Seasonal and elevational variations of black carbon and dust in snow and ice in the Solu-Khumbu, Nepal and estimated radiative forcings

Black carbon (BC) and dust deposited on snow and glacier surfaces can reduce the surface albedo, accelerate snow and ice melt, and trigger albedo feedback. Assessing BC and dust concentrations in snow and ice in the Himalaya is of interest because this region borders large BC and dust sources, and seasonal snow and glacier ice in this region are an important source of water resources. Snow and ice samples were collected from crevasse profiles and snow pits at elevations between 5400 and 6400 m a.s.l. from Mera glacier located in the Solu-Khumbu region of Nepal during spring and fall 2009, providing the first observational data of BC concentrations in snow and ice from the southern slope of the Himalaya. The samples were measured for Fe concentrations (used as a dust proxy) via ICP-MS, total impurity content gravimetrically, and BC concentrations using a Single Particle Soot Photometer (SP2). Measured BC concentrations underestimate actual BC concentrations due to changes to the sample during storage and loss of BC particles in the ultrasonic nebulizer; thus, we correct for the underestimated BC mass. BC and Fe concentrations are substantially higher at elevations \u3c 6000 m due to post-depositional processes including melt and sublimation and greater loading in the lower troposphere. Because the largest areal extent of snow and ice resides at elevations \u3c 6000 m, the higher BC and dust concentrations at these elevations can reduce the snow and glacier albedo over large areas, accelerating melt, affecting glacier mass balance and water resources, and contributing to a positive climate forcing. Radiative transfer modeling constrained by measurements at 5400 m at Mera La indicates that BC concentrations in the winter–spring snow/ice horizons are sufficient to reduce albedo by 6–10% relative to clean snow, corresponding to localized instantaneous radiative forcings of 75–120 W m−2. The other bulk impurity concentrations, when treated separately as dust, reduce albedo by 40–42% relative to clean snow and give localized instantaneous radiative forcings of 488 to 525 W m−2. Adding the BC absorption to the other impurities results in additional radiative forcings of 3 W m−2. The BC and Fe concentrations were used to further examine relative absorption of BC and dust. When dust concentrations are high, dust dominates absorption, snow albedo reduction, and radiative forcing, and the impact of BC may be negligible, confirming the radiative transfer modeling. When impurity concentrations are low, the absorption by BC and dust may be comparable; however, due to the low impurity concentrations, albedo reductions are small. While these results suggest that the snow albedo and radiative forcing effect of dust is considerably greater than BC, there are several sources of uncertainty. Further observational studies are needed to address the contribution of BC, dust, and colored organics to albedo reductions and snow and ice melt, and to characterize the time variation of radiative forcing

The Post-Wildfire Impact of Burn Severity and Age on Black Carbon Snow Deposition and Implications for Snow Water Resources, Cascade Range, Washington

Wildfires in the snow zone affect ablation by removing forest canopy, which enhances surface solar irradiance, and depositing light absorbing particles [LAPs, such as black carbon (BC)] on the snowpack, reducing snow albedo. How variations in BC deposition affects post-wildfire snowmelt timing is poorly known and highly relevant to water resources. We present a field-based analysis of BC variability across five sites of varying burn age and burn severity in the Cascade Range, Washington State, United States. Single particle soot photometer (SP2) analyses of BC snow concentrations were used to assess the impact of BC on snow albedo, and radiative transfer modeling was used to estimate the radiative effect of BC on snowmelt. Results were compared to Snowpack Telemetry (SNOTEL) data from one site that burned in 2012 and another in a proximal unburned forest. We show that post-wildfire forests provide a significant source of BC to the snowpack, and this effect increases by an order of magnitude in regions of high versus low burn severity, and decreased by two orders of magnitude over a decade. There is a shift in the timing of snowmelt, with snow disappearance occurring on average 19 ± 9 days earlier post-wildfire (2013–19) relative to pre-wildfire (1983–2012). This study improves understanding of the connection between wildfire activity and snowmelt, which is of high relevance as climate change models project further decreases in snowpack and increases in wildfire activity in the Washington Cascades

The spectral and chemical measurement of pollutants on snow near South Pole, Antarctica

Remote sensing of light-absorbing particles (LAPs), or dark colored impurities, such as black carbon (BC) and dust on snow, is a key remaining challenge in cryospheric surface characterization and application to snow, ice, and climate models. We present a quantitative data set of in situ snow reflectance, measured and modeled albedo, and BC and trace element concentrations from clean to heavily fossil fuel emission contaminated snow near South Pole, Antarctica. Over 380 snow reflectance spectra (350–2500 nm) and 28 surface snow samples were collected at seven distinct sites in the austral summer season of 2014–2015. Snow samples were analyzed for BC concentration via a single particle soot photometer and for trace element concentration via an inductively coupled plasma mass spectrometer. Snow impurity concentrations ranged from 0.14 to 7000 part per billion (ppb) BC, 9.5 to 1200 ppb sulfur, 0.19 to 660 ppb iron, 0.013 to 1.9 ppb chromium, 0.13 to 120 ppb copper, 0.63 to 6.3 ppb zinc, 0.45 to 82 parts per trillion (ppt) arsenic, 0.0028 to 6.1 ppb cadmium, 0.062 to 22 ppb barium, and 0.0044 to 6.2 ppb lead. Broadband visible to shortwave infrared albedo ranged from 0.85 in pristine snow to 0.62 in contaminated snow. LAP radiative forcing, the enhanced surface absorption due to BC and trace elements, spanned from \u3c1 W m­–2 for clean snow to ~70 W m­–2 for snow with high BC and trace element content. Measured snow reflectance differed from modeled snow albedo due to specific impurity-dependent absorption features, which we recommend be further studied and improved in snow albedo models

Case study of spatial and temporal variability of snow cover, grain size, albedo and radiative forcing in the Sierra Nevada and Rocky Mountain snowpack derived from imaging spectroscopy

Quantifying the spatial distribution and temporal change in mountain snow cover, microphysical and optical properties is important to improve our understanding of the local energy balance and the related snowmelt and hydrological processes. In this paper, we analyze changes of snow cover, optical-equivalent snow grain size (radius), snow albedo and radiative forcing by light-absorbing impurities in snow and ice (LAISI) with respect to terrain elevation and aspect at multiple dates during the snowmelt period. These snow properties are derived from the NASA/JPL Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data from 2009 in California's Sierra Nevada and from 2011 in Colorado's Rocky Mountains, USA. Our results show a linearly decreasing snow cover during the ablation period in May and June in the Rocky Mountains and a snowfall-driven change in snow cover in the Sierra Nevada between February and May. At the same time, the snow grain size is increasing primarily at higher elevations and north-facing slopes from 200 microns to 800 microns on average. We find that intense snowmelt renders the mean grain size almost invariant with respect to elevation and aspect. Our results confirm the inverse relationship between snow albedo and grain size, as well as between snow albedo and radiative forcing by LAISI. At both study sites, the mean snow albedo value decreases from approximately 0.7 to 0.5 during the ablation period. The mean snow grain size increased from approximately 150 to 650 microns. The mean radiative forcing increases from 20 W m−2 up to 200 W m−2 during the ablation period. The variability of snow albedo and grain size decreases in general with the progression of the ablation period. The spatial variability of the snow albedo and grain size decreases through the melt season while the spatial variability of radiative forcing remains constant.</p

Improving snow albedo processes in WRF/SSiB regional climate model to assess impact of dust and black carbon in snow on surface energy balance and hydrology over western U.S.

Two important factors that control snow albedo are snow grain growth and presence of light‐absorbing impurities (aerosols) in snow. However, current regional climate models do not include such processes in a physically based manner in their land surface models. We improve snow albedo calculations in the Simplified Simple Biosphere (SSiB) land surface model coupled with the Weather Research and Forecasting (WRF) regional climate model (RCM), by incorporating the physically based SNow ICe And Radiative (SNICAR) scheme. SNICAR simulates snow albedo evolution due to snow aging and presence of aerosols in snow. The land surface model is further modified to account for deposition, movement, and removal by meltwater of such impurities in the snowpack. This paper presents model development technique, validation with in situ observations, and preliminary results from RCM simulations investigating the impact of such impurities in snow on surface energy and water budgets. By including snow‐aerosol interactions, the new land surface model is able to realistically simulate observed snow albedo, snow grain size, dust in snow, and surface water and energy balances in offline simulations for a location in western U.S. Preliminary results with the fully coupled RCM show that over western U.S., realistic aerosol deposition in snow induces a springtime average radiative forcing of 16 W/m2 due to a 6% albedo reduction, a regional surface warming of 0.84°C, and a snowpack reduction of 11 mm.Key PointsIncluding snow aging and aerosols in snow improves offline and WRF snow simulationsDust and black/organic carbon exerts nontrivial radiative forcing in western U.S.RCM simulation shows temperature increase and snow mass loss from aerosols in snowPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111782/1/jgrd52045.pd

Low serum albumin levels prior to pediatric allogeneic HCT are associated with increased need for critical care interventions and increased 6-month mortality

Poor nutritional status in HCT patients is a negative prognostic factor. There are no pediatric studies evaluating albumin levels prior to HCT and need for critical care interventions. We hypothesized that pediatric patients with low albumin levels, routinely measured 30 days (±10 days) prior to allogeneic HCT, have a higher risk of critical care interventions in the post-transplant period. We performed a 5-year retrospective study of pediatric patients who underwent allogeneic HCT for any indication. Patients were categorized based on albumin level. Hypoalbuminemia was defined as <3.1 g/dL. A total of 73 patients were included, with a median age of 7.4 years (IQR 3.3, 13.2). Patients with hypoalbuminemia had higher needs for critical care interventions including non-invasive ventilation (44% vs 8%, P=.01), mechanical ventilation (67% vs 17%, P<.01), and vasoactive therapy (56% vs 16%, P=.01). Patients with hypoalbuminemia also had a higher 6-month mortality (56% vs 17%, P=.02). Our data demonstrate that children undergoing allogeneic HCT with hypoalbuminemia in the pretransplant period are more likely to require critical care interventions and have higher 6-month mortality. These findings identify an at-risk population in which nutritional improvements may be instituted prior to HCT in hopes of improving outcomes

Factors influencing time to diagnosis and treatment among pediatric oncology patients in Kenya

Early diagnosis and start of treatment are fundamental goals in cancer care. This study determines the time lag and the factors that influence the time to diagnosis and start of treatment. Study participants were parents of childhood cancer patients diagnosed between August 2013 and July 2014 in a hospital in Kenya. Patient, physician, diagnosis, treatment, health care system, and total delay were explored using a questionnaire. Demographic and medical data were collected from the patients' medical records. Parents of 99 childhood cancer patients were interviewed (response rate: 80%). Median total delay was 102 (9–1021) days. Median patient delay (4 days) was significantly shorter than health care system delay (median 87 days; P < .001). Diagnosis delay (median 94 days) was significantly longer than treatment delay (median 6 days; P < .001). days. Lack of health insurance at diagnosis and use of alternative medicine before attending conventional health services were associated with a significantly longer patient delay (P = .041 and P = .017, respectively). The type of cancer had a significant effect on treatment delay (P = .020). The type of health facility attended affected only patient delay (P = .03). Gender, age at diagnosis, stage of disease, parents' education level or income, and distance from hospital did not have a significant effect on the length of any type of delay. Training on childhood cancer should be included in the curricula for medical training institutes. In-service workshops should be held for the health workers already working. Families must be obligated to get health insurance. Families should be encourage to attend conventional health facilities and informed on symptoms of cancer through mass media

Impact of Performance-Based Financing on effective coverage for curative child health services in Burkina Faso: Evidence from a quasi-experimental design.

OBJECTIVE: To evaluate the impact of Performance-Based Financing (PBF) on effective coverage of child curative health services in primary healthcare facilities in Burkina Faso. METHODS: An impact evaluation of a PBF pilot programme, using an experiment nested within a quasi-experimental design, was carried out in 12 intervention and 12 comparison districts in six regions of Burkina Faso. Across the 24 districts, primary healthcare facilities (537 both at baseline and endline) and households (baseline = 7978 endline = 7898) were surveyed. Within these households, 12 350 and 15 021 under-five-year-olds caretakers were interviewed at baseline and endline respectively. Linking service quality to service utilisation, we used difference-in-differences to estimate the impact of PBF on effective coverage of curative child health services. RESULTS: Our study failed to detect any effect of PBF on effective coverage. Looking specifically into quality of care indicators, we detected a positive effect of PBF on structural elements of quality of care related to general service readiness, but not on the overall facility quality score, capturing both service readiness and the content of childcare. CONCLUSION: The current study makes a unique contribution to PBF literature, as this is the first study assessing PBF impact on effective coverage for curative child health services in low-income settings. The absence of any significant effects of PBF on effective coverage suggests that PBF programmes require a stronger design focus on quality of care elements especially when implemented in a context of free healthcare policy

Global snow mass measurements and the effect of stratigraphic detail on inversion of microwave brightness temperatures

Snow provides large seasonal storage of freshwater, and information about the distribution of snow mass as Snow Water Equivalent (SWE) is important for hydrological planning and detecting climate change impacts. Large regional disagreements remain between estimates from reanalyses, remote sensing and modelling. Assimilating passive microwave information improves SWE estimates in many regions but the assimilation must account for how microwave scattering depends on snow stratigraphy. Physical snow models can estimate snow stratigraphy, but users must consider the computational expense of model complexity versus acceptable errors. Using data from the National Aeronautics and Space Administration Cold Land Processes Experiment (NASA CLPX) and the Helsinki University of Technology (HUT) microwave emission model of layered snowpacks, it is shown that simulations of the brightness temperature difference between 19 GHz and 37 GHz vertically polarised microwaves are consistent with Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and Special Sensor Microwave Imager (SSM/I) retrievals once known stratigraphic information is used. Simulated brightness temperature differences for an individual snow profile depend on the provided stratigraphic detail. Relative to a profile defined at the 10 cm resolution of density and temperature measurements, the error introduced by simplification to a single layer of average properties increases approximately linearly with snow mass. If this brightness temperature error is converted into SWE using a traditional retrieval method then it is equivalent to ±13 mm SWE (7% of total) at a depth of 100 cm. This error is reduced to ±5.6 mm SWE (3 % of total) for a two-layer model