A versatile method for computing optimized snow albedo from spectrally fixed radiative variables: VALHALLA v1.0

Abstract. In climate models, the snow albedo scheme generally calculates only a narrowband or broadband albedo, which leads to significant uncertainties. Here, we present the Versatile ALbedo calculation metHod based on spectrALLy fixed radiative vAriables (VALHALLA version 1.0) to optimize spectral snow albedo calculation. For this optimization, the energy absorbed by the snowpack is calculated by the spectral albedo model Two-streAm Radiative TransfEr in Snow (TARTES) and the spectral irradiance model Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART). This calculation takes into account the spectral characteristics of the incident radiation and the optical properties of the snow based on an analytical approximation of the radiative transfer of snow. For this method, 30 wavelengths, called tie points (TPs), and 16 reference irradiance profiles are calculated to incorporate the absorbed energy and the reference irradiance. The absorbed energy is then interpolated for each wavelength between two TPs with adequate kernel functions derived from radiative transfer theory for snow and the atmosphere. We show that the accuracy of the absorbed energy calculation primarily depends on the adaptation of the irradiance of the reference profile to that of the simulation (absolute difference <1 W m−2 for broadband absorbed energy and absolute difference <0.005 for broadband albedo). In addition to the performance in terms of accuracy and calculation time, the method is adaptable to any atmospheric input (broadband, narrowband) and is easily adaptable for integration into a radiative scheme of a global or regional climate model.

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A versatile method for computing optimized snow albedo from spectrally fixed radiative variables: VALHALLA v1.0

10.5194/gmd-2020-442 ◽

2021 ◽

Author(s):

Florent Veillon ◽

Marie Dumont ◽

Charles Amory ◽

Mathieu Fructus

Keyword(s):

Radiative Transfer ◽

Climate Models ◽

Spectral Characteristics ◽

Incident Radiation ◽

Kernel Functions ◽

Spectral Irradiance ◽

Absolute Difference ◽

Energy Calculation ◽

Absorbed Energy ◽

Snow Albedo

Abstract. In climate models, the snow albedo scheme generally calculates only a narrowband or broadband albedo, which leads to significant uncertainties. Here, we present the Versatile ALbedo calculation metHod based on spectrALLy fixed radiative vAriables (VALHALLA, version 1.0), to optimize spectral snow albedo calculation. For this optimization, the energy absorbed by the snowpack is calculated by the spectral albedo model Two-streAm Radiative TransfEr in Snow (TARTES) and the spectral irradiance model Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART). This calculation takes into account the spectral characteristics of the incident radiation and the optical properties of the snow, based on an analytical approximation of the radiative transfer of snow. For this method, 30 wavelengths, called tie points (tps), and 16 reference irradiance profiles are calculated to incorporate the absorbed energy and the reference irradiance. The absorbed energy is then interpolated for each wavelength between two tps with adequate kernel functions derived from radiative transfer theory for snow and the atmosphere. We show that the accuracy of the absorbed energy calculation primarily depends on the adaptation of the irradiance of the reference profile to that of the simulation (absolute difference

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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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A module to convert spectral to narrowband snow albedo for use in climate models: SNOWBAL v1.2

Geoscientific Model Development ◽

10.5194/gmd-12-5157-2019 ◽

2019 ◽

Vol 12 (12) ◽

pp. 5157-5175 ◽

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):

Water Vapor ◽

Radiative Transfer ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Greenland Ice Sheet ◽

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 1.2) 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 Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), cycle 33R1, atmospheric radiation scheme based on the Rapid Radiation Transfer Model, which is embedded in the Regional Atmospheric Climate Model version 2.3p2 (RACMO2). This coupling allows to explicitly account for the effect of clouds, water vapor, 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 IFS 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), cloud content and water vapor. 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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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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Intercomparison of Arctic Regional Climate Models: Modeling Clouds and Radiation for SHEBA in May 1998

Journal of Climate ◽

10.1175/jcli3854.1 ◽

2006 ◽

Vol 19 (17) ◽

pp. 4167-4178 ◽

Cited By ~ 47

Author(s):

Jun Inoue ◽

Jiping Liu ◽

James O. Pinto ◽

Judith A. Curry

Keyword(s):

Radiative Transfer ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Cloud Microphysics ◽

Surface Heat ◽

The Arctic ◽

Surface Boundary ◽

Simulated Surface

Abstract To improve simulations of the Arctic climate and to quantify climate model errors, four regional climate models [the Arctic Regional Climate System Model (ARCSYM), the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS), the High-Resolution Limited-Area Model (HIRHAM), and the Rossby Center Atmospheric Model (RCA)] have simulated the annual Surface Heat Budget of the Arctic Ocean (SHEBA) under the Arctic Regional Climate Model Intercomparison Project (ARCMIP). The same lateral boundary and ocean surface boundary conditions (i.e., ice concentration and surface temperature) drive all of the models. This study evaluated modeled surface heat fluxes and cloud fields during May 1998, a month that included the onset of the surface icemelt. In general, observations agreed with simulated surface pressure and near-surface air properties. Simulation errors due to surface fluxes and cloud effects biased the net simulated surface heat flux, which in turn affected the timing of the simulated icemelt. Modeled cloud geometry and precipitation suggest that the RCA model produced the most accurate cloud scheme, followed by the HIRHAM model. Evaluation of a relationship between cloud water paths and radiation showed that a radiative transfer scheme in ARCSYM was closely matched with the observation when liquid clouds were dominant. Clouds and radiation are of course closely linked, and an additional comparison of the radiative transfer codes for ARCSYM and COAMPS was performed for clear-sky conditions, thereby excluding cloud effects. Overall, the schemes for radiative transfer in ARCSYM and for cloud microphysics in RCA potentially have some advantages for modeling the springtime Arctic.

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RadNet 1.0: Exploring deep learning architectures for longwave radiative transfer

10.5194/gmd-2019-327 ◽

2020 ◽

Author(s):

Ying Liu ◽

Rodrigo Caballero ◽

Joy Merwin Monteiro

Keyword(s):

Neural Network ◽

Radiative Transfer ◽

Climate Models ◽

Climate Model ◽

Mean Squared Error ◽

Network Models ◽

Radiative Heating ◽

Human Populations ◽

Heating Rates ◽

Neural Network Models

Abstract. Simulating global and regional climate at high resolution is essential to study the effects of climate change and capture extreme events affecting human populations. To achieve this goal, the scalability of climate models and the efficiency of individual model components are both important. Radiative transfer is among the most computationally expensive components in a typical climate model. Here we attempt to model this component using a neural network. We aim to study the feasibility of replacing an explicit, physics-based computation of longwave radiative transfer by a neural network emulator, and assessing the resultant performance gains. We compare multiple neural-network architectures, including a convolutional neural network and our results suggest that the performance loss from the use of convolutional networks is not offset by gains in accuracy. We train the networks with and without noise added to the input profiles and find that adding noise improves the ability of the networks to generalise beyond the training set. Prediction of radiative heating rates using our neural network models achieve up to 370x speedup on a GTX 1080 GPU setup and 11x speedup on a Xeon CPU setup compared to the a state of the art radiative transfer library running on the same Xeon CPU. Furthermore, our neural network models yield less than 0.1 Kelvin per day mean squared error across all pressure levels. Upon introducing this component into a single column model, we find that the time evolution of the temperature and humidity profiles are physically reasonable, though the model is conservative in its prediction of heating rates in regions where the optical depth changes quickly. Differences exist in the equilibrium climate simulated when using the neural networks, which are attributed to small systematic errors that accumulate over time. Thus, we find that the accuracy of the neural network in the "offline" mode does not reflect its performance when coupled with other components.

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The MOSAiC sea ice albedo record: its context and role for informing improved surface radiative budgets in a climate model

10.5194/egusphere-egu21-8585 ◽

2021 ◽

Author(s):

Bonnie Light ◽

Marika Holland ◽

Madison Smith ◽

Donald Perovich ◽

Melinda Webster ◽

...

Keyword(s):

Optical Properties ◽

Radiative Transfer ◽

Sea Ice ◽

Climate Models ◽

Climate Model ◽

Heat Budget ◽

Shortwave Radiation ◽

The Arctic ◽

Data Sets ◽

Seasonal Evolution

Sea ice albedo is both a driver and a consequence of summer melt evolution. The ability to collect comprehensive observations and develop accurate, general, and consistent physics-based models is central to our quantitative understanding of sea ice mass and heat budgets and a variety of associated feedback processes. Sea ice albedos recorded during the MOSAiC field campaign have extended our knowledge of the optical properties of specific ice types as well as their seasonal evolution. This new dataset complements and extends observations made during the Surface Heat Budget of the Arctic Ocean (SHEBA) campaign in the Beaufort Sea in 1998. It also presents an opportunity to improve numerical treatment of shortwave radiation partitioning by sea ice covers in climate models. Specifically, the observations include spectral and broadband albedo measurements made by observers on the surface for two classes of measurement: 1) individual ice types including snow covered ice (prior to and during melt), bare melting ice, ponded ice, and sediment-laden ice, and 2) time series measured over the full seasonal cycles. The MOSAiC and SHEBA data sets show remarkable similarity with respect to the steady spectral albedo of bare, melting summer ice and the seasonal evolution measured over representative survey lines. Both data sets include coordinated physical property characterization, which is key to the development and refinement of radiative transfer treatment in climate modeling.In this work, we compare the observational record with results generated from runs of the CESM2 model. The Community Earth System Model (CESM2) is a coupled climate model that includes a physics-based radiative transfer treatment for sea ice. This model relies on a 2-stream delta-Eddington solution with prescribed ice-type-specific inherent optical properties. Specifically, we consider newly available sub-gridscale diagnostics in the model that detail the radiative partitioning for individual surface types and thickness categories. Comparisons between observed and modeled values are considered for the albedo of individual surface types, aggregate albedo estimates, and their seasonal progression. In particular, we use these comparisons to derive a quantitative picture of the overall partitioning of shortwave radiation by the ice cover, and how it has changed over past decades. These results can help pinpoint where the most substantial model upgrades can be accomplished as well as where the best observational investments should be made.

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

10.5194/tc-2020-118 ◽

2020 ◽

Author(s):

Christiaan T. van Dalum ◽

Willem Jan van de Berg ◽

Stef Lhermitte ◽

Michiel R. van den Broeke

Keyword(s):

Regional Climate Model ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Atmospheric Conditions ◽

Snow Albedo ◽

In Situ Data ◽

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 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 model TARTES and the spectral-to-narrowband albedo module SNOWBAL, 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 PROMICE and K-transect in-situ data and 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 smaller than 0.001 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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Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18041992 ◽

2021 ◽

Vol 18 (4) ◽

pp. 1992

Author(s):

Weijia Qian ◽

Howard H. Chang

Keyword(s):

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Warm Season ◽

Climate Science ◽

Regional Climate Models ◽

Health Impact ◽

Quantile Mapping ◽

Ed Visits

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

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