Preserved in Portico This version is not peer-reviewed
Causal Pathways for Temperature Predictability from Snow Depth
: Received: 2 May 2017 / Approved: 3 May 2017 / Online: 3 May 2017 (09:24:43 CEST)
: Received: 29 June 2017 / Approved: 30 June 2017 / Online: 30 June 2017 (12:04:04 CEST)
: Received: 22 August 2017 / Approved: 23 August 2017 / Online: 23 August 2017 (08:05:47 CEST)
A peer-reviewed article of this Preprint also exists.
Journal reference: J. Climate 2017
Subseasonal-to-seasonal (S2S) weather forecasting has improved in recent years, thanks partly to better representation of physical variables in models. For instance, realistic initializations of snow and soil moisture in models yield enhanced predictability on S2S time scales. Snow depth and soil moisture also mediate month-to-month persistence of near-surface air temperature. Here the role of snow depth as predictor of temperature one month ahead in the Northern Hemisphere is probed via two causal pathways. Through the first pathway, snow depth anomalies in month 1 cause snow depth anomalies in month 2, which then cause temperature anomalies in month 2. This pathway represents the snow–albedo feedback, as well as cooling due to insulation, emissivity and heat loss. It is active from fall to summer, and its effect peaks in March/April in the midlatitudes and in May/June at high latitudes. A complementary second pathway, where snow depth anomalies in month 1 cause soil moisture anomalies in month 2, which then cause temperature anomalies in month 2 through soil moisture–temperature feedbacks, is only active in spring and summer. Its effect peaks later in the warm season than the effect of the first pathway. Geographically, snow depth mediates north of, and soil moisture south of, the areas with the highest temperature predictability from snow depth. These results indicate that the two pathways describe complementary physical mechanisms. The first pathway embodies month-to-month persistence of snow depth, and the second pathway represents melting of snow from one month to the next.
soil moisture; snow cover; snow depth; seasonal prediction; land–surface feedbacks; cryosphere
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