The Alpine snow-albedo feedback in regional climate models

Winter, Kevin; Kotlarski, Sven; Scherrer, Simon C.; Schär, Christoph

doi:10.1007/s00382-016-3130-7

Cited by 43 publications

(27 citation statements)

References 35 publications

(52 reference statements)

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“…While factors (1) to (4) can explain parts of the elevation dependency of temperature changes in individual seasons and individual parts of the European continent, the most prominent influence was found for factor (5), suggesting an important contribution of the snow‐albedo feedback. This is in line with previous modelling studies (Giorgi et al , ; Fyfe and Flato, ; Winter, ) and can to some extent also be derived from near‐surface temperature records (Ceppi et al , ; Scherrer et al , ).…”

Section: Climate Scenariossupporting

confidence: 92%

The elevation dependency of 21st century European climate change: an RCM ensemble perspective

Kotlarski

Lüthi

Schär

2015

Intl Journal of Climatology

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The ENSEMBLES regional climate model (RCM) ensemble is analysed with respect to the elevation‐dependency of 21st century near‐surface climate change over Europe. Fifteen experiments carried out by 11 different RCMs, driven by 6 different global climate models (GCMs) are considered, all of them assuming the SRES A1B emission scenario. Model evaluation for the historical period yields an approximate reproduction of observed temperature–elevation and precipitation–elevation profiles, but some distortion due to high‐elevation cold and wet biases. Climate change signals until the end of the 21st century show characteristic elevation dependencies over many parts of Europe and in many seasons. Except for northern Europe maximum warming typically occurs at medium to high elevations, leading to a decrease of near‐surface temperature lapse rates. The model agreement on the vertical temperature change profile is high; it does not depend on the driving GCM although the latter strongly determines the overall warming magnitude. Warming anomalies can often be related to changes of surface snow cover, and a contribution of the snow‐albedo feedback to the anomalous medium‐ and high‐elevation warming is likely. Also climate change signals of seasonal precipitation sums can considerably depend on elevation, but peculiarities highly depend on the region and season considered. Moreover, the vertical precipitation change profile appears to be less related to elevation itself but is strongly influenced by horizontal change patterns within the individual analysis domains and by the relative location of grid cells with respect to major orographic obstacles. Medium‐ to high‐elevation anomalies of large‐scale climate change signals as identified in this work represent an added value of dynamical downscaling as these elevations are typically not represented by the driving GCMs.

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Section: Climate Scenariossupporting

confidence: 92%

The elevation dependency of 21st century European climate change: an RCM ensemble perspective

Kotlarski

Lüthi

Schär

2015

Intl Journal of Climatology

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“…Other recent work focused on the European Alps has attempted to quantify the SAF using station observations [ Scherrer et al , ] and a suite of RCMs with 25 km horizontal grids [ Winter et al , ]. Results show large variability in SAF strength between RCMs and a substantial overestimate of springtime SAF strength relative to observations in several RCMs [ Winter et al , ]. However, the key uncertainties of this study include the following: limitations of the observational SAF estimates used, which are based on a handful of station observations, and the uncertain applicability of these results to higher‐resolution RCM simulations.…”

Section: Introductionmentioning

confidence: 76%

“…Since their study only considered a relatively small region and a single year, the generality of the results is unknown. Other recent work focused on the European Alps has attempted to quantify the SAF using station observations [ Scherrer et al , ] and a suite of RCMs with 25 km horizontal grids [ Winter et al , ]. Results show large variability in SAF strength between RCMs and a substantial overestimate of springtime SAF strength relative to observations in several RCMs [ Winter et al , ].…”

Section: Introductionmentioning

confidence: 97%

“…Studies applying RCMs over mountainous regions indicate a strong local enhancement of climate warming in regions of snow loss associated with the SAF [ Giorgi et al , ; Lynch et al , ; Salathé et al , ; Im et al , ; Kotlarski et al , , ; Letcher and Minder , ]. This SAF‐enhanced warming can impart an elevation dependence to the climate change signal by amplifying warming in the elevation band where the largest reductions in snow cover occur [e.g., Kotlarski et al , , ; Winter et al , ]. The strength, seasonality, and spatial structure of SAF‐enhanced warming depend on model grid spacing such that a grid spacing of 12 km or finer may be necessary to adequately simulate patterns of snow cover and SAFs [ Letcher and Minder , ].…”

Section: Introductionmentioning

confidence: 99%

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An evaluation of high‐resolution regional climate model simulations of snow cover and albedo over the Rocky Mountains, with implications for the simulated snow‐albedo feedback

Minder

Letcher

Skiles³

2016

JGR Atmospheres

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The snow‐albedo feedback (SAF) strongly influences climate over midlatitude mountainous regions. However, over these regions the skill of regional climate models (RCMs) at simulating properties such as snow cover and surface albedo is poorly characterized. These properties are evaluated in a pair of 7 year long high‐resolution RCM simulations with the Weather Research and Forecasting model over the central Rocky Mountains. Key differences between the simulations include the computational domain (regional versus continental) and land surface model used (Noah versus Noah‐MP). Simulations are evaluated against high‐resolution satellite estimates of snow cover and albedo from the Moderate Resolution Imaging Spectroradiometer. Both simulations generally reproduce the observed seasonal and spatial variability of snow cover and also exhibit important biases. One simulation substantially overpredicts subpixel fractional snow cover over snowy pixels (by up to 0.4) causing large positive biases in surface albedo, likely due in part to inadequate representation of canopy effects. The other simulation exhibits a negative bias in areal snow extent (as much as 19% of the analysis domain). Surface measurements reveal large positive biases in snow albedo (exceeding 0.2) during late spring caused by neglecting radiative effects of impurities deposited onto snow. Semi‐idealized climate change experiments show substantially different magnitudes of SAF‐enhanced warming in the two simulations that can be tied to the differences in snow cover in their control climates. More confident projections of regional climate change over mountains will require further work to evaluate and improve representation of snow cover and albedo in RCMs.

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“…The spatial distribution of snow on the ground is of paramount importance in cold regions. Changes in patterns of snow accumulation strongly influence global climate systems [D ery and Yau, 2002;Barnett et al, 2008;Callaghan et al, 2011], local climatology [Terzago et al, 2014;Euskirchen et al, 2016;Winter et al, 2017], as well as the rate and timing of snowmelt [Luce et al, 1998;Nolin, 2012;Ellis et al, 2013]. Understanding the physical processes of snow accumulation is important to hydrologists since predictions and forecasts can be made regarding water availability from snow [Maurer, 2007;Haddeland et al, 2011;Fang et al, 2013;Guan et al, 2013] and shifts in climate [Marks et al, 1998;L opez-Moreno et al, 2014;Rasouli et al, 2014;L opez-Moreno et al, 2016].…”

Section: Introductionmentioning

confidence: 99%

Modeling blowing snow accumulation downwind of an obstruction: The Ohara Eulerian particle distribution equation

Kinar

2017

Water Resources Research

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An equation was proposed to model the height of blowing snow accumulation downwind of an obstacle such as vegetation, a snow fence, a building, or a topographic feature. The equation does not require aerodynamic flow condition parameters such as wind speed, allowing for the spatial distribution of snow to be determined at locations where meteorological data is not available. However, snow particle diffusion, drift, and erosion coefficients must be estimated for application of the equation. These coefficients can be used to provide insight into the relative magnitude of blowing snow processes at a field location. Further research is required to determine efficient methods for coefficient estimation. The equation could be used with other models of wind‐transported snow to predict snow accumulation downwind of an obstacle without the need for wind speed adjustments or correction equations. Applications for this equation include the design of snow fences, and the use of this equation with other hydrological models to predict snow distribution, climate change, drought, flooding, and avalanches.

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The Alpine snow-albedo feedback in regional climate models

Cited by 43 publications

References 35 publications

The elevation dependency of 21st century European climate change: an RCM ensemble perspective

The elevation dependency of 21st century European climate change: an RCM ensemble perspective

An evaluation of high‐resolution regional climate model simulations of snow cover and albedo over the Rocky Mountains, with implications for the simulated snow‐albedo feedback

Modeling blowing snow accumulation downwind of an obstruction: The Ohara Eulerian particle distribution equation

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