NEITHER dead or alive, knife-wound or gunshot victims will be cooled down and placed in suspended animation later this month, as a groundbreaking emergency technique is tested out for the first time....
The technique involves replacing all of a patient's blood with a cold saline solution, which rapidly cools the body and stops almost all cellular activity. "If a patient comes to us two hours after dying you can't bring them back to life. But if they're dying and you suspend them, you have a chance to bring them back after their structural problems have been fixed," says surgeon Peter Rhee at the University of Arizona in Tucson, who helped develop the technique.
The benefits of cooling, or induced hypothermia, have been known for decades. At normal body temperature – around 37 °C – cells need a regular oxygen supply to produce energy. When the heart stops beating, blood no longer carries oxygen to cells. Without oxygen the brain can only survive for about 5 minutes before the damage is irreversible.
However, at lower temperatures, cells need less oxygen because all chemical reactions slow down. This explains why people who fall into icy lakes can sometimes be revived more than half an hour after they have stopped breathing.
Posts tagged ‘temperature’
I try to make it a habit to criticize bad analyses from "my side" of certain debates. I find this to be a good habit that keeps one from falling for poorly constructed but ideologically tempting arguments.
Here is my example this week, from climate skeptic Steven Goddard. I generally enjoy his work, and have quoted him before, but this is a bad chart (this is global temperatures as measured by satellite and aggregated by RSS).
He is trying to show that the last 17+ years has no temperature trend. Fine. But by trying to put a trend line on the earlier period, it results in a mess that understates warming in earlier years. He ends up with 17 years with a zero trend and 20 years with a 0.05 per decade trend. Add these up and one would expect 0.1C total warming. But in fact over this entire period there was, by this data set, 0.3C-0.4C of warming. He left most of the warming out in the the step between the two lines.
Now there are times this might be appropriate. For example, in the measurement of ocean heat content, there is a step change that occurs right at the point where the measurement approach changed from ship records to the ARGO floats. One might argue that it is wrong to make a trend through the transition point because the step change was an artifact of the measurement change. But in this case there was no such measurement change. And while there was a crazy El Nino year in 1998, I have heard no argument from any quarter as to why there might have been some fundamental change in the climate system around 1997.
So I call foul. Take the trend line off the blue portion and the graph is much better.
I have not been blogging climate much because none of the debates ever change. So here are some quick updates
- 67% to 90% of all warming in climate forecasts still from assumptions of strong positive feedback in the climate system, rather than from CO2 warming per se (ie models still assuming about 1 degree in CO2 warming is multiplied 3-10 times by positive feedbacks)
- Studies are still mixed about the direction of feedbacks, with as many showing negative as positive feedback. No study that I have seen supports positive feedbacks as large as those used in many climate models
- As a result, climate models are systematically exaggerating warming (from Roy Spenser, click to enlarge). Note that the conformance through 1998 is nothing to get excited about -- most models were rewritten after that date and likely had plugs and adjustments to force the historical match.
- To defend the forecasts, modellers are increasingly blaming natural effects like solar cycles on the miss, natural effects that the same modellers insisted were inherently trivial contributions when skeptics used them to explain part of the temperature rise from 1978-1998.
- By the way, 1978-1998 is still the only period since 1940 when temperatures actually rose, such that increasingly all catastrophic forecasts rely on extrapolations from this one 20-year period. Seriously, look for yourself.
- Alarmists are still blaming every two or three sigma weather pattern on CO2 on global warming (polar vortex, sigh).
- Even when weather is moderate, media hyping of weather events has everyone convinced weather is more extreme, when it is not. (effect explained in context of Summer of the Shark)
- My temperature forecast from 2007 still is doing well. Back in '07 I regressed temperature history to a linear trend plus a sine wave.
My dad grew up in farm country in Iowa. He told me stories of the early days of commodity futures when a number of farmers lost a lot of money betting the wrong way. The error they made is that they would look at their local weather and assume everyone was experiencing the same. For example, some guy in Iowa would be experiencing a drought and facing a poor corn crop, and would buy corn futures assuming the crop would be bad everywhere. Unfortunately, this was often not the case.
A few climate sites have monthly contests to predict the next month's average global temperature anomaly. Apparently, everyone really missed in the January betting. Since most of the participants were American, they assumed that really cold weather in the US would translate to falling global temperatures. They were wrong. The global temperature anomaly in January actually rose a bit.
This is a variation of the same effect I often point out in the opposite direction -- that heat waves in even seemingly large areas do not necessarily mean anything for global temperatures. The US is only about 2% of the global surface area (land and ocean) and since the cold spell was in the eastern half of the US, it therefore affected perhaps 1% of the globe. And remember, on average, some area representing 1% of the globe should constantly be seeing a 100-year high or low for that particular day. It's just how averages work.
No particularly point here, except to emphasize just how facile it is to try to draw conclusions about global temperature trends from regional weather events.
The journal Nature has finally caught up to the fact that ocean cycles may influence global surface temperature trends. Climate alarmists refused to acknowledge this when temperatures were rising and the cycles were in their warm phase, but now are grasping at these cycles for an explanation of the 15+ year hiatus in warming as a way to avoid abandoning high climate sensitivity assumptions (ie the sensitivity of global temperatures to CO2 concentrations, which IMO are exaggerated by implausible assumptions of positive feedback).
I cannot find my first use of this chart, but here is a version I was using over 5 years ago. I know I was using it long before that
It will be interesting to see if they find a way to blame cycles for cooling in the last 10-15 years but not for the warming in the 80's and 90's.
Next step -- alarmists have the same epiphany about the sun, and blame non-warming on a low solar cycle without simultaneously giving previous high solar cycles any credit for warming. For Nature's benefit, here is another chart they might use (from the same 2008 blog post). The number 50 below is selected arbitrarily, but does a good job of highlighting solar activity in the second half of the 20th century vs. the first half.
I won't repeat the analysis, you need to see it here. Here is the chart in question:
My argument is that the smoothing and relatively low sampling intervals in the early data very likely mask variations similar to what we are seeing in the last 100 years -- ie they greatly exaggerate the smoothness of history and create a false impression that recent temperature changes are unprecedented (also the grey range bands are self-evidently garbage, but that is another story).
Drum's response was that "it was published in Science." Apparently, this sort of appeal to authority is what passes for data analysis in the climate world.
Well, maybe I did not explain the issue well. So I found a political analysis that may help Kevin Drum see the problem. This is from an actual blog post by Dave Manuel (this seems to be such a common data analysis fallacy that I found an example on the first page of my first Google search). It is an analysis of average GDP growth by President. I don't know this Dave Manuel guy and can't comment on the data quality, but let's assume the data is correct for a moment. Quoting from his post:
Here are the individual performances of each president since 1948:
1948-1952 (Harry S. Truman, Democrat), +4.82%
1953-1960 (Dwight D. Eisenhower, Republican), +3%
1961-1964 (John F. Kennedy / Lyndon B. Johnson, Democrat), +4.65%
1965-1968 (Lyndon B. Johnson, Democrat), +5.05%
1969-1972 (Richard Nixon, Republican), +3%
1973-1976 (Richard Nixon / Gerald Ford, Republican), +2.6%
1977-1980 (Jimmy Carter, Democrat), +3.25%
1981-1988 (Ronald Reagan, Republican), 3.4%
1989-1992 (George H. W. Bush, Republican), 2.17%
1993-2000 (Bill Clinton, Democrat), 3.88%
2001-2008 (George W. Bush, Republican), +2.09%
2009 (Barack Obama, Democrat), -2.6%
Let's put this data in a chart:
Look, a hockey stick , right? Obama is the worst, right?
In fact there is a big problem with this analysis, even if the data is correct. And I bet Kevin Drum can get it right away, even though it is the exact same problem as on his climate chart.
The problem is that a single year of Obama's is compared to four or eight years for other presidents. These earlier presidents may well have had individual down economic years - in fact, Reagan's first year was almost certainly a down year for GDP. But that kind of volatility is masked because the data points for the other presidents represent much more time, effectively smoothing variability.
Now, this chart has a difference in sampling frequency of 4-8x between the previous presidents and Obama. This made a huge difference here, but it is a trivial difference compared to the 1 million times greater sampling frequency of modern temperature data vs. historical data obtained by looking at proxies (such as ice cores and tree rings). And, unlike this chart, the method of sampling is very different across time with temperature - thermometers today are far more reliable and linear measurement devices than trees or ice. In our GDP example, this problem roughly equates to trying to compare the GDP under Obama (with all the economic data we collate today) to, say, the economic growth rate under Henry the VIII. Or perhaps under Ramses II. If I showed that GDP growth in a single month under Obama was less than the average over 66 years under Ramses II, and tried to draw some conclusion from that, I think someone might challenge my analysis. Unless of course it appears in Science, then it must be beyond question.
If You Don't Like People Saying That Climate Science is Absurd, Stop Publishing Absurd Un-Scientific Charts
Kevin Drum can't believe the folks at the National Review are still calling global warming science a "myth". As is usual for global warming supporters, he wraps himself in the mantle of science while implying that those who don't toe the line on the declared consensus are somehow anti-science.
Readers will know that as a lukewarmer, I have as little patience with outright CO2 warming deniers as I do with those declaring a catastrophe (for my views read this and this). But if you are going to simply be thunderstruck that some people don't trust climate scientists, then don't post a chart that is a great example of why people think that a lot of global warming science is garbage. Here is Drum's chart:
The problem is that his chart is a splice of multiple data series with very different time resolutions. The series up to about 1850 has data points taken at best every 50 years and likely at 100-200 year or more intervals. It is smoothed so that temperature shifts less than 200 years or so in length won't show up and are smoothed out.
In contrast, the data series after 1850 has data sampled every day or even hour. It has a sampling interval 6 orders of magnitude (over a million times) more frequent. It by definition is smoothed on a time scale substantially shorter than the rest of the data.
In addition, these two data sets use entirely different measurement techniques. The modern data comes from thermometers and satellites, measurement approaches that we understand fairly well. The earlier data comes from some sort of proxy analysis (ice cores, tree rings, sediments, etc.) While we know these proxies generally change with temperature, there are still a lot of questions as to their accuracy and, perhaps more importantly for us here, whether they vary linearly or have any sort of attenuation of the peaks. For example, recent warming has not shown up as strongly in tree ring proxies, raising the question of whether they may also be missing rapid temperature changes or peaks in earlier data for which we don't have thermometers to back-check them (this is an oft-discussed problem called proxy divergence).
The problem is not the accuracy of the data for the last 100 years, though we could quibble this it is perhaps exaggerated by a few tenths of a degree. The problem is with the historic data and using it as a valid comparison to recent data. Even a 100 year increase of about a degree would, in the data series before 1850, be at most a single data point. If the sampling is on 200 year intervals, there is a 50-50 chance a 100 year spike would be missed entirely in the historic data. And even if it were in the data as a single data point, it would be smoothed out at this data scale.
Do you really think that there was never a 100-year period in those last 10,000 years where the temperatures varied by more than 0.1F, as implied by this chart? This chart has a data set that is smoothed to signals no finer than about 200 years and compares it to recent data with no such filter. It is like comparing the annualized GDP increase for the last quarter to the average annual GDP increase for the entire 19th century. It is easy to demonstrate how silly this is. If you cut the chart off at say 1950, before much anthropogenic effect will have occurred, it would still look like this, with an anomalous spike at the right (just a bit shorter). If you believe this analysis, you have to believe that there is an unprecedented spike at the end even without anthropogenic effects.
There are several other issues with this chart that makes it laughably bad for someone to use in the context of arguing that he is the true defender of scientific integrity
- The grey range band is if anything an even bigger scientific absurdity than the main data line. Are they really trying to argue that there were no years, or decades, or even whole centuries that never deviated from a 0.7F baseline anomaly by more than 0.3F for the entire 4000 year period from 7500 years ago to 3500 years ago? I will bet just about anything that the error bars on this analysis should be more than 0.3F, much less the range of variability around the mean. Any natural scientist worth his or her salt would laugh this out of the room. It is absurd. But here it is presented as climate science in the exact same article that the author expresses dismay that anyone would distrust climate science.
- A more minor point, but one that disguises the sampling frequency problem a bit, is that the last dark brown shaded area on the right that is labelled "the last 100 years" is actually at least 300 years wide. Based on the scale, a hundred years should be about one dot on the x axis. This means that 100 years is less than the width of the red line, and the last 60 years or the real anthropogenic period is less than half the width of the red line. We are talking about a temperature change whose duration is half the width of the red line, which hopefully gives you some idea why I say the data sampling and smoothing processes would disguise any past periods similar to the most recent one.
Update: Kevin Drum posted a defense of this chart on Twitter. Here it is: "It was published in Science." Well folks, there is climate debate in a nutshell. An 1000-word dissection of what appears to be wrong with a particular analysis retorted by a five-word appeal to authority.
Update #2: I have explained the issue with a parallel flawed analysis from politics where Drum is more likely to see the flaws.
Though this is hilarious, I am pretty sure Thomas Lovejoy is serious when he writes
But the complete candor and transparency of the [IPCC] panel’s findings should be recognized and applauded. This is science sticking with the facts. It does not mean that global warming is not a problem; indeed it is a really big problem.
This is a howler. Two quick examples. First, every past IPCC report summary has had estimates for climate sensitivity, ie the amount of temperature increase they expect for a doubling of CO2 levels. Coming into this IPCC report, emerging evidence from recent studies has been that the climate sensitivity is much lower than previous estimates. So what did the "transparent" IPCC do? They, for the first time, just left out the estimate rather than be forced to publish one that was lower than the last report.
The second example relates to the fact that temperatures have been flat over the last 15-17 years and as a result, every single climate model has overestimated temperatures. By a lot. In a draft version, the IPCC created this chart (the red dots were added by Steve McIntyre after the chart was made as the new data came in).
This chart was consistent with a number of peer-reviewed studies that assessed the performance of climate models. Well, this chart was a little too much "candor" for the transparent IPCC, so they replaced it with this chart in the final draft:
What a mess! They have made the area we want to look at between 1990 and the present really tiny, and then they have somehow shifted the forecast envelopes down on several of the past reports so that suddenly current measurements are within the bands. They also hide the bottom of the fourth assessment band (orange FAR) so you can't see that observations are out of the envelope of the last report. No one so far can figure out how they got the numbers in this chart, and it does not match any peer-reviewed work. Steve McIntyre is trying to figure it out.
OK, so now that we are on the subject of climate models, here is the second hilarious thing Lovejoy said:
Does the leveling-off of temperatures mean that the climate models used to track them are seriously flawed? Not really. It is important to remember that models are used so that we can understand where the Earth system is headed.
Does this make any sense at all? Try it in a different context: The Fed said the fact that their economic models failed to predict what actually happened over the last 15 years is irrelevant because the models are only used to see where the economy is headed.
The consistent theme of this report is declining certainty and declining chances of catastrophe, two facts that the IPCC works as hard as possible to obfuscate but which still come out pretty clearly as one reads the report.
After over 15 years of no warming, which the IPCC still cannot explain, and with climate sensitivity numbers dropping so much in recent studies that the IPCC left climate sensitivity estimates out of their summary report rather than address the drop, the Weather Channel is running this headline on their site:
The IPCC does claim more confidence that warming over the past 60 years is partly or mostly due to man (I have not yet seen the exact wording they landed on), from 90% to 95%. But this is odd given that the warming all came from 1978 to 1998 (see for yourself in temperature data about halfway through this post). Temperatures are flat or cooling for the other 40 years of the period. The IPCC cannot explain these 40 years of no warming in the context of high temperature sensitivities to CO2. And, they can't explain why they can be 95% confident of what drove temperatures in the 20 year period of 1978-1998 but simultaneously have no clue what drove temperatures in the other years.
At some point I will read the thing and comment further.
The Phoenix New Times blog had a fairly remarkable story on a record-hot Phoenix summer. The core of the article is a chart from the NOAA. There are three things to notice in it:
- The article actually acknowledges that higher temperatures were due to higher night-time lows rather than higher daytime highs Any mention of this is exceedingly rare in media stories on temperatures, perhaps because the idea of a higher low is confusing to communicate
- It actually attributes urban warming to the urban heat island effect
- It makes no mention of global warming
Here is the graphic:
This puts me in the odd role of switching sides, so to speak, and observing that greenhouse warming could very likely manifest itself as rising nighttime lows (rather than rising daytime highs). I can only assume the surrounding area of Arizona did not see the same sort of records, which would support the theory that this is a UHI effect.
Phoenix has a huge urban heat island effect, which my son actually measured. At 9-10 in the evening, we measured a temperature differential of 8-12F from city center to rural areas outside the city. By the way, this is a fabulous science fair project if you know a junior high or high school student trying to do something different than growing bean plants under different color lights.
In this post, I want to discuss my just-for-fun model of global temperatures I developed 6 years ago. But more importantly, I am going to come back to some lessons about natural climate drivers and historic temperature trends that should have great relevance to the upcoming IPCC report.
In 2007, for my first climate video, I created an admittedly simplistic model of global temperatures. I did not try to model any details within the climate system. Instead, I attempted to tease out a very few (it ended up being three) trends from the historic temperature data and simply projected them forward. Each of these trends has a logic grounded in physical processes, but the values I used were pure regression rather than any bottom up calculation from physics. Here they are:
- A long term trend of 0.4C warming per century. This can be thought of as a sort of base natural rate for the post-little ice age era.
- An additional linear trend beginning in 1945 of an additional 0.35C per century. This represents combined effects of CO2 (whose effects should largely appear after mid-century) and higher solar activity in the second half of the 20th century (Note that this is way, way below the mainstream estimates in the IPCC of the historic contribution of CO2, as it implies the maximum historic contribution is less than 0.2C)
- A cyclic trend that looks like a sine wave centered on zero (such that over time it adds nothing to the long term trend) with a period of about 63 years. Think of this as representing the net effect of cyclical climate processes such as the PDO and AMO.
Put in graphical form, here are these three drivers (the left axis in both is degrees C, re-centered to match the centering of Hadley CRUT4 temperature anomalies). The two linear trends (click on any image in this post to enlarge it)
And the cyclic trend:
These two charts are simply added and then can be compared to actual temperatures. This is the way the comparison looked in 2007 when I first created this "model"
The historic match is no great feat. The model was admittedly tuned to match history (yes, unlike the pros who all tune their models, I admit it). The linear trends as well as the sine wave period and amplitude were adjusted to make the fit work.
However, it is instructive to note that a simple model of a linear trend plus sine wave matches history so well, particularly since it assumes such a small contribution from CO2 (yet matches history well) and since in prior IPCC reports, the IPCC and most modelers simply refused to include cyclic functions like AMO and PDO in their models. You will note that the Coyote Climate Model was projecting a flattening, even a decrease in temperatures when everyone else in the climate community was projecting that blue temperature line heading up and to the right.
So, how are we doing? I never really meant the model to have predictive power. I built it just to make some points about the potential role of cyclic functions in the historic temperature trend. But based on updated Hadley CRUT4 data through July, 2013, this is how we are doing:
Not too shabby. Anyway, I do not insist on the model, but I do want to come back to a few points about temperature modeling and cyclic climate processes in light of the new IPCC report coming soon.
The decisions of climate modelers do not always make sense or seem consistent. The best framework I can find for explaining their choices is to hypothesize that every choice is driven by trying to make the forecast future temperature increase as large as possible. In past IPCC reports, modelers refused to acknowledge any natural or cyclic effects on global temperatures, and actually made statements that a) variations in the sun's output were too small to change temperatures in any measurable way and b) it was not necessary to include cyclic processes like the PDO and AMO in their climate models.
I do not know why these decisions were made, but they had the effect of maximizing the amount of past warming that could be attributed to CO2, thus maximizing potential climate sensitivity numbers and future warming forecasts. The reason for this was that the IPCC based nearly the totality of their conclusions about past warming rates and CO2 from the period 1978-1998. They may talk about "since 1950", but you can see from the chart above that all of the warming since 1950 actually happened in that narrow 20 year window. During that 20-year window, though, solar activity, the PDO and the AMO were also all peaking or in their warm phases. So if the IPCC were to acknowledge that any of those natural effects had any influence on temperatures, they would have to reduce the amount of warming scored to CO2 between 1978 and 1998 and thus their large future warming forecasts would have become even harder to justify.
Now, fast forward to today. Global temperatures have been flat since about 1998, or for about 15 years or so. This is difficult to explain for the IPCC, since about none of the 60+ models in their ensembles predicted this kind of pause in warming. In fact, temperature trends over the last 15 years have fallen below the 95% confidence level of nearly every climate model used by the IPCC. So scientists must either change their models (eek!) or else they must explain why they still are correct but missed the last 15 years of flat temperatures.
The IPCC is likely to take the latter course. Rumor has it that they will attribute the warming pause to... ocean cycles and the sun (those things the IPCC said last time were irrelevant). As you can see from my model above, this is entirely plausible. My model has an underlying 0.75C per century trend after 1945, but even with this trend actual temperatures hit a 30-year flat spot after the year 2000. So it is entirely possible for an underlying trend to be temporarily masked by cyclical factors.
BUT. And this is a big but. You can also see from my model that you can't assume that these factors caused the current "pause" in warming without also acknowledging that they contributed to the warming from 1978-1998, something the IPCC seems loath to do. I do not know how the ICC is going to deal with this. I hate to think the worst of people, but I do not think it is beyond them to say that these factors offset greenhouse warming for the last 15 years but did not increase warming the 20 years before that.
We shall see. To be continued....
Update: Seriously, on a relative basis, I am kicking ass
Catastrophic Anthropogenic Climate Change is the magic theory -- every bit of evidence proves it. More rain, less rain, harder rain, drought, floods, more tornadoes, fewer tornadoes, hotter weather, colder weather, more hurricanes, fewer hurricane -- they all prove the theory. It is the theory that it is impossible not to confirm. Example
It will take climate scientists many months to complete studies into whether manmade global warming made the Boulder flood more likely to occur, but the amount by which this event has exceeded past events suggests that manmade warming may have played some role by making the event worse than it otherwise would have been...
An increase in the frequency and intensity of extreme precipitation events is expected to take place even though annual precipitation amounts are projected to decrease in the Southwest. Colorado sits right along the dividing line between the areas where average annual precipitation is expected to increase, and the region that is expected to become drier as a result of climate change.
That may translate into more frequent, sharp swings between drought and flood, as has recently been the case. Last year, after all, was Colorado's second-driest on record, with the warmest spring and warmest summer on record, leading to an intense drought that is only just easing.
Generally one wants to point to a data trend to prove a theory, but look at that last paragraph. Global warming is truly unique because it can be verified by there being no trend.
I hate to make this point for the five millionth time, but here goes: It is virtually impossible (and takes far more data, by orders of magnitude, than we posses) to prove a shift in the mean of any phenomenon simply by highlighting occasional tail-of-the-distribution events. The best way to prove a mean shift is to actually, you know, track the mean. The problem is that the trend data lines for all these phenomenon -- droughts, wet weather, tornadoes, hurricanes -- show no trend, so the only tool supporters of the theory have at their disposal is to scream "global warming" as loud as they can every time there is a tail-of-the-distribution event.
Let's do some math: They claim this flood was a one in one thousand year event. That strikes me as false precision, because we have only been observing this phenomenon with any reliability for 100 years, but I will accept their figure for now. Let's say this was indeed a one in 1000 year flood that it occurred over, say, half the area of Colorado (again a generous assumption, it was actually less that that).
Colorado is about 270,000 KM^2 so half would be 135,000 KM^2. The land area of the world (we really should include oceans for this but we will give these folks every break) is about 150,000,000 km^2. That means that half of Colorado is a bit less than 1/1000 of the world land area.
Our intuition tells us that a 1 in 1000 year storm is so rare that to have one means something weird or unusual or even unnatural must be going on. But by the math above, since this storm covered 1/1000 of the land surface of the Earth, we should see one such storm on average every year somewhere in the world. This is not some "biblical" unprecedented event - it is freaking expected, somewhere, every year. Over the same area we should also see a 1 in 1000 year drought, a 1 in 1000 year temperature high, and a one in one thousand year temperature low -- every single damn year. Good news if you are a newspaper and feed off of this stuff, but bad news for anyone trying to draw conclusions about the shifts in means and averages from such events.
This month, the world will get a new report from a United Nations panel about the science of climate change. Scientists will soon meet in Stockholm to put the finishing touches on the document, and behind the scenes, two big fights are brewing....
In the second case, we have mainstream science that says if the amount of carbon dioxide in the atmosphere doubles, which is well on its way to happening, the long-term rise in the temperature of the earth will be at least 3.6 degrees Fahrenheit, but more likely above 5 degrees. We have outlier science that says the rise could come in well below 3 degrees.
In this case, the drafters of the report lowered the bottom end in a range of temperatures for how much the earth could warm, treating the outlier science as credible.
The interesting part is that "mainstream science" is based mainly on theory and climate models that over the last 20 years have not made accurate predictions (overestimating warming significantly). "Outlier science" is in a lot of cases based on actual observations of temperatures along with other variables like infrared radiation returning to space. The author, through his nomenclature, is essentially disparaging observational data that is petulantly refusing to match up to model predictions. But of course skeptics are anti-science.
Drafts seen by Reuters of the study by the U.N. panel of experts, due to be published next month, say it is at least 95 percent likely that human activities - chiefly the burning of fossil fuels - are the main cause of warming since the 1950s.
That is up from at least 90 percent in the last report in 2007, 66 percent in 2001, and just over 50 in 1995, steadily squeezing out the arguments by a small minority of scientists that natural variations in the climate might be to blame.
I have three quick reactions to this
- The IPCC has always adopted words like "main cause" or "substantial cause." They have not even had enough certainly to use the word "majority cause" -- they want to keep it looser than that. If man causes 30% and every other cause is at 10% or less, is man the main cause? No one knows. So that is how we get to the absurd situation where folks are trumpeting being 95% confident in a statement that is purposely vaguely worded -- so vague that the vast majority of people who sign it would likely disagree with one another on exactly what they have agreed to.
- The entirety of the post-1950 temperature rise occurred between 1978 and 1998 (see below a chart based on the Hadley CRUT4 database, the same one used by the IPCC
Note that temperatures fell from 1945 to about 1975, and have been flat from about 1998 to 2013. This is not some hidden fact - it was the very fact that the warming slope was so steep in the short period from 1978-1998 that contributed to the alarm. The current 15 years with no warming was not predicted and remains unexplained (at least in the context of the assumption of high temperature sensitivities to CO2). The IPCC is in a quandary here, because they can't just say that natural variation counter-acted warming for 15 years, because this would imply a magnitude to natural variability that might have explained the 20 year rise from 1978-1998 as easily as it might explain the warming hiatus over the last 15 years (or in the 30 years preceding 1978).
- This lead statement by the IPCC continues to be one of the great bait and switches of all time. Most leading skeptics (excluding those of the talk show host or politician variety) accept that CO2 is a greenhouse gas and is contributing to some warming of the Earth. This statement by the IPCC says nothing about the real issue, which is what is the future sensitivity of the Earth's temperatures to rising CO2 - is it high, driven by large positive feedbacks, or more modest, driven by zero to negative feedbacks. Skeptics don't disagree that man has cause some warming, but believe that future warming forecasts are exaggerated and that the negative effects of warming (e.g. tornadoes, fires, hurricanes) are grossly exaggerated.
Its OK not to know something -- in fact, that is an important part of scientific detachment, to admit what one does not know. But what the hell does being 95% confident in a vague statement mean? Choose which of these is science:
- Masses are attracted to each other in proportion to the product of their masses and inversely proportional to the square of their distance of separation.
- We are 95% certain that gravity is the main cause of my papers remaining on my desk
The other day I posted a graph from Roy Spencer comparing climate model predictions to actual measurements in the tropical mid-troposphere (the zone on Earth where climate models predict the most warming due to large assumed water vapor positive feedbacks). The graph is a powerful indictment of the accuracy of climate models.
Spencer has an article (or perhaps a blog post) in the Financial Post with the same results, and includes a graph that does a pretty good job of simplifying the messy spaghetti graph in the original version. Except for one problem. Nowhere is it correctly labelled. One would assume looking at it that it is a graph of global surface temperatures, which is what most folks are used to seeing in global warming articles. But in fact it is a graph of temperatures in the mid-troposphere, between 20 degrees North and 20 degrees South latitude. He mentions that it is for tropical troposphere in the text of the article, but it is not labelled as such on the graph. There is a very good reason for that narrow focus, but now the graph will end up on Google image search, and people will start crying "bullsh*t" because they will compare the numbers to global surface temperature data and it won't match.
I respect Spencer's work but he did not do a good job with this.
Dr. Roy Spencer has compared the output of 73 climate models to actual recent temperature measurements. He has focused on temperatures in the mid-troposphere in the tropics -- this is not the same as global surface temperatures but is of course related. The reason for this focus is 1) we have some good space-based data sources for temperatures in this region that don't suffer the same biases and limitations as surface thermometers and 2) This is the zone that catastrophic anthropogenic global warming theory says should be seeing the most warming, due to positive feedback effects of water vapor. The lines are the model results for temperatures, the dots are the actuals.
I continue to suspect that the main source of disagreement is that the models’ positive feedbacks are too strong…and possibly of even the wrong sign.
The lack of a tropical upper tropospheric hotspot in the observations is the main reason for the disconnect in the above plots, and as I have been pointing out this is probably rooted in differences in water vapor feedback. The models exhibit strongly positive water vapor feedback, which ends up causing a strong upper tropospheric warming response (the “hot spot”), while the observation’s lack of a hot spot would be consistent with little water vapor feedback.
The warming from manmade CO2 without positive feedbacks would be about 1.3C per doubling of CO2 concentrations, a fraction of the 3-10C predicted by these climate models. If the climate, like most other long-term stable natural systems, is dominated by negative feedbacks, the sensitivity would be likely less than 1C. Either way, the resulting predicted warming from manmade CO2 over the rest of this century would likely be less than 1 degree C.
I have not had the time to write much about climate of late, but after several years of arguing over emails (an activity with which I quickly grew bored), the field is heating up again, as it were.
As I have said many times, the key missing science in the whole climate debate centers around climate sensitivity, or the expected temperature increase from a doubling of CO2 concentrations in the atmosphere (as reference, CO2 in the industrial age has increased from about 270 ppm to close to 400 ppm, or about half a doubling).
In my many speeches and this video (soon to be updated, if I can just find the time to finish it), I have argued that climate computer models have exaggerated climate sensitivity. This Wikipedia page is a pretty good rehash of the alarmist position on climate sensitivity. According to this standard alarmist position, here is the distribution of studies which represent the potential values for sensitivity - note that virtually none are below 2°C.
The problem is that these are all made with computer models. They are not based on observational data. Yes, all these models nominally backcast history reasonably correctly (look at that chart above and think about that statement for a minute, see if you can spot the problem). But many an investor has been bankrupted by models that correctly backcast history. The guys putting together tranches of mortgages for securities all had models. What has been missing is any validation of these numbers with actual, you know, observations of nature.
Way back 6 or 7 years ago I began taking these numbers and projecting them backwards. In other words, if climate sensitivity is really, say, at 4°C, then what should that imply about historical temperature increases since the pre-industrial age? Let's do a back of the envelope with the 4°C example. We are at just about half of a doubling of CO2 concentrations, but since sensitivity is a logarithmic curve, this implies we should have seen about 57% of the temperature increase that we would expect from a full doubling of CO2. Applied to the 4°C sensitivity figure, this means that if sensitivity really is 4°C, we should have seen a 2.3°C global temperature increase over the last 150 years or so. Which we certainly have not -- instead we have seen 0.8°C from all causes, only one of which is CO2.
So these high sensitivity models are over-predicting history. Even a 2°C sensitivity over-predicts the amount of warming we have seen historically. So how do they make the numbers fit? The models are tuned and tweaked with a number of assumptions. Time delays are one -- the oceans act as a huge flywheel on world temperatures and tend to add large lags to getting to the ultimate sensitivity figure. But even this was not enough for high sensitivity models to back-cast accurately. To make their models accurately predict history, their authors have had to ignore every other source of warming (which is why they have been so vociferous in downplaying the sun and ocean cycles, at least until they needed these to explain the lack of warming over the last decade). Further, they have added man-made cooling factors, particularly from sulfate aerosols, that offset some of the man-made warming with man-made cooling.
Which brings us back to the problem I hinted at with the chart above and its distribution of sensitivities. Did you spot the problem? All these models claim to accurately back-cast history, but how can a model with a 2°C sensitivity and an 11°C sensitivity both accurately model the last 100 years? One way they do it is by using a plug variable, and many models use aerosol cooling as the plug. Why? Well, unlike natural cooling factors, it is anthropogenic, so they can still claim catastrophe once we clean up the aerosols. Also, for years the values of aerosol cooling were really uncertain, so ironically the lack of good science on them allowed scientists to assume a wide range of values. Below is from a selection of climate models, and shows that the higher the climate sensitivity in the model, the higher the negative forcing (cooling) effect assumed from aerosols. This has to be, or the models would not back-cast.
The reasons that these models had such high sensitivities is that they assumed the climate was dominated by net positive feedback, meaning there were processes in the climate system that would take small amounts of initial warming from CO2 and multiply them many times. The generally accepted value for sensitivity without these feedbacks is 1.2°C or 1.3°C (via work by Michael Mann over a decade ago). So all the rest of the warming, in fact the entire catastrophe that is predicted, comes not from CO2 but from this positive feedback that multiplies this modest 1.2°C many times.
I have argued, as have many other skeptics, that this assumption of net positive feedback is not based on good science, and in fact most long-term stable natural systems are dominated by negative feedback (note that you can certainly identify individual processes, like ice albedo, that are certainly a positive feedback, but we are talking about the net effect of all such processes combined). Based on a skepticism about strong positive feedback, and the magnitude of past warming in relation to CO2 increases, I have always argued that the climate sensitivity is perhaps 1.2°C and maybe less, but that we should not expect more than a degree of warming from CO2 in the next century, hardly catastrophic.
One of the interesting things you might notice from the Wikipedia page is that they do not reference any sensitivity study more recent than 2007 (except for a literature review in 2008). One reason might be that over the last 5 years there have been a series of studies that have begun to lower the expected value of the sensitivity number. What many of these studies have in common is that they are based on actual observational data over the last 100 years, rather than computer models (by the way, for those of you who like to fool with Wikipedia, don't bother on climate pages -- the editors of these pages will reverse any change attempting to bring balance to their articles in a matter of minutes). These studies include a wide range of natural effects, such as ocean cycles, left out of the earlier models. And, as real numbers have been put on aerosol concentrations and their effects, much lower values have been assigned to aerosol cooling, thus reducing the amount of warming that could be coming from CO2.
Recent studies based on observational approaches are coming up with much lower numbers. ECS, or equilibrium climate sensitivity numbers (what we would expect in temperature increases if we waited hundreds or thousands of years for all time delays to be overcome) has been coming in between 1.6°C and 2.0°C. Values for TCS, or transient climate sensitivity, or what we might expect to see in our lifetimes, has been coming in around 1.3°C per doubling of CO2 concentrations.
Yesterday saw the publication of a paper in a prestigious journal,Nature Geoscience, from a high-profile international team led by Oxford scientists. The contributors include 14 lead authors of the forthcoming Intergovernmental Panel on Climate Change scientific report; two are lead authors of the crucial chapter 10: professors Myles Allen and Gabriele Hegerl.
So this study is about as authoritative as you can get. It uses the most robust method, of analysing the Earth’s heat budget over the past hundred years or so, to estimate a “transient climate response” — the amount of warming that, with rising emissions, the world is likely to experience by the time carbon dioxide levels have doubled since pre-industrial times.
The most likely estimate is 1.3C. Even if we reach doubled carbon dioxide in just 50 years, we can expect the world to be about two-thirds of a degree warmer than it is now, maybe a bit more if other greenhouse gases increase too….
This is still tough work, likely with a lot of necessary improvement, because it is really hard to dis-aggregate multiple drivers in such a complex system. There may, for example, be causative variables we don't even know about so by definition were not included in the study. However, it is nice to see that folks are out there trying to solve the problem with real observations of Nature, and not via computer auto-eroticism.
Postscript: Alarmists have certainly not quit the field. The current emerging hypothesis to defend high sensitivities is to say that the heat is going directly into the deep oceans. At some level this is sensible -- the vast majority of the heat carrying capacity (80-90%) of the Earth's surface is in the oceans, not in the atmosphere, and so they are the best place to measure warming. Skeptics have said this for years. But in the top 700 meters or so of the ocean, as measured by ARGO floats, ocean heating over the last 10 years (since these more advanced measuring devices were launched) has been only about 15% of what we might predict with high sensitivity models. So when alarmists say today that the heat is going into the oceans, they say the deep oceans -- ie that the heat from global warming is not going into the air or the first 700 meters of ocean but directly into ocean layers beneath that. Again, this is marginally possible by some funky dynamics, but just like the aerosol defense that has fallen apart of late, this defense of high sensitivity forecasts is completely unproven. But the science is settled, of course.
Well, I have zero desire to score political points off the tragedy in Oklahoma, but unfortunately others are more than eager to do so. As a result, it is necessary to put a few facts on the table to refute the absurd claim that this tornado is somehow attributable to CO2.
- I really should not have to say this, but there is no mechanism by which CO2 has ever been accused of causing tornadoes except via the intervening step of warming. Without warming, CO2 can't be the cause (even with warming, the evidence is weak, since tornadoes are cause more by temperature differentials, than by temperature per se). So it is worth noting that there have been no unusually warm temperatures in the area of late, and in fact the US has had one of its coolest springs in several decades.
- I should also not have to say this, but major tornadoes occurred in Oklahoma at much lower CO2 levels.
- In fact, if anything the trend in major tornadoes in the US over the last several decades is down
- And, this is actually a really, really low tornado year so far. So its hard to figure an argument that says that global warming reduced tornadoes in general but caused this one in particular
For years, readers of this site know that I have argued that:
- CO2 is indeed a greenhouse gas, and since man is increasing its atmospheric concentration, there is likely some anthropogenic contribution to warming
- Most forecasts, including those of the IPCC, grossly exaggerate temperature sensitivity to CO2 by assuming absurd levels of net positive feedback in the climate system
- Past temperature changes are not consistent with high climate sensitivities
Recently, there have been a whole spate of studies based on actual observations rather than computer models that have been arriving at climate sensitivity numbers far below the IPCC number. While the IPCC settled on 3C per doubling of CO2, it strongly implied that all the risk was to the upside, and many other prominent folks who typically get fawning attention in the media have proposed much higher numbers.
In fact, recent studies are coming in closer to 1.5C - 2C. I actually still think these numbers will turn out to be high. For several years now my money has been on a number from 0.8 to 1 C, sensitivity numbers that imply a small amount of negative feedback rather than positive feedback, a safer choice in my mind since most long-term stable natural systems are dominated by negative feedback.
Anyway, in an article that was as surprising as it is welcome, NY Times climate writer Andy Revkin has quite an article recently, finally acknowledging in the paper of record that maybe those skeptics who have argued for alower sensitivity number kind of sort of have a point.
“Worse than we thought” has been one of the most durable phrases lately among those pushing for urgent action to stem the buildup of greenhouse gases linked to global warming.
But on one critically important metric — how hot the planet will get from a doubling of the pre-industrial concentration of greenhouse gases, a k a “climate sensitivity” — someclimate researchers with substantial publication records are shifting toward the lower end of the warming spectrum.
By the way, this is the only metric that matters. All the other BS about "climate change" and "dirty weather" are meaningless without warming. CO2 cannot change the climate or raise sea levels or any of that other stuff by any mechanism we understand or that has even been postulated, except via warming. Anyway, to continue:
But while plenty of other climate scientists hold firm to the idea that the full range of possible outcomes, including a disruptively dangerous warming of more than 4.5 degrees C. (8 degrees F.), remain in play, it’s getting harder to see why the high-end projections are given much weight.
This is also not a “single-study syndrome” situation, where one outlier research paper is used to cast doubt on a bigger body of work — as Skeptical Science asserted over the weekend. That post focused on the as-yet-unpublished paper finding lower sensitivity that was inadvisedly promoted recently by the Research Council of Norway.
In fact, there is an accumulating body of reviewed, published researchshaving away the high end of the range of possible warming estimates from doubled carbon dioxide levels. Chief among climate scientists critical of the high-sensitivity holdouts is James Annan, an experienced climate modeler based in Japan who contributed to the 2007 science report from the Intergovernmental Panel on Climate Change. By 2006, he was already diverging from his colleagues a bit.
The whole thing is good. Of course, for Revkin, this is no excuse to slow down all the actions supposedly demanded by global warming, such as substantially raising the price and scarcity of hydrocarbons. Which to me simply demonstrates that people who have been against hydrocarbons have always been against them as an almost aesthetic choice, and climate change and global warming were mere excuses to push the agenda. After all, as there certainly are tradeoffs to limiting economic growth and energy use and raising the price of energy, how can a reduction in postulated harms from fossil fuels NOT change the balance point one chooses in managing their use?
PS- I thought this was a great post mortem on Hurricane Sandy and the whole notion that this one data point proves the global warming trend:
In this case several factors not directly related to climate change converged to generate the event. On Sandy’s way north, it ran into a vast high-pressure system over Canada, which prevented it from continuing in that direction, as hurricanes normally do, and forced it to turn west. Then, because it traveled about 300 miles over open water before making landfall, it piled up an unusually large storm surge. An infrequent jet-stream reversal helped maintain and fuel the storm. As if all that weren’t bad enough, a full moon was occurring, so the moon, the earth, and the sun were in a straight line, increasing the moon’s and sun’s gravitational effects on the tides, thus lifting the high tide even higher. Add to this that the wind and water, though not quite at hurricane levels, struck an area rarely hit by storms of this magnitude so the structures were more vulnerable and a disaster occurred.
The last one is a key for me -- you have cities on the Atlantic Ocean that seemed to build and act as if they were immune from ocean storms. From my perspective growing up on the gulf coast, where one practically expects any structure one builds on the coast to be swept away every thirty years or so, this is a big contributing factor no one really talks about.
She goes on to say that rising sea levels may have made the storm worse, but I demonstrated that it couldn't have added more than a few percentage points to the surge.
Silver chooses to focus on individuals working in a tight competition and their motives and individual biases, which he understands and explains well. For him, modeling is a man versus wild type thing, working with your wits in a finite universe to win the chess game.
He spends very little time on the question of how people act inside larger systems, where a given modeler might be more interested in keeping their job or getting a big bonus than in making their model as accurate as possible.
In other words, Silver crafts an argument which ignores politics. This is Silver’s blind spot: in the real world politics often trump accuracy, and accurate mathematical models don’t matter as much as he hopes they would....
My conclusion: Nate Silver is a man who deeply believes in experts, even when the evidence is not good that they have aligned incentives with the public.
Distrust the experts
Call me “asinine,” but I have less faith in the experts than Nate Silver: I don’t want to trust the very people who got us into this mess, while benefitting from it, to also be in charge of cleaning it up. And, being part of the Occupy movement, I obviously think that this is the time for mass movements.
Like Ms. O'Neill, I distrust "authorities" as well, and have a real problem with debates that quickly fall into dueling appeals to authority. She is focusing here on overt politics, but subtler pressure and signalling are important as well. For example, since "believing" in climate alarmism in many circles is equated with a sort of positive morality (and being skeptical of such findings equated with being a bad person) there is an underlying peer pressure that is different from overt politics but just as damaging to scientific rigor. Here is an example from the comments at Judith Curry's blog discussing research on climate sensitivity (which is the temperature response predicted if atmospheric levels of CO2 double).
While many estimates have been made, the consensus value often used is ~3°C. Like the porridge in “The Three Bears”, this value is just right – not so great as to lack credibility, and not so small as to seem benign.
Huybers (2010) showed that the treatment of clouds was the “principal source of uncertainty in models”. Indeed, his Table I shows that whereas the response of the climate system to clouds by various models varied from 0.04 to 0.37 (a wide spread), the variation of net feedback from clouds varied only from 0.49 to 0.73 (a much narrower relative range). He then examined several possible sources of compensation between climate sensitivity and radiative forcing. He concluded:
“Model conditioning need not be restricted to calibration of parameters against observations, but could also include more nebulous adjustment of parameters, for example, to fit expectations, maintain accepted conventions, or increase accord with other model results. These more nebulous adjustments are referred to as ‘tuning’.” He suggested that one example of possible tuning is that “reported values of climate sensitivity are anchored near the 3±1.5°C range initially suggested by the ad hoc study group on carbon dioxide and climate (1979) and that these were not changed because of a lack of compelling reason to do so”.
Huybers (2010) went on to say:
“More recently reported values of climate sensitivity have not deviated substantially. The implication is that the reported values of climate sensitivity are, in a sense, tuned to maintain accepted convention.”
Translated into simple terms, the implication is that climate modelers have been heavily influenced by the early (1979) estimate that doubling of CO2 from pre-industrial levels would raise global temperatures 3±1.5°C. Modelers have chosen to compensate their widely varying estimates of climate sensitivity by adopting cloud feedback values countering the effect of climate sensitivity, thus keeping the final estimate of temperature rise due to doubling within limits preset in their minds.
There is a LOT of bad behavior out there by models. I know that to be true because I used to be a modeler myself. What laymen do not understand is that it is way too easy to tune and tweak and plug models to get a preconceived answer -- and the more complex the model, the easier this is to do in a non-transparent way. Here is one example, related again to climate sensitivity
When I looked at historic temperature and CO2 levels, it was impossible for me to see how they could be in any way consistent with the high climate sensitivities that were coming out of the IPCC models. Even if all past warming were attributed to CO2 (a heroic assertion in and of itself) the temperature increases we have seen in the past imply a climate sensitivity closer to 1 rather than 3 or 5 or even 10 (I show this analysis in more depth in this video).
My skepticism was increased when several skeptics pointed out a problem that should have been obvious. The ten or twelve IPCC climate models all had very different climate sensitivities — how, if they have different climate sensitivities, do they all nearly exactly model past temperatures? If each embodies a correct model of the climate, and each has a different climate sensitivity, only one (at most) should replicate observed data. But they all do. It is like someone saying she has ten clocks all showing a different time but asserting that all are correct (or worse, as the IPCC does, claiming that the average must be the right time).
The answer to this paradox came in a 2007 study by climate modeler Jeffrey Kiehl. To understand his findings, we need to understand a bit of background on aerosols. Aerosols are man-made pollutants, mainly combustion products, that are thought to have the effect of cooling the Earth’s climate.
What Kiehl demonstrated was that these aerosols are likely the answer to my old question about how models with high sensitivities are able to accurately model historic temperatures. When simulating history, scientists add aerosols to their high-sensitivity models in sufficient quantities to cool them to match historic temperatures. Then, since such aerosols are much easier to eliminate as combustion products than is CO2, they assume these aerosols go away in the future, allowing their models to produce enormous amounts of future warming.
Specifically, when he looked at the climate models used by the IPCC, Kiehl found they all used very different assumptions for aerosol cooling and, most significantly, he found that each of these varying assumptions were exactly what was required to combine with that model’s unique sensitivity assumptions to reproduce historical temperatures. In my terminology, aerosol cooling was the plug variable.
By the way, this aerosol issue is central to recent work that is pointing to a much lower climate sensitivity to CO2 than has been reported in past IPCC reports.
The other day I linked my Forbes column that showed that there was no upward trend in global hurricane number and strength, the number of US hurricane strikes, or the number of October hurricanes. Given these trends, anyone who wants to claim Sandy is proof of global warming is forced to extrapolate from a single data point.
Since I wrote that, Bob Tisdale had an interesting article on Sandy. The theoretical link between global warming and more and stronger Atlantic hurricanes has not been fully proven, but the theory says that warmer waters will provide energy for more and larger storms (like Sandy). Thus the theory is that global warming has heated up the waters through which hurricanes pass and that feed these hurricanes' strength.
Bob Tisdale took a look at the historical trends in sea surface temperatures in the area bounded by Sandy's storm track. These are the temperature trends for the waters that fueled Sandy. This is what he got:
If he has done the analysis right, this means there is no warming trend over the last 60+ years in the ocean waters that fed Sandy. This means that the unusually warm seas that fed Sandy's growth were simply a random event, an outlier which appears from this chart to be unrelated to any long-term temperature trend.
Update: I challenge you to find any article arguing that Sandy was caused by anthropogenic global warming that actually includes a long term trend chart (other than global temperatures) in the article. The only one I have seen is a hurricane strike chart that is cut off in the 1950's (despite data that goes back over 100 years) because this is the only cherry-picked cut off point that delivers an upward trend. If you find one, email me the link, I would like to see it.
I have a new article up at Forbes on how crazy it is to extrapolate conclusions about the speed and direction of climate change from a single data point.
Positing a trend from a single database without any supporting historical information has become a common media practice in discussing climate. As I wrote several months ago, the media did the same thing with the hot summer, arguing frequently that this recent hot dry summer proved a trend for extreme temperatures, drought, and forest fires. In fact, none of these are the case — this summer was not unprecedented on any of these dimensions and no upward trend is detectable in long-term drought or fire data. Despite a pretty clear history of warming over the last century, it is even hard to establish any trend in high temperature extremes (in large part because much of the warming has been in warmer night-time lows rather than in daytime highs). See here for the data.
As I said in that earlier article, when the media posits a trend, demand a trendline, not just a single data point.
To this end, I try to bring so actual trend data to the trend discussion.
... and it was fun to see my charts in it! The lecture is reprinted here (pdf) or here (html). The charts I did are around pages 6-7 of the pdf, the ones showing the projected curve of global warming for various climate sensitivities, and backing into what that should imply for current warming. In short, even if you don't think warming in the surface temperature record is exaggerated, there still has not been anywhere near the amount of warming one would expect for the types of higher sensitivities in the IPCC and other climate models. Warming to date, even if not exaggerated and all attributed to man-made and not natural causes, is consistent with far less catastrophic, and more incremental, future warming numbers.
These charts come right out of the IPCC formula for the relationship between CO2 concentrations and warming, a formula first proposed by Michael Mann. I explained these charts in depth around the 10 minute mark of this video, and returned to them to make the point about past warming around the 62 minute mark. This is a shorter video, just three minutes, that covers the same ground. Watching it again, I am struck by how relevant it is as a critique five years later, and by how depressing it is that this critique still has not penetrated mainstream discussion of climate. In fact, I am going to embed it below:
The older slides Ridley uses, which are cleaner (I went back and forth on the best way to portray this stuff) can be found here.
By the way, Ridley wrote an awesome piece for Wired more generally about catastrophism which is very much worth a read.
I know I hammer this home constantly, but it is often worth a reminder. The issue in the scientific debate over catastrophic man-made global warming theory is not whether CO2 is a greenhouse gas, or even the approximate magnitude of warming from CO2 directly, but around feedbacks. Patrick Moore, Greenpeace founder, said it very well:
What most people don't realize, partly because the media never explains it, is that there is no dispute over whether CO2 is a greenhouse gas, and all else being equal would result in a warming of the climate. The fundamental dispute is about water in the atmosphere, either in the form of water vapour (a gas) or clouds (water in liquid form). It is generally accepted that a warmer climate will result in more water evaporating from the land and sea and therefore resulting in a higher level of water in the atmosphere, partly because the warmer the air is the more water it can hold. All of the models used by the IPCC assume that this increase in water vapour will result in a positive feedback in the order of 3-4 times the increase in temperature that would be caused by the increase in CO2 alone.
Many scientists do not agree with this, or do not agree that we know enough about the impact of increased water to predict the outcome. Some scientists believe increased water will have a negative feedback instead, due to increased cloud cover. It all depends on how much, and a t what altitudes, latitudes and times of day that water is in the form of a gas (vapour) or a liquid (clouds). So if a certain increase in CO2 would theoretically cause a 1.0C increase in temperature, then if water caused a 3-4 times positive feedback the temperature would actually increase by 3-4C. This is why the warming predicted by the models is so large. Whereas if there was a negative feedback of 0.5 times then the temperature would only rise 0.5C.
My new column is up, comparing coverage of this summer's heat wave to "Summer of the Shark"
Before I discuss the 2012 global warming version of this process, let's take a step back to 2001 and the "Summer of the Shark." The media hysteria began in early July, when a young boy was bitten by a shark on a beach in Florida. Subsequent attacks received breathless media coverage, up to and including near-nightly footage from TV helicopters of swimming sharks. Until the 9/11 attacks, sharks were the third biggest story of the year as measured by the time dedicated to it on the three major broadcast networks' news shows.
Through this coverage, Americans were left with a strong impression that something unusual was happening -- that an unprecedented number of shark attacks were occurring in that year, and the media dedicated endless coverage to speculation by various "experts" as to the cause of this sharp increase in attacks.
Except there was one problem -- there was no sharp increase in attacks. In the year 2001, five people died in 76 shark attacks. However, just a year earlier, 12 people had died in 85 attacks. The data showed that 2001 actually was a down year for shark attacks.
This summer we have been absolutely bombarded with stories about the summer heat wave in the United States. The constant drumbeat of this coverage is being jumped on by many as evidence of catastrophic man-made global warming....
What the Summer of the Shark needed, and what this summer’s US heatwave needs, is a little context. Specifically, if we are going to talk about supposed “trends”, then we should look at the data series in question over time. So let’s do so.
I go on to present a number of data series on temperatures, temperature maximums, droughts, and fires. Enjoy.