Why would you use a surface data set for modern temperature. Sure I can see the need to use a surface reconstruction to get an idea of what temperature was before the satalites were launched in the late 70s but continuing to use this outdated technology because its poor ad hoc nature suits your purpose is in a word stupid. That is like trusting an x-ray over a CT scan when diagnosing cancer. Its just stupid. The tecnhology used by in the surface reconstruction. Stevenson screens are 19th century technology. The technology used for the RSS satellite data set. Its amazing that so many on the warmmonger side have actually convinced themselves that the surface reconstruction is superior to the satellite record. Their bias ans delusion knows no bound. Now watch contrails fly off into a rant about all the problems with the satellite data pulled straight from Wikipedia which ignores that most of the "problems" are common to all reconstructions surface and satellite and usually less of an issue with the satellite data.
Why would you use satellite data with a know cooling bias? You'll notice that HadCRUT4 and GISS are both slightly cooler than UAH, the other satellite record. Which of the four major datasets appears to be the outlier here? I can't wait for you to show us how Dr. Roy Spencer is wrong in his analysis of recent RSS temperature trends.
For how long has there been no statistically-significant warming? In the following graph, the zone of statistical insignificance is shown in pale blue. Since the entire central trend-line falls within that zone, the warming since February 1996, more than 17 years ago, is not statistically distinguishable from zero. HadCRUt4: revision or revisionism?
Since it can take up to 20 years for temperature trends to reach the 95% confidence level, my guess would be about 18 years. Remember when skeptics jumped all over Phil Jones' statement in 2010 that there was "no statistically significant warming since 1995"? Remember when he followed up in 2011 that warming since 1995 had reached statistical significance? Is it any wonder that skeptics keep shifting the start date as the warming continues? Next year they'll be saying there's been not statistically significant warming since 1997.
Ha Ha Ha such a deluded post. News flash the el nino you were hoping and praying for turned into el nada. Now we are going to get a strong la nina which will push all trends down, way down. I expect a repeat of 1945-1976 with strong la nina domination of the pacific helping a prolonged cooling.
When NOAA puts the chances of El Niño at 60-65% during the Northern Hemisphere fall and winter, Who is being deluded here? Besides, when PDO cycles have a 20 to 30 year period, and we've only been in the cooling phase for about 15 years, your prediction could be half right. But when is the prolonged cooling going to start? Unlike the -.1 °C cooling from 1945 to 1976, temperature has risen .1 °C since the PDO cooling phase started in 1999.
I know, unlike most of the other people who deny AGW, you're in a class all by yourself. Lags are only present when there is feedback involved, such as the case with CO2, H2O and temperature. When there is a direct cause and effect link between PDO and surface temperature, the results would be much more immediate, just as they were during the last warm phase from 1977 and 1999.
In systems analysis you cannot assume that net positive and negative lags are equal. Just because the system responds quickly to a positive input does not mean that the system response has the same lag in response to a negative input. Lags are always present and exist on multiple orders. You can have multiple lags of different order present in the same system at the sane time. Your system response can be increasing as you have overcome a first order lag while being dampened by second and third order lags. All of this is basic systems analysis which is why so many engineers do not fall for AGW propoganda. Its clear that climate scientivists have no systems expertise at all yet what they are doing is at its core systems analysis which is an engineering skill. The physics is beyond our understanding so we are doing blackbox analysis. That is systems analysis. Scientivists make claims that tell anyone with a modicum of systems experience that scientivists dont know what they are doing.
This discussion of "lags" is a distraction. There are multiple timescales associated with the climate system response (even without feedbacks) but this doesn't make the problem a free-for-all. It is observably the case that the system's atmosphere and mixed layer respond rather rapidly to the diurnal cycle, seasonal cycle, volcanic eruptions, ENSO, etc, and with some slow component associated with the deep ocean, especially for the anthropogenic problem of interest. Climate models know about the heat capacity of the various reservoirs in the system, and so the lags we see in modeled responses are emergent, not fit to some pre-defined functional form. And the behavior looks very much like the real world-- e.g., we don't spontaneously see rapid cooling to some volcano 400 years ago, although several people have reported a deep ocean heat content residual to volcanoes 100 years ago that would presumably have a residual impact on surface temperature that is extremely small and undetectable. The seasonal cycle in models is also well-behaved. These fast and slow components are often separable in a rather useful way (e.g., this paper). Any epistemological framework for how we understand (or don't understand) climate needs to be first and foremost constrained by the emergent simplicity we observe in the real world between forcing and response.
Does the physicist want to argue systems with the engineer. This should be fun. That is like the guy thinking he can go from his church flag football league to the NFL. Did you evr even taken a single signals and systems class?
You can argue all you want. I know many engineers who think they know everything about how we should study planetary atmospheres and physics in general. What you think you know needs to apply sensibly to the system we are actually talking about (climate) and be congruent with the type of emergent simplicity I am speeaking of.
The models all of this is based on are notorious for not including things like ENSO or AMO, for one because they cannot predict either because they don't understand enough about them. They cannot model cloud formation accurately, They do not model a host of things they do not understand or do not have accurate data for. For instance, deep ocean temperatures. The hypothesis now is that is where the warming is hiding yet since the ocean is the biggest heat sink we are talking about a change of thousandths of degree to match the alleged heat missing which is well inside the margin of error for the measuring system. On top of that, the actual measuring period are quite short to really understand trends. So modelling is valuable as a tool to check against what is known and have recently shown that not enough is known to be able to model correctly. Most of the money has been put into modeling and more should be put into field work. Unfortunately the models and science have been used politically. Not enough is really known about how the sun affects the planet because it is not just total radiance but sun spot activity, solar wind, and magnetic variations that all affect the earth but are incompletely understood.
No you simply know that in the real world companies dont let PhD do everything in house. Every good firm hires engineers to do their systems analysis. The PhDs in physics have to send their ideas to the engineering department who do a systems analysis and tell the PhD if he is on the right track or full of (*)(*)(*)(*). Its usually the latter. The reason these engineers exist is that in private companies unlike universities they cant afford to be wrong most of the time. PhDs would rather over simplify the system and ignore all known unknowns and not even think that unknown unknowns even exist. The very first thing you learned to do in your first physics class was to over simplify the system. You continue to do that to this day.
Windigo- You simply don't know what you're talking about with respect to climate analysis. Projecting that onto what degree someone got, where people work, what class they took, etc, isn't very productive. Your time would be better spent reading the literature or buying a textbook on climate, and then attempting to make a coherent argument.
All current coupled models produce ENSO and AMO, etc as an emergent property of their dynamics. If we're going to criticize models, which there's always plenty of room to do in an intelligent way, let's at least understand what they do and what they are intended for. Of course there are things we don't understand. That doesn't mean we don't understand anything.This is a critical fallacy that keeps pseudoscience in all fields alive.
No they dont the produce overly simplified and quasi periodic events that in no way are actual physical models for the actual event. When the base code for most climate models was first written we didnt even know of the existence of most of the ocean cycles. They were patched in after the fact, poorly.
Once again, you have no clue what you are talking about. Read Ch. 9 in the AR5 WG1 report or start with this paper. ENSO is a quasi-periodic manifestation of internal variability, and present-day models have improved considerably in its representation, and most models now bear considerable similarity to observed ENSO properties, which also extends to how ENSO projects onto regional climate anomalies. There's much more than just its periodicity. There's always room to criticize whether the spectra, amplitude, teleconnections are robust (or whether the observations are long enough to test model performance) but to say they don't produce such shows immediately that the commenter has never even looked at model output. I'm sure there are thousands of papers on this topic of how we model ENSO. I'll be the first to tell you that models are not perfect and contain a lot of approximations...demanding perfection will just lead to disappointment, but they are demonstrably skillful and also just one tool in the study of climate. There are numerous examples to look at to see model skill, and a lot of documentation of their biases and performance (e.g., see this, this, this, etc). But you need to first understand why we use models, the philosophy, and what we expect to tease out of them (see this TED talk), and what we can/cannot tease out of them before it's worth interrogating model output in any detail (ENSO behavior, sea ice, or otherwise). Then, once we interrogate model output, we can test explanations for why they may not be so good in some respects and very good at others, and whether it matters. We cannot have this discussion without the basics, however, so I'll leave it at that until you do a bit of reading on the subject. Talk about the church football team...
There is a huge difference between actually modeling the ENSO which is a wind an solar driven physical process that we still lack enough understanding to trully model and simply having a random number generator throw out random Pacific warming and colling every decade give or take 3 years. The fact thst you actually think that a randomly generated pattern is adequate just speaks to ivory tower ignorance. You arent modeling the real climate you are playing Simcity.
All irrelevant...this isnt how enso is modeled. Thanks for trying but I hope you put more pride in your career as an engineer than in your education in climate.
That is yhe basics of how yhe various ENSO oscillator models work. The most funny thing with the different conceptual ENSO oscillator models being used most are capable of producing ENSO like oscillations despite each claiming to simulate a different proposed mechanism. That means that the ENSO is what is called in systems as a known unknown.
I'm seeing a fine example of the Engineer's Arrogance Fallacy. And I say that as an engineer. It's when an engineer declares how, in a remarkable coincidence, his specialty just happens to perfectly explain whatever is being discussed, no matter what the topic is. And it always supports his conclusions. And if it's outside his specialty, it doesn't matter, by definition. Climate science is such a complex field, it requires generalists who understand multiple disciplines. Tunnel-vision engineers aren't capable of seeing the big picture, so they always mess it up badly.
