Radiative Transfer in Climate Models

1988 ◽  
pp. 223-283 ◽  
Author(s):  
Y. Fouquart
Keyword(s):  
Radiative Transfer ◽  
Climate Models

Evaluation of shortwave radiation fluxes in the multi-layer SPARTACUS-Urban scheme using DART

2021 ◽  
Author(s):  
Megan Stretton ◽  
William Morrison ◽  
Robin Hogan ◽  
Sue Grimmond
Keyword(s):  
Radiative Transfer ◽  
Urban Form ◽  
Climate Models ◽  
Weather Prediction ◽  
Computational Cost ◽  
Three Dimensional ◽  
Shortwave Radiation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Urban Structure

<p>The heterogenous structure of cities impacts radiative exchanges (e.g. albedo and heat storage). Numerical weather prediction (NWP) models often characterise the urban structure with an infinite street canyon – but this does not capture the three-dimensional urban form. SPARTACUS-Urban (SU) - a fast, multi-layer radiative transfer model designed for NWP - is evaluated using the explicit Discrete Anisotropic Radiative Transfer (DART) model for shortwave fluxes across several model domains – from a regular array of cubes to real cities .</p><p>SU agrees with DART (errors < 5.5% for all variables) when the SU assumptions of building distribution are fulfilled (e.g. randomly distribution). For real-world areas with pitched roofs, SU underestimates the albedo (< 10%) and shortwave transmission to the surface (< 15%), and overestimates wall-plus-roof absorption (9-27%), with errors increasing with solar zenith angle. SU should be beneficial to weather and climate models, as it allows more realistic urban form (cf. most schemes) without large increases in computational cost.</p>

scholarly journals Evaluation of Cirrus Parameterizations for Radiative Flux Computations in Climate Models Using TOVS–ScaRaB Satellite Observations

2007 ◽  
Vol 20 (17) ◽  
pp. 4459-4475 ◽  
Author(s):  
C. J. Stubenrauch ◽  
F. Eddounia ◽  
J. M. Edwards ◽  
A. Macke
Keyword(s):  
Optical Properties ◽  
Radiative Transfer ◽  
Climate Models ◽  
Radiative Flux ◽  
Satellite Observations ◽  
Radiation Budget ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Ice Crystal ◽  
Infrared Observation

Abstract Combined simultaneous satellite observations are used to evaluate the performance of parameterizations of the microphysical and optical properties of cirrus clouds used for radiative flux computations in climate models. Atmospheric and cirrus properties retrieved from Television and Infrared Observation Satellite (TIROS-N) Operational Vertical Sounder (TOVS) observations are given as input to the radiative transfer model developed for the Met Office climate model to simulate radiative fluxes at the top of the atmosphere (TOA). Simulated cirrus shortwave (SW) albedos are then compared to those retrieved from collocated Scanner for Radiation Budget (ScaRaB) observations. For the retrieval, special care has been given to angular direction models. Three parameterizations of cirrus ice crystal optical properties are represented in the Met Office radiative transfer model. These parameterizations are based on different physical approximations and different hypotheses on crystal habit. One parameterization assumes pristine ice crystals and two ice crystal aggregates. By relating the cirrus ice water path (IWP) retrieved from the effective infrared emissivity to the cirrus SW albedo, differences between the parameterizations are amplified. This study shows that pristine crystals seem to be plausible only for cirrus with IWP less than 30 g m−2. For larger IWP, ice crystal aggregates lead to cirrus SW albedos in better agreement with the observations. The data also indicate that climate models should allow the cirrus effective ice crystal diameter (De) to increase with IWP, especially in the range up to 30 g m−2. For cirrus with IWP less than 20 g m−2, this would lead to SW albedos that are about 0.02 higher than the ones of a constant De of 55 μm.

scholarly journals Mesoscale Simulations of Australian Direct Normal Irradiance, Featuring an Extreme Dust Event

2018 ◽  
Vol 57 (3) ◽  
pp. 493-515 ◽  
Author(s):  
S. K. Mukkavilli ◽  
A. A. Prasad ◽  
R. A. Taylor ◽  
A. Troccoli ◽  
M. J. Kay
Keyword(s):  
Radiative Transfer ◽  
Climate Models ◽  
Global Climate ◽  
Weather Prediction ◽  
Wrf Model ◽  
Global Climate Models ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Dust Event ◽  
Direct Normal Irradiance

AbstractDirect normal irradiance (DNI) is the main input for concentrating solar power (CSP) technologies—an important component in future energy scenarios. DNI forecast accuracy is sensitive to radiative transfer schemes (RTSs) and microphysics in numerical weather prediction (NWP) models. Additionally, NWP models have large regional aerosol uncertainties. Dust aerosols can significantly attenuate DNI in extreme cases, with marked consequences for applications such as CSP. To date, studies have not compared the skill of different physical parameterization schemes for predicting hourly DNI under varying aerosol conditions over Australia. The authors address this gap by aiming to provide the first Weather and Forecasting (WRF) Model DNI benchmarks for Australia as baselines for assessing future aerosol-assimilated models. Annual and day-ahead simulations against ground measurements at selected sites focusing on an extreme dust event are run. Model biases are assessed for five shortwave RTSs at 30- and 10-km grid resolutions, along with the Thompson aerosol-aware scheme in three different microphysics configurations: no aerosols, fixed optical properties, and monthly climatologies. From the annual simulation, the best schemes were the Rapid Radiative Transfer Model for global climate models (RRTMG), followed by the new Goddard and Dudhia schemes, despite the relative simplicity of the latter. These top three RTSs all had 1.4–70.8 W m−2 lower mean absolute error than persistence. RRTMG with monthly aerosol climatologies was the best combination. The extreme dust event had large DNI mean bias overpredictions (up to 4.6 times), compared to background aerosol results. Dust storm–aware DNI forecasts could benefit from RRTMG with high-resolution aerosol inputs.

Intercomparison of Arctic Regional Climate Models: Modeling Clouds and Radiation for SHEBA in May 1998

2006 ◽  
Vol 19 (17) ◽  
pp. 4167-4178 ◽  
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.

scholarly journals Re-Examining the First Climate Models: Climate Sensitivity of a Modern Radiative–Convective Equilibrium Model

2019 ◽  
Vol 32 (23) ◽  
pp. 8111-8125 ◽  
Author(s):  
Lukas Kluft ◽  
Sally Dacie ◽  
Stefan A. Buehler ◽  
Hauke Schmidt ◽  
Bjorn Stevens
Keyword(s):  
Relative Humidity ◽  
Radiative Transfer ◽  
Temperature Profile ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Lapse Rate ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Humidity Profile ◽  
Clear Sky

Abstract We revisit clear-sky one-dimensional radiative–convective equilibrium (1D-RCE) and determine its equilibrium climate sensitivity to a CO2 doubling (ECS) and associated uncertainty. Our 1D-RCE model, named konrad, uses the Rapid Radiative Transfer Model for GCMs (RRTMG) to calculate radiative fluxes in the same way as in comprehensive climate models. The simulated radiative feedbacks are verified by a line-by-line radiative transfer model, with which we also investigate their spectral distribution. Changing the model configuration of konrad enables a clear separation between the water vapor and the lapse rate feedbacks, as well as the interaction between the two. We find that the radiative feedback and ECS are sensitive to the chosen relative humidity profile, resulting in an ECS range of 2.09–2.40 K. Using larger CO2 forcings we find that the radiative feedback changes up to 10% for surface temperatures of 291–299 K. Although the ECS is similar to previous studies, it arises from the compensation of a larger clear-sky forcing (4.7 W m−2) and more strongly negative feedbacks (−2.3 W m−2 K−1). The lapse rate feedback and the feedback from the interaction of lapse rate and humidity compensate each other, but the degree of compensation depends on the relative humidity profile. Additionally, the temperature profile is investigated in a warming climate. The temperature change at the convective top is half as large as at the surface, consistent with the proportionally higher anvil temperature hypothesis, as long as the humidity is consistently coupled to the temperature profile.

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

2018 ◽  
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.

scholarly journals RadNet 1.0: Exploring deep learning architectures for longwave radiative transfer

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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