No, the President does not hold the ultimate power. The President cannot create new laws. The President cannot allocate spending. The President can attempt to deny Congress a new law, but Congress can override the President. And Congress can impeach the President - the President cannot remove a member of Congress. Congress holds the power, the President simply applies it as best he believes it should be applied.
Regulations arise out of legislation. At some point, a law was written that drove the creation of those regulations. It's why you can challenge regulations on the grounds they exceed the original intent or scope of the underlying law. But regulations are, in fact, the "details" about the law.
Go light up in front of the FBI or Federal court in your State, then. The Feds will still bust you. Just because local jurisdictions no longer emphasize enforcement of pot laws, doesn't mean they not exist. Feds arrested plenty of people in Colorado where pot was legal from a State standpoint...
Partially correct; you have to pay a use tax for products bought out-of-state. It is always pegged at the same rate as a sales tax, but it is not a sales tax - it is a use tax. And the purchaser must report it to their own state, for all out-of-state products you brought back to your state or purchased and had delivered.
Influenced and driven are diferent things. YOU influence the climate, but do you drive it? The bottom line is that the models simple don't match up with the actual evidence - which is exactly what you espouse you want. Dr. Spencer shows that very thing. So if the models are provably wrong (empirical data doesn't support the models), then why should we continue to rely upon the models to predict what could happen in the future?
Apple, just freaking buy them already. You'd have an instant 10+ million consumers, ecosystem with much better audio than Amazon, Google, or Microsoft - and can build up as you want. Crack open the checkbook, Tim, and write out a $3 billion check. And it's yours.
Mandatory overtime is legal in America, and is fairly common, so I don't see what the big deal is here. 11 hours of work isn't going to kill anyone, and the majority likely appreciated the extra pay.
You do know that Dr. Spencer does not deny warming? He's just questioning if it's driven by CO2 - because the CO2-based models don't match up with actual measurements. Plot CO2 increases relative to temperature and you'll find there is no correlation. Yet we continue to use models and decide policy for billions of people based upon the conclusion that it is all CO2 that drives any warming we are experiencing.
So just to clarify: your position is to keep using failed models (provably so) rather than start over? Ignore the data, it's the model and desired outcomes that matter?
Note that Dr. Spencer is not a "denialist", he's a NASA scientist who believes we are warming, but that CO2 is not the primary cause. He does what a scientist should do: collects data, and then draws conclusions. If his hypothesis doesn't stand up to the data, he tosses the hypothesis (which is pretty much exactly the opposite of what the IPCC does). And he's using the 90 models from the IPCC - not cherry picked at all. These are the actual models the IPCC uses to reach its claims. And continues to use, even though the data shows the models are wrong. Additionally, the data shows the warming "pausing" for around 10-12 years, from ~2002 to 2015.
In fact, there is ONE model that actually seems to fit the measured data: the oceanic oscillations model of Professor Don Easterbrook. His model not only tracks the historical record - but seems to match the current satellite record as well. I know that Professor Easterbrook is typically ignored by a lot of the pro-AGW side because he's not a "climatologist", but he is a geologist with a lot of good training, and a background in oceanic/land interactions. More importantly, his model actually fits better than those from the climatologists. Shall we ignore his model - even though it is better - because his background isn't "acceptable"?
I don't. Perhaps those who have models should work on improving them, because what they have now don't work at all for predicting what could happen. Or should we just accept continued reliance on models which are provably incorrect by a factor of 2 or more?
Methinks you have reading issues. From the article:
I’ve updated our comparison of 90 climate models versus observations for global average surface temperatures through 2013, and we still see that >95% of the models have over-forecast the warming trend since 1979, whether we use their own surface temperature dataset (HadCRUT4), or our satellite dataset of lower tropospheric temperatures (UAH)
OVER-forecast the warming trend. Then the graph (which should be easy to read) shows that 88 of 90 models show MORE warming than actual measurements, and the mean of those models is over twice that of the actual data. How you get from that to "underestimating", I'd like to see...
Ahhh. I see, an award winning NASA scientist, who presents data rather than models, is a quack. Attack the messenger, not the message - brilliant strategy!
Here you go. It was in my original post. Just take a look at the data, look at what the models predict. The data shows about 0.3 deg C increase in temperature. The mean of the models is over twice that. Many are pushing 3 to 4 times the actual measurements.
assuming Roy Spencer is correct.
uh-huh. You get to assume that the lone wolf is correct, but if I argue that knowledgeable people, who have studied the problem are correct i'm engaging in some sort of "if all your friends jumped in a lake" argument.
"my friends" believe CAGW because knowledgeable people who have studied the problem believe it.
Just look at the data. Dr. Spencer is presenting actual, measured data. The others? They are models, not data. So when actual measured data and models conflict - who wins? Who do you believe?
I don't think you know what you are saying. Only two of the models are more conservative than the actual measurements - the other 88 are over the measurements, and most by a factor of 3 or more.
Einstein on consensus. Just takes one set of data to invalidate a model. The fact that so many work so hard to obfuscate that fact of science - by appealing to consensus - makes most skeptics dig in further. It's OK to say your models are wrong - and get to work making them better.
And yes, Spencer's actual, measured data (NOT a model) starts from 1979 - a relatively short time! GIven that there is so much divergence between the model and actual measured data over such a short time, wouldn't that lead one to conclude that the errors in the model are not 2nd or 3rd order effects, but primary effects? If you can get factors of 2 or more divergence in a relatively short time - what confidence does that provide when extrapolating those models to 2, 3 or more times longer durations?
Sure, models are simplifications - but in this case the models are off by more than twice their error bars. And they continue to diverge even further. At what point do we choose to ignore what the "models say for the future" and go all-in on new models? Or do we keep basing decisions on the outputs of provably inaccurate models? If you were doing a circuit based upon V=I^3/R^5, and your data as you increased I for a given R was way off from what your model, at what point would you stop, examine the data and basic theories, and try to build another model?
When empirical data and theoretical models don't match - which do you trust? Dr. Spencer does what any good scientist or engineer should do - go with the actual empirical data.
This says NOTHING about whether or not climate change is happening or whether or not man is causing it; what it IS saying is that the models used to predict what could happen are turning out to be quite invalid, as they do not match the actual data. When the model fails to predict or match actual measurements - it's the model that needs to be corrected, yes?
Dr. Roy Spencer, funded solely by Government grants (not "Big Oil"), lays out the actual data and shows that 95% of all climate models agree that actual measured data is wrong. The models, basically, do not model actually all that well. Puts a bit of a damper on the whole "models assume we have negative carbon output!" kind of thing, doesn't it?
Counter-example: pretty much any bluetooth headset. They seem to work well for voice communications, even in noisy environment. Beamforming for earbuds/headsets is actually pretty simple as you can be reasonably assured of where the wearer's mouth is relative to your microphones. And the processing is quite mundane, even being a "freebie" feature in nearly every BT chipset (CSR, TI, etc). A few weeks of tuning, and your POLQA score should be well over 3.5 unless you really 1) suck at tuning the parameters for voice capture, or 2) chose really bad microphones with low SNR and high THD.
Slashdot Mirror
No, the President does not hold the ultimate power. The President cannot create new laws. The President cannot allocate spending. The President can attempt to deny Congress a new law, but Congress can override the President. And Congress can impeach the President - the President cannot remove a member of Congress. Congress holds the power, the President simply applies it as best he believes it should be applied.
But see, we're being robbed by the State, so it's OK because it's altruistic!
Regulations arise out of legislation. At some point, a law was written that drove the creation of those regulations. It's why you can challenge regulations on the grounds they exceed the original intent or scope of the underlying law. But regulations are, in fact, the "details" about the law.
Go light up in front of the FBI or Federal court in your State, then. The Feds will still bust you. Just because local jurisdictions no longer emphasize enforcement of pot laws, doesn't mean they not exist. Feds arrested plenty of people in Colorado where pot was legal from a State standpoint...
That law would get tossed on the grounds it contradicts Federal law. State law cannot override Federal law.
Partially correct; you have to pay a use tax for products bought out-of-state. It is always pegged at the same rate as a sales tax, but it is not a sales tax - it is a use tax. And the purchaser must report it to their own state, for all out-of-state products you brought back to your state or purchased and had delivered.
So what dataset supports the model results? If your theoretical model and empirical data don't match - which do you choose to believe?
Because his is actual data; the model results are simply outputs of, well, models.
Influenced and driven are diferent things. YOU influence the climate, but do you drive it? The bottom line is that the models simple don't match up with the actual evidence - which is exactly what you espouse you want. Dr. Spencer shows that very thing. So if the models are provably wrong (empirical data doesn't support the models), then why should we continue to rely upon the models to predict what could happen in the future?
Apple, just freaking buy them already. You'd have an instant 10+ million consumers, ecosystem with much better audio than Amazon, Google, or Microsoft - and can build up as you want. Crack open the checkbook, Tim, and write out a $3 billion check. And it's yours.
Mandatory overtime is legal in America, and is fairly common, so I don't see what the big deal is here. 11 hours of work isn't going to kill anyone, and the majority likely appreciated the extra pay.
Except for minors and teens. Sorry about that...
You do know that Dr. Spencer does not deny warming? He's just questioning if it's driven by CO2 - because the CO2-based models don't match up with actual measurements. Plot CO2 increases relative to temperature and you'll find there is no correlation. Yet we continue to use models and decide policy for billions of people based upon the conclusion that it is all CO2 that drives any warming we are experiencing.
So just to clarify: your position is to keep using failed models (provably so) rather than start over? Ignore the data, it's the model and desired outcomes that matter?
Note that Dr. Spencer is not a "denialist", he's a NASA scientist who believes we are warming, but that CO2 is not the primary cause. He does what a scientist should do: collects data, and then draws conclusions. If his hypothesis doesn't stand up to the data, he tosses the hypothesis (which is pretty much exactly the opposite of what the IPCC does). And he's using the 90 models from the IPCC - not cherry picked at all. These are the actual models the IPCC uses to reach its claims. And continues to use, even though the data shows the models are wrong. Additionally, the data shows the warming "pausing" for around 10-12 years, from ~2002 to 2015.
In fact, there is ONE model that actually seems to fit the measured data: the oceanic oscillations model of Professor Don Easterbrook. His model not only tracks the historical record - but seems to match the current satellite record as well. I know that Professor Easterbrook is typically ignored by a lot of the pro-AGW side because he's not a "climatologist", but he is a geologist with a lot of good training, and a background in oceanic/land interactions. More importantly, his model actually fits better than those from the climatologists. Shall we ignore his model - even though it is better - because his background isn't "acceptable"?
I don't. Perhaps those who have models should work on improving them, because what they have now don't work at all for predicting what could happen. Or should we just accept continued reliance on models which are provably incorrect by a factor of 2 or more?
I’ve updated our comparison of 90 climate models versus observations for global average surface temperatures through 2013, and we still see that >95% of the models have over-forecast the warming trend since 1979, whether we use their own surface temperature dataset (HadCRUT4), or our satellite dataset of lower tropospheric temperatures (UAH)
OVER-forecast the warming trend. Then the graph (which should be easy to read) shows that 88 of 90 models show MORE warming than actual measurements, and the mean of those models is over twice that of the actual data. How you get from that to "underestimating", I'd like to see...
Ahhh. I see, an award winning NASA scientist, who presents data rather than models, is a quack. Attack the messenger, not the message - brilliant strategy!
Does not invalidate his data, though.
Here you go. It was in my original post. Just take a look at the data, look at what the models predict. The data shows about 0.3 deg C increase in temperature. The mean of the models is over twice that. Many are pushing 3 to 4 times the actual measurements.
assuming Roy Spencer is correct. uh-huh. You get to assume that the lone wolf is correct, but if I argue that knowledgeable people, who have studied the problem are correct i'm engaging in some sort of "if all your friends jumped in a lake" argument.
"my friends" believe CAGW because knowledgeable people who have studied the problem believe it.
Just look at the data. Dr. Spencer is presenting actual, measured data. The others? They are models, not data. So when actual measured data and models conflict - who wins? Who do you believe?
I don't think you know what you are saying. Only two of the models are more conservative than the actual measurements - the other 88 are over the measurements, and most by a factor of 3 or more.
Einstein on consensus. Just takes one set of data to invalidate a model. The fact that so many work so hard to obfuscate that fact of science - by appealing to consensus - makes most skeptics dig in further. It's OK to say your models are wrong - and get to work making them better.
And yes, Spencer's actual, measured data (NOT a model) starts from 1979 - a relatively short time! GIven that there is so much divergence between the model and actual measured data over such a short time, wouldn't that lead one to conclude that the errors in the model are not 2nd or 3rd order effects, but primary effects? If you can get factors of 2 or more divergence in a relatively short time - what confidence does that provide when extrapolating those models to 2, 3 or more times longer durations?
Sure, models are simplifications - but in this case the models are off by more than twice their error bars. And they continue to diverge even further. At what point do we choose to ignore what the "models say for the future" and go all-in on new models? Or do we keep basing decisions on the outputs of provably inaccurate models? If you were doing a circuit based upon V=I^3/R^5, and your data as you increased I for a given R was way off from what your model, at what point would you stop, examine the data and basic theories, and try to build another model?
When empirical data and theoretical models don't match - which do you trust? Dr. Spencer does what any good scientist or engineer should do - go with the actual empirical data.
This says NOTHING about whether or not climate change is happening or whether or not man is causing it; what it IS saying is that the models used to predict what could happen are turning out to be quite invalid, as they do not match the actual data. When the model fails to predict or match actual measurements - it's the model that needs to be corrected, yes?
Dr. Roy Spencer, funded solely by Government grants (not "Big Oil"), lays out the actual data and shows that 95% of all climate models agree that actual measured data is wrong. The models, basically, do not model actually all that well. Puts a bit of a damper on the whole "models assume we have negative carbon output!" kind of thing, doesn't it?
Counter-example: pretty much any bluetooth headset. They seem to work well for voice communications, even in noisy environment. Beamforming for earbuds/headsets is actually pretty simple as you can be reasonably assured of where the wearer's mouth is relative to your microphones. And the processing is quite mundane, even being a "freebie" feature in nearly every BT chipset (CSR, TI, etc). A few weeks of tuning, and your POLQA score should be well over 3.5 unless you really 1) suck at tuning the parameters for voice capture, or 2) chose really bad microphones with low SNR and high THD.
Anything more than zero would be an issue, wouldn't it?