The "Pause" - A Review of Its Significance and Importance to Climate Science 77

[rconnor]

----------Introduction---------
A comparison of recent temperature trends in isolation of earlier data, say 1998-present, to long(er)-term temperature trends, say 1970-present, reveals that more recent temperature trends are lower than long-term temperature trends. This has led many, including many prominent climate scientists, to refer to the recent period as a “pause”, “hiatus” or “slowdown”. While in isolation of any other context besides two temperature trends, the term “pause” or “hiatus” may be quasi-accurate, much more context is required to determine whether these terms are statistically and, more importantly, physically accurate.

It should be noted that most times when these terms are used by climate scientists, they keep the quotation marks to indicate the mention-form of the word and are not implying an actual physical pause or hiatus in climate change. The subsequent research into the physical mechanism behind the “pause” has continually demonstrated that it is not indicative of a pause in climate change nor does it suggest a drastic reduction in our estimates of climate sensitivity. However, this fact appears to be lost on many who see the “pause” as some kind of death-blow to the anthropogenic climate change theory or to the relevancy of climate change models.

While this subject has been discussed repeatedly in these forums, it has never been the focus but rather used as a jet-pack style argument to change the conversation from the subject at hand to the “pause” (“Well that can’t be right because the Earth hasn’t warmed in X years!”). Revisiting past threads, I cannot find an example of where someone attempted to defend the “pause” as a valid argument against anthropogenic climate change. It is brought up, debunked and then not defended (and then gets brought up again 5 posts later). The hope is to discuss the scientific literature surrounding the “pause” to help readers understand why the “pause” is simply not a valid argument. While some points have been discussed (usually by me) before, this post does contain new research as well as 2014 and 2015 temperature data, which shed even more light on the topic. The post will be split into three parts: 1) the introduction (and a brief discussion on satellite versus surface station temperature data sets), 2) Does the “pause” suggest that climate change is not due to anthropogenic CO2? and 3) Does the “pause” suggest that climate models are deeply flawed?

------Why I Will Be Using Ground-Based Temperature Data Sets-------
Prior to going into the meat of the discussion, I feel it necessary to discuss why I will be using ground-based temperature data sets and not satellite data sets. Perhaps one of the most hypocritical and confused (or purposefully misleading) arguments on many “skeptic” blogs is the disdain for all ground-based temperature data sets and the promotion of satellite temperature data sets. The main contention with ground-based temperature data sets is that they do not include raw data and require homogenization techniques to produce their end result. While I am not here (in this thread) to discuss the validity of such techniques, it is crucial to understand that satellite temperature data sets go through a much more involved and complex set of calculations, adjustments and homogenizations to get from their raw data to their end product. Both what they measure and where they measure it are very important and highlights the deep confusion (or purposeful misdirection) of “skeptic” arguments that ground-based temperatures are rubbish and satellite-based temperatures are “better”.

[ul][li]Satellites measure radiances in different wavelength bands, not temperature. These measurements are mathematically inverted to obtain indirect inferences of temperature (Uddstrom 1988). Satellite data is closer to paleoclimate temperature reconstructions than modern ground-based temperature data in this way.[/li]
[li]Satellite record is constructed from a series of satellites, meaning the data is not fully homogeneous (Christy et al, 1998). Various homogenization techniques are required to create the record. (RSS information)[/li]
[li]Satellites have to infer the temperature at various altitudes by attempting to mathematically remove the influence of other layers and other interference (RSS information). This is a very difficult thing to do and the methods have gone through multiple challenges and revisions. (Mears and Wentz 2005, Mears et al 2011, Fu et al 2004)[/li]
[li]Satellites do not measure surface temperatures. The closest to “surface” temperatures they get are TLT which is an loose combination of the atmosphere centered roughly around 5 km. It is also not even a direct measurement channel (which themselves are not measuring temperature directly) but a mathematically adjustment of other channels. Furthermore, due to the amount of adjustments involved, TLT has constantly required revisions to correct errors and biases (Christy et al 1998, Fu et al 2005).[/li]
[li]See the discussion on Satellite data sets in IPCC Report (section 3.4.1.2)[/li]
[li]Satellite data and the large amount of homogenization and adjustments required to turn the raw data into useful temperature data are still being question to this day. Unlike ground-based adjustments which lead to trivial changes in trends (from the infamous Karl et al 2015), recent research shows that corrections of perhaps 30% are required for satellite data (Weng et al 2013 .[/li][/ul]

None of this is meant to say the satellite temperature data is “wrong” but it very clearly highlights the deep-set confusion in the “skeptic” camp about temperature data sets. If one finds themselves dismissing ground-based temperature data sets because they require homogenization or adjustments while claiming satellite temperature data sets are superior have simply been lead astray by “skeptics” or are trying to lead others astray. Furthermore, it clearly demonstrates that any attempt to compare satellite data (which measures the troposphere) to the surface temperature output of models is completely misguided (*cough*John Christy *cough*). It is for these reasons that I will use ground-based data in the rest of the post.

Again, I would like to state that I do not wish this to be a focal point of this discussion. I am merely outline why I will be using ground-based temperature data sets and my justification for that as, undoubtedly, someone would claim I should be using satellite temperature datasets. In fact, I appear to be in pretty good company; Carl Mears, one of the chief researchers of RSS (and the same Mears from all the papers above), stated:

My particular dataset (RSS tropospheric temperatures from MSU/AMSU satellites) show less warming than would be expected when compared to the surface temperatures. All datasets contain errors. In this case, I would trust the surface data a little more because the difference between the long term trends in the various surface datasets (NOAA, NASA GISS, HADCRUT, Berkeley etc) are closer to each other than the long term trends from the different satellite datasets. This suggests that the satellite datasets contain more “structural uncertainty” than the surface dataset

If this is a topic of interest to people, perhaps starting your own thread would be advisable as I will not be responding to comments on temperature data sets on this thread. Now, onto the actual discussion…

 

[controlnovice]

From the link suggested by SAITAETGrad (Link):

The very first comment about the course is interesting:
"Yes, what an utterly bizzare thing. I wanted to learn about climate change science. This course is about how to convince people of a thing you are certain about while assuming they are wrong, even though you do not really understand the thing yourself. It seems more like an evangelizing seminar than anyting related to science...I guess if your job is debating science you don't understand, good for you. I just want to learn the facts, not be convinced without them."

And this is what's it become. The religious right (no matter the religion) try to say their book is the truth. The Climate Change people try to say their 'book' is the truth. They call anyone who doesn't believe heritics, deniers, etc...

It's interesting that a scientist who is a skeptic of the now famous Mann hockey stick graph is being sued by Mann himself. Someone merely questions the data, and how it was gathered, and they get sued.

And just this summer, a US Senator is suggesting to use RICO laws against Climate Change skeptics.

It's a way to silence the discussion. Skeptics will stop discussing for fear of legal action. Reminds me of speaking with Tibetans and Cubans and what their Gov'ts have done...

And why does the OP put skeptics in quotes? Just another way to diminish those who don't believe in the Gospel.

The one thing GW scientist have on their side: Can't prove a negative.

______________________________________________________________________________
This is normally the space where people post something insightful.

 

[GTTofAK]

From Rconnors Durack et al 2014

Using satellite altimetry observations and a large suite of climate models, we conclude that observed estimates of 0–700 dbar global ocean warming since 1970 are likely biased low.

Yep more reanalysis. Model results being sold as actual data. When the data doesn't agree with you simply pretend model results are data. Alarmist scientists will always have a retort because science media is so ignorant that they cant tell the difference between data and models. Ocean heat content not doing what you want it to do? Simply pretend model runs are actual data problem solved.

Very few people actaully know what reanalysis is. So alarmist scientists can easily parse words to make their model runs sound like actual observations. Didn't expect Rconnor to understand what Durak et. al. actually was. Trust but verify.

 

[cranky108]

Looks like other political debates. You don't get good answers from those on the far left or far right.

Then the personal attacks start to happen.

Just give me what you are proposing to do about it, the cost, and how you intend to pay for the cost. Then give me the options that can reduce the costs.
Then as part of the public I will make my own decision.

Many of us don't want the big hand of government shoving things at us. I mean what next, a 55 MPH speed limit so our cars will operate at the most efficient speed?

If the data set is too complicated for me to understand, then it can't be believed.

 

[JNieman]

If the data set is too complicated for me to understand, then it can't be believed.

I think that quite adequately sums up the problem we have with the voting populace in this country.

I wonder... do your customers understand your calculations or analysis reports?

 

[GTTofAK]

If the data set is too complicated for me to understand, then it can't be believed.

You realize that this is an engineering forum. Most of what "climate science" does isn't that complicated. TYou might call climate science a repository for the less gifted minds. The smarter students tend to go into other fields like Engineering, mathematics, chemistry. Climate science seems to get the refuse. Top of the bottom bottom of the top if you will.

I'd put my money on the intellect of 100 random engineers up against 100 random climate scientists any day of the week and twice on Sunday.

 

[moltenmetal]

GTTofAK: put "intellect" up against actually being educated in and having fully read the literature related to a particular area of study, and I'll choose the latter, thanks. Science does suffer from "groupthink" from time to time, but it is remarkably able to self-correct.

 

[beej67]

Could you expand on this. To me, there are many unsupported jumps in reasoning here. I think it’s important to understand that climate sensitivity numbers do not represent what the future temperature rise will be, they represent the temperature rise at a doubling of CO2 concentrations from pre-industrial levels (i.e. at 560 ppm).

Yeah, and when estimates for that swing by a factor of 3, it vastly impacts the ROI of mitigation vs preparation vs some blend of both, policy-wise. You cannot establish policy without a model that reliably predicts the effects of that policy.

My program at Georgia Tech built the Hydrologic Decision Support System for the Nile River Basin. The very short story of that, was that all the countries bordering the Nile were about ready to bomb each other over water management, because nobody could agree on what the effects of certain policies were. Conversations would go like this: "We aught to be able to irrigate as much as we want out of Lake Victoria, because it won't affect your water Egypt." "Yes it will." "No it won't." (someone throws a chair)

We put together a very detailed basin model, through which all the countries could try different policies in theory, so they could know exactly what the results would be from a particular policy. The model must be predictive, or it is useless in crafting policy.

IPCC AR5 can't even agree what the "do nothing" case is. "It could be 1.5 degrees centigrade increase per doubling, or, you know, it could be something like triple that." That is not predictive. And until it's predictive, it's useless for crafting policy.

You never have 100% assurance of the future when you do risk assessment exercises, you know this. You always work off the likelihood of events happening and the possible impacts. Furthermore, when doing risk assessment exercises, uncertainty is not your friend. Especially when the vast majority of the science that comes out says it will likely be very problematic.

Yeah no kidding. That's why we have to get the ECS right before we go off making policy changes that have the potential to stunt or destroy third world economies.

Thanks for linking the storage term information. I'll read through it. You're backwards on this though:

Furthermore, if “storage terms of energy caught up in chemical bonds during photosynthesis” were missing from the energy balance it would mean the energy imbalance is LARGER than originally thought as more energy would be trapped inside the system. This would mean the planet is more sensitivity to CO2 than previously believed.

No. It would mean that the loss of vegetative mass associated with land cover change from human expansion would cause less solar energy to be stored and interned into the earth itself, and less stored means anthropogenic land cover changes put more into the measurable environment. It would mean that anthropogenic warming had more than the one source IPCC claims, and a model calibrated to one source might be in truth representing warming from both sources.


Hydrology, Drainage Analysis, Flood Studies, and Complex Stormwater Litigation for Atlanta and the South East -

http://www.campbellcivil.com

 

[beej67]

Your link just talked about storage of sensible heat and latent heat, not about energy storage in chemical bonds.

Hydrology, Drainage Analysis, Flood Studies, and Complex Stormwater Litigation for Atlanta and the South East -

http://www.campbellcivil.com

 

[rb1957]

"... (someone throws a chair)" ... only a chair, you're doing well.

"We put together a very detailed basin model, through which all the countries could try different policies in theory, so they could know exactly what the results would be from a particular policy." ... really? your model could predict "exactly" what would happen ?? at best your model may be a neutral representation of the real world and may respond fairly to the different proposals. Worse, the model unwittingly (or possibly wittingly, smile) favoured one proposal. Worst case is the model didn't predict well, and the consequences were dire, and someone in the desert got skrewed, and maybe GT got sued too ? (or possibly worse ??)

another day in paradise, or is paradise one day closer ?

 

[rconnor]

No. It would mean that the loss of vegetative mass associated with land cover change from human expansion would cause less solar energy to be stored and interned into the earth itself, and less stored means anthropogenic land cover changes put more into the measurable environment.

If change in the energy absorbed by photosynthesis was (1) significant, (2) increased “available” energy is then absorbed by the “measurable environment” and (3) missing from the energy balance currently than this would be extra energy on top of the current CO2 energy imbalance, not in place of it. So even if you manage to demonstrate those three factors (which you need to do first), you can only conclude that climate change will be worse than previously thought because, in addition to the CO2 imbalance, we also have extra energy being released from the vegetal mass. Now, I don’t believe that all three of the factors are true but even if they were, it certainly wouldn’t help your main argument.

Luckily, your idea does not appear to be at all correct because all three factors are either untrue or very questionable. The Earth’s atmosphere, oceans and land masses absorb ~3,850,000 EJ/year from solar energy, while only 3,000 EJ/year is absorbed by photosynthesis (source) or 0.08% of the total energy absorbed (i.e. insignificant). But it’s worse, 0.08% is the total absorbed by photosynthesis, so we are talking about a fraction of 0.08%. Let’s say we reduce half the world’s biomass (which is crazy), that means we are talking about 0.04% of the total energy absorbed by the planet (i.e. extremely insignificant). To put this in perspective, OHC has increased by 11,111 EJ/year from 2005 to 2014 (source). So even if, magically, we destroyed every plant, tree and blade of grass on the planet in a year, the “extra” energy, now not absorbed by photosynthesis, would amount to ~1/4 of the energy absorbed by the oceans for that year alone (and I highly doubt the extra energy would be our biggest problem in that situation).

The second factor requires that all of the “extra” available energy would be absorbed by the “measurable environment” (i.e. atmosphere, ice sheets, oceans). Decreasing biomass coverage increases the albedo (AR5 WGI 8.3.5), which means some of the “extra” energy, now not absorbed by photosynthesis, would not be absorbed but reflected back to space. So now we are talking about a fraction of 0.04%. So while factor two may not be completely false, it further demonstrates how completely irrelevant it is.

The third factor, that it currently isn’t factored into the energy balance, I believe is untrue. As far as I can tell, the solar energy “absorbed” by the surface includes photosynthesis as well as other factors (source). Furthermore, most of the energy “trapped” by photosynthesis is released shortly after (dies and rots or gets eaten, etc.), so it’s not really a missing term at all, it just gets transformed into other terms. But even if it were true that photosynthesis isn’t included, we are still talking about a fraction of 0.04%. And that’s assuming we remove 50% of the Earth’s biomass.

Changes in energy storage by photosynthesis are irrelevant compared to the energy imbalanced observed on the planet today. There is no way that changes in photosynthesis could be responsible for climate change. Despite it being off-topic, despite you, rather arrogantly, proclaiming that climate science was wrong and your idea was right, despite you having done, seemingly, no research prior to making these claims, I still took the time to do all the research for you…and your idea turned out to be completely irrelevant (as I stated from the beginning).

Again beej67, you need to realize that mitigation measures already require substantial decreases in deforestation, and more likely reforestation. So we are somewhat, if not accidently, on the same page.

 

[cranky108]

Interesting note, Solar panels change the albedo of the area they are installed, so is installing solar panels in the south west actually increasing a warming trend?
Any studies on that?

And maybe changing the albedo of our living areas is an answer. Start with changing your home roof color to white.

 

[GTTofAK]

rconnor

You say that

3) The Newest Data Continues to Undermine the “Pause”

And you then proceed to reference Karl et. al. as the "new data". Karl et. al. isn't new data but a new adjustment. Is it not disingenuous to call an adjustment to the data data.

I'd further like to hear how you justify Karl's approach of adjusting good data up to match bad data. Would not the correct method be to adjust the bad data down to match the good data? Of course such an adjustment wouldn't get rid of the pause. It would simply increase the 1976-2001 warming trend. The choice of adjusting good buoy data to match bad ship intake data seems to be done for the express purpose of eliminating the pause and not correcting error caused by ship intake heat.

You say you dont want to start a Karl flame war but sadly most of your argument is dependent on Karl et. al. being correct. Any statistical analysis done on the NCDC and GISS data sets that use the Karl adjustment are dependent on that adjustment being correct. You simply cant ignore it. A scientific paper does not stand alone. It is dependent on all the others it directly or indirectly uses. Any statistical analysis of the GISS and NCDC/NOAA datasets are dependent on the accuracy and legitimacy of the Karl adjustment.

I hypothesize that hte reason you dont want to get in a discussion over Karl is you know prima fascia that it is very hard to defend adjusting good data to match bad data. So you would rather hope to avoid such a discussion.

 

[rconnor]

---Part 2: Does the “pause” suggest that climate models are wrong/climate sensitivity is lower than expected?---
In Part 1 we discussed the physics and statistics surrounding the “pause”. It was demonstrated that:
[ul 1][li]OHC has continued to rise by a large amount while solar activity has been dwindling, demonstrating that the radiative imbalance is still present[/li]
[li]ENSO has played a very large role in the apparent lower trends as of late. When comparing ENSO neutral years to ENSO neutral years, there is a steady warming trend throughout the “pause”[/li]
[li]New data, including 2014 and 2015 temperatures and updated research, demonstrates that the warming is larger than appeared a few years back[/li]
[li]Statistically speaking, the "pause" never existed[/li][/ul]

This clearly demonstrates that the “pause” does not, in any way, suggest that climate change has stopped nor does it suggest that climate change may not be due to anthropogenic CO2 emissions. However, this does not directly address concerns that temperature outputs from climate models have been higher than observed temperatures as of late. As sensitivity estimates that stem from climate models are important to understanding the extent of future warming, the subject is very important within climate change discussions. However, as with most topics in climate change, many people have a limited understanding of the situation and lack critical context , often developed by reading blogs of non-experts rather than reading peer-reviewed papers from experts.

In Part 2 we will incorporate the information discussed in Part 1 to provide necessary context to examine and compare model outputs and observed temperatures. The post will be broken out into the following sections: 1) how models handle internal variability, 2) using updated research, how have models compared to observations, 3) are models/sensitivity estimates wrong?.

1) How Models Handle Internal Variability
As demonstrated in Part 1, internal variability can have a large impact on short-term trends. ENSO played a huge part in the appearance of a “pause” (and it is only an appearance) but so did volcanic activity and solar activity. So, prior to comparing models to observations, it’s essential to understand how models incorporate these factors and what influence they would have on the outputs.

A) ENSO
Let’s start with ENSO, which is by far the strongest factor during this period. Currently, we have no ability to predict ENSO events outside of a few months ahead (and even at that, we can still be surprised). So, in models, ENSO events are treated as stochastic (Kleeman and Moore 1997, Kleeman and Moore 1998). Different model runs will develop different ENSO states at different years depending on how they simulate trade winds for that year. One run will have an El Nino in 2016, neutral in 2017 and a La Nina in 2018 while another might be neutral in all three years. Therefore, while no model is trying to estimate ENSO events correctly, some runs will, accidently, match up with observed states while others will not.

This begs the very sensible question, “Does this not significantly impact long-term projections?" (note: the question, “Could it be that the 20th century warming was due to ENSO?” shares the same answer and same reason for that answer). Drawing on the research and data surrounding ENSO, the answer is no, ENSO does not appear to have a significant impact on long-term projections, nor does it have the ability to impact long-term temperature trends. To figure out why, I’d encourage readers to review my post on this subject at 12 Feb 15 23:45 of this thread. Below is a brief rundown of the points made in that post but for more details and supporting references, please see the original post:
[ul 1][li]ENSO is episodic[/li]
[li]ENSO is roughly cyclical[/li]
[li]ENSO has no notable long-term increase in the intensity of El Nino’s or La Nina’s[/li]
[li]ENSO has had no notable long-term impact on pre-industrial temperature trends[/li]
[li]ENSO is not a driver of changes in climate[/li]
[li]ENSO only causes surface temperature to temporarily deviate from the “average”, it does not impact the “average”[/li]
[li]ENSO does not significantly impact the TOA energy balance[/li]
[li]ENSO has no inherent mechanism that could have a major impact on long-term trends[/li][/ul]
Furthermore, while ENSO can dominate year-to-year variability, we already can see that the underlying warming trend caused by CO2 dominates the variability of ENSO in the long-term. The 1995 El Nino was colder than any 21st century La Nina year (and colder than any 21st century year).

So while ENSO does not have the ability to impact long-term temperature trends and therefore does not impact long-term temperature projections, it does significantly impact short-term trends and short-term projections. Short-term trends will be impacted by the imposed year-to-year variability, as discussed in Part 1. Short-term projections will be impacted by the fact that the “average” model run is somewhere closer to an ENSO neutral state. This is why comparing short-term trends against the “average” model run is foolish. You aren’t comparing apples to apples. For a better comparison, you need compare runs that accurately captured the ENSO states for the short-term time period. Two such papers looked at this, Risbey et al 2014, which compared models that matched the observed ENSO state with observed temperatures, and Kosaka and Xie 2013, which used observed Pacific Ocean states as an input to a model and then compared with observed temperatures. Both find the agreement was quite good. Below is a graph from Kosaka and Xie 2013 (Purple Line – HIST – model output temperature if assuming ENSO neutral years (similar to the “average” model run), Red Line – POGA-H – model output temperature matching pacific ocean states to oberservations, Black Line – Observations – NASA GISS temperature data):
[image

http://blog.chron.com/climateabyss/files/2013/08/KX1.gif

]

B) Volcanoes
Like ENSO, volcanic activity is unpredictable. Models do not try to predict volcanic activity (even indirectly). This is a non-issue as volcanic activity has only a temporary impact of ~2 years. They do, however, allow climate scientists to test the dynamic response of climate models to rapid, mass injections of aerosols. So, in hindcasts, volcanic aerosol data is used as an input for climate models (Taylor et al 2012). Models do quite well at reproducing global temperatures during large volcanic events (such as Pinatubo 1992). At this point, I think it’s important to point out that parameterization in climate models is not done to match global temperatures. Parameterization is done to better simulate the physics of the sub-process. As indicated in Mauritsen et al 2012, “The MPI-ESM was not tuned to better fit the 20th Century. In fact, we only had the capability to run the full 20th Century simulation according to the CMIP5-protocol after the point in time when the model was frozen.”

In forecasting, volcanic aerosols (by way of stratospheric aerosol optical depth, SAOD) were assumed to be negligible past 2000 and thus set to zero in models (AR5 WGI Box TS.3). However, subsequent research has indicated that this assumption may not be valid. Solomon et al 2011, Santer et al 2014 and Ridley et al 2014 demonstrate that, since 2005, there has been a non-trivial increase in SAOD caused by smaller volcanic events.

By omitting the rise in SAOD, models have, incorrectly, ignored the cooling impact of smaller volcanic events since 2005. This would lead models to read warmer than they should over this period. See Ridley et al 2014 Figure 3, below, for a graphical representation of this issue. Correcting for this error would bring models more in-line with observations (without adjusting any major elements of models that would impact sensitivity).
[image

http://1.bp.blogspot.com/-JXWosXizuvU/VLDUAvGTlyI/AAAAAAAAHiU/gC4XhbCKOkE/s1600/Fig3Ridley14.png

]
“Fig. 3 - (a) Estimated global mean radiative forcing is shown for datasets from Sato et al. (orange), Vernier et al. (blue) and AERONET mean (black) with 25th to 75th percentile range (grey). The dotted line indicates the baseline model used in many climate model studies to date, which includes no stratospheric aerosol changes after 2000.
(b) The temperature anomaly, relative to the baseline model, including the AERONET mean (black), median (white), and 25th to 75th percentile range (grey), Vernier et al. (blue), and Sato et al. (orange) forcing computed for each dataset
(c) the total global temperature change predicted by the Bern 2.5cc EMIC in response to combined anthropogenic and natural forcing, including the reduced warming when considering the stratospheric aerosol forcing from the three datasets.”

C) Solar
While the 11-year solar cycle is very repeatable and therefore predictable, longer-term variance in solar activity is not. Recently, the maximums of the 11-year cycle have been lower than expected and the minimums have lasted longer than predicted in models (solar activity since 1950). Research shows the impact is small; Foster and Rahmstorf 2011 found the cooling sun was responsible for -0.014 deg C per decade from 1979 to 2010 and Lean and Rind 2008 found an impact of -0.004 deg C per decade from 1979 to 2005. But it does provide a slight warming bias to model projections.

Again, the long-term impact is rather unimportant. Changes in solar activity are simply not large enough to significantly impact long-term trends amidst the much stronger forcing of increased CO2 levels. Even if we were to enter a new Grand Solar Minimum, it could not significant impact and certainly would not revise global warming (1). Feulner & Rahmstorf 2010 concluded that if, somehow (i.e. magically), we were stuck in a Grand Solar Minimum, it would only reduce 2100 temperature by 0.26 deg C, compared to 3.7 deg C (A1B) or 4.5 deg C (A2) of total warming.
[image

http://s11.postimg.org/dxnzizwgz/feulner2010_graph.jpg

]

As discussed in Part 1, ENSO, volcanic activity and solar activity have all had an impact on temperature trends during the first part of the 21st century but due to their random nature, their impact was not captured in models. This will impact model vs. observed temperature comparisons. It is important to note that models were never designed to predict these events nor were models ever intended on perfectly matching all short-term trends. However, these events do not have a significant impact on long-term trends, which is what models are designed to project. In the case of ENSO and volcanoes, the effects are too short-lived to impact long-term trends. In the case of Solar (and ENSO, for that matter), the effects are too weak to impact long-term trends. More importantly, while all of these natural events have added a short-term cooling effect to global temperatures, they do not impact CO2 sensitivity estimates. Therefore, if updating models with the proper natural forcings corrects most of the difference between model outputs and observations, then we can conclude that the “pause” does not put sensitivity estimates into question nor does it proves models are significantly flawed.

2) Incorporating Up-to-Date Research into Model/Observation Comparisons
As outlined in Part 1, there has been a lot of new research published in the past two years that is extremely relevant to understanding the “pause” (in fact, it completely eliminates the “pause” as a discernible change in temperature trends). This research is perhaps more important when comparing model outputs and observed temperatures during the “pause”.

In the previous section, we examined various natural events that have lead to a short-term cooling effect that were not captured in climate models. Schmidt et al 2014 looked at these factors and used the new research to update natural forcings in models. They then compared the up-to-date models with observations.

The effects of ENSO were outlined in Kosaka and Xie 2013. The effects of volcanic aerosols were discussed in Santer et al 2014 and Ridley et al 2014. However, Ridley et al 2014 was published after Schmidt et al 2014, so Santer et al 2014 and Vernier et al 2011 was used (note: Vernier et al 2011 was heavily used by Santer and Ridley). On top of natural aerosols, Schmidt et al 2014 updated anthropogenic aerosols as well, using Shindell et al 2013 and Bellouin et al 2011. Solar forcing were updated based on Lean 2009.

However, it wasn’t just models that required updates. Observed temperature data sets were also improved with up-to-date research. As discussed in Part 1, the coverage of most temperature data sets is very sparse in the Arctic, where warming is the most severe. This means that most temperature data sets underestimate the warming. Cowtan and Way 2013 (2014 updated) used kriging to improve the coverage of HadCRUT. Schmidt et al 2014 incorporated these changes.

Incorporating the updated research, Schmidt et al 2014 provided a much more accurate comparison between model outputs and observations. Note that none of the changes applied to the models impact the climate sensitivity nor do they affect how CO2 would interact with the climate nor do they affect any parameterization within the models. The improvements purely correct the random natural variances of ENSO, volcanic activity and solar activity to match with observations. None of these have the ability to impact the trends in the long-term. The results of Schmidt et al 2014 are illustrated below:
[image

http://s23.postimg.org/vraz3r81n/Global_El_Nino_Solar.png

]

But Schmidt et al 2014 was not the only paper to look into this issue. Huber and Knutti 2014 also used the latest research (at the time) to perform an updated model/observation comparison. Again, nothing related to parameterization nor anything impacting CO2 sensitivity estimates was changed. They concluded:

We conclude that there is little evidence for a systematic overestimation of the temperature response to increasing atmospheric CO2 concentrations in the CMIP5 ensemble

[image

http://s15.postimg.org/6phylabfv/Huber_and_Knutti_2014_graph.jpg

]
(Dashed Yellow Line – previous model mean, Solid Yellow Line – updated model mean, Solid Black Line – HadCRUT, Dashed Black Line – HadCRUT with Cowtan and Way 2013 kriging)

Yet we know from Part 1 that since Schmidt et al 2014 and Huber and Knutti 2014 were released, even more data and research has come out. Karl et al 2015 included adjustments to NOAA NCEI/NCDC data set, which was not used in either study. It should be noted that Karl et al 2015 lowered long-term trends in NOAA NCEI but increased trends since 1998. So despite all the noise surrounding Karl et al 2015, it actually does very little to impact the model/observation comparison. However, complete 2014 and 2015 (to date) data is very relevant to the conversion. Gavin Schmidt (of Schmidt et al 2014) has updated his analysis using 2014 and 2015 data (to June). The image below represents perhaps the most complete and up-to-date comparison out there.
[image

http://s10.postimg.org/9f8to9x8p/schmidt_et_al_2014_updated_2015_data.jpg

]

Lastly, new research has been published that corrects a (rather obvious) error in past model/observation comparisons. Cowtan et al 2015 pointed out that model temperature outputs commonly seen are surface air temperatures while observed temperature data sets are a blend of surface air temperatures and sea surface temperatures. This means that model/observation comparisons, such as in AR5, are not really comparing the same thing. As sea surface temperatures are cooler than surface air temperatures, this will lead to an inherent, but artificial, disagreement. The study concluded that this issue accounted for 38% of the discrepancy in trend between models and observations. As you see, where you take the measurements can greatly change the results and thus. (Hence why using satellite temperature data sets, which calculate the temperature somewhere around 5km above the surface, is not appropriate when comparing against model outputs at the surface.)

3) Are Models/Sensitivity Estimates Wrong?
When attempting to determine whether climate models, and therefore sensitivity estimates, are accurate, context is everything. Both Part 1 and Part 2 (up to this point) were all about providing that context. The length and detail contained therein speaks to the fact that these questions are not as straight forward as many people think. The major take-aways are the following:
[ul][li]Natural variability can significantly impact short-term trends. Over the recent period (1998-2013), natural variability has had a temporary cooling impact. However, none of these factors will have a significant impact on long-term trends.[/li]
[li]Climate models are designed for long-term projections where short-term noise cancels out or becomes irrelevant. Care should be taken when reviewing short-term trends.[/li]
[li]Climate models do not attempt to accurately estimate year-to-year ENSO states or volcanic activity. Again, this has no significant impact on their long-term projections but does impact short-term projections.[/li]
[li]Comparison of short-term trends is only applicable if natural variability is accounted for. Otherwise, you are asking models to do something they were never designed or intended to do.[/li][/ul]

Therefore, when someone looks at model projections versus observations and notices that, since 2005, observations are on the low end of model runs (see AR5 SPM Figure 1.4), it would be wrong to conclude that models are flawed or sensitivity estimates are overestimated. You must first examine the impact natural variability has had on that period. The fact that natural variability has had a cooling effect during that period means that it makes complete sense that observations would sit on the lower end of the model range. Note that the opposite is true as well – 1998 was an anomalously hot year (due to ENSO) and sits near the upper end of the model range. So, not only are observations not outside the range of model runs, they sit where you’d expect them to sit given the state of natural variability over that period.

The next step would be to simulate the model run with the observed state for ENSO, volcanic activity and solar activity input into the model. By doing this, you can determine whether the discrepancy is due to natural variability or something else, and more important to long-term projections, like parameterization or CO2 sensitivity. Schmidt et al 2014 and Huber and Knutti 2014 are two examples of this. Both of them demonstrate that when you correct for things that models were never intended to predict in the short-term (ENSO, volcanic activity, etc.), models agree with observations extremely well. Therefore, the recent discrepancy between models and observations can be attributed to short-term variability that models were never designed to predict and not due to some fundamental issue related to the physics, parameterization or CO2 sensitivity estimates. Furthermore, and perhaps more importantly, what this analysis demonstrates is that the underlying physics, parameterization and CO2 sensitivity estimates are quite accurate.

Part 2 Conclusion (TL;DR)
A proper analysis of model outputs versus observed temperatures during the “pause” demonstrates that:
[ul 1][li]The time period is too short to draw any conclusions (1)[/li]
[li]Models have NOT failed to predict temperature trends. Even without correcting for the short-term noise, observations sit within the range of model runs (exactly where you’d expect given the short-term cooling bias). (1, 2, 3, 4)[/li]
[li]The minor, short-term deviation is caused by effects that are inherently not predictable (ENSO, volcanic activity, solar activity and anthropogenic aerosols) and not issues related to the underlying physics, parameterization or anything impacting CO2 sensitivity estimates . (1, 2, 3, 4, 5)[/li]
[li]When these effects are accounted for, model trends match observed temperature trends remarkably well. This provides more assurance that the underlying physics, parameterization and CO2 sensitivity estimates, which were unchanged, are accurate within the range of models. (1, 2, 3)[/li][/ul]

Thus, every time someone says something along the lines of “models have failed to predict temperature trends, therefore the theory is wrong”, there argument is 1) inconclusive and insignificant, 2) fundamentally and demonstrably false, 3) a non-sequitur and 4) actually validates the antithesis are its original assertion. As statements like that are central to the “skeptic” position, we begin to understand why that position is not all that skeptical. When you take the time to understand the science, the research, the data and the context that surrounds it (as any proper skeptic would), rather than eye-ball a small section of the data and come to a convenient conclusion (as a “skeptic” would do), two things become very clear:
[ul][li]The “pause” does not, in any way, suggest that climate change has stopped nor does it suggest that climate change may not be due to anthropogenic CO2 emissions.[/li]
[li]The discrepancy between model outputs and temperature observations during the “pause” does not suggest that climate models are flawed nor does it suggest that climate sensitivity is lower than expected. In fact, a proper analysis of the “pause” period provides more support that the underlying physics, parameterization and climate sensitivity estimates are accurate within the range of models.[/li][/ul]

 

[GTTofAK]

OHC has continued to rise by a large amount while solar activity has been dwindling, demonstrating that the radiative imbalance is still present

Says a model not actual the data. You just dont know what a reanalysis is. Here is what actual experts say about such reanalysis

Wunsch and Heimbach (2013) wrote, “clear warnings have appeared in the literature—that spurious trends and values are artifacts of changing observation systems (see, e.g., Elliott and Gaffen, 1991; Marshall et al., 2002; Thompson et al., 2008)—the reanalysis are rarely used appropriately, meaning with the recognition that they are subject to large errors

Do you get that?

When comparing ENSO neutral years to ENSO neutral years, there is a steady warming trend throughout the “pause”

How so your garaphic doesn't show that.

There are clearly only 3 "ENSO neutral years" in the pause according your graphic, firs that isn't a large enough sample to say anything. 2nd there appears to be no such trend.

New data, including 2014 and 2015 temperatures and updated research, demonstrates that the warming is larger than appeared a few years back

You keep saying "new data" adjustments to old data is not new data. Validity of Karl et. al. or not this continued instance by you to call an adjustment new data is insulting and disingenuous.

Statistically speaking, the "pause" never existed

After you throw out the satellite dataset because

Satellites measure radiances in different wavelength bands, not temperature. These measurements are mathematically inverted to obtain indirect inferences of temperature (Uddstrom 1988). Satellite data is closer to paleoclimate temperature reconstructions than modern ground-based temperature data in this way.

This just shows right off the start of your long winded post that you dont really know what you are talking about because all thermometers infer temperature from some proxy, be it thermal expansion of liquid, or impedance of metal. You were wrong. You were blatantly wrong to a level that would disqualify you from any further credibility, on anything engineering related were you testifying as an expert. Seriously how does an ME not know how thermometers work? You still refuse to say "ooops" because so much of your argument is appeal to authority. You cant be wrong. You write this long diatribe of cherry picked studies and data sets, most of which you misrepresent like calling adjustments and model reanalysis "data", yet you dont even know how a thermometer works?

Please lol.

 

[GTTofAK]

ENSO is episodic
ENSO is roughly cyclical
ENSO has no notable long-term increase in the intensity of El Nino’s or La Nina’s
ENSO has had no notable long-term impact on pre-industrial temperature trends
ENSO is not a driver of changes in climate
ENSO only causes surface temperature to temporarily deviate from the “average”, it does not impact the “average”
ENSO does not significantly impact the TOA energy balance
ENSO has no inherent mechanism that could have a major impact on long-term trends

Please we cannot even model the ENSO. We know so little about it nothing you listed here as fact is fact. As Dr. Curry noted we are currently playing catch up with the ENSO because the pigs at the trough climate alarmists have diverted most climate funding to AGW that we know so little about eh earths actual cycles. What you clam the ENSO is and isn't are simply assumptions that fit with your predetermined view. Its pure hyperbole sold as a scientific analysis. When we an accuracy model the ENSO then you can say what it is and isn't. Until then you are guessing. You cant argue what the ENSO does and does not do based on models that do not work.

Lets just take this one that I destroyed the last time you tried to argue these false assumptions

ENSO does not significantly impact the TOA energy balance

It looks to me that during the current La Nina phase it has significantly affected the outgoing short wave radiation.

You couldn't respond in that thread and you wont respond now because you dont care if what you said is true or not. You simply want it to be true. Yo say it doesn't effect TOA but the data sure as hell suggests it does. Maybe a reanalysis of the data is in order. That seems to be the go to scam these days. Who needs actual data when you can do a reanalysis and call model output data.

 

[rb1957]

@rconnor,
I apologise 'cause I didn't do justice to your long post. I stopped at the last figure for your ENSO analysis. you don't see a difference between the purple (model) line and the black (obs.) line ?

another day in paradise, or is paradise one day closer ?

 

[rconnor]

Note I have edited Part 2 to include Cowtan et al 2015 which I forgot to include the first time. It’s very relevant research to the topic.

rb1957, no worries, I understand it’s long. However, I feel it’s all important information to being able to properly understand the subject. You’re welcome to read the conclusion at the end if the post is too long for you.

As to your question, you’re talking about Kosaka and Xie 2013, correct? If so, yes I see the difference, so did the authors. In fact, it’s the entire point of the paper. There’s a difference between the purple line (model) and black line (observations) but when you input the observed state of the Pacific (i.e. ENSO state) and change nothing else, you get the red line (POGA-H) which matches the black line (observations) very well. This suggest that the discrepancy between models and observations is not due to overestimations of CO2 sensitivity in models but short-term internal variability.

If I misunderstood your intent, please clarify.

 

[GTTofAK]

It occurred to me last night the Rconnor is using a rather clever trick, clever for him that unlike many of his other tricks it took a whole day for it to occur to me normally his tricks are plainly obvious.

Rconnor begins his long diatribe of cherry picked studies by first throwing out all evidence the disagrees with his predetermined conclusion. He throws out all the satellite data sets because he doesn't know how a thermometer works. However, he still has no trouble using satellites for other things like TOA. Never expected consistency from a committed zealot.

However despite throwing out the satellite data sets because he doesn't know how a thermometer works Rconnor still sets uses the satellite data sets to define the pause.

Take his anlaysis of the ENSO

When comparing ENSO neutral years to ENSO neutral years, there is a steady warming trend throughout the “pause”

Notice something fishy? Well there is no trend during "the pause" if we defined as what we see in the surface record which start in the early 2000s depending on which surface data set you use.

No Rconnor extends his analysis all the way back to the mid 90s. The only data sets that have a pause that extend back that far are the satellite datasets.

You would think that because Rconnor has declared that he will not be using the satellites because he doesn't know how a thermometer works Rconnor would put his time frame around the surface record. But no Rconnor has no trouble using the the satellite's time frame and applying that time frame to surface measurements so long as it increases his trend. Rconnor likes to pull many tricks but I have to hand it to him on this one. This little deceit was actually pretty good. I didn't catch it right away. 'I'm not going to use the satellite datasets but I'm still going to set my time frame to the satellites to get better trends' is a rather subtle deception that even the best scam detectors might not catch.

 

[beej67]

If change in the energy absorbed by photosynthesis was (1) significant, (2) increased “available” energy is then absorbed by the “measurable environment” and (3) missing from the energy balance currently than this would be extra energy on top of the current CO2 energy imbalance, not in place of it. So even if you manage to demonstrate those three factors (which you need to do first), you can only conclude that climate change will be worse than previously thought because, in addition to the CO2 imbalance, we also have extra energy being released from the vegetal mass. Now, I don’t believe that all three of the factors are true but even if they were, it certainly wouldn’t help your main argument.

Umm, no.

We already know how much climate change we've had. We have records. If we discovered that there were additional drivers for how much climate change we've already had, then that would not mean that we've magically had more climate change. It would mean that the climate change we've already had was a combination of the terms we knew before and the terms we recently discovered. It would mean some of it was due to CO2 and some of it was due to (other stuff). Which means less of it so far has been due to CO2 than you presume when you presume that all of it is due to CO2.

That you can't seem to even understand that, tells me you're lost in the weeds and failing to see the bigger picture.

Decreasing biomass coverage increases the albedo (AR5 WGI 8.3.5)

Yeah, this is the bit that claims a concrete tennis court and dense tree canopy have the same net effect on the global climate, and paving over the entire planet in a carbon neutral fashion would cool us off. They are missing something. Take that to the bank.

Again beej67, you need to realize that mitigation measures already require substantial decreases in deforestation, and more likely reforestation. So we are somewhat, if not accidently, on the same page.

Not if the IPCC AR5 is correct. They seem to think that deforesting the entire planet would cool us off. Read your own link. At least they do admit this:

there is low agreement on the sign of the net change in global mean temperature as a result of land use change. {8.3.5}

 

[rb1957]

@rconnor,
yes, we were talking about the same graph. For me the red line is close to meaningless since it is the purple line "retuned" in some manner to better follow observations, and so it follows observations better.

another day in paradise, or is paradise one day closer ?