Potential for western US seasonal snowpack prediction

Abstract

Western US snowpack-snow that accumulates on the ground in the mountains-plays a critical role in regional hydroclimate and water supply, with 80% of snowmelt runoff being used for agriculture. While climate projections provide estimates of snowpack loss by the end of the century and weather forecasts provide predictions of weather conditions out to 2 weeks, less progress has been made for snow predictions at seasonal timescales (months to 2 years), crucial for regional agricultural decisions (e.g., plant choice and quantity). Seasonal predictions with climate models first took the form of El Niño predictions 3 decades ago, with hydroclimate predictions emerging more recently. While the field has been focused on single-season predictions (3 months or less), we are now poised to advance our predictions beyond this timeframe. Utilizing observations, climate indices, and a suite of global climate models, we demonstrate the feasibility of seasonal snowpack predictions and quantify the limits of predictive skill 8 months in advance. This physically based dynamic system outperforms observation-based statistical predictions made on July 1 for March snowpack everywhere except the southern Sierra Nevada, a region where prediction skill is nonexistent for every predictor presently tested. Additionally, in the absence of externally forced negative trends in snowpack, narrow maritime mountain ranges with high hydroclimate variability pose a challenge for seasonal prediction in our present system; natural snowpack variability may inherently be unpredictable at this timescale. This work highlights present prediction system successes and gives cause for optimism for developing seasonal predictions for societal needs.

Keywords: climate; cryosphere; seasonal prediction; snowpack; water.

Fig. 1.

Mean March snowpack climatology for 1981–2016 at each model resolution (noted in the column headers). Observations (Bottom) are taken from snowpack point measurements (Right) and are regridded to native model grids. Simulated climatology of ensemble mean spring prediction values from previous July (8 mo in advance; Top) from three GFDL AOGCMs (described in Materials and Methods). Spatial resolution is coarsest in Left and finest in Right (200 km is the lowest resolution).

Fig. 2.

As in Fig. 1, observed (Bottom) and ensemble mean simulated AOGCM March predictions from previous July (Top) of snowpack anomalies in 2012–2015 relative to 1981–2016 mean. Note that, for simulated plots, points have been masked for only those with climatological (1981–2016) simulated SWE greater or equal to 1 cm.

Fig. 3.

Mountain range snowpack prediction skill measured by correlations (Spearman) between observed March snowpack and predictors available July 1 from AOGCM models (triangles, circles) or climate indices (squares) where higher absolute values represent greater skill, shown for (A) various mountain ranges and (B) ranges aggregated in increasing scale. Dashed lines provided for the value of the higher-resolution multimodel (50 km and 25 km) prediction for snowpack over the entire mountainous WUS (0.48) and the negative value (−0.48) to provide a reference for correlations with climate indices. Inset provided for ranges in highest-resolution model; the 200-km model has no ranges for northern and southern Sierra Nevada, Oregon Cascades, or Arizona and New Mexico (SI Appendix, Fig. S1).

Fig. 4.

Skill measured by correlation (Spearman) for temperature (A and B), precipitation (C and D), and storminess defined by 850-mb wind v-component following ref. (E and F) between November 1980 and February 2015 from July 1 initialization versus 0.5° observations. Points without statistical significance (P > 0.1) have been masked in white. Note that, unlike other prediction analyses, this figure provides predictions 4 mo in advance (from July 1 for November through February) to parse potential sources of predictability of March snowpack. Precipitation and temperature were downloaded from the University of East Anglia Climate Research Unit (https://crudata.uea.ac.uk/cru/data/hrg/). Wind data are from the European Reanalysis Interim product from the European Centre for Medium-Range Weather Forecasts (38).