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Author: Mary Szatkowski
Publisher:
ISBN:
Category : Snow
Languages : en
Pages : 0
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Book Description
Understanding and modeling the permafrost system, hydrologic cycle, energy balance, and biologic systems in the Arctic are dependent, in part, on snow depth and snow distribution. Point-source snow measurements provide ground-truth observations of snow depth and snow water equivalent, although these measurements may be limited in their spatial and temporal distributions. Satellite-derived remote sensing products and gridded model output provide spatial coverage of snow properties, but their applicability is affected by their balance of resolution, computational speed, and accuracy confidence. The goal of this research is to assess the performance of three snow data products derived from remote sensing techniques as well as model output across the North Slope of Alaska with the International Land Model Benchmarking (ILAMB) Project software. Historic ground-based snow data, collected by agencies, academia, and industry, and dating from 1902 to 2021, was curated to create an ILAMB-compatible benchmark dataset for end-of-winter (EOW) snow depth and snow water equivalent (SWE) for the evaluation of the three snow data products: Canadian Sea Ice and Snow Evolution (CanSISE) network SWE; Arctic Boreal Vulnerability Experiment (ABoVE) snow depth; and Energy Exascale Earth System Model (E3SM) Earth Land Model (ELM) snow depth. The ILAMB evaluation results showed that the ABoVE data product is effective in providing the average EOW snow depth for regions of the North Slope but lacks representation of interannual and spatial variability of snow depth. Comparatively, the CanSISE data product and ELM results are inaccurate in magnitude for applicability across the North Slope of Alaska in addition to lacking representation of snow condition spatial variability. In interpreting ILAMB results, factors to consider were representation bias from inconsistent benchmark site distribution throughout the evaluated time period, the range of dates considered to represent the spring snow data, and uncertainty within the individual benchmark values. Future analysis of the same datasets with ILAMB could include diagnostic tests to understand the sources of error better. Thorough spring snow data collection should continue on the North Slope of Alaska to inform and improve Earth System Models.
Author: Mary Szatkowski
Publisher:
ISBN:
Category : Snow
Languages : en
Pages : 0
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Book Description
Understanding and modeling the permafrost system, hydrologic cycle, energy balance, and biologic systems in the Arctic are dependent, in part, on snow depth and snow distribution. Point-source snow measurements provide ground-truth observations of snow depth and snow water equivalent, although these measurements may be limited in their spatial and temporal distributions. Satellite-derived remote sensing products and gridded model output provide spatial coverage of snow properties, but their applicability is affected by their balance of resolution, computational speed, and accuracy confidence. The goal of this research is to assess the performance of three snow data products derived from remote sensing techniques as well as model output across the North Slope of Alaska with the International Land Model Benchmarking (ILAMB) Project software. Historic ground-based snow data, collected by agencies, academia, and industry, and dating from 1902 to 2021, was curated to create an ILAMB-compatible benchmark dataset for end-of-winter (EOW) snow depth and snow water equivalent (SWE) for the evaluation of the three snow data products: Canadian Sea Ice and Snow Evolution (CanSISE) network SWE; Arctic Boreal Vulnerability Experiment (ABoVE) snow depth; and Energy Exascale Earth System Model (E3SM) Earth Land Model (ELM) snow depth. The ILAMB evaluation results showed that the ABoVE data product is effective in providing the average EOW snow depth for regions of the North Slope but lacks representation of interannual and spatial variability of snow depth. Comparatively, the CanSISE data product and ELM results are inaccurate in magnitude for applicability across the North Slope of Alaska in addition to lacking representation of snow condition spatial variability. In interpreting ILAMB results, factors to consider were representation bias from inconsistent benchmark site distribution throughout the evaluated time period, the range of dates considered to represent the spring snow data, and uncertainty within the individual benchmark values. Future analysis of the same datasets with ILAMB could include diagnostic tests to understand the sources of error better. Thorough spring snow data collection should continue on the North Slope of Alaska to inform and improve Earth System Models.
Author: Molly E. Tedesche
Publisher:
ISBN:
Category : Cryosphere
Languages : en
Pages : 462
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Book Description
Perennial snowfields, such as those found in the Brooks Range of Alaska, are a critical component of the cryosphere. They serve as habitat for an array of wildlife, some of which are crucial for rural subsistence hunters. Snowfields also influence hydrology, vegetation, permafrost, and have the potential to preserve valuable archaeological artifacts. In this study, perennial snowfield extents in the Brooks Range are derived from satellite remote sensing, field acquired data, and snowmelt modeling. The remote sensing data are used to map and quantify snow cover area changes across multiple temporal scales, spatial resolutions, and geographic sub-domains. Perennial snowfield classification techniques were developed using optical multi-spectral imagery from NASA Landsat and European Space Agency Sentinel-2 satellites. A Synthetic Aperture Radar change detection algorithm was also developed to quantify snow cover area using Sentinel-1 data. Results of the remote sensing analyses were compared to helicopter and manually collected field data. Also, a snowfield melt model was developed using an adaptation of the temperature index method to determine probability of melt via binary logistic regression in two dimensions. The logistic temperature melt model considers summer season snow cover area changes per pixel in remotely sensed products and relationships to several independent variables, including elevation-lapse-adjusted air temperature and terrain-adjusted solar radiation. Evaluations of the Synthetic Aperture Radar change detection algorithm via comparison with results from optical imagery analysis, as well as via comparison with field acquired data, indicate that the radar algorithm performs best in small, focused geographic sub-domains. The multi-spectral approach appears to perform similarly well within multiple geographic domain sizes. This may be the result of synthetic aperture radar algorithm dependency on backscatter thresholding techniques and slope corrections in mountainous complex topography. Results indicate that perennial snowfield extents in the Brooks Range are decreasing over decadal time scales, with short-lived, interannual and seasonal increases. Results also show that perennial snowfields are more persistent at higher elevations over time with notable consistency in at least one of the Brooks Range sub-domains of this study, Gates of the Arctic National Park and Preserve. Climate change may be altering the distribution, elevation, melt behavior, and overall extents of the Brooks Range perennial snowfields. Such changes could have significant implications for hydrology, wildlife, vegetation, and subsistence hunting in rural Alaska.
Author: Joanna C. Young
Publisher:
ISBN:
Category : Glaciers
Languages : en
Pages : 166
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Book Description
Glaciers of Alaska, USA, and Northwestern Canada are shedding mass at one of the highest rates of any mountain glacier system, with significant impact at the global and local scales. Despite advances in satellite and airborne technologies, fully characterizing the temporal evolution of glacier mass change in individual watersheds remains a challenge. Temperature index modeling is an approach that can be used to expand on sparse ground observations, and that can help bridge the gap between regional and individual watershed estimates of the time series of glacier mass change. Here we present a study on temperature index modeling of glacier-wide mass balance for the large Kahiltna Glacier (502 km2, 270 to 6100 m in elevation) in the Central Alaska Range, using a combination of ground observations and past climate data products. We reproduce mass changes from 1991 to 2011, and assess model performance by comparing our results to several field and remote sensing datasets. First, we compare our results to a 20-year record of mass balance measurements at a National Park Service index site at the glacier's equilibrium line altitude. We find low correlation between index site measurements and modeled glacier-wide balances (R2 = 0.24), indicating that the index site may not be representative of the glacier-wide mass balance regime. We compare next to glacier-wide mass balances derived from airborne laser altimetry, to assess the model's long-term mass change estimates. We find disagreement between the mean annual balances for 1995 to 2010 (-0.95 ±0.49 m w.e. yr−1 from the model versus -0.69 +0.07/-0.08 m w.e. yr−1 from laser altimetry). To validate the laser altimetry methods, we then compare estimates from 1951 to 2011 from laser altimetry and digital elevation model differencing, finding close agreement (-0.48 +0.08/-0.09 m w.e. yr−1 and -0.41 ±0.26 m w.e. yr−1, respectively), and lending strength to the laser altimetry centerline extrapolation techniques. We also examine estimates derived from regionally-downscaled satellite gravimetry. While gravimetry likely underestimates long-term mass loss for this glacier (-0.36 ±0.13 m w.e. yr−1 for 2003 to 2010), it correlates well to individual modeled annual balances (R2 = 0.72) and to the time series of mass balance at an ablation stake location (R2 = 0.81). Given ongoing refinements to gravimetry downscaling and geodetic techniques, our results point to the potential for integrating multiple methods to obtain the most information on subannual and long-term mass changes at the basin scale for remote sites such as the Kahiltna Glacier.
Author: Carl S. Benson
Publisher: Washington, D.C. : Arctic Institute of North America
ISBN:
Category : Snow surveys
Languages : en
Pages : 47
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Book Description
The snow cover of Arctic Alaska includes two distinct types which are separated by the Brooks Range. On the Arctic Slope the snow has a wind-swept, continuous surface which resembles that of the Greenland or Antarctic Ice Sheets. The snow of Interior Alaska, between the Brooks and Alaska Ranges, lies in heavily forested land with patches of smooth continuous snow cover occurring on lakes and swamps and on hilltops above timberline. It is possible to differentiate between Arctic and Pacific source areas for snow deposited on the Arctic Slope. The heavy isotopes of hydrogen and oxygen are more abundant in precipitation from the Pacific. The electrical conductance of melt waters derived from Pacific snowfall is also higher than that from Arctic storms. More investigations are needed, but present information suggests that Arctic sources make up nearly half of the precipitation on the Arctic Slope. This is an important factor in considerations on the origin and maintenance of the Pleistocene North American Ice Sheet. (Author).
Author: Ming Zhao
Publisher:
ISBN: 9781303050398
Category : Arctic regions
Languages : en
Pages : 93
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Book Description
The Arctic is a region that is very sensitive to global climate change while also experiencing significant changes in its surface air temperature, sea-ice cover, atmospheric circulation, precipitation, snowfall, biogeochemical cycling, and land surface. Although previous studies have shown that the arctic clouds play an important role in the arctic climate changes, the arctic clouds are poorly understood and simulated in climate model due to limited observations. Furthermore, most of the studies were based on short-term experiments and typically only cover the warm seasons, which do not provide a full understanding of the seasonal cycle of arctic clouds. To address the above concerns and to improve our understanding of arctic clouds, six years of observational and retrieval data from 1999 to 2004 at the Atmospheric Radiation Management (ARM) Climate Research Facility (ACRF) North Slope of Alaska (NSA) Barrow site are used to understand the arctic clouds and related radiative processes. In particular, we focus on the liquid-ice mass partition in the mixed-phase cloud layer. Statistical results show that aerosol type and concentration are important factors that impact the mixed-phase stratus (MPS) cloud microphysical properties: liquid water path (LWP) and liquid water fraction (LWF) decrease with the increase of cloud condensation nuclei (CCN) number concentration; the high dust loading and dust occurrence in the spring are possible reasons for the much lower LWF than the other seasons. The importance of liquid-ice mass partition on surface radiation budgets was analyzed by comparing cloud longwave radiative forcings under the same LWP but different ice water path (IWP) ranges. Results show the ice phase enhance the surface cloud longwave (LW) forcing by 8~9 W m−2 in the moderately thin MPS. This result provides an observational evidence on the aerosol glaciation effect in the moderately thin MPS, which is largely unknown so far. The above new insights are important to guide the model parameterizations of liquid-ice mass partition in arctic mixed-phase clouds, and are served as a test bed to cloud models and cloud microphysical schemes. The observational data between 1999 and 2007 are used to assess the performance of the European Center for Medium-Range Weather Forecasts (ECMWF) model in the Arctic region. The ECMWF model-simulated near-surface humidity had seasonal dependent biases as large as 20%, while also experiencing difficulty representing boundary layer (BL) temperature inversion height and strength during the transition seasons. Although the ECMWF model captured the seasonal variation of surface heat fluxes, it had sensible heat flux biases over 20 W m−2 in most of the cold months. Furthermore, even though the model captured the general seasonal variations of low-level cloud fraction (LCF) and LWP, it still overestimated the LCF by 20% or more and underestimated the LWP over 50% in the cold season. On average, the ECMWF model underestimated LWP by ~30 g m−2 but more accurately predicted ice water path for BL clouds. For BL mixed-phase clouds, the model predicted water-ice mass partition was significantly lower than the observations, largely due to the temperature dependence of water-ice mass partition used in the model. The new cloud and BL schemes of the ECMWF model that were implemented after 2003 only resulted in minor improvements in BL cloud simulations in summer. These results indicate that significant improvements in cold season BL and mixed-phase cloud processes in the model are needed. In this study, single-layer MPS clouds were simulated by the Weather Research and Forecasting (WRF) model under different microphysical schemes and different ice nuclei (IN) number concentrations. Results show that by using proper IN concentration, the WRF model incorporated with Morrison microphysical scheme can reasonably capture the observed seasonal differences in temperature dependent liquid-ice mass partition. However, WRF simulations underestimate both LWP and IWP indicating its deficiency in capturing the radiative impacts of arctic MPS clouds.
Author:
Publisher:
ISBN:
Category : Power resources
Languages : en
Pages : 716
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Author: Nicholas E. Wayand
Publisher:
ISBN:
Category :
Languages : en
Pages : 174
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Book Description
The frequent mid-winter accumulation and ablation cycles of snowpack within the rain-snow transitional zone play an important role for the maritime basins along the western U.S. mountain ranges. Representation of transitional snowpack within hydrological models has remained a challenge, largely because surface and meteorological conditions frequently remain near the freezing point, which allows large errors in modeled accumulation or ablation to result from small forcing or structural errors. This research aims to improve model representation of accumulation and ablation processes by utilizing new observations within the transitional snow zone combined with novel methods of model evaluation. The importance of mid-winter snowmelt during historical flooding events was assessed over three maritime basins in the western US. A physically-based snow model was coupled with an idealized basin representation to quantity how the characteristics of each basin combined with storm strength to control the distribution of snowmelt over a basin. Snowmelt contributions to total basin runoff ranged from 7-29% during historic flooding events between 1980 and 2008. However, poor meteorological forcing data were found to be a major limitation in model evaluation. In response to this limitation, a historical snow study site at Snoqualmie Pass within the Washington Cascades was updated in October 2012 with meteorological, soil, and snow observations to provide an ideal site for model evaluation within the transitional snow zone where existing observations are extremely sparse. The data set includes complete meteorological forcing required for snow models: air temperature, total precipitation, wind speed, specific humidity, air pressure, short- and longwave irradiance. Historical (1980-2015) observations include snow board new snow accumulation, multiple measurements of total snow depth, and manual snow pits, while more recent years (2012-2015) include sub-daily surface temperature, snowpack drainage, soil moisture and temperature profiles, and eddy co-variance derived turbulent heat flux; in short an ideal site to test different hypothesis about snow processes. This unique observational data set was used to illustrate how a novel process-based approach can diagnose model errors in snow accumulation processes (precipitation partitioning, new snow density, and compaction). The main source of model error on each day was identified by comparing observed snow board measurements to a “modeled snow board.” Results found that even after in-situ calibration, new snow density errors were the most common, occurring 53% of available days, followed by precipitation partition errors (43%) and compaction errors (18%). Daily errors canceled out on annual time scales during all years except the anomalously warm winter of 2014-2015. The partitioning of precipitation into rain or snow during water year 2015 was further examined by evaluating surface-based and mesoscale-model-based predictions. Observations of precipitation phase from a disdrometer at Snoqualmie Pass and nearby snow depth sensors were used to evaluate both methods. With calibration, the skill of surface-based methods was greatly improved by using air temperature from a nearby higher-elevation station, which was less impacted by surface inversions at the pass. Without any form of a prior calibration, we found a hybrid method that combines surface-based predictions with output from the Weather Research and Forecasting mesoscale model, to have comparable skill to calibrated surface-based methods. These results suggest that phase prediction in mountain passes can be improved by incorporating observations or models of the atmosphere aloft.
Author: Nicholas Dawson
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Improved knowledge of the cryosphere state is paramount for continued model development and for accurate estimates of fresh water supply. This work focuses on evaluation and potential improvements of current snow datasets over the United States. Snow in mountainous terrain is most difficult to quantify due to the slope, aspect, and remote nature of the environment. Due to the difficulty of measuring snow quantities in the mountains, the initial study creates a new method to upscale point measurements to area averages for comparison to initial snow quantities in numerical weather prediction models. The new method is robust and cross validation of the method results in a relatively low mean absolute error of 18% for snow depth (SD). Operational models at the National Centers for Environmental Prediction which use Air Force Weather Agency (AFWA) snow depth data for initialization were found to underestimate snow depth by 77% on average. Larger error is observed in areas that are more mountainous. Additionally, SD data from the Canadian Meteorological Center, which is used for some model evaluations, performed similarly to models initialized with AFWA data. The use of constant snow density for snow water equivalent (SWE) initialization for models which utilize AFWA data exacerbates poor SD performance with dismal SWE estimates. A remedy for the constant snow density utilized in NCEP snow initializations is presented in the next study which creates a new snow density parameterization (SNODEN). SNODEN is evaluated against observations and performance is compared with offline land surface models from the National Land Data Assimilation System (NLDAS) as well as the Snow Data Assimilation System (SNODAS). SNODEN has less error overall and reproduces the temporal evolution of snow density better than all evaluated products. SNODEN is also able to estimate snow density for up to 10 snow layers which may be useful for land surface models as well as conversion of remotely-sensed SD to SWE. Due to the poor performance of previously evaluated snow products, the last study evaluates openly-available remotely-sensed snow datasets to better understand the strengths and weaknesses of current global SWE datasets. A new SWE dataset developed at the University of Arizona is used for evaluation. While the UA SWE data has already been stringently evaluated, confidence is further increased by favorable comparison of UA snow cover, created from UA SWE, with multiple snow cover extent products. Poor performance of remotely-sensed SWE is still evident even in products which combine ground observations with remotely-sensed data. Grid boxes that are predominantly tree covered have a mean absolute difference up to 87% of mean SWE and SWE less than 5 cm is routinely overestimated by 100% or more. Additionally, snow covered area derived from global SWE datasets have mean absolute errors of 20%-154% of mean snow covered area.
Author: Intergovernmental Panel on Climate Change (IPCC)
Publisher: Cambridge University Press
ISBN: 9781009157971
Category : Science
Languages : en
Pages : 755
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Book Description
The Intergovernmental Panel on Climate Change (IPCC) is the leading international body for assessing the science related to climate change. It provides policymakers with regular assessments of the scientific basis of human-induced climate change, its impacts and future risks, and options for adaptation and mitigation. This IPCC Special Report on the Ocean and Cryosphere in a Changing Climate is the most comprehensive and up-to-date assessment of the observed and projected changes to the ocean and cryosphere and their associated impacts and risks, with a focus on resilience, risk management response options, and adaptation measures, considering both their potential and limitations. It brings together knowledge on physical and biogeochemical changes, the interplay with ecosystem changes, and the implications for human communities. It serves policymakers, decision makers, stakeholders, and all interested parties with unbiased, up-to-date, policy-relevant information. This title is also available as Open Access on Cambridge Core.
Author: National Research Council
Publisher: National Academies Press
ISBN: 0309054494
Category : Science
Languages : en
Pages : 645
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Book Description
This volume reflects the current state of scientific knowledge about natural climate variability on decade-to-century time scales. It covers a wide range of relevant subjects, including the characteristics of the atmosphere and ocean environments as well as the methods used to describe and analyze them, such as proxy data and numerical models. They clearly demonstrate the range, persistence, and magnitude of climate variability as represented by many different indicators. Not only do natural climate variations have important socioeconomic effects, but they must be better understood before possible anthropogenic effects (from greenhouse gas emissions, for instance) can be evaluated. A topical essay introduces each of the disciplines represented, providing the nonscientist with a perspective on the field and linking the papers to the larger issues in climate research. In its conclusions section, the book evaluates progress in the different areas and makes recommendations for the direction and conduct of future climate research. This book, while consisting of technical papers, is also accessible to the interested layperson.
