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Title: Spring Onset Predictability in the North American Multimodel Ensemble

Abstract

Abstract The predictability of spring onset is assessed using an index of its interannual variability (the “extended spring index” or SI‐x) and output from the North American Multimodel Ensemble reforecast experiment. The input data to compute SI‐x were treated with a daily joint bias correction approach, and the SI‐x outputs computed from the North American Multimodel Ensemble were postprocessed using an ensemble model output statistic approach—nonhomogeneous Gaussian regression. This ensemble model output statistic approach was used to quantify the effects of training period length and ensemble size on forecast skill. The lead time for predicting the timing of spring onset is found to be from 10 to 60 days, with the higher end of this range located along a narrow band between 35°N to 45°N in the eastern United States. Using continuous rank probability scores and skill score (SS) thresholds, this study demonstrates that ranges of positive predictability of SI‐x fall into two categories: 10–40 and 40–60 days. Using higher skill thresholds (SS equal to 0.1 and 0.2), predictability is confined to a lower range with values around 10–30 days. The postprocessing work using joint bias correction improves the predictive skill for SI‐x relative to the untreated input data set. Using nonhomogeneous Gaussianmore » regression, a positive change in the SS is noted in regions where the skill with joint bias correction shows evidence of improvement. These findings suggest that the start of spring might be predictable on intraseasonal time horizons, which in turn could be useful for farmers, growers, and stakeholders making decisions on these time scales.« less

Authors:
ORCiD logo [1];  [1];  [1]
  1. Department of Earth and Atmospheric Sciences Cornell University Ithaca NY USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1441242
Alternate Identifier(s):
OSTI ID: 1441243
Resource Type:
Published Article
Journal Name:
Journal of Geophysical Research: Atmospheres
Additional Journal Information:
Journal Name: Journal of Geophysical Research: Atmospheres Journal Volume: 123 Journal Issue: 11; Journal ID: ISSN 2169-897X
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English

Citation Formats

Carrillo, Carlos M., Ault, Toby R., and Wilks, Daniel S. Spring Onset Predictability in the North American Multimodel Ensemble. United States: N. p., 2018. Web. doi:10.1029/2018JD028597.
Carrillo, Carlos M., Ault, Toby R., & Wilks, Daniel S. Spring Onset Predictability in the North American Multimodel Ensemble. United States. https://doi.org/10.1029/2018JD028597
Carrillo, Carlos M., Ault, Toby R., and Wilks, Daniel S. Tue . "Spring Onset Predictability in the North American Multimodel Ensemble". United States. https://doi.org/10.1029/2018JD028597.
@article{osti_1441242,
title = {Spring Onset Predictability in the North American Multimodel Ensemble},
author = {Carrillo, Carlos M. and Ault, Toby R. and Wilks, Daniel S.},
abstractNote = {Abstract The predictability of spring onset is assessed using an index of its interannual variability (the “extended spring index” or SI‐x) and output from the North American Multimodel Ensemble reforecast experiment. The input data to compute SI‐x were treated with a daily joint bias correction approach, and the SI‐x outputs computed from the North American Multimodel Ensemble were postprocessed using an ensemble model output statistic approach—nonhomogeneous Gaussian regression. This ensemble model output statistic approach was used to quantify the effects of training period length and ensemble size on forecast skill. The lead time for predicting the timing of spring onset is found to be from 10 to 60 days, with the higher end of this range located along a narrow band between 35°N to 45°N in the eastern United States. Using continuous rank probability scores and skill score (SS) thresholds, this study demonstrates that ranges of positive predictability of SI‐x fall into two categories: 10–40 and 40–60 days. Using higher skill thresholds (SS equal to 0.1 and 0.2), predictability is confined to a lower range with values around 10–30 days. The postprocessing work using joint bias correction improves the predictive skill for SI‐x relative to the untreated input data set. Using nonhomogeneous Gaussian regression, a positive change in the SS is noted in regions where the skill with joint bias correction shows evidence of improvement. These findings suggest that the start of spring might be predictable on intraseasonal time horizons, which in turn could be useful for farmers, growers, and stakeholders making decisions on these time scales.},
doi = {10.1029/2018JD028597},
journal = {Journal of Geophysical Research: Atmospheres},
number = 11,
volume = 123,
place = {United States},
year = {Tue Jun 12 00:00:00 EDT 2018},
month = {Tue Jun 12 00:00:00 EDT 2018}
}

Journal Article:
Free Publicly Available Full Text
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https://doi.org/10.1029/2018JD028597

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