http://esr.ibiblio.org/?p=5297
"The most recent climate model simulations used in the AR5 indicate that the warming stagnation since 1998 is no longer consistent with model projections even at the 2% confidence level."
Kudos to them for publishing these, though. If they are rotten, it ought to be easy to tell.
Or, hell, any global warming denialist to publish predictions at all, or build any model at all beyond their day jobs of quote mining and writing clever PR releases that prey on the public's scientific illiteracy and fossil fuel barons' desire to violate other people's lives and properties with untrammeled carbon pollution.
This is not a political position, it is an expert's evaluation after having looked at the code and documentation for some of the better models. You simply can't parameterize away as much of the physics as they do, or impose conservation conditions by hand the way they do, and expect a long-term integration to produce anything but the crudest approximation to reality.
System-wide averages are likely accurate in terms of scale. That is, global heat content is increasing at the order of 1 W/m2, probably not 0.1E-2 W/m2 and certainly not 10 W/m2. This is important, because 1 W/m2 is consistent with observtions and likely problematic in terms of local climate. The global economy is heavily optimized to the current climate, and even relatively modest changes would turn trillions of dollars of investment into malinvestment. This is a bad thing, if you care about global capitalism.
Climate scientists are not computational physicists. They have not spent most of their careers modelling systems that are ultimately subject to laboratory testing, and as such they have not seen their best laid plans gang aft agley.
I have long wondered how much of the hubris in climate prediction was coming from the policy level and how much was coming from the scientists themselves. This release suggests that it really is coming from the scientists, and that's unfortunate. Twenty years from now most of these predictions will prove to be false. That falsity will be in all directions: some will have temperatures or rainfalls far too low, some far too high. And the enemies of science will use that to further attack us.
Unfortunately, when I stand up and say this, I get attacked as an enemy of science by people who think they love science, but who are actually drawn to it simply as a useful stick with which to beat their political opponents.
Also, using the phrase "global warming denialist" distracts from your point. When some scientists thought neutrinos traveled faster than light, they weren't called "light speed denialists". Have some respect for alternate scientific opinions.
The problem with GW predictions is that most of them are wrong and the ones that are correct aren't correct for the right reasons.
> Noting that “buoy data have been proven to be more accurate than ship data,” the new study applies a new “bias correction” to address the difference between them.
swoon
During the Climategate fiasco, Raymond's ability to read other peoples' source code (or at least his honesty about it) was called into question when he was caught quote-mining analysis software written by the CRU researchers, presenting a commented-out section of source code used for analyzing counterfactuals as evidence of deliberate data manipulation. When confronted with the fact that scientists as a general rule are scrupulously honest, Raymond claimed it was a case of an "error cascade," a concept that makes sense in computer science and other places where all data goes through a single potential failure point, but in areas where outside data and multiple lines of evidence are used for verification, doesn't entirely make sense. (He was curiously silent when all the researchers involved were exonerated of scientific misconduct.)
"My favorite part of the "many eyes" argument is how few bugs were found by the two eyes of Eric (the originator of the statement). All the many eyes are apparently attached to a lot of hands that type lots of words about many eyes, and never actually audit code." -Theo De Raadt http://marc.info/?l=openbsd-tech&m=129261032213320
> Most of the environmental movement is composed of
innocent Gaianists, but not all of it. There’s a hard
core that’s sort of a zombie remnant of Soviet psyops.[0] http://www.skepticalscience.com/ipcc-global-warming-projecti...
[1] http://www.realclimate.org/index.php/archives/2015/06/noaa-t...
[2] www.theguardian.com/environment/climate-consensus-97-per-cent/2014/jul/21/realistic-climate-models-accurately-predicted-global-warming
[3] http://skepticalscience.com/curry-mcintyre-resist-ipcc-model...
That doesn't feel like a particularly strong response to ESR's claim. Taken as a response to that claim, it feels kind of like: "our predictions were wildly inaccurate, but it's a complicated system with short-term and long-term factors and the short-term factors caused more warming just before we made our predictions and less just after, and that threw us off".
Which doesn't inspire confidence. If you can't predict the short-term factors, you need to widen your confidence intervals. I feel like at the very least, you should be making predictions like "conditional on these factors staying within these bounds, we expect this amount of warming". Then if that condition doesn't hold, you don't lose any bayes points.
(I recognize that the article wasn't written as a response to ESR, I'm not even sure it was a response to the same thing that ESR is talking about. But you offered it as a response, so that's how I'm evaluating it.)
The greatest changes in the new NOAA surface temperature analysis is to the ocean temperatures since 1998. This seems rather ironic, since this is the period where there is the greatest coverage of data with the highest quality of measurements – ARGO buoys and satellites don’t show a warming trend. Nevertheless, the NOAA team finds a substantial increase in the ocean surface temperature anomaly trend since 1998. ... The global surface temperature datasets are clearly a moving target. So while I’m sure this latest analysis from NOAA will be regarded as politically useful for the Obama administration, I don’t regard it as a particularly useful contribution to our scientific understanding of what is going on.
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The link above also includes more on some of the specifics of the new adjustments NOAA made.
Quote: 'These results do not support the notion of a “slowdown” in the increase of global surface temperature.'
http://dataserver3.nccs.nasa.gov/thredds/catalog/bypass/NEX-...
for the catalog. At that point you can browse the dataset, add the data as a data source, subset it (serverside), and visualize it. I and others have made a bunch of videos of how to use the IDV[2].
1. Visited https://www.unidata.ucar.edu/software/idv/webstart/IDV/
2. Got Unidata IDV running locally
3. I was then able to add the data source via URL and saw two options in "Field Selector": "Image Collection" and "Omni Control".
I've tried selecting either field and clicking "Create Display", but nothing ever appears in the "Displays" tab. Would welcome further advice or more specific reference to directions among the 16 videos in your YouTube playlist. Thank you!
This may be related to some of the errors[1] that seem to occur on startup of IDV.
What kind of agriculture? Every plant species is different, and various growing techniques are suited to different conditions.
What kind of habitat? Every dwelling is different, and every person has unique requirements of it.
See the problem?
I have 25 mbs which equals to 1041:40:00 (hh:mm:ss) That is 25 mbs perfect connection with no errors or drops. 1041 hours equals 43+ days.
You don't need to download the entire thing to get the gist of it, although it probably would have helped if they also delivered a digest version with for example yearly or 5-yearly averages.
Edit: There is also a THREDDS data catalog present which allows you to extract slices or subsets of the entire data. Other than that it's missing a nice visualization tool I'd say its a pretty reasonable way of making such a huge amount of data available.
EDIT: I should be clear. I would not be writing the code. I'd do the legwork to find someone (and pay them) who is familiar working with OSM to process the data, create the dataset, and build a javascript frontend to visualize it. The result would be open source. I would accept no funds whatsoever for this.
I don't have the time to do the project, but that doesn't mean I can find someone who does, and get them paid to do it and make the results freely available.
Lets say you express the predicted error in the data not as a percentage of temperature but as a time along the trendline. That would imply beyond a certain estimated error horizon, it doesn't really matter which year you download as long as you get the decade vaguely correct, once the temperature error figure exceeds a couple years of travel along the trend lines.
By example lets say today, here, 2080 CE, is 35C +/- 2C (to make the math easy) but the trend line is 0.1C/yr (unrealistic, but make the math easy) then +/- 2C is the same thing as saying +/- 20 years along the trendline, so I don't really need the 2080 data set I can do "roughly as well" with any data set downloaded from 2070 to 2090.
Yeah yeah I know calculus and the derivative is likely not constant or even linear and this is a really simplified way to look at statistics, but the general plan holds as a way to cut back on downloaded data required. Based on REAL statistical analysis you could come up with a formula that says you'll only increase the error bars 10% wider at year X if you skip every Y years of data. Where I'm guessing Y might exceed a decade at the extreme future years of the run. That could save an enormous amount of bandwidth without significantly impacting a visualization or analysis.
"Someone else" has to download it all, run the stat analysis, then tell us all, obviously it doesn't scale for all of us to download it all, run our own stat analysis, then go back in time and only download every Y-th year at or beyond year X. So thanks in advance, "Someone else"!
1 year of data would mean 120GB files or so (assuming 100 years total)
Breaking it down by lattitude would mean 800MB lattide-year average data sets. Breaking it down by longitude would be very helpful too, and not much reason to not make that breakdown in addition to the lattitude one, which means 5mb data chunks.
Its almost as though they are being intentionally uncooperative by dumping 12TB.
Hopefully, some group makes a web interface that allows downloads in these manageable chunk sizes.
What am I missing?
I know this because of the high pressure area that's currently sitting here and will take time to move.
Last weekend, however, it would have been practically impossible to tell whether it would rain here or not. There was a low coming in off the Atlantic, and if it had gone a little further North or South, it would have rained there instead of here.
Forecasters are nearly 100% accurate with what the weather will be. It's just the time and location of that weather that are tricky!