Phase matching the solar cycle to Global temperature data; Unclear results.

January 14, 2014

So I decided to use the method I used to earlier investigate a possible solar cycle impact on US temps, to see if I could find a *global* solar cycle signal. Answer? Ehhhhhh (waves flat hand in manner of the universal gesture for “not much there, there.”)

HADCRUT4SolarCycleBlue is the average temperature profile (with low frequency component and volcanic signal removed) of temperature anomalies over a solar cycle, in months from minimum, and in red, the average sunspot cycle, standardized to have mean zero and the same standard deviation. If there is a signal of the solar cycle here, it’s highly out of phase and still difficult to find in the noise.  To be sure, there is a minimum in temperatures around 32 months after the sunspot minimum and a maximum about fourteen months before, but there are all these random wiggles obscuring any clear relationship even with some lag. Nevertheless, if we essentially “fish” for a signal, we can at least get something that isn’t zero-since the solar brightness does vary, even if climate were highly insensitive to perturbation (moreso than even I think it is) there should be some change from the small variation in solar brightness, and if there are amplifying mechanisms then the sensitivity would have to be very small indeed to accommodate almost no actual temperature change over the solar cycle. So if we make it so the mid points between solar cycle extrema line up with the mid point between temperature extrema, we get a lag of about 45 months, consistent with what we found for the US. And we get a bit of a relationship:

HADCRUT4SolarRegressionAnd we can take the regression and lag, and get the short term solar effect from the solar cycle on temperatures:

HADCRUT4ShortTermSunspotSignalInterestingly, this seems to imply that typical solar cycles have a temperature variation of about .05 K. The IPCC report, and frankly the work of a lot of scientists I respect, cite a number twice this large. The heck gives?

I have a theory. The main cite for the estimate of the solar cycle signal is Douglass and Calder. But Douglass and Calder estimate the magnitude of the solar cycle signal on lower tropospheric temperature. Since variations (though not trends) tend to be larger in the troposphere, it isn’t terribly surprising that there should be a larger signal in the troposphere. I also view the removal of ENSO from climate data increasingly as an erroneous and philosophically wrong approach to signal detection in climate. ENSO is a part of the climate system, it is not in some manner magically immune to radiative forcing. Lastly my approach to accounting for the confounding impact of volcanic eruptions is, I personally believe, superior.

It is worth noting that just because the impact of the sunspot cycle itself, over the short term, is a small effect, does not preclude the possibility of secular trends in solar activity, damped by the oceans thermal inertia, causing long term climate trends. To answer that question we need a full model that accounts for those effects, contains the right sensitivity and response time, and an accurate history of the forcing from all solar effects. These amount essentially to a long list of unknowns.

It’s also worth nothing that if the forcing over the solar cycle is actually rather large, due to a cosmic ray effect on clouds, such a small temperature change would require a low sensitivity or an inordinately high degree of thermal inertia-the latter however is probably inconsistent with relatively short time lags observed for solar and volcanic effects. Of course, even if there weren’t an additional forcing apart from solar brightness alone, such a small signal is compatible with a low sensitivity.

The Curious Case of NOAA-12

January 13, 2014

Much has been made of the differences between the UAH and RSS satellite data products for the lower troposphere layer average temperature anomalies. But the vast majority of the commentary on this issue is ignorant of the underlying data issues. Below is a plot of the differences between the two (note: I downloaded RSS from KNMI as the non-anomaly data, then anomalized to the 1981-2010 annual cycle to match UAH. I did this because UAH does not cut out some of the high latitude southern hemisphere data in their reported global averages but RSS does, whereas the KNMI non-anomalized data for RSS has the same spatial coverage. However, the differences are minimal as far as I can tell. I also rounded to 2 decimal places to match.)

UAHRSSDifferenceAlso present are the averages of 11 point and 13 point centered averages. I have highlighted two periods which are of interest. How did I select the dates? It wasn’t by looking at the discontinuities themselves. Rather, from my readings of various papers by John Christy, I saw that the transition from NOAA-11 to NOAA-12 was of particular interest in the differences of trends between the two satellites. So I identified the date at which NOAA-12 became operational; September 1991, and I identified the date at which the next satellite after NOAA-12, NOAA-14 came on line; April 1995. This would be the period during which the effect of the NOAA-11 and NOAA-12 transition should be most apparent, and indeed we see a continuous warming of RSS relative to UAH during that period. The later period, on the other hand, was identified as the period during which UAH was making use of AQUA as a data backbone. That was from August of 2002 to the end of 2009. At the time, non-AQUA satellites were diurnally drifting warm, as such they needed cooling corrections applied to them: the fact that RSS cooled during this period relative to UAH strongly indicates that RSS’s diurnal drift adjustment (which was not necessary for AQUA) is excessive.

Clarifying note: RSS also makes use of AQUA, however, it does not treat it the same way as UAH does. UAH treated AQUA as superior for assessing the trend over the period to other satellites (hence “backbone”) whereas RSS treated it as equal to the other satellites after applying their diurnal adjustment.

So we can be pretty confident that, at lest during that period, RSS is cooling excessively. This suggests that RSS should probably also be wrong about the earlier, warm shift, too, since it would also arise in that manner from excessive corrections by RSS.

But just to be sure, can we check the data against something else? For example, during a short term period, surface temps and LT’s roughly move together, albeit with different magnitudes. The answer might be yes. Herein, I will use GISS surface temperature data (downloaded from KNMI, 2500 km smoothing, anomalized to 1981-2010 mean, values from December 1978-November 2013). First, let’s detrend all the data: we only are interested in the spurious shift over a short period, not making data all agree in their long term trends, since their long term trends agreeing is a hypothesis we wish to be able to test. Next, let’s remove seasonal noise by taking the averages of 11 point and 13 point centered averages. That looks like this:

GISSvariationversusSatelliteVariationBlue is the average of the two satellite datasets: that way, we aren’t assuming either is superior to the other. Red is GISS Next, we estimate the tropospheric amplification factor by linear regression: the best fit slope is about 1.337. We use that factor to multiply GISS detrended anomalies, both smoothed and unsmoothed. Now, we compare those, by taking differences, to the UAH and RSS detrended anomalies (smoothed and unsmoothed) over the period of interest involving the spurious shift with NOAA-12:

GISSSatellitestepRed and dark red are RSS-GISS, blue and black UAH-GISS, green and purple are RSS-UAH. Both satellite datasets warming relative to GISS over the period of interest, but RSS definitely warms more. The differences of the smoothed endpoints for RSS-GISS, UAH-GISS, and RSS-UAH, are ~0.0787 K, ~0.0121 K, and ~0.0622 K, respectively. The linear trends are ~0.0389 K/yr, ~0.0216 K/yr, and ~0.0160 K/yr, respectively. GISS appears to confirm that RSS warms spuriously during this period, and even suggests the possibility that UAH warms spuriously over this period, too.

If I correct for RSS’s spurious shift, the differences now look like this:

UAHRSSDifferenceNOAA12CorrectedNotice that now, before the AQUA period, there is essentially no difference between UAH and RSS in trend terms. And remember: RSS would be expected to be wrong over this period and UAH right, due to the way in which they differently handle the AQUA data, which doesn’t need diurnal drift correction. So if I correct RSS for the drift over the AQUA period (and generously assume that there was no additional drift before or after) The differences now look like this:


What we see is that two simple corrections, which are based on a combination of independent data and understanding of the underlying satellites, removes a lot of the distinctive features of the differences between the two datasets. However, it appears that the trend difference between the two is largely unchanged, because these two errors mostly balance one another out. RSS is still cooling relative to UAH over the entire dataset, and it is hard to determine the origin of the remaining discrepancies. If we return to the NOAA-12 discrepancy, UAH looked like it might have a slight warm bias, too. If I correct for that, it will bring the UAH trend down closer to RSS’s corrected trend. However, this doesn’t account for the whole remaining discrepancy, which remains about .01  K/decade of RSS cooling relative to UAH. Another possibility is that I need to extend the AQUA correction forwards and backwards in time a bit, since the satellites that drifted during that period in RSS probably drifted before and after, to. If I extend it backwards to the start of NOAA-15 in December of 1998, and forward to the present, the difference in long term trends reverses, and now RSS warms slightly relative to UAH. It looks like we have a plausible explanation for the UAH-RSS divergences, and slight variations in those adjustments for the differences can switch which warms more or less relative to the other. Here is our final estimate for both datasets adjusted for spurious shifts and trends:

CorrectedUAHandRSSRed is RSS and blue is UAH, with corrections applied to both for shifts relative to GISS during the NOAA-12 step, and RSS corrected for spurious cooling since NOAA-15.

Lessing Colding

January 9, 2014

Something tells me that I’m not having an entirely healthy reaction to the fact that people are actually taking the idea that cold weather is caused by warming the Earth seriously. Because I’m mostly just amused by it.

But the reality is that we have a deadly serious situation here. The State Science Institute is now in on the idea, so it seems that this is actually going to get some traction. Something like half of the population can readily become convince of virtually any sort of absurdity. All that appears necessary is that it is the “Liberal” position to believe it is so. And cognition grinds to screeching halt, all basic logic and reason out the window. The few that might worry that such an uncritical acceptance of an idea that is absurd on it’s face, need not: out is trotted one of the Kathedersozialisten, to give the veneer of “expertise” and “science” to the sort of thing you’d expect out of the Annals of Improbable Research.

I take that back, some of those articles make a lot more sense.

Well, okay, let’s take the idea that global warming is more colding, seriously. Let’s regard it as a hypothesis for us to test. So, when the mean temperature rises, do we get colder cold weather?

In a word: No.

The reader is encouraged to look it up for themselves, because NCDC’s climate at a glance page isn’t working right now, but:

Generally, January is the coldest month of the year, and since the 1970’s, ie the period of the “late twentieth century warming” that is allegedly exclusively anthropogenic in origin, this month has seen more warming than the other months of the year in the Continental United States. But we can go further than that.

Repeating an earlier analysis, now with data through 2013: I can ask two questions: ranking days within the year by temperature, which days warm most and least? As before, the answer is that warming of the coldest days is the greatest. This time I also include 2 sigma bounds and a 6th order polynomial fit for no particular reason:


It actually turns out that the warming of the coldest days is statistical significant (that is, more than 2 sigma greater than zero), but the warming of the warmest day is not.

I can ask a second question, too: since it is evident that cold days are more variable (larger uncertainty in the regressions) I can ask the question instead, by regressing against the mean annual temperature instead of time, when the mean annual temperature changes by one K, how much on average does the temperature of each day by rank change? It turns out the answer looks like this:


What we see is that a change of the mean annual temperature by one degree is on average accompanied by changes on days by temperature rank that is mostly not statistically significantly different from 1 to 1 (a uniform warming throughout the year) except for the fact that many of the coldest days, in fact the 61 coldest days, all warm more than one K when the mean annual temperature warms one K, and a few warmer than median days that warm less than a one K when the mean annual temperature warms one K, statistically significantly. And although most days are not significantly different from uniform warming when the annual temperature warms, the general rule is that the warmer days warm less when the mean annual temperature warms and the colder days warm more.

Perhaps instead of warming, we ought to call it less colding.

So the claim that extreme cold occurs because of warming, doesn’t stand up to scrutiny.

A New Global Surface Temperature Index and “Unprecedented” Non-Warming

January 6, 2014

So it occurred to me it should be easy to create a global temperature surface temperature index using the Berkley Earth land surface data, and the recent HADSST3 reconstruction, the only sea surface temperature index which has been corrected at all for the post World War II discontinuity. Just take the anomalies of Land Surface temperature, multiply them by fraction land area (.29) (more precisely, fraction non ocean) and add add it to HADSST3 times fraction ocean area (.71). This results in this index:

BESTHADSST3Don’t be too worried about that last datapoint shooting off like that: it’s September, because that was where BEST ended when I downloaded…I think a week ago? Anyway, it looks to me like something in the BEST algorithm results in spuriously too high or low final anomaly values, some kind of endpoint problem. It doesn’t appear in other datasets at all which pretty much verfies that it is spurious, and I have seen similar spurious final anomaly problems with BEST in the cool direction. As they update the data, they disappear to be replaced by spikes in the new latest anomaly.

Regardless, I’ve done some interesting analyses. For one thing, I’ve made some improvements to my volcanic eruption profile detection, the lag in this index is closer to seven months and the effect is ever so slightly larger. I can explain some of my newer methodology if anyone likes. At any rate, the removal of volcanic eruption effects has some interesting results. First, in the original data, it’s worthwhile to compare the trend from December 1978 (when UAH satellite anomalies begin) to the end of the data, which is currently September 2013, with the highest earlier trend of the same length in the data (which happens to be from November 1907 to August 1942):


The two trends are almost parallel, and it is doubtful the trends are statistically significantly different. But keep in mind, during the latter period there where two volcanic eruptions, in the early 80s and early 90s, while no volcanoes erupted in the earlier period. So I remove my most recent estimate of the volcanic eruption effect, which has the following result on this comparison:


Now, taking the volcanic eruptions into account, it turns out that the earlier warming was faster than the later one, albeit by an obviously negligible amount. Nevertheless, this means that a trend which most of the alarmed scientists concede was probably natural, was actually larger than the trend that is supposedly exclusively driven by anthropogenic forcing. And keep in mind again, we don’t actually know what caused the earlier warming, as even models which include larger changes in solar irradiance than probably actually occurred, and volcanoes, fail to reproduce the warming at the appropriate magnitude. So the question for people who buy the “attribution argument” is this: how do you know whatever caused the earlier warming didn’t in large part cause the late warming? Because if you don’t know, then it could have, and therefore the attribution argument-that nothing else could have caused the recent warming-falls apart.

Finally, let me state something about the recent “pause.” It is sometimes argued that, just because there has been a negative trend in the last ten years (and no significant trend for something like 15) does not mean that anything is really amiss: they point to two periods in the last 30 years, during a warming trend that latter continued, of about ten years in length. Those making these arguments, whether they be prominent, respected scientists at NOAA or NCAR (Easterling and Wehrner, Trenberth and Fasullo) or internet trolls (I find increasingly, in terms of intelligence, there is no difference) it highlights either the ignorance, incompetence, or dishonest of these individuals (in the latter case, who are federally funded to provide intelligent honest analysis to the American taxpayer!). Why do I have such a harsh judgement? Because: while it is technically true that there were indeed pauses during those periods, those pauses are associated with periods impacted by major volcanic eruptions, and it was those which caused the warming to halt during those periods: there is no indication that a similarly large eruption happened recently in the time frame necessary to be responsible for the halting of warming. Indeed, if we look at ten year trends by end date, and identify the period when they first become almost continuously positive (the 120 month trend ending in October of 1979 (that is, beginning in November 1969) so we may identify this as the start of the warming period) and look at trends in data before and after correction for the effects of volcanic eruptions, we find that both earlier, very brief halts in warming, disappear, and the warming trend becomes unambiguously continuous until recently, when it stops and goes negative:


The above shows 120 month trends, in K per annum, red without volcanic effects removed, blue with volcanic effects removed. You can see that in fact there was only one (very brief) period when the ten year trends dipped negative before, the later period associated with Pinatubo did not, in fact, go negative, merely very close to zero. Second, we see that picking periods beginning in about 1992 for trends is an excellent way to exaggerate warming to give the impression of a rapid rate, but this is dishonest because the cause of this elevation of the trend is due Pinatubo occurring at the beginning of such trends (eruption in 1991, results in maximum dip around about 1992). I recall some other “scientists” of the SS “skeptics are conspiracy theorists” crowd using exactly that to argue the trends are on par with models, but such dishonest scumbags are really not worth dignifying by naming them. What we see is that the halt in warming is without precedent in the recent warming period. As such, something *is* amiss with predictions of not only continued, but *accelerated* warming. The something that is amiss appears to be that:

  1. Sensitivity has been very significantly over estimated and
  2. Natural climate variability, whatever the cause, has been under estimated.

The former undermines the claim of drastic future warming, the latter undermines the claim that recent warming was uniquely attributable to anthropogenic forcing.

Let’s be absolutely clear: that represents a complete vindication of the skeptical position and a refutation of the alarmed position.

Presented without comment: An invitation to discussion.

December 22, 2013


The auto-correlation of nonlinearly “detrended” HADCRUT4 anomalies (from a 1850-2012 baseline), for lags up to 30 years. What do you make of it?

US 12 month average temperatures to take largest nosedive since the 1930’s?

December 16, 2013

I just saw this post on US temperatures by Pat Michaels and Chip Knappenberger, and I would recommend readers here give it a look. This line in particular is interesting:

But even if the rest of the month is not quite cold enough to push the entire year into negative territory, the 2013 annual temperate will still be markedly colder than last year’s record high, and will be the largest year-over-year decrease in the annual temperature on record, underscoring the “outlier” nature of the 2012 temperatures.

I have to agree that this is true, although, I think it is worth noting, as usual with “unprecedented” events, just how unprecedented they truly are depends in significant part on how you define them. If December averages at what it has averaged so far, the January to December average temperature in the US will drop about 3.12 degrees Fahrenheit, the previous record holder being 1934 to 35, which was a drop of 2.21 degrees Fahrenheit. If December cools down to average 27.6°F, the drop will be about 3.2 degrees: either way it shatters the record for a drop of Jan-Dec average temperatures. Buuuuut….This does not represent the largest drop of an twelve month average from the previous twelve month average. The average temperature from from April 1935-March 1936 was ~3.39°F below the April to March average of 1934-35, which represents the fastest drop of a twelve month average from the previous, in the entire USHCN record going back to 1895. However, Jan-Dec 2013 from Jan-Dec 2012 will represent the largest drop of all twelve month averages from the previous since then, and the third largest drop in the entire record (the second being March 1935-February 1936 from March 1934-February 1935. That’s still pretty far back to have to go (over 70 years) to find any drop larger.

12monthaverageminuspreviousUSHCNThe above represents what I am talking about: in red is the difference of each 12 month average from the previous 12 month average, by end date. In black are the values for January to December periods, and the green and purple dots represent projected values for 2013-2012. All this goes to show that one should never make too big a deal out of one of those warm spikes like 2012 (or a cold spike for that matter). The almost inevitable result is that the next year will cool down (or warm up) in opposition to the spike because it is a transient weather event. And the larger the event, the more dramatic the pendulum swing can be.

Warm weather part deux

December 14, 2013

Previously, I began an investigation into why we have recently had warm weather in December here in Florida, when most of the rest of the country has not. I identified the likely culprit as being a teleconnection between US weather and warm anomalies in the middle of the Northern Pacific. To investigate this further, I decided to use HADSST3 to examine the anomalies (relative to the mean value for all Decembers available) in the region in question, which I estimate to be about 30-50N and 180-208E. Downloading the data from KNMI, I then looked at the years I had identified as probably matching our recent pattern historically (below average US, above average Florida). As it turns out, the average anomaly relative to the long term mean for those Decembers was about .4 K above average, which confirms my suspicion, I think, that our recent pattern has a bit to do with warm water in that region: there is at least some association between warm anomalies there and a pattern of below average temperatures for the US as a whole and above average temperatures for Florida, in the month of December. Of course, this is probably not the only phenomenon that can be associated with a warm Florida and a cold US: at least some of the years I selected had below average temperatures for that region (those that didn’t, had an average anomaly of ~.87K). At any rate, I figure it was worth looking into a couple of additional details. First, the PDO: For the official PDO data, the average December value in the years (excluding 1897, not available) I selected as analogs, was in fact about -0.68 below the long term mean (1900-2012) and the value for the years where there was, in fact, a warm anomaly in the region I selected, was about -1.2 below the long term mean, which confirms that the analog Decembers were generally during negative PDO conditions. In fact, in the subsample of years with actual positive temperature anomalies relative to the long term mean in the selected region, only 1992 had a PDO value for that December above the long term mean; it seems probable that the actual reason for the cold weather in the US that year was the eruption of Pinatubo in 1991, which probably also disrupted the PDO pattern’s connection to weather phenomena somewhat. I think this more or less confirms my diagnosis: a negative PDO (warm central North Pacific Ocean) is teleconnected to warm December weather in the South East US, a link especially strong during La Nina years, but present to a lesser extent, and in  a reduced area, in years without a La Nina. Similarly, a negative PDO is associated with cold weather in December in much of the US, and this is even more true absent a La Nina.

Well, there was one other question my mother had, which was whether there was some connection to solar activity in the reduced temperatures in much of the US. Well, it took a lot of work to get, not much of an answer, honestly. The following shows the average monthly (degrees Fahrenheit) anomaly (blue) and average annual smooth (average of 11 month and 13 month centered averages, black) of USHCN data, in months from date of sunspot minima (data from here, minima determined by lowest value of the average of an 11 month centered average and a 13 month centered average, in a cycle), as well as the average of sunspots over the same periods, minus 64.81525 and divided by 184.91169136522 (red):

SolarCycleUSHCNIt is not obvious to me that there is any instantaneous effect of solar activity on temperatures in the US. Looking closely, it looks as though there might be a delayed effect, although there is a lot of noise. This shows the above, minus the blue curve:

SolarCycleUSHCN2And then with the sunspots shifted to three years later:

SolarCycleUSHCN3Well, okay. So it’s hard to say if there is a relationship: there might be, but the data is very noisy, and it’s hard to detect. But, if there is about a 3 year lag, then since last minima was around 2008, we might still be feeling it’s lingering effects in our cold weather here. Maybe. It’s hard to say.

EDIT: Thinking on it, I remembered how, by removing the effects of long term variations, I was better able to discern the effects of volcanic eruptions on the temperature record. So I used the smoothing technique (10 times!) to take the long term variations out of the temperature data. So this is the new phase plot:


The new normalization factors for the sunspots are minus 63.1120103092784 divide by 202.857354779726, and I then lag them by 75 months to get the green curve (I also used the averaged cycle lagged 125 months to get the values from before 84 months before minima). That would put us, presently, a little before the full effects of the minima in 2008 would be fully felt, but only a year and four months away. The best fit linear regression coefficient for anomalies on this cycle is  0.516260800957563. This, then, shows the lag short term impact of sunspots (thermal inertia only crudely dealt with by the lag):


So, I think my answer is, yes, low solar activity might be contributing a little bit to recent cold weather in the US. Of course, the multidecadal impacts of Solar Activity can’t be well resolved by this sort of analysis. Nevertheless the impact does appear to be there, and it is not negligible.

Something Else Curious…

December 13, 2013

In a recent conversation, I expressed some skepticism about the accuracy of the conversion of CO2 emissions scenarios, into estimates of concentrations. This is important because it adds an additional layer of inaccuracy of future estimates of global warming due to future increases in CO2: It is currently my opinion that the emissions scenarios themselves are not to be taken seriously as accurate but even if they were, the carbon cycle models appear to overestimate how much an increase in emissions leads to an increase in concentrations. This will tend to lead to overestimating future climate forcing and therefore, future warming.

In investigating the issue further, I came across another uh, curiosity. From here, one can download estimates of global fossil fuel burning CO2 emissions back to 1751. From here you can download historic CO2 concentrations (Mauna Loa back to 1959, global averages back to 1980), and from here, you can get ice core estimates of concentrations. According to this, our handy dandy conversion factor for ppm to metric tons of CO2 should be 7769028871.39107 tons/ppm. So from there, we can estimate the net mass flux into the atmosphere each year. It is from doing something like this that people estimate the so called “airborne fraction.” We’ve discussed that before, (see also discussion at Jeff’s blog). At any rate, compare emissions from fossil fuel burning to actual mass flux in and out of the atmosphere, and we get a graph like this:

CO2MassFluxGreen is metric tons of emissions from fossil fuel burning, black is the ice core based mass flux, red the Mauna Loa based mass flux, and blue the global avergae concentration based mass flux. We can also take the differences:

CO2SourceSinkWhich shows that non fossil fuel burning sources and sinks of CO2 represent a large and growing net sink of CO2. But that’s not really what I deemed especially curious. What I deemed curious is, that before about 1910, these other sources and sinks represented, usually, a net source of CO2. Hm. That’s kinda interesting to me.

Now, as I understand it, there is also a contribution to total anthropogenic emissions from land use, which could mean that one interpretation of the above result should be that before about 1910, land use emissions contributed more to rising CO2 than fossil fuel burning. Hm. Does that make sense to anyone else? Well, there are estimates of emissions from land use from 1850-2005. Converting from teragrams to metric tons of carbon and from carbon to CO2, I can get comparable measures of land use emissions, and fossil fuel emissions. This is the ratio of Land Use emissions to total emissions:

LandUsetoTotalRatioRoughly, this aligns with what I just said-around the beginning of the twentieth century, fossil fuel burning started to represent about half of all emissions, and represented an increasing fraction of emissions thereafter. I can also (estimating emissions from land use after 2005 by extrapolating the rate at which the ratio between land use and fossil burning emissions declined from 1996-2005) find the net natural CO2 mass flux: the difference between the change in the amount of atmosphere, and the anthropogenic emissions (land use + fossil burning):

NetNaturalMassFluxCO2Okay so finally we get to the really curious thing. What was the natural net source of CO2 in the early 1880’s? I don’t think it’s Krakatoa: in the first place, it appears in other cases volcanic eruptions strengthened the natural sinks, besides which the net natural inflow starts before then. Or perhaps it is simply that human emissions were underestimated around that period? It’s hard for me to say.

Either way, however, we can see something very important from the above: An increasingly large amount of CO2 is never making it into the atmosphere, but is instead being absorbed by nature. An increasingly strong net natural sink would imply some interesting things: first, that future increases in CO2 that is in the atmosphere (and thus exerting a warming influence) will probably be significantly smaller than future emissions. Second, that nature is adaptive, and tends to stabilize itself in spite of our activities. Third, if emissions underwent a sudden decrease by a large fraction, the atmospheric concentration could, if sinks have a significant inertia in their size, actually decrease almost immediately. This last point is quite contrary to the idea that the CO2 concentration would remain elevated for thousands of years even if human emissions went to zero immediately. Such claims appear, to me, to be quite erroneous. Which I take as all the more reason to doubt the carbon cycle models used to estimate future CO2 concentrations can be trusted as accurate.

But please note: while I am skeptical of carbon cycle modeling, that skepticism does not extend to the attribution of increasing CO2 to human emissions: for me, it’s a simple matter of algebra: The amount of CO2 we have emitted is more than enough to account for the increase in atmospheric CO2. As such, it is illogical to suggest natural CO2 could account for the increase, since the natural CO2 mass flow to the atmosphere has had to be large and negative. That is, nature has taken CO2 out of the atmosphere, not put it in.

Something curious…

December 12, 2013

The 20th Century Reanalysis is an effort to use surface pressure observations to create a record of atmospheric conditions through out the 20th Century-in fact, it extends back into the 19th century to some extent. You can in fact download some data from KNMI, but I was messing around with it on the ESRL composites page for it to see if I could investigate what it might hint at for some of those earlier years in my last post (although I was somewhat frustrated by the fact that I can’t change their base period. Anyway, I decided it would be kind of interesting to ask what the near surface layer (1000 mb) temperature trends are over the whole dataset from 1871-2011-or rather, what the difference between the period 2001-2011 and 1871-1881 is in various places. I produced an interesting plot, which I changed the colors on to more clearly show the temperature change’s signs:

20thCenturyReanalysisLongTermChangeInterestingly, it looks like much of the US (and a few other places, including Turkey and much of the region called Levant) show long term *cooling* in this data! Keep in mind, these are *estimates* of the temperature, by a weather model, based on surface pressure measurements and apparently some sea surface temperatures. At any rate, I regarded this as sufficiently surprising as to warrant further investigation: while a reanalysis is likely to contain biases and errors, especially where not tightly constrained by observations or where new data sources are added in or taken out, it nevertheless raises some eyebrows: barometric pressure over the US is presumably very well characterized by an extensive observational network, and presumably has been for a long time. If a realistic simulation of weather processes can then take those measures of surface pressure and accurately estimate the temperature (a vaguely similar idea is not without precedent), then it raises the question whether this could be an indication of a problem with the estimate of the long term temperature trend over the US. At least for the satellite period, I have generally found the US data from NCDC (USHCN) to be pretty good in quality, agreeing well with well supported satellite analyses, but I am open to the possibility that the estimates of long term trends could be off. Now, partly it seemed to me a possibility this was merely because USHCN, going back as it does to 1895, may simply not capture some unusually warm years in the late 19th century in the US (a period of history especially dear to my heart, let me talk about it endlessly to you at some point). Well, NCDC does extend their full temperature dataset for the globe back to 1880, so it is possible to make a comparison of the 20th Century Reanalysis with the NCDC data to find, whence the difference, if any?

First, I focus on the region 24-49N 235-293E, which roughly delineates the US and surrounding coastal areas (and some of Canada and Mexico) and get the NCDC data from KNMI, and the same from KNMI for the 20th Century Reanalysis surface temps-rebaselining both to 1880-2009 to cover all the full years (er, except the last, just realized 20th Century does have all of 2010. Oh well, doesn’t matter much) they share. I then subtracting the NCDC data from the 20th Century Reanalysis data. The result was this residual:

NCDCminus20thCenturyUS1Green is the monthly differences, red is the 12 month running mean, black is the linear OLS trend over the period 1880-2010. Clearly, the 20th Century Reanalysis shows a significantly warmer early US than does NCDC. Specifically, before about 1916, it almost always runs warmer, and it also runs warmer during much of the Great Depression and WWII, but from about the 50’s onwards, it runs consistently cooler, but also pretty flat: in fact, examining the period from 1979-2010, the Reanalsis-NCDC residual warms slightly by ~0.067 K/Century-almost not at all, which is pretty consistent with the conclusion we have had here at Hypothesis Testing: the Data in the US is pretty good over the satellite period. However, because of the differences above, it seems likely to me that the all important question of “warmest year” in the US would probably have been different in this data, at least prior to us having that big temperature spike in 2012-the sort of thing that happens from time to time in most places on Earth, by the way. The question is whether that is an artifact of the reanalysis, or the reanalysis may be capturing a real feature of the climate of the US over the 20th Century. It would be interesting to examine the surface pressure data for evidence of biases and inhomogeneity, and the surface temperature data for possible uncorrected inhomogeneities.

Incidentally, the difference doesn’t seem to be merely a function of the sea surface temperature data or data outside the US. The differences look very similar over the “core” US (31-40 N, 240-280 E):

NCDCminus20thCenturyUS2Anyway, I find these differences quite intriguing. It’s not at all obvious to me why they arise, or which dataset to “believe” better represents the US Temperature record. So anyway. Curious…

Why is it so warm here? (And not elsewhere)

December 11, 2013

So my mother asked me the question the other day: why is it that the rest of the US (or at least, the lower 48 states) has been so cold lately, but here in Florida, it’s been so hot? Well, my first thought was to ask what ENSO was currently doing to see if that would offer a clue: no dice, ENSO is doing nothing right about now. So I told her “I don’t know mom, weather is just weird sometimes” and let the matter rest there. But the question “Why?” kept eating at me. Finally I decide to do a bit of a “forensic” analysis. First, I decided to ask the question: when, in the past, has the US as a whole, seen Decembers colder than average, while at the same time Florida, as a whole, has seen Decembers warmer than average? It turns out that since 1895 (relative to the 1895-2012 mean), the answer is in the years 1897, 1902, 1911, 1916, 1919, 1924, 1926, 1932, 1948, 1951, 1961, 1964, 1967, 1972, 1978, 1990, 1992, 2008, and 2009. Now, looking at the years since 1948, I can create composites for those from ESRL’s composite page. The result looks like this:


A couple of things stood out to me: that these years tend to feature a warm Antarctic and a cold Arctic, at least in the Reanalysis. But on the other hand, the reanalysis is least reliable in those areas due to sparse data. Moreover, this December has, so far, been warmer than average in Alaska, where the above map shows it cold. One interesting place where the maps *do* match, however, is the North Pacific, where much of it is above average in temperature in both. This got me to thinking, “what about the PDO? ENSO is neutral, but the PDO is probably negative right now.” And sure enough, yup, it is! Which got me wondering, again, how the PDO correlates with December Weather in the US. This is the plot:


Indeed, Florida is in the Negatively correlated region, so a negative PDO would tend to be associated with a warm December in Florida-but oddly enough, also much of the rest of the US. However, negative PDO values are also often associated with La Nina conditions, which presently are not prevailing. This lessens the impact of the PDO in being associated with a cold US *generally*. So the explanation for our warm weather and the rest of the US’s cold weather? I think, but need to investigate further, that we can attribute it to the conditions in the Pacific: the presence of warm water in the middle of the North Pacific, in the absence of cold water in the Equatorial Pacific:  Cold PDO-No La Nina pattern.