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

An extraordinary gathering at the United Nations on September 21 may have permanently changed how the world deals with antibiotic resistance, which is believed to kill 700,000 people around the world each year.

During the UN meeting, the entire assembly signed on to a political declaration that calls antibiotic resistance “the greatest and most urgent global risk.” But it is what they do next that will determine whether the threat can really be contained.
http://news.nationalgeographic.com/2016/09/antibiotic-resistance-bacteria-disease-united-nations-health/?linkId=29137110
 
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TownieDeac said:
Better read https://www.washingtonpost.com/news...ght-climate-change-and-protect-food-security/
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Actually, it is just more doom and gloom predictions that will not come true - just like their predecessors. Or are they "super serial" this time? This article makes the same kinds of predictions debunked in sailor's article - they just changed the numbers and the dates.

Climate change has already begun to affect the world’s food production, a new report from the United Nations warns — and unless significant action is taken, it could put millions more people at risk of hunger and poverty in the next few decades.
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Here’s a list of predictions made with much fanfare and extensive coverage in the media in the 1970s, when I was young and green, in both senses of the word:

the population explosion would be unstoppable;
global famine would be inevitable;
crop yields would fall;
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TownieDeac said:
"debunked"
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Yes, by the IPCC...ya know the consensus pushers.

The ironic thing about that article is that it gets to the primary issue the Shrub administration was advocating, but back then it was rubbish because they made the case that the cause of warming didn't matter.
 
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birdman said:
Temps have not been flat since 2000. That is just not true. One outlier record setting El Niño in 1998 does not undermine decades of a positive trend. Unless you cut your time series off at just the right year. I suggest you use a little more rigor in your time series regression models in the future to avoid erroneous conclusions.
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Decades of a positive trend?? Depends on where you cut off your time series doesn't it?
 
ImTheCaptain said:
pour doesn't believe in modeling
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Modeling that has never been predictive when the standard for observational science is....that the model is predictive. No I don't, and neither should anyone else. If light didn't bend around the sun, we wouldn't just keep believing in the theory of relativity because it "has" to be true.
 
pourdeac said:
Decades of a positive trend?? Depends on where you cut off your time series doesn't it?
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Where would you like to cut it off? How about we use all of the available data instead of harping on an awful analysis and arbitrarily start in 1998?
 
pourdeac said:
Modeling that has never been predictive when the standard for observational science is....that the model is predictive. No I don't, and neither should anyone else. If light didn't bend around the sun, we wouldn't just keep believing in the theory of relativity because it "has" to be true.
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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.
 
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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abandon all hope, ye who enter here
 
birdman said:
Where would you like to cut it off? How about we use all of the available data instead of harping on an awful analysis and arbitrarily start in 1998?
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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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