Claiming that the forced climate response must be larger
than the weather noise for climate prediction on all time scales is just silly.
Not exact matches
[Response: Over short periods the size of the
weather noise is significantly larger
than the structural differences in the models.
Progress is continuing apace; and when (I choose to use that word rather
than «if») the science becomes more robust, and when (or if) the corresponding climate trends toward volatility of
weather emerge clearly from the background
noise of «natural» daily
weather, then more and more governments will find motivation to act.
The advantage of the ocean heat content changes for detecting climate changes is that there is less
noise than in the surface temperature record due to the
weather that affects the atmospheric measurements, but that has much less impact below the ocean mixed layer.
My opinion, judging from the amount of unforced (and hence unpredictable)
weather noise is that you would not have been able to say that even a perfect model was clearly better
than this.
But right now they're not much different
than the short - term
weather noise we've seen previously.
There is just too much
noise in my opinion and the
weather determines the monthly average more
than any ongoing anomaly.
But when, as with the Antarctica
weather station data we used, there is not only a lot of missing data and «
noise» but also greatly time - varying patterns of missingness (which stations have data missing), ridge regression (both mridge and iridge) can be expected to, and does, perform significantly better
than TTLS.
That is exactly what Schmidt is doing when he is «generating»
weather noise in his GCMs even if the model does something infinitely crudest
than DNS.
AR5 3.2.2.3 says of it «Overall, the SST data should be regarded as more reliable because averaging of fewer samples is needed for SST
than for HadMAT to remove synoptic
weather noise.