Via Eurekalert, a press release about projections of “Melting Marches” from the Heidi Cullen frozone team who says loss of freezing zones is “worse than we thought”. Minnesotans for Global Warming say “YES!”.
New Climate Central projection map shows local and national retreat of freezing temperatures in March
PRINCETON, NJ. On the last day of the month, Climate Central has just published an interactive animated map showing what we might expect in Marches to come as the climate warms. Developed by Climate Central scientists, the map uses special high-resolution projections covering the Lower 48 states to show where average March temperatures are expected to be above or below freezing each decade this century. The map also compares projections under a low, reduced carbon pollution scenario versus a high one that extends current trends.
Under the high scenario, Climate Central’s work shows majority or complete loss, by the end of the century, of these freezing zones in every state analyzed. Minnesota, Montana and North Dakota would lose the most total below-freezing area, while seven other states, from Arizona to Wisconsin, are projected to lose all they currently have. A table on the group’s website lists details state by state.
The projections promise earlier starts for gardeners, farmers, and golf enthusiasts. At the same time, they would mean earlier snowmelt. In the American West, early snowmelt years have already been linked to drier rivers and forests later in the summer, and very much higher wildfire activity – projected to intensify with further warming. Scientists also expect challenges for irrigation supplies and cold-water stream life like trout.
“These maps imply future changes the research community is only beginning to appreciate,” said Climate Central scientist Dr. Ben Strauss.
Climate Central is a nonprofit group of journalists and scientists dedicated to communicating the best and latest climate science.
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DirkH (05:17:56) :Clouds still don’t work. Influence of aerosols still unknown. Humidity still parameterized.
Not quite sure what you mean by ‘don’t work’. The point is that the effect of omissions/deficiencies of GCM’s can be TESTED by comparing their statistical properties with those of the real atmosphere. It turns out that they do incredibly well giving a certain level of confidence in the results.
Living in Michigan, this looks awesome. I don’t see a problem. Maybe if I hold onto my house for another 50 years, it’ll be worth something …
DirkH (08:15:12) :
I don’t think they even attempt by now to realistically simulate changes in humidity. Or maybe they try but still fail. Given that H2O is way more important as a GHG, this results in a completely failed simulation.
Think again. In addition to three-dimensional temperature, pressure, density and wind fields, the GISS model for example, has an explicit three-dimensional humidity field.
http://www.giss.nasa.gov/research/modeling/gcms.html
“DirkH (08:15:12) :
[…]
humidity. Or maybe they try but still fail. Given that H2O is way more important as a GHG, this results in a completely failed simulation.”
I must be sounding very cross. Let me put it this way: The situation climate simulation as a science is in is comparable to the situation of AI in the 60ies. There was a lot of euphoria that real soon now we would have thinking machines. Then came the crash; funding was cut in the 70ies when it became obvious that no significant progress was made.
But today, we have wonderful machines like Google or Wolfram Alpha that do really useful things and retrieve or compute information for us better than anything else in humans history. (Enter “1 furlong in yards” in Google for instance, or go to wolfram alpha and enter “largest airplane”)
What happened?
After the big pipe dreams of government sponsored projects evaporated, some people sat down and thought “How can we still make money out of this and deliver a useful service?” and things like search engines, Googles pagerank algorithm (and some Eliza-like trickery for the unit conversions) and whatever makes Wolfram alpha tick were created. Little things really, but working.
The same might very well happen to climate science and GCM’s. All the bravardo about the GCM’s will evaporate when the funding runs out – we are past the hype and into the crash.
There is a pattern here: Hype-Crash-Steady growth.
JohnH quotes;
“The prices we pay for our goods do not reflect one key cost: the damage that their production does to the planet’s climate system,” said Bob Ward, of the Grantham Research Institute on Climate Change at the LSE. “We need to find ways to extract payment from those who cause that damage and then use that money to fund developing nations so that they can protect themselves from the worst effects of global warming.”>>
Perhaps it is time to accept both the prognostications and the solutions proposed by the AGW crowd. Both the temperature record and the models show tropical temperatures to be pretty stable with slight increases and temperate and arctic regions to be highly variable and with much larger increases. Based on the logic in the quote above, and the facts on the ground we arrive at the following:
1. The ill effects of global warming will be borne by the temperate zones
2. The bulk of developing countries being in tropical zones, they are already protected from the bulk of the changes from global warming
3. As the developing countries are desperately poor they need continued assistance from the developed countries to ensure their food supply continues, disaster relief is delivered, vaccines are produced and distributed and many other things that are produced in the temperate zones and so are threatened by global warming.
Therefor, for the good of the world, taxes should be collected from developing countries in tropical zones and re-distrubuted to the sources of production in developed countries in the temperate zones. This will allow developed countries to put programs in place to protect themselves from the worst effects of global warming, and continue to produce enough extra food and other products to provide as hand outs to developing nations in tropical zones.
Of course, this does not provide a solution in regard to the corrupt governments in the developing countries scooping up the hand outs for themselves while their populations starve, or of corrupt UN officials slicing a cut off the top for themselves when distributing them (remember Oil for Food?) but it does ensure a continued world wide food supply.
As the countries with the lowest standards of living will stand to benefit the most from continued production in developed countries, of course they should be taxed the highest. Countries with the most proximity to the poles should be paid the most in transfers.
Prime Minister Stephen Harper of Canada and Russia’s Vladimir Putin have already considered this plan, and are organizing a meeting with Iceland, Norway, Finland, Denmark and the Governer of Alaska to discussed the plan. The United States other than Alaska was specifically excluded on the basis that the continental United States does not fall into the required latitude zones to be eligible for benefits, and some states may actually fall into the taxation zones. (Several commentators have suggested that this is simply posturing on Canada’s part and is a reaction from Canada’s foreign minister having been hit by Hillary Clinton with her purse at the G8 summit just before she asked for more troops for Afghanistan)
In the southern hemisphere, Australia has said that the plan has merit, though Chile is threatening action at the UN based on their own formula which classes the entire country at the same latitude as its most southern tip. Argentina has meanwhile indicated that they may drop their claim on the Falklands in return for a land swap in northern Ireland.
Speculation that this mechanism could be used in a similar fashion to resolve middle east peace negotiations by swapping both population and land were dashed when both sides instantly claimed Antarctica as their traditional homeland.
Asked about the likelihood of global warming coming to fruition in this manner, both Harper and Putin replied “Yes, we remain optimistic”.
“Tom W (08:24:08) :
DirkH (05:17:56) :Clouds still don’t work. Influence of aerosols still unknown. Humidity still parameterized.
Not quite sure what you mean by ‘don’t work’. The point is that the effect of omissions/deficiencies of GCM’s can be TESTED by comparing their statistical properties with those of the real atmosphere. It turns out that they do incredibly well giving a certain level of confidence in the results.”
Dumb luck.
Tom W:
Yes, and that certain level would be approaching zero. GCM’s do not demonstrate predictive skill. Taking the ensemble of 50 odd models and saying the observed state is in there somewhere is not useful. Almost any future state can be found in one of the models. The error range is too wide to have any value. If you could pick one model with reasonable error ranges and demonstrate some predictive skill over a decade you might have something. Right now it is nothing.
Tom W (08:33:13) :
“It turns out that they do incredibly well giving a certain level of confidence in the results.”
The operative word here being “incredible”
From Webster’s dictionary:
incredible (adj.) – 1. not credible; unbelievable 2. seeming too unusual or improbable to be possible.
Tom W (08:24:08) :
Bull! You can’t discount a dominant feedback and with a little hand waving say its all good.
“Tom W (08:33:13) :
DirkH (08:15:12) :
I don’t think they even attempt by now to realistically simulate changes in humidity. Or maybe they try but still fail. Given that H2O is way more important as a GHG, this results in a completely failed simulation.
Think again. In addition to three-dimensional temperature, pressure, density and wind fields, the GISS model for example, has an explicit three-dimensional humidity field.
http://www.giss.nasa.gov/research/modeling/gcms.html
”
From the page you’re linking to:
“They also consider, often in parameterized form, the physical processes within the boxes, including sources and sinks of these quantities. ”
A physical process “considered in parameterized form”? Well, i did say that they parameterize things they don’t know to improve their hindcasting. Sophisticated curve-fitting. Thanks for the confirmation.
I see that everyone conveniently forgets the temperatures just a few weeks ago which were far below freezing across most of the country.
Arizona probably hasn’t forgotten. Forecast was for snow yesterday. And today, the Grand Canyon is freezing (0C).
Arctic ice is not disappearing! 30yrs of GW has not significantly altered Arctic sea ice extent maximum… 1 of 2 important indicator reports!
http://nsidc.org/data/seaice_index/images/daily_images/N_stddev_timeseries.png
March (month end averages) NSIDC (sea ice extent)
30 yrs ago
1981 Southern Hemisphere = 3.7 million sq km
1981 Northern Hemisphere = 15.6 million sq km
Total = 19.3 million sq km
Record Arctic minimum extent year (Sept 2007- 4.28 Mkm2).
2007 Southern Hemisphere = 4.1 million sq km
2007 Northern Hemisphere = 14.6 million sq km
Total = 18.7 million sq km
Last yr.
2009 Southern Hemisphere = 5.0 million sq km
2009 Northern Hemisphere = 15.2 million sq km
Total = 20.2 million sq km
This yr.
2010 Southern Hemisphere = 4.0 million sq km
2010 Northern Hemisphere = 15.1 million sq km
Total = 19.1 million sq km
Northern Hemisphere Plate
Southern Hemisphere Plate
1979-2000 Southern Hemisphere Mar. mean = 4.3 million sq km
1979-2000 Northern Hemisphere Mar. mean = 15.7 million sq km
Total Feb. mean = 20.5 million sq km
“Think again. In addition to three-dimensional temperature, pressure, density and wind fields, the GISS model for example, has an explicit three-dimensional humidity field.”
http://www.giss.nasa.gov/research/modeling/gcms.html
Interesting that Tom W. links to one of the worst of the GCMs…
Much better choice here (they even document what they do, unlike GISS)
Frank K. (08:14:11) :Anyway, if you demonstrate to me that the mathematical problem as defined by the differential equations (and that means all of them, not just the “Navier-Stokes” equations) and initial/boundary conditions used in modern climate models are well-posed and solvable numerically, that would be wonderful. Take your time.
Since the Navier-Stokes equations haven’t been shown to be well-posed, it is hardly surprising that the equations used in GCM haven’t either. Once again you complain about an issue that applies equally to acoustics, aeronautics, etc. Why don’t you hassle acoustical and aeronautical engineers?
Let me repeat; The simulated flows can be tested by comparing them with real atmospheric flows. The comparisons suggest that the imperfections that you want to use to dismiss the models are of secondary importance.
Tom W;
Not quite sure what you mean by ‘don’t work’. The point is that the effect of omissions/deficiencies of GCM’s can be TESTED by comparing their statistical properties with those of the real atmosphere. It turns out that they do incredibly well giving a certain level of confidence in the results.>>
Yes, we think that the omissions/deficiencies cancel each other out and so arrive at a good approximation. On that basis I could select a dozen different sets of omissions/deficiencies that arrive at a good approximation for the tiny time period of detailed (but incomplete) climate data, and still have a good approximation.
X=4
A-B=X=4
If I assume that A=8 and B=4, then my model yields X=4 and so is correct.
If I assume that A=1000 and B=996, then my model yields X=4 and so is correct. I may have been able to measure X=4, but if I have millions of variables that collectively define it, and I know that some of them are wrong, then any assumptions about any of them are valid either for a brief moment in time, or are correct by a matter of chance in the range of infinity to one.
Interesting that Tom W. links to one of the worst of the GCMs…
Yep, even the worst GCM does what DirkH claimed they didn’t.
“Whom the gods would destroy they first make mad”
jeez (08:44:16) :
Yes, and that certain level would be approaching zero. GCM’s do not demonstrate predictive skill. Taking the ensemble of 50 odd models and saying the observed state is in there somewhere is not useful. Almost any future state can be found in one of the models>>
Oh I don’t know. They got the entire history of earth out of one tree didn’t they?
“Frank K. (08:49:52) :
[…]
Much better choice here (they even document what they do, unlike GISS)”
Very interesting. Now lets look at this page in that documentation:
http://www.ccsm.ucar.edu/models/atm-cam/docs/description/
They describe how fractional cloud coverage and humidity and whatnot are working in their cells… Hmm. This sounds all pretty weather-like to me. So wait… They simulate weather (of course, using the right parameters to have a good hindcasting 😉 ) to arrive at long term climate predictions, right? Was it not said that beyond 3 days we can’t forecast weather very well? And has it not been said that, well, weather is difficult to forecast but climate, no problem, just do an ensemble of simulations and you have it? For 100 years into the future?
Now if there is not already a name for a unit of bogosity, i would suggest “Hansen” for it.
I think the simulators are doing a very interesting thing here, but it’s not suited to predict the future climate. It’s very interesting stuff, it’s only used for a purpose it can’t fulfill.
davidmhoffer (08:55:10) :Yes, we think that the omissions/deficiencies cancel each other out and so arrive at a good approximation.
No, we DEMONSTRATE that certain models with deficiencies/omissions do a good job in reproducing the more important statistical properties of the atmosphere.
“Tom W (08:58:34) :
Interesting that Tom W. links to one of the worst of the GCMs…
Yep, even the worst GCM does what DirkH claimed they didn’t.”
What did i say? Hmmm… let’s look it up, shall we?
“Humidity still parameterized.”
What does the link you gave say:
“They also consider, often in parameterized form, the physical processes within the boxes, including sources and sinks of these quantities. ”
Do you see a contradiction? I don’t. But it’s a minor point. I learned that they actually do attempt to model humidity, thank you for that. I am also optimistic that they have already fine-tuned their parameterizations to hindcast the last 10 years of non-warming.
“Let me repeat; The simulated flows can be tested by comparing them with real atmospheric flows. The comparisons suggest that the imperfections that you want to use to dismiss the models are of secondary importance.”
Errr…are we talking about “climate” here or short term weather phenomena? If the latter I would agree, if it’s the former, then that is called a “hindcast” – in my 20 year’s of experience with high end industrial CFD, knowing the answer beforehand has always led to superb results (after tuning the models, of course).
Climate modelers (some, not all) appear to suffer from what I call the “flat plate” syndrome. The belief is that if a CFD code can predict the correct shear stress distribution on a flat plate, then surely it should get the same level of accuracy when you solve for the flow over a 747 aircraft. I mean were solving the same equations and all, right? You know, those well-posed Navier-Stokes equations. Right? Likewise, GCM hindcasts appear to held up as the gold standard in numerical climatology, such that if one can get a good “result” from a hindcast, then surely using the same methods to calculate the climate 90 years into the future will yield a similar level of accuracy. So much so that I can generate press releases, receive Nobel prizes, scare the public, impress my colleagues on the “Weather Channel”…
DirkH (09:02:20) : Very interesting. Now let’s look at this page in that documentation:
http://www.ccsm.ucar.edu/models/atm-cam/docs/description/
They describe….
“This page” is the table of contents of a document with 7 chapters and 3 appendices. So once again nobody has a clue what you are talking about.
Sorry,
Total Feb. mean = 20.5 million sq km
Should read Feb mean = 20.0 million sq km – GK
Oh the horrors of a longer growing season! AH!!!
I wonder if they “modelled” it by taking later weeks in the month and into April as their basis i.e. for March 2020 use the avg map for the first week in March up to the present, 2030, the second week avg, ……2090, the current avg last week in April. If one were going to do modelling, this would seem like a good way to do it. It also shows the arbitrariness of the model.