight, monthly mean temperatures exhibit a systematic bias relative to the local midnight standard (Baker, 1975). In the U.S. Cooperative Observer Network, the ending hour of the 24-hour climatological day typically varies from station to station and can change at a given station during its period of record. The TOB-adjustment software uses an empirical model to estimate and adjust the monthly temperature values so that they more closely resemble values based on the local midnight summary period. The metadata archive is used to determine the time of observation for any given period in a station's observational history.

and

The USHCN version 2 "pairwise" homogenization algorithm addresses these and other issues according to the following steps, which are described in detail in Menne and Williams (2009). At present, only temperature series are evaluated for artificial changepoints.
First, a series of monthly temperature differences is formed between numerous pairs of station series in a region. Specifically, difference series are calculated between each target station series and a number (up to 40) of highly correlated series from nearby stations. In effect, a matrix of difference series is formed for a large fraction of all possible combinations of station series pairs in each localized region. The station pool for this pairwise comparison of series includes USHCN stations as well as other U.S. Cooperative Observer Network stations.
Tests for undocumented changepoints are then applied to each paired difference series. A hierarchy of changepoint models is used to distinguish whether the changepoint appears to be a change in mean with no trend (Alexandersson and Moberg, 1997), a change in mean within a general trend (Wang, 2003), or a change in mean coincident with a change in trend (Lund and Reeves, 2002) . Since all difference series are comprised of values from two series, a changepoint date in any one difference series is temporarily attributed to both station series used to calculate the differences. The result is a matrix of potential changepoint dates for each station series.
The full matrix of changepoint dates is then "unconfounded" by identifying the series common to multiple paired-difference series that have the same changepoint date. Since each series is paired with a unique set of neighboring series, it is possible to determine whether more than one nearby series share the same changepoint date.
The magnitude of each relative changepoint is calculated using the most appropriate two-phase regression model (e.g., a jump in mean with no trend in the series, a jump in mean within a general linear trend, etc.). This magnitude is used to estimate the "window of uncertainty" for each changepoint date since the most probable date of an undocumented changepoint is subject to some sampling uncertainty, the magnitude of which is a function of the size of the changepoint. Any cluster of undocumented changepoint dates that falls within overlapping windows of uncertainty is conflated to a single changepoint date according to
a known change date as documented in the target station's history archive (meaning the discontinuity does not appear to be undocumented), or
the most common undocumented changepoint date within the uncertainty window (meaning the discontinuity appears to be truly undocumented)
Finally, multiple pairwise estimates of relative step change magnitude are re-calculated (as a simple difference in mean) at all documented and undocumented discontinuities attributed to the target series. The range of the pairwise estimates for each target step change is used to calculate confidence limits for the magnitude of the discontinuity. Adjustments are made to the target series using the estimates for each shift in the series.

and

Estimation of Missing Values

Following the homogenization process, estimates for missing data are calculated using a weighted average of values from highly correlated neighboring stations. The weights are determined using a procedure similar to the SHAP routine. This program, called FILNET, uses the results from the TOB and homogenization algorithms to obtain a more accurate estimate of the climatological relationship between stations. The FILNET program also estimates data across intervals in a station record where discontinuities occur in a short time interval, which prevents the reliable estimation of appropriate adjustments.

and your favorite

Urbanization Effects

In the original USHCN, the regression-based approach of Karl et al. (1988) was employed to account for urban heat islands. In contrast, no specific urban correction is applied in USHCN version 2 because the change-point detection algorithm effectively accounts for any "local" trend at any individual station. In other words, the impact of urbanization and other changes in land use is likely small in USHCN version 2. Figure 2 - the minimum temperature time series for Reno, Nevada - provides anecdotal evidence in this regard. In brief, the black line represents unadjusted data, and the blue line represents fully adjusted data. The unadjusted data clearly indicate that the station at Reno experienced both major step changes (e.g., a move from the city to the airport during the 1930s) and trend changes (e.g., a possible growing urban heat island beginning in the 1970s). In contrast, the fully adjusted (homogenized) data indicate that both the step-type changes and the trend changes have been effectively addressed through the change-point detection process used in USHCN version 2.

An example:

Figure 1. (a) Mean annual unadjusted and fully adjusted minimum temperatures at Reno, Nevada. Error bars indicating the magnitude of uncertainty (±1 standard error) were calculated via 100 Monte Carlo simulations that sampled within the range of the pairwise estimates for the magnitude of each inhomogeneity; (b) difference between minimum temperatures at Reno and the mean from its 10 nearest neighbors.

http://cdiac.ornl.gov/epubs/ndp/ushcn/monthly_doc.html#steps

 

[IanC]

You have seen ample evidence of just that. Like it or not, some climate scientists are willing to alter the record in order to gain money, fame, and political power.

You have shown nothing but that adjustments were made to the data - and explanations were given for those adjustments. You have NOT shown that those adjustments weren't called for and you have NOT provided any evidence that those making the adustments had ulterior motives of any sort. You see the adjustments and simply assume that they were done for deceptive and malicious purposes when you have no evidence indicating that at all. They call that PREJUDICE. In severe cases, they call it BIGOTRY. In either case, it is the result of the application of ignorance.

Really? Let's hear a rational scientifically sound reason for altering temperatures prior to 1960.

but we dont like the 'explanations'!

From: Tom Wigley <[email protected]>
To: Phil Jones <[email protected]>
Subject: 1940s
Date: Sun, 27 Sep 2009 23:25:38 -0600
Cc: Ben Santer <[email protected]>

<x-flowed>
Phil,

Here are some speculations on correcting SSTs to partly
explain the 1940s warming blip.

If you look at the attached plot you will see that the
land also shows the 1940s blip (as I'm sure you know).

So, if we could reduce the ocean blip by, say, 0.15 degC,
then this would be significant for the global mean -- but
we'd still have to explain the land blip.

I've chosen 0.15 here deliberately. This still leaves an
ocean blip, and i think one needs to have some form of
ocean blip to explain the land blip (via either some common
forcing, or ocean forcing land, or vice versa, or all of
these). When you look at other blips, the land blips are
1.5 to 2 times (roughly) the ocean blips -- higher sensitivity
plus thermal inertia effects. My 0.15 adjustment leaves things
consistent with this, so you can see where I am coming from.

Removing ENSO does not affect this.

It would be good to remove at least part of the 1940s blip,
but we are still left with "why the blip".

Let me go further. If you look at NH vs SH and the aerosol
effect (qualitatively or with MAGICC) then with a reduced
ocean blip we get continuous warming in the SH, and a cooling
in the NH -- just as one would expect with mainly NH aerosols.

The other interesting thing is (as Foukal et al. note -- from
MAGICC) that the 1910-40 warming cannot be solar. The Sun can
get at most 10% of this with Wang et al solar, less with Foukal
solar. So this may well be NADW, as Sarah and I noted in 1987
(and also Schlesinger later). A reduced SST blip in the 1940s
makes the 1910-40 warming larger than the SH (which it
currently is not) -- but not really enough.

So ... why was the SH so cold around 1910? Another SST problem?
(SH/NH data also attached.)

This stuff is in a report I am writing for EPRI, so I'd
appreciate any comments you (and Ben) might have.

Tom.

</x-flowed>

Attachment Converted: "c:\eudora\attach\TTHEMIS.xls"

Attachment Converted: "c:\eudora\attach\TTLVSO.XLS"

 

[Abraham3]

What is it you think this (I assume stolen) email says about the adjustments to the various temperature records?

 

[SSDD]

You are the one with the burden of proof.

However, since you will very likely make no effort in that regard, being satisfied with your prejudices:

Time of Observation Bias Adjustments

Next, monthly temperature values were adjusted for the time-of-observation bias (Karl, et al. 1986; Vose et al., 2003). The Time of Observation Bias (TOB) arises when the 24-hour daily summary period at a station begins and ends at an hour other than local midnight. When the summary period ends at an hour other than midnight, monthly mean temperatures exhibit a systematic bias relative to the local midnight standard (Baker, 1975). In the U.S. Cooperative Observer Network, the ending hour of the 24-hour climatological day typically varies from station to station and can change at a given station during its period of record. The TOB-adjustment software uses an empirical model to estimate and adjust the monthly temperature values so that they more closely resemble values based on the local midnight summary period. The metadata archive is used to determine the time of observation for any given period in a station's observational history.

and

The USHCN version 2 "pairwise" homogenization algorithm addresses these and other issues according to the following steps, which are described in detail in Menne and Williams (2009). At present, only temperature series are evaluated for artificial changepoints.
First, a series of monthly temperature differences is formed between numerous pairs of station series in a region. Specifically, difference series are calculated between each target station series and a number (up to 40) of highly correlated series from nearby stations. In effect, a matrix of difference series is formed for a large fraction of all possible combinations of station series pairs in each localized region. The station pool for this pairwise comparison of series includes USHCN stations as well as other U.S. Cooperative Observer Network stations.
Tests for undocumented changepoints are then applied to each paired difference series. A hierarchy of changepoint models is used to distinguish whether the changepoint appears to be a change in mean with no trend (Alexandersson and Moberg, 1997), a change in mean within a general trend (Wang, 2003), or a change in mean coincident with a change in trend (Lund and Reeves, 2002) . Since all difference series are comprised of values from two series, a changepoint date in any one difference series is temporarily attributed to both station series used to calculate the differences. The result is a matrix of potential changepoint dates for each station series.
The full matrix of changepoint dates is then "unconfounded" by identifying the series common to multiple paired-difference series that have the same changepoint date. Since each series is paired with a unique set of neighboring series, it is possible to determine whether more than one nearby series share the same changepoint date.
The magnitude of each relative changepoint is calculated using the most appropriate two-phase regression model (e.g., a jump in mean with no trend in the series, a jump in mean within a general linear trend, etc.). This magnitude is used to estimate the "window of uncertainty" for each changepoint date since the most probable date of an undocumented changepoint is subject to some sampling uncertainty, the magnitude of which is a function of the size of the changepoint. Any cluster of undocumented changepoint dates that falls within overlapping windows of uncertainty is conflated to a single changepoint date according to
a known change date as documented in the target station's history archive (meaning the discontinuity does not appear to be undocumented), or
the most common undocumented changepoint date within the uncertainty window (meaning the discontinuity appears to be truly undocumented)
Finally, multiple pairwise estimates of relative step change magnitude are re-calculated (as a simple difference in mean) at all documented and undocumented discontinuities attributed to the target series. The range of the pairwise estimates for each target step change is used to calculate confidence limits for the magnitude of the discontinuity. Adjustments are made to the target series using the estimates for each shift in the series.

and

Estimation of Missing Values

Following the homogenization process, estimates for missing data are calculated using a weighted average of values from highly correlated neighboring stations. The weights are determined using a procedure similar to the SHAP routine. This program, called FILNET, uses the results from the TOB and homogenization algorithms to obtain a more accurate estimate of the climatological relationship between stations. The FILNET program also estimates data across intervals in a station record where discontinuities occur in a short time interval, which prevents the reliable estimation of appropriate adjustments.

and your favorite

Urbanization Effects

In the original USHCN, the regression-based approach of Karl et al. (1988) was employed to account for urban heat islands. In contrast, no specific urban correction is applied in USHCN version 2 because the change-point detection algorithm effectively accounts for any "local" trend at any individual station. In other words, the impact of urbanization and other changes in land use is likely small in USHCN version 2. Figure 2 - the minimum temperature time series for Reno, Nevada - provides anecdotal evidence in this regard. In brief, the black line represents unadjusted data, and the blue line represents fully adjusted data. The unadjusted data clearly indicate that the station at Reno experienced both major step changes (e.g., a move from the city to the airport during the 1930s) and trend changes (e.g., a possible growing urban heat island beginning in the 1970s). In contrast, the fully adjusted (homogenized) data indicate that both the step-type changes and the trend changes have been effectively addressed through the change-point detection process used in USHCN version 2.

An example:

Figure 1. (a) Mean annual unadjusted and fully adjusted minimum temperatures at Reno, Nevada. Error bars indicating the magnitude of uncertainty (±1 standard error) were calculated via 100 Monte Carlo simulations that sampled within the range of the pairwise estimates for the magnitude of each inhomogeneity; (b) difference between minimum temperatures at Reno and the mean from its 10 nearest neighbors.

http://cdiac.ornl.gov/epubs/ndp/ushcn/monthly_doc.html#steps

I asked for a rational and scientifically sound reason for altering the record prior to 1960. Got one...because what you posted isn't

 

[SSDD]

What is it you think this (I assume stolen) email says about the adjustments to the various temperature records?

It is an attempt to rationalize altering
Data

 

[IanC]

You are the one with the burden of proof.

However, since you will very likely make no effort in that regard, being satisfied with your prejudices:

Time of Observation Bias Adjustments

Next, monthly temperature values were adjusted for the time-of-observation bias (Karl, et al. 1986; Vose et al., 2003). The Time of Observation Bias (TOB) arises when the 24-hour daily summary period at a station begins and ends at an hour other than local midnight. When the summary period ends at an hour other than midnight, monthly mean temperatures exhibit a systematic bias relative to the local midnight standard (Baker, 1975). In the U.S. Cooperative Observer Network, the ending hour of the 24-hour climatological day typically varies from station to station and can change at a given station during its period of record. The TOB-adjustment software uses an empirical model to estimate and adjust the monthly temperature values so that they more closely resemble values based on the local midnight summary period. The metadata archive is used to determine the time of observation for any given period in a station's observational history.

and

The USHCN version 2 "pairwise" homogenization algorithm addresses these and other issues according to the following steps, which are described in detail in Menne and Williams (2009). At present, only temperature series are evaluated for artificial changepoints.
First, a series of monthly temperature differences is formed between numerous pairs of station series in a region. Specifically, difference series are calculated between each target station series and a number (up to 40) of highly correlated series from nearby stations. In effect, a matrix of difference series is formed for a large fraction of all possible combinations of station series pairs in each localized region. The station pool for this pairwise comparison of series includes USHCN stations as well as other U.S. Cooperative Observer Network stations.
Tests for undocumented changepoints are then applied to each paired difference series. A hierarchy of changepoint models is used to distinguish whether the changepoint appears to be a change in mean with no trend (Alexandersson and Moberg, 1997), a change in mean within a general trend (Wang, 2003), or a change in mean coincident with a change in trend (Lund and Reeves, 2002) . Since all difference series are comprised of values from two series, a changepoint date in any one difference series is temporarily attributed to both station series used to calculate the differences. The result is a matrix of potential changepoint dates for each station series.
The full matrix of changepoint dates is then "unconfounded" by identifying the series common to multiple paired-difference series that have the same changepoint date. Since each series is paired with a unique set of neighboring series, it is possible to determine whether more than one nearby series share the same changepoint date.
The magnitude of each relative changepoint is calculated using the most appropriate two-phase regression model (e.g., a jump in mean with no trend in the series, a jump in mean within a general linear trend, etc.). This magnitude is used to estimate the "window of uncertainty" for each changepoint date since the most probable date of an undocumented changepoint is subject to some sampling uncertainty, the magnitude of which is a function of the size of the changepoint. Any cluster of undocumented changepoint dates that falls within overlapping windows of uncertainty is conflated to a single changepoint date according to
a known change date as documented in the target station's history archive (meaning the discontinuity does not appear to be undocumented), or
the most common undocumented changepoint date within the uncertainty window (meaning the discontinuity appears to be truly undocumented)
Finally, multiple pairwise estimates of relative step change magnitude are re-calculated (as a simple difference in mean) at all documented and undocumented discontinuities attributed to the target series. The range of the pairwise estimates for each target step change is used to calculate confidence limits for the magnitude of the discontinuity. Adjustments are made to the target series using the estimates for each shift in the series.

and

Estimation of Missing Values

Following the homogenization process, estimates for missing data are calculated using a weighted average of values from highly correlated neighboring stations. The weights are determined using a procedure similar to the SHAP routine. This program, called FILNET, uses the results from the TOB and homogenization algorithms to obtain a more accurate estimate of the climatological relationship between stations. The FILNET program also estimates data across intervals in a station record where discontinuities occur in a short time interval, which prevents the reliable estimation of appropriate adjustments.

and your favorite

Urbanization Effects

In the original USHCN, the regression-based approach of Karl et al. (1988) was employed to account for urban heat islands. In contrast, no specific urban correction is applied in USHCN version 2 because the change-point detection algorithm effectively accounts for any "local" trend at any individual station. In other words, the impact of urbanization and other changes in land use is likely small in USHCN version 2. Figure 2 - the minimum temperature time series for Reno, Nevada - provides anecdotal evidence in this regard. In brief, the black line represents unadjusted data, and the blue line represents fully adjusted data. The unadjusted data clearly indicate that the station at Reno experienced both major step changes (e.g., a move from the city to the airport during the 1930s) and trend changes (e.g., a possible growing urban heat island beginning in the 1970s). In contrast, the fully adjusted (homogenized) data indicate that both the step-type changes and the trend changes have been effectively addressed through the change-point detection process used in USHCN version 2.

An example:

Figure 1. (a) Mean annual unadjusted and fully adjusted minimum temperatures at Reno, Nevada. Error bars indicating the magnitude of uncertainty (±1 standard error) were calculated via 100 Monte Carlo simulations that sampled within the range of the pairwise estimates for the magnitude of each inhomogeneity; (b) difference between minimum temperatures at Reno and the mean from its 10 nearest neighbors.

Long-Term Monthly Climate Records from Stations Across the Contiguous United States

the homogenization algorithm is faulty for many reasons. the main reason is that it is not checked for realistic results but is simply a blackbox computation that is automatically accepted. Icelandic stations are well documented and properly adjusted but when the algorithm spots trends counter to what it anticipates, the computer code simply cuts the record into pieces and rearranges them into a more acceptable shape. when questioned, there was no explanation other than to point to the generic web site that Abe has quoted.

to reiterate-- a cooling trend is likely to be 'corrected' even if it is authentic, a warming trend is likely to be accepted even if it is spurious. the homogenization process adds a large increase to the warming trend, but in a non-transparent way that is difficult to track down or remove.

 

[SwimExpert]

Physic equations that show co2, water vapor and methane are green house gases

Science also shows that wood is combustible. And that the world is covered in trees. Uh-oh, global burning!!

And that passes your expert judgement in logical argumentation as a valid and meaningful response?

Mine, and Aristotle's. Rebutting an argument by presenting an alternative one of comparable form, yet produces false conclusions is one of the oldest means to show an argument as failing.

 

[Theowl32]

Abraham still has not proven that man made global warming is a fact. There is no consensus in the scientific community. Period.

Him and people like him ignore all of the scams that have generated hundreds of MILLIONS of dollars.

When CBS is even reporting these types of scams, you know what we are dealing with.



60 minutes


It is really unreal what has happened, and how gullible people are. It truly is unreal.

 

[Abraham3]

Abraham still has not proven that man made global warming is a fact. There is no consensus in the scientific community. Period.

I will never prove ANYTHING in the natural sciences. Neither will anyone else. But I can show evidence demonstrating the likelihood (or unlikelihood) of all manner of things. Regarding the consensus of climate scientists in support of the IPCC position that the primary cause of the last 150 years' global warming is human activity (GHG emissions and deforestation), I offer the following:

Here is an abbreviated version of the information noted in

http://en.wikipedia.org/wiki/Scientific_opinion_on_climate_change

and

http://en.wikipedia.org/wiki/Surveys...climate_change

1) 2004, Science Historian Naomi Oreskes conducted a study of the scientific literature on climate change:
Out of 928 papers' abstracts from refereed scientific journals between 1993 and 2003, NONE disagreed with the consensus position (AGW).

2) 2007, Harris Interactive surveyed 489 randomly selected member of either the AMS or the AGU:
97% agreed that temperatures had increased over the prior 100 years
84% said they personally believed human-induced warming was occurring
74% agreed that scientific evidence substantiates human-induced warming is taking place
5% said they thought human activity did NOT contribute to greenhouse warming

3) 2008, Dennis Bray and Hans von Storch invited 2,058 climate scientists from 34 different countries to participate in a web-bases survey.
373(18.2%) of invited scientists responded.
o To the question "How convinced are you that climate change, whether natural or anthropogenic, is occurring now?", ALL respondents answered that they agreed to some small extent, some large extent or very much. NONE responded that they did not agree at all.
o To the question "How convinced are you that most of recent or near future climate change is, or will be, a result of anthropogenic causes?"98.65% of respondents agreed to a small extent, a large extent or very much. 1.35% did not agree at all.

4) 2009, Peter Doran and Maggie Zimmerman, at UI at Chicago, polled 10,257 Earth scientists and received responses from 3,146 of them. Results were analyzed both globally and by specialization. 79 respondents listed climate science as their area of expertise AND had published more than 50% of their recent peer-reviewed papers on the subject of climate change.
Among the 79 actively publishing climate scientists:
o 96.2% (76) believed that mean global temperatures had risen compared to pre-1800s levels.
o 94.9% (75) believed that human activity is a significant factor in changing mean global temperatures
Among ALL 3,146 Earth scientist respondents:
o 90% agreed that temperatures had risen compared to pre-1800s levels
o 82% agreed that humans signficantly influenced global temperatures

5) 2010, Proceedings of the National Academy of Sciences of the US, Anderegg, Prall, Harold, and Schneider, 2010, reviewed publication and citation data for 1,372 climate researchers and found:
o 97-98% of the climate researchers most actively publishing in the field support the tenets of ACC (Anthropogenic Climate Change) outlined by the Intergovernmental Panel on Climate Change
o the relative climate expertise and scientific prominence of the researchers unconvinced of ACC are substantially below that of the convinced researchers

6) 2011, Farnsworth and Lichter, Repeated the 2007, Harris Interactive survey of AMS and AGU members. Published in the International Journal of Public Opinion Research a survey and analysis of 489 scientists working in academia, government, and industry.
o 97% agreed that global temperatures have risen over the past century
o 84% agreed that "human-induced greenhouse warming" is now occurring
o 5% disagreed with the idea that human activity is a significant cause of global
warming

7) 2013, Environmental Research Letters, John Cook, Dana Nuccitelli, Sarah A Green, Mark Richardson, Bärbel Winkler, Rob Painting, Robert Way, Peter Jacobs and Andrew Skuce reviewed 11,944 abstracts of scientific papers, finding 4,014 which discussed the cause of recent global warming and reporting that 97.1% endorsed the consensus position that humans are causing global warming.

8) Additionally, the authors of the studies were invited to categorise their own research papers. Among the 1,381 authors who chose to participate, 97.2% rated their own papers as supporting the AGW consensus.

9) 2014, James Lawrence Powell, a former member of the National Science Board and current executive director of the National Physical Science Consortium, analyzed 13,950 published research papers on global warming and climate change between 1991 and 2012 and a follow-up analysis of 2,258 peer-reviewed climate articles with 9,136 authors published between November 2012 and December 2013 and found:
o 24 out of 13,950 (0.172%) rejected anthropogenic global warming [leaving 99.828%]
o 1 out of the 2,258 (0.044%) papers in the follow-up rejected anthropogenic global warming [leaving 99.956%]

***********************

So, Owl, do you still believe no consensus among climate scientists has been shown to exist?

 

[westwall]

Okay. Done. Now how does that modify the author's statement that Rossby waves are driven by the delta T between the tropics and the poles and that the reason the US is getting such shit weather is the warming of the Arctic?

"The outflow from the cell creates harmonic waves in the atmosphere known as Rossby waves. These ultra-long waves play an important role in determining the path of the jet stream, which travels within the transitional zone between the tropopause and the Ferrel cell. By acting as a heat sink, the Polar cell also balances the Hadley cell in the Earth&#8217;s energy equation."

and


Ferrel cell
The Ferrel cell, theorized by William Ferrel (1817&#8211;1891), is a secondary circulation feature, dependent for its existence upon the Hadley cell and the Polar cell. It behaves much as an atmospheric ball bearing between the Hadley cell and the Polar cell, and comes about as a result of the eddy circulations (the high and low pressure areas) of the mid-latitudes. For this reason it is sometimes known as the "zone of mixing." At its southern extent (in the Northern hemisphere), it overrides the Hadley cell, and at its northern extent, it overrides the Polar cell. Just as the Trade Winds can be found below the Hadley cell, the Westerlies can be found beneath the Ferrel cell. Thus, strong high pressure areas which divert the prevailing westerlies, such as a Siberian high (which could be considered an extension of the Arctic high), could be said to override the Ferrel cell, making it discontinuous.
While the Hadley and Polar cells are truly closed loops, the Ferrel cell is not, and the telling point is in the Westerlies, which are more formally known as "the Prevailing Westerlies." While the Trade Winds and the Polar Easterlies have nothing over which to prevail, their parent circulation cells having taken care of any competition they might have to face, the Westerlies are at the mercy of passing weather systems. While upper-level winds are essentially westerly, surface winds can vary sharply and abruptly in direction. A low moving polewards or a high moving equator wards maintains or even accelerates a westerly flow; the local passage of a cold front may change that in a matter of minutes, and frequently does. A strong high moving polewards may bring easterly winds for days.
The base of the Ferrel cell is characterized by the movement of air masses, and the location of these air masses is influenced in part by the location of the jet stream, which acts as a collector for the air carried aloft by surface lows (a look at a weather map will show that surface lows follow the jet stream). The overall movement of surface air is from the 30th latitude to the 60th. However, the upper flow of the Ferrel cell is not well defined. This is in part because it is intermediary between the Hadley and Polar cells, with neither a strong heat source nor a strong cold sink to drive convection and, in part, because of the effects on the upper atmosphere of surface eddies, which act as destabilizing influences.

Atmospheric circulation - Wikipedia, the free encyclopedia

You're going to have to convince me that I'm wrong when I suspect that prior to this thread you'd never heard of Rossby waves or Ferrel cells and that you looked this article up and pulled the term from the text. Now I hadn't either, but I made it pretty clear I was just passing on an article I'd read. This would be an attempt to falsify your creds - a failure to give credit where credit was due (Wikipedia) and I may just have to neg you for it. ;-)

Nothing. You seemed to be attempting to better your knowledge so as any good per fessor would, I pointed you in the direction of more information.

 

[mamooth]

As an illustration of just how much heat has gotten pushed into the oceans, this plot shows the average temps from only over the land, from the BEST data set that denialists swore was the greatest data set ever, at least until they found it disagreed with them. And to think some people are actually crazy enough to say there's been no warming. It's funny, what cults can get people to say.

Wood for Trees: Notes

 

[mamooth]

Ummmm, no. I'm correct....

World Climate Report » Sea Level Rise: Still Slowing Down

"Deliberate use of obsolete data when better data is available" fallacy.

You used a 2011 article, right smack dab when the massive rains over interior Australia had temporarily dropped sea levels by 7mm. And you use that obsolete data in preference to current data, which shows the fast rebound from that 2011 drop and the continuing sea level climb.

 

[Abraham3]

Okay. Done. Now how does that modify the author's statement that Rossby waves are driven by the delta T between the tropics and the poles and that the reason the US is getting such shit weather is the warming of the Arctic?

"The outflow from the cell creates harmonic waves in the atmosphere known as Rossby waves. These ultra-long waves play an important role in determining the path of the jet stream, which travels within the transitional zone between the tropopause and the Ferrel cell. By acting as a heat sink, the Polar cell also balances the Hadley cell in the Earth’s energy equation."

and

Ferrel cell
The Ferrel cell, theorized by William Ferrel (1817–1891), is a secondary circulation feature, dependent for its existence upon the Hadley cell and the Polar cell. It behaves much as an atmospheric ball bearing between the Hadley cell and the Polar cell, and comes about as a result of the eddy circulations (the high and low pressure areas) of the mid-latitudes. For this reason it is sometimes known as the "zone of mixing." At its southern extent (in the Northern hemisphere), it overrides the Hadley cell, and at its northern extent, it overrides the Polar cell. Just as the Trade Winds can be found below the Hadley cell, the Westerlies can be found beneath the Ferrel cell. Thus, strong high pressure areas which divert the prevailing westerlies, such as a Siberian high (which could be considered an extension of the Arctic high), could be said to override the Ferrel cell, making it discontinuous.
While the Hadley and Polar cells are truly closed loops, the Ferrel cell is not, and the telling point is in the Westerlies, which are more formally known as "the Prevailing Westerlies." While the Trade Winds and the Polar Easterlies have nothing over which to prevail, their parent circulation cells having taken care of any competition they might have to face, the Westerlies are at the mercy of passing weather systems. While upper-level winds are essentially westerly, surface winds can vary sharply and abruptly in direction. A low moving polewards or a high moving equator wards maintains or even accelerates a westerly flow; the local passage of a cold front may change that in a matter of minutes, and frequently does. A strong high moving polewards may bring easterly winds for days.
The base of the Ferrel cell is characterized by the movement of air masses, and the location of these air masses is influenced in part by the location of the jet stream, which acts as a collector for the air carried aloft by surface lows (a look at a weather map will show that surface lows follow the jet stream). The overall movement of surface air is from the 30th latitude to the 60th. However, the upper flow of the Ferrel cell is not well defined. This is in part because it is intermediary between the Hadley and Polar cells, with neither a strong heat source nor a strong cold sink to drive convection and, in part, because of the effects on the upper atmosphere of surface eddies, which act as destabilizing influences.

Atmospheric circulation - Wikipedia, the free encyclopedia

You're going to have to convince me that I'm wrong when I suspect that prior to this thread you'd never heard of Rossby waves or Ferrel cells and that you looked this article up and pulled the term from the text. Now I hadn't either, but I made it pretty clear I was just passing on an article I'd read. This would be an attempt to falsify your creds - a failure to give credit where credit was due (Wikipedia) and I may just have to neg you for it. ;-)

Nothing. You seemed to be attempting to better your knowledge so as any good per fessor would, I pointed you in the direction of more information.

But you failed to support your argument.

 

[mamooth]

to reiterate-- a cooling trend is likely to be 'corrected' even if it is authentic, a warming trend is likely to be accepted even if it is spurious. the homogenization process adds a large increase to the warming trend, but in a non-transparent way that is difficult to track down or remove.

Nobody in the field cares about McIntyre's claims any more, because McIntyre doesn't have a clue and refuses to get one. People tried to help him out and explain to him where he botched it, but that just brought more abuse from McIntyre, so people wised up and no longer give him the attention he craves.

 

[SwimExpert]

Have you ever heard anyone say that AGW was a process that has taken place throughout the "overall history of the Earth"? No. AGW is a process that has been taking place since the beginning of the Industrial Revolution and became a serious issue in the 20th century.....Once again, the climate is not a "geological activity". It does not operate on a geological scale.

Then I don't want to hear anything more about ice cores.

 

[Abraham3]

The oldest ice cores ever taken go back 800,000 years. Let's look on that geological clock diagram and see where that puts us.

Well, that barely visible bump indicating the first hominids is 2 million years back. So our ice core would be 40% of that.

Yeah, that works.

We could even work out the actual time on the clock. Let's say the clock is scaled to 24 hours. Let's see:

(800,000/4,527,000,000) = (x/24)
x=24 * (800/000/4,527,000,000)
x= 0.00424121935056328694499668654738 hours
x=15.268 seconds

Yeah. Man, now THAT'S geological!

 

[SwimExpert]

The oldest ice cores ever taken go back 800,000 years.

Either 125 years, or not 125 years. You cannot have it both ways.

 

[Abraham3]

I haven't the faintest idea what 125 years you're talking about.