Contradictory curves = no cause and effect (apparently)


I found the following graphs at Joanna Nova’s post

Not FOUR degrees, 1.4 degrees

I think you can see that 1992-1993 the temps are well below the curve in the above graph.

Now look at those same years in the following graph.

SPPI is a site that has an axe to grind, but if these graphs run true, what could anyone ever be thinking if they assert that we get stronger storms when the global temperature rises?

Yes, 1998 has a big peak in that cyclonic energy graph AND has a huge peak in the blobal temperature graph, so one could – on that basis alone – start to think there is a connection.

But look at the cyclonic graph’s HIGHEST peak and that is right when the global temperature graph is at its lowest.

This strongly suggests the two phenomena are not connected.

But let’s not just look at those two sets of peaks and valleys.  Look at 1985.  Temps way down, cyclonic energy pretty much bout average.

2001 vs 1985:  Global temp about 0.5º C warmer in 2001, but the cyclonic energy is lower than  1985.

If there is a correspondence, it sure doesn’t jump out at you, does it?

Notice that I am just pointing at something, and not coming to a firm conclusion?  I say “apparently” there is no cause and effect, even though the curves seem to clearly show there isn’t a connection.  That is because one pair of charts doesn’t prove anything.  They are just part of the picture.  When the picture is confusing, it is wrong to come to conclusions.  It is enough to just file it away and maybe come back later, when more information is known.

Sometimes that is all you can do.

BTW, when trying to figure something out that includes data, I found years ago it is sometimes very useful to look at extreme cases in the numbers and see what is going on at those points.  When testing out formulas I write, I do that, to see if it is giving the right trend.  It will tell me if I probably made a mistake.  Not always!  Sometimes our intuitive idea of what is going on isn’t correct.  That is why science is so wrapped up in numbers.  Science is REALLY a lot into assigning numbers to things – just so they can begin to predict outcomes reliably.

When scientific predictions don’t pan out, it is a pretty good indication that the underlying thinking is not correct yet.

. . . . Steve

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