Details on the October ENSO Diagnostic Discussion: Trust, but verify.

Although Portugal and California are similar in their basic climate (e.g. both have a Mediterranean climate), what happens to one in response to an El Nino does not at all necessarily have bearing on what happens in to the other. Further, recent rains in Portugal have little influence on the ENSO situation. ENSO may affect rainfall, but the rainfall does not feedback onto ENSO in any substantial manner. Of course, Portugal has much less of a late winter impact from ENSO than does southern California.

This is an excellent question. In developing dynamical  models, historical data is indeed used to validate/verify/evaluate model performance, and also to tune some of the parameters used in the models. Therefore, dynamical models are expected to do slightly better in hindcast model than in real-time mode. But the difference between their performance during the two periods (hindcast vs. real-time) is normally smaller than that found in statistical models, which train completely using hindcast data. A good portion of a dynamical model has explicit and completely represented physics, and that part is not tuned. Only where the physics needs to be abridged, as for example in representing tropical convection using grid points larger than the spatial scale of the convection, is there wiggle room for some tuning. But the question is very good. We note in the graph that the errors during the last 2 to 3 years, the real-time period, look a bit larger than those during the hindcast period. That should make us a little bit more worried about how well we can trust the model for the current prediction of weak El Nino coming in the next several months. If there is an error similar to that of the last 2 years (where the outcome was cooler than the forecast), we could end up getting only a marginal El Nino condition instead of a full-blown weak El Nino as predicted. On the other hand, assuming the error will be as that found during the last 2 years is also questionable. A sample of two cases is highly inadequate for coming to a conclusion or decision. The bottom line is that we must wait and see, and should trust the model to at least a moderate extent.

The three missed forecasts for La Nina were those in the autumns of 2000, 2005 and 2008, as shown on the graph. All 3 cases ended up having weak La Nina. As for the ensuing winters in southern California (the part of the state most strongly affected by ENSO, especially in late winter), during Jan-Feb-Mar season we did observe drier than average conditions in 2001 (a few months after the missed forecasts were made), drier than average conditions in 2009, and in 2006 it was wetter than normal in central and northern California but near to slightly below normal in southern California. So we see that the observations did tend to follow the expected impacts, but not unfailingly. That is a typical rate of observing ENSO teleconnections--they show a tendency but not a guaranteed outcome. 

Yes, we are still in an "El Nino Watch" meaning we are expecting a full blown (weak) El Nino as of mid October 2014:

http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory…

Yes, there are satellites which are relied upon to give us a complete picture of many tropical Pacific Ocean variables, such as sea surface temperatures, sea level height (which is an indicator of heat content below the surface of the ocean), and rainfall.  I'm not familiar *which* satellites are relied upon, but suffice it to say without them, it would be difficult to measure what is happening with ENSO.

Tom did a nice job describing the IPCC consensus on how ENSO relates to climate change here:

http://www.climate.gov/news-features/blogs/enso/enso-climate-change-hea…

There will be future ENSO blog articles that touch on some of the cutting edge work done in this area. 

Good observation.  Since 2000, ENSO has shifted to lower variability, meaning we don't see the bigger amplitude events that we saw in the 80s/90s.  The models in general have more difficulty forecasting weaker events and capturing the correct onset time.  All those cases of misses/false alarms during OND since 2000, occured with the weaker events (< +/-1degC).  The jury is out on whether this weaker ENSO variability is associated with climate change and it is very difficult to determine with the short lenght of the historical record.  There are certainly papers that project weaker variability but there are also papers that suggest strong variability. 

Here is a nice article from the NOAA Northwest Fisheries Science Center that discusses the warm temperatures seen nearly along the entire eastern North Pacific Ocean:

http://www.nwfsc.noaa.gov/news/features/food_chain/index.cfm

I agree with their asessment that the cause is not straightforward at this time.  However, while we have not officially entered El Nino conditions at this point (ENSO depends on physics in/over the Pacific Ocean near the equator), it is not unusual for certain atmospheric and oceanic variables to behave "El Nino-like" prior to onset.  It is possible that warmer waters is consistent with the evolution *toward* El Nino, though the exceptionally above-average temperatures are certainly unusual and do not accompany every El Nino (or evolution toward El Nino).   

Good question.  The statistical and dynamical models shown in the popular IRI/CPC plume of models do not all have the same "initial condtions," meaning they do not all have the exact same input data.  Dynamical models often have a huge amount of atmospheric and ocean data, while the statistical models only receive input from less than a handful of variables (most often sea surface temperature).   Also, the input data into these models can be based upon different time averages.  In particular the statistical models often rely on monthly or seasonal (3-month) averaged inputs, while the dynamical models generally rely on daily or shorter averages.  So we tend to believe the dynamical models are at an advantage simply because they see the most recent observational evolution better than many of the statistical models.  However, there are certainly cases where statistical models performed better, so clearly this is only part of the story.  But overall, in the last 10 years, we have generally seen better model performance from the dyanamical models over the statistical.  Here is one paper that explores that:

http://journals.ametsoc.org/doi/full/10.1175/BAMS-D-11-00111.1 (is free/open access)