Investigation of black carbon climate effects in the Arctic in winter and spring
Graphical abstract
Introduction
Over the past several decades, the Arctic has experienced a rapid warming at a rate almost twice of the global average, and this phenomenon is known as Arctic amplification (Chen et al., 2019; Cohen et al., 2014; Sand et al., 2016; Shindell and Faluvegi, 2009). Arctic amplification has substantial influences on the local environment, such as the significant loss of sea ice as well as the melting of glacier and snow cover (Bokhorst et al., 2016; Kim and Kim, 2020; Szafraniec, 2018; Zheng et al., 2018), the increase of methane release from permafrost degradation (Walter Anthony et al., 2016), and the considerable effects on ecosystem carbon budgets and human societies (Ito et al., 2020; Moon et al., 2019). Furthermore, it can be also closely associated with mid-latitude weather and climate (Cohen et al., 2014, Cohen et al., 2020; Coumou et al., 2018). Evidence suggests that next to the warming caused by greenhouse gases, short-lived climate forcers, such as methane, tropospheric ozone, and black carbon-containing aerosols, can substantially affect the Arctic climate (AMAP, 2015; Quinn et al., 2008; Sand et al., 2016; Stjern et al., 2019; Yang et al., 2014).
Black carbon (BC), emitted from incomplete combustion of fossil fuels and biomass, is the major light-absorbing component of aerosols that plays an important role on global and regional climate (Bond et al., 2013; Jacobson, 2004). The climate effects of BC spans from aerosol–radiation interaction (direct effect), through influences on cloud optical properties and lifetime, cloud cover, and precipitation (indirect effect), to rapid adjustments involving modification of atmospheric stability and humidity and consequent modification of clouds (semi-direct effect) (Forkel et al., 2012; Stjern et al., 2017). Additionally, BC has other forcing mechanism when it deposits on snow and ice. Such mechanism enhances surface absorption of solar radiation by reducing albedo and thus accelerates snow and ice melting (Flanner et al., 2007; Kang et al., 2019; Skiles et al., 2018). The Arctic is very sensitive to climate change due to its highly reflective surface with widely covered snow and ice, thus BC is especially important for the Arctic climate.
Both observations and model results showed that BC had a clearly seasonal variation pattern in the Arctic, with high concentration levels in winter-spring (it's called Arctic haze) and low levels in summer (AMAP, 2015; Ikeda et al., 2017; Law and Stohl, 2007; Xu et al., 2017; Zhu et al., 2020). Many previous studies indicated that BC contributed to the Arctic warming by absorbing solar radiation in atmosphere and through snow and ice, and the reduction of BC was considered as the effective mitigation (Flanner et al., 2007; Jacobson, 2010; Namazi et al., 2015; Qian et al., 2014; Quinn et al., 2008; Sand et al., 2013; Shindell et al., 2012). Sand et al. (2016) quantified the Arctic temperature response to short-lived climate forcers, and the results showed that BC in atmosphere and snow led to large surface warming of 0.48 (0.33–0.66) K. Stjern et al. (2019) investigated the Arctic temperature and precipitation responses to perturbations of individual climate drivers. They found that BC caused the strongest surface warming in winter and much stronger upper-level warming in summer, and the BC response was also stronger in all energy budget processes of precipitation changes in the Arctic. Flanner (2013) analyzed how local and global BC influenced the Arctic climate, and they concluded that Arctic BC affected the surface temperature highly depended on its vertical location and extra-Arctic BC affected the temperature by changing poleward heat flux. While Sand et al. (2013) revealed that the local emissions were more important for the Arctic climate, with BC emitted within the Arctic led to an almost five times larger surface temperature response compared to same amount of BC emitted from mid-latitudes. Generally, many studies have focused on atmospheric forcing as well as snow and ice forcing by BC linked with different emission sources, and temperature response in the Arctic. However, the BC–cloud–radiation interactions and its effects on meteorology as well as atmospheric stability in different seasons have not been studied extensively in this region. That forms the main objective of this study.
Aerosol–cloud–radiation interactions and feedbacks cannot be accurately simulated by offline coupled air quality models, which requires the use of fully coupled online meteorology-chemistry models since they provide the possibility for feedback modelling (Briant et al., 2017; Forkel et al., 2015; Forkel et al., 2012; Grell and Baklanov, 2011; Zhang et al., 2010). Among them, the high-resolution WRF-Chem model is very typical and widely used in the studies of air quality and feedback effects (Chen et al., 2020; Ding et al., 2019; Forkel et al., 2012; Kumar et al., 2014; Wu et al., 2019; Yang et al., 2019; Yang et al., 2018; Yang et al., 2020). By applying WRF-Chem, Stofferahn and Boybeyi, 2017a, Stofferahn and Boybeyi, 2017b analyzed both total and separating aerosol effects on the Arctic surface temperature during the diurnal cycle. Those two studies suggested that aerosols caused the surface cooling during the daytime and warming during the nighttime, among which the indirect effects played a dominated role. Additionally, WRF-Chem was also applied in the Arctic for several studies involving aerosol concentrations as well as transports, emissions impacts, and cloud properties (Chen et al., 2020; Eckhardt et al., 2015; Marelle et al., 2015, Marelle et al., 2016; Sotiropoulou et al., 2019). Therefore, this study attempts to explore the effects of atmospheric BC in the Arctic using WRF-Chem during Arctic haze, for this period presents high BC loadings. Section 2 introduces the model details, experiment design, and observation data used. In Section 3, the model performance is evaluated firstly, then the BC effects on surface radiation, meteorological variables, and atmospheric stability are analyzed. Section 4 provides the conclusions.
Section snippets
WRF-Chem model and configuration
This study used the Weather Research and Forecasting Model (Skamarock et al., 2005) coupled with Chemistry (Grell et al., 2005) (WRF-Chem, version 3.6.1) to investigate the total effects of BC (i.e., the direct, semi-direct, and indirect effects) in the Arctic (Here the Arctic region is defined as areas north of the Arctic circle (about 67 N°)). WRF-Chem is a mesoscale non-hydrostatic and fully coupled online chemistry model, which has various modules considering complex physical and chemical
Validation of simulated meteorological variables and BC concentration
In this section, evaluations of meteorological variables and BC concentration were conducted for CON experiments. Correlation coefficient (R) and root mean square error (RMSE) were calculated for T2, TD2, SLP, WS10, and BC concentration. Since meteorology plays a driving role for transport pattern in aerosol simulation, the meteorological variables simulated by WRF-Chem at three Arctic sites were validated using in-situ observations from NCEI and ERA-Interim reanalysis data firstly (Fig. 2).
Conclusions
In this study, numerical simulations were conducted in the Arctic in winter and spring from 2015 to 2017 using WRF-Chem model to investigate the climate effects of atmospheric BC on surface radiation, meteorology, and atmospheric stability. First of all, the model performance on meteorological variables and BC concentration was evaluated using in-situ observations and ERA-Interim reanalysis data. Generally, WRF-Chem reproduced the temporal variations of meteorological variables (i.e., T2, TD2,
CRediT authorship contribution statement
Xintong Chen: Conceptualization, Methodology, Formal analysis, Writing - original draft, Writing - review & editing. Shichang Kang: Conceptualization, Writing - review & editing. Junhua Yang: Conceptualization, Methodology, Writing - review & editing. Zhenming Ji: Writing - review & editing.
Declaration of competing interest
The authors declare that they have no conflict of interest to this work.
Acknowledgements
This study was supported by the Chinese Academy of Sciences (QYZDY-SSW-DQC021, QYZDJ-SSW-DQC039), the National Natural Science Foundation of China (41721091, 41630754), and the State Key Laboratory of Cryospheric Science (SKLCS-ZZ-2020). We are grateful to the Mesoscale and Microscale Meteorology Laboratory (MMM) of the National Center for Atmospheric Research (NCAR) for making WRF-Chem model. We would like to acknowledge the NOAA's National Centers for Environmental Information (NCEI) for
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