Evaluation of a Coupled Snow and Energy Balance Model for Zhadang Glacier, Tibetan Plateau, Using Glaciological Measurements and Time-Lapse Photography

Huintjes, Eva; Sauter, Tobias; Schröter, Benjamin; Maussion, Fabien; Yang, Wei; Kropáček, Jan; Buchroithner, Manfred; Scherer, Dieter; Kang, Shichang; Schneider, Christoph

doi:10.1657/aaar0014-073

Cited by 67 publications

(115 citation statements)

References 64 publications

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“…This constraint makes it challenging to infer on micrometeorological conditions from a limited number of observations. Glaciological modeling studies typically circumvent this obstacle by constructing meteorological forcing fields, e.g., for temperature and wind, from scattered observations using fixed or variable lapse rates (e.g., Greuell and Böhm, 1998;Carturan et al, 2015;Ayala et al, 2015;Petersen et al, 2013;Huintjes et al, 2015;Weidemann et al, 2013;Jarosch et al, 2012). The interpolated fields then serve for the estimation of turbulent fluxes at any given point on the glacier.…”

Section: T Sauter and S P Galos: Effects Of Local Advection On Thementioning

confidence: 99%

“…10). The simulated (LES) wind velocities and temperatures at the two sites are linearly extrapolated across the glacier to obtain a temperature field (e.g., Paul and Kotlarski, 2010;Machguth et al, 2009;Huintjes et al, 2015;Weidemann et al, 2013;Jarosch et al, 2012). For comparison, we also estimate a second temperature field using a simple thermodynamic glacier wind model that accounts for air temperature variations along the flow line.…”

Section: Reliability Of the Les Experimentsmentioning

confidence: 99%

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Effects of local advection on the spatial sensible heat flux variation on a mountain glacier

Sauter

Galos

2016

The Cryosphere

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Abstract. Distributed mass balance models, which translate micrometeorological conditions into local melt rates, have proven deficient to reflect the energy flux variability on mountain glaciers. This deficiency is predominantly related to shortcomings in the representation of local processes in the forcing data. We found by means of idealized large-eddy simulations that heat advection, associated with local wind systems, causes small-scale sensible heat flux variations by up to 100 W m −2 during clear sky conditions. Here we show that process understanding at a few observation sites is insufficient to infer the wind and temperature distributions across the glacier. The glacier-wide hourly averaged sensible heat fluxes are both over-and underestimated by up to 16 W m −2 when using extrapolated temperature and wind fields. The sign and magnitude of the differences depend on the site selection, which is used for extrapolation as well as on the large-scale flow direction. Our results demonstrate how the shortcomings in the local sensible heat flux estimates are related to topographic effects and the insufficient characterization of the temperature advection process.

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Section: T Sauter and S P Galos: Effects Of Local Advection On Thementioning

confidence: 99%

Section: Reliability Of the Les Experimentsmentioning

confidence: 99%

Effects of local advection on the spatial sensible heat flux variation on a mountain glacier

Sauter

Galos

2016

The Cryosphere

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“…Detailed measurement specifications are listed in Table 1. However, only the Xiao Dongkemadi station provides the directly measured LST values, which were obtained through an Apogee Precision Infrared Thermocouple Sensor (IRTS-P) with an accuracy of 0.3 K over the glacier surface (Huintjes et al, 2015). The LSTs of the Qinghai and Ngari stations were derived based on the Stefan-Boltzmann law and the thermal radiative transfer theory:…”

Section: Ground Measurementsmentioning

confidence: 99%

Evaluation of cloud effects on air temperature estimation using MODIS LST based on ground measurements over the Tibetan Plateau

Zhang

et al. 2016

Atmos. Chem. Phys.

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Abstract. Moderate Resolution Imaging Spectroradiometer (MODIS) daytime and nighttime land surface temperature (LST) data are often used as proxies for estimating daily maximum (T max ) and minimum (T min ) air temperatures, especially for remote mountainous areas due to the sparseness of ground measurements. However, the Tibetan Plateau (TP) has a high daily cloud cover fraction (> 45 %), which may affect the air temperature (T air ) estimation accuracy. This study comprehensively analyzes the effects of clouds on T air estimation based on MODIS LST using detailed halfhourly ground measurements and daily meteorological station observations collected from the TP. It is shown that erroneous rates of MODIS nighttime cloud detection are obviously higher than those achieved in daytime. Large errors in MODIS nighttime LST data were found to be introduced by undetected clouds and thus reduce the T min estimation accuracy. However, for T max estimation, clouds are mainly found to reduce the estimation accuracy by affecting the essential relationship between T max and daytime LST. The errors of T max estimation are obviously larger than those of T min and could be attributed to larger MODIS daytime LST errors that result from higher degrees of LST heterogeneity within MODIS pixel compared to those of nighttime LST. Constraining MODIS observations to non-cloudy observations can efficiently screen data samples for accurate T min estimation using MODIS nighttime LST. As a result, the present study reveals the effects of clouds on T max and T min estimation through MODIS daytime and nighttime LST, respectively, so as to help improve the T air estimation accuracy and alleviate the severe air temperature data sparseness issues over the TP.

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“…Some models, including SnowModel (Liston and Elder, 2006a), have the option to calculate the state properties of multiple snow layers. A few models, including the COupled Snowpack and Ice surface energy and MAss balance model (COSIMA; Huintjes et al, 2015), calculate the sensible and latent energy of both snow and ice. Wolverine glacier is on the Kenai Peninsula in relatively close proximity to the Gulf of Alaska while Gulkana is in the Alaska Range and much further from a large body of water (Fig.…”

Section: Cryosphere Energy Balance Representationsmentioning

confidence: 99%

How much cryosphere model complexity is just right? Exploration using the conceptual cryosphere hydrology framework

2016

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Abstract. Making meaningful projections of the impacts that possible future climates would have on water resources in mountain regions requires understanding how cryosphere hydrology model performance changes under altered climate conditions and when the model is applied to ungaged catchments. Further, if we are to develop better models, we must understand which specific process representations limit model performance. This article presents a modeling tool, named the Conceptual Cryosphere Hydrology Framework (CCHF), that enables implementing and evaluating a wide range of cryosphere modeling hypotheses. The CCHF represents cryosphere hydrology systems using a set of coupled process modules that allows easily interchanging individual module representations and includes analysis tools to evaluate model outputs. CCHF version 1 (Mosier, 2016) implements model formulations that require only precipitation and temperature as climate inputs -for example variations on simple degree-index (SDI) or enhanced temperature index (ETI) formulations -because these model structures are often applied in data-sparse mountain regions, and perform relatively well over short periods, but their calibration is known to change based on climate and geography. Using CCHF, we implement seven existing and novel models, including one existing SDI model, two existing ETI models, and four novel models that utilize a combination of existing and novel module representations. The novel module representations include a heat transfer formulation with net longwave radiation and a snowpack internal energy formulation that uses an approximation of the cold content. We assess the models for the Gulkana and Wolverine glaciated watersheds in Alaska, which have markedly different climates and contain long-term US Geological Survey benchmark glaciers. Overall we find that the best performing models are those that are more physically consistent and representative, but no single model performs best for all of our model evaluation criteria.

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Evaluation of a Coupled Snow and Energy Balance Model for Zhadang Glacier, Tibetan Plateau, Using Glaciological Measurements and Time-Lapse Photography

Cited by 67 publications

References 64 publications

Effects of local advection on the spatial sensible heat flux variation on a mountain glacier

Effects of local advection on the spatial sensible heat flux variation on a mountain glacier

Evaluation of cloud effects on air temperature estimation using MODIS LST based on ground measurements over the Tibetan Plateau

How much cryosphere model complexity is just right? Exploration using the conceptual cryosphere hydrology framework

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