Model-Data Comparison: Australia Land Surface Air Temperatures & Anomalies – not so ‘angry’ after all

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.

INTRODUCTION

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.

OVERVIEW

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.

Figure 1

Figure 1

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.

Figure 2

Figure 2

HHHHHHHHH

Figure 3

Figure 3

HHHHHHHHH

Figure 4

Figure 4

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.

Figure 5

Figure 5

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.

Figure 6

Figure 6

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.

CLOSING

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

Hemispheric Sea Ice Area

Global Precipitation

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.

Figure 7

Figure 7

#######

Figure 8

Figure 8

(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.

Thanks, Nick.

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49 thoughts on “Model-Data Comparison: Australia Land Surface Air Temperatures & Anomalies – not so ‘angry’ after all

  1. It is hardly news that the models and observations don’t match. I surprised that the “scientists” continue to hype the model results.

  2. Excellent work. Clears any doubt about whether it is a media and BOM beat up. It’s sad to see the 150 climate researchers at the BOM sell out!

  3. Bob,
    You don’t need to put together your own data from grids that don’t really fit. The BoM has the data you need here. And on my calc, the trend is 0.0863°/decade for Dec-Feb from Dec 79 to Feb 13.

  4. Thanks Bob. My point in publishing the “angry” Australian claim by Karoly was that given the broad reach of WUWT, somebody would see what a load of codswallop it was and write a rebuttal, and I was right.
    UPDATE: I see Nick is here working on an angle to defend the Karoly and Lewis govsci effort, so I’m even more sure it’s codswallop.

  5. Bob,
    On the question of what region you are actually analyzing – I calculate the area of your rectangle at 17.2 million sq km. The area of Australia is 7.7 million. That would make your calc more than half ocean.

  6. I think it is about time that we just made evrything line up, Satelitte, Thermometer, Proxy, Models, etc.

    The methodology is simple.

    Starting with the most accurate instrument of Global coverage UAH & RSS (combined) then OLS adjust the thermometer series so that they conform during the whole of the periods of overlap and thence on down the line right to the models.

    Basically treat every other temperature source as a proxy rather than a measurement when compared to the most accurate source.

    As the models have oviously (to most anyway) widley overoptimistic this would help to tame them somewhat!

  7. I live in Merimbula, NSW, Australia.

    I have not seen a true Summer here for many years. Merimbula is a Summer Tourist destination on the South East Coast and I work in the Tourism Industry.

    We have now entered Winter. (I notice that the start of Summer/Winter dates vary from USA to AU). This is the 3rd kindest Summer in a row and possibly the strangest Winter. Temps are about normal, we do not have frost here, but Bega, a few miles inland does.

    What I have noticed for the 1st time in the 30 years I have lived in Merimbula is green grass at this time of the year. This has been a wet winter but that is not unusual. Normally the grass rots with rain and low temps or browns off with dry conditions and cold. This year is different. Green grass growing in the middle of Winter.

    I have never cut green grass at this time of the year until now. Can only see CO2 doing it’s thing.

  8. Nick Stokes says: “You don’t need to put together your own data from grids that don’t really fit.”

    The coordinates I used capture Australia perfectly:

    Would you like me to shift the lower latitude up to eliminate Tasmania? I don’t really think it impacts the results.

    Nick Stokes says: “On the question of what region you are actually analyzing – I calculate the area of your rectangle at 17.2 million sq km. The area of Australia is 7.7 million. That would make your calc more than half ocean.”

    Nick, you appear to be forgetting that the GHCN-CAMS dataset is a land surface temperature dataset, no sea surface temperatures. I also used the “land only” option at KNMI for the outputs of the climate models.

    Would you like me to show you the comparisons with the sea surface temperatures in the model outputs? They drop the absolute temperatures down a little and lower the trend a little too. But then I’m not comparing apples to apples, am I?

    Wanna try again?

  9. Thanks.
    In Fig. 4 you use red for the Multi-Model-Mean. But in the title of the graph, you use a red font for the GHCN-CAMS-data. An Inconvenient Inconsistency :-)

  10. Bob,
    “The coordinates I used capture Australia perfectly:”
    Well, they include Australia, but also a lot of ocean. So what does it mean when you say on the diagrams that you are analyzing this area?

    But the main thing then seems to be that you have a large discrepancy with the BoM figures. This shows in Fig 3, where you have the 12/13 summer cooler than 82/83, where BoM has it warmer. And you have a negative summer trend, whereas the BoM data has a substantial positive one.

  11. cassandraclub says: “In Fig. 4 you use red for the Multi-Model-Mean. But in the title of the graph, you use a red font for the GHCN-CAMS-data. An Inconvenient Inconsistency :-)”

    Thanks for noticing it. Corrected. That’s what I get for working a 20-hr day with color-coded graphs.

  12. Bob: Have you ever thought of adding extra longer period averages to the graphs you display? Such as in Fig 1 for example.

    With just a single 13 month filter you cannot ‘see’ where the longer freqency energy is located.

    Try adding others with increasing spans and you will see this more easily I believe. I have plotted a series of 12, 16, 18, …. on the UAH data and the results are very interesting

  13. I was curious about the discrepancy between GHCN-CAMS and the BoM data. The GHCN-CAMS is a reanalysis set. I looked up the paper which introduced it, and saw in the abstract this clear warning:
    “The study also reveals that there are clear biases between the observed surface air temperature and the existing Reanalysis data sets, and they vary in space and seasons. Therefore the Reanalysis 2 m temperature data sets may not be suitable for model forcing and validation.”

  14. My models say it is getting warmer.

    My data says it is not.

    Gradually, models must yield to the dictates of data.

    Key word . . . Gradually.

  15. Fred from Canuckistan says:
    June 28, 2013 at 6:07 am

    “My models say it is getting warmer.

    My data says it is not.

    Gradually, models must yield to the dictates of data.

    Key word . . . Gradually.”

    As I observed above, we can force them all into alingment by making their OLS trends align wiht reality. That tames them real quick.

  16. Fred from Canuckistan says: June 28, 2013 at 6:07 am
    “Gradually, models must yield to the dictates of data.”

    But in this case the “data” is reanalysis – ie modelled.

  17. ‘Angry summer’ sounds like ‘angry God’ to me…..~500 years of rationalism and science, and the words have changed, but not the concept.

  18. The “climate science” in Aus is, basically, a buttress in the support of Ohbummas climate action. A carbon tax was tested in Aus (The EU ETS is an odd ball given their economic woes), BUT it was all based on a certain stability in a certain country. That looks dubious now.

  19. Adjusted data, homogenised data is not real, observed and recorded, data is. Using “adjusted” data in computer models just exacerbates the “problem”.

  20. Nick Stokes says: “Well, they include Australia, but also a lot of ocean.”

    But there’s no sea surface temperature data included, so why are you discussing it?

    Nick Stokes says: “So what does it mean when you say on the diagrams that you are analyzing this area?”

    Please rephrase your question.

    Nick Stokes says: “But the main thing then seems to be that you have a large discrepancy with the BoM figures. This shows in Fig 3, where you have the 12/13 summer cooler than 82/83, where BoM has it warmer. And you have a negative summer trend, whereas the BoM data has a substantial positive one.”

    I present data, Nick. If you like, you can verify my results using the KNMI Climate Explorer:

    http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

    Let’s make an assumption for the sake of discussion: Let’s assume there’s a seasonal bias in the GHCN-CAMS data that doesn’t exist in the summer data of the other land surface temperature datasets. While that would impact the summer model-data comparison, it doesn’t impact the overall comparisons shown in Figures 5 and 6. The models are still showing trends 3.5 to almost 4 times higher than observations. Bottom line: The models show no skill at being able to simulate Australia land surface temperatures.

    Regards

  21. 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).

    Then why not take the random component out of the models and quit with the pretence you can model natural variability?

    Good work, Bob.

  22. Australia’s climate is so controlled by the ENSO, it is not even worth trying to model it in a climate model. Its ridiculous really.

  23. Bob,
    “I present data, Nick.”
    You’ve presented reanalysis data, which the authors say is not suitable for validating models because of biases.

    “While that would impact the summer model-data comparison, it doesn’t impact the overall comparisons shown in Figures 5 and 6.”
    But it does affect your claim that
    “the data I had downloaded indicated Australia summertime temperatures in 2013 weren’t remarkable “ and “not so angry after all”.

    Fig 6 does not use GHCN/CAMS, so yes, it is unaffected. I think it is likely that the CMIP 5 average will show a higher trend for annual temperature than measured for Australia in this period. How significant this is, I don’t know.

  24. Once again we see folks here using re analysis data without fully disclosing that the data they use is modelled.

    Now, If I used re analysis data without disclosing it people would scream bloody murder.

    Further, Bob has used RCP 8.5. You all realize that RCP 8.5 is the projection at the highest end of the scenarios, ie Bob has compared re analysis data to the highest projections.
    And to make matters worse he has used re analysis data against the authors advice.

    When the NAS said that bristlecones should be avoided, we screamed when mann used bristlecones. Here the authors tell us that the data should not be used for model validation.
    Yet bob ignores that.

    If you want an estmate for australia that uses the exact dimensions of the country

    http://berkeleyearth.lbl.gov/regions/australia

  25. Steven Mosher says: “Now, If I used re analysis data without disclosing it people would scream bloody murder.”

    And you, Steven, are screaming bloody murder and being quite obnoxious about it. See update 3.

    Steven Mosher says: “Further, Bob has used RCP 8.5. You all realize that RCP 8.5 is the projection at the highest end of the scenarios, ie Bob has compared re analysis data to the highest projections.”

    Did you read my post, Steve? I explained why I used RCP8.5, instead of RCP6.0, which I is the scenario I normally use. Are you even aware that Lewis and Karoly (2013) used RCP8.5? Also, for the period of January 1979 to May 2013, the vast majority of the model output is hindcast not projection. Are you aware of that, Steven?

    Also, please present to all of those reading this thread the difference the use of RCP6.0 (instead of RCP8.5) would have made on the results of Figures 7 and 8. Are you aware that many more models and ensemble members are archived for CMIP5 at KNMI using RCP8.5 than RCP6.0, so that the hindcasts are a little different? Go ahead, plot them for the coordinates I used and for the time period I used. You’re going to be surprised. I wasn’t. I’ve examined them many times previously. And for this post, I had originally used RCP6.0 so I already know the outcome. All I have to do is clean up the graphs.

    Steven Mosher says: “If you want an estmate for australia that uses the exact dimensions of the country”

    Why would I be interested in the exact dimensions of Australia, when the coordinates I used capture its land surfaces just right?:

    In closing this comment, Steven, I await your presentation of my Figures 7 & 8 using the RCP6.0-based model mean. If I have to do it, I won’t be as pleasant in my reply to you as I was with this one.

    When I make mistakes, Steven, I correct them. Do you?

  26. Modeling “scientists” mix in stochastic BS, give it a few shakes, and then turn around and argue that it needs to be averaged out. To alert jane & joe public this tangled twist might look like not so welcome 2-faced government- & university-style smoke & mirrors.

    Suggestion for someone with the time for it:

    Calculate the cost-savings of simply replacing all climate models with monotonically increasing straight or curved lines.

    i.e.
    Remove the tangled twist of expensive 2-faced smoke & mirrors
    and show ALL THAT’S LEFT at the root of stochastic deception.

  27. Of what utility is A VERY EXPENSIVE standard error
    (…that’s based on patently untenable assumptions)?

    Answer:
    Political deception.

  28. Mosh, what’s with you? Bob Tisdale and Willis Eschenbach do a tremendous amount of work day in and day out while you sit around on your throne criticizing them. Anyone that is as productive as these two is bound to do something that may be quite right or even totally wrong. The readers of WUWT are quick to catch things, so in essence it is a place of high, in public review. In my mind you are batting about 50% in what you catch. That’s not a bad batting average by anyone’s measurement, but you lose points galore with your holier than thou attitude. Please apologize to Bob and Willis and henceforth make your review points in a more professional manner.

  29. Reality. Not a ruler in sight, nor anything else to get alarmed about.

    http://postimg.org/image/jicwcfid7/full (1800×900, 201.04 kB)
    http://postimg.org/image/o5iye75q3/full (1800×1500, 338.2 kB)

    As Bill says above, Australia’s climate is so controlled by ENSO, it’s not even worth trying to model it. Where is the model that can successfully predict ENSO (and major volcanoes) decades in advance, iteration after iteration?

    If you think you can discriminate a miniscule warming signal due to increased CO2 out of that level of variation in what is patently a chaotic non-linear system of immense complexity you are truly living in cloud cuckoo land. Put your silly linear regression toys away and look at climate evolving as it has always done. Chaotically due to perturbations over time scales we can’t even estimate the number and extent of.

  30. PaulC writes:
    “What I have noticed for the 1st time in the 30 years I have lived in Merimbula is green grass at this time of the year. This has been a wet winter but that is not unusual. Normally the grass rots with rain and low temps or browns off with dry conditions and cold. This year is different. Green grass growing in the middle of Winter.”

    This is interesting. I live in Canberra, not too far away and I have noticed similar things. Trees began budding up and producing new shoots through May and into June, and my roses flowered quite strongly coming into June, and are still flowering. I am no gardener, but I don’t recall that happening before…

  31. Whatever the best model might be, it does not appear in discussion presented to the general public. The main Australian science body, the CSIRO, has claimed that future warming for Australia will be “up to” five degrees Celsius by 2070, almost 1.0 degrees per decade. This figure was repeated widely by the media, generally without any qualification of uncertainty, or mention of any lower possible figures. It was then used and specifically quoted by (now ex-) Prime Minister Gillard as a justification for a carbon tax. (The Australian newspaper 4 July 2011)

    The models that should be discussed and compared to the real world are those that are actually being used to drive public policy.

  32. For those who live in Canberra and Merimbula if you watch carefully no two seasons are ever the same. Then there are some balmy ones. If you watch the birds and animals you will see that they too do things differently. When you live on the land and are at the whims of the seasons for your income you learn a lot of things. The Aborigines understood all this. Don’t be fooled into thinking that this is evidence for AGW

  33. I and other Canberra friends have noticed that the grass is a lot greener in midwinter than usual. It’s usually brown and dead-looking by this date. Of course seasons are variable, as is everything else in weather and climate. We can talk all we like about weather and climate, but both are far beyong mere human influence!

  34. PaulC June 28 5:16am

    The speculation of PaulC who lives in Merimbula, HWS, Austraila is quite clear but seems to have been misunderstood by many..

    Increased CO2 increases plant growth. It allows plants to increase their efficiency in the use of “scarce” resources — whether those scarce resources be sunlight hours, water, temperature, soil nutrients etc

    In other words increased CO2 makes up for a lack of those other things.

    This means plants will start their growth cycles earlier and continue later. They will begin to extend their ranges into areas that were previously hostile to them. (Frost lines moving higher up mountains, dry areas seeing the arrival of new growth. And animals and birds will follow behind the plants. One can suggest that during periods of much higher CO2 levels in the distant past the earth was a much greener place than it is today)

    In still other words, to sum it up — increased CO2 makes the earth greener. All that greenies have attributed to “increased global temperature” (and demanded that we believe it is a dangerous thing foretelling the doom of the planet) is most likely due to increase CO2 fertilization. So greening the earth is a bad thing? I guess it is if it refutes ACGW.

    There was a recent article on WUWT (if I am not mistaken) that talked about increased plant growth in semi-arid regions. Probably others articles along this line that I missed.

    Thank you PaulC.

    Eugene WR Gallun

  35. Slightly OT – looking at SSTs right now there is something resembling a La Nina in both the Pacific and Atlantic.

    The Pacific one has been there some time and its not clear if it will become a full La Nina or not.

  36. Phlogiston:

    I live in eastern Indonesia where it picks up the La Nina early, we are getting rain in the dry season again since early June, same as in 2010, indicating La Nina. Nobody does any research here, but the area picks up pacific conditions quickly.

  37. Steven Mosher, as a reminder, you said, “Further, Bob has used RCP 8.5. You all realize that RCP 8.5 is the projection at the highest end of the scenarios, ie Bob has compared re analysis data to the highest projections.”

    I then replied:

    Did you read my post, Steve? I explained why I used RCP8.5, instead of RCP6.0, which I [sic] is the scenario I normally use. Are you even aware that Lewis and Karoly (2013) used RCP8.5? Also, for the period of January 1979 to May 2013, the vast majority of the model output is hindcast not projection. Are you aware of that, Steven?

    Also, please present to all of those reading this thread the difference the use of RCP6.0 (instead of RCP8.5) would have made on the results of Figures 7 and 8. Are you aware that many more models and ensemble members are archived for CMIP5 at KNMI using RCP8.5 than RCP6.0, so that the hindcasts are a little different? Go ahead, plot them for the coordinates I used and for the time period I used. You’re going to be surprised. I wasn’t. I’ve examined them many times previously. And for this post, I had originally used RCP6.0 so I already know the outcome. All I have to do is clean up the graphs.

    More than 24 hours have passed, Steven, and you have elected to ignore my request that you confirm your accusation that RCP8.5 provides a higher trend during the period illustrated.

    The following gif animation is my Figure 7 above with comparisons of the CMIP5 multi-model ensemble mean of the Australia summertime simulations using RCP6.0 and RCP8.5 scenarios. You, Steven Mosher, accused me of using the RCP8.5 because it would bias my results, making the models look worse. As you can see, your accusation was baseless, because the hindcast using the RCP8.5 scenario has a lower hindcast trend than the RCP6.0 scenario:

    Australia Summertime Temp RCP6.0 and RCP8.5

    The same holds true for the monthly data in my Figure 8, Steven:

    Australia Temp RCP6.0 and RCP8.5

    So your attempt to undermine my post failed, Steven.

    In total, you provided a link to a Berkeley Earth Surface Temperature webpage that was useless in this discussion. Your accusation about my use of the RCP8.5 output was without merit. And you simply piled onto Nick Stokes’s discovery that GHCN-CAMS was a reconstruction. It was Nick who did the research.

    In summary, you, Steven Mosher, offered nothing of value to this thread. You’ve simply wasted my time and the time of those who have read your comment. Your behavior is becoming troll-like here at WUWT.

    Have a nice day.

  38. phlogiston & thingodonta: It seems we’re having off season ENSO events. Last summer there was the mini El Nino, which shows up really well in the weekly NINO3.4 data:

    This year we have La Nina conditions early, but they’re toward the East Pacific, not in the central Pacific where La Ninas are typically strongest. There should be a downwelling Kelvin wave coming along. It should offset some of the cooling we’re seeing. (That was back-to-back sentences in which the operative word was “should”.)

    phlogiston, one can hope those Atlantic La Nina-like conditions work their way into the North Atlantic and help to drive its SST down a little.

    Regards

  39. 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 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’s post helps to highlight some of the problems with the Australian land surface temperature data:

    http://wattsupwiththat.com/2013/06/28/australia-and-acorn-sat/

    Regards

  40. thingodonta says:
    June 29, 2013 at 6:12 am
    Phlogiston:

    I live in eastern Indonesia where it picks up the La Nina early, we are getting rain in the dry season again since early June, same as in 2010, indicating La Nina. Nobody does any research here, but the area picks up pacific conditions quickly.

    Anyone who looks out of the window or walks outside, anyone who feels the sun or rain, warm or cold, researches climate. The elites chose poorly when they chose climate as a field to politicize. Did they think no-one would notice when their warming predictions fail?

  41. Bob Tisdale says:
    June 29, 2013 at 8:49 am
    phlogiston & thingodonta: It seems we’re having off season ENSO events. Last summer there was the mini El Nino, which shows up really well in the weekly NINO3.4 data:

    This year we have La Nina conditions early, but they’re toward the East Pacific, not in the central Pacific where La Ninas are typically strongest. There should be a downwelling Kelvin wave coming along. It should offset some of the cooling we’re seeing. (That was back-to-back sentences in which the operative word was “should”.)

    phlogiston, one can hope those Atlantic La Nina-like conditions work their way into the North Atlantic and help to drive its SST down a little.

    Regards

    I guess the Peruvian upwelling and cold tongue is building, albeit more slowly than the UNISYS prediction which always seems to be jumping the gun.

    In the Atlantic maybe the Carribean current could take some of that cold Atlantic-ENSO “La Nina” (“Msichana?”) water north across the equator. The Atlantic seems divided between a warm east and cold west just now.

  42. Bob Tisdale says:
    June 29, 2013 at 8:59 am

    and then Nick Stokes reminded us all that GHCN-CAMS was a reanalysis, not data.

    Models are getting everywhere like viruses.

  43. phlogiston says: “Models are getting everywhere like viruses.”

    CRUTEM4 and HADSST3 are model outputs. If memory serves, the UKMO provides a suite of 100 ensemble members.

    Regards

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