Claim: Ice core data shows CO2 controls temperature.
Response: CO2 levels in ice cores lag temperatures by 800+ years. The cause cannot follow the effect. Indeed, CO2 lags temperature on all timescales. Temperature changes drive CO2 levels. [At an epistemological level, ice core data cannot “show” anything other than what it is, ice core data. An interpreter of the data is necessary. And every interpreter has underlying assumptions.]
Claim: Other forces, like El Nino/La Nina, volcanoes and solar irradiance cannot alone explain all of the variability we’ve observed. Therefore, global temperature change cannot be understood without taking greenhouse gas emissions into account.
Response: Judith Curry has addressed this claim here.
[But even so, this is a logical fallacy known as ad ignorantum, an argument from ignorance. Just because something is not known does not make something else true.]
Claim: Florida is facing an impending disaster from sea level rise.
Response: Global sea levels naturally fluctuate. The rise in sea level has decelerated over the past 8,000 years, decelerated over the 20th century, decelerated 31% since 2002 and decelerated 44% since 2004 to less than 7 inches per century. There is no evidence of an acceleration of sea level rise. There is no evidence of any effect of mankind on sea levels. Indeed, rising sea levels in particular areas relate to land subsidence.
[This false thinking commits the fallacy of composition, piecing together an argument regarding the “whole” (global climate causing sea level rise) from a few fragments of specifics (in this case modeling results).]
Mathematician Kurt Gödel’s incompleteness theorems suggest that no matter how much climate mathematical modeling one does of the parts, it cannot reproduce the “whole.” The “whole” is always much more than what the sum of “parts” suggest. And in this case the global climate is much, much more than what any mathematical modeling of the parts suggest, and always will be. Observational science gives us an idea of physical state of the “whole.” Mathematical modeling cannot do this. And this is why observational science should be preferred and supplemented with modeling rather than the other way around (where only modeling is considered).