Beijing Climate Center Earth System Model version 1 (BCC-ESM1): model description and evaluation of aerosol simulations

Wu, Tongwen; Zhang, Fang; Zhang, Jie; Wei, Jie; Zhang, Yanwu; Wu, Fang; Li, Laurent; Yan, Jinghui; Liu, Xiaohong; Lu, Xiao; Tan, Haiyue; Zhang, Lin; Wang, Jun; Hu, Aixue

doi:10.5194/gmd-13-977-2020

Cited by 79 publications

(31 citation statements)

References 77 publications

(116 reference statements)

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“…Overall, nine coupled ocean-atmosphere climate models performed the SSP3-7.0 and SSP3-7.0-lowNTCF simulations, including CNRM-ESM2-1 (Séférian et al, 2019;Michou et al, 2019), MIROC6 (Takemura et al, 2005(Takemura et al, , 2009Tatebe et al, 2019), MPI-ESM1-2-HAM (Mauritsen et al, 2019;Neubauer et al, 2019;Tegen et al, 2019), NorESM2-LM (Seland et al, 2020), BCC-ESM1 (Wu et al, 2019(Wu et al, , 2020, GFDL-ESM4 (John et al, 2018;Horowitz et al, 2018;Dunne et al, 2020;Horowitz et al, 2020), CESM2-WACCM (Emmons et al, 2020;Gettelman et al, 2019;Tilmes et al, 2019), UKESM1-0-LL (Sellar et al, 2019), and MRI-ESM2-0 (Yukimoto et al, 2019). However, the first four models (CNRM-ESM2-1, MIROC6, MPI-ESM1-2-HAM, NorESM2-LM) lack interactive tropospheric chemistry schemes and therefore include identical ozone evolution in both SSP3-7.0 and SSP3-7.0-lowNTCF simulations (as recommended by AerChemMIP).…”

Section: Aerchemmip Modelsmentioning

confidence: 99%

“…Furthermore, studies show that the hemispheric contrast in aerosol forcing has shifted the tropical rainbelt southward, which is associated with a weakening of the west African monsoon and the occurrence of the Sahel drought of the mid-1980s (Rotstayn and Lohmann, 2002;Biasutti and Giannini, 2006;Allen and Sherwood, 2011;Ackerley et al, 2011;Chang et al, 2011;Biasutti, 2013;Hwang et al, 2013;Dong et al, 2014;Allen et al, 2015;Undorf et al, 2018). The observed precipitation decrease during recent decades over most of the areas affected by the south and east Asian monsoon can also be explained by the dominance of aerosol radiative effects suppressing precipitation over the expected precipitation enhancement due to increased GHGs (Wang et al, 2013;Song et al, 2014;Li et al, 2015;Xie et al, 2016;Krishnan et al, 2016;Guo et al, 2016;Lau and Kim, 2017;Zhang et al, 2017;Lin et al, 2018;Liu et al, 2018).…”

Section: Introductionmentioning

confidence: 97%

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Climate and air quality impacts due to mitigation of non-methane near-term climate forcers

et al. 2020

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Abstract. It is important to understand how future environmental policies will impact both climate change and air pollution. Although targeting near-term climate forcers (NTCFs), defined here as aerosols, tropospheric ozone, and precursor gases, should improve air quality, NTCF reductions will also impact climate. Prior assessments of the impact of NTCF mitigation on air quality and climate have been limited. This is related to the idealized nature of some prior studies, simplified treatment of aerosols and chemically reactive gases, as well as a lack of a sufficiently large number of models to quantify model diversity and robust responses. Here, we quantify the 2015–2055 climate and air quality effects of non-methane NTCFs using nine state-of-the-art chemistry–climate model simulations conducted for the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP). Simulations are driven by two future scenarios featuring similar increases in greenhouse gases (GHGs) but with “weak” (SSP3-7.0) versus “strong” (SSP3-7.0-lowNTCF) levels of air quality control measures. As SSP3-7.0 lacks climate policy and has the highest levels of NTCFs, our results (e.g., surface warming) represent an upper bound. Unsurprisingly, we find significant improvements in air quality under NTCF mitigation (strong versus weak air quality controls). Surface fine particulate matter (PM2.5) and ozone (O3) decrease by -2.2±0.32 µg m−3 and -4.6±0.88 ppb, respectively (changes quoted here are for the entire 2015–2055 time period; uncertainty represents the 95 % confidence interval), over global land surfaces, with larger reductions in some regions including south and southeast Asia. Non-methane NTCF mitigation, however, leads to additional climate change due to the removal of aerosol which causes a net warming effect, including global mean surface temperature and precipitation increases of 0.25±0.12 K and 0.03±0.012 mm d−1, respectively. Similarly, increases in extreme weather indices, including the hottest and wettest days, also occur. Regionally, the largest warming and wetting occurs over Asia, including central and north Asia (0.66±0.20 K and 0.03±0.02 mm d−1), south Asia (0.47±0.16 K and 0.17±0.09 mm d−1), and east Asia (0.46±0.20 K and 0.15±0.06 mm d−1). Relatively large warming and wetting of the Arctic also occur at 0.59±0.36 K and 0.04±0.02 mm d−1, respectively. Similar surface warming occurs in model simulations with aerosol-only mitigation, implying weak cooling due to ozone reductions. Our findings suggest that future policies that aggressively target non-methane NTCF reductions will improve air quality but will lead to additional surface warming, particularly in Asia and the Arctic. Policies that address other NTCFs including methane, as well as carbon dioxide emissions, must also be adopted to meet climate mitigation goals.

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Section: Aerchemmip Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Climate and air quality impacts due to mitigation of non-methane near-term climate forcers

et al. 2020

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“…CC BY 4.0 License. relevant publications (Wu et al, 2008(Wu et al, , 2010Wu, 2012;Wu et al, 2013;Lu et al, 2013;Wu et al, 2019;Lu et al, 2020a;Wu et al, 2020). The dynamic core in BCC-AGCM3-MR uses the spectral framework as described in Wu et al (2008), in which explicit time difference scheme is applied to vorticity equation, semi-implicit time difference scheme for divergence, temperature, and surface pressure equations, and semi-Lagrangian tracer transport scheme is used for water vapor, liquid cloud water and ice cloud water.…”

Section: Atmosphere Modelmentioning

confidence: 99%

“…The dynamic core in BCC-AGCM3-MR uses the spectral framework as described in Wu et al (2008), in which explicit time difference scheme is applied to vorticity equation, semi-implicit time difference scheme for divergence, temperature, and surface pressure equations, and semi-Lagrangian tracer transport scheme is used for water vapor, liquid cloud water and ice cloud water. The main model physics in BCC-AGCM3-MR was described in Wu et al (2019), which includes the modified scheme of deep convection suggested by Wu (2012), a new diagnostic scheme of cloud amount (Wu et al, 2019), shallow convection transport scheme (Hack, 1994), the stratiform cloud microphysics followed the framework of non-convective cloud processes in NCAR Community Atmosphere Model version 3 (CAM3, Collins et al, 2004) but a noticeable treatment for indirect effects of aerosols through mechanisms of clouds and precipitation, the radiative transfer parameterization that originally implemented in CAM3, a modified boundary layer turbulence parameterization based on the eddy diffusivity approach (Holtslag and Boville, 1993), and treatments of gravity waves that are generated by a variety of sources including orography and convection (Lu et al, 2020a).…”

Section: Atmosphere Modelmentioning

confidence: 99%

“…The general performance of QBO in BCC-CSM2-MR was evaluated in Wu et al (2019). A detailed assessment of the underlying mechanism involving wave dynamics and the associated forcing to drive QBO is presented in Lu et al (2020a). The simulated QBO has stronger amplitudes in BCC-CSM2-HR than in BCC-CSM2-MR. As the https://doi.org/10.5194/gmd-2020-284 Preprint.…”

Section: The Stratospheric Quasi-biennial Oscillationmentioning

confidence: 99%

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BCC-CSM2-HR: A High-Resolution Version of the Beijing Climate Center Climate System Model

Wu¹,

Yu²,

Lu³

et al. 2020

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Abstract. BCC-CSM2-HR is a high-resolution version of the Beijing Climate Center (BCC) Climate System Model. Its development is on the basis of the medium-resolution version BCC-CSM2-MR which is the baseline for BCC participation to the Coupled Model Intercomparison Project Phase 6 (CMIP6). This study documents the high-resolution model, highlights major improvements in the representation of atmospheric dynamic core and physical processes. BCC-CSM2-HR is evaluated for present-day climate simulations from 1971 to 2000, which are performed under CMIP6-prescribed historical forcing, in comparison with its previous medium-resolution version BCC-CSM2-MR. We focus on basic atmospheric mean states over the globe and variabilities in the tropics including the tropic cyclones (TCs), the El Niño–Southern Oscillation (ENSO), the Madden-Julian Oscillation (MJO), and the quasi-biennial oscillation (QBO) in the stratosphere. It is shown that BCC-CSM2-HR keeps well the global energy balance and can realistically reproduce main patterns of atmosphere temperature and wind, precipitation, land surface air temperature and sea surface temperature. It also improves in the spatial patterns of sea ice and associated seasonal variations in both hemispheres. The bias of double intertropical convergence zone (ITCZ), obvious in BCC-CSM2-MR, is almost disappeared in BCC-CSM2-HR. TC activity in the tropics is increased with resolution enhanced. The cycle of ENSO, the eastward propagative feature and convection intensity of MJO, the downward propagation of QBO in BCC-CSM2-HR are all in a better agreement with observation than their counterparts in BCC-CSM2-MR. We also note some weakness in BCC-CSM2-HR, such as the excessive cloudiness in the eastern basin of the tropical Pacific with cold Sea Surface Temperature (SST) biases and the insufficient number of tropical cyclones in the North Atlantic.

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Multi-stage Ensemble-learning-based Model Fusion for Surface Ozone Simulations: A Focus on CMIP6 Models

Sun

Archibald

2021

Preprint

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Accurately simulating the geographical distribution and temporal variability of global surface ozone has long been one of the principal components of chemistry-climate modelling. However, the simulation outcomes have been reported to vary significantly as a result of the complex mixture of uncertain factors that control the tropospheric ozone budget. Settling the cross-model discrepancies to achieve higher accuracy predictions of surface ozone is thus a task of priority, and methods that overcome structural biases in models going beyond naïve averaging of model simulations are urgently required. Building on the Coupled Model Intercomparison Project Phase 6 (CMIP6), we have transplanted a conventional ensemble learning approach, and also constructed an innovative 2-stage enhanced space-time Bayesian neural network to fuse an ensemble of 57 simulations together with a prescribed ozone dataset, both of which have realised outstanding performances (R 2 > 0.95, RMSE < 2.12 ppbv). The conventional ensemble learning approach is computationally cheaper and results in higher overall performance, but at the expense of oceanic ozone being overestimated and the learning process being uninterpretable. The Bayesian approach performs better in spatial generalisation and enables perceivable interpretability, but induces heavier computational burdens. Both of these multi-stage machine learning-based approaches provide frameworks for improving the fidelity of composition-climate model outputs for uses in future impact studies.

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Beijing Climate Center Earth System Model version 1 (BCC-ESM1): model description and evaluation of aerosol simulations

Cited by 79 publications

References 77 publications

Climate and air quality impacts due to mitigation of non-methane near-term climate forcers

Climate and air quality impacts due to mitigation of non-methane near-term climate forcers

BCC-CSM2-HR: A High-Resolution Version of the Beijing Climate Center Climate System Model

Multi-stage Ensemble-learning-based Model Fusion for Surface Ozone Simulations: A Focus on CMIP6 Models

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