Mike Wallace writes about his new paper, with some references to his previous posts here:
First, a post on streamflow forecasting using solar cycles for high altitude catchments.
The new paper covers other directions as well, but perhaps of greater interest is its identification of a different ENSO parameter which ties everything together, namely the trade wind velocities at 800 mb across the western equatorial pacific. Until this point, solar climate researchers have not been able to identify a ‘bottom up’ or ‘top down’ solar cycle signature. The intermittent evidence challenged a comprehensive picture. This paper identifies a lagged solar cycle
signature from the bottom through to the TOA across the footprint of note.
The paper is here:
Michael G. Wallace, Department of Nanoscience and Microsystems Engineering (NSME), University of New Mexico, Albuquerque, NM, USA
Trade winds localized within the Western Equatorial Pacific express lagged and statistically significant correlations to sunspot numbers as well as to streamflow in rivers of the Southern Rocky Mountains. Both correlation sets were integrated in a linear regression analysis to produce relatively accurate sub-decadal streamflow forecasts for an annual and a 5-year average. In comparison to the autocorrelation technique, the prototyped method yielded the highest correlations, the highest goodness-of-fit scores, and the lowest root mean squared errors, for both the 5-year average and the annual average assignments. Of all of the cases examined, the highest Kolmogorov-Smirnov test scores between observation and prediction were found for the single solar-based forecast 5 years in advance for the 60-month average streamflow of the Animas River in New Mexico.
From the paper:
Summary and conclusions
Considerations of past research regarding solar cycles, Hadley and Walker circulation patterns, and streamflow characteristics of several mid-latitude, high-altitude watersheds have pointed to the potential for improved multi-annual to sub-decadal forecasting of streamflows in targeted locations. Such conditions appear to apply to
Northern Hemisphere watersheds of the Himalayas as well as the Southern Rocky Mountains of the Western USA.
Conditions also appear favorable in some Southern Hemisphere watersheds of the Andes, with the caveat that forecast spans are expected to be shorter in potential. The initial examples studied do not yet define any limit throughout the American Cordillera.
In the development of these conclusions, a set of correlations and linear regressions were explored for key sequential features based on previous published research and currently available solar (SSN), trade wind (TWWP), outgoing longwave radiation (OLR), geopotential height (Z), divergence of latent heat (LEDIV), and other indexes. Equivalent exercises were applied towards potential connections of some of those parameters to streamflow datasets for candidate streams of the RockyMountains. A two-stage regression-based forecasting approach was then applied to exploit some of the highest lagged correlations that were identified. The forecasts were compared to forecasts for the same streamflow
datasets via a conventional autocorrelation technique.
The training forecasts based on the new CRMA applications were generally the most accurate of all featured methods, for the series considered under a 5-year trailing average. Through a sequence of solar and trade wind regression exercises, forecasts for this set were advanced as far as 6 years into the future. The forecasts under the new method were also found to be more accurate than the conventional method under an annual average with a 2-year lead forecast approach, although the fidelity of all results was diminished in comparison to the 5-year average set of forecasts. An additional but limited investigation demonstrated high-fidelity 5-year trailing average forecasts for the Animas River based directly on solar cycles taking place 5 years in advance.
The success of the proposed methodology is expected to apply to other regions meeting the target criteria, including the Ganges River in India and the Rio Biobío in Chile. Subsequent exploration of monthly correlations between the TWWP and sunspot cycles suggests that, for appropriate locations, advances of hydroclimate forecasting accuracy with monthly resolution yet multi-annual lead times may also be possible through the new technique.