Remember, it was Christy who once bragged that his UAH satellite dataset was accurate to within 0.01C and infallible when it showed little or no warming. But, then after it was shown that he failed to account for orbital decay which result in a massive underestimation of the warming his tune changed quickly. He did eventually correct his dataset and now concedes that the Earth is, in fact, warming. And even as recently has a few months ago people in the academic community are still throwing up red flags that his dataset still has numerous significant problems. And how about Judith Curry. She was once a member of the Berkeley Earth team which was convened by skeptics to challenge the scientific record on the global warming record. It was quite a bombshell when they released their first dataset in 2012 which showed that not only did NASA, NOAA, etc. not fraudulently manipulate data as deniers claim, but that they are likely underestimating the warming. Naturally Judith Curry jumped ship and now claims that her role was minimal and advisory at best. She's even been critical of the work she helped publish and which had significant funding from skeptical groups. Isn't that interesting? It kind of makes you wonder if she feared losing funding for her "hurricane forecast" business that allegedly exists for the benefit of oil companies and which she has never published any details of her forecasts.
That is what you claim. I have communicated with Dr. Lindzen and those accusations are horrendous. I never talk like that about any of your side. As to Judith, more of what you claim. Why in her writings is she not making such claims? What propaganda on my part can you mean?
There are at a minimum two issues. 1. Is earth Warming. I have seen some say no. But the majority on my side, Curry and Lindzen say of course. 2. Man does this. I do not accept a bit of blame. So stop blaming me. I truthfully no longer care. I get sick of the name calling.
You do not debate. You insist i warm Earth on purpose. I am tired of your name calling. You complain all the time yet you do not have me jumping you for your whining.
And what does that have to do with Lindzen's bad science? Are you stating that since you talked to him, it means all his claims are right, and smoking isn't correlated with lung cancer? If you want to call their bad science horrendous, that's your choice. However, I wouldn't call their science horrendous. Scientists make mistakes. It's not a big deal to put forth a new theory and see it proven wrong. Just admit you were wrong and move on. What's horrible is how most denier scientists react to being proven wrong, which is to scream "fraud!" at the people who pointed out their error. Well, yeah. If our side doesn't act badly, it's hard to accuse them of acting badly. https://judithcurry.com/2013/10/10/the-stadium-wave/ --- “The stadium wave signal predicts that the current pause in global warming could extend into the 2030s,” Wyatt said, the paper’s lead author. Curry added, “This prediction is in contrast to the recently released IPCC AR5 Report that projects an imminent resumption of the warming, likely to be in the range of a 0.3 to 0.7 degree Celsius rise in global mean surface temperature from 2016 to 2035.” Curry is the chair of the Department of Earth and Atmospheric Sciences at the Georgia Institute of Technology. --- In 2013, Curry agreed that there would be no global warming into the 2030s. 2014-2015-2016 were record-breaking warm years. She was completely wrong. I said on her part, not yours. Her bad science isn't the propaganda. Her baseless accusations of fraud are the propaganda.
More good data from Curry. https://judithcurry.com/2018/06/07/...nal-to-interannual-predictability/#more-24138 Beyond ENSO: new signals of seasonal to interannual predictability Posted on June 7, 2018 by curryja | Leave a comment by Judith Curry My new talk on improving seasonal to interannual climate predictions. This week, I am attending the Weather Risk Management Conference (WRMA) in Miami. Utility of climate forecasts for risk mgt In providing forecasts for the private sector, I’ve come to realize that there is a gap between climate forecast information and the needs of users. Even if the scientific community believes certain information is valuable, users may not. There is also a gap between how users value forecast information compared to its quality. CFAN has focused its forecast efforts on bridging this gap, by providing predictions of extreme eventsand objective assessments of forecast confidence. Ensembles and probabilistic weather forecasting This figure illustrates the concept of probabilistic weather forecasting, using a global ensemble model prediction system. For each forecast, the global model produces an ensemble of multiple forecasts, initialized with slightly different conditions. The ECMWF model has an ensemble size of 51 forecasts. A single forecast (say the gold dot) may be rather far away from the actual observed outcome (the red dot). If the ensemble is large enough, meaningful probabilistic forecasts can be provided. The objective of the probability forecast is to bound the observed outcome (the red dot) in a probability space (reflected by the darker blue region) that is much smaller than climatology (the lighter blue region). The actual model prediction is characterized by the green region. The potential predictability of the model is characterized by the dark blue region. This potential predictability can be realized in a prediction through forecast calibration and ensemble interpretation techniques. Ensembles and probabilistic climate prediction The challenge for probabilistic climateprediction is that in order to encompass the observed outcome, the ensemble size needs to be very large and becomes as large as the climatology. The challenge is even greater when climate is changing, such as the slow creep of global warming or an abrupt shift in a climate regime such as the Atlantic Multidecadal Oscillation. Predictability, prediction and scenarios For weather prediction timescales of 2 weeks or less, ensemble prediction methods can provide meaningful probabilities. This time horizon is being extended into the subseasonal time frame, potentially out to 6 weeks. However forecasts beyond two months often show little skill, and probabilistic forecasts can actually mislead decision makers. On seasonal time scales, predictive insights are typically provided, whereby a forecaster integrates the model predictions with an analysis of analogues and perhaps some statistical forecast techniques. A predictability gap is seen around 1 year, where there is very little predictability. At longer time scales some predictably is recovered, associated with longer-term climate regimes. However at longer time horizons, predictions become increasingly uncertain. Possible future scenarios can be enumerated but are not ranked, e.g. because of ambiguity. A useful long range forecast keeps open the possibility of being wrong or being surprised, associated with abrupt changes or black swan events. Anticipating abrupt changes or black swan events would be most valuable. The key forecasting challenge is is to extend the time horizon for meaningful probability forecasts, and to extend the time horizon for predictive insights of the likelihood of future events. How can we extend these forecast horizons? ECMWF ENSO prediction skill Improvements to global climate models are helping extend the time horizon for meaningful probabilistic forecasts. This slide compares El Nino Southern Oscillation forecasts from the latest version of the European model with the previous model version. The y-axis is the initialization month, and the x-axis represents the forecast time horizon in months. The colors represent the strength of the correlation between historical forecasts and observations. The most notable feature of this diagram is the spring predictability barrier. If you initialize a forecast in April, it will rapidly lose skill by July, and the correlation coefficient drops below 0.7 (which is reflected by the white region). However, if the forecast is initialized in July, the forecast skill remains strong for 7 months and beyond. The skill for the new version of the ECMWF forecast model is shown on the right, and we see substantial improvement. While the color schemes are slightly different, you can see that the white region, indicating correlation below 0.7, is much smaller, indicating that the model performs much better during the springtime predictability barrier. The improved skill in Version 5 is attributed to improvements to the ocean model and also to parameterizations of tropical convection. Climate prediction: signal to noise problem On timescales beyond a few weeks, the challenge is to identify the predictable components. Predictable components include Any long-term trend Regimes and teleconnection patterns And any cyclical or seasonal effects Once you identify and isolate the predictable components, you can ride the wave. The challenge is to separate the predictable components from the ‘noise’. These include Unpredictable chaotic components Random weather variability Model error The biggest challenge is regime shifts, particularly when the shifts were triggered by random events. You may recall that in 2015, it really looked like an El Nino wanted develop. However, its development was thwarted by random but strong easterly wind outburst in the tropical Pacific. This failed 2015 El Nino set the stage for the super El Nino of 2016. Climate prediction: can we beat climatology? The big challenge in making a climate prediction is whether you can beat climatology. Forecast skill depends on several things. When the forecast is initialized relative to the annual cycle is an important determinant of skill. Also, a forecast initialized during a well-established regime, such as an El Nino, are more skillful. One of the most important predictive insights that a forecaster can provide is whether the current forecast can beat climatology The forecast windows of opportunityapproach identifies windows in time and space when expected forecast skill is higher than usual because of the presence of certain phases of large-scale circulation patterns. I often use a ‘poker’ analogy when explaining this to energy traders – you need to know whether to ‘hold’ or ‘fold’. In forecasting terms, this is the difference between a forecast with high or low confidence. Data-driven prediction methods
Where do you derive that Dr. Richard Lindzen has taught and used bad science? Maybe you think it is a different Lindzen. Nobody alive on this planet can predict climate for the next year, much less for the number of years told to us by the alarmists. Lindzen never discussed smoking with me. But he did tell me of his 245 papers and sent me the link. i have posted this here a number of times. This allows many to realize he is not working alone and cooperated globally with other scientists. Lindzen gets trashed yet he was so valuable he was part of IPCC for a time and he departed the group due to their politics. Frankly the alarmists drives me most. They are crazy. Show me a prediction made by them say in 1990 that came true?
hold on here, you were complaining earlier about rude behavior calling people names(a fair observation to be sure) but aren't you doing the same repeatedly using "alarmist" and now "crazy"
that's how the peer review process is supposed to work...present the hypothesis and the evidence that goes with it and see if it withstands scrutiny of your peers...it's a brutal intimidating process but that's why it works, the expertise of the peers will shred it if they find the hypothesis isn't supported by the evidence.
And out of those 245 papers I could only find ONE that talked of climate change not happening and it was published in a medical journal As for the rest. Here are his claims all neatly debunked by science https://www.skepticalscience.com/skeptic_Richard_Lindzen.htm
She makes her living due to climate changing. I have repeated over and over climate changes. Do you make your living due to climate changing?
Well, on crazy you have a good point. Folks, I am bewildered and erred by calling them crazy. Alarmist is not rude.
Let's try this on for size since it has worked for billions of years. Climate is always changing. The reasons are so varied it depends on where on the earth one is, what the landscape is like, and the proximity to the two poles. I use as an example of the climate that produced first the great glaciers that covered much of north America at one point that once the warming proceeded, said glaciers melted and deposited the great lakes in the north of the USA and the South of Canada. We also can cite the glacier periods that created Yosemite park. They came and carved the canyons and left. They returned later and carved more and left. And returned one more time to carve and left. Clearly humans played no role.
so why is it changing NOW? It is a big system and like all big systems requires some input to overcome inertia What is currently causing the change?
Then what are the drivers? Again you are dodging " Because it can" is not an answer We know what has caused the changes in the past, Milankovitch cycles, volcanic activity , Solar cycles etc
Covered in the previous post. And you'll ignore it. That's an example of propaganda, being how it's a false claim pushed for political purposes. The climate science predictions have been very good. If you don't know that, it indicates your knowledge of the science is poor. Our knowledge of the science is excellent, so we know with 100% certainty that your claim is false, same as if a flat-earther told us the earth was flat. It doesn't matter if the flat-earther sincerely believes it, it's still a false claim.
