Forget global climate models, ‘local climate models’ to predict change are the next big thing

Think-Globally-Act-Locally-globe

From the “climate is just global weather on a local scale” department:

Dartmouth-Led Team Develops Method to Predict Local Climate Change

HANOVER, N.H. – Feb. 18, 2016 – Global climate models are essential for climate prediction and assessing the impacts of climate change across large areas, but a Dartmouth College-led team has developed a new method to project future climate scenarios at the local level.

The method can be used in any mountainous or hilly area with a reasonable number of weather stations measuring temperature and precipitation.

The findings appear in the Journal of Hydrometeorology. The team includes researchers from Dartmouth, the University of Vermont and Columbia University.

Global models can simulate the earth’s climate hundreds of years into the future, and have been used to evaluate climate impacts on water, air temperature, human health, extreme precipitation, wildfire, agriculture, snowfall, and other applications. But both global climate models — and global models that have been downscaled to increase the data’s spatial resolution, analogous to increasing the number of pixels used in a digital image — aren’t accurate at local and regional levels. That makes them insufficient for modeling of potential climate impacts on small watersheds, such as those in the mountainous northeastern United States, which are a critical socioeconomic resource for Vermont, New York, New Hampshire, Maine and southern Quebec.

To address this limitation, the researchers developed a method to generate high-resolution climate datasets for assessing local climate change impacts on the Lake Champlain basin in Vermont, including changes in water quantity and quality flowing into Lake Champlain.  They did this by finding the relationships between temperature and elevation and between precipitation and elevation, and then using those relationships to create a high-resolution temperature and precipitation dataset from a relatively coarse-resolution dataset and high-resolution elevation data.

“Compared to weather station observations, our high-resolution dataset better captures both temperature and precipitation, especially in cases where there is a large error in the coarse-resolution dataset and the elevation adjustment is large,” says lead author Jonathan Winter, an assistant professor of geography whose research explores climate prediction and the impacts of climate variability and change on water resources and agriculture. “Improved climate datasets at higher resolutions make assessments of climate variability and climate change impacts both more accurate and more location specific.”

This work is part of a National Science Foundation-funded project to help create policies on land use and management to reduce toxic algal blooms caused by nutrient pollution in Lake Champlain.

###

Development and Evaluation of High-Resolution Climate Simulations over the Mountainous Northeastern United States

Abstract

The mountain regions of the Northeastern US are a critical socioeconomic resource for Vermont, New York State, New Hampshire, Maine, and Southern Quebec. While global climate models (GCMs) are important tools for climate change risk assessment at regional scales, even the increased spatial resolution of statistically downscaled GCMs (commonly ~1/8°) is not sufficient for hydrologic, ecologic, and land-use modeling of small watersheds within the mountainous Northeast. To address this limitation, we develop an ensemble of topographically downscaled, high-resolution (30”), daily 2-m maximum air temperature, 2-m minimum air temperature, and precipitation simulations for the mountainous Northeast by applying an additional level of downscaling to intermediately downscaled (1/8°) data using high-resolution topography and station observations. We first derive observed relationships between 2-m air temperature and elevation, and precipitation and elevation. Then, these relationships are combined with spatial interpolation to enhance the resolution of intermediately downscaled GCM simulations. The resulting topographically downscaled dataset is analyzed for its ability to reproduce station observations. We find that topographic downscaling adds value to intermediately downscaled maximum and minimum 2-m air temperature at high elevation stations, as well as moderately improves domain-averaged maximum and minimum 2-m air temperature. Topographic downscaling improves mean precipitation but not daily probability distributions of precipitation. Overall, we show that the utility of topographic downscaling is dependent on the initial bias of the intermediately downscaled product and the magnitude of the elevation adjustment. As the initial bias or elevation adjustment increase, more value is added to the topographically downscaled product.

112 thoughts on “Forget global climate models, ‘local climate models’ to predict change are the next big thing

  1. How is this any different than long range weather forecasting? How will it be any more accurate? Another silly scam to keep the gravy train full of gravy and rolling along forever.

    • I don’t see how it could be done locally. Even weather forecasts need information from what’s going on globally – or at least what’s going on in the respective hemisphere…I certainly don’t see how they can predict local climate any better than they do global climate or local weather. And I can actually predict as well as any computer. Take any climate zone, and 20 years from now, unless something pretty drastic happens to the sun or throws the planet out of its orbit, the climate will be pretty much the same as what it’s been in the past. Same goes for 100 years out. :)

    • ” improving GCM “projections”

      Projections are predictions that don’t have the confidence to call themselves the right name. Validation is the key. Do these new models make useful predictions that are confirmed by experiment, or are they simply rehashing old data?

    • I can’t say they are right, obviously. I am sure they do not account for station microsite. But it is an interesting approach: matching conditions with readings and inferring results (which can be checked).

  2. “Global models can simulate the earth’s climate hundreds of years into the future…”

    and there we have it right there.

    The daily horoscope will have twice the accuracy of this nonsense. But, it serves the same purpose; some people enjoy being told what is “going to happen”, even if its completely made up, and there are always plenty of people around to make money from doing so.

    No matter how many versions of this come up, it’s always the same old scam, over and over again.

      • “Compared to weather station observations, our high-resolution dataset better captures both temperature and precipitation, especially in cases where there is a large error in the coarse-resolution dataset and the elevation adjustment is large,

        “large error in the coarse-resolution dataset and the elevation adjustment is large,”

        Hmm, more analysis done on very dodgy statistics, may as well use ancient Irish Celtic runic symbols in an attempt to divine world climate patterns.

        Straight away, when you first read the abstract is smacks you in the face, the smell rises high, even though may well be earnest BS, it is still climate modelling and therefore clinging on to those light shaft tendrils of moonbeam guessology, ie first yr undergrad standard speculative BS.

    • By how much research money they can pull into the department for next year , of course.

      What did you think it could mean?

      • We can sense rising desperation and they beat this drum until it bursts. They all know this el Nino will be followed by a very wicked witch of a la Nina freezing everyone in the Northeast USA where major media lives.

  3. Yeah, we know the drill. Any and all “change” is “consistent with the models”; if not the GCMs, then the LCMs.

  4. “Global models can simulate the earth’s climate hundreds of years into the future, and have been used to evaluate climate impacts on water, air temperature, human health, extreme precipitation, wildfire, agriculture, snowfall, and other applications.”

    Let’s check CAGW’s global model results:

    Water (floods and drought): WRONG–(no global increasing trends for 65 years)
    Air-Temps: WRONG– (off by over 2 standard deviations for 20 years and soon to exceed 3+ SDs)
    Human Health: WRONG: Predicted 50 million climate refugees… There haven’t been any.
    Extreeme Precipitation: WRONG– No global increasing trends for 50~100 years for: thunderstorms, sub-tropical storms, hurricanes and cyclones. Perhaps a small increase in global precipation, but this is a good thing…
    Wildfires: WRONG– Any changes caused by stupid EPA/Forest & Fishery regs limiting controlled burns.
    Agriculture: WRONG– higher CO2 levels have increased crop yields and forest growth by 25% since 1850…
    Snowfall– WRONG– global snowfall trends are flat, perhaps slightly higher, which is a good thing..

    How much longer can this CAGW scam continue when NONE of its dire predictions are coming even close to reflecting reality…

    Oh, l see…. Alarmists now want to shift to selective one-off local weather events to extrapolate global climate trends…

    I guess Alarmists have to come up with something to keep the grant gravy train chugging along…

    • Well said, SAMURAI.

      You other commenters have done a fine job already of cutting this Junk Science Gravy Train off at the pass, but, just for future reference, I’m going to go ahead and post this thought. At first glance (until you see just how off their methods and conclusions are), I thought: “Oh, I see. Now, they are going to try to fool people into thinking that the enormous gaps in their computer grid are no problem anymore…

      That is, that they were (I at first thought) trying to pretend that the following problems are solved by their above “methods”:

      {

      Computer analysis requires that the earth be ‘cut’ into small, separate areas (actually volumes), each being analysed for heat input/outputs and other gas/vapour fluxes. Even so the computational analysis domain size (basic computer grid elements) is huge, 150km x 150km by 1km high, with the current computer power. It is so large that the effects of even the very large clouds are not individually included; and that includes clouds in our visual horizon. The spatial resolution is therefore very poor. Supercomputers cannot give us the accuracy we need.

      (From Dr. Geoffrey G. Duffy Report linked and quoted here: https://wattsupwiththat.com/2008/09/04/even-doubling-or-tripling-the-amount-of-co2-will-have-little-impact-on-temps/ )

      Also discussed in detail by Dr. Christopher Essex in his “6 Impossible Things” lecture video linked here:

      https://wattsupwiththat.com/2015/02/20/believing-in-six-impossible-things-before-breakfast-and-climate-models/

      Dr. Essex in above lecture on physics equations not yet solved and computer math gross inadequacies for climate modeling {with approx. times in video}:

      {25:17} 1. Solving the closure problem. {i.e., the “basic physics” equations have not even been SOLVED yet, e.g., the flow of fluids equation “Navier-Stokes Equations” — we still can’t even figure out what the flow of water in a PIPE would be if there were any turbulence}

      {30:20} 2. a. Impossible Thing #2: Computers with infinite representation and math skill. {gross overestimation and far, far, misplaced confidence in the ability of computers to do math accurately (esp. over many iterations) — in this section he discusses the 100 km square gaps {specifically mentioned at about 46:00} (i.e., cell size) — e.g., to analyze air movement, the cell would need to be, per Komogorov microscale, 1mm (aerosols even smaller, microns)).

      At about 44:00, Dr. Essex discusses the fact that even IF the basic equations were known, there isn’t enough time since time began to calculate even just a TEN–year forecast, even at super-fast speeds it would take approx. 10 to the 20th power years (the universe is only 10 to the 10th power years old)}.

      ***********************************************************************

      All the above is just, for THIS particular thread, FYI and a heads up to WUWTers to be ready to refute any attempts by AGWers to wave “Oh, but, NOW we have much smaller grid cells for our ‘data’ analysis” scarves to pull a climate model rabbit out of a hat: that is, no matter how fine you slice it (computer grid, etc…), it’s still baloney.

      *********************************************
      *********************************************

      Personals:

      kim: Thank you for the Sandy “Bergler” (Berger) correction for my (who knows why, lol), Scooter Libby mis-remembering.

      Mark Stoval: Thank you, so much. That you want me here DOES matter. You matter. Thank you for bothering to say something.

      Marcus! — Anthony sent me a kind e mail with his explanation. So, of course, I am back (already, heh). Just kind of distracted with some personal stuff, so not commenting as much lately.

      Janice

      • At about 44:00, Dr. Essex discusses the fact that even IF the basic equations were known, there isn’t enough time since time began to calculate even just a TEN–year forecast, even at super-fast speeds it would take approx. 10 to the 20th power years (the universe is only 10 to the 10th power years old)}.

        Engineering is all about choosing the right approximation. To do that, you have to understand what you’re working on. If we understood the climate sufficiently, we could apply the right approximations and produce models that would give accurate results in a useful time period.

        We don’t understand the climate well enough so we are reduced to curve fitting exercises and pretending that they have some predictive skill (which they don’t).

        My favorite equation:

        f(garbage) = garbage

      • Thanks, TonyL!

        Great equation, commiebob (when oh when are you going to give up communism!!!) — f(commiebob comment) = lol. :)

      • Janice – I missed the kerfuffle so don’t have a clue what happened (been busy and largely away from the computer for a couple of days except for quick skims of articles and not much in the way of comments). No doubt I will find out what it was all about when I catch up on the threads. I just want to say I’m glad you’re back.

        Thank you, Anthony, for emailing Janice and putting whatever right. That’s a big thing because you do not have quiet days! Cheers to you! :)

      • Hi, A. D. Everard — thank you, very much. Yes, the “kerfuffle” is in the rear view mirror, now. And, yes, indeed, it was very generous of Anthony to take the time. He is a man of honor — and kindness.

        Sure hope your writing is STILL (you were publishing some last time we “spoke” :) ) humming along — your great heart is the fuel that runs the finely tuned writing skills mental engine of yours (you can really tell the difference when someone is writing creatively only from their head, versus heart. Clunk… clunk….. clunk. Heh. Well, we all have our “clunk” days (just to acknowledge my own, I mean).

      • Janice: I too am glad you are back. I’m just a silent admirer and have always loved reading your posts. You are very informative and you post lots of what I call ‘food for thought’.

        Thanks PeterK

      • Well! A “silent admirer!” Thanks for the gift of your words of affirmation, Peter K.. How very kind. Man alive, all the angst and tears (my problem: I’m too sensitive and I realize that, just reporting a fact) over that “incident” on Sunday was worth it to get so much kindness in exchange! Wow.

        Janice

      • I have to disagree, Ms. Janice. To completely model the climate on an actual first-principles basis, yes, you need a Matrix (the fictional one by the Wachowskis). However, that is not necessary to get a representation of a system. Trying to claim that it is required is missing the point and invoking the perfect solution fallacy.

        In fact, you can do a simple model in a single equation.
        T= A *log(CO2 – CO20) + T0
        It involves some pretty hefty assumptions (that everything aside from CO2 is constant), and the value of the result is limited. However, it also bypasses all the problems pointed out by you, and it is very useful for the purpose intended, which is to get a reasonable estimate of the the average warming resulting from a change in CO2.

        The problem is that the more precise and detailed a prediction you want, the closer you get to the Matrix requirement in power, and the more systems you have to take into account. You end up replacing your single tuned variable with 20 or more, each with their own errors. By the end, the errors involved massively overwhelm the prediction, as we have seen with global climate models. Trying to downscale to local areas produces results that are directly contradictory between different models because the vast averaging areas that smooth out the gobal model discrepancies are gone.

      • Hi, Ben (of Houston),

        It involves some pretty hefty assumptions (that everything aside from CO2 is constant), and the value of the result is limited. However, it also bypasses all the problems pointed out by you, and it is very useful for the purpose intended, which is to get a reasonable estimate of the average warming resulting from a change in CO2.

        I appreciate your take on my comment, but… (ahem):

        1. Those “hefty assumptions” (everything constant — yeah) pretty much negate such a model’s being “very useful,” regardless of the computer resources needed.

        Further,

        2. Your underlying premise is mistaken: there is no evidence that CO2 can cause any warming of any climate zone of the earth at all, not even a square the size of the all-you-can-eat plate at Mike Bloomberg’s Diner.

        Unless….. Unless they are inside a real greenhouse! So THAT’s where they’ve been… . And all this time we thought they were out in the woods, freezing and miserable. They’ve been sitting in the woods INSIDE A COMFY GREENHOUSE!! Fiddling with the CO2 settings, earning $45.00/hour … eating pizza…. playing video games (on their breaks)…. TAX PAYERS OF AMERICA, UNITE!!! ;)

        **********************
        — lol, he or she was probably complimenting you, Mr. Ben, but, just in case: THANK YOU, ATHELSTAN! :)

    • Really? 100’s of years into the future? Climate models (GCM’s) are an evolution of weather prediction models. which at best are about 50% accurate out to a few weeks and fail miserably when it comes to predicting the magnitude and timing of short term locally natural variability.

  5. Gar—Bage In garbage Out. Global climate models are essential for climate prediction and assessing the impacts of climate change

  6. From what they’ve presented here, they have an algorithm for filling in (interpolating) between measured and predicted data points. No fundamentally new predictive capability..

  7. “Global models can simulate the earth’s climate hundreds of years into the future…”

    and there you have it.

  8. Maybe now they can predict how wind farms can change the local weather patterns like they are changing here in Texas.

    • Much of the weather pattern change in texas was caused by the decrease of southern gulf jet stream that dissipated back in 2006 and came back last year. This is similar to the weather pattern that was prevalent during the Dust Bowl.

  9. The Graphics Research Lab at the University of Utah has been using limited data, (e.g., an artists sketch), and then “filling in the pixels” for years. So the “climate researchers,” (sic) reported here have just, basically, ripped off a process that’s been used, (very successfully, see, among others: Newell, Warnock, Catmull), for commercial production of cartoons.

  10. “Global models can simulate the earth’s climate hundreds of years into the future, and have been used to evaluate climate impacts on water, air temperature, human health, extreme precipitation, wildfire, agriculture, snowfall, and other applications.”

    That´s where I stopped reading. Igorant morons.

      • Lol — yes, fossilsage, no doubt!

        For any English-not-strong-language readers, here is how “laboratories” pronounced with a British accent sounds to an American English-as-first-language speaker:

        luh-BO-duh-trihs (s sounds like “z” in zebra)

      • Depends where they are from. Jordy, Liverpudlian or Mancunian…I can never understand what they say, and I am British born and bred (Strong in the arm and thick in the head).

      • Hi, Patrick MJD — lol, well, almost all my data come from British TV (like that great 1970’s comedy, “Good Neighbors” — I still love watching those re-runs of Jeddy and Mahgoh and Bahbruh (and well, Tohm, not so much, VERY obnoxious character!)) or movies. So… whatever John Gielgud, Diana Rigg, Judi Dench, Josh Ackland, et. al.’s accents are… . (i.e., pre-1980’s, for the most part — after that, the British (and world-wide) raunchiness and acting standards plummeted … and that’s okay — there are PLENTY of fine older shows!)

        Not “cockney” (or the like)– that is an ugly, almost-unintelligible, accent to my ear.

        Oh, Pmjd, pshaw!, your mind is STRONG and SHARP — along with your arm, too, no doubt. So… maybe you are an anomaly, lol.

        Well, chiddy-oh!

        #(:))

  11. More bait and switch, and muddying the water. Like the ‘heat hid in the bottom of the ocean’, or ‘satellites measure the troposphere and we live in the ground’.
    So, I guess that whole ‘weather is not climate’ thing is history?
    At least until the propaganda machine workshops it and decides it’s not again.
    I wonder how many jobs were funded by this study.

  12. So, next year weather same as last year weather.
    Pretty safe “model”.
    In other words No change in climate(or not much) just weather.

    Course these “models” are validated..how?

    • Matt
      ‘Worthless’ – to you and me, yes. For sure. And to most reading here.

      But to the academic careers of the loons whose names are appended?

      Jonathan M. Winter
      Department of Geography, Department of Earth Sciences, Dartmouth College, Hanover, NH 03755
      Brian Beckage
      Department of Plant Biology, University of Vermont, Burlington, VT 05405
      Gabriela Bucini
      Department of Plant Biology, University of Vermont, Burlington, VT 05405
      Radley M. Horton
      Columbia University, NASA Goddard Institute for Space Studies, New York, NY 10025
      Patrick J. Clemins
      Department of Computer Science, University of Vermont, Burlington, VT 05405

      “Compared to weather station observations, our high-resolution dataset better captures both temperature and precipitation, especially in cases where there is a large error in the coarse-resolution dataset and the elevation adjustment is large,” says lead author Jonathan Winter, an assistant professor of geography whose research explores climate prediction and the impacts of climate variability and change on water resources and agriculture. “Improved climate datasets at higher resolutions make assessments of climate variability and climate change impacts both more accurate and more location specific.”

      Translated
      PJ Clemins – our computer guy – took some numbers, ran them through a model, and served us up tenure – we hope.

      Auto
      PS – the translate is not pure G**gle translate.

  13. “This work is part of a National Science Foundation-funded project to help create policies on land use and management to reduce toxic algal blooms caused by nutrient pollution in Lake Champlain.”
    ———————————————————————————————————————————–
    They already know how to do this.

    From: http://www.healthvermont.gov/enviro/bg_algae/bgalgae_fact.aspx

    “Algae blooms are likely to occur during sunny, calm weather when high concentrations of nutrients are present in water. The two important nutrients that can cause a bloom are phosphorus and nitrogen, found in animal and human waste and fertilizers.
    To help decrease nutrients flowing into streams, ponds and lakes:
    • Don’t use more lawn fertilizers than the recommended amount, and keep fertilizers out of storm drains and off driveways and sidewalks.
    • Maintain or plant native plants around shorelines and streams. Native plants don’t require fertilizers and help filter water.
    • Properly care for and maintain your septic system.
    • Do not allow livestock to drink or defecate in streams or lakes. Don’t overfeed waterfowl.
    • Take steps to prevent soil erosion”

  14. ““Compared to weather station observations, our high-resolution dataset better captures both temperature and precipitation…..” (the actual data the weather stations capture)
    So their data is “better” than the real data. And that is possible how?
    Welcome to yet another episode of Fantasy Island.

  15. “Compared to weather station observations, our high-resolution dataset better captures both temperature and precipitation… ”

    Observations are so old school.

    • ““Compared to weather station observations, our high-resolution dataset better captures both temperature and precipitation… ””

      How the **** do they know that ????

    • Michael,
      My personal climate model is to keep the sixteenth of an inch next to my skin at a comfortable temperature.
      {Cribbed, for sure. Of course.}
      Can’t remember who from.
      Possibly one of the Catastrophic Global Cooling gooroos from the 70s. Possibly.

      Auto – a believer in layers [yes, in London. Note, do, the location of that thin layer of air.]

  16. Well, they’ve shown they can measure more things more accurately locally thats for sure.

    Whether they’ve shown anything else is a moot point.

    You still have to make assumptions in models and you still have to wait a heck of a long time to know whether your assumptions were valid or not.

    I have to say that long-term local climate change prediction sounds like a very high-degree-of-difficulty dive.

    Very small changes in how depressions track can have huge impacts on local precipitation after all.

    Far more sensible to ask what happened the past 200 years and then ask why change should be any different in the future.

    • “Very small changes in how depressions track can have huge impacts on local precipitation after all”

      Not to mention how T-storms track. A couple of week ago here in eastern Virginia by the bay we got ~ 3″ snow. Up the road a piece in Richmun’ they got up to 15″ or so. That’s about 50 miles. In summer we frequently lounge, dry and warm in the sun, sipping wine on the deck watching the T-storms pummeling Whitestone, VA two miles across the river. Now how big across are those cells?

  17. Global models can simulate the earth’s climate hundreds of years into the future,

    They can be run for hundreds of years. They won’t be right, but they can run as long as you want.

    • global models that have been downscaled to increase the data’s spatial resolution, analogous to increasing the number of pixels used in a digital image — aren’t accurate at local and regional levels.

      Errm, well to be honest they are not accurate on a global scale either but that’s not important. Now, look over here , I have something else I’d like to show you….

  18. You disbelievers, the world is entering thermagddon – faux Nobel Prize winner Michael Mann told me so it must be true.

    Evidence is everywhere (if not just make it up):
    “Photographer captures heartbreaking image of an Arctic polar bear which ‘starved to death as a result of climate change’ ”
    http://www.dailymail.co.uk/news/article-3452894/Photographer-captures-heartbreaking-image-Arctic-polar-bear-starved-death-result-climate-change.html

    Polar bears never starved to death before climatism! It is all man’s fault! Enough to make you cry in your beer.

  19. Ok, aside from the total lack of confidence in climate modeling, isn’t this an improvement in thinking? We know there is no such thing as a global climate and all climate is more or less local, and at a human level most of the effects of climate (weather) are experienced locally. So shouldn’t the climate be studied in local general areas (in context of course) to better identify what changes might be taking place and what effects that may have generally speaking? As it is right now, no scientist can tell me if Southern California is likely to get hotter, colder, drier, wetter, windier, calmer, or anything that would help me to plan for the future. Having scientists tell me what they think the whole globe is doing doesn’t tell me what might happen locally.

    • No, it is no improvement, because the agenda isn’t science. They are still basing their prognostications on the pseudoscientific global models, albeit tailored for more local climates. They can add all the science food (just barely) they want to the climate garbage; it’s still garbage.

      • Actually, I have a dart board with different coloured areas representing, rain, sun, hail, etc and the areas on the dart board are sized for the percentages of days on average per year that we get rain, sun, hail, etc. I throw my dart at the dart board every night before I go to bed for the next days weather prediction from exactly 20 feet out.

        My weather board is running at almost 22% accuracy.

      • Peter, that is so funny! I can just hear you chuckle when the dart strikes home in “snow” in the middle of summer (assuming you live within 50 deg. latitude of the equator). Heh. 22%. lol
        :)

  20. The resulting topographically downscaled dataset is analyzed for its ability to reproduce station observations.

    That’s just curve fitting. It should read:

    The resulting topographically downscaled dataset is analyzed for its ability to predict station observations.

    Folks who are bold enough to make predictions should be held to account for those predictions. Instead, failed prognosticators like John Holdren continue to thrive.

  21. Gee whiz…they found a more accurate way of estimating past temperature and precipitation values on local levels than GCMs can. Shocking.

    If a “skeptic” came up with this, we’d just hear about correlation is not causation, how we need to use physics to explan the connected relationships between elevation, temperature, and precipitation, etc.

  22. My brain seized when I read these phrases in the abstract:

    “intermediately downscaled GCM simulations” and “statistically downscaled GCMs”

      • I thought it was Steve McQueen and his teenage friends who killed the Blob, circa 1958. Oh no, they froze the Blob and transported it to the Arctic where it might have thawed out by now.

      • Just fill your 55 Chevy with explosives and then aim it straight at the California Blob and watch it blow up Venice Beach.

      • I used to have a ’57 Chev pickup. That would make for a decent payload. Wish I still had that truck. It would be worth quite a bit as compared to the 500 dollars it cost me in 1970.

  23. Global models can simulate the earth’s climate hundreds of years into the future…

    Yes they can, and we can make lunar colonies on the moon. Just that it is important to note we have have never done that and may never do that even though we “can”.

    … to create a high-resolution temperature and precipitation dataset from a relatively coarse-resolution dataset…

    You can’t make a high resolution dataset from a coarse dataset. It is impossible. Your resolution is limited by the course data. At that point you are simply manufacturing data, making stuff up no matter what the algorithms. Take a picture of a car down the street with a smart phone. If you can zoom in and see the license plate number you have high enough resolution to the task of identifying the licence number. If you zoom in and cannot discern the license plate number no amount of blowing up or manipulation will ever show you the license plate number. The data is simply not there.

    It is regrettable they are unable to teach common sense at Dartmouth.

    • You can’t make a high resolution dataset from a coarse dataset. … The data is simply not there.

      +1

    • “They did this by finding the relationships between temperature and elevation and between precipitation and elevation, and then using those relationships to create a high-resolution temperature and precipitation dataset from a relatively coarse-resolution dataset and high-resolution elevation data.”

      In other words it is yet another set of linear models they are going to use to make up data sets describing a highly complex non-linear dynamical system. If you submitted something like that in a freshman science course you would be placed on a watch list and sent for psychiatric evaluation and counselling. CAGW climate ‘science’ is now completely decoupled from the constraints of the real physical world and even the scientific method itself. It is blatantly and unashamedly pure witchcraft.

  24. So they’ve nearly eaten all the cookies and want to crack open another jar. This has been asked before but I will ask it again – Where are the adults on this planet? These kiddies need to be rounded up, soundly chastised for the mess they’ve made and the waste they’ve caused, and finally put to bed. Playtime is OVER.

  25. Well if one climate model has a 4% chance of being close enough to be a success, and you have 100 local models, then you have a 400% chance of being right! Yay!!

  26. So they have invented a whole new area of climate study that will, of course, need a whole lot of new funding dollars from the government, ie. taxpayers. If their predictions at the local level are as far-off as their laughable global predictions of climate (and doom for us all) have been it should be funny to read all about it. Speaking of laughable and funny, I thought Jonathan Winters was a comedian, not a professor of geography. I guess, in this case, it amounts to the same thing. Why is a professor of geography involved in climate research anyway? Not enough money in geography?

    • Three cents… (smile),

      Reading the above article, one gets the impression that those “scientists,” however clever they are at sophistry, are not all that bright… . Mr. Winter was probably added to the team the second day, after the rest got lost trying to find their way across the parking lot. He can read a map and learned how to use a compass in Boy Scouts (before he went to the dark side). His name was Summers, but, after a week, they had to change it because it really, really, messed up those “researchers'” minds when they tried to remember what to shout out when they got lost, “Mister S_ … {look left at snow piled at edge of parking lot…. look up at dark, cloudy, sky…… Mister Sss …….smm … hm….. (sigh)…. Mister…. Snow?? Help me I’m lost! ….” — and no one answered and that made them really upset.

      • Hi, James :)

        Onward! “Forgetting what lies behind, and pressing on toward what lies ahead… .” (a fave)

        Thanks for saying so!

        I tell you, James, WUWT is a real character builder for me. Toughens me up, but, I get worn down sometimes and need to learn to take breaks BEFORE this… and that … and this … aren’t rolling off like water off a duck’s back anymore, but accumulate and then a straw comes long and… .

        Janice (back to her old feisty self)

  27. Quote – “Compared to weather station observations, our high-resolution dataset better captures both temperature and precipitation…”

    Observations are SO old-school. The REAL world is in my computer!

  28. Excellent news. I am forever challenging alarmists to name a place, say what the climate was 30 years ago and what it is now. Not once have any of them responded with anything other than insults or generalities.

  29. I predict that, in 6 months time, local temperatures will be 60 Centigrade above what they were last weekend, here in Ottawa.

  30. Shows the lows some scientists are willing to stoop to, to keep funding rolling in, and advance their careers.
    Accuracy and truthfulness, in short, integrity, is not an important criteria to them.

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