Global search for autumn‐lead sea surface salinity predictors of winter precipitation in southwestern United States

Author Posting. © American Geophysical Union, 2018. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 45 (2018): 8445-8454, doi:10.1029/2018GL079293.Sea surface salinity (SSS) is sensitive to changes in ocean evaporation and precipitation, that is, to changes in the oceanic water cycle. Through the close connection between the oceanic and terrestrial water cycle, SSS can be used as an indicator of rainfall on land. Here we search globally for teleconnections between autumn‐lead September‐October‐November SSS signals and winter December‐January‐February precipitation over southwestern United States. The SSS‐based model (R2 = 0.61) outperforms the sea surface temperature‐based model (R2 = 0.54). Further, a fresh tropical Pacific in autumn, indicated by low SSS, corresponds with wet winters. Recent studies suggest that anomalously high rainfall in the tropics may excite Rossby waves that can export water to the extratropics. Thus, incorporating SSS, a sensitive indicator of regional oceanic rainfall, can enhance the accuracy of existing precipitation prediction frameworks that rely on sea surface temperature‐based climate indices and, by extension, improve watershed management.NSF Grant Numbers: ICER‐1663704, ICER‐1663138, DGE1144152, DGE1745303; Woods Hole Oceanographic Institution2019-02-2

Quantifying the influence of agricultural fires in northwest India on urban air pollution in Delhi, India

Since at least the 1980s, many farmers in northwest India have switched to mechanized combine harvesting to boost efficiency. This harvesting technique leaves abundant crop residue on the fields, which farmers typically burn to prepare their fields for subsequent planting. A key question is to what extent the large quantity of smoke emitted by these fires contributes to the already severe pollution in Delhi and across other parts of the heavily populated Indo-Gangetic Plain located downwind of the fires. Using a combination of observed and modeled variables, including surface measurements of PM2.5, we quantify the magnitude of the influence of agricultural fire emissions on surface air pollution in Delhi. With surface measurements, we first derive the signal of regional PM2.5 enhancements (i.e. the pollution above an anthropogenic baseline) during each post-monsoon burning season for 2012–2016. We next use the Stochastic Time-Inverted Lagrangian Transport model (STILT) to simulate surface PM2.5 using five fire emission inventories. We reproduce up to 25% of the weekly variability in total observed PM2.5 using STILT. Depending on year and emission inventory, our method attributes 7.0%–78% of the maximum observed PM2.5 enhancements in Delhi to fires. The large range in these attribution estimates points to the uncertainties in fire emission parameterizations, especially in regions where thick smoke may interfere with hotspots of fire radiative power. Although our model can generally reproduce the largest PM2.5 enhancements in Delhi air quality for 1–3 consecutive days each fire season, it fails to capture many smaller daily enhancements, which we attribute to the challenge of detecting small fires in the satellite retrieval. By quantifying the influence of upwind agricultural fire emissions on Delhi air pollution, our work underscores the potential health benefits of changes in farming practices to reduce fires

Missing emissions from post-monsoon agricultural fires in northwestern India: regional limitations of MODIS burned area and active fire products

A rising source of outdoor emissions in northwestern India is crop residue burning, occurring after the monsoon (kharif) and winter (rabi) crop harvests. In particular, post-monsoon rice residue burning, which occurs annually from October to November and is linked to increasing mechanization, coincides with meteorological conditions that enhance short-term air quality degradation. Here we examine the Global Fire Emissions Database (GFED), whose bottom-up emissions are based on the 500-m burned area product, MCD64A1, derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations. Using a household survey from 2016, we find that MCD64A1 tends to underestimate burned area in many surveyed villages, leading to poor representation of small, scattered fires and consequent spatial biases in model results. To more accurately allocate such small fires and resolve sub-village heterogeneity, we use an experimental hybrid MODIS-Landsat method (ModL2T) to map burned area at 30-m spatial resolution, which results in 44 ± 21% higher burned area than MCD64A1 and up to 105 ± 52% increase in dry matter emissions over GFEDv4s. In our validation and assessments, we find that ModL2T performs better relative to MCD64A1 in terms of bias and omission error, but may introduce commission error due to conflation of burning with harvest and still underestimate burned area due to Landsat's coarse temporal resolution (every 16 days). We conclude that while MODIS and Landsat provide more than two decades worth of observations, their spatio-temporal resolution is too coarse to overcome several region-specific challenges: small median landholding size (1–3 ha), quick harvest-to-sowing turnover period, prevalence of partial burning, and increasing haziness. To further constrain agricultural fire emissions in northwestern India and improve model estimates of associated public health impacts, integration of finer resolution imagery, as well as better understanding of the spatial patterns in burn rates, burn practices, and fuel loading, is requisite

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, air quality and health impacts 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 trends 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 increase in relative humidity, leading to over fivefold increase in poor visibility days. Given this crucial aerosol-radiation-meteorological feedback driving the smog intensification, results from this study would help inform mitigation strategies supporting stronger region-wide measures, which are critical for solving the smog challenge in India

Rapid growth and high cloud-forming potential of anthropogenic sulfate aerosol in a thermal power plant plume during COVID lockdown in India

The COVID lockdown presented an interesting opportunity to study the anthropogenic emissions from different sectors under relatively cleaner conditions in India. The complex interplays of power production, industry, and transport could be dissected due to the significantly reduced influence of the latter two emission sources. Here, based on measurements of cloud condensation nuclei (CCN) activity and chemical composition of atmospheric aerosols during the lockdown, we report an episodic event resulting from distinct meteorological conditions. This event was marked by rapid growth and high hygroscopicity of new aerosol particles formed in the SO2 plume from a large coal-fired power plant in Southern India. These sulfate-rich particles had high CCN activity and number concentration, indicating high cloud-forming potential. Examining the sensitivity of CCN properties under relatively clean conditions provides important new clues to delineate the contributions of different anthropogenic emission sectors and further to understand their perturbations of past and future climate forcing

Public health impacts of the severe haze in Equatorial Asia in September-October 2015: demonstration of a new framework for informing fire management strategies to reduce downwind smoke exposure

In September–October 2015, El Niño and positive Indian Ocean Dipole conditions set the stage for massive fires in Sumatra and Kalimantan (Indonesian Borneo), leading to persistently hazardous levels of smoke pollution across much of Equatorial Asia. Here we quantify the emission sources and health impacts of this haze episode and compare the sources and impacts to an event of similar magnitude occurring under similar meteorological conditions in September–October 2006. Using the adjoint of the GEOS-Chem chemical transport model, we first calculate the influence of potential fire emissions across the domain on smoke concentrations in three receptor areas downwind—Indonesia, Malaysia, and Singapore—during the 2006 event. This step maps the sensitivity of each receptor to fire emissions in each grid cell upwind. We then combine these sensitivities with 2006 and 2015 fire emission inventories from the Global Fire Assimilation System (GFAS) to estimate the resulting population-weighted smoke exposure. This method, which assumes similar smoke transport pathways in 2006 and 2015, allows near real-time assessment of smoke pollution exposure, and therefore the consequent morbidity and premature mortality, due to severe haze. Our approach also provides rapid assessment of the relative contribution of fire emissions generated in a specific province to smoke-related health impacts in the receptor areas. We estimate that haze in 2015 resulted in 100 300 excess deaths across Indonesia, Malaysia and Singapore, more than double those of the 2006 event, with much of the increase due to fires in Indonesia's South Sumatra Province. The model framework we introduce in this study can rapidly identify those areas where land use management to reduce and/or avoid fires would yield the greatest benefit to human health, both nationally and regionally

Numerical simulation and analysis of the transition to turbelence in boundary layer flows

The transition from laminar to turbulent flow has always been a complicated and intractable process which can hardly be explained completely so far. Although a series of models were built to study the transition, no manageable one could exhibit its entire characteristics and make an accurate prediction when the transition would occur. In this context, Assoc. Prof. Vladimir V. Kulish has come up with a new model that views the velocity fluctuations during a transition from laminar to turbulent flow as a fractal time series. Hence, the author would conduct two types of experiments, i.e. Open Channel Flow Experiment and Boundary Layer Flow Experiment, to verify this model and explore the validity of a prediction of transition. FRASTSAN program was used throughout this project to analyze the fractal time series and generate the results of Fractal Dimension, Jeffrey Divergence Measure and Hurst Exponent, respectively. By comparing and analyzing plots of the results in each channel, interpretations on the above-mentioned three measures were able to explain the observations from velocity data, and the prediction of a transition was found to be consistent with that made from the velocity data. Thus, it could be concluded that the model is credible and can make a dependable prediction of a laminar-turbulent transition. However, certain limitations cannot be neglected in multifractal analysis. As a result, some recommendations based on these limitations would be made to future researchers and work.Bachelor of Engineerin