Unquestionably, the U.N. Intergovernmental Panel on Climate Change (IPCC) was formed to build the scientific case for humanity being the primary cause of global warming. Such a goal is fundamentally unscientific, as it is hostile to alternative hypotheses for the causes of climate change.

The most glaring example of this bias has been the lack of interest on the IPCC’s part in figuring out to what extent climate change is simply the result of natural, internal cycles in the climate system. In Chapter 9 of the latest (4th) IPCC report, entitled “Understanding and Attributing Climate Change”, you would think the issue of external versus internal forcing would be thoroughly addressed. But you would be wrong.

The IPCC is totally obsessed with external forcing, that is, energy imbalances imposed upon the climate system that are NOT the result of the natural, internal workings of the system. For instance, a search through Chapter 9 for the phrase “external forcing” yields a total of 91 uses of that term. A search for the phrase “internal forcing” yields…(wait for it)…zero uses. Can we really believe that the IPCC has ruled out natural sources of global warming when such a glaring blind spot exists?

Admittedly, we really do not understand internal sources of climate change. Weather AND climate involves chaotic processes, most of which we may never understand, let alone predict. While chaos in weather is exhibited on time scales of days to weeks, chaotic changes in the ocean circulation could have time scales as long as hundreds of years, and we know that cloud formation – providing the Earth’s natural sun shade – is strongly influenced by the ocean.

Thus, small changes in ocean circulation can lead to small changes in the Earth’s albedo (how much sunlight is reflected back to space), which in turn can lead to global warming or cooling. The IPCC’s view (which is never explicitly stated) that such changes in the climate system do not occur is little more than faith on their part.

The IPCC’s pundits like to claim that the published evidence for humanity causing warming greatly outweighs any published evidence against it. This appeal to majority opinion on their part is pretty selective, though. They had no trouble discarding hundreds of research papers supporting evidence for the Medieval Warm Period or the Little Ice Age when they so uncritically embraced the infamous “Hockey Stick” reconstructions of past temperature change.

Despite a wide variety of previous temperature proxies gathered from around the world (see figure below) that so clearly showed that centuries with global warming and cooling are the rule, not the exception, the Hockey Stick was mostly based upon some cherry-picked tree rings combined with the assumption that significant warming is a uniquely modern phenomenon.

As such, they rejected the prevailing “scientific consensus” in favor of a minority view that supported their desired outcome. I suspect that they do not even recognize their own hypocrisy.

As I have discussed before, the IPCC’s neglect of natural variability in the climate system ends up leading to circular reasoning on their part. They ignore the effect of natural cloud variations when trying to diagnose feedback, which then leads to overestimates of climate sensitivity. This, in turn, causes them to conclude that increasing carbon dioxide concentrations alone are sufficient to explain global warming, and so no natural forcings of climate change need be found.

But all they have done is reasoned themselves in a circle. By ignoring natural variability, they can end up claiming that natural variability does not exist. Admittedly, their position is internally consistent. But then, so is all circular reasoning.

Our re-submitted paper to the Journal of Geophysical Research entitled “On the Diagnosis of Radiative Feedback in the Presence of Unknown Radiative Forcing” will hopefully lead to a little more diversity being permitted in the global warming debate.

I don’t think the IPCC scientists are as opposed to this as are their self-appointed spokespersons, like Al Gore and numerous environmental writers in the media who get to over-simplify the climate issue without ever being corrected by the IPCC. Natural climate change continues to be the 800 lb gorilla in the room, and I suspect that some within the IPCC are slowly becoming aware of its existence.

Since the global average sea surface temperature (SST) anomalies (departures from average) hit a peak a couple of months ago, I thought it would be a good time to see how they are progressing. Here’s a plot of running 11-day SST anomalies for the global oceans (60N to 60S latitude):

As can be seen, at least for the time being, temperatures have returned to the long-term average. Of course, this says nothing about what will happen in the future. I have also plotted the linear trend line, which is for entertainment purposes only.

The SSTs come from the AMSR-E instrument on NASA’s Aqua satellite, and are computed and archived at Remote Sensing Systems (Frank Wentz). I believe them to be the most precise record of subtle SST changes available, albeit only since mid-2002.

It is claimed by the IPCC that there are ‘fingerprints’ associated with global warming which can be tied to humanity’s greenhouse gas emissions, as if the signatures were somehow unique like real fingerprints.

But I have never been convinced that there is ANY fingerprint of anthropogenic warming. And the reason is that any sufficiently strong radiative warming influence – for instance, a small (even unmeasurable) decrease in cloud cover letting in slightly more sunlight starting back in the late 1970’s or 1980’s– would have had the same effect.

The intent of the following figure from Chapter 9 in the latest (AR4) version IPCC report is to convince the reader that greenhouse gas emissions have been tested against all other sources of warming, and that GHGs are the only agent that can cause substantial warming. (The snarky reference to “proof” is my addition.)

But all the figure demonstrates is that the warming influence of GHGs is stronger than that from a couple of other known external forcing mechanisms, specifically a very small increase in the sun’s output, and a change in ozone. It says absolutely nothing about the possibility that warming might have been simply part of a natural, internal fluctuation (cycle, if you wish) in the climate system.

For instance, the famous “hot spot” seen in the figure has become a hot topic in recent years since at least two satellite temperature datasets (including our UAH dataset), and most radiosonde data analyses suggest the tropical hotspot does not exist. Some have claimed that this somehow invalidates the hypothesis that anthropogenic greenhouse gas emissions are responsible for global warming.

But the hotspot is not a unique signature of manmade greenhouse gases. It simply reflects anomalous heating of the troposphere — no matter what its source. Anomalous heating gets spread throughout the depth of the troposphere by convection, and greater temperature rise in the upper troposphere than in the lower troposphere is because of latent heat release (rainfall formation) there.

For instance, a natural decrease in cloud cover would have had the same effect. It would lead to increased solar warming of the ocean, followed by warming and humidifying of the global atmosphere and an acceleration of the hydrologic cycle.

Thus, while possibly significant from the standpoint of indicating problems with feedbacks in climate models, the lack of a hotspot no more disproves manmade global warming than the existence of the hotspot would have proved manmade global warming. At most, it would be evidence that the warming influence of increasing GHGs in the models has been exaggerated, probably due to exaggerated positive feedback from water vapor.

The same is true of the supposed fingerprint of greater warming over land than over the ocean, of which there is some observational evidence. But this would also be caused by a slight decrease in cloud cover…even if that decrease only occurred over the ocean (Compo, G.P., and P. D. Sardeshmukh, 2009).

What you find in the AR4 report is artfully constructed prose about how patterns of warming are “consistent with” that expected from manmade greenhouse gases. But “consistent with” is not “proof of”.

The AR4 authors are careful to refer to “natural external factors” that have been ruled out as potential causes, like those seen in the above figure. I can only assume this is was deliberate attempt to cover themselves just in case most warming eventually gets traced to natural internal changes in the climate system, rather than to that exceedingly scarce atmospheric constituent that is necessary for life of Earth – carbon dioxide.

YR MON GLOBE NH SH TROPICS
2009 1 +0.304 +0.443 +0.165 -0.036
2009 2 +0.347 +0.678 +0.016 +0.051
2009 3 +0.206 +0.310 +0.103 -0.149
2009 4 +0.090 +0.124 +0.056 -0.014
2009 5 +0.045 +0.046 +0.044 -0.166
2009 6 +0.003 +0.031 -0.025 -0.003
2009 7 +0.411 +0.212 +0.610 +0.427
2009 8 +0.229 +0.282 +0.177 +0.456
2009 9 +0.424 +0.554 +0.295 +0.516

The global-average lower tropospheric temperature anomaly in September 2009 rebounded again, from +0.23 deg. C in August to +0.42 deg. C in September. The tropics and Northern Hemisphere continue to dominate the signal.

NOTE: For those who are monitoring the daily progress of global-average temperatures here, we are still working on switching from NOAA-15 to Aqua AMSU, which will provide more accurate tracking on a daily basis. We will be including both our lower troposphere (LT) and mid-tropospheric (MT) pre-processing of the data. We have added the global sea surface temperature anomalies from the AMSR-E instrument on board the NASA Aqua satellite, computed from files at Remote Sensing Systems, although we are still not done adjusting the display range of those data.

[This is an update on research progress we have made into determining just how sensitive the climate system is to increasing atmospheric greenhouse gas concentrations.]

While published studies are beginning to suggest that net feedbacks in the climate system could be negative for year-to-year variations (e.g., our 2007 paper, and the new study by Lindzen and Choi, 2009), there remains the question of whether the same can be said of long-term climate sensitivity (and therefore, of the strength of future global warming).

Even if we find observational evidence of an insensitive climate system for year-to-year fluctuations in the climate system, it could be that the system’s long term response to more carbon dioxide is very sensitive. I’m not saying I believe that is the case – I don’t – but it is possible. This question of a potentially large difference in short-term and long-term responses of the climate system has been bothering me for many months.

Significantly, as far as I know, the climate modelers have not yet demonstrated that there is any short-term behavior in their models which is also a good predictor of how much global warming those models project for our future. It needs to be something we can measure, something we can test with real observations. Just because all of the models behave more-or-less like the real climate system does not mean the range of warming they produce encompasses the truth.

For instance, computing feedback parameters (a measure of how much the radiative balance of the Earth changes in response to a temperature change) would be the most obvious test. But I’ve diagnosed feedback parameters from 7- to 10-year subsets of the models’ long-term global warming simulations, and they have virtually no correlation with those models known long-term feedbacks. (I am quite sure I know the reason for this…which is the subject of our JGR paper now being revised…I just don’t know a good way around it).

But I refuse to give up searching. This is because the most important feedbacks in the climate system – clouds and water vapor – have inherently short time scales…minutes for individual clouds, to days or weeks for large regional cloud systems and changes in free-tropospheric water vapor. So, I still believe that there MUST be one or more short term “markers” of long term climate sensitivity.

Well, this past week I think I finally found one. I’m going to be a little evasive about exactly what that marker is because, in this case, the finding is too important to give away to another researcher who will beat me to publishing it (insert smiley here).

What I will say is that the marker ‘index’ is related to how the climate models behave during sudden warming events and the cooling that follows them. In the IPCC climate models, these warming/cooling events typically have time scales of several months, and are self-generated as ‘natural variability’ within the models. (I’m not concerned that I’ve given it away, since the marker is not obvious…as my associate Danny Braswell asked, “What made you think of that?”)

The following plot shows how this ‘mystery index’ is related to the net feedback parameters diagnosed in those 18 climate models by Forster and Taylor (2006). As can be seen, it explains 50% of the variance among the different models. The best I have been able to do up to this point is less than 10% explained variance, which for a sample size of 18 models might as well be zero.

Also plotted is the range of values of this index from 9 years of CERES satellite measurements computed in the same manner as with the models’ output. As can be seen, the satellite data support lower climate sensitivity (larger feedback parameter) than any of the climate models…but not nearly as low as the 6 Watts per sq. meter per degree found for tropical climate variations by us and others.

For a doubling of atmospheric carbon dioxide, the satellite measurements would correspond to about 1.6 to 2.0 deg. C of warming, compared to the 18 IPCC models’ range shown, which corresponds to warming of from about 2.0 to 4.2 deg. C.

The relatively short length of record of our best satellite data (9 years) appears to be the limiting factor in this analysis. The model results shown in the above figure come from 50 years of output from each of the 18 models, while the satellite range of results comes from only 9 years of CERES data (March 2000 through December 2008). The index needs to be computed from as many strong warming events as can be found, because the marker only emerges when a number of them are averaged together.

Despite this drawback, the finding of this short-term marker of long-term climate sensitivity is at least a step in the right direction. I will post progress on this issue as the evidence unfolds. Hopefully, more robust markers can be found that show even a stronger relationship to long-term warming in the models, and which will produce greater confidence when tested with relatively short periods of satellite data.

As I work on finishing our forcing/feedback paper for re-submission to Journal of Geophysical Research – a process that has been going on for months now – I keep finding new pieces of evidence in the data that keep changing the paper’s focus in small ways.

For instance, yesterday I realized that NASA Langley has recently updated their CERES global radiative budget measurement dataset through 2008 (it had previously ran from March 2000 through August 2007).

I’ve been anxiously awaiting this update because of the major global cooling event we saw during late 2007 and early 2008. A plot of daily running 91-day global averages in UAH lower tropospheric (LT) temperature anomalies is shown below, which reveals the dramatic 2007-08 cool event.

I was especially interested to see if this was caused by a natural increase in low clouds reducing the amount of sunlight absorbed by the climate system. As readers of my blog know, I believe that most climate change – including “global warming” – in the last 100 years or more has been caused by natural changes in low cloud cover, which in turn have been caused by natural, chaotic fluctuations in global circulation patterns in the atmosphere-ocean system. The leading candidate for this, in my opinion, is the Pacific Decadal Oscillation…possibly augmented by more frequent El Nino activity in the last 30 years.

Now that we have 9 years of CERES data from the Terra satellite, we can more closely examine a possible low cloud connection to climate change. The next figure shows the changes in the Earth’s net radiative balance as measured by the Terra CERES system. By “net” I mean the sum of reflected shortwave energy (sunlight), or “SW”, and emitted longwave energy (infrared) or “LW”.

The changes in the radiative balance of the Earth seen above can be thought of conceptually in terms of forcing and feedback, which are combined together in some unknown proportion that varies over time. Making the interpretation even more uncertain is that some proportion of the feedback is due not only to radiative forcing, but also to non-radiative forcing of temperature change.

So the variations we see in the above chart is the combined result of three processes: (1) radiative forcing (both internal and external), which can be expected to cause a temperature change; (2) radiative feedback upon any radiatively forced temperature changes; and (3) radiative feedback upon any NON-radiatively forced temperature changes (e.g., from tropical intraseasonal oscillations in rainfall). It turns out that feedback can only be uniquely measured in response to NON-radiatively forced temperature changes, but that’s a different discussion.

The SW component of the total flux measured by CERES looks like this…note the large spike upward in reflected sunlight coinciding with the late 2007 cooling:

And here’s the LW (infrared) component…note the very low emission late in 2007, a portion of which must be from the colder atmosphere emitting less infrared radiation.

As I discuss at length in the paper I am preparing, the physical interpretation of which of these 3 processes is dominant is helped by drawing a phase space diagram of the Net (LW+SW) radiative flux anomalies versus temperature anomalies (now shown as monthly running 3-month averages), which shows that the 2007-08 cooling event has a classic radiative forcing signature:

The spiral (or loop) pattern is the result of the fact that the temperature response of the ocean lags the forcing. This is in contrast to feedback, a process for which there is no time lag. The dashed line represents the feedback I believe to be operating in the climate system on these interannual (year-to-year) time scales, around 6 W m^-2 K^-1 as we published in 2007…and as Lindzen and Choi (2009) recently published from the older Earth Radiation Budget Satellite data.

The ability to separate forcing from feedback is crucial in the global warming debate. While this signature of internal radiative forcing of the 2007-08 event is clear, it is not possible to determine the feedback in response to that temperature change – it’s signature is overwhelmed by the radiative forcing.

Since the fluctuations in Net (LW+SW) radiative flux are a combination of forcing and feedback, we can use the tropospheric temperature variations to remove an estimate of the feedback component in order to isolate the forcing. [While experts will questions this step, it is entirely consistent with the procedures of Forster and Gregory (2006 J. Climate) and Forster and Taylor (2006 J. of Climate), who subtracted known radiative forcings from the total flux to isolate the feedback].

The method is simple: The forcing equals the Net flux minus the feedback parameter (6 W m^-2 K^-1) times the LT temperature variations shown in the first figure above. The result looks like this:

What we see are 3 major peaks in radiant energy loss forcing the system: in 2000, 2004, and late 2007. If you look at the features in the separate SW and LW plots above, it is obvious the main signature is in the SW…probably due to natural increases in cloud cover, mostly low clouds, causing internal radiative forcing of the system

If we instead assume a much smaller feedback parameter, say in the mid-range of what the IPCC models exhibit, 1.5 W m^-2 K^-1, then the estimate of the radiative forcing looks like this:

Note the trend lines in either case show a net increase of at least 1 W m^-2 in the radiant energy entering the climate system. The anthropogenic greenhouse gas component of this would be (I believe) about 0.4 W m^-2, or a little less that half. I’ll update this if someone gives me a better estimate.

So, what might all of this mean in the climate debate? First, nature can cause some pretty substantial forcings…what if these occur on the time scales associated with global warming (decades to centuries)?

But what is really curious is that the 9-year change in radiative forcing (warming influence) of the system seen in the last two figures is at least TWICE that expected from the carbon dioxide component alone, and yet essentially no warming has occurred over that period (see first illustration above). How could this be, if the climate system is as sensitive as the IPCC claims it to be?

YR MON GLOBE NH SH TROPICS
2009 1 +0.304 +0.443 +0.165 -0.036
2009 2 +0.347 +0.678 +0.016 +0.051
2009 3 +0.206 +0.310 +0.103 -0.149
2009 4 +0.090 +0.124 +0.056 -0.014
2009 5 +0.045 +0.046 +0.044 -0.166
2009 6 +0.003 +0.031 -0.025 -0.003
2009 7 +0.412 +0.212 +0.610 +0.427
2009 8 +0.231 +0.284 +0.179 +0.455

August 2009 saw a modest fall in the global average tropospheric temperature anomaly, from +0.41 deg. C in July to +0.23 deg. C in August. The tropical and Northern Hemispheric troposphere remain quite warm, but the Southern Hemisphere cooled by over 0.4 deg. C in the last month.

NOTE: For those who are monitoring the daily progress of global-average temperatures here, we are still working on switching from NOAA-15 to Aqua AMSU, which will provide more accurate tracking on a daily basis. We will be including both our lower troposphere (LT) and mid-tropospheric (MT) pre-processing of the data. We will also be adding global sea surface temperature anomalies from the AMSR-E instrument on board the NASA Aqua satellite.

This is the first of what might turn into a series of monthly updates of some maritime climate parameters monitored by the AMSR-E instrument on NASA’s Aqua satellite. All monthly statistics have been computed by me from daily global gridpoint data produced and archived by Remote Sensing Systems (RSS) under the direction of Frank Wentz, a member of our U.S. AMSR-E Science Team. Since Aqua was launched in 2002, the data are available only since June, 2002. A description of how these products were derived, and where they reside, is provided here.

There are 5 “ocean products”: sea surface temperature [SST]; near-surface wind speed; vertically-integrated water vapor; vertically integrated cloud water; and rain rate. I will present time series of monthly anomalies averaged over the global, ice-free oceans (56 deg. N to 56 deg. S latitude), and separately for the deep tropics (20 deg. N to 20 deg. S latitude). ‘Anomalies’ are departures from the average seasonal cycles in those parameters, which will be recomputed as each new month of data is added.

GLOBAL OCEANS

In the first figure below are plotted the 5 ocean products for the global ice free-oceans (56N to 56S). As can be seen in the top panel, SSTs in August cooled slightly from the unusually warm conditions experienced in July.

I have added linear trend lines to each time series, which you are free to misinterpret as you wish. 😉 Since the AMSR-E period of record is only 7.25 years long, a calculated trend won’t have much meaning…although it will be interesting to see how long it takes before the climate system obeys the UN’s command to warm, and the SST trend line begins to go uphill again.

How these different variables change relative to each other is illustrated in the following lag-correlation plot of SST versus the other variables. “PDO” is the Pacific Decadal Oscillation Index, while “SOI” is the Southern Oscillation Index (negative for El Nino, positive for La Nina). A discussion of these curves is provided later, below.

TROPICAL OCEANS

The next figure shows the ocean product anomalies for just the deep tropics, 20N to 20S latitude….

…and the lag correlation plot for the deep tropics is next:

DISCUSSION

Using the 20N-20S lag correlation plot as an example, you can see that total integrated water vapor is highly correlated with SST, which in turn is highly correlated with El Nino conditions (negative SOI values).

Also note that sea surface temperature tends to peak after months of anomalously low wind conditions, then falls as wind speeds increase.

Cloud water and rain rates increase as SST increases, reaching a maximum 1 to 3 months after the SST peaked.

My previous post described what I called “smoking gun” evidence of a spurious drift in the NOAA sea surface temperature (SST) product when compared to SSTs from the TRMM satellite Microwave Imager (TMI). The drift seemed to be mostly confined to 2001, almost a ‘step’ jump. The moored buoy validation statistics of the TMI sea surface temperatures from Frank Wentz’s web site (SSMI.com) suggested that the TMI SSTs had good long-term stability.

But 2001 was also the year that the TRMM satellite was boosted into a higher orbit, which concerned me. I asked Frank about the effect of this event on the TMI SSTs (which also come from his web site). Frank couldn’t remember the details, but said he spent quite a bit of time correcting for the altitude change on the retrieved SSTs since the microwave emission of the sea surface depends upon the TMI instrument’s view angle with respect to the local vertical.

I know from our many years of work together on the AMSR-E Science Team that Frank is indeed a careful researcher, yet it seemed like more than a coincidence that the TMI and NOAA sea surface temperatures diverged during the same year as the orbit boost. So, I went back to see what might have caused the problem. I went back and thought about the different ways in which one can compute area averages from satellite data.

To make a long story short, because the orbit boost caused the TMI to be able to “see” to slightly higher latitudes, the way in which individual latitude bands are handled has a significant impact on the resulting temperature anomalies that are computed over time. The previous results I presented were for the 40N to 40S latitude band, which is nominally what the TMI instrument sees today. But before 2001, the latitudinal extent was slightly smaller than it was after 2001.

As shown in the following figure, if I restrict the latitude range to 38N to 38S, which was always covered during the entire TRMM mission, I find that the divergence between the TMI and NOAA average SST measurements essentially disappears.

Even though I was processing the NOAA and TMI datasets in the same manner, I should NOT have been. This is because there were not as many gridpoints over cooler SST regions going into the ‘global’ averages before the satellite altitude boost as after the boost. So, for example, one must be very careful in computing a latitude band average, say from 39N to 40N, to make sure that there has been no long-term change in the sampling of that band.

Based upon the above comparisons, I would now say there is no statistically significant difference in the SST trends since 1998 between TMI, the NOAA ERSSTv3b product, and the HadSST2 product. And it does look like July 2009 might well have experienced a warmer SST anomaly than July 1998, as was originally claimed by NOAA. (Remember, TMI can not see all of the global oceans, just equatorward of about 40 deg. N and S latitude.)

In the bottom panel of the above figure, I also have a comparison between the TMI and AMSR-E sea surface temperatures, which are available only since June of 2002 from the Aqua satellite. As can be seen, there is no evidence of a calibration (or sampling) drift in that comparison either.

So, what’s the moral of this story? Always question your results…even after finding the obvious errors. And maybe I should eliminate the term ‘smoking gun evidence’ from any results I describe in the future.

Oh…and don’t believe everything you read on the internet.

IMPORTANT (2:15 p.m. CDT, 8/31/09): The results of this post have been superseded.

After crunching data this week from two of our satellite-based microwave sensors, and from NOAA’s official sea surface temperature (SST) product ERSST v3b, I think the evidence is pretty clear:

The ERSST v3b product has a spurious warming since 1998 of about 0.2 deg. C, most of which occurred as a jump in 2001.

The following three panels tell the story. In the first panel I’ve plotted the TRMM Microwave Imager (TMI) SST anomalies (blue) for the latitude band 40N to 40S. I’ve also plotted SST anomalies from the more recently launched AMSR-E instrument (red), computed over the same latitude band, to show that they are nearly identical. (These SST retrievals do not have any time-dependent adjustments based upon buoy data). The orange curve is anomalies for the entire global (ice-free) oceans, which shows there is little difference with the more restricted latitude band.

In the second panel above I’ve added the NOAA ERSST v3b SST anomalies (magenta), calculated over the same latitude band (40N to 40S) and time period as is available from TRMM.

The third panel above shows the difference [ERSST minus TMI], which reveals an abrupt shift in 2001. The reason why I trust the microwave SST is shown in the following plot, where validation statistics are displayed for match-ups between satellite measurements and moored buoy SST measurements. The horizontal green line is a regression fit to the data. (An average seasonal cycle, and 0.15 deg. C cool skin bias have been removed from these data…neither affects the trend, however.)

I also checked the TMI wind speed retrievals, and there is no evidence of anything unusual happening during 2001. I have no idea how such a large warm bias could have entered into the ERSST dataset, but I’d say the evidence is pretty clear that one exists.

Finally, the 0.15 to 0.20 deg. C warm bias in the NOAA SST product makes it virtually certain that July 2009 was not, as NOAA reported, a record high for global sea surface temperatures.