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Abstract Accurate precipitation estimates are critical to simulating seasonal snowpack evolution. We conduct and evaluate high‐resolution (4‐km) snowpack simulations over the western United States (WUS) mountains in Water Year 2013 using the Noah with multi‐parameterization (Noah‐MP) land surface model driven by precipitation forcing from convection‐permitting (4‐km) Weather Research and Forecasting (WRF) modeling and four widely used high‐resolution datasets that are derived from statistical interpolation based on in situ measurements. Substantial differences in the precipitation amount among these five datasets, particularly over the western and northern portions of WUS mountains, significantly affect simulated snow water equivalent (SWE) and snow depth (SD) but have relatively limited effects on snow cover fraction (SCF) and surface albedo. WRF generally captures observed precipitation patterns and results in an overall best‐performed SWE and SD in the western and northern portions of WUS mountains, where the statistically interpolated datasets lead to underpredicted precipitation, SWE, and SD. Over the interior WUS mountains, all the datasets consistently underestimate precipitation, causing significant negative biases in SWE and SD, among which the results driven by the WRF precipitation show an average performance. Further analysis reveals systematic positive biases in SCF and surface albedo across the WUS mountains, with similar bias patterns and magnitudes for simulations driven by different precipitation datasets, suggesting an urgent need to improve the Noah‐MP snowpack physics. This study highlights that convection‐permitting modeling with proper configurations can have added values in providing decent precipitation for high‐resolution snowpack simulations over the WUS mountains in a typical ENSO‐neutral year, particularly over observation‐scarce regions.
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Improving predictions of snow resources using midlatitude SSTs with convergent cross mapping
Abstract Effective water resource management in the western United States (WUS) is possible only with accurate monitoring and forecasting of seasonal snowpack. Seasonal snowpack, a major water source for the WUS, is declining due to anthropogenic climate change. Overprinted on this trends is year-to-year variance in snowpack extent and mass due to influences from teleconnections related to the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO). Recently in the 2015 and 2016 winters, extreme droughts in the coastal WUS, mainly the Pacific Northwest (PNW) states of Washington and Oregon were linked with anomalously warm sea surface temperatures (SST) in northeastern Pacific Ocean. Here, we use convergent cross maps (CCMs) to analyze time series of SSTs and snow water equivalent (SWE) in the PNW. For some ecoregions, we show that extratropical SSTs may have a stronger influence on snowfall and snow accumulation in the PNW compared to tropical indices of climatic variability. Cold (warm) SSTs in the northeast Pacific lead to high (low) snow years. CCMs also performed better in recreating SWE anomalies compared to linear regressions with lagged predictor variables. Accounting for the influence of SSTs may help water resource managers to better predict and prepare for extreme snow events in the future.
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- Award ID(s):
- 2402498
- PAR ID:
- 10587381
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Environmental Research: Climate
- ISSN:
- 2752-5295
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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