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Montane snowpack in the Sierra Nevada provides critical water resources for ecological functions and downstream communities. Forest removal allows us to manage the snowpack in montane forests and mitigate the effect of climate on water resources. Little is known about the mid- to long-term effects that changing snowpack following forest disturbance has on tree re-growth, and how tree re-growth might in turn affect snowpack accumulation and melt. We use a 1-m resolution process-based snow model (SnowPALM) coupled with a stand-scale ecohydrological model (RHESSys) that resolves water, energy and carbon cycling to represent tree growth, and to quantify how trees and snowpack co-evolve following two disturbance scenarios (thinning and clearcutting) over a period of 40 years in a small 100 m x 234 m mid-elevation forested area in the Sierra Nevada, California. We first calculate the impact of forest disturbance on the snowpack assuming no tree regrowth and then we compare it with scenarios that include the feedback of trees regrowth on the snowpack. Without tree regrowth, snow accumulation and melt volume increase on average by roughly 5 % and 13 % following thinning and clearcutting, respectively. With tree regrowth, a regrowth rate of 0.75 and 1.15 m/decade are found for thinning and clearcutting, respectively, along with a decrease of melt volumes of 2.5 to 0.9 mm/decade, respectively. About 50 % of the snowmelt volume gains from forest thinning are lost after 40 years of regrowth, whereas only about 7 % is lost from clearcutting after the same period, which are largely explained by changes to canopy interception and sublimation. This proof-of-concept study is expected to shed light into the coevolution of montane forests and snowpack response to forest disturbance. 

Abstract California experienced a historic run of nine consecutive landfalling atmospheric rivers (ARs) in three weeks’ time during winter 2022/23. Following three years of drought from 2020 to 2022, intense landfalling ARs across California in December 2022–January 2023 were responsible for bringing reservoirs back to historical averages and producing damaging floods and debris flows. In recent years, the Center for Western Weather and Water Extremes and collaborating institutions have developed and routinely provided to end users peer-reviewed experimental seasonal (1–6 month lead time) and subseasonal (2–6 week lead time) prediction tools for western U.S. ARs, circulation regimes, and precipitation. Here, we evaluate the performance of experimental seasonal precipitation forecasts for winter 2022/23, along with experimental subseasonal AR activity and circulation forecasts during the December 2022 regime shift from dry conditions to persistent troughing and record AR-driven wetness over the western United States. Experimental seasonal precipitation forecasts were too dry across Southern California (likely due to their overreliance on La Niña), and the observed above-normal precipitation across Northern and Central California was underpredicted. However, experimental subseasonal forecasts skillfully captured the regime shift from dry to wet conditions in late December 2022 at 2–3 week lead time. During this time, an active MJO shift from phases 4 and 5 to 6 and 7 occurred, which historically tilts the odds toward increased AR activity over California. New experimental seasonal and subseasonal synthesis forecast products, designed to aggregate information across institutions and methods, are introduced in the context of this historic winter to provide situational awareness guidance to western U.S. water managers. 

Abstract The Sierra Nevada has experienced unprecedented wildfires and reduced snowmelt runoff in recent decades, due partially to anthropogenic climate change and over a century of fire suppression. To address these challenges, public land agencies are planning forest restoration treatments, which have the potential to both increase water availability and reduce the likelihood of uncontrollable wildfires. However, the impact of forest restoration on snowpack is site specific and not well understood across gradients of climate and topography. To improve our understanding of how forest restoration might impact snowpack across diverse conditions in the central Sierra Nevada, we run the high‐resolution (1 m) energy and mass balance Snow Physics and Lidar Mapping (SnowPALM) model across five 23–75 km²subdomains in the region where forest thinning is planned or recently completed. We conduct two virtual thinning experiments by removing all trees shorter than 10 or 20 m tall and rerunning SnowPALM to calculate the change in meltwater input. Our results indicate heterogeneous responses to thinning due to differences in climate and wind across our five central Sierra Nevada subdomains. We also predict the largest increases in snow retention when thinning forests with tall (7–20 m) and dense (40–70% canopy cover) trees, highlighting the importance of pre‐thinning vegetation structure. We develop a decision support tool using a random forests model to determine which regions would most benefit from thinning. In many locations, we expect major forest restoration to increase snow accumulation, while other areas with short and sparse canopies, as well as sunny and windy climates, are more likely to see decreased snowpack following thinning. Our decision support tool provides stand‐scale (30 m) information to land managers across the central Sierra Nevada region to best take advantage of climate and existing forest structure to obtain the greatest snowpack benefits from forest restoration.