Evaluation of preindustrial to present-day black carbon and its albedo forcing from Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP)

Lee, Y.H.; Lamarque, Jean‐François; Flanner, M.; Jiao, C.; Shindell, D. T.; Berntsen, Terje; Bisiaux, M. M.; Cao, Junji; Collins, William; Curran, Mark A. J.; Edwards, Ross; Faluvegi, G.; Ghan, Steven J.; Horowitz, Larry W.; McConnell, Joseph R.; Ming, Jing; Myhre, Gunnar; Nagashima, T.; Naik, Vaishali; Rumbold, Steven T.; Skeie, Ragnhild Bieltvedt; Sudo, Kengo; Takemura, Toshihiko; Thevenon, Florian; Xu, Bin; Yoon, Jin‐Ho

doi:10.5194/acp-13-2607-2013

Cited by 137 publications

(115 citation statements)

References 101 publications

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“…has been observed in other studies, pointing to changes in biomass burning emissions as the cause (Wang et al, 2011). This feature arises from the emissions inventory used, in particular, the assumed size of the aerosol emitted, and has an important impact in both direct (e.g., Lee et al, 2013b) and indirect effects (e.g., Wang et al, 2011;Bauer and Menon, 2012). The N d fields in Fig.…”

Section: Overview Of the Simulationsmentioning

confidence: 83%

Understanding the contributions of aerosol properties and parameterization discrepancies to droplet number variability in a global climate model

Betancourt

Nenes

2014

Atmos. Chem. Phys.

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Abstract. Aerosol indirect effects in climate models strongly depend on the representation of the aerosol activation process. In this study, we assess the process-level differences across activation parameterizations that contribute to droplet number uncertainty by using the adjoints of the AbdulRazzak and Ghan (2000) and Fountoukis and Nenes (2005) droplet activation parameterizations in the framework of the Community Atmospheric Model version 5.1 (CAM5.1). The adjoint sensitivities of N d to relevant input parameters are used to (i) unravel the spatially resolved contribution of aerosol number, mass, and chemical composition to changes in N d between present-day and pre-industrial simulations and (ii) identify the key variables responsible for the differences in N d fields and aerosol indirect effect estimates when different activation schemes are used within the same modeling framework. The sensitivities are computed online at minimal computational cost. Changes in aerosol number and aerosol mass concentrations were found to contribute to N d differences much more strongly than chemical composition effects. The main sources of discrepancy between the activation parameterizations considered were the treatment of the water uptake by coarse mode particles, and the sensitivity of the parameterized N d accumulation mode aerosol geometric mean diameter. These two factors explain the different predictions of N d over land and over oceans when these parameterizations are employed. Discrepancies in the sensitivity to aerosol size are responsible for an exaggerated response to aerosol volume changes over heavily polluted regions. Because these regions are collocated with areas of deep clouds, their impact on shortwave cloud forcing is amplified through liquid water path changes. The same framework is also utilized to efficiently explore droplet number uncertainty attributable to hygroscopicity parameter of organic aerosol (primary and secondary). Comparisons between the parameterization-derived sensitivities of droplet number against predictions with detailed numerical simulations of the activation process were performed to validate the physical consistency of the adjoint sensitivities.

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Section: Overview Of the Simulationsmentioning

confidence: 83%

Understanding the contributions of aerosol properties and parameterization discrepancies to droplet number variability in a global climate model

Betancourt

Nenes

2014

Atmos. Chem. Phys.

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“…We also explore the sensitivity of model-measurement comparisons to meltwater removal efficiency, one of the key uncertainties in simulated BC-insnow forcing Bond et al, 2013), and consistency between model meteorology and deposition. We have also applied the framework developed here in recent collaborative efforts to quantify radiative effects from AC-CMIP models (Lee et al, 2013b;Shindell et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…North America is the major contributor to Arctic ozone and BC deposited on Greenland, whereas European emissions dominate the total BC deposition elsewhere in the Arctic. Lee et al (2013b) evaluated historical BC aerosols simulated by 8 ACCMIP models against observations. They found that year 2000 global atmospheric BC burden varies by about a factor of 3 among models, despite all models applying the same emissions.…”

Section: Introductionmentioning

confidence: 99%

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An AeroCom assessment of black carbon in Arctic snow and sea ice

et al. 2014

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Abstract. Though many global aerosols models prognose surface deposition, only a few models have been used to directly simulate the radiative effect from black carbon (BC) deposition to snow and sea ice. Here, we apply aerosol deposition fields from 25 models contributing to two phases of the Aerosol Comparisons between Observations and Models (AeroCom) project to simulate and evaluate within-snow BC concentrations and radiative effect in the Arctic. We accomplish this by driving the offline land and sea ice components of the Community Earth System Model with different deposition fields and meteorological conditions from 2004 to 2009, during which an extensive field campaign of BC measurements in Arctic snow occurred. We find that models generally underestimate BC concentrations in snow in northern Russia and Norway, while overestimating BC amounts elsewhere in the Arctic. Although simulated BC distributions in snow are poorly correlated with measurements, mean values are reasonable. The multi-model mean (range) bias in BC concentrations, sampled over the same grid cells, snow depths, and months of measurements, are for a more recent phase of AeroCom models (phase II), compared to the observational mean of 19.2 ng g −1 . Factors determining model BC concentrations in Arctic snow include Arctic BC emissions, transport of extra-Arctic aerosols, precipitation, deposition efficiency of aerosols within the Arctic, and meltwater removal of particles in snow. Sensitivity studies show that the model-measurement evaluation is only weakly affected by meltwater scavenging efficiency because most measurements were conducted in non-melting snow. The Arctic (60-90 • N) atmospheric residence time for BC in phase II models ranges from 3.7 to 23.2 days, implying large inter-model variation in local BC deposition efficiency. Combined with the fact that most Arctic BC deposition originates from extra-Arctic emissions, these results suggest that aerosol removal processes are a leading source of variation in model performance. The multi-model mean (full range) of Arctic radiative effect from BC in snow is 0.15 (0.07-0.25) W m −2 and 0.18 (0.06-0.28) W m −2 in phase I and phase II models, respectively. After correcting for model biases relative to observed BC concentrations in different regions of the Arctic, we obtain a multi-model mean Arctic radiative effect of 0.17 W m −2 for the combined AeroCom ensembles. Finally, there is a high correlation between modeled BC concentrations sampled over the observational sites and the Arctic as a whole, indicating that the field campaign provided a reasonable sample of the Arctic.

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“…However, because no other study has shown the potential of reanalysis ozone for model evaluation, this study focuses on tropospheric ozone only. ACCMIP models have been used to calculate historic and future radiative and chemically important species and their coupling with the broader climate system Lee et al, 2013;Naik et al, 2013;Stevenson et al, 2013;Shindell et al, 2013;Voulgarakis et al, 2013;Young et al, 2013). We characterize ACCMIP models in simulating global distributions and the seasonal variation of ozone from the lower troposphere to the lower stratosphere.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of ACCMIP ozone simulations and ozonesonde sampling biases using a satellite-based multi-constituent chemical reanalysis

Miyazaki

Bowman

2017

Atmos. Chem. Phys.

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Abstract. The Atmospheric Chemistry Climate Model Intercomparison Project (ACCMIP) ensemble ozone simulations for the present day from the 2000 decade simulation results are evaluated by a state-of-the-art multi-constituent atmospheric chemical reanalysis that ingests multiple satellite data including the Tropospheric Emission Spectrometer (TES), the Microwave Limb Sounder (MLS), the Ozone Monitoring Instrument (OMI), and the Measurement of Pollution in the Troposphere (MOPITT) for [2005][2006][2007][2008][2009]. Validation of the chemical reanalysis against global ozonesondes shows good agreement throughout the free troposphere and lower stratosphere for both seasonal and year-to-year variations, with an annual mean bias of less than 0.9 ppb in the middle and upper troposphere at the tropics and midlatitudes. The reanalysis provides comprehensive spatiotemporal evaluation of chemistry-model performance that compliments direct ozonesonde comparisons, which are shown to suffer from significant sampling bias. The reanalysis reveals that the ACCMIP ensemble mean overestimates ozone in the northern extratropics by 6-11 ppb while underestimating by up to 18 ppb in the southern tropics over the Atlantic in the lower troposphere. Most models underestimate the spatial variability of the annual mean lower tropospheric concentrations in the extratropics of both hemispheres by up to 70 %. The ensemble mean also overestimates the seasonal amplitude by 25-70 % in the northern extratropics and overestimates the inter-hemispheric gradient by about 30 % in the lower and middle troposphere. A part of the discrepancies can be attributed to the 5-year reanalysis data for the decadal model simulations. However, these differences are less evident with the current sonde network. To estimate ozonesonde sampling biases, we computed model bias separately for global coverage and the ozonesonde network. The ozonesonde sampling bias in the evaluated model bias for the seasonal mean concentration relative to global coverage is 40-50 % over the western Pacific and east Indian Ocean and reaches 110 % over the equatorial Americas and up to 80 % for the global tropics. In contrast, the ozonesonde sampling bias is typically smaller than 30 % for the Arctic regions in the lower and middle troposphere. These systematic biases have implications for ozone radiative forcing and the response of chemistry to climate that can be further quantified as the satellite observational record extends to multiple decades.

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Evaluation of preindustrial to present-day black carbon and its albedo forcing from Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP)

Cited by 137 publications

References 101 publications

Understanding the contributions of aerosol properties and parameterization discrepancies to droplet number variability in a global climate model

Understanding the contributions of aerosol properties and parameterization discrepancies to droplet number variability in a global climate model

An AeroCom assessment of black carbon in Arctic snow and sea ice

Evaluation of ACCMIP ozone simulations and ozonesonde sampling biases using a satellite-based multi-constituent chemical reanalysis

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