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There is a continuously increasing need for reliable feature detection and tracking tools based on objective analysis principles for use with meteorological data. Many tools have been developed over the previous 2 decades that attempt to address this need but most have limitations on the type of data they can be used with, feature computational and/or memory expenses that make them unwieldy with larger datasets, or require some form of data reduction prior to use that limits the tool's utility. The Tracking and Object-Based Analysis of Clouds (tobac) Python package is a modular, open-source tool that improves on the overall generality and utility of past tools. A number of scientific improvements (three spatial dimensions, splits and mergers of features, an internal spectral filtering tool) and procedural enhancements (increased computational efficiency, internal regridding of data, and treatments for periodic boundary conditions) have been included in tobac as a part of the tobac v1.5 update. These improvements have made tobac one of the most robust, powerful, and flexible identification and tracking tools in our field to date and expand its potential use in other fields. Future plans for tobac v2 are also discussed. [ABSTRACT FROM AUTHOR]

In this research, we delve into the influence of cloud condensation nuclei (CCN) and ice-nucleating particle (INP) concentrations on the morphology and abundance of ice particles in mixed-phase clouds, emphasizing the consequential impact of ice particle shape, number, and size on cloud dynamics and microphysics. Leveraging the synergy of the Advanced Microphysics Prediction System (AMPS) and the Kinematic Driver (KiD) model, we conducted simulations to capture cloud microphysics across diverse CCN and INP concentrations. The Passive and Active Microwave radiative TRAnsfer (PAMTRA) radar forward simulator further augmented our study, offering insights into how the concentrations of CCN and INPs affect radar reflectivities. Our experimental framework encompassed CCN concentrations ranging from 10 to 5000 cm -3 and INP concentrations from 0.001 to 10 L -1. Central to our findings is the observation that higher INP concentrations yield smaller ice particles, while an increase in CCN concentrations leads to a subtle growth in their dimensions. Consistent with existing literature, our results spotlight oblate-like crystals as dominant between temperatures of −20 and −16 °C. Notably, high-INP scenarios unveiled a significant prevalence of irregular polycrystals. The aspect ratio (AR) of ice particles exhibited a decline with the rise in both CCN and INP concentrations, highlighting the nuanced interrelation between CCN levels and ice particle shape, especially its ramifications on the riming mechanism. The forward-simulated radar reflectivities, spanning from -11.83 dBZ (low INP, 0.001 L -1) to 4.65 dBZ (high INP, 10 L -1), elucidate the complex dynamics between CCN and INPs in determining mixed-phase cloud characteristics. Comparable differences in radar reflectivity were also reported from observational studies of stratiform mixed-phase clouds in contrasting aerosol environments. Our meticulous analysis of KiD-AMPS simulation outputs, coupled with insights into aerosol-driven microphysical changes, thus underscores the significance of this study in refining our ability to understand and interpret observations and climate projections. [ABSTRACT FROM AUTHOR]

There is a continuously increasing need for reliable feature detection and tracking tools based on objective analysis principles for use with meteorological data. Many tools have been developed over the previous two decades that attempt to address this need, but most have limitations on the type of data they can be used with; computational and/or memory expenses that make them unwieldy with larger datasets; or require some form of data reduction prior to use that limits the tool's utility. The Tracking and Object-Based Analysis of Clouds (tobac) Python package is a modular, open-source tool that improves on the overall generality and utility of past tools. A number of scientific improvements (three spatial dimensions, splits and mergers of features, an internal spectral filtering tool) and procedural enhancements (increased computational efficiency, internal regridding of data, and treatments for periodic boundary conditions) have been included in tobac as a part of the tobac v1.5 update. These improvements have made tobac one of the most robust, powerful, and flexible identification and tracking tools in our field to date and expand its potential use in other fields. Future plans for tobac v2 are also discussed. [ABSTRACT FROM AUTHOR]

In this research, we delve into the influence of cloud condensation nuclei (CCN) and ice-nucleating particles (INP) concentrations on the morphology and abundance of ice particles in mixed-phase clouds, emphasizing the consequential impact of ice particle shape, number, and size on cloud dynamics and microphysics. Leveraging the synergy of the Advanced Microphysics Prediction System (AMPS) and the Kinematic Driver (KiD) model, we conducted simulations to capture cloud microphysics across diverse CCN and INP concentrations. The Passive and Active Microwave radiative TRAnsfer (PAMTRA) radar forward simulator further augmented our study, offering insights into how the concentrations of CCN and INP affect radar reflectivities. Our experimental framework encompassed CCN concentrations ranging from 10 to 5000 cm−3 and INP concentrations from 0.001 to 10 L−1. Central to our findings are the observation that increased INP concentrations yield smaller ice particles, while a surge in CCN concentrations leads to a subtle growth in their dimensions. Consistent with existing literature, our results spotlight plate-like crystals as dominant between temperatures of −20 to −16 °C. Notably, high INP scenarios unveiled a significant prevalence of irregular polycrystals. The Aspect Ratio (AR) of ice particles exhibited a decline with the rise in both CCN and INP concentrations, highlighting the nuanced interrelation between CCN levels and ice particle shape, especially its ramifications on the riming mechanism. The forward-simulated radar reflectivities, spanning from −11.83 dBZ (low-INP, 0.001 L−1) to 4.65 dBZ (high-INP, 10 L−1), elucidate the complex dynamics between CCN and INP in determining mixed-phase cloud characteristics. Comparable differences in radar reflectivity were also reported from observational studies of stratiform mixed-phase clouds in contrasting aerosol environments. Our meticulous analysis of KiD-AMPS simulation outputs, coupled with insights into aerosol-driven microphysical changes, thus underscores the significance of this study in refining our ability to understand and interpret observations and climate projections. [ABSTRACT FROM AUTHOR]

In this research, we delve into the influence of cloud condensation nuclei (CCN) and ice-nucleating particles (INP) concentrations on the morphology and abundance of ice particles in mixed-phase clouds, emphasizing the consequential impact of ice particle shape, number, and size on cloud dynamics and microphysics. Leveraging the synergy of the Advanced Microphysics Prediction System (AMPS) and the Kinematic Driver (KiD) model, we conducted simulations to capture cloud microphysics across diverse CCN and INP concentrations. The Passive and Active Microwave radiative TRAnsfer (PAMTRA) radar forward simulator further augmented our study, offering insights into how the concentrations of CCN and INP affect radar reflectivities. Our experimental framework encompassed CCN concentrations ranging from 10 to 5000 cm-3 and INP concentrations from 0.001 to 10 L-1. Central to our findings are the observation that increased INP concentrations yield smaller ice particles, while a surge in CCN concentrations leads to a subtle growth in their dimensions. Consistent with existing literature, our results spotlight plate-like crystals as dominant between temperatures of -20 to -16 °C. Notably, high INP scenarios unveiled a significant prevalence of irregular polycrystals. The Aspect Ratio (AR) of ice particles exhibited a decline with the rise in both CCN and INP concentrations, highlighting the nuanced interrelation between CCN levels and ice particle shape, especially its ramifications on the riming mechanism. The forward-simulated radar reflectivities, spanning from -11.83 dBZ (low-INP, 0.001 L-1) to 4.65 dBZ (high-INP, 10 L-1), elucidate the complex dynamics between CCN and INP in determining mixed-phase cloud characteristics. Comparable differences in radar reflectivity were also reported from observational studies of stratiform mixed-phase clouds in contrasting aerosol environments. Our meticulous analysis of KiD-AMPS simulation outputs, coupled with insights into aerosol-driven microphysical changes, thus underscores the significance of this study in refining our ability to understand and interpret obser vations and climate projections. [ABSTRACT FROM AUTHOR]

Wildfire smoke is known as a highly absorptive aerosol type in the shortwave wavelength range. The absorption of sunlight by optically thick smoke layers results in heating of the ambient air. This heating is translated into self-lofting of the smoke up to more than 1 km in altitude per day. This study aims for a detailed analysis of tropospheric and stratospheric smoke lofting rates based on simulations and observations. The main goal is to demonstrate that radiative heating of intense smoke plumes is capable of lofting them from the lower and middle free troposphere (injection heights) up to the tropopause without the need of pyrocumulonimbus (pyroCb) convection. The further subsequent ascent within the lower stratosphere (caused by self-lofting) is already well documented in the literature. Simulations of absorbed solar radiation by smoke particles and resulting heating rates, which are then converted into lofting rates, are conducted by using the ECRAD (European Centre for Medium-Range Weather Forecasts Radiation) scheme. As input parameters thermodynamic profiles from CAMS (Copernicus Atmosphere Monitoring Service) reanalysis data, aerosol profiles from ground-based lidar observations, radiosonde potential temperature profiles, CALIOP (Cloud–Aerosol Lidar with Orthogonal Polarization) aerosol measurements, and MODIS (Moderate Resolution Imaging Spectroradiometer) aerosol optical depth retrievals were used. The sensitivity analysis revealed that the lofting rate strongly depends on aerosol optical thickness (AOT), layer depth, layer height, and black carbon (BC) fraction. We also looked at the influence of different meteorological parameters such as cloudiness, relative humidity, and potential temperature gradient. To demonstrate the applicability of our self-lofting model, we compared our simulations with the lofting processes in the stratosphere observed with CALIOP after major pyroCb events (Canadian fires in 2017, Australian fires in 2019–2020). We analyzed long-term CALIOP observations of smoke layers and plumes evolving in the UTLS (upper troposphere and lower stratosphere) height region over Siberia and the adjacent Arctic Ocean during the summer season of 2019. Our results indicate that self-lofting contributed to the vertical transport of smoke. We hypothesize that the formation of a near-tropopause aerosol layer, observed with CALIOP, was the result of self-lofting processes because this is in line with the simulations. Furthermore, Raman-lidar-based aerosol typing (in Leipzig and the High Arctic) clearly indicated the dominance of smoke in the UTLS aerosol layer since August 2019, most probably also the result of smoke self-lofting. [ABSTRACT FROM AUTHOR]

Wildfires are a significant source of absorbing aerosols in the atmosphere. Especially extreme fires, such as those during the 2019–2020 Australian wildfire season (Black Summer fires), can have considerable large-scale effects. In this context, the climate impact of extreme wildfires not only unfolds because of the emitted carbon dioxide, but also due to smoke aerosol released as high as the stratosphere. The overall aerosol effects depend on a variety of factors, such as the amount emitted, the injection height, and the composition of the burned material, and is therefore subject to considerable uncertainty. In the present study, we address the global impact caused by the exceptionally strong and high-reaching smoke emissions from the Australian wildfires using simulations with a global aerosol-climate model. We show that the absorption of solar radiation by the black carbon contained in the emitted smoke led to a shortwave radiative forcing of more than +5 W m−2 in the southern mid-latitudes of the lower stratosphere. Subsequent adjustment processes in the stratosphere slowed down the diabatically driven meridional circulation, thus redistributing the heating perturbation on a global scale. As a result of these stratospheric adjustments, a positive temperature perturbation developed in both hemispheres leading to additional longwave radiation emitted back to space. According to the model results, this adjustment occurred in the stratosphere within the first two months after the event. At the top of atmosphere (TOA), the net effective radiative forcing (ERF) in the southern hemisphere was initially dominated by the instantaneous positive radiative forcing of about +0.5 W m−2, for which the positive sign resulted mainly from the presence of clouds above the Southern Ocean. The longwave adjustments led to a compensation of the initially net positive TOA ERF, which is seen in the southern hemisphere, the tropics and the northern mid-latitudes. The changes in the lower stratosphere also affected the upper troposphere through a thermodynamic downward coupling mechanism in the model. Subsequently, increased temperatures were also obtained in the upper troposphere, causing a decrease in relative humidity, cirrus amount, and the ice water path. As a result, surface precipitation also decreased, which was accompanied by a weakening of the tropospheric circulation due to the given energetic constraints. In general, it appears that the radiative effects of smoke from single extreme wildfire events can lead to global impacts that affect the interplay of tropospheric and stratospheric cycles in complex ways. This emphasizes that future changes in extreme wildfires need to be included in projections of aerosol radiative forcing. [ABSTRACT FROM AUTHOR]

More than 1 Tg smoke aerosol was emitted into the atmosphere by the exceptional 2019-2020 Southeast Australian wildfires. Triggered by the extreme fire heat, several deep pyroconvective events carried the smoke directly into the stratosphere. Once there, smoke aerosol remained airborne considerably longer than in lower atmospheric layers. The thick plumes traveled eastward thereby being distributed across the high and mid-latitude Southern Hemisphere enhancing the atmospheric opacity. Due to the increased atmospheric lifetime of the smoke plume its radiative effect increased compared to smoke that remains lower altitudes. Global models describing aerosol-climate impacts show significant uncertainties regarding the emission height of aerosols from intense wildfires. Here, we demonstrate by combination of aerosol-climate modeling and lidar observations the importance of the representation of those high-altitude fire smoke layers for estimating the atmospheric energy budget. In this observation-based approach, the Australian wildfire emissions by pyroconvection are explicitly prescribed to the lower stratosphere in different scenarios. The 2019-2020 Australian fires caused a significant top-of-atmosphere hemispheric instantaneous direct radiative forcing signal that reached a magnitude comparable to the radiative forcing induced by anthropogenic absorbing aerosol. Up to +0.50 W m-2 instantaneous direct radiative forcing was modeled at top of the atmosphere, averaged for the Southern Hemisphere for January to March 2020 under all-sky conditions. While at the surface, an instantaneous solar radiative forcing of up to -0.81 W m-2 was found for clear-sky conditions, depending on the model configuration. Since extreme wildfires are expected to occur more frequently in the rapidly changing climate, our findings suggest that deep wildfire plumes must be adequately considered in climate projections in order to obtain reasonable estimates of atmospheric energy budget changes. [ABSTRACT FROM AUTHOR]