PowerPoint Presentation - Presentation at Yale University

lModel-based estimates of natural internal variability are an integral component of D&A research

lWhy do we rely on models for these estimates?

∆They can be used to perform the control experiments that we can’t conduct in the real world

lWhy is it difficult to estimate natural internal variability from observations?

∆We want to estimate noise on multi-decadal to century timescales

∆Most observational records are too short for this purpose

∆Signal and noise are convolved – difficult to achieve unambiguous partitioning

Estimating the “noise” of natural internal variability


	Detecting “significant” climate change means showing that a given change in climate is unusual relative to some yardstick that you have of “normal” behavior.
	If you feel hot, and measure a body temperature of 102^oF, then you’ve “detected” a fever - you know that that this elevated temperature is outside the limits of “normal” body temperature.
	But this measurement doesn’t give you the cause of the fever.
	To deduce the cause, you’ll probably need to undergo some more sophisticated tests - perhaps urine and blood analyses, maybe even x-rays or CAT scans.
	Once you have the results from these tests, it’s much easier to narrow down the potential causes of the fever.
	This is attribution - the process of establishing cause and effect.