Global Outgoing Longwave Radiation Research Articles

A Decomposition of the Atmospheric and Surface Contributions to the Outgoing Longwave Radiation

AbstractThe outgoing longwave radiation (OLR), which consists of the thermal radiation from both the atmosphere and surface, is of critical importance to the Earth radiation energy budget. To understand the global OLR distribution, it is important to quantify the varying atmospheric and surface contributions. In this work, we present such a quantification using radiative transfer computations based on global reanalysis atmospheric data. By dissecting the OLR simulated following the radiative transfer equation, we quantitatively measure the layer‐wise atmospheric contributions to OLR and compare it to the surface contribution in different spectral bands. One focus of this study is on the OLR in the far‐infrared (FIR), for which new satellites are expected to provide unprecedented measurements. We find that around 45% of the global mean OLR is radiated in the FIR and in polar regions the FIR contribution can increase to 60%. Our vertical decomposition of OLR discloses that although the atmospheric contribution generally surpasses the surface contribution, the enhanced FIR contribution in the polar regions mainly results from a relatively stronger surface (as opposed to atmospheric) contribution. This is allowed by the opening of the atmospheric window in the FIR in these extremely dry regions. Our analysis also reveals that the tropopause layer makes a minimum contribution to the OLR, which may be a unique spectroscopic feature of the Earth atmosphere. Clouds are found to reduce the atmospheric contribution in the FIR while enhancing it in the mid‐infrared.

Evaluation of Reprocessed Fengyun-3B Global Outgoing Longwave Radiation Data: Comparison with CERES OLR

Evaluation of Reprocessed Fengyun-3B Global Outgoing Longwave Radiation Data: Comparison with CERES OLR

Estimating the Earth’s Outgoing Longwave Radiation Measured from a Moon-Based Platform

Moon-based Earth observations have attracted significant attention across many large-scale phenomena. As the only natural satellite of the Earth, and having a stable lunar surface as well as a particular orbit, Moon-based Earth observations allow the Earth to be viewed as a single point. Furthermore, in contrast with artificial satellites, the varied inclination of Moon-based observations can improve angular samplings of specific locations on Earth. However, the potential for estimating the global outgoing longwave radiation (OLR) from the Earth with such a platform has not yet been fully explored. To evaluate the possibility of calculating OLR using specific Earth observation geometry, we constructed a model to estimate Moon-based OLR measurements and investigated the potential of a Moon-based platform to acquire the necessary data to estimate global mean OLR. The primary method of our study is the discretization of the observational scope into various elements and the consequent integration of the OLR of all elements. Our results indicate that a Moon-based platform is suitable for global sampling related to the calculation of global mean OLR. By separating the geometric and anisotropic factors from the measurement calculations, we ensured that measured values include the effects of the Moon-based Earth observation geometry and the anisotropy of the scenes in the observational scope. Although our results indicate that higher measured values can be achieved if the platform is located near the center of the lunar disk, a maximum difference between locations of approximately 9 × 10−4 W m−2 indicates that the effect of location is too small to remarkably improve observation performance of the platform. In conclusion, our analysis demonstrates that a Moon-based platform has the potential to provide continuous, adequate, and long-term data for estimating global mean OLR.

Cloud scattering impact on thermal radiative transfer and global longwave radiation

The potential importance of longwave (LW) cloud scattering has been recognized but the actual estimate of this effect on thermal radiation varies greatly among different studies. General circulation models (GCMs) generally neglect or simplify the multiple scattering in the LW. In this study, we use a rigorous radiative transfer algorithm to explicitly consider LW multiple-scattering and apply the GCM to quantify the impact of cloud LW scattering on thermal radiation fluxes. Our study shows that the cloud scattering effect on downward thermal radiation at the surface is concentrated in the infrared atmospheric window spectrum (800–1250 cm−1). The scattering effect on the outgoing longwave radiation (OLR) is also present in the window region over low clouds but it is mainly in the far-infrared spectrum (300–600 cm−1) over high clouds. For clouds with small to moderate optical depth (τ < 10), the scattering effect on thermal fluxes shows large variation with the cloud τ and has a maximum at an optical depth of ∼3. For opaque clouds, the scattering effect approaches an asymptote and is smaller and less important.The 2-stream radiative transfer scheme could have an error over 10% with an RMS error around 3.5%–4.0% in the calculated LW flux. This algorithm error of the 2-stream approximation could readily exceed the no-scattering error in the LW, and thus it is worthless to include the time-consuming computation of multiple scattering in a 2-stream radiative transfer scheme. However, the calculation error rapidly decreases as stream number increases and the RMS error in LW flux using the 4-stream scheme is under 0.3%, an accuracy sufficient for most climate studies. We implement the 4-stream discrete-ordinate algorithm in the GISS GCM and run the GCM for 20 years with and without the LW scattering effect, respectively. When cloud LW scattering is included, we find that the global annual mean OLR is reduced by 2.7 W/m2, and the downward surface flux and the net atmospheric absorption are increased by 1.6 W/m2 and 1.8 W/m2, respectively. Using one year of ISCCP clouds and running the standalone radiative transfer offline, the global annual mean non-scattering errors in OLR, surface LW downward flux and net atmospheric absorption are 3.6 W/m2, −1.1 W/m2, and −2.5 W/m2, respectively. The global scattering impact of 2.7 W/m2 on the OLR is small when compared to the typical global OLR value of 240 W/m2, but it is significant when compared to cloud LW radiative forcing (30 W/m2) and net cloud forcing (−14 W/m2). Overall, the effect of neglecting scattering on the thermal fluxes is comparable to the reported clear sky radiative effect of doubling CO2.

Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydrostatic atmospheric model

We propose a deep learning approach for identifying tropical cyclones (TCs) and their precursors. Twenty year simulated outgoing longwave radiation (OLR) calculated using a cloud-resolving global atmospheric simulation is used for training two-dimensional deep convolutional neural networks (CNNs). The CNNs are trained with 50,000 TCs and their precursors and 500,000 non-TC data for binary classification. Ensemble CNN classifiers are applied to 10 year independent global OLR data for detecting precursors and TCs. The performance of the CNNs is investigated for various basins, seasons, and lead times. The CNN model successfully detects TCs and their precursors in the western North Pacific in the period from July to November with a probability of detection (POD) of 79.9–89.1% and a false alarm ratio (FAR) of 32.8–53.4%. Detection results include 91.2%, 77.8%, and 74.8% of precursors 2, 5, and 7 days before their formation, respectively, in the western North Pacific. Furthermore, although the detection performance is correlated with the amount of training data and TC lifetimes, it is possible to achieve high detectability with a POD exceeding 70% and a FAR below 50% during TC season for several ocean basins, such as the North Atlantic, with a limited sample size and short lifetime.

Logarithmic radiative effect of water vapor and spectral kernels

AbstractRadiative kernels have become a useful tool in climate analysis. A set of spectral kernels is calculated using a moderate resolution atmospheric transmission code MODTRAN and implemented in diagnosing spectrally decomposed global outgoing longwave radiation (OLR) changes. It is found that the effect of water vapor on the OLR is in proportion to the logarithm of its concentration. Spectral analysis discloses that this logarithmic dependency mainly results from water vapor absorption bands (0–560 cm−1 and 1250–1850 cm−1), while in the window region (800–1250 cm−1), the effect scales more linearly to its concentration. The logarithmic and linear effects in the respective spectral regions are validated by the calculations of a benchmark line‐by‐line radiative transfer model LBLRTM. The analysis based on LBLRTM‐calculated second‐order kernels shows that the nonlinear (logarithmic) effect results from the damping of the OLR sensitivity to layer‐wise water vapor perturbation by both intra‐ and inter‐layer effects. Given that different scaling approaches suit different spectral regions, it is advisable to apply the kernels in a hybrid manner in diagnosing the water vapor radiative effect. Applying logarithmic scaling in the water vapor absorption bands where absorption is strong and linear scaling in the window region where absorption is weak can generally constrain the error to within 10% of the overall OLR change for up to eightfold water vapor perturbations.

Evaluation of ACCMIP outgoing longwave radiation from tropospheric ozone using TES satellite observations

Abstract. We use simultaneous observations of tropospheric ozone and outgoing longwave radiation (OLR) sensitivity to tropospheric ozone from the Tropospheric Emission Spectrometer (TES) to evaluate model tropospheric ozone and its effect on OLR simulated by a suite of chemistry-climate models that participated in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). The ensemble mean of ACCMIP models show a persistent but modest tropospheric ozone low bias (5–20 ppb) in the Southern Hemisphere (SH) and modest high bias (5–10 ppb) in the Northern Hemisphere (NH) relative to TES ozone for 2005–2010. These ozone biases have a significant impact on the OLR. Using TES instantaneous radiative kernels (IRK), we show that the ACCMIP ensemble mean tropospheric ozone low bias leads up to 120 mW m−2 OLR high bias locally but zonally compensating errors reduce the global OLR high bias to 39 ± 41 m Wm−2 relative to TES data. We show that there is a correlation (R2 = 0.59) between the magnitude of the ACCMIP OLR bias and the deviation of the ACCMIP preindustrial to present day (1750–2010) ozone radiative forcing (RF) from the ensemble ozone RF mean. However, this correlation is driven primarily by models whose absolute OLR bias from tropospheric ozone exceeds 100 m Wm−2. Removing these models leads to a mean ozone radiative forcing of 394 ± 42 m Wm−2. The mean is about the same and the standard deviation is about 30% lower than an ensemble ozone RF of 384 ± 60 m Wm−2 derived from 14 of the 16 ACCMIP models reported in a companion ACCMIP study. These results point towards a profitable direction of combining satellite observations and chemistry-climate model simulations to reduce uncertainty in ozone radiative forcing.

Estimating the outgoing longwave radiation from the FY-3B satellite visible infrared radiometer Channel 5 radiance observations

Based on the infrared radiation transfer model, the outgoing longwave radiation (OLR) and Channel 5 radiance of Fengyun-3B (FY-3B) satellite visible infrared radiometer (VIRR) were simulated for 3812 global soundings. Using regression analysis of the simulations, an inverse model, which connected the flux equivalent brightness temperature with the channel brightness temperature, was derived. By applying the model to the FY-3B VIRR L1 data, the global OLR data at the time of the passing of the FY-3B were processed. The quality of the data was validated by comparing it with that of the NOAA-18 satellite’s advanced very high resolution radiometer (AVHRR). The validation results show root mean square errors in the range 10–13 W/M2 when comparing the daily average OLR of the VIRR with that of the NOAA-18 AVHRR, and the correlation coefficients were in the range 0.97–0.98. The larger RMSE is mainly due to the different passing times of the two satellites for the specific locations on the Earth. An example of the OLR data and its preliminary applications are given.

Correlation analysis of persistent heavy rainfall events in the vicinity of the Yangtze River valley and global outgoing longwave radiation in the preceding month

Based on the National Oceanic and Atmospheric Administration (NOAA) daily satellite dataset of global outgoing longwave radiation (OLR) for the period of 1974–2004 and the NCEP-NCAR reanalysis for 1971–2004, the linkage between persistent heavy rainfall (PHR) events in the vicinity of the Yangtze River valley and global OLR leading up to those events (with 1- to 30-day lag) was investigated. The results reveal that there is a significant connection between the initiation of PHR events over the study area and anomalous convective activity over the tropical Indian Ocean, maritime continent, and tropical western Pacific Ocean. During the 30-day period prior to the onset of PHR events, the major significantly anomalous convective centers have an apparent dipole structure, always with enhanced convection in the west and suppressed convection in the east. This dipole structure continuously shifts eastward with time during the 30-day lead period. The influence of the anomalous convective activity over the tropical oceans on the initiation of PHR events over the study area is achieved via an interaction between tropical and extratropical latitudes. More specifically, anomalous convective activity weakens the Walker circulation cell over the tropical Indian Ocean first. This is followed by a weakening of the Indian summer monsoon background state and the excitation and dispersion of Rossby wave activity over Eurasia. Finally, a major modulation of the large scale background circulation occurs. As a result, the condition of a phase-lock among major large scale circulation features favoring PHR events is established over the study area.

Estimating the Satellite Equatorial Crossing Time Biases in the Daily, Global Outgoing Longwave Radiation Dataset

Due to its long record length (approximately 25 years), the outgoing longwave radiation (OLR) dataset has been used in a multitude of climatological studies including studies on tropical circulation and convection, the El Niño–Southern Oscillation (ENSO) phenomenon, and the earth's radiation budget. Although many of the climatological studies using OLR have proven invaluable, proper interpretation of the low-frequency components of the data could be limited by the presence of biases introduced by changes in the satellite equatorial crossing time (ECT). Since long-term global changes could be masked or contaminated by this instrumental bias, it is necessary to take steps to ensure that the daily, global OLR dataset is free from such biases and is as accurate as possible. The goal of this study is to derive a method for estimating the ECT biases in the daily, global OLR dataset. Our analysis utilizes a Procrustes targeted empirical orthogonal function rotation (REOF) on an interpolated OLR dataset to try to isolate and remove the two major ECT biases—afternoon satellite orbital drift and the abrupt transitions from a morning satellite to an afternoon satellite—from the dataset. Two targeted REOF analyses are performed to separate and distinguish between these two artificial satellite bias modes. A “common ECT” of approximately 0245 LST is established for the dataset by removing an estimate of these two ECT biases. Results from the analysis indicate that changes in ECTs can cause large regional biases over both ocean and tropical landmasses. The afternoon satellite ECT drift-bias accounts for 0.4% of the pentad anomaly variance. During a single satellite series (e.g., NOAA-11), the afternoon drift-bias can introduce a difference as large as 10.5 W m−2 in the OLR values collected over most tropical landmasses. The morning to afternoon satellite transition bias accounts for 0.9% of the pentad anomaly variance, and is shown to cause a bias of 12 W m−2 in the OLR values over most tropical landmasses during the NOAA-SR satellite series. The data are corrected by removing a statistically derived synthetic eigenvector that is associated with each of the ECT bias modes. This synthetic eigenvector is used instead of the exact values of the satellite bias eigenvector to ensure that only the artificial variability is removed from the dataset. The two REOF modes produced in this study are nearly orthogonal to each other having a correlation of only 0.17. This near orthogonality suggests that the use of the two-mode method presented in this study can more adequately describe the individual nature of each of the two ECT biases than a single REOF mode examined in previous studies. However, due to the presence of other forms of variability, it is likely that this study's estimate of the ECT bias includes ECT-related bias as well as some aspects of variability that may be associated with sensor changes, intersatellite calibration and/or natural climate variability. The strengths and limitations of the above technique are discussed, as are suggestions for future efforts.

Volcanic aerosols and interannual variation of high clouds

Interannual variability of high‐level cloudiness (HC) is examined using global outgoing longwave radiation (OLR). Variations of HC are analyzed versus a measure of global stratospheric aerosol amount and an El Nino index. Volcanic aerosols are apparently associated with widespread increases of up to 10% in an OLR‐based HC index. The most significant effects occurred in middle latitudes and persisted for several years after major eruptions. El Nino is found to be associated with decreased cloud activity in the subtropics. This study suggests that volcanic aerosols can significantly modify global cloudiness, and that stratospheric aerosol loading can be an important variable controlling the interannual variations of high level clouds and climate.

Relation Between OLR and Precipitation in The World

Global 2.5°mesh daily OLR (Outgoing Longwave Radiation) data observed by NOAA from 1974-87 were analyzed and compared with precipitation data at various stations over the world. Global OLR distributions clearly show seasonal climatic variations and the characteristics of El-Nino and La-Nina. Pseudo-reflectivity defined as Angot value minus OLR was introduced as an index of cloud activities and compared with precipitation on the ground. Pseudo-reflectivity usually shows the positive correlation with precipitation in the low latitude areas, but often shows negative one in the middle to high latitude areas. Analyses of its correlation with daily precipitation over Thailand revealed that the use of pseudo-reflectivity replacing original OLR did not improve the potential capability of estimating precipitation by satellite.

Further analysis of the global outgoing longwave radiative flux observed by Nimbus

A multiyear decrease in the total global outgoing longwave radiation (OLR) is revealed by the Nimbus 7 data set. The size of the change is of the order of 1 W m−2. The data have been analyzed using spectral harmonic decomposition on the monthly averages. The two lowest order fitting coefficients (i.e., C00 and C10) directly yield the temporal behavior of the global OLR and the hemispheric difference in OLR between the northern and southern hemispheres, respectively. Using a hypothetical detrending technique, it is found that the decreasing signal in the global OLR cannot be ascribed to a drift in the instrumental sensitivity. The secular change in terms of decreasing with time is also found in C10. This represents that the OLR from the northern hemisphere is decreasing faster than that from the Southern hemisphere. Thus a single detrending fit to the instrumental sensitivity cannot remove the drift in the OLR. As a previous study showed that the annually averaged C00 variability is strongly correlated with observed variability of global mean surface air temperature (Ts), we show that C10 variability is also consistent with the relative variabilities of the hemispheric mean Ts. Finally, the analysis on the solar insolation during the same period shows that the decreasing insolation may be a contributing factor to the decrease of OLR.

Planetary-Scale Aspects of Outgoing Longwave Radiation and vorticity over the Global Tropics during Winter

Abstract Fourier analysis was applied to outgoing longwave radiation (OLR) and 200 mb vorticity (VOR) during winter 1974–75, over the global tropics from 20°N to 20°S. Significant OLR and VOR zonal variance (33%) is contained in low wavenumbers (1–4) over the equatorial tropics. Empirical orthogonal function (EOF) analysis was performed on OLR and VOR Fourier coefficients, am, bm (m = 1−4), over the tropical domain. Spectral analyses for the eight largest eigenvectors exhibit marked peaks at periods of about 15–30 days. Only 15–30 day filtered, planetary-scale (1–4) OLR and VOR fields are examined. OLR standard deviations reveal extremely large values over Indonesia and the equatorial Pacific with a maximum at 5°S, 115°E. VOR standard deviations are minimum along the equator with maxima at 5–10° north and south. Correlations between OLR at 5°S, 115°E and global OLR reveal a geographically coherent pattern with OLR over Indonesia out of phase with OLR over the equatorial Indian Ocean and central equatorial...