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Lectro was RIGHT--post1626--(climate related)

what about ice core data?

undeniable.jpg
 
pourdeac said:
Or 1978 where the CO2 crowd loves to start their awful analysis?....which also coincides with when the method of measuring solar output happened to change BTW. Completely coincidental I'm sure.

I'd prefer to go back to the original temp data sets that showed a pretty strong warming trend well before CO2 was ever released by humans but of course, those data have become so poisoned by the CO2 crowd I have no idea if you even believe them credible or not. This would be before all the "if the data helps demonize CO2 it's true" and "if the data refutes the CO2 correlation it must be wrong and changed" started. It's amazing how that happened every single time.
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So what was wrong with these data, that you previously dismissed. Please don't offer a flippant condescending response. I am an ornithologist and ecologist with an emphasis on quantitative ecology and population management, not a climate scientist. But I do have an interest in this because many of the wildlife populations that I/we study and manage are potentially greatly affect by climate change. For example, I study migratory red knots that winter in Tierra del Fuego and breed arctic Canada all indications are that the population is crashing (beginning as of ~1999) and there are two primary hypotheses: 1) conditions in the arctic (snow depth, cyclical precipitation) are strongly implicated as causal factors, or 2) food resources during migration at a stopover site in Delaware Bay were drastically reduced in the late 1990s because of unregulated fishing of Atlantic horseshoe crabs, thus affecting survival and reproduction. The referenced data and graphic seem to use actual measure temperatures back to the 1850's and use some other data source to estimate temps prior to that back 20000 years. That is a nice time series if you ask me.
 
birdman said:
You don't seem to understand what a statistical model of observational data is, but I can maybe help a little. When building statistical models of observational data we attempt to explain the observed patterns of the data and attempt to attribute the variation in those data to causes; with observational studies we often use correlative analyses to draw inference about how the world works. Yes, correlation is not causation, but that just means we need to be careful with our inferences it does not mean we should not use or trust a correlative study. There is nothing causal about time in a time trend analysis anyway, time typically is included to represent some other environmental variable that covaries with time. In this realm of modeling, we can compare the fit of a statistical regression model where time is the independent variable and global temperature is the dependent variable other with a null model, or intercept only model, that has no correlative relationship. We could even do models with percent of green land cover, or better yet atmospheric carbon as the independent variables. The formal comparison of the statistical models can involve things like evaluating r-squared values, or, better yet, throw it all into an information theoretic approach to directly compare model fit for multiple models simultaneously. Again, This type of statistical modeling is retrospective and attempts to explain patterns in data. They type predictive modeling that you are referring to in this post is kind of a different class of modeling, theory building and testing; statistical modeling is a type of theory building and testing but as I said before, it is focused on explaining the past, not necessarily predicting the future. Predictions about the future can be derived from a good statistical modeling analysis, but given the stochastic nature of the systems we are discussing here, wide variability and uncertainty in future projects are expected. That is where risk assessment comes in and we as a society have to assess what are we willing to risk give the uncertainty in the model predictions.

However, the original discussion here was about a time series analysis that infamously cuts that data off at a specific year to tell an advantageous story to climate deniers. So just like I am no longer referring the 97% consensus at your suggestion, I suggest you drop the global temperatures haven't changed in 20 years line because it is just plain wrong.
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LOL...good synopsis but you are forgetting one thing. I'm not the one making predictions, the people building the models are. THEY are building the theoretical statistical models explaining the past and THEY have then used those models to be predictive in order to describe the future risk of CO2....and been wrong all the time, about the past correlation AND future. Should we as a society establish risk based on modeling that is wrong 100% of the time?? LOL. So maybe you should go explain to them how they are all wrong, instead of trying to convince me.

Correlative models like these are tools. I use them all the time...and I'm not the one who decided they were infallible. They created a model that suggested CO2 might be an issue. Great. One can create models showing it's mostly solar. Linus Pauling believed in those models. We know basically nothing about most climate drivers. Nothing. Plus the magnitude of change is so tiny, just about ANY driver of climate could be statistically shown to correlate with temp because it's really within the noise of our measurements (if you're honest). That's how irrelevant the modeling really is.

But it still stands that an observational model like these that is not predictive is not valid or believable in science. That is fundamental Science 101. The models are tools to provide a possible rationale that needs to be proven. But here they have been wrong 100% of the time. Heck the original correlation wasn't even statistically meaningful, which is why they've had to backtrack and admit that there is no statistical correlation pre-1978. They didn't even apply stats correctly.
 
pourdeac said:
LOL...good synopsis but you are forgetting one thing. I'm not the one making predictions, the people building the models are. THEY are building the theoretical statistical models explaining the past and THEY have then used those models to be predictive in order to describe the future risk of CO2....and been wrong all the time, about the past correlation AND future. Should we as a society establish risk based on modeling that is wrong 100% of the time?? LOL. So maybe you should go explain to them how they are all wrong, instead of trying to convince me.

Correlative models like these are tools. I use them all the time...and I'm not the one who decided they were infallible. They created a model that suggested CO2 might be an issue. Great. One can create models showing it's mostly solar. Linus Pauling believed in those models. We know basically nothing about most climate drivers. Nothing. Plus the magnitude of change is so tiny, just about ANY driver of climate could be statistically shown to correlate with temp because it's really within the noise of our measurements (if you're honest). That's how irrelevant the modeling really is.

But it still stands that an observational model like these that is not predictive is not valid or believable in science. That is fundamental Science 101. The models are tools to provide a possible rationale that needs to be proven. But here they have been wrong 100% of the time. Heck the original correlation wasn't even statistically meaningful, which is why they've had to backtrack and admit that there is no statistical correlation pre-1978. They didn't even apply stats correctly.
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Please expand on this. Give sources and specific instances of where the predictions are wrong.
 
When you here things like 9 out of 10 scientist agree that smoking is bad for you, and your all like who the fuck is the 1. Pourdeac, Pourdeac is that 1.
 
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Only 60 Years of Farming Left If Soil Degradation Continues
Generating three centimeters of top soil takes 1,000 years, and if current rates of degradation continue all of the world's top soil could be gone within 60 years, a senior UN official said
The causes of soil destruction include chemical-heavy farming techniques, deforestation which increases erosion, and global warming. The earth under our feet is too often ignored by policymakers, experts said.
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https://www.scientificamerican.com/article/only-60-years-of-farming-left-if-soil-degradation-continues/
 
WFcatamount22 said:
When you here things like 9 out of 10 scientist agree that smoking is bad for you, and your all like who the fuck is the 1. Pourdeac, Pourdeac is that 1.
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Pourdeac, you must be wrong. How dare you stand alone against the the masses. This issue has been decided long ago. Your intransigence is just holding up progress. We do not need independent thinkers such as yourself to defy what we already know to be true. Good science is based on group-think. While no one is really able to poke holes in your logic, the numbers suggest you are wrong.

Just take the L and conform. You are standing in the way of history. Resistance is futile when there are so many billions of dollars riding on this.

Please step aside.
 
knowell said:
Pourdeac, you must be wrong. How dare you stand alone against the the masses. This issue has been decided long ago. Your intransigence is just holding up progress. We do not need independent thinkers such as yourself to defy what we already know to be true. Good science is based on group-think. While no one is really able to poke holes in your logic, the numbers suggest you are wrong.

Just take the L and conform. You are standing in the way of history. Resistance is futile when there are so many billions of dollars riding on this.

Please step aside.
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You'll notice Knowell, that is has been just about a month and Pourdeac still hasn't offered up one citation for the "100% wrong" claim that he mad. Earnestly willing to discuss this but your boy has offered nothing but flippant dismissive responses without backing up his claims.
 
birdman said:
You'll notice Knowell, that is has been just about a month and Pourdeac still hasn't offered up one citation for the "100% wrong" claim that he mad. Earnestly willing to discuss this but your boy has offered nothing but flippant dismissive responses without backing up his claims.
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He quits this thread every now and then because it's like banging his head up against the wall. He has explained himself in detail plenty of times, but nobody bothers to acknowledge it.
 
EatLeadCommie said:
He quits this thread every now and then because it's like banging his head up against the wall. He has explained himself in detail plenty of times, but nobody bothers to acknowledge it.
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Sure, seems like a potentially valid conclusion. An equally valid conclusion might be that when pressed for citations and support of his claim that 100% of climate change predictions have turned out to be wrong, he couldn't find any references so he stopped posting here. We won't know which is true unless pour comes back to enlighten us all.
 
pour's having no problem posting in the other forums...
 
birdman said:
You'll notice Knowell, that is has been just about a month and Pourdeac still hasn't offered up one citation for the "100% wrong" claim that he mad. Earnestly willing to discuss this but your boy has offered nothing but flippant dismissive responses without backing up his claims.
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LOL...other than having done this several times in the past on these boards over the past 15 years and I didn't want to go back yet again and dig it all up.....that's because it's been 100% wrong. They have never predicted anything accurately. When you look at the modeling from 16 years ago in the 1990 IPCC report some were predicting 6° increases by the end of the century. Figure 9 from the first IPCC report shows basically a 4° increase with "business as usual" emission (and that was their best guess if you look at Figure 8), now they are predicting maybe 1° maybe 2°. How they can even do that is a mystery (see below).

In section 11.2 they even state their only approach is observational modeling and about the need for its predictability BTW..exactingly my eralier point.

It's really easy to explain why they've been wrong. We just don't know anything about the real drivers. THEY even STATE that they don't know so you don't even have to believe my opinion. Here's an example from the original IPCC report.

The condensation of water is the main energy source of the atmospheric heat engine and the transport of water vapoui by the atmospheric circulation is a key process in the redistribution of the sun s energy in the Earth system Water vapour is also an important greenhouse gas The vertical distribution of latent heating in precipitating clouds has a large effect on the large-scale circulation of the atmosphere Precipitating clouds also play an important role in the general circulation through their effect on vertical transport of heat, moisture and momentum The inflow of fresh water at high latitudes is a major lactor in determining sea water buoyancy which forces the ocean cuculation The rates of accumulation of snow and the ablation of ice in the Antarctic and Greenland ice sheets are important sources ot uncertainty for sea level rise dunng the next century (Section 9) Changes in the hydrological regime, precipitation and evapoiation and consequent change in soil moisture and the availability of liesh water resources, are the most serious potential consequences of impending climate change in terms ot its elfect on man
Unfortunately, present quantitative knowledge of the Large-scale water budget is still very poor For example, it has not yet been possible to measure or deduce from existing measurements either global precipitation or global evaporation
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So the main mechanism of the atmospheric heat engine is the water cycle and water is also a greenhouse gas....more potent than CO2 BTW.....but they are stating that they do not understand the water cycle....AT ALL. Think about that one. We STILL don't know. If the water cycle lengthens slightly, that explains all of it.

The focus has just recently turned to the oceans as THE factor...because they can't explain the big pause in warming so they have concluded it must be the ocean acting as a heat sink.......duh. Even Michael Mann is hedging his bets (I also predicted years ago). Here is how the original models handled oceans as stated in the 1990 IPCC report.
The oceans are represented in most climate models in a very simplified way that does not propertly simulate the oceans ability to absorb heat and hnce retard global warming. These so called equilibrium models provide an estimate of eventual climate changes but not the rate at which these will take place. In order to predict the evolution of climate realistically, it is necessary to develop further a new generation of models in which the atmosphere and oceans are fully coupled....
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So here they are ADMITTING that they do not have realistic models....and that the only way to generate them would be to develop new models which couple the atmosphere and oceans....which comically has yet to occur. They seem to have just stumbled onto the fact that ocean temperature cycles exist in short, medium and, long 200+ year cycles. I pointed that out years ago on here.

Of course until the recent IPCC report they also believed in the CO2 driven /temp correlation throughout the 20th century.......LOL. Oops...epic modeling fail. That one fell due to improper stats, which was another problem back in 1990.

You can go through these reports and find them admitting to major gaping holes all over the place....not my words, theirs. Shall I continue? Want to talk solar? Maybe the outer atmosphere cooling and not warming?

Haha....and watch the recent spike in temps reverse now that the super El Nino is gone. The CO2 crowd has been all excited about the spike because it "proves" warming but we've had a very high surface temps in this El Nino, including the formation of the "blob" in the Pacific. Now that it's gone....temps will likely fall back, probably below the recent averages.
 
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Pour, you are some sort of physicist, correct? That might explain your intolerance of wide prediction intervals.

I asked for a citation for the statement that 100% of climate predictions are wrong and you respond with two or theee 25 year old predictions that were off by a few degrees. Ok, color me convinced.

You seem to have really high expectations for precision of prediction intervals. The systems these folks are trying to model are very stochastic and their data and parameters are subject to high parametric uncertainty. When you just look at the median prediction without considering the prediction interval, of course it is going to be wrong. I'll grant you that the number often reported is the measure of central tendency and not the upper and lower bound, but that is a failure of science communication, not a failure of science. To me, making a model to predict the future of a highly stochastic and uncertain system then pointing out caveats like, 'this is all highly dependent on the H2O cycle and we have high uncertainty in how that cycle works', is exactly how this should be done. In an applied context and management focused science we can't always wait until the model is perfect before making a prediction, we have to use the best available information to construct a model that will hopefully make useful predictions to help decision makers make good decisions. Waiting until you have all the information before build a model and making predictions might make the predictions more accurate, but you might also be waiting until it is too late for your model to be useful; i.e. Too late to do anything the amazingly precise prediction you have.
 
You have to make sure the captions are turned on so you can understand what they are saying.
 
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