Evaluation of snow cover fraction for regional climate simulations in the Sierra Nevada

Abstract. Mountain seasonal snow cover is undergoing major changes due to global climate change. Assessments of future snow cover usually rely on physical based models, and often include post-processed meteorology. Alternatively, here we propose a direct statistical adjustment of snow cover fraction from regional climate models by using long-term remote sensing observations. We compared different bias correction routines (delta change, quantile mapping, and quantile delta mapping) and explore a downscaling based on historical observations for the Greater Alpine Region in Europe. All bias correction methods adjust for systematic biases, for example due to topographic smoothing, and reduce model spread in future projections. Averaged over the study region and whole year, snow cover fraction decreases from 12.5 % in 2000–2020 to 10.4 (8.9, 11.5; model spread) % in 2071–2100 under RCP2.6, and 6.4 (4.1, 7.8) % under RCP8.5. In addition, changes strongly depended on season and altitude. The comparison of the statistical downscaling to a high-resolution physical based model yields similar results for the altitude range covered by the climate models, but different altitudinal gradients of change above and below. We found trend-preserving bias correction methods (delta change, quantile delta mapping) more plausible for snow cover fraction than quantile mapping. Downscaling showed potential but requires further research. Since climate model and remote sensing observations are available globally, the proposed methods are potentially widely applicable, but are limited to snow cover fraction only.

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Snow cover sensitivity to black carbon deposition in the Himalaya: from atmospheric and ice core measurements to regional climate simulations

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-13-31013-2013 ◽

2013 ◽

Vol 13 (11) ◽

pp. 31013-31040

Author(s):

M. Ménégoz ◽

G. Krinner ◽

Y. Balkanski ◽

O. Boucher ◽

A. Cozic ◽

...

Keyword(s):

Snow Cover ◽

Black Carbon ◽

Regional Climate ◽

Ice Core ◽

Shortwave Radiation ◽

Monsoon Period ◽

Climate Simulations ◽

Snow Cover Duration ◽

The Impact ◽

Annual Duration

Abstract. We applied a climate-chemistry model to evaluate the impact of black carbon (BC) deposition on the Himalayan snow cover from 1998 to 2008. Using a stretched grid with a resolution of 50 km over this complex topography, the model reproduces reasonably well the observations of both the snow cover duration and the seasonal cycle of the atmospheric BC concentration including a maximum in atmospheric BC during the pre-monsoon period. Comparing the simulated BC concentrations in the snow with observations is challenging because of the high spatial variability and the complex vertical distribution of BC in the snow. We estimate that both wet and dry BC depositions affect the Himalayan snow cover reducing its annual duration by one to eight days. The resulting increase of the net shortwave radiation at the surface reaches an annual mean of 1 to 3 W m−2, leading to a localised warming of 0.05 to 0.3 °C.

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Supplementary material to "Bias correction and downscaling of snow cover fraction projections from regional climate models using remote sensing for the European Alps"

10.5194/hess-2021-449-supplement ◽

2021 ◽

Author(s):

Michael Matiu ◽

Florian Hanzer

Keyword(s):

Remote Sensing ◽

Snow Cover ◽

Bias Correction ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

European Alps ◽

Snow Cover Fraction ◽

Supplementary Material

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Biascorrected projections of snow cover fraction from EURO-CORDEX regional climate models with MODIS remote sensing observations for the European Alps

10.5194/egusphere-egu2020-4610 ◽

2020 ◽

Author(s):

Michael Matiu ◽

Marcello Petitta ◽

Claudia Notarnicola ◽

Marc Zebisch

Keyword(s):

Remote Sensing ◽

Snow Cover ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Climate Projections ◽

European Alps ◽

Hydrological Models ◽

Snow Cover Fraction

Snow is a key environmental parameter in mountains, and in this changing climate reductions in snow are expected. Traditionally, future estimates of snow are based on dedicated snow/hydrological models forced by climate projections, which, however, are computationally intensive and which decouple hydrology from climate forcing. Recently, regional climate models (RCM) have been used as an alternative, although snow is only an auxiliary parameter in RCMs and not as accurately represented as compared to dedicated snow models. Nonetheless, RCMs encompass the climate-hydrology feedbacks, cover large areas, and have recently become available in moderate horizontal resolutions.Here, we skip the need to biascorrect the input variables to the snow/hydrological models (i.e. temperature, precipitation, &#8230;) and use observations to directly biascorrect the target variable, i.e. snow cover. Quantile delta mapping (QDM), a trend preserving bias correction method, is used to correct biases in EURO-CORDEX RCMs that provide snow cover fraction as output (CCLM4-8-17, ALADIN63, WRF331F, WRF381P, RACMO22E, RCA4) using remote sensing observations of snow cover from MODIS for the European Alps. As such, snow cover biases were accounted for, which originated mostly from orographic mismatches as well as temperature and precipitation biases. Model ensemble means were calculated for two emission scenarios (rcp26 and rcp85; with 6 and 21 GCM-RCM combinations available). The biascorrected projections can be used to put the climate model projections into context of current observations thus facilitating interpretations.These are results from CliRSnow, a project that aims at providing bias corrected and downscaled projections of snow cover for the whole Alpine region until 2100. This project has received funding from the European Union&#8217;s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 795310.

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Evaluation of Arctic Land Snow Cover Characteristics, Surface Albedo, and Temperature during the Transition Seasons from Regional Climate Model Simulations and Satellite Data

Advances in Meteorology ◽

10.1155/2014/604157 ◽

2014 ◽

Vol 2014 ◽

pp. 1-15 ◽

Cited By ~ 12

Author(s):

X. Zhou ◽

H. Matthes ◽

A. Rinke ◽

K. Klehmet ◽

B. Heim ◽

...

Keyword(s):

Surface Temperature ◽

Regional Climate Model ◽

Snow Cover ◽

Land Surface Temperature ◽

Land Surface ◽

Surface Albedo ◽

Snow Water Equivalent ◽

Climate Model ◽

Regional Climate ◽

Snow Cover Fraction

This paper evaluates the simulated Arctic land snow cover duration, snow water equivalent, snow cover fraction, surface albedo, and land surface temperature in the regional climate model HIRHAM5 during 2008–2010, compared with various satellite and reanalysis data and one further regional climate model (COSMO-CLM). HIRHAM5 shows a general agreement in the spatial patterns and annual course of these variables, although distinct biases for specific regions and months are obvious. The most prominent biases occur for east Siberian deciduous forest albedo, which is overestimated in the simulation for snow covered conditions in spring. This may be caused by the simplified albedo parameterization (e.g., nonconsideration of different forest types and neglecting the effect of fallen leaves and branches on snow for deciduous tree forest). The land surface temperature biases mirror the albedo biases in their spatial and temporal structures. The snow cover fraction and albedo biases can explain the simulated land surface temperature bias of ca. −3°C over the Siberian forest area in spring.

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