Understanding Top‐of‐Atmosphere Flux Bias in the AeroCom Phase III Models: A Clear‐Sky Perspective

Su, Wenying; Liang, Lusheng; Myhre, Gunnar; Thorsen, Tyler J.; Loeb, Norman G.; Schuster, Gregory L.; Ginoux, Paul; Paulot, Fabien; Neubauer, David; Checa‐Garcia, Ramiro; Matsui, Hitoshi; Tsigaridis, Kostas; Skeie, Ragnhild Bieltvedt; Takemura, Toshihiko; Bauer, Susanne E.; Schulz, Mıchael

doi:10.1029/2021ms002584

Cited by 6 publications

(3 citation statements)

References 92 publications

(169 reference statements)

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“…The representation of aerosols in CMIP6 models remains discrepant, especially in heavily polluted areas, desert and polar regions (Cherian & Quaas, 2020;Lapere et al, 2023). The discrepancies can result from various factors, including aerosol emissions and deposition, the representation of microphysics (such as size distribution and hygroscopicity) as well as the vertical distribution of aerosols (Li et al, 2015; Q.-R. Yu et al, 2019;Guo et al, 2021;Su et al, 2021;Lapere et al, 2023). Many studies have highlighted the uncertainty in aerosol optical properties.…”

Section: Introductionmentioning

confidence: 99%

A Dissection of the Inter-model Spread of the Aerosol Direct Radiative Effect in CMIP6 Models

Huang

2023

Preprint

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The all-sky Aerosol Direct Radiative Effect (ADRE) varies considerably among global climate models (GCMs), which results from differences in both aerosol and atmospheric states and radiative transfer calculations. This study uses a novel regression method to analyze the inter-model spread of ADRE among the GCMs of the Sixth Coupled Model Intercomparison Projects (CMIP6). The key state variables examined include scattering and absorbing aerosol optical depth, surface albedo, and shortwave cloud radiative effect. We find that differences in state variables and radiative transfer explain 67% and 17%, respectively, of the global ADRE anomaly. The ADRE anomaly in different models is dominated by different factors, which sometimes leads to compensating effects. For example, aerosol optical depth differences explain most of the ADRE anomaly in the CNRM-ESM2-1 and GFDL-ESM4 models, while radiative transfer evaluation of the aerosol single scattering effect dominates the ADRE anomaly in the HadGEM3-GC31-LL, MPI-ESM-1-2-HAM, and MRI-ESM2-0 models.

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Section: Introductionmentioning

confidence: 99%

A Dissection of the Inter-model Spread of the Aerosol Direct Radiative Effect in CMIP6 Models

Huang

2023

Preprint

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“…The discrepancies can result from various factors, including aerosol emissions and deposition, the representation of microphysics (such as size distribution and hygroscopicity) as well as the vertical distribution of aerosols (Li et al., 2015; Q.‐R. Yu et al., 2019; Guo et al., 2021; Su et al., 2021; Lapere et al., 2023). Many studies have highlighted the uncertainty in aerosol optical properties.…”

Section: Introductionmentioning

confidence: 99%

A Dissection of the Inter‐Model Spread of the Aerosol Direct Radiative Effect in CMIP6 Models

Yu,

Huang

2023

Geophysical Research Letters

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The all‐sky Aerosol Direct Radiative Effect (ADRE) varies considerably among global climate models (GCMs), which results from differences in aerosol and atmospheric states and ADRE sensitivity to aerosol‐related radiative processes. This study uses a regression method to analyze the inter‐model spread of ADRE among the GCMs of the Sixth Coupled Model Intercomparison Projects (CMIP6). The key state variables examined include scattering and absorbing aerosol optical depth, surface albedo, and shortwave cloud radiative effect. We find that differences in state variables and radiative sensitivity explain 67% and 17%, respectively, of the global ADRE anomaly. The ADRE anomaly in different models is dominated by different factors, which sometimes leads to compensating effects. For the global mean ADRE anomaly, aerosol optical depth differences dominate in CNRM‐ESM2‐1 and GFDL‐ESM4 models, while ADRE sensitivity variations to aerosol‐only scattering effect dominate in HadGEM3‐GC31‐LL, MPI‐ESM‐1‐2‐HAM, and MRI‐ESM2‐0 models.

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“…Thus, the ADM-based approach may be limited by complex terrains and clouds in the effort to resolve the 3D radiative fluxes and global energy balance at TOA [27]. Uncertainties in TOA fluxes can be caused by changes in accompanying images and errors in cloud properties [27,28] and aerosol optical depths [29]. The number of daytime measurements is also limited: they are collected only two local times a day.…”

Section: Introductionmentioning

confidence: 99%

An Investigation on Seasonal and Diurnal Cycles of TOA Shortwave Radiations from DSCOVR/EPIC, CERES, MERRA-2, and ERA5

Lim

Kim

et al. 2021

Remote Sensing

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Reflected shortwave (SW) solar radiations at the top of atmosphere from Clouds and the Earth’s Radiant Energy System (CERES), Modern Era-Retrospective analysis for Research and Applications version 2 (MERRA-2), and ECMWF Reanalysis 5th Generation (ERA5) are examined to better understand their differences in spatial and temporal variations (seasonal and diurnal cycle timescale) with respect to the observations from the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) satellite. Comparisons between two reanalyses (MERRA-2 and ERA5) and EPIC reveal that MERRA-2 has a generally larger deviation from EPIC than ERA5 in terms of the SW radiance and diurnal variability in all seasons, which can be attributed to larger cloud biases in MERRA-2. MERRA-2 produces more ice/liquid water content than ERA5 over the tropical warm pool, leading to positive SW biases in cloud and radiance, while both reanalyses underestimate the observed SW radiance from EPIC in the stratus-topped region off the western coast of US/Mexico in the boreal summer. Himalaya/Tibet region in the boreal spring/summer and the midlatitude Southern Hemisphere in the boreal winter are the regions where MERRA-2 and ERA5 deviate largely from EPIC, but their deviations have the opposite sign. Vertical structures of cloud ice/liquid water content explain reasonably well these contrasting differences between the two reanalyses. As two independent observations, CERES and EPIC agree well with each other in terms of the SW radiance maps, showing 2–3% mean absolute errors over the tropical midlatitudes. The CERES-EPIC consistency further confirms that the reanalyses still have challenges in representing the SW flux and its global distribution. In the CERES-EPIC observation differences, CERES slightly overestimates the diurnal cycle (as a function of local solar time) of the observed EPIC irradiance in the morning and underestimates it in the afternoon, while the opposite is the case in the reanalyses.

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Understanding Top‐of‐Atmosphere Flux Bias in the AeroCom Phase III Models: A Clear‐Sky Perspective

Cited by 6 publications

References 92 publications

A Dissection of the Inter-model Spread of the Aerosol Direct Radiative Effect in CMIP6 Models

A Dissection of the Inter-model Spread of the Aerosol Direct Radiative Effect in CMIP6 Models

A Dissection of the Inter‐Model Spread of the Aerosol Direct Radiative Effect in CMIP6 Models

An Investigation on Seasonal and Diurnal Cycles of TOA Shortwave Radiations from DSCOVR/EPIC, CERES, MERRA-2, and ERA5

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