Extreme smog challenge of India intensified by increasing lower tropospheric stability

Extreme smog in India widely impacts air quality in late autumn and winter months. While the links between emissions and air quality are well-recognized, the association of smog and its intensification with climatic trends in the lower troposphere, where aerosol pollution and its radiative effects manifest, are not understood well. Here we use long-term satellite data to show a significant increase in aerosol exceedances over northern India, resulting in sustained atmospheric warming and surface cooling over the last two decades. We find several lines of evidence suggesting these aerosol radiative effects have induced a multidecadal (1980-2019) strengthening of lower tropospheric stability and an increase in relative humidity, leading to over fivefold increase in poor visibility days. Given this crucial aerosol-radiation-meteorological feedback driving the smog intensification, we anticipate results from this study will help inform mitigation strategies supporting stronger region-wide measures, which are critical for solving the smog challenge in India.

Time dependence of climate sensitivity

<p>Climate sensitivity is a measure for the global mean temperature change of the earth in response to a given radiative forcing. In an experiment with an instantaneous forcing by e.g. a doubling of the atmospheric CO<sub>2</sub> content the radiative imbalance at the top of the atmosphere can be regarded as a function of the global mean temperature change. In such an experiment the climate sensitivity can be approximated by linearly extrapolating to zero the TOA imbalance where equilibrium is obtained. The thus derived value is usually referred to as effective climate sensitivity. It has been established however, that the effective climate sensitivity changes over time. While the reason for this change is not clear, most recent investigations of the abrupt4xCO2 experiments of multiple members of the CMIP5 archive point to a delay in warming of the eastern tropical Pacific region relative to the global average in the multi model mean. Due to high stability in this region the heat is trapped there close to the surface which reduces the local lower tropospheric stability. The trapping of the warming close to the surface implies that the longwave cooling is less efficient in this region and its delayed warming relative to the global average increases global climate sensitivity over time. The decrease in lower tropospheric stability furthermore reduces low cloud cover leading to less negative low cloud feedback which causes additional warming.</p><p>We investigate the delayed warming in the eastern Pacific region in more detail in terms of its effects on stability as well as clouds for individual members and multi model means of both the CMIP5 and CMIP6 archives. We find that in the multi model mean, the CMIP6 members show an even larger delayed warming than the CMIP5 members. Furthermore, the individual members of both archives generally exhibit the same pattern of delayed eastern tropical Pacific warming and a corresponding decrease in lower tropospheric stability in the same region, which indicates robustness of the earlier results based on the CMIP5 multi model mean. Additionally, there is a decrease in liquid water content in the lower atmospheric layers, confirming the influence of reduced lower tropospheric stability on low clouds. However, there are several further regions such as the Southern Ocean with a consistent delayed warming and reduced stability, which might influence climate sensitivity as well.</p>

A Novel Framework for Evaluating and Improving Parameterized Subtropical Marine Boundary Layer Cloudiness

A single-column model (SCM) is used to simulate a variety of environmental conditions between Los Angeles, California, and Hawaii in order to identify physical elements of parameterizations that are required to reproduce the observed behavior of marine boundary layer (MBL) cloudiness. The SCM is composed of the JPL eddy-diffusivity/mass-flux (EDMF) mixing formulation and the RRTMG radiation model. Model forcings are provided by the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA2). Simulated low cloud cover (LCC), rain rate, albedo, and liquid water path are compared to collocated pixel-level observations from A-Train satellites. This framework ensures that the JPL EDMF is able to simulate a continuum of real-world conditions. First, the JPL EDMF is shown to reproduce the observed mean LCC as a function of lower-tropospheric stability. Joint probability distributions of lower-tropospheric cloud fraction, height, and lower-tropospheric stability (LTS) show that the JPL EDMF improves upon its MERRA2 input but struggles to match the frequency of observed intermediate-range LCC. We then illustrate the physical roles of plume lateral entrainment and eddy-diffusivity mixing length in producing a realistic behavior of LCC as a function of LTS. In low-LTS conditions, LCC is mostly sensitive to the ability of convection to mix moist air out of the MBL. In high-LTS conditions, LCC is also sensitive to the turbulent mixing of free-tropospheric air into the MBL. In the intermediate LTS regime typical of stratocumulus–cumulus transition there is proportional sensitivity to both mixing mechanisms, emphasizing the utility of a combined eddy-diffusivity/mass-flux approach for representing mixing processes.

Time Scales of Clouds and Cloud-Controlling Variables in Subtropical Stratocumulus from a Lagrangian Perspective

The Lagrangian evolution of cloud cover and cloud-controlling variables is well approximated using red noise processes with different autocorrelation time scales for each variable. Trajectories within the subtropical marine boundary layer are generated using winds from ECMWF Re-Analysis data for low cloud decks in four eastern subtropical ocean basins. Cloud cover, liquid water path, and boundary layer depth are sampled at 12-h intervals using A-Train satellites, and droplet concentration is sampled every 24 h. Lower-tropospheric stability and vertical velocity are sampled concurrently using reanalysis data. Samples are converted to seasonal and diurnal anomalies. Data are spatially averaged over a range of length scales. The e-folding decay times τ for autocorrelation are calculated for each variable based on lag times of 12, 24, 36, and 48 h. Using lag 24 h and an averaging radius of 100 km, τ ≈ 15–17 h for liquid water path and vertical velocity, τ ≈ 19 h for cloud cover, τ ≈ 24–25 h for boundary layer depth and droplet concentration, and τ ≈ 53 h for lower-tropospheric stability. Time scales vary somewhat between regions and are shortest in the eastern Indian Ocean. Decay time τ increases with averaging scale and the autocorrelation e-folding length of a variable at a fixed time. Diurnal analysis shows cloud cover anomalies have a stronger memory during morning breakup, while other variables show stronger memory as clouds reform in the evening. Lagrangian cloud anomalies are less persistent than anomalies at a fixed location. For the latter, estimated τ values can vary significantly at different lag times, so a red noise assumption is inappropriate.

Effects of long-range aerosol transport on the microphysical properties of low-level liquid clouds in the Arctic

The properties of low-level liquid clouds in the Arctic can be altered by long-range pollution transport to the region. Satellite, tracer transport model, and meteorological data sets are used here to determine a net aerosol–cloud interaction (ACInet) parameter that expresses the ratio of relative changes in cloud microphysical properties to relative variations in pollution concentrations while accounting for dry or wet scavenging of aerosols en route to the Arctic. For a period between 2008 and 2010, ACInet is calculated as a function of the cloud liquid water path, temperature, altitude, specific humidity, and lower tropospheric stability. For all data, ACInet averages 0.12 ± 0.02 for cloud-droplet effective radius and 0.16 ± 0.02 for cloud optical depth. It increases with specific humidity and lower tropospheric stability and is highest when pollution concentrations are low. Carefully controlling for meteorological conditions we find that the liquid water path of arctic clouds does not respond strongly to aerosols within pollution plumes. Or, not stratifying the data according to meteorological state can lead to artificially exaggerated calculations of the magnitude of the impacts of pollution on arctic clouds.

On the Contribution of Longwave Radiation to Global Climate Model Biases in Arctic Lower Tropospheric Stability

Previous research has found that global climate models (GCMs) usually simulate greater lower tropospheric stabilities compared to reanalysis data. To understand the origins of this bias, the authors examine hindcast simulations initialized with reanalysis data of six GCMs and find that four of the six models simulate within five days a positive bias in Arctic lower tropospheric stability during the Arctic polar night over sea ice regions. These biases in lower tropospheric stability are mainly due to cold biases in surface temperature, as very small potential temperature biases exist aloft. Similar to previous research, polar night surface temperature biases in the hindcast runs relate to all-sky downwelling longwave radiation in the models, which very much relates to the cloud liquid water. Also found herein are clear-sky longwave radiation biases and a fairly large clear-sky longwave radiation bias in the day one hindcast. This clear-sky longwave bias is analyzed by running the same radiation transfer model for each model’s temperature and moisture profile, and the model spread in clear-sky downwelling longwave radiation with the same radiative transfer model is found to be much less, suggesting that model differences other than temperature and moisture are aiding in the spread in downwelling longwave radiation. The six models were also analyzed in Atmospheric Model Intercomparison Project (AMIP) mode to determine if hindcast simulations are analogous to free-running simulations. Similar winter lower tropospheric stability biases occur in four of the six models with surface temperature biases relating to the winter lower tropospheric stability values.