scholarly journals On the Approximation of Local and Linear Radiative Damping in the Middle Atmosphere

2010 ◽  
Vol 67 (6) ◽  
pp. 2070-2085 ◽  
Author(s):  
Peter Hitchcock ◽  
Theodore G. Shepherd ◽  
Shigeo Yoden
Keyword(s):  
Climate Model ◽  
Middle Atmosphere ◽  
Mechanistic Modeling ◽  
Radiative Heating ◽  
Spatiotemporal Variability ◽  
Quadratic Term ◽  
Heating Rates ◽  
Temperature State ◽  
Large Fluctuations ◽  
Damping Rate

Abstract The validity of approximating radiative heating rates in the middle atmosphere by a local linear relaxation to a reference temperature state (i.e., “Newtonian cooling”) is investigated. Using radiative heating rate and temperature output from a chemistry–climate model with realistic spatiotemporal variability and realistic chemical and radiative parameterizations, it is found that a linear regression model can capture more than 80% of the variance in longwave heating rates throughout most of the stratosphere and mesosphere, provided that the damping rate is allowed to vary with height, latitude, and season. The linear model describes departures from the climatological mean, not from radiative equilibrium. Photochemical damping rates in the upper stratosphere are similarly diagnosed. Three important exceptions, however, are found. The approximation of linearity breaks down near the edges of the polar vortices in both hemispheres. This nonlinearity can be well captured by including a quadratic term. The use of a scale-independent damping rate is not well justified in the lower tropical stratosphere because of the presence of a broad spectrum of vertical scales. The local assumption fails entirely during the breakup of the Antarctic vortex, where large fluctuations in temperature near the top of the vortex influence longwave heating rates within the quiescent region below. These results are relevant for mechanistic modeling studies of the middle atmosphere, particularly those investigating the final Antarctic warming.

Aerosol influences on radiative heating rates in the Asian tropopause aerosol layer

2021 ◽  
Author(s):  
Jie Gao ◽  
Jonathon Wright
Keyword(s):  
Radiative Transfer ◽  
Radiative Forcing ◽  
Asian Monsoon ◽  
Volcanic Eruptions ◽  
Radiative Heating ◽  
Spatiotemporal Variability ◽  
Radiation Budget ◽  
Aerosol Layer ◽  
Heating Rates ◽  
Aerosol Analysis

<p>The Asian Tropopause Aerosol Layer (ATAL) has emerged over recent decades to play an increasingly prominent role in the upper troposphere and lower stratosphere above the Asian monsoon region. Although the effects of the ATAL on the surface and top-of-atmosphere radiation budget have been examined by several studies, the processes and effects by which the ATAL alters radiative transfer within the tropopause layer have been much less discussed. We have used a conditional composite approach to investigate aerosol mixing ratios and their impacts on radiative heating rates in the Asian monsoon tropopause layer in MERRA-2. We have then subsampled in time based on known volcanic eruptions and the evolution of emission and data assimilation inputs to the MERRA-2 aerosol analysis to isolate the ATAL contribution and compare it to radiative heating signatures in the monsoon anticyclone region after volcanic eruptions. The results indicate that the ATAL impact on radiative heating rates in this region is on the order of 0.1 K/day, similar to that associated with ozone variability in MERRA-2 but weaker than cloud radiative effects at these altitudes. We have validated these results and tested their sensitivity to variations in the vertical structure and composition of ATAL aerosols using offline radiative transfer simulations. The idealized simulations produce similar but slightly stronger responses of radiative heating rates to the ATAL and are in good agreement with previous estimates of the top-of-atmosphere radiative forcing. Although the ATAL perturbations inferred from MERRA-2 are only about 10% of mean heating rates at these levels, their spatial distribution suggests potential implications for both isentropic and diabatic transport within the monsoon anticyclone, which should be examined in future work. Our results are limited by uncertainties in the composition and spatiotemporal variability of the ATAL, and reflect only the conditions in this layer as represented by MERRA-2. Targeted observations and model simulations are needed to adequately constrain the uncertainties, particularly with respect to the relative proportions and contributions of nitrate aerosols, which are not included in the MERRA-2 aerosol analysis.</p>

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.

scholarly journals Application and evaluation of McICA scheme with new radiation code in BCC_AGCM2.0.1

2013 ◽  
Vol 6 (3) ◽  
pp. 4933-4982 ◽  
Author(s):  
H. Zhang ◽  
X. Jing ◽  
J. Li
Keyword(s):  
Climate Model ◽  
Atmospheric Temperature ◽  
Horizontal Distribution ◽  
Radiative Heating ◽  
Specific Humidity ◽  
Storm Tracks ◽  
Heating Rates ◽  
Radiative Forcings ◽  
Horizontal Inhomogeneity ◽  
The Tropics

Abstract. This research incorporates the Monte Carlo Independent Column Approximation (McICA) scheme with the correlated k-distribution BCC-RAD radiation model into the climate model BCC_AGCM2.0.1 and examines the impacts on modeled climate through several simulations with variations in cloud structures. Results from experiments with consistent sub-grid cloud structures show that both clear-sky radiation fluxes and cloud radiative forcings (CRFs) calculated by the new scheme are mostly improved relative to those calculated from the original one. The modeled atmospheric temperature and specific humidity are also improved due to changes in the radiative heating rates. The vertical overlap of fractional clouds and horizontal distribution of cloud condensation are important for computing CRFs. The maximum changes in seasonal CRF using the general overlap assumption (GenO) with different decorrelation depths (Lcf) are larger than 10 and 20 Wm2 for longwave (LW) CRF and shortwave (SW) CRF, respectively, mostly located in the Tropics and mid-latitude storm tracks. Larger (smaller) Lcf in the Tropics (mid-latitude storm tracks) yield better cloud fraction and CRF compared with observations. The inclusion of an observation-based horizontal inhomogeneity of cloud condensation has a distinct impact on LW CRF and SW CRF, with global means of ∼1.2 Wm−2 and ∼3.7 Wm−2 at the top of atmosphere, respectively, making these much closer to observations. These results prove the reliability of the new model configuration to be used in BCC_AGCM2.0.1 for climate simulations, and also indicate that more detailed real-world information on cloud structures should be obtained to constrain cloud settings in McICA in the future.

scholarly journals Application and evaluation of a new radiation code under McICA scheme in BCC_AGCM2.0.1

2014 ◽  
Vol 7 (3) ◽  
pp. 737-754 ◽  
Author(s):  
H. Zhang ◽  
X. Jing ◽  
J. Li
Keyword(s):  
Climate Model ◽  
Atmospheric Temperature ◽  
Radiative Heating ◽  
Specific Humidity ◽  
Climate Simulation ◽  
Heating Rates ◽  
Radiative Forcings ◽  
Radiation Fluxes ◽  
World Information ◽  
Horizontal Inhomogeneity

Abstract. This research incorporates the correlated k distribution BCC-RAD radiation model into the climate model BCC_AGCM2.0.1 and examines the change in climate simulation by implementation of the new radiation algorithm. It is shown that both clear-sky radiation fluxes and cloud radiative forcings (CRFs) are improved. The modeled atmospheric temperature and specific humidity are also improved due to changes in radiative heating rates, which most likely stem from the revised treatment of gaseous absorption. Subgrid cloud variability, including vertical overlap of fractional clouds and horizontal inhomogeneity in cloud condensate, is addressed by using the Monte Carlo Independent Column Approximation (McICA) method. In McICA, a cloud-type-dependent function for cloud fraction decorrelation length, which gives zonal mean results very close to the observations of CloudSat/CALIPSO, is developed. Compared to utilizing a globally constant decorrelation length, the maximum changes in seasonal CRFs by the new scheme can be as large as 10 and 20 W m−2 for longwave (LW) and shortwave (SW) CRFs, respectively, mostly located in the tropics. The inclusion of an observation-based horizontal inhomogeneity of cloud condensate has also a significant impact on CRFs, with global means of ~ 1.5 W m−2 and ~ 3.7 Wm−2 for LW and SW CRFs at the top of atmosphere (TOA), respectively. Generally, incorporating McICA and horizontal inhomogeneity of cloud condensate in the BCC-RAD model reduces global mean TOA and surface SW and LW flux biases in BCC_AGCM2.0.1. These results demonstrate the feasibility of the new model configuration to be used in BCC_AGCM2.0.1 for climate simulations, and also indicate that more detailed real-world information on cloud structures should be obtained to constrain cloud settings in McICA in the future.

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

2020 ◽  
Vol 13 (9) ◽  
pp. 4399-4412 ◽  
Author(s):  
Ying Liu ◽  
Rodrigo Caballero ◽  
Joy Merwin Monteiro
Keyword(s):  
Neural Network ◽  
Radiative Transfer ◽  
Climate Model ◽  
Mean Squared Error ◽  
Network Models ◽  
Radiative Heating ◽  
Human Populations ◽  
Heating Rates ◽  
Neural Network Models ◽  
The Neural Network

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 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 conventional 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 370× speedup on a GTX 1080 GPU setup and 11× 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 K d−1 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 is 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 network, 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.

GISS Model E2.2: A Climate Model Optimized for the Middle Atmosphere—2. Validation of Large‐Scale Transport and Evaluation of Climate Response

2020 ◽  
Vol 125 (24) ◽  
Author(s):  
Clara Orbe ◽  
David Rind ◽  
Jeffrey Jonas ◽  
Larissa Nazarenko ◽  
Greg Faluvegi ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Model ◽  
Middle Atmosphere ◽  
Climate Response

scholarly journals Polar Middle Atmospheric Responses to Medium Energy Electron (MEE) Precipitation Using Numerical Model Simulations

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 133
Author(s):  
Ji-Hee Lee ◽  
Geonhwa Jee ◽  
Young-Sil Kwak ◽  
Heejin Hwang ◽  
Annika Seppälä ◽  
...  
Keyword(s):  
Climate Model ◽  
Middle Atmosphere ◽  
Meridional Circulation ◽  
Stratospheric Ozone ◽  
High Energy ◽  
Chemical Changes ◽  
Temperature Changes ◽  
Mesospheric Temperature ◽  
High Energy Electrons ◽  
Circulation Changes

Energetic particle precipitation (EPP) is known to be an important source of chemical changes in the polar middle atmosphere in winter. Recent modeling studies further suggest that chemical changes induced by EPP can also cause dynamic changes in the middle atmosphere. In this study, we investigated the atmospheric responses to the precipitation of medium-to-high energy electrons (MEEs) over the period 2005–2013 using the Specific Dynamics Whole Atmosphere Community Climate Model (SD-WACCM). Our results show that the MEE precipitation significantly increases the amounts of NOx and HOx, resulting in mesospheric and stratospheric ozone losses by up to 60% and 25% respectively during polar winter. The MEE-induced ozone loss generally increases the temperature in the lower mesosphere but decreases the temperature in the upper mesosphere with large year-to-year variability, not only by radiative effects but also by adiabatic effects. The adiabatic effects by meridional circulation changes may be dominant for the mesospheric temperature changes. In particular, the meridional circulation changes occasionally act in opposite ways to vary the temperature in terms of height variations, especially at around the solar minimum period with low geomagnetic activity, which cancels out the temperature changes to make the average small in the polar mesosphere for the 9-year period.

scholarly journals Middle atmosphere response to the solar cycle in irradiance and ionizing particle precipitation

2011 ◽  
Vol 11 (10) ◽  
pp. 5045-5077 ◽  
Author(s):  
K. Semeniuk ◽  
V. I. Fomichev ◽  
J. C. McConnell ◽  
C. Fu ◽  
S. M. L. Melo ◽  
...  
Keyword(s):  
Solar Cycle ◽  
Radiative Forcing ◽  
Climate Model ◽  
Middle Atmosphere ◽  
High Energy ◽  
Galactic Cosmic Rays ◽  
Particle Precipitation ◽  
Solar Cycle Variation ◽  
Cycle Variation ◽  
The Impact

Abstract. The impact of NOx and HOx production by three types of energetic particle precipitation (EPP), auroral zone medium and high energy electrons, solar proton events and galactic cosmic rays on the middle atmosphere is examined using a chemistry climate model. This process study uses ensemble simulations forced by transient EPP derived from observations with one-year repeating sea surface temperatures and fixed chemical boundary conditions for cases with and without solar cycle in irradiance. Our model results show a wintertime polar stratosphere ozone reduction of between 3 and 10 % in agreement with previous studies. EPP is found to modulate the radiative solar cycle effect in the middle atmosphere in a significant way, bringing temperature and ozone variations closer to observed patterns. The Southern Hemisphere polar vortex undergoes an intensification from solar minimum to solar maximum instead of a weakening. This changes the solar cycle variation of the Brewer-Dobson circulation, with a weakening during solar maxima compared to solar minima. In response, the tropical tropopause temperature manifests a statistically significant solar cycle variation resulting in about 4 % more water vapour transported into the lower tropical stratosphere during solar maxima compared to solar minima. This has implications for surface temperature variation due to the associated change in radiative forcing.

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