Satellite-based estimation of the aerosol forcing contribution to the global land surface temperature in the recent decade
Graphical abstract
Introduction
The temperature is a crucial driver of the Earth's climate and eco-environmental evolution. The abnormal temperature change (i.e., global warming) triggers serious problems, that is, climate anomalies (Wolf et al., 2010), hydrological extreme events (Dai, 2011; Feng and Zhang, 2015), and even biodiversity loss (Fonty et al., 2009). Therefore, it is critical to monitor the temperature change and identify the controlling factors for climate adaption and environment management. The aerosol forcing, including the direct, indirect, and semi-direct effects, has a significant impact on the temperature and has attracted increasing attention from researchers (Andreae et al., 2005; Garrett and Zhao, 2006; Huang et al., 2006; Penner et al., 1992; Scott et al., 2017; Yang et al., 2018; Zhao and Garrett, 2015; Zhao et al., 2015). Physically, the direct forcing refers to the reflection and absorption of incoming solar radiation (Boucher et al., 2013; Yang et al., 2016a; Yang et al., 2016b). The indirect aerosol forcing is the modification of the microphysics, lifetime, and thermal emissivity of clouds. Firstly, some aerosols act as cloud condensation nuclei (CCN), which helps to generate brighter clouds that reflect more incoming solar radiation (Kim and Ramanathan, 2008; Seinfeld et al., 2016; Twomey, 1977). Secondly, aerosols enhance the cloud lifetime by inhibiting precipitation (Albrecht, 1989; Small et al., 2009). Thirdly, aerosols in thin water clouds enhance the longwave cloud emissivity (Garrett et al., 2009; Garrett and Zhao, 2006; Zhao and Garrett, 2015). The semi-direct forcing is characterized by the radiation absorption of several aerosol types (i.e., black carbon, BC) further evaporate the clouds, which enables more solar radiation to reach the air and surface (Allen and Sherwood, 2010; Stofferahn and Boybeyi, 2017). Generally, the direct effect on the radiation reflection and indirect effects on the cloud condensation nuclei (CNN) and lifetime of clouds tend to cool the world, while the indirect effect of warming on the longwave cloud emissivity (particularly in the Arctic winter) and semi-direct effect on the cloud evaporation may offset the cooling effects (Garrett and Zhao, 2006; Gettelman et al., 2015; Myhre et al., 2013; Satheesh and Ramanathan, 2000). The net aerosol forcing on subtle details of how the direct, indirect, and semi-direct effects are coupled. Because of the complex coupled forcing, the sign and magnitude of the aerosol forcing are strongly heterogeneous over land. The identification of the net impact (warming or cooling) is therefore a great challenge with respect to research on global changes.
Various climate models have been used to estimate the aerosol forcing. However, the results are controversial, both with respect to the sign and magnitude. For example, the fifth Intergovernmental Panel on Climate Change (IPCC) reported that aerosols have a net cooling effect on the global temperature (Myhre et al., 2013). By contrast, Andreae et al. (2005) suggested that the aerosol cooling is expected to decline and may result in a hot future when the effect is relative to greenhouse gas forcing. With respect to the magnitude, some researchers suggested that aerosols have a strong impact on the temperature change. Gao et al. (2015) showed that aerosols warm the atmosphere by 0.1–0.5 K and cool the surface by 0.8–2.8 K in the North China Plain. Samset et al. (2018) revealed that removing aerosols induces global mean surface heating by 0.5–1.1 K. However, other studies suggested that the aerosol forcing might be overestimated. Lohmann and Lesins (2002) demonstrated that the indirect anthropogenic aerosol effect is overestimated by −1.4 to −0.85 W/m2 compared with the observed clouds. Gettelman et al. (2015) pointed out that the change in anthropogenic sulfate-induced net global radiative forcing is small. This controversy leaves a great gap for climate prediction, which needs to be investigated.
The controversy mentioned above is mainly attributed to unavoidable model uncertainties. Although physically explicit, the uncertainties of the models originate from the initial conditions, model errors, and prediction scenarios (Bonan and Doney, 2018). The initial conditions refer to the differences in the model parameter input, where small differences produce different climate trajectories. Carslaw et al. (2013) showed that the uncertainties in the natural and anthropogenic emissions cause 45% and 34% of the variance of aerosol forcing, respectively. The model error stems from assumptions concerning the physical and chemical properties of aerosols as well as their interaction with climate, that is, unreliable simulations of aerosol–cloud interactions or cloud parameterization schemes. Fan et al. (2018) showed that large uncertainties in the cloud fraction are mainly due to aerosol–cloud interaction schemes rather than aerosol simulations. Different models result in different aerosol forcings because of strong discrepancies (Gettelman et al., 2015; Kim and Ramanathan, 2008). Even for the same model, different aerosol radiative forcings can be obtained by using different aerosol-related cloud schemes. Xie et al. (2013) showed strong sensitivities of the surface and top-of-atmosphere (TOA) radiative forcing to dust aerosols based on an ice nuclei (IN) scheme. To reduce this type of uncertainty, the modeling results are usually validated or calibrated using satellite or in situ observation data (Lohmann and Lesins, 2002; Ma et al., 2018; Xie et al., 2013). The third source of uncertainty is mainly due to boundary conditions of the models, represented by various modeling results at different scales (global or regional, inter- or intraannual), and the forcing type (total forcing or anthropogenic forcing; LevyII et al., 2013; Stier et al., 2007; Stott et al., 2000; Wang et al., 2016). Considering these uncertainties, researchers suggested that direct observations (satellite and in situ) are needed to accurately capture the aerosol forcing (Bellouin et al., 2005; Satheesh and Ramanathan, 2000; Seinfeld et al., 2016).
Several researchers carried out experiments using ground observations. Based on the ground aerosol and radiation measurements near Barrow in Alaska, Garrett and Zhao (2006) reported 1–1.6 K surface warming in the Arctic winter originating from longwave cloud thermal emissivity due to aerosol pollution. The temporal variability of the forcing, ranging from 12.2 Wm−2 in February to 11.8 Wm−2 in August, was further identified Zhao and Garrett (2015). However, the spatial representativeness of ground observations is limited (Feng et al., 2015; Shi et al., 2018). Satellite observations help to capture geographic phenomena and processes on a large scale. The wide application of satellites refers to the spatial mapping of the air pollution concentration (Zou et al., 2016, Zou et al., 2019). The relationships between aerosols and potential factors (i.e., land cover and climate) were also analyzed to capture the dominant influences at difference scales (Feng et al., 2017b, Feng et al., 2019). However, due to the complex interactions of various influencing factors, precise satellite-based estimations of the aerosol forcing are still challenging.
This study aims to evaluate the aerosol forcing on the global land surface temperature (Ts) based on satellite observations. The datasets include the global aerosols (represented by the Aerosol Optical Depth, AOD), Ts, and relevant auxiliary data (i.e., radiation, atmospheric, and surface properties). Based on these datasets, a semi-physical framework was developed to estimate the Ts based on the Blackbody radiation and surface radiation budget. Subsequently, the aerosol forcing was evaluated based on the Ts difference between the changing aerosol scenario and baseline scenario with a fixed aerosol amount. The methods and corresponding datasets are described in Section 2. The results and discussion are presented in Section 3. The conclusions are provided in Section 4. The results of this study help to reduce the uncertainty and validate the model results and support the relative climate adaption and environmental management.
Section snippets
Methodology
Based on the Blackbody radiation law, the temperature reflects the radiation of an object, which can be described as (Campbell and Wynne, 2011):where R is the radiation of an object, σ = 5.6697 × 10−8 [W/(m2 ∙ K4)] is the Stefan–Boltzmann constant, T is the temperature (K), and ε is the thermal emissivity.
As shown in Eq. (1), the Ts can be estimated using the surface radiation, which is characterized by longwave emittance (RL↑). Based on the surface radiation budget (Rn = (1 − αs)Rs ↓ + RL
Global AOD and Ts patterns
Fig. 1 shows the spatial and temporal patterns of the observed AOD and Ts from 2001 to 2016. Several studies suggested the polar-orbiting satellite observations might miss diurnal peak AOD due to the relatively coarse temporal resolution when compared to the geostationary satellite (i.e., the Himawari-8/AHI) (Shang et al., 2019). This study focuses on the long-term trend, which would eliminate the uncertainty originated from daily scale. The multi-year mean of the global AOD is 0.198 ± 0.146,
Conclusions
In this study, the aerosol forcing on the global temperature was evaluated by using a semi-physical framework of satellite observations. Several conclusions can be drawn:
The framework was developed based on the Blackbody radiation and surface radiation budget laws. The results demonstrate that the framework can be used to estimate the Ts; the global mean Ts and RMSE of Ts are 0.62 and 1.48 K, respectively. Therefore, the proposed framework is reliable and can be used to evaluate the aerosol
Acknowledgments
This work was in part supported by the National Key Research and Development Program [grant number 2016YFC0206205], National Natural Science Foundation of China [grant number 41501034], and Natural Science Foundation of the Hunan Province [grant number 2018JJ2498]. We highly appreciate the editors and anonymous reviewers for their constructive comments on this manuscript.
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2022, Advances in Space ResearchCitation Excerpt :Most of the data sets above started from 2001. Furthermore, significant changes of the global radiation and climate change have been reported in the recent decade (Brown and Caldeira, 2017; Rahmstorf et al., 2017; Feng and Zou, 2019b; Feng et al., 2020). According to the previous studies, the appropriate resolution could be 1° × 1° at a global scale when considering the overall pattern and spatial details of the global radiation (Feng et al., 2020).
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2022, Advances in Space ResearchCitation Excerpt :On the other hand, air pollution also has a significant impact on the land surface temperature. Aerosols affect the surface radiation budget through direct, indirect and semi-indirect effects, and thus exerts influence on the surface temperature (Andreae et al., 2005; Feng and Zou, 2019; Reddington et al., 2015) Notably, aerosols will retain heat and increase the urban heat island intensity, which would form a positive feedback loop between air pollution and UHI effect (Cao et al., 2016). Thus, it is imperative to investigate the correlation between UHI and air pollution.