Showing papers on "Snowpack published in 2022"

Increasing wildfire impacts on snowpack in the western U.S.

Abstract: Significance The snowpack of the western United States is critical for both ecosystem and human water supply. As wildfire area increases in snow zones throughout the West, the wildfire influence on the snowpack will also grow. The impacts of wildfire on snowpack are likely to be both regionally and locally variable because of differences in the snowpack energy balance. In some ecoregions, wildfire will likely cause declines in both peak snow accumulation and snow duration, with low latitude and clear-sky regions most vulnerable to these changes. Since shorter snow duration lengthens the growing season, the fire effects on the snowpack may cascade into consequences for forest regeneration after fire.

The critical benefits of snowpack insulation and snowmelt for winter wheat productivity

Abstract: How climate change will affect overwintering crops is largely unknown due to the complex and understudied interactions among temperature, rainfall and snowpack. Increases in average winter temperature should release cold limitations yet warming-induced reductions of snowpack thickness should lead to decreased insulation effects and more exposure to freezing. Here, using statistical models, we show that the presence of snowpack weakens yield sensitivity to freezing stress by 22% during 1999–2019. By 2080–2100, we project that reduced snow cover insulation will offset up to one-third of the yield benefit (8.8 ± 1.1% for RCP 4.5 and 11.8 ± 1.4% for RCP 8.5) from reduced frost stress across the United States. Furthermore, by 2080–2100 future decline in wheat growing season snowfall (source of snowmelt) will drive a yield loss greater than the yield benefit from increasing rainfall. Explicitly considering these factors is critical to predict the climate change impacts on winter wheat production in snowy regions. The authors consider the complex effects of climate change on winter wheat in the United States. They show that snow cover insulation weakened yield sensitivity to freezing stress by 22% from 1999 to 2019, but project that future reduced snow cover will offset up to one-third of the yield benefit from reduced frost.

Black carbon footprint of human presence in Antarctica

Abstract: Black carbon (BC) from fossil fuel and biomass combustion darkens the snow and makes it melt sooner. The BC footprint of research activities and tourism in Antarctica has likely increased as human presence in the continent has surged in recent decades. Here, we report on measurements of the BC concentration in snow samples from 28 sites across a transect of about 2,000 km from the northern tip of Antarctica (62°S) to the southern Ellsworth Mountains (79°S). Our surveys show that BC content in snow surrounding research facilities and popular shore tourist-landing sites is considerably above background levels measured elsewhere in the continent. The resulting radiative forcing is accelerating snow melting and shrinking the snowpack on BC-impacted areas on the Antarctic Peninsula and associated archipelagos by up to 23 mm water equivalent (w.e.) every summer.

Variability in observed stable water isotopes in snowpack across a mountainous watershed in Colorado

Abstract: Isotopic information from 81 snowpits was collected over a 5‐year period in a large, Colorado watershed. Data spans gradients in elevation, aspect, vegetation, and seasonal climate. They are combined with overlapping campaigns for water isotopes in precipitation and snowmelt, and a land‐surface model for detailed estimates of snowfall and climate at sample locations. Snowfall isotopic inputs, describe the majority of δ18O snowpack variability. Aspect is a secondary control, with slightly more enriched conditions on east and north facing slopes. This is attributed to preservation of seasonally enriched snowfall and vapour loss in the early winter. Sublimation, expressed by decreases in snowpack d‐excess in comparison to snowfall contributions, increases at low elevation and when seasonal temperature and solar radiation are high. At peak snow accumulation, post‐depositional fractionation appears to occur in the top 25 ± 14% of the snowpack due to melt‐freeze redistribution of lighter isotopes deeper into the snowpack and vapour loss to the atmosphere during intermittent periods of low relative humidity and high windspeed. Relative depth of fractionation increases when winter daytime temperatures are high and winter precipitation is low. Once isothermal, snowpack isotopic homogenization and enrichment was observed with initial snowmelt isotopically depleted in comparison to snowpack and enriching over time. The rate of δ18O increase (d‐excess decrease) in snowmelt was 0.02‰ per day per 100‐m elevation loss. Isotopic data suggests elevation dictates snowpack and snowmelt evolution by controlling early snow persistence (or absence), isotopic lapse rates in precipitation and the ratio of energy to snow availability. Hydrologic tracer studies using stable water isotopes in basins of large topographic relief will require adjustment for these elevational controls to properly constrain stream water sourcing from snowmelt.

Asymmetric emergence of low-to-no snow in the midlatitudes of the American Cordillera

Abstract: Abstract Societies and ecosystems within and downstream of mountains rely on seasonal snowmelt to satisfy their water demands. Anthropogenic climate change has reduced mountain snowpacks worldwide, altering snowmelt magnitude and timing. Here the global warming level leading to widespread and persistent mountain snowpack decline, termed low-to-no snow, is estimated for the world’s most latitudinally contiguous mountain range, the American Cordillera. We show that a combination of dynamical, thermodynamical and hypsometric factors results in an asymmetric emergence of low-to-no-snow conditions within the midlatitudes of the American Cordillera. Low-to-no-snow emergence occurs approximately 20 years earlier in the southern hemisphere, at a third of the local warming level, and coincides with runoff efficiency declines (8% average) in both dry and wet years. The prevention of a low-to-no-snow future in either hemisphere requires the level of global warming to be held to, at most, +2.5 °C.

The sensitivity of snow hydrology to changes in air temperature and precipitation in three North American headwater basins

Abstract: Whether or not the impact of warming on mountain snow and runoff can be offset by precipitation increases has not been well examined, but it is crucially important for future downstream water supply. Using the physically based Cold Regions Hydrological Modelling Platform (CRHM), elasticity (percent change in runoff divided by change in a climate forcing) and the sensitivity of snow regimes to perturbations were investigated in three well-instrumented mountain research basins spanning the northern North American Cordillera. Hourly meteorological observations were perturbed using air temperature and precipitation changes and were then used to force hydrological models for each basin. In all three basins, lower temperature sensitivities of annual runoff volume (≤ 6% °C−1) and higher sensitivities of peak snowpack (−17% °C−1) showed that annual runoff was far less sensitive to temperature than the snow regime. Higher and lower precipitation elasticities of annual runoff (1.5 – 2.1) and peak snowpack (0.7 – 1.1) indicated that the runoff change is primarily attributed to precipitation change and, secondarily, to warming. A low discrepancy between observed and simulated precipitation elasticities showed that the model results are reliable, and one can conduct sensitivity analysis. The air temperature elasticities, however, must be interpreted with care as the projected warmings range beyond the observed temperatures and, hence, it is not possible to test their reliability. Simulations using multiple elevations showed that the timing of peak snowpack was most sensitive to temperature. For the range of warming expected from North American climate model simulations, the impacts of warming on annual runoff, but not on peak snowpack, can be offset by the size of precipitation increases projected for the near-future period 2041–2070. To offset the impact of 2 °C warming on annual runoff, precipitation would need to increase by less than 5% in all three basins. To offset the impact of 2 °C warming on peak snowpack, however, precipitation would need to increase by 12% in Wolf Creek in Yukon Territory, 18% in Marmot Creek in the Canadian Rockies, and an amount greater than the maximum projected at Reynolds Mountain in Idaho. The role of increased precipitation as a compensator for the impact of warming on snowpack is more effective at the highest elevations and higher latitudes. Increased precipitation leads to resilient and strongly coupled snow and runoff regimes, contrasting sharply with the sensitive and weakly coupled regimes at low elevations and in temperate climate zones.

Sentinel-1 time series for mapping snow cover depletion and timing of snowmelt in Arctic periglacial environments: case study from Zackenberg and Kobbefjord, Greenland

Abstract: Abstract. Snow cover (SC) and timing of snowmelt are key regulators of a wide range of Arctic ecosystem functions. Both are strongly influenced by the amplified Arctic warming and essential variables to understand environmental changes and their dynamics. This study evaluates the potential of Sentinel-1 (S-1) synthetic aperture radar (SAR) time series for monitoring SC depletion and snowmelt with high spatiotemporal resolution to capture their understudied small-scale heterogeneity. We use 97 dual-polarized S-1 SAR images acquired over northeastern Greenland and 94 over southwestern Greenland in the interferometric wide swath mode from the years 2017 and 2018. Comparison of S-1 intensity against SC fraction maps derived from orthorectified terrestrial time-lapse imagery indicates that SAR backscatter can increase before a decrease in SC fraction is observed. Hence, the increase in backscatter is related to changing snowpack properties during the runoff phase as well as decreasing SC fraction. We here present a novel empirical approach based on the temporal evolution of the SAR signal to identify start of runoff (SOR), end of snow cover (EOS) and SC extent for each S-1 observation date during melt using backscatter thresholds as well as the derivative. Comparison of SC with orthorectified time-lapse imagery indicates that HV polarization outperforms HH when using a global threshold. The derivative avoids manual selection of thresholds and adapts to different environmental settings and seasonal conditions. With a global configuration (threshold: 4 dB; polarization: HV) as well as with the derivative, the overall accuracy of SC maps was in all cases above 75 % and in more than half of cases above 90 %. Based on the physical principle of SAR backscatter during snowmelt, our approach is expected to work well in other low-vegetation areas and, hence, could support large-scale SC monitoring at high spatiotemporal resolution (20 m, 6 d) with high accuracy.

Monitoring the daily evolution and extent of snow drought

Abstract: Abstract. Snow droughts are commonly defined as below-average snowpack at a point in time, typically 1 April in the western United States (wUS). This definition is valuable for interpreting the state of the snowpack but obscures the temporal evolution of snow drought. Borrowing from dynamical systems theory, we applied phase diagrams to visually examine the daily evolution of snow water equivalent (SWE) and accumulated precipitation conditions in maritime, intermountain, and continental snow climates in the wUS using station observations as well as spatially distributed estimates of SWE and precipitation. Using a percentile-based drought definition, phase diagrams of SWE and precipitation highlighted decision-relevant aspects of snow drought such as onset, evolution, and termination. The phase diagram approach can be used in tandem with spatially distributed estimates of daily SWE and precipitation to reveal variability in snow drought type and extent. When combined with streamflow or other environmental data, phase diagrams and spatial estimates of snow drought conditions can help inform drought monitoring and early warning systems and help link snow drought type and evolution to impacts on ecosystems, water resources, and recreation. A web tool is introduced allowing users to create real-time or historic snow drought phase diagrams.

Wildfire impacts on western United States snowpacks

Abstract: Mountain snowpacks provide 53–78% of water used for irrigation, municipalities, and industrial consumption in the western United States. Snowpacks serve as natural reservoirs during the winter months and play an essential role in water storage for human consumption and ecosystem functions. However, wildfires across the West are increasing in severity, size, and frequency, progressively putting snowpacks at risk as they burn further into the seasonal snow zone. Following a fire, snow disappears 4–23 days earlier and melt rates increase by up to 57%. In a high burn severity fire in the Oregon Cascades, the black carbon and charred woody debris shed from burned trees onto the snowpack decreased the snow albedo by 40%. Canopy cover loss causes a 60% increase in solar radiation reaching the snow surface. Together, these effects produce a 200% increase in net shortwave radiation absorbed by the snowpack. This mini-review synthesizes the implications of wildfire for snow hydrology in mountainous watersheds with the primary aim to characterize wildfires' varied influences on the volume and timing of water resources across time scales (daily to decadal), space (plot to watershed) and burn severity (low to high). The increase in the geographical overlap between fire and snow poses unique challenges for managing snow-dominated watersheds and highlights deficiencies in research and operational snow hydrologic modeling, emphasizing the need for additional field and remote-sensing observations and model experiments.

Isolating forest process effects on modelled snowpack density and snow water equivalent

Abstract: Understanding how the presence of a forest canopy influences the underlying snowpack is critical to making accurate model predictions of bulk snow density and snow water equivalent (SWE). To investigate the relative importance of forest processes on snow density and SWE, we applied the SUMMA model at three sites representing diverse snow climates in Colorado (USA), Oregon (USA), and Alberta (Canada) for 5 years. First, control simulations were run for open and forest sites. Comparisons to observations showed the uncalibrated model with NLDAS‐2 forcing performed reasonably. Then, experiments were completed to isolate how forest processes affected modelled snowpack density and SWE, including: (1) mass reduction due to interception loss, (2) changes in the phase and amount of water delivered from the canopy to the underlying snow, (3) varying new snow density from reduced wind speed, and (4) modification of incoming longwave and shortwave radiation. Delivery effects (2) increased forest snowpack density relative to open areas, often more than 30%. Mass effects (1) and wind effects (3) decreased forest snowpack density, but generally by less than 6%. The radiation experiment (4) yielded negligible to positive effects (i.e., 0%–10%) on snowpack density. Delivery effects on density were greatest at the warmest times in the season and at the warmest site (Oregon): higher temperatures increased interception and melted intercepted snow, which then dripped to the underlying snowpack. In contrast, mass effects and radiation effects were shown to have the greatest impact on forest‐to‐open SWE differences, yielding differences greater than 30%. The study highlights the importance of delivery effects in models and the need for new types of observations to characterize how canopies influence the flux of water to the snow surface.

Observations and simulations of new snow density in the drifting snow-dominated environment of Antarctica

Abstract: Owing to drifting snow processes, snow accumulation and surface density in polar environments are variable in space and time. We present new field data of manual measurements, repeat terrestrial laser scanning and snow micro-penetrometry from Dronning Maud Land, Antarctica, showing the density of new snow accumulations. We combine these data with published drifting snow mass flux observations, to evaluate the performance of the 1-D, detailed, physics-based snow cover model SNOWPACK in representing drifting snow and surface density. For two sites in East Antarctica with multiple years of data, we found a coefficient of determination for the simulated drifting snow of r2 = 0.42 and r2 = 0.50, respectively. The field observations show the existence of low-density snow accumulations during low wind conditions. Successive high wind speed events generally erode these low-density layers while producing spatially variable erosion/deposition patterns with typical length scales of a few metres. We found that a model setup that is able to represent low-density snow accumulating during low wind speed conditions, as well as subsequent snow erosion and redeposition at higher densities during drifting snow events was mostly able to describe the observed temporal variability of surface density in the field.

Effects of spatial and temporal variability in surface water inputs on streamflow generation and cessation in the rain–snow transition zone

Abstract: Abstract. Climate change affects precipitation phase, which can propagate into changes in streamflow timing and magnitude. This study examines how the spatial and temporal distribution of rainfall and snowmelt affects discharge in rain–snow transition zones. These zones experience large year-to-year variations in precipitation phase, cover a significant area of mountain catchments globally, and might extend to higher elevations under future climate change. We used observations from 11 weather stations and snow depths measured from one aerial lidar survey to force a spatially distributed snowpack model (iSnobal/Automated Water Supply Model) in a semiarid, 1.8 km2 headwater catchment. We focused on surface water input (SWI; the summation of rainfall and snowmelt on the soil) for 4 years with contrasting climatological conditions (wet, dry, rainy, and snowy) and compared simulated SWI to measured discharge. A strong spatial agreement between snow depth from the lidar survey and model (r2 = 0.88) was observed, with a median Nash–Sutcliffe efficiency (NSE) of 0.65 for simulated and measured snow depths at snow depth stations for all modeled years (0.75 for normalized snow depths). The spatial pattern of SWI was consistent between the 4 years, with north-facing slopes producing 1.09–1.25 times more SWI than south-facing slopes, and snowdrifts producing up to 6 times more SWI than the catchment average. Annual discharge in the catchment was not significantly correlated with the fraction of precipitation falling as snow; instead, it was correlated with the magnitude of precipitation and spring snow and rain. Stream cessation depended on total and spring precipitation, as well as on the melt-out date of the snowdrifts. These results highlight the importance of the heterogeneity of SWI at the rain–snow transition zone for streamflow generation and cessation, and emphasize the need for spatially distributed modeling or monitoring of both snowpack and rainfall dynamics.

Rain-on-Snow (ROS) Understudied in Sea Ice Remote Sensing: A Multi-Sensor Analysis of ROS during MOSAiC

Abstract: Abstract. Arctic rain-on-snow (ROS) deposits liquid water onto existing snowpacks. Upon refreezing, this can form icy crusts at the surface or within the snowpack. By altering radar backscatter and microwave emissivity, ROS over sea ice can influence the accuracy of sea ice variables retrieved from satellite radar altimetry, scatterometers, and passive microwave radiometers. During the Arctic Ocean MOSAiC Expedition, there was an unprecedented opportunity to observe a ROS event using in situ active and passive microwave instruments similar to those deployed on satellite platforms. During liquid water accumulation in the snowpack, there was a four-fold decrease in radar energy returned at Ku- and Ka-bands. After the snowpack refroze and ice layers formed, this decrease was followed by a six-fold increase in returned energy. Besides altering the radar backscatter, analysis of the returned waveforms shows the waveform shape changed in response to rain and refreezing. Microwave emissivity at 19 and 89 GHz increased with increasing liquid water content and decreased as the snowpack refroze, yet subsequent ice layers altered the polarization difference. Corresponding analysis of CryoSat-2 waveform shape and backscatter as well as AMSR2 brightness temperatures further shows the rain/refreeze was significant enough to impact satellite returns. Our analysis provides the first detailed in situ analysis of the impacts of ROS and subsequent refreezing on both active and passive microwave observations, providing important baseline knowledge for detecting ROS over sea ice and assessing their impacts on satellite-derived sea ice variables.

Pervasive alterations to snow-dominated ecosystem functions under climate change

Abstract: Significance Climate change will alter the mean ecohydrological state, but little is known about potential changes in ecohydrological variability that are required to inform climate change adaptation and mitigation strategies. Our results demonstrate pervasive alterations to the variability of water fluxes, water storage, and disturbance by the end of this century. Projected warming will reduce winter snow accumulation and increase the fraction of snow that melts during winter, blurring the seasonal distinction between periods of winter snow accumulation and its subsequent melt in the spring and summer. Notably, we find that in the future, runoff quantity and timing will be less predictable from snow, more closely reflecting the stochastic character of precipitation—findings that have critical implications for water resource management.

An SVM-Based Snow Detection Algorithm for GNSS-R Snow Depth Retrievals

Abstract: The signal-to-noise ratio (SNR) is important observations in global navigation satellite system-reflectometry (GNSS-R) technology. The oscillation frequency in the SNR arc is sensitive to different reflecting surfaces and can be used to build height model to track the variation of snow depth. However, it is difficult to obtain retrieval results with snow depth of zero in the actual snow depth retrieval experiments based on GNSS-R technology, which indicates that the classical model has nonnegligible retrieval errors in the snow-free state. This study aims to realize the detection of ground truth information before snow depth retrieval, i.e., classification of snow-free state and snow-covered state. Machine learning was introduced to achieve the aforementioned purpose and the SNR arc was used as the input data. Compared with the current mainstream topography correction algorithms, the algorithm proposed in this study does not rely on any priori ground measured data and has theoretical universality. The detection results can constrain the retrieval snow depth in the snow-free state and, thus, improve the retrieval accuracy. The experimental results for the 2014 seasonal snowpack at P351 station in Idaho, USA, show that the detection results obtained based on support vector machines agree well with the measured snow depth provided by the SNOTEL network, and the overall detection accuracy can reach about 96%. The daily snowpack state is determined by the majority of SNR arcs detected during the day and is only considered reliable if the percentage exceeds 75%. Only one day of the detection results was inaccurate and only 8 days (8/365) did not reach the set threshold of 75%. With the help of the detection results, the root-mean-square error of snow depth retrieval can be reduced from 20 cm in the classical algorithm to 15 cm, which results in a 25% improvement in retrieval accuracy. Moreover, this study broadens the application value of GNSS signals and provides a reference for the application of SNR in the detection field.

Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing

Abstract: Abstract. Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 × 106 km2 of Earth's surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth's climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ −13 % per decade) as Arctic summer sea ice. More than one-sixth of the world's population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth's cold regions' ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of water stored as snow on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations are not able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high-socio-economic-value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-band synthetic aperture radar (SAR) for global monitoring of SWE. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and application communities on progress made in recent decades and sets the stage for a new era in SWE remote sensing from SAR measurements.

Future of Winter in Northeastern North America: Climate Indicators Portray Warming and Snow Loss That Will Impact Ecosystems and Communities

Abstract: Winters in northeastern North America have warmed faster than summers, with impacts on ecosystems and society. Global climate models (GCMs) indicate that winters will continue to warm and lose snow in the future, but uncertainty remains regarding the magnitude of warming. Here, we project future trends in winter indicators under lower and higher climate-warming scenarios based on emission levels across northeastern North America at a fine spatial scale (1/16°) relevant to climate-related decision making. Under both climate scenarios, winters continue to warm with coincident increases in days above freezing, decreases in days with snow cover, and fewer nights below freezing. Deep snowpacks become increasingly short-lived, decreasing from a historical baseline of 2 months of subnivium habitat to <1 month under the warmer, higher-emissions climate scenario. Warmer winter temperatures allow invasive pests such as Adelges tsugae (Hemlock Woolly Adelgid) and Dendroctonus frontalis (Southern Pine Beetle) to expand their range northward due to reduced overwinter mortality. The higher elevations remain more resilient to winter warming compared to more southerly and coastal regions. Decreases in natural snowpack and warmer temperatures point toward a need for adaptation and mitigation in the multi-million-dollar winter-recreation and forest-management economies.

Long-term trend of snow water equivalent in the Italian Alps

Abstract: Snow stores a significant amount of water in mountain regions. The decrease of water storage in the snowpack can have relevant impacts on water supply for mountain and lowland areas that rely on snow melting. In this work, we modelled the Snow Water Equivalent (SWE) using daily snow depth (HS) data obtained from 19 historical HS measurement stations located in the southern European Alps (Italy). Then, we analysed the long-term (1930–2020) variability of the monthly Standardised SWE Index (SSWEI) and its links with climate change and large-scale atmospheric forcings (teleconnection indices). We found a marked variability in monthly SSWEI, with the lowermost values generally occurring in the last few decades (1991–2020), irrespective of elevation. In this recent period, highly negative values occurred at the snow season tails, mostly in spring. We found large-scale atmospheric patterns (North Atlantic Oscillation, Atlantic Multi-decadal Oscillation, and Artic Oscillation) and precipitation to be interconnected with SSWEI oscillations, although this relation changed after the 1980s, especially at low and medium elevations. This change occurred in correspondence of highly positive air temperature anomalies. In the last decades, we found increasing air temperature to be the main driver for the pronounced snow mass loss and persistent snow-drought conditions.

Comparison of model and ground observations finds snowpack and blowing snow aerosols both contribute to Arctic tropospheric reactive bromine

Abstract: Abstract. Reactive halogens play a prominent role in the atmospheric chemistry of the Arctic during springtime. Field measurements and modeling studies suggest that halogens are emitted into the atmosphere from snowpack and reactions on wind-blown snow-sourced aerosols. The relative importance of snowpack and blowing snow sources is still debated, both at local scales and regionally throughout the Arctic. To understand the implications of these halogen sources on a pan-Arctic scale, we simulate Arctic reactive bromine chemistry in the atmospheric chemical transport model GEOS-Chem. Two mechanisms are included: (1) a blowing snow sea salt aerosol formation mechanism and (2) a snowpack mechanism assuming uniform molecular bromine production from all snow surfaces. We compare simulations including neither mechanism, each mechanism individually, and both mechanisms to examine conditions where one process may dominate or the mechanisms may interact. We compare the models using these mechanisms to observations of bromine monoxide (BrO) derived from multiple-axis differential optical absorption spectroscopy (MAX-DOAS) instruments on O-Buoy platforms on the sea ice and at a coastal site in Utqiaġvik, Alaska, during spring 2015. Model estimations of hourly and monthly average BrO are improved by assuming a constant yield of 0.1 % molecular bromine from all snowpack surfaces on ozone deposition. The blowing snow aerosol mechanism increases modeled BrO by providing more bromide-rich aerosol surface area for reactive bromine recycling. The snowpack mechanism led to increased model BrO across the Arctic Ocean with maximum production in coastal regions, whereas the blowing snow aerosol mechanism increases BrO in specific areas due to high surface wind speeds. Our uniform snowpack source has a greater impact on BrO mixing ratios than the blowing snow source. Model results best replicate several features of BrO observations during spring 2015 when using both mechanisms in conjunction, adding evidence that these mechanisms are both active during the Arctic spring. Extending our transport model throughout the entire year leads to predictions of enhanced fall BrO that are not supported by observations.

Spring Land Temperature in Tibetan Plateau and Global-Scale Summer Precipitation – Initialization and Improved Prediction

Abstract: Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface temperatures show a lag correlation with summer precipitation in several remote regions, but current global land-atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-meter temperature over the TP in the LS4P experiment, results from a multi-model ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hot spot” regions identified here; the ensemble means in some “hot spots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.