Meltwater runoff in a changing climate (1951–2099) at Chhota Shigri Glacier, Western Himalaya, Northern India

Engelhardt, Markus; Leclercq, P. W.; Eidhammer, T.; Kumar, Pankaj; Landgren, Oskar; Rasmussen, Roy

doi:10.1017/aog.2017.13

Cited by 28 publications

(28 citation statements)

References 64 publications

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“…Our decision to use dynamically downscaled reanalysis data as model forcing data rather than meteorological observations is based on a simple model experiment. We found that hydrologic simulations perform equivalent to other studies when using observations as input (Hegdahl et al, 2016;Li et al, 2015) yet significantly better when using dynamically downscaled reanalysis data (see supporting information for details; Diggle & Ribeiro, 2007;Engelhardt et al, 2017). Figure 2 introduces our methodology which provides a concurrent instantaneous RFS calculation for direct comparison with MODDRFS .…”

Section: Study Location and Meteorological Forcingmentioning

confidence: 82%

Assessing Satellite‐Derived Radiative Forcing From Snow Impurities Through Inverse Hydrologic Modeling

Matt

Burkhart

2018

Geophysical Research Letters

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Light‐absorbing impurities in snow and ice (LAISI) lower the snow albedo and cause accelerated snowmelt. The radiative forcing caused by LAISI is in this connection the key variable in understanding LAISI‐snowpack dynamics. Here we present an approach combining distributed hydrologic model simulations and remotely sensed radiative forcing from LAISI in order to improve model predictions of radiative forcing impacts. In a case study, we assess the seasonal cycle of instantaneous at‐surface clear‐sky radiative forcing from LAISI as predicted by model and satellite observations for a river basin located at the southern slope of the Himalayas. By scaling dust depositions, we optimize simulated radiative forcing conditioned on satellite observations. The optimized model predicts that LAISI‐induced radiative forcing in snow contributes to 4.1% to 5.8% of the annual discharge. The presented approach has a wide range of applications as it provides a novel method to constrain and evaluate measures of LAISI‐induced radiative forcing.

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Section: Study Location and Meteorological Forcingmentioning

confidence: 82%

Assessing Satellite‐Derived Radiative Forcing From Snow Impurities Through Inverse Hydrologic Modeling

Matt

Burkhart

2018

Geophysical Research Letters

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“…Since peaks represented by only one grid point increase the wave energy in the high frequency part of the spectrum, leading to unphysical atmospheric perturbations, the topography was smoothed by a 3 × 3 moving window algorithm (Guo and Chen, 1994, p.34). A similar type of smoothing, which is common when using the weather research and forecasting pre-processing system, was performed in previous studies employing ICAR (Gutmann et al, 2016;Engelhardt et al, 2017).…”

Section: Digital Elevation Modelmentioning

confidence: 99%

“…Gutmann et al (2016) used the North American Regional Reanalysis (NARR), which has a 32 km grid spacing (Mesinger et al, 2006). Engelhardt et al (2017) use output from the Norwegian Earth System Model (NorESM), downscaled to a grid spacing of 25 km by the regional climate model REMO, as ICAR input for a simulation period from 2006 to 2099. In this study, ERAI are preferred over regional reanalysis data sets because of their global availability and thus more widespread applicability.…”

Section: Forcing Data and Referencementioning

confidence: 99%

“…At the time of writing, ICAR has been evaluated in an idealized hill experiment, as well as by comparing monthly precipitation fields generated by ICAR for Colorado, USA, with WRF output and an observation-based gridded dataset (Gutmann et al, 2016). Furthermore, ICAR was employed to generate downscaled atmospheric fields as input for a glacier mass balance model to simulate meltwater runoff in the western Himalayas (Engelhardt et al, 2017). Recently Bernhardt et al (2018) applied ICAR to investigate differences in precipitation patterns and amounts for a domain in the European Alps due to the choice of the microphysics scheme and associated parameters.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Added Value of the Intermediate Complexity Atmospheric Research Model (ICAR) for Precipitation in Complex Topography

Horak¹,

Hofer²,

Maussion³

et al. 2018

Preprint

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Abstract. The coarse grid spacing of global circulation models necessitates the application of climate downscaling to investigate the local impact of a changing global climate. Difficulties arise for data sparse regions in complex topography which are computationally demanding for dynamic downscaling and often not suitable for statistical downscaling due to the lack of high quality observational data. The Intermediate Complexity Atmospheric Research Model (ICAR) is a physics-based model that can be applied without relying on measurements for training and is computationally more efficient than dynamic downscaling models. This study presents the first in-depth evaluation of multi-year precipitation time series generated with ICAR on a 4 x 4 km² grid for the South Island of New Zealand for the eleven-year period from 2007 to 2017. It focuses on complex topography and evaluates ICAR at 16 weather stations, eleven of which are situated in the Southern Alps between 700 m MSL and 2150 m MSL. ICAR is diagnosed with standard skill scores and the effect of model top elevation, topography, season, atmospheric background state and synoptic weather patterns on these scores are investigated. The results show a strong dependence of ICAR skill on the choice of the model top elevation, with the highest scores obtained for 4 km above topography. Furthermore, ICAR is found to provide added value over its ERA-Interim reanalysis forcing data set for alpine weather stations, improving mean squared errors (MSE) up to 53%. It performs similarly during all seasons with an MSE minimum during winter, while flow of higher linearity and atmospheric stability were found to increase skill scores. ICAR scores are highest during weather patterns associated with flow perpendicular to the Southern Alps and lowest for flow parallel to the alpine range. While measured precipitation is underestimated by ICAR, these results show the skill of ICAR in a real-world application, and may be improved upon by further observational tuning or bias correction techniques. Based on these findings ICAR shows the potential to generate downscaled fields for long term impact studies in data sparse regions with complex topography.

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“…At the time of writing, ICAR has been evaluated in an idealized hill experiment, as well as by comparing monthly precipitation fields generated by ICAR for Colorado, USA, with WRF output and an observation-based gridded data set (Gutmann et al, 2016). Furthermore, ICAR was employed to generate downscaled atmospheric fields as input for a glacier mass balance model to simulate meltwater runoff in the western Himalayas (Engelhardt et al, 2017). Recently Bernhardt et al (2018) applied ICAR to investigate differences in precipitation patterns and amounts for a domain in the European Alps, emerging from the choice of the microphysics scheme and associated parameters.…”

Section: Introductionmentioning

confidence: 99%

Assessing the added value of the Intermediate Complexity Atmospheric Research (ICAR) model for precipitation in complex topography

Horak

Hofer

Maussion

et al. 2019

Hydrol. Earth Syst. Sci.

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Abstract. The coarse grid spacing of global circulation models necessitates the application of downscaling techniques to investigate the local impact of a changing global climate. Difficulties arise for data-sparse regions in complex topography, as they are computationally demanding for dynamic downscaling and often not suitable for statistical downscaling due to the lack of high-quality observational data. The Intermediate Complexity Atmospheric Research (ICAR) model is a physics-based model that can be applied without relying on measurements for training and is computationally more efficient than dynamic downscaling models. This study presents the first in-depth evaluation of multiyear precipitation time series generated with ICAR on a 4×4 km2 grid for the South Island of New Zealand for an 11-year period, ranging from 2007 to 2017. It focuses on complex topography and evaluates ICAR at 16 weather stations, 11 of which are situated in the Southern Alps between 700 and 2150mm.s.l (m m.s.l refers to meters above mean sea level). ICAR is assessed with standard skill scores, and the effect of model top elevation, topography, season, atmospheric background state and synoptic weather patterns on these scores are investigated. The results show a strong dependence of ICAR skill on the choice of the model top elevation, with the highest scores obtained for 4 km above topography. Furthermore, ICAR is found to provide added value over its ERA-Interim reanalysis forcing data set for alpine weather stations, improving the median of mean squared errors (MSEs) by 30 % and up to 53 %. It performs similarly during all seasons with a MSE minimum during winter, while flow linearity and atmospheric stability are found to increase skill scores. ICAR scores are highest during weather patterns associated with flow perpendicular to the Southern Alps and lowest for flow parallel to the alpine range. While measured precipitation is underestimated by ICAR, these results show the skill of ICAR in a real-world application, and may be improved upon by further observational calibration or bias correction techniques. Based on these findings ICAR shows the potential to generate downscaled fields for long-term impact studies in data-sparse regions with complex topography.

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Meltwater runoff in a changing climate (1951–2099) at Chhota Shigri Glacier, Western Himalaya, Northern India

Cited by 28 publications

References 64 publications

Assessing Satellite‐Derived Radiative Forcing From Snow Impurities Through Inverse Hydrologic Modeling

Assessing Satellite‐Derived Radiative Forcing From Snow Impurities Through Inverse Hydrologic Modeling

Assessing the Added Value of the Intermediate Complexity Atmospheric Research Model (ICAR) for Precipitation in Complex Topography

Assessing the added value of the Intermediate Complexity Atmospheric Research (ICAR) model for precipitation in complex topography

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