Evaluation of snow cover fraction for regional climate simulations in the Sierra Nevada

Mountain snow cover plays an important role in regional climate due to its high albedo, its effects on atmospheric convection, and its influence on lower-elevation runoff. Snowpack water storage is also a critical water resource and understanding how it varies is of great social value. Unfortunately, in situ measurements of snow cover are not widespread; therefore, models are often depended on to assess snowpack and snow cover variability. Here, we use a new satellite-derived snow product to evaluate the ability of the Weather Research and Forecasting (WRF) regional climate model with the Noah land surface model with multiparameterization options (Noah-MP) to simulate snow cover fraction (SCF) and snow water equivalent (SWE) on a 3 km domain over the central Sierra Nevada. WRF/Noah-MP SWE simulations improve upon previous versions of the Noah land surface model by removing the early bias in snow melt. As a result, WRF/Noah-MP now accurately simulates spatial variations in SWE. Additionally, WRF/Noah-MP correctly identifies the areas where snow is present and captures large-scale variability in SCF. Temporal RMSE of the domain-average SCF was 1863.9 km2 (24%). However, our study reveals that WRF/Noah-MP struggles to simulate SCF at the scale of individual grid cells. The equation used to calculate SCF fails to produce temporal variations in grid-scale SCF and depletion occurs too rapidly. Therefore SCF is a nearly binary metric inmountain environments. Sensitivity tests of the equation may improve simulation of SCF during accumulation or melt but does not remove the bias for the entire snow season. Though WRF/Noah-MP accurately simulates the presence or absence of snow, high-resolution, reliable SCF measurements may only be attainable if snow depletion equations are designed specifically for complex topographical areas

Tracking the impacts of precipitation phase changes through the hydrologic cycle in snowy regions: From precipitation to reservoir storage

Cool season precipitation plays a critical role in regional water resource management in the western United States. Throughout the twenty-first century, regional precipitation will be impacted by rising temperatures and changing circulation patterns. Changes to precipitation magnitude remain challenging to project; however, precipitation phase is largely dependent on temperature, and temperature predictions from global climate models are generally in agreement. To understand the implications of this dependence, we investigate projected patterns in changing precipitation phase for mountain areas of the western United States over the twenty-first century and how shifts from snow to rain may impact runoff. We downscale two bias-corrected global climate models for historical and end-century decades with the Weather Research and Forecasting (WRF) regional climate model to estimate precipitation phase and spatial patterns at high spatial resolution (9 km). For future decades, we use the RCP 8.5 scenario, which may be considered a very high baseline emissions scenario to quantify snow season differences over major mountain chains in the western U.S. Under this scenario, the average annual snowfall fraction over the Sierra Nevada decreases by >45% by the end of the century. In contrast, for the colder Rocky Mountains, the snowfall fraction decreases by 29%. Streamflow peaks in basins draining the Sierra Nevada are projected to arrive nearly a month earlier by the end of the century. By coupling WRF with a water resources model, we estimate that California reservoirs will shift towards earlier maximum storage by 1–2 months, suggesting that water management strategies will need to adapt to changes in streamflow magnitude and timing

Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling

The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models is used to assess the uncertainty in SWE and total snow storage (SWS) estimation over North America during the 2009–2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where, even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In midlatitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation–snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty, and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the midlatitudes

Characterizing Biases in Mountain Snow Accumulation From Global Data Sets

Mountain snow has a fundamental role in regional water budgets through its seasonal accumulation, storage, and melt. However, characterizing snow accumulation over large regions remains difficult because of limited observational networks and the inability of available satellite instruments to remotely sense snow depth or water equivalent in mountains. Models offer some ability to estimate snow water storage (SWS) on mountain range to continental scales. Here we compare four commonly used global data sets to understand whether there is a consensus regarding mountain SWS estimates among them. The data sets—European Centre for Medium-Range Weather Forecasts Reanalysis-Interim, Global Land Data Assimilation System, Modern-Era Retrospective Analysis for Research and Applications version 2, and Variable Infiltration Capacity—agree to within ±36% of the four–data set average for total global SWS. When mountain areas are extracted using a new seasonal mountain snow classification data set, the four data products have more agreement, where all are within ±21% of the seasonal SWS for mountain regions. However, when compared to high-resolution (9 km) simulations of SWS from the Weather Research and Forecasting (WRF) regional model, the four global products differ from WRF-estimated North American mountain snow accumulation by 40–66%, with a negative bias up to 651 km3, comparable to the annual streamflow of the Mississippi River. If we extend the North America SWS bias to global mountains, the global data sets may miss as much as 1,500 km3 of SWS, equivalent to 4% of the flow in all the world's rivers. The potential difference of SWS suggests more work must be done to characterize water resources in snow-dominated regions, particularly in mountains

Extending the utility of space-borne snow water equivalent observations over vegetated areas with data assimilation

<jats:p>Abstract. Snow is a vital component of the earth system, yet no snow-focused satellite remote sensing platform currently exists. In this study, we investigate how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use a fraternal twin observing system simulation experiment, specifically investigating how much snow simulated using widely used models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24∘×37∘ domain in the western USA and Canada, simulating snow at 250 m resolution and hourly time steps in water year 2019. We perform two data assimilation experiments, including (1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals and (2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that, relative to a nature run, or assumed true simulation of snow evolution, assimilating synthetic SWE observations improved average SWE biases at maximum snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14 %, to within 1 %. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at the time of maximum snow volume were 111 mm and average SWE biases were on the order of 150 %. Here the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18 %) and the SWE mean absolute error (27 mm). Simulations employing data assimilation also improved estimates of the temporal evolution of both SWE and runoff, even in spring snowmelt periods when melting snow and high snow liquid water content prevented synthetic SWE retrievals. In fact, in the Upper Colorado River region, melt-season SWE biases were improved from 63 % to within 1 %, and the Nash–Sutcliffe efficiency of runoff improved from −2.59 to 0.22. These results demonstrate the value of data assimilation and a snow-focused globally relevant remote sensing platform for improving the characterization of SWE and associated water availability. </jats:p&gt

DataSheet1_Tracking the impacts of precipitation phase changes through the hydrologic cycle in snowy regions: From precipitation to reservoir storage.docx

Cool season precipitation plays a critical role in regional water resource management in the western United States. Throughout the twenty-first century, regional precipitation will be impacted by rising temperatures and changing circulation patterns. Changes to precipitation magnitude remain challenging to project; however, precipitation phase is largely dependent on temperature, and temperature predictions from global climate models are generally in agreement. To understand the implications of this dependence, we investigate projected patterns in changing precipitation phase for mountain areas of the western United States over the twenty-first century and how shifts from snow to rain may impact runoff. We downscale two bias-corrected global climate models for historical and end-century decades with the Weather Research and Forecasting (WRF) regional climate model to estimate precipitation phase and spatial patterns at high spatial resolution (9 km). For future decades, we use the RCP 8.5 scenario, which may be considered a very high baseline emissions scenario to quantify snow season differences over major mountain chains in the western U.S. Under this scenario, the average annual snowfall fraction over the Sierra Nevada decreases by >45% by the end of the century. In contrast, for the colder Rocky Mountains, the snowfall fraction decreases by 29%. Streamflow peaks in basins draining the Sierra Nevada are projected to arrive nearly a month earlier by the end of the century. By coupling WRF with a water resources model, we estimate that California reservoirs will shift towards earlier maximum storage by 1–2 months, suggesting that water management strategies will need to adapt to changes in streamflow magnitude and timing.</p

Virtual Reality for Anxiety Disorders: Rethinking a Field in Expansion

The principal aim to this chapter is to present the latest ideas in virtual reality (VR), some of which have already been applied to the field of anxiety disorders, and others are still pending to be materialized. More than 20 years ago, VR emerged as an exposure tool in order to provide patients and therapists with more appealing ways of delivering a technique that was undoubtedly effective but also rejected and thus underused. Throughout these years, many improvements were achieved. The first section of the chapter describes those improvements, both considering the research progresses and the applications in the real world. In a second part, our main interest is to expand the discussion of the new applications of VR beyond its already known role as an exposure tool. In particular, VR is enabling the materialization of numerous ideas that were previously confined to a merely philosophical discussion in the field of cognitive sciences. That is, VR has the enormous potential of providing feasible ways to explore nonclassical ways of cognition, such as embodied and situated information processing. Despite the fact that many of these developments are not fully developed, and not specifically designed for anxiety disorders, we want to introduce these new ideas in a context in which VR is experiencing an enormous transformation