Physics‐based narrowband optical parameters for snow albedo simulation in climate models

Evaluation of Snow Albedo in Land Models for Weather and Climate Studies

Journal of Applied Meteorology and Climatology ◽

10.1175/2009jamc2134.1 ◽

2010 ◽

Vol 49 (3) ◽

pp. 363-380 ◽

Cited By ~ 46

Author(s):

Zhuo Wang ◽

Xubin Zeng

Keyword(s):

Climate Models ◽

Snow Water Equivalent ◽

Weather Forecasting ◽

Vegetation Type ◽

Global Model ◽

Forest Site ◽

Snow Albedo ◽

Community Land Model ◽

In Situ Data ◽

Forest Sites

Abstract Snow albedo plays an important role in land models for weather, climate, and hydrometeorological studies, but its treatment in various land models still contains significant deficiencies. Complementary to previous studies that evaluated the snow albedo as part of an overall land model study, the snow albedo formulations as used in four major weather forecasting and climate models [European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP) “Noah” land model, National Center for Atmospheric Research (NCAR) Community Land Model (CLM3), and NCEP global model] were directly evaluated here using multiyear Boreal Ecosystem–Atmosphere Study (BOREAS) in situ data over grass and forest sites. First, four idealized cases over grass and forest sites were designed to understand better the different albedo formulations in these models. Then the BOREAS data were used to evaluate snow albedo and relevant formulations and to identify deficiencies of each model. Based on these analyses, suggestions that involve only minor changes in parameters or formulations were made to significantly reduce these deficiencies of each model. For the ECMWF land model, using the square root of snow water equivalent (SWE), rather than SWE itself, in the computation of snow fraction would significantly reduce the underestimation of albedo over grass. For the NCEP Noah land model, reducing (increasing) the critical SWE for full snow cover over short (tall) vegetation would reduce the underestimate (overestimate) of snow albedo over the grass (forest) site. For the NCAR CLM3, revising the coefficient used in the ground snow-fraction computation would substantially reduce the albedo underestimation over grass. For the albedo formulations in the NCEP global model, replacing the globally constant fresh snow albedo by the vegetation-type-dependent Moderate-Resolution Imaging Spectroradiometer (MODIS) maximum snow albedo would significantly improve the overestimation of model albedo over forest.

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Assessing Snow Albedo Feedback in Simulated Climate Change

Journal of Climate ◽

10.1175/jcli3750.1 ◽

2006 ◽

Vol 19 (11) ◽

pp. 2617-2630 ◽

Cited By ~ 83

Author(s):

Xin Qu ◽

Alex Hall

Keyword(s):

Climate Change ◽

Standard Deviation ◽

Northern Hemisphere ◽

Surface Albedo ◽

Climate Models ◽

Attenuation Effect ◽

Albedo Feedback ◽

Snow Albedo ◽

The Mean ◽

Planetary Albedo

Abstract In this paper, the two factors controlling Northern Hemisphere springtime snow albedo feedback in transient climate change are isolated and quantified based on scenario runs of 17 climate models used in the Intergovernmental Panel on Climate Change Fourth Assessment Report. The first factor is the dependence of planetary albedo on surface albedo, representing the atmosphere's attenuation effect on surface albedo anomalies. It is potentially a major source of divergence in simulations of snow albedo feedback because of large differences in simulated cloud fields in Northern Hemisphere land areas. To calculate the dependence, an analytical model governing planetary albedo was developed. Detailed validations of the analytical model for two of the simulations are shown, version 3 of the Community Climate System Model (CCSM3) and the Geophysical Fluid Dynamics Laboratory global coupled Climate Model 2.0 (CM2.0), demonstrating that it facilitates a highly accurate calculation of the dependence of planetary albedo on surface albedo given readily available simulation output. In all simulations it is found that surface albedo anomalies are attenuated by approximately half in Northern Hemisphere land areas as they are transformed into planetary albedo anomalies. The intermodel standard deviation in the dependence of planetary albedo on surface albedo is surprisingly small, less than 10% of the mean. Moreover, when an observational estimate of this factor is calculated by applying the same method to the satellite-based International Satellite Cloud Climatology Project (ISCCP) data, it is found that most simulations agree with ISCCP values to within about 10%, despite further disagreements between observed and simulated cloud fields. This suggests that even large relative errors in simulated cloud fields do not result in significant error in this factor, enhancing confidence in climate models. The second factor, related exclusively to surface processes, is the change in surface albedo associated with an anthropogenically induced temperature change in Northern Hemisphere land areas. It exhibits much more intermodel variability. The standard deviation is about ⅓ of the mean, with the largest value being approximately 3 times larger than the smallest. Therefore this factor is unquestionably the main source of the large divergence in simulations of snow albedo feedback. To reduce the divergence, attention should be focused on differing parameterizations of snow processes, rather than intermodel variations in the attenuation effect of the atmosphere on surface albedo anomalies.

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Evaluation of a new snow albedo scheme in RACMO2 for the Greenland ice sheet

10.5194/egusphere-egu2020-15261 ◽

2020 ◽

Author(s):

Christiaan van Dalum ◽

Willem Jan van de Berg ◽

Stef Lhermitte ◽

Michiel van den Broeke

Keyword(s):

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Atmospheric Conditions ◽

Surface Mass ◽

Snow Albedo ◽

In Situ Data ◽

Snow And Ice

Snow and ice albedo schemes in present day climate models often lack a sophisticated radiation penetration scheme and are limited to a broadband albedo. In this study, we evaluate a new snow albedo scheme in the regional climate model RACMO2 that uses the two-stream radiative transfer in snow model TARTES and the spectral-to-narrowband albedo module SNOWBAL for the Greenland ice sheet. Additionally, the bare ice albedo parameterization has been updated. The snow and ice albedo output of the updated version of RACMO2, referred to as RACMO2.3p3, is evaluated using PROMICE and K-transect in-situ data and MODIS remote-sensing observations. Generally, the RACMO2.3p3 albedo is in very good agreement with satellite observations, leading to a domain-averaged bias of only -0.012. Some discrepancies are, however, observed for regions close to the ice margin. Compared to the previous iteration RACMO2.3p2, the albedo of RACMO2.3p3 is considerably higher in the bare ice zone during the ablation season, as atmospheric conditions now alter the bare ice albedo. For most other regions, however, the albedo of RACMO2.3p3 is lower due to spectral effects, radiation penetration, snow metamorphism or a delayed firn-ice transition. Furthermore, a white-out effect during cloudy conditions is captured and the snow albedo shows a low sensitivity to low soot concentrations. The surface mass balance of RACMO2.3p3 compares well with observations. Subsurface heating, however, now leads to increased melt and refreezing in south Greenland, changing the snow structure.

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Evaluation of a new snow albedo scheme for the Greenland ice sheet in the Regional Atmospheric Climate Model (RACMO2)

The Cryosphere ◽

10.5194/tc-14-3645-2020 ◽

2020 ◽

Vol 14 (11) ◽

pp. 3645-3662

Author(s):

Christiaan T. van Dalum ◽

Willem Jan van de Berg ◽

Stef Lhermitte ◽

Michiel R. van den Broeke

Keyword(s):

Climate Models ◽

Climate Model ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Atmospheric Conditions ◽

Snow Albedo ◽

In Situ Data ◽

Moderate Resolution Imaging Spectroradiometer ◽

Regional Atmospheric Climate Model ◽

Snow And Ice

Abstract. Snow and ice albedo schemes in present-day climate models often lack a sophisticated radiation penetration scheme and do not explicitly include spectral albedo variations. In this study, we evaluate a new snow albedo scheme in the Regional Atmospheric Climate Model (RACMO2) for the Greenland ice sheet, version 2.3p3, that includes these processes. The new albedo scheme uses the Two-streAm Radiative TransfEr in Snow (TARTES) model and the Spectral-to-NarrOWBand ALbedo (SNOWBAL) module, version 1.2. Additionally, the bare-ice albedo parameterization has been updated. The snow and ice broadband and narrowband albedo output of the updated version of RACMO2 is evaluated using the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and Kangerlussuaq transect (K-transect) in situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) remote-sensing observations. Generally, the modeled narrowband and broadband albedo is in very good agreement with satellite observations, leading to a negligible domain-averaged broadband albedo bias for the interior. Some discrepancies are, however, observed close to the ice margin. Compared to the previous model version, RACMO2.3p2, the broadband albedo is considerably higher in the bare-ice zone during the ablation season, as atmospheric conditions now alter the bare-ice broadband albedo. For most other regions, however, the updated broadband albedo is lower due to spectral effects, radiation penetration or enhanced snow metamorphism.

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Land-atmosphere interactions in sub-polar and alpine climates in the CORDEX FPS LUCAS models: Part II. The role of changing vegetation

10.5194/tc-2021-291 ◽

2021 ◽

Author(s):

Priscilla A. Mooney ◽

Diana Rechid ◽

Edouard L. Davin ◽

Eleni Katragkou ◽

Natalie de Noblet-Ducoudré ◽

...

Keyword(s):

Land Cover ◽

Land Surface ◽

Climate Models ◽

Regional Climate ◽

Model Development ◽

Regional Climate Models ◽

Sensitivity Index ◽

Land Surface Models ◽

Snow Albedo ◽

Surface Models

Abstract. Land cover in sub-polar and alpine regions of northern and eastern Europe have already begun changing due to natural and anthropogenic changes such as afforestation. This will impact the regional climate and hydrology upon which societies in these regions are highly reliant. This study aims to identify the impacts of afforestation/reforestation (hereafter afforestation) on snow and the snow-albedo effect, and highlight potential improvements for future model development. The study uses an ensemble of nine regional climate models for two different idealised experiments covering a 30-year period; one experiment replaces most land cover in Europe with forest while the other experiment replaces all forested areas with grass. The ensemble consists of nine regional climate models composed of different combinations of five regional atmospheric models and six land surface models. Results show that afforestation reduces the snow-albedo sensitivity index and enhances snow melt. While the direction of change is robustly modelled, there is still uncertainty in the magnitude of change. Greatest differences between models emerge in the snowmelt season. One regional climate model uses different land surface models which shows consistent changes between the three simulations during the accumulation period but differs in the snowmelt season. Together these results point to the need for further model development in representing both grass-snow and forest-snow interactions during the snowmelt season. Pathways to accomplishing this include 1) a more sophisticated representation of forest structure, 2) kilometer scale simulations, and 3) more observational studies on vegetation-snow interactions in Northern Europe.

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Evaluation of snow albedo feedback simulated by CMIP6 coupled climate models

10.5194/egusphere-egu2020-4309 ◽

2020 ◽

Author(s):

jiangling hu ◽

duoying ji

Keyword(s):

Climate Change ◽

Northern Hemisphere ◽

Land Surface ◽

Climate Models ◽

Coupled Model ◽

Cmip5 Models ◽

Simulation Research ◽

Albedo Feedback ◽

Snow Albedo ◽

Autumn And Winter

As the land surface warms, a subsequent reduction in snow and ice cover reveals a less reflective surface that absorbs more solar radiation, which further enhances the initial warming. This positive feedback climate mechanism is the snow albedo feedback (SAF), which will exacerbate climate warming and is the second largest contributor to Arctic amplification. Snow albedo feedback will increase the sensitivity of climate change in the northern hemisphere, which affects the accuracy of climate models in simulation research of climate change, and further affects the credibility of future climate prediction results.Using the latest generation of climate models from CMIP6 (Coupled Model Intercomparison Project Version 6), we analyze seasonal cycle snow albedo feedback in Northern Hemisphere extratropics. We find that the strongest SAF strength is in spring (mean: -1.34 %K-1), second strongest is autumn (mean: -1.01 %K-1), the weakest is in summer (mean: -0.18 %K-1). Except summer, the SAF strength is approximately 0.15% K-1 larger than CMIP5 models in the other three seasons. The spread of spring SAF strength (range: -1.09 to -1.37% K-1) is larger than CMIP5 models. Oppositely, the spread of summer SAF strength (range: 0.20 to -0.56% K-1) is smaller than CMIP5 models. When compared with CMIP5 models, the spread of autumn and winter SAF strength have not changed much.

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A module to convert spectral to narrowband snow albedo for use in climate models: SNOWBAL v1.0

10.5194/gmd-2018-175 ◽

2018 ◽

Cited By ~ 1

Author(s):

Christiaan T. van Dalum ◽

Willem Jan van de Berg ◽

Quentin Libois ◽

Ghislain Picard ◽

Michiel R. van den Broeke

Keyword(s):

Radiative Transfer ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Greenland Ice Sheet ◽

Atmospheric Model ◽

Shortwave Radiation ◽

Transfer Model ◽

Snow Albedo ◽

Radiation Scheme

Abstract. Snow albedo schemes in regional climate models often lack a sophisticated radiation penetration scheme and generally compute only a broadband albedo. Here, we present the Spectral-to-NarrOWBand ALbedo module (SNOWBAL, version 1.0) to couple effectively a spectral albedo model with a narrowband radiation scheme. Specifically, the Two-streAm Radiative TransfEr in Snow model (TARTES) is coupled with the European Center for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System atmospheric radiation scheme based on the rapid radiation transfer model, which is embedded in the regional climate model RACMO2. This coupling allows to explicitly account for the effect of clouds, snow impurities and snow metamorphism on albedo. Firstly, we present a narrowband albedo method to project the spectral albedos of TARTES onto the 14 spectral bands of the ECMWF shortwave radiation scheme using a representative wavelength (RW) for each band. Using TARTES and spectral downwelling surface irradiance derived with the DIScrete Ordinate Radiative Transfer atmospheric model, we show that RWs primarily depend on the solar zenith angle (SZA) and cloud content. Secondly, we compare the TARTES narrowband albedo, using offline RACMO2 results for South Greenland, with the broadband albedo parameterizations of Gardner and Sharp (2010), currently implemented in RACMO2, and the multi-layered parameterization of Kuipers Munneke et al. (2011, PKM). The actual absence of radiation penetration in RACMO2 leads on average to a higher albedo compared with TARTES narrowband albedo. Furthermore, large differences between the TARTES narrowband albedo and PKM and RACMO2 are observed for high SZA and clear-sky conditions, and after melt events when the snowpack is very inhomogeneous. This highlights the importance of accounting for spectral albedo and radiation penetration to simulate the energy budget of the Greenland ice sheet.

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Intercomparison and validation of snow albedo parameterization schemes in climate models

Climate Dynamics ◽

10.1007/s00382-005-0037-0 ◽

2005 ◽

Vol 25 (4) ◽

pp. 351-362 ◽

Cited By ~ 60

Author(s):

Christina A. Pedersen ◽

Jan-Gunnar Winther

Keyword(s):

Climate Models ◽

Snow Albedo ◽

Parameterization Schemes

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Enhancement of snow albedo reduction and radiative forcing due to coated black carbon in snow

The Cryosphere ◽

10.5194/tc-15-2255-2021 ◽

2021 ◽

Vol 15 (5) ◽

pp. 2255-2272

Author(s):

Wei Pu ◽

Tenglong Shi ◽

Jiecan Cui ◽

Yang Chen ◽

Yue Zhou ◽

...

Keyword(s):

Black Carbon ◽

Light Absorption ◽

Radiative Forcing ◽

Climate Models ◽

Northern China ◽

Climate Impact ◽

Arctic Sea Ice ◽

The Arctic ◽

Core Shell ◽

Snow Albedo

Abstract. When black carbon (BC) is mixed internally with other atmospheric particles, the BC light absorption effect is enhanced. This study explicitly resolved the optical properties of coated BC in snow based on the core / shell Mie theory and the Snow, Ice, and Aerosol Radiative (SNICAR) model. Our results indicated that the BC coating effect enhances the reduction in snow albedo by a factor ranging from 1.1–1.8 for a nonabsorbing shell and 1.1–1.3 for an absorbing shell, depending on the BC concentration, snow grain radius, and core / shell ratio. We developed parameterizations of the BC coating effect for application to climate models, which provides a convenient way to accurately estimate the climate impact of BC in snow. Finally, based on a comprehensive set of in situ measurements across the Northern Hemisphere, we determined that the contribution of the BC coating effect to snow light absorption exceeds that of dust over northern China. Notably, high enhancements of snow albedo reduction due to the BC coating effect were found in the Arctic and Tibetan Plateau, suggesting a greater contribution of BC to the retreat of Arctic sea ice and Tibetan glaciers.

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Satellite data processing for optimization of snow albedo parameterization in Noah LSM

10.5194/egusphere-egu21-14192 ◽

2021 ◽

Author(s):

Seung Yeon Lee ◽

Sujung Lim ◽

Ebony Lee ◽

Seon Ki Park

Keyword(s):

Land Cover ◽

Data Processing ◽

Land Surface ◽

Climate Models ◽

Fitness Function ◽

Surface Model ◽

Snow Albedo ◽

Modis Data ◽

Surface Atmosphere ◽

Parameter Values

To improve the predictability of weather/climate models, a prediction system capable of simulating the land surface-atmosphere interaction is essential. In the land surface model (LSM), the parameter values are applied differently depending on the land cover type. Previous studies reported that the Noah LSM underestimated the snow-related variables such as snow albedo, snow depth, and snow cover, compared to actual observations. In this study, among various processes in Noah LSM, we optimize several parameters related to snow albedo, using the genetic algorithm (GA) and satellite (MODIS) data: the parameters to be optimized include 1) the threshold value of the amount of snow with full coverage, , 2) the distribution shape coefficient related to the maximum albedo of new snowfall, and 3) the maximum albedo coefficient. We propose the MODIS data processing method to extract representative snow albedo values, rather than the point (pixel) values, for different land cover types in a 10 km by 10 km area around a model gridpoint &#11834; the representative values are used to calculate the fitness function in the GA optimization. The snow albedo simulation by Noah LSM has alleviated the underestimation problem with the optimized parameter values: it showed better results with the parameter values optimized using the representative values than those optimized using the point values. We expect to see further improvement in the weather/climate simluations using the coupled land surface-atmosphere model (e.g., WRF-Noah LSM) by implementing the optimized parameter values related to snow albedo.

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