![climate-model-1[1]](http://wattsupwiththat.files.wordpress.com/2013/09/climate-model-11.jpg?quality=83)
Computer models that simulate the climate are an integral part of providing climate information, in particular for future changes in the climate. Overall, climate modeling has made enormous progress in the past several decades, but meeting the information needs of users will require further advances in the coming decades.
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The fundamental science of greenhouse gas-induced climate change is simple and compelling. However, genuine and important uncertainties remain (e.g., the response of clouds,
ecosystems, and the polar regions) and need to be considered in developing scientifically based strategies for societal response to climate change.
Description:
As climate change has pushed climate patterns outside of historic norms, the need for detailed projections is growing across all sectors, including agriculture, insurance, and emergency preparedness planning. A National Strategy for Advancing Climate Modeling emphasizes the needs for climate models to evolve substantially in order to deliver climate projections at the scale and level of detail desired by decision makers, this report finds. Despite much recent progress in developing reliable climate models, there are still efficiencies to be gained across the large and diverse U.S. climate modeling community. Evolving to a more unified climate modeling enterprise-in particular by developing a common software infrastructure shared by all climate researchers and holding an annual climate modeling forum-could help speed progress.
Throughout this report, several recommendations and guidelines are outlined to accelerate progress in climate modeling. The U.S. supports several climate models, each conceptually similar but with components assembled with slightly different software and data output standards. If all U.S. climate models employed a single software system, it could simplify testing and migration to new computing hardware, and allow scientists to compare and interchange climate model components, such as land surface or ocean models. A National Strategy for Advancing Climate Modeling recommends an annual U.S. climate modeling forum be held to help bring the nation’s diverse modeling communities together with the users of climate data. This would provide climate model data users with an opportunity to learn more about the strengths and limitations of models and provide input to modelers on their needs and provide a venue for discussions of priorities for the national modeling enterprise, and bring disparate climate science communities together to design common modeling experiments.
In addition, A National Strategy for Advancing Climate Modeling explains that U.S. climate modelers will need to address an expanding breadth of scientific problems while striving to make predictions and projections more accurate. Progress toward this goal can be made through a combination of increasing model resolution, advances in observations, improved model physics, and more complete representations of the Earth system. To address the computing needs of the climate modeling community, the report suggests a two-pronged approach that involves the continued use and upgrading of existing climate-dedicated computing resources at modeling centers, together with research on how to effectively exploit the more complex computer hardware systems expected over the next 10 to 20 years.
h/t to Steve Milloy of junkscience.com
Related articles
- Global warming wildly off (foxnews.com)
- Climate models over predicted global warming (indiavision.com)
See also this video from Bob Tisdale: A Video Preview of “Climate Models Fail”
get everyone using same model = centralized assumption control = easier thought-policing = transparent consolidation of administrative power = monopoly = unhealthy opposite of diversity needed for survival
Even the IPCC years back recognized that climate modelling using GCM’s was not simply hard, it was impossible.
The largest movement of water on the planet, the twice daily ocean tides cannot be calculated with any accuracy using this approach. If you try, if you build an ocean tide model using first principles it will quickly diverge from reality.
Similarly, if you try and model the stock markets, animal populations, or any other physical process. Round off errors quickly overwhelm the accuracy of the result. So why do scientists persist in the illusion that somehow climate is different?
Consider just a simple example. Enter 1/3 into a calculator. The result is 1.33333333333… on forever. However, calculators (and computers) cannot store infinitely large numbers, so eventually they round off the result. A similar thing happens with multiplication.
So, no matter how precise your calculations, as you start performing millions and millions of calculations, the round off errors start getting larger and larger. As you try and extend these calculations into the future (or past) they become less and less accurate until the result are meaningless.
the result is 0.333333…
The climate models have been, are, and will be going forward in a word, USELESS!
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I beg to differ. They have been very useful to those out to make a dishonest buck and/or a power grab.
But it is becoming clearer to those using them that the jig is just about up.
Be alert for the next “lever” after the hockey stick has served it’s purpose.
“The fundamental science of greenhouse gas-induced climate change is simple and compelling.”?? Since when? If the models are so fundamentally wrong, why do people still assume the so-called underlying science is fundamentally right. It simply doesn’t follow and doesnt make any any sense!
I’m not sure it would be that difficult to greatly improve climate models.
1) Remove positive feedbacks and replace with negative cloud feedbacks
2) Add in major ocean cycles (ENSO,PDO,AMO, etc.)
3) Determine equilibrium solar attractor state and apply corrections as needed.
Might actually get pretty close in the near term.
I think eventually a global climate model system will be developed. Greenhouse gasses will be better understood to play a small part, possibly even no part in terms of global weather pattern variations. I also think solar variability will play a small part, possibly even no part as well. And I think it will eventually be possible to forecast into the next decade and even multiple decades but with a variety of “scenarios”, mostly of the oceanic kind. Then as the future unfolds, one or two of them will rise to the top. However, those one or two will not be set as the best forever. My hunch is that a restart will be necessary every 10 years or so. Just my back of the envelope guess.
So, for now, the models are useless.
I also think that the real money will be in research on oceanic-atmospheric teleconnections related to equatorial SW IR absorption at the surface, transport in terms of holding onto heat, moving it, releasing it in varying amounts, and at varying time scales. Greenhouse gasses and solar variability will fall by the wayside.
New Animations
There’s so much attention focused on anomalies in the solar/climate discussion that sometimes newcomers forget that ENSO is just a small thing that rides on a big thing called the terrestrial year.
In the past I shared a bunch of annnual cycle climatology map animations. The files were large and the format was “.apng”, which doesn’t run on all browsers. To ease reach to a wider audience, I’ve slimmed the images down to “.gif” and piled groups of variables into each animation (instead of just 1).
Ocean & atmosphere are coupled, as are temperature, mass, & velocity. The aim is to visually aid awareness of multivariately coupled circulatory topology that pulses spatially as well as temporally with the solar cycle, having inescapable implications that are apparently rather unintuitive for mainstreamers who don’t adequately appreciate the role of wind in ocean evaporation, currents, welling, ice-transport, & mixing more generally.
5 new climate animations:
1. sun, temperature, wind, & ozone
climatology attractor (average annual cycle) map animation: equator-pole insolation & temperatue gradients, semiannual midlatitude westerly winds = westerlies = mean jet streams, & ozone
2. water = hydrology
climatology attractor (average annual cycle) map animation: multivariate hydrology in the context of sunlight, temperature, pressure, wind, & welling
3. cloud cover
climatology attractor (average annual cycle) map animation: low, mid level, high, & total cloud cover
4. sun, temperature, & wind
climatology attractor (average annual cycle) map animation: visualizing & understanding terrestrial 200hPa semiannual midlatitude westerly winds = mean terrestrial jet streams
5. pressure, wind, waves, & gyres
climatology attractor (average annual cycle) map animation: visualizing & understanding coherence of terrestrial surface pressure, wind, waves, & currents (ocean gyres)
Credits:
a) The ocean significant wave height (SWH) climatology attractor (average annual cycle) map animation was assembled using Australian Department of Defence images developed from data provided by the GlobWave Project
b) All other climatology attractor (average annual cycle) map animations have been assembled using JRA-25 Atlas images. JRA-25 long-term reanalysis is a collaboration of Japan Meteorological Agency (JMA) & Central Research Institute of Electric Power Industry (CRIEPI).
These new animations are strategic supplements to help everyone understand solar Schwabe modulation of annually cycling terrestrial insolation (heat engine) gradients. I’m drafting a concise extension of the STC101 article to address ozone & hurricanes.
NB: Solar-terrestrial-climate attractor observations are robust against:
1) switching summary methods.
2) changing the resolution of sunspot data (e.g. from monthly to annual).
3) substituting daily atmospheric angular momentum data for daily length of day data.
4) substituting the famously “ironed flat” TSI reconstruction for sunspot numbers.
5) converting sunspot numbers to simple “low” (-1) & “high” (+1) values.
(The proposed comparatively tiny adjustments to sunspot numbers also have no effect.)
#5 is the clincher that underscores the physical importance of frequency shift.
I smell a grant!
It is good to see a recognition of the fundamental, epistemological problem: When is computer code to be considered science? In other words: Is it PacMan or is it Science?
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gregjxn,
Yes, that is a salient question on a epistemological level.
What is this mere limited use tool created by man that we call modeling doing in any broad theory of knowledge?
John