A document by Qiurun Yu. Click on the document to view its contents.

Increasing carbon dioxide (CO2) in the atmosphere usually reduces Earth’s outgoing longwave radiation (OLR). The unusual case of Antarctica, where CO2 enhances OLR and implies a negative forcing, has previously been explained by the strong near-surface inversion or extremely low surface temperature. However, negative forcing can occasionally be found in the Arctic and tropics where neither of these explanations applies. Here, we examine the changes in infrared opacity from CO2 doubling in these low or negative forcing climate states, which shows the predominant role of the stratospheric contribution to the broadband forcing. Negative forcing in today’s climate demands a combination of strong negative forcing caused by a steep stratospheric temperature inversion and a weaker positive forcing in the atmospheric window, which can be caused by a low surface temperature or a strong high cloud masking effect. Contrary to conventional wisdom, the near-surface inversion has little impact on the forcing.

To facilitate the quantification of the stratospheric aerosol radiative effect, this study generates a set of aerosol direct radiative effect (ADRE) kernels based on MERRA-2 reanalysis data. These radiative kernels measure the sensitivities of ADRE to perturbations in scattering and absorbing aerosol optical depth (AOD), respectively. Both broadband and band-by-band radiative kernels are developed to account for the wavelength dependency of ADRE. The broadband kernels are then emulated by a multivariate regression model, which predicts the kernel values from a handful of predictors, including the top-of-atmosphere (TOA) insolation, TOA reflectance, and stratospheric AOD. These kernels offer an efficient and versatile way to assess the ADRE of stratospheric aerosols. The ADREs of the 2022 Hunga volcano eruption and the 2020 Australia wildfire are estimated from the kernels and validated against radiative transfer model-calculated results. The Hunga eruption induced a global mean cooling forcing of -0.46 W/m² throughout 2022, while the Australia wildfire caused a warming forcing of +0.28 W/m² from January to August. The kernel estimation can capture over 90% of the ADRE variance with relative error within 10%, in these assessments. The results demonstrate the spectral dependencies of stratospheric ADRE and highlight the distinct radiative sensitivity of stratospheric aerosols, which differs significantly from that of tropospheric aerosols.

Radiation energy balance at the top of the atmosphere (TOA) is a critical boundary condition for the Earth climate. It is essential to validate it in the global climate models (GCM) on both global and regional scales. However, the comparison of overall radiation field is known to conceal compensating errors. Here we use a new set of radiative kernels to diagnose the radiation biases by different geophysical variables in the latest GCMs. We find although clouds remain a primary cause of radiation biases, the radiation biases caused by non-cloud variables are of comparable magnitudes. Many GCMs tend to have a cold bias in the air temperature and a moist bias in the tropospheric humidity, which lead to considerable biases in TOA radiation budget but are compensated by cloud biases. These findings signify the importance of validating the GCM-simulated radiation fields, with respect to both the overall and component radiation biases.

Downwelling longwave radiation (DLR) is an important part of the surface energy budget. Spectral trends in the DLR provide insight into the radiative drivers of climate change. In this research, we process and analyze a 23-year downwelling longwave radiance record measured by the Atmospheric Emitted Radiance Interferometers (AERI) at the Southern Great Plains (SGP) site of the Atmospheric Radiation Program. Two AERIs were deployed at SGP with an overlapping observation period of about 10 years, which allows us to examine the consistency and accuracy of the measurements and to characterize discrepancies between them due to undetected instrumentation errors. Using the 23-year record, we analyze the all-sky radiance trends in DLR, which reflects the associated surface warming trend at SGP during this same period and also the complex changes in meteorological conditions. For instance, the observed radiance in the CO2 absorption band follows closely the near-surface air temperature variations. The changes in the sky fraction of clear-sky and thick cloudy-sky scenes offset the radiance changes in the window band. Our analysis shows that the radiance trend uncertainty in the DLR record to date mainly results from the climate internal variability rather than the measurement error, which highlights the importance of continuing the DLR spectral measurements to unambiguously detect and attribute climate change.

The Asian Tropopause Aerosol Layer (ATAL) has emerged in recent decades to play a prominent role in the upper troposphere and lower stratosphere above the Asian monsoon. Although ATAL effects on surface and top-of-atmosphere radiation budgets are well established, the magnitude and variability of ATAL effects on radiative transfer within the tropopause layer remain poorly constrained. Here, we investigate the impacts of various aerosol types and layer structures on clear-sky shortwave radiative heating in the Asian monsoon tropopause layer using reanalysis products and offline radiative transfer simulations. ATAL effects on shortwave radiative heating based on the MERRA-2 aerosol reanalysis are on the order of 10% of mean clear-sky radiative heating within the tropopause layer, although discrepancies among recent reanalysis and forecast products suggest that this ratio could be as small as ~5% or as large as ~25%. Uncertainties in surface and top-of-atmosphere flux effects are also large, with values spanning one order of magnitude at the top-of-atmosphere. ATAL effects on radiative heating peak between 150 hPa and 80 hPa (360 K–400 K potential temperature) along the southern flank of the anticyclone. Clear-sky and all-sky shortwave heating are at local minima in this vertical range, which is situated between the positive influences of monsoon-enhanced water vapor and the negative influence of the ‘ozone valley’ in the monsoon lower stratosphere. ATAL effects also extend further toward the west, where diabatic vertical velocities remain upward despite descent in pressure coordinates.

The radiative forcing of carbon dioxide (CO2) at the top-of-atmosphere (TOA) has a rich spatial structure and has implications for large-scale climate changes, such as poleward energy transport and tropical circulation change. Beyond the TOA, additional CO2 increases downwelling longwave at the surface, and this change in flux is the surface CO2 forcing. Here, we thoroughly evaluate the spatiotemporal variation of the instantaneous, longwave CO2 radiative forcing at both the TOA and surface. The instantaneous forcing is calculated with a radiative transfer model using ERA5 reanalysis fields. Multivariate regression models show that the broadband forcing at the TOA and surface are well-predicted by local temperatures, humidity, and cloud radiative effects. The difference between the TOA and surface forcing, the atmospheric forcing, can be either positive or negative and is mostly controlled by the column water vapor, with little explicit dependence on the surface temperature. The role of local variables on the TOA forcing is also assessed by partitioning the change in radiative flux to the component emitted by the surface vs. that emitted by the atmosphere. In cold, dry regions, the surface and atmospheric contribution partially cancel out, leading to locally weak or even negative TOA forcing. In contrast, in the warm, moist regions, the surface and atmospheric components strengthen each other, resulting in overall larger TOA forcing. The relative contribution of surface and atmosphere to the TOA forcing depends on the optical thickness in the current climate, which, in turn, is controlled by the column water vapor.

The 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, for which new satellites are expected to provide unprecedented measurements. We find that around 45% of the global mean OLR is radiated in the far-infrared and in polar regions the far-infrared contribution can increase to 60%. Our vertical decomposition of OLR discloses that the enhanced far-infrared contribution in the polar regions mainly results from a stronger surface (as opposed to atmosphere) contribution. 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 far-infrared while enhancing it in the mid-infrared.

A neural network model is developed to measure shortwave radiative feedbacks. This model is trained with the atmospheric data of the fifth-generation European Centre for Medium-Range Weather Forecasts Reanalysis to emulate the atmospheric shortwave radiation fluxes, which thus allows direct and computationally efficient evaluation of the radiative feedbacks following the straightforward perturbation method. This model is tested for computing the radiative feedbacks of surface albedo, water vapor and cloud in the Arctic climate change. In both idealized and real climate change scenarios, the model can accurately quantify the albedo feedback compared to the truth computed by a radiative transfer model, while the linear kernel method severely overestimates the albedo feedback during Arctic warming. The neural network model can capture the nonlinearity in the surface albedo feedback and its dependency on the cloud amount.