Why 'Climate Change' Alarmists Will NEVER Debate Skeptics

I understand. People are trying to tell me that my city is warming because we are entering this thing called "summer." But we all know how evil those scientists are so its all a lie. The warming is only happening in some places and in some cities. There are some cities that are even cooling! Last weekend was very cold!

 

Can you show these estimates are completely unreliable?

 

Because it works. The techniques used in climate science are no different than when they're used every other discipline of science. The thing with climate science is that the use of interpolation and other statistical techniques isn't even that impressive compared to what's being done in the other disciplines.

 

Why haven't we read papers by iamanonman? Why is temperature confused with climate?

 

Why is semantics confused with semantics?

I've just been accused... because someone is a dumb troll. Maybe the white race cannot be affiliated with Global Warming...

I'm perplexed.

 

I have, actually. So many times, I've lost count. As in, have cited, discussed, analyzed or otherwise used this time and again to demonstrate the bias in the data sets that have been used to fuel the trending data for most all temp warming over the proceeding 2 decades.

Ask this question. If there isn't a single thermometer in a region of over 10K square miles, how or why should an estimate of it's temperature fluctuations ever be considered reliable? No, make that broader. If there aren't thermometers in areas over 1 million square miles, why would you assume that the estimates are remotely reliable?

 

Impressive? As in, demonstrably inaccurate? So, you assert that "it works". Not exactly a very useful standard as it doesn't offer a qualification for quality. It works, suggests that because you're too lazy to instrument, you can simply guess to overcome the obvious bias of the data you no longer like to induce warming that isn't there, etc. Right? So, when you say, "Because it works", it seems perhaps a little self defeating since you cannot demonstrate real quality.

 

Wait, you are only looking at two decades? I am talking about 150 years worth of temperature data.

 

I doubt it. Of course, temp data sets have been "restated" using the same set of assumed data estimations. But, clearly, it doesn't matter to the conversation of asking why estimations are being valued at all.

 

Really? When the pseudo scientists use false hockey stick graphs and skew temperatures by using only those in cities and excluding temperatures in rural areas.

 

Again, what is wrong with the estimates for 150 years worth of data? How are they overestimating?

 

So, words do, in fact, matter. We have empirical data recorded manually that represent the historic temp data record. We also have "estimates" that were developed using models (which spit out results based on what the CO2 impact is assumed to have created, which FYI is incredibly inaccurate when compared to the actual historic temp records) which are being used to provide more data points in the data set that is historic. Of course, this isn't problematic, right? So, take temp records from the 1930s as an example. The "estimated" record diverges significantly by several degrees (as in the estimated temps are significantly lower than the historic temps recorded). Why? If the model demonstrably produces garbage, why give them weight or credibility?

 

It's only the conventional surface station datasets that must do the infilling or interpolation. Reanalysis uses a completely different method to measure every location on Earth and compute a global mean temperature. Reanalysis and conventional datasets agree with each other within a reasonable margin of error.

 

They don't use only cities. Conventional datasets use surface stations (land, ship, buoy) from all over the world both rural and urban (and over the ocean). And for the urban stations they specifically reduce the observed temperature from cities to offset the urban heat island effect. You are okay with them adjusting urban observations downward aren't you?

 

Which reanalysis uses CO2 concentration as part of its logic for measuring the temperature? None of the reanalysis datasets I look at or which are referenced by the academic community do this.

 

We collect upwards of 100 million observations per day and assimilate them into reanalysis datasets which do not make any adjustments to the inputs at all. All they do is form 3D fields of the atmosphere that best fit the observations. And guess what...reanalysis confirms that conventional datasets that use infilling or homogenization techniques like NASA GISS are working pretty well.

 

Well, is NASA credible for something?

It's actually a good question for me, and the person who disparaged me.

 

Yes they are. And so are the dozens of other institutions which produce similar datasets.

 

One hundred and fifty years ago

Climate Alarmists Skew Data to Make Sea Level Rise Appear ...
https://principia-scientific.org/climate-alarmists-skew-data-to-make-sea-level-rise-appear-worse/
December 14, 2017 - Climate Alarmists Skew Data to Make Sea Level Rise Appear ... have been “corrected” in order to skew the data even more, ... Principia Scientific International ...

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Is the Sea Level Stable at Aden, Yemen?
https://link.springer.com/article/10.1007/s41748-017-0020-z
December 2017 - Analysis of the tide gauge data of Aden, Yemen shows that without arbitrary alignment of data, Aden exhibits very stable sea level conditions like those in Mumbai, India and Karachi, Pakistan, without any significant sea level trend.

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Climate change research is globally skewed -- ScienceDaily
https://www.sciencedaily.com/releases/2014/01/140122091736.htm
It could have political and societal consequences if there are regional shortages of climate scientists... Climate change research is globally skewed. ... have been .. "The tendency is a geographical bias where climate knowledge is produced mainly in the northern hemisphere, while the most vulnerable countries are found in the southern hemisphere. The challenge for the scientific community is to improve cooperation and knowledge sharing across geographical and cultural barriers, but also between practitioners and academics."

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Little Ice Age
https://en.wikipedia.org/wiki/Little_Ice_Age
The Little Ice Age ended in the latter half of the 19th century or early in ... not be maintained through... in the earth's orbit .. "The NASA Earth Observatory notes three particularly cold intervals: one beginning about 1650, another about 1770, and the last in 1850, all separated by intervals of slight warming.[5] The Intergovernmental Panel on Climate Change Third Assessment Report considered the timing and areas affected by the Little Ice Age suggested largely-independent regional climate changes rather than a globally-synchronous increased glaciation. At most, there was modest cooling of the Northern Hemisphere during the period." [10]

 

Bully for you. I wouldn't accept your assertion that "the academic community" don't do this, as I have cited that they, in fact, do. But whatever floats your boat.

 

So, if they "work pretty well" do the models so often get the temp data for locations so dramatically wrong? Take today, for example. The model for temp prediction for today is roughly 12F higher than the actual temp for this time period for today. But hey, guess which temp data the model is using? So, if by "pretty well" you mean unreliable, not useful, or otherwise entirely dismissable, they I guess you're right.

 

I'm talking about our measurement of the temperature at a specific moment in time. I wasn't talking about numerical weather prediction.

But, here are some interesting stats for NWP. NOAA's premiere global weather model (the GFS) has an RMS error on 1000 mb (near surface) temperatures of 0.8C for a 24 hour forecast. Even for a 120 hour forecast it is only 2.5C. And the worlds best model (the ECMWF) has an RMS error of only 0.65C for a 24 hour forecast. You can see the verification stats here. And again, these are the stats for forecasts. Obviously our measurement of the temperature at a specific moment in time is much better. One problem that's pretty ubiquitous in public perception of model skill is that there a negative selection bias. People only remember when models get things really wrong (and it does happen). They never remember when models gets things mostly right within a reasonable margin of error (which is the majority of the time). Think about it...how often do you post here when a model gets the high or low temperature in your area right?

 

Then all you have to do is point me to one reanalysis dataset that uses CO2 concentration specifically to derive a temperature as part of it's computation.

 

An interesting notion. This "within the margin of error" which then produces the majority of the inaccuracy that we then see vs actuals, right? So, "statistically" you discount these "errors" because you anticipated them. The reality though is that programmatically, these are structural errors in the actual calculations based on assumptions that are a basic part of the modeling process. And the observation is, those errors are the driving force behind the majority of the warming that we see in the estimated temp series. And because it is, that makes those "within the margin errors" quite significant, actually.

 

No it doesn't. Models and reanalysis divide the Earth into a grid mesh using 100,000+ cells for a modern implementation. Even if the RMS error of a temperature in an individual cell is 2C then the error of the mean over the whole Earth is S/sqrt(N) where S = 2 and N = 100000 so E = 2/sqrt(100,000) = 0.006C using pure statistical theory. In reality the RMS error is slightly higher than that for a global mean temperature anomaly for various reasons, but it's still on the order of 0.01C for a reanalysis dataset. The conventional datasets (like NASA GISS) have errors of about 0.05C for their global mean temperature anomalies. By the way, it's this error margin that forces the dataset publishers to give probabilities on yearly temperature rankings. Every measurement has error. This is ubiquitous in all disciplines of science. If you don't like these concepts then your going to be dissatisfied with pretty much all disciplines of science.