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Tropospheric and stratospheric wildfire smoke profiling with lidar: mass, surface area, CCN, and INP retrieval
2021, Ansmann, Albert, Ohneiser, Kevin, Mamouri, Rodanthi-Elisavet, Knopf, Daniel A., Veselovskii, Igor, Baars, Holger, Engelmann, Ronny, Foth, Andreas, Jimenez, Cristofer, Seifert, Patric, Barja, Boris
We present retrievals of tropospheric and stratospheric height profiles of particle mass, volume, surface area, and number concentrations in the case of wildfire smoke layers as well as estimates of smoke-related cloud condensation nuclei (CCN) and ice-nucleating particle (INP) concentrations from backscatter lidar measurements on the ground and in space. Conversion factors used to convert the optical measurements into microphysical properties play a central role in the data analysis, in addition to estimates of the smoke extinction-to-backscatter ratios required to obtain smoke extinction coefficients. The set of needed conversion parameters for wildfire smoke is derived from AERONET observations of major smoke events, e.g., in western Canada in August 2017, California in September 2020, and southeastern Australia in January-February 2020 as well as from AERONET long-term observations of smoke in the Amazon region, southern Africa, and Southeast Asia. The new smoke analysis scheme is applied to CALIPSO observations of tropospheric smoke plumes over the United States in September 2020 and to ground-based lidar observation in Punta Arenas, in southern Chile, in aged Australian smoke layers in the stratosphere in January 2020. These case studies show the potential of spaceborne and ground-based lidars to document large-scale and long-lasting wildfire smoke events in detail and thus to provide valuable information for climate, cloud, and air chemistry modeling efforts performed to investigate the role of wildfire smoke in the atmospheric system. © 2021 Albert Ansmann et al.
Diurnal variation of midlatitudinal NO3 column abundance over table mountain facility, California
2011, Chen, C.M., Cageao, R.P., Lawrence, L., Stutz, J., Salawitch, R.J., Jourdain, L., Li, Q., Sander, S.P.
The column abundance of NO3 was measured over Table Mountain Facility, CA (34.4° 117.7° W) from May 2003 through September 2004, using lunar occultation near full moon with a grating spectrometer. The NO 3 column retrieval was performed with the differential optical absorption spectroscopy (DOAS) technique using both the 623 and 662 nm NO 3 absorption bands. Other spectral features such as Fraunhofer lines and absorption from water vapor and oxygen were removed using solar spectra obtained at different airmass factors. We observed a seasonal variation, with nocturnally averaged NO3 columns between 5-7 × 1013 molec cm-2 during October through March, and 5-22 × 10 13 molec cm-2 during April through September. A subset of the data, with diurnal variability vastly different from the temporal profile obtained from one-dimensional stratospheric model calculations, clearly has boundary layer contributions; this was confirmed by simultaneous long-path DOAS measurements. However, even the NO3 columns that did follow the modeled time evolution were often much larger than modeled stratospheric partial columns constrained by realistic temperatures and ozone concentrations. This discrepancy is attributed to substantial tropospheric NO3 in the free troposphere, which may have the same time dependence as stratospheric NO 3.
Characterizing the sectoral development of cities
2021, Rybski, Diego, Pradhan, Prajal, Shutters, Shade T., Butsic, Van, Kropp, Jürgen P., Xue, Bing
Previous research has identified a predictive model of how a nation’s distribution of gross domestic product (GDP) among agriculture (a), industry (i), and services (s) changes as a country develops. Here we use this national model to analyze the composition of GDP for US Metropolitan Statistical Areas (MSA) over time. To characterize the transfer of GDP shares between the sectors in the course of economic development we explore a simple system of differential equations proposed in the country-level model. Fitting the model to more than 120 MSAs we find that according to the obtained parameters MSAs can be classified into 6 groups (consecutive, high industry, re-industrializing; each of them also with reversed development direction). The consecutive transfer (a → i → s) is common but does not represent all MSAs examined. At the 95% confidence level, 40% of MSAs belong to types exhibiting an increasing share of GDP from agriculture. In California, such MSAs, which we classify as part of an agriculture renaissance, are found in the Central Valley.