UPDATE: Corrected Figures 2 through 6.
UPDATE 2: Corrected the color-coding in the title block of Figure 4. (Thanks to blogger cassandraclub for noticing it.)
UPDATE 3: I removed the word Anomalies from Figures 2, 3 and 4. And I’ve added two graphs using BOM data for Australia at the end of the post.
In the wake of the heat wave in Australia last summer, I had promised Jo Nova a post about Australia land surface air temperatures. That email exchange took place a couple of months ago. I began work on it a few days ago, the graphs were done, but I hadn’t written the text. Much to my amazement, Anthony Watts published a post about the press release for the Lewis and Karoly (2013) paper Anthropogenic contributions to Australia’s record summer temperatures of 2013 (paywalled). Anthony’s post was titled Claim: Humans play role in Australia’s “angry” hot summer. Lewis and Karoly (2013) were blaming human-induced global warming for the heat wave, but the data I had downloaded indicated Australia summertime temperatures in 2013 weren’t remarkable and the models showed no skill at being able to simulate Australia land surface temperatures.
Please keep in mind that I did not prepare my post about Lewis and Karoly (2013) but the post does shed some light on the paper. Please read Anthony’s post and the abstract of the paper linked above.
For the land surface temperature dataset, I used NOAA’s GHCN-CAMS. It is available for download on a gridded basis through the KNMI Climate Explorer. It has the best spatial coverage of the surface temperature datasets that are regularly updated, because it relies on other surface temperature data in addition to the GHCN data. And it’s also available in absolute form, where other datasets are presented as anomalies. Unfortunately, it has a higher warming trend than the GHCN-only datasets.
I used the coordinates of 45S-10S, 110E-155E for Australia. Figure 1 is a time-series graph of the Australia land surface temperature anomalies from January 1948 (the start of the dataset) to present (May 2013). There is very obvious shift in the data around 1977—possibly a lagged aftereffect of the 1976 Pacific Climate Shift—so I started my comparison in 1979, which is a common start year for surface temperature data presentations.
I had originally looked at the months of January to March, but those commenting on the thread at the WUWT post were also defining the Australian summer as November to January and December to February. So I threw together a couple of additional graphs. One other note: I typically use RCP6.0 for the scenario in my CMIP5-based (IPCC AR5) model-data comparisons, because it’s similar to the A1B scenario, which was the one used most often in CMIP4-based studies. But Lewis and Karoly (2013) went all out and used RCP8.5, so I changed model scenarios for this post. I did not, however, make any other effort to make this post agree with Lewis and Karoly (2013). They picked 9 CMIP5-based models for their study and I used all the 39 models with their 81 ensemble members.
SUMMERTIME MODEL-DATA COMPARISON
The following three graphs compare the 3-month average Australia land surface temperatures (not anomalies), based on the GCHN-CAMS data and the multi-model ensemble mean of the RCP8.5-based models stored in the CMIP5 archive. Figure 2 uses November to January, Figure 3 is for December to February, and Figure 4 includes January to March. As illustrated, no matter which 3-month periods you look at, there wasn’t anything unusual about the land surface temperature for the 2013 season. The other thing that really stands out is the fact that, based on the linear trends, summertime surface temperatures haven’t warmed since 1979. The linear trends are basically flat. On other hand, the models show that summertime land surface temperatures should have warmed at a rate of about 0.22 to 0.236 deg C per decade. Oops, they missed yet again.
MONTHLY MODEL-DATA COMPARISON
That’s not to say that Australia land surface temperatures haven’t warmed since 1979. The monthly data shows that Australia land surface temperatures warmed at a rate of about 0.07 deg C per decade. However, the models show that if greenhouse gases were responsible for the warming, Australia land surface temperature anomalies should have warmed at a rate that’s more than 3 times faster. The modelers still overshot the mark by a sizeable amount.
And as a reference, I’ve replaced the observations-based data with CRUTEM4 in Figure 6, to confirm that the GHCN-CAMS data does show a little extra warming.
Note: The trends in Figures 5 and 6 are based on the monthly data and model outputs, not on the smoothed versions.
STANDARD BLURB ABOUT THE USE OF THE MODEL MEAN
We’ve published numerous posts that include model-data comparisons. If history repeats itself, proponents of manmade global warming will complain in comments that I’ve only presented the model mean in the above graphs and not the full ensemble. In an effort to suppress their need to complain once again, I’ve borrowed parts of the discussion from the post Blog Memo to John Hockenberry Regarding PBS Report “Climate of Doubt”.
The model mean provides the best representation of the manmade greenhouse gas-driven scenario—not the individual model runs, which contain noise created by the models. For this, I’ll provide two references:
The first is a comment made by Gavin Schmidt (climatologist and climate modeler at the NASA Goddard Institute for Space Studies—GISS). He is one of the contributors to the website RealClimate. The following quotes are from the thread of the RealClimate post Decadal predictions. At comment 49, dated 30 Sep 2009 at 6:18 AM, a blogger posed this question:
If a single simulation is not a good predictor of reality how can the average of many simulations, each of which is a poor predictor of reality, be a better predictor, or indeed claim to have any residual of reality?
Gavin Schmidt replied with a general discussion of models:
Any single realisation can be thought of as being made up of two components – a forced signal and a random realisation of the internal variability (‘noise’). By definition the random component will uncorrelated across different realisations and when you average together many examples you get the forced component (i.e. the ensemble mean).
To paraphrase Gavin Schmidt, we’re not interested in the random component (noise) inherent in the individual simulations; we’re interested in the forced component, which represents the modeler’s best guess of the effects of manmade greenhouse gases on the variable being simulated.
The quote by Gavin Schmidt is supported by a similar statement from the National Center for Atmospheric Research (NCAR). I’ve quoted the following in numerous blog posts and in my recently published ebook. Sometime over the past few months, NCAR elected to remove that educational webpage from its website. Luckily the Wayback Machine has a copy. NCAR wrote on that FAQ webpage that had been part of an introductory discussion about climate models (my boldface):
Averaging over a multi-member ensemble of model climate runs gives a measure of the average model response to the forcings imposed on the model. Unless you are interested in a particular ensemble member where the initial conditions make a difference in your work, averaging of several ensemble members will give you best representation of a scenario.
In summary, we are definitely not interested in the models’ internally created noise, and we are not interested in the results of individual responses of ensemble members to initial conditions. So, in the graphs, we exclude the visual noise of the individual ensemble members and present only the model mean, because the model mean is the best representation of how the models are programmed and tuned to respond to manmade greenhouse gases.
We can add Australia land surface temperatures to the list of variables the CMIP5 climate models show no skill at simulating. The others include:
Alaska Land Surface Air Temperatures
Daily Maximum and Minimum Temperatures and the Diurnal Temperature Range
Satellite-Era Sea Surface Temperatures
Global Surface Temperatures (Land+Ocean) Since 1880
And we recently illustrated and discussed in the post Meehl et al (2013) Are Also Looking for Trenberth’s Missing Heat that the climate models used by Meehl et al (2013) show no evidence that they are capable of simulating how warm water is transported from the tropics to the mid-latitudes at the surface of the Pacific Ocean, so why should we believe they can simulate warm water being transported to depths below 700 meters without warming the waters above 700 meters?
That list is growing quite large.
UPDATE 3: Nick Stokes was correct to point out that I’ve presented a reanalysis with GHCN-CAMS and not data. I can’t complain that Balmaseda et al (2013) are presenting a reanalysis, not data, while looking for Trenberth’s missing heat (see here), and then present a reanalysis without showing the difference between the reanalysis and data.
If we assume that the BOM data is correct, then compared to the BOM land surface air temperature data for Australia, it appears the GHCN-CAMS reanalysis has a cooling bias during summer months. Regardless, as shown in Figures 7 and 8, the RCP8.5-based CMIP5 climate models overestimate the summertime warming rate by 2.4 times and overestimate the monthly warming rate by 3.2 times. Again, the models show no skill at being able to simulate Australia land surface air temperatures.
(The trends in Figure 8 are based on the monthly data and model outputs, not the smoothed values.)
So this is another example of the difference between data and a reanalysis. Consider that when examining Balmaseda et al (2013), discussed here.
To the visitors who’ve read this far: My apologies for this post. It was NOT a good one. First I posted the wrong graphs, then we discovered I had the color-coding wrong on one of the graphs, and then Nick Stokes over at the cross post at WUWT reminded us all that GHCN-CAMS was a reanalysis, not data. My want to present the summertime surface temperatures in absolute form backfired, because of my use of the GHCN-CAMS reanalysis. But I admitted the error, and corrected it.
On the bright side, Willis Eschenbach’s post helps to highlight some of the problems with the Australian land surface temperature data:
Bob: Ah well. Always check your sources. And then re-check, as everyone always misses the obvious until someone else points out just what you forgot. Only way to find what others MAY have misssed though.
I would like your opinion on these two summary 🙂 views of Temperature data.
RichardLH: Have you written something so that you can share what you’ve done? If so, consider presenting it for a guest post at WUWT.
And lets hope your prediction for 2014 comes to pass. Every year that goes by without a hint of warming makes it harder and harder for the AGW enthusiasts.
I have tried but obviously if failed to reach the requirements for publishing 😦
It all fits together so well now.
There are very strong 3 year (37 months), 4 year, (weak 7 year) and strong 12 year cycles visible in both the satellite record and the thermometer record. As evidenced by UAH and CET.
That is so 3, 4 3+4 and 3*4 it just cannot be co-incidence.
The steps for simple low pass frequnecy analysis are as below.
Original from DR R Spencer (http://www.drroyspencer.com)
Fig 1 – Original UAH Global data Dr R Spencer (http://www.drroyspencer.com)
Additional cascaded low pass filters of 12-37 months added
Fig 2 – Additional cascaded low pass filters added to the presentation from Fig 1
Comparison between individual filter stage outputs (IPCC CO2 temperature rate, the final stage, can be set as required)
Fig 3 – Comparison of the various filter stages available for the whole record so far
Remember, this is not adding cycles to the record, it is showing cycles that are recorded as being there.
I think I have a handle on what it could all be. I also can think of things to look for in the ENSO but that is for the future.
The difference between confirmation and confirmation bias is small tough 🙂
And if you want where this is all headed (possibly).
RichardLH: Something to consider about the cycles. As you noted, El Nino and La Nina events are responsible for many of the variations in the temperature record. Many say they are chaotic. Then there are volcanic eruptions. There were two strong eruptions in the 30+ years of the lower troposphere temperature record, and they likely impact your analysis.
Also, have you stopped over at TallblokesTalkshop?
They have published guest posts that are similar to yours.
I tried there and everywhere.
Pehaps my posts are just too oblique. I have not cliamed a new climate model (though I am suggesting one is possible – just standard fluid mechaics and lateral gravitational tides:-) )
Perhaps that would get more attention!
I would commend by rather classical bandpass frequnency alanysuis tool for all climate related data.
Makes no assumtions about cycls, normals, etc. Just shows areas to look for them.
Oh I don’t think that ENSO is that unpredicatable now ;-). Think of a long period slow wave moving North to South moved by the lateral gravitational tides caused by the sun and moon at 50-60 degrees relative to the orbits.
Makes 37 month, 4 years and the 12 year harmonc easy to understand.
If you make the 60+ year a thermohaline response (given its magnitude) and off you go.
My current theory. Lateral orbital tides modulating the Polar/Ferrel Cell boundary and thermohaline response describes most behaviour seen.
Ice ages occur when conditions allow 2 cell rather than a 3 cell atmosphere.
There are problems in all these data (the actual data I mean). We cannot analyse ‘trend’ in data that are not homogeneous through time. Temperature data for specific sites includes shifts, or step changes, that can be attributed to specific non-trend impacts (including for instance, instrument changes and station moves). In order to expose the true trend, these need to be removed.
Eye-balling Figure 1, there seemed to be shifts (relative to the long-term average) around 1960 (to 1975), then again in 2000. There was a well-documents shift also in 1948; the 1977 shift has been well documented, as has been one around 2000. So the data is not homogeneous. So the question is: what is the trend? Is it the cumulated effect of the ‘steps’ or is a real time-related trend? Is it spurious, or is it real is the question that needs to be answered.
El Nino cycling is also a ‘non-trend’ which affects statistical significance and autocorrelation within a series, but not the trend itself. ENSO can also be attributed and removed as a clean-up measure before testing the significance of the trend. (ENSO biases against the possibility of detecting significance.)
Australia’s temperature series is dominated by the extent and aridity of the landscape. This was known and explained succinctly by Chief Meteorologist Henry Chamberlain Russell as long ago as 14 January 1896, in Broken Hill’s Barrier Miner (January 14th 1896) newspaper, under the heading “A Sydney-side Black Monday; a scorcher everywhere”.
Based on anecdotal and limited empirical evidence, I don’t think the intensity and impact of the heatwave around that time has ever been exceeded. It was during that heatwave event that around 400 people died, with many perishing in Sydney.
Together with data from the Bureau of Meteorology I have been researching anecdotal climate information using TROVE which is a facility run by the National Library of Australia. TROVE is a searchable archive of Australia’s newspapers (from way-back).
It is clear that the intensity of extreme events surrounding the climate shift in 1896 far exceeded any other climate change event.
We had drought and bushfires; cyclones, hurricanes; we had ships lost due to the “Maitland gale” which roared up the NSW South Coast; then we had blizzard conditions for several winters, with snow falling heavily in Dubbo and snow reported as far west as Louth and Bourke.
Unfortunately we don’t have too much in the way of climate data that goes back that far, but the anecdotal record (with some reported data) is compelling.
Another problem for Australia’s record is that we have few long-term >100 yr.) continuous datasets. We also have hardly any alpine data – that is data from stations with an altitude exceeding 1500 m.
Stations west of the Great Dividing Range cop a lot of dry (high-pressure cells) air from the interior – this shows up as as westerly and south westerly winds in summer (for the south of the continent); and in winter for the zone generally north of a westward line from about Cowra (NSW) (Our anticyclones spin anticlockwise; in the N-hemisphere they spin clockwise!)
If the interior of the continent is dry; these winds will be warmer than if Lake Eyre is full and there has been good spring rain across South Australia and Western Australia. This conceptually at least, leads to a lagged temperature-rainfall relationship, but its beyond my statistical skills to analyse for it.
I think David Karoly has tried to put the cart ahead of the horse with his view that warming causes drought! By ignoring the role of water in dissipating heat, he is simply fooling around with the laws of thermodynamics.
It is unarguable that sensible heat increases as the landscape dries out, and it is this sensible heat that results in hot air and extreme conditions. (David Karoly of course is not a meteorologist.)
My summary view is that we need to look at the climate system in a broader context than just temperature alone.