Beginning with impacts on winter (November-March) temperature, the chart below shows the median temperature anomaly for strongly negative, near-neutral, and strongly positive phases of 4 different large-scale sea surface temperature (SST) patterns. I've included the Atlantic Multidecadal Oscillation (AMO) for good measure as I've mentioned it a few times recently. Note that the data are from 1930-2015; strongly negative and strongly positive are defined as the bottom 10 and top 10 years respectively, while near-neutral consists of the 20 years in the middle of the 86-year distribution. Also note that I de-trended the temperatures before calculating the anomalies so that relatively warm and cold anomalies are more evenly distributed through the history.
The PDO and ENSO (El Niño/La Niña) impacts on temperature are as expected, with a negative PDO and La Niña both bringing cold winters, and with El Niño showing a less reliable warm influence than a positive PDO phase. The NPM appears to have little effect on temperature, but surprisingly the AMO phase is modestly influential, with a more positive phase correlated with warmer conditions.
Similar charts for winter precipitation and snowfall are shown below; the PDO and ENSO impacts are as expected, and the impact of the NPM is "opposite" to that of the PDO and ENSO, as I showed in my September talk. It's interesting to note that the strongly positive NPM phase brings more than twice as much precipitation as the strongly negative phase (median 3.65" vs 1.44"), but the snowfall is only 78% higher (55" vs 31").
The charts below show parallel results for the other seasons of the year, defined as April-May for spring, June-August for summer, and September-October for autumn. Vertical scales are left unchanged to highlight the relative magnitude of temperature anomalies and precipitation amounts in different seasons. There's a lot that could be said about the different patterns, but I'll leave additional comments for another time.
If you were to use these values to create a forecast for the next year, how would you weight them and what would that forecast be? Actually, you wound want to do some validation using previous climate periods. Either way, it would be interesting to see if there is some kind of predictive power in this.
ReplyDeleteI would put a reference to the NPM in the post for those who didn't follow your talk. And with the Blob decreasing in intensity, I expect that it's influence will be decreasing.
Eric, a simple forecasting approach - as I think you've suggested before - would be to perform a multivariate regression with the lagged index values. There would be a small amount of predictability, but most of the variance would be unexplained - otherwise seasonal forecasting would be easy. Nevertheless the AMO adds a bit of intrigue and it might be worth a simple experiment to see what pops out.
DeleteI added the NPM link, thanks.
Thank you Richard for a very nice synopsis...I've bookmarked this for future reference...and Eric's suggestion re: predictive power would be a future treat to review.
ReplyDeleteI'm interested in the mechanisms by which the major ocean anomalies might affect Interior Alaska's climate. Given the database for Fairbanks, I wonder if wind plot diagrams vary with the seasonal phase of the anomalies? Also, is cloud cover forced by their strength?
Gary
As I see it, the ocean anomalies affect the preferred circulation patterns, i.e. the location and strength of ridges and troughs, and these changes produce variations in airmass characteristics, wind, and cloud cover - which all affect the outcome in any given spot. It also seems clear to me that the North Pacific anomalies in particular directly affect the temperature of the low-level air throughout the region, i.e. a warm ocean begets warm air that is then transported throughout Alaska. But of course pinning down physical cause and effect is always a challenge in the climate system.
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