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1. Improving predictions of snow resources using midlatitude SSTs with convergent cross mapping

   https://doi.org/10.1088/2752-5295/add362  

   Kim, Haejo; Fastovich, David; Bhattacharya, Tripti; Tuttle, Sam (May 2025, Environmental Research: Climate)

   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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2. California’s 2023 snow deluge: Contextualizing an extreme snow year against future climate change

   https://doi.org/10.1073/pnas.2320600121  

   Marshall, Adrienne M.; Abatzoglou, John T.; Rahimi, Stefan; Lettenmaier, Dennis P.; Hall, Alex (May 2024, Proceedings of the National Academy of Sciences)

   The increasing prevalence of low snow conditions in a warming climate has attracted substantial attention in recent years, but a focus exclusively on low snow leaves high snow years relatively underexplored. However, these large snow years are hydrologically and economically important in regions where snow is critical for water resources. Here, we introduce the term “snow deluge” and use anomalously high snowpack in California’s Sierra Nevada during the 2023 water year as a case study. Snow monitoring sites across the state had a median 41 y return interval for April 1 snow water equivalent (SWE). Similarly, a process-based snow model showed a 54 y return interval for statewide April 1 SWE (90% CI: 38 to 109 y). While snow droughts can result from either warm or dry conditions, snow deluges require both cool and wet conditions. Relative to the last century, cool-season temperature and precipitation during California’s 2023 snow deluge were both moderately anomalous, while temperature was highly anomalous relative to recent climatology. Downscaled climate models in the Shared Socioeconomic Pathway-370 scenario indicate that California snow deluges—which we define as the 20 y April 1 SWE event—are projected to decline with climate change (58% decline by late century), although less so than median snow years (73% decline by late century). This pattern occurs across the western United States. Changes to snow deluge, and discrepancies between snow deluge and median snow year changes, could impact water resources and ecosystems. Understanding these changes is therefore critical to appropriate climate adaptation. 

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3. Sources of seasonal water supply forecast uncertainty during snow drought in the Sierra Nevada

   https://doi.org/10.1111/1752-1688.13221  

   Boardman, Elijah_N; Renshaw, Carl_E; Shriver, Robert_K; Walters, Reggie; McGurk, Bruce; Painter, Thomas_H; Deems, Jeffrey_S; Bormann, Kat_J; Lewis, Gabriel_M; Dethier, Evan_N; et al (July 2024, JAWRA Journal of the American Water Resources Association)

   Abstract Uncertainty attribution in water supply forecasting is crucial to improve forecast skill and increase confidence in seasonal water management planning. We develop a framework to quantify fractional forecast uncertainty and partition it between (1) snowpack quantification methods, (2) variability in post‐forecast precipitation, and (3) runoff model errors. We demonstrate the uncertainty framework with statistical runoff models in the upper Tuolumne and Merced River basins (California, USA) using snow observations at two endmember spatial resolutions: a simple snow pillow index and full‐catchment snow water equivalent (SWE) maps at 50 m resolution from the Airborne Snow Observatories. Bayesian forecast simulations demonstrate a nonlinear decrease in the skill of statistical water supply forecasts during warm snow droughts, when a low fraction of winter precipitation remains as SWE. Forecast skill similarly decreases during dry snow droughts, when winter precipitation is low. During a shift away from snow‐dominance, the uncertainty of forecasts using snow pillow data increases about 1.9 times faster than analogous forecasts using full‐catchment SWE maps in the study area. Replacing the snow pillow index with full‐catchment SWE data reduces statistical forecast uncertainty by 39% on average across all tested climate conditions. Attributing water supply forecast uncertainty to reducible error sources reveals opportunities to improve forecast reliability in a warmer future climate. 

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4. A Database of Snow on Sea Ice in the Central Arctic Collected during the MOSAiC expedition

   https://doi.org/10.1038/s41597-023-02273-1  

   Macfarlane, Amy R.; Schneebeli, Martin; Dadic, Ruzica; Tavri, Aikaterini; Immerz, Antonia; Polashenski, Chris; Krampe, Daniela; Clemens-Sewall, David; Wagner, David N.; Perovich, Donald K.; et al (June 2023, Scientific Data)

   Abstract Snow plays an essential role in the Arctic as the interface between the sea ice and the atmosphere. Optical properties, thermal conductivity and mass distribution are critical to understanding the complex Arctic sea ice system’s energy balance and mass distribution. By conducting measurements from October 2019 to September 2020 on the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we have produced a dataset capturing the year-long evolution of the physical properties of the snow and surface scattering layer, a highly porous surface layer on Arctic sea ice that evolves due to preferential melt at the ice grain boundaries. The dataset includes measurements of snow during MOSAiC. Measurements included profiles of depth, density, temperature, snow water equivalent, penetration resistance, stable water isotope, salinity and microcomputer tomography samples. Most snowpit sites were visited and measured weekly to capture the temporal evolution of the physical properties of snow. The compiled dataset includes 576 snowpits and describes snow conditions during the MOSAiC expedition. 

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5. Persistent net release of carbon dioxide and methane from an Alaskan lowland boreal peatland complex

   https://doi.org/10.1111/gcb.17139  

   Euskirchen, Eugénie_S; Edgar, Colin_W; Kane, Evan_S; Waldrop, Mark_P; Neumann, Rebecca_B; Manies, Kristen_L; Douglas, Thomas_A; Dieleman, Catherine; Jones, Miriam_C; Turetsky, Merritt_R (January 2024, Global Change Biology)

   Abstract Permafrost degradation in peatlands is altering vegetation and soil properties and impacting net carbon storage. We studied four adjacent sites in Alaska with varied permafrost regimes, including a black spruce forest on a peat plateau with permafrost, two collapse scar bogs of different ages formed following thermokarst, and a rich fen without permafrost. Measurements included year‐round eddy covariance estimates of net carbon dioxide (CO₂), mid‐April to October methane (CH₄) emissions, and environmental variables. From 2011 to 2022, annual rainfall was above the historical average, snow water equivalent increased, and snow‐season duration shortened due to later snow return. Seasonally thawed active layer depths also increased. During this period, all ecosystems acted as slight annual sources of CO₂(13–59 g C m⁻² year⁻¹) and stronger sources of CH₄(11–14 g CH₄ m⁻²from ~April to October). The interannual variability of net ecosystem exchange was high, approximately ±100 g C m⁻² year⁻¹, or twice what has been previously reported across other boreal sites. Net CO₂release was positively related to increased summer rainfall and winter snow water equivalent and later snow return. Controls over CH₄emissions were related to increased soil moisture and inundation status. The dominant emitter of carbon was the rich fen, which, in addition to being a source of CO₂, was also the largest CH₄emitter. These results suggest that the future carbon‐source strength of boreal lowlands in Interior Alaska may be determined by the area occupied by minerotrophic fens, which are expected to become more abundant as permafrost thaw increases hydrologic connectivity. Since our measurements occur within close proximity of each other (≤1 km²), this study also has implications for the spatial scale and data used in benchmarking carbon cycle models and emphasizes the necessity of long‐term measurements to identify carbon cycle process changes in a warming climate. 

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