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Michael D. Fromm, Lanning M. Penn, John J. Cahir, and Hans A. Panofsky

Abstract

Multiple linear regression is used to relate monthly means and year-to-year changes of the monthly mean planetary albedo and infrared flux leaving the atmosphere, as measured by NOAA satellites, to certain meteorological quantities. Physical predictors are selected which are likely to influence cloudiness, such as temperature, relative humidity and wind. Such predictors can be readily obtained from numerical models.

Forty-two months of polar orbiter measurements of radiation fluxes and objective analyses from NMC's operational model were related. Continental and oceanic samples were evaluated separately. Checks on the model consisted of independent tests and comparison with estimation of radiation fluxes in which predictors were functions of latitude, longitude and time of year only. Physical predictors are consistently superior, with the single exception of oceanic albedo, where there was little difference.

In the case of the infrared flux, 93 and 84% of the variance in the monthly means is explained over land and ocean, respectively. The Planck function computed from a humidity (cloud) sensitive radiating temperature is the dominant predictor, with other humidity predictors also useful. Between 60 and 72% of the variance of the albedo is explained; results over land again are superior. Relative humidity and midtropospheric wind speed variables dominate in this case. Greater success with infrared is probably attributable to a failure to adequately estimate the effect of low-topped clouds, which impact the albedo differentially. Over land, patterns of year-to-year changes of visible and infrared fluxes (surrogates for anomalies) are predicted well and are consistent with observed changes in rainfall and cloudiness. Over the means the skill for year-to-year changes is low, possibly because low-topped clouds are more common, but also because analyses are poorer there.

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Douglas R. Allen, Michael D. Fromm, George P. Kablick III, and Gerald E. Nedoluha

Abstract

The Australian bushfires of 2019/20 produced an unusually large number of pyrocumulonimbus (pyroCb) that injected huge amounts of smoke into the lower stratosphere. The pyroCbs from 29 December 2019 to 4 January 2020 were particularly intense, producing hemispheric-wide aerosol that persisted for months. One plume from this so-called Australian New Year (ANY) event evolved into a stratospheric aerosol mass ~1000 km across and several kilometers thick. This plume initially moved eastward toward South America in January, then reversed course and moved westward passing south of Australia in February and eventually reached South Africa in early March. The peculiar motion was related to the steady rise in plume potential temperature of ~8 K day−1 in January and ~6 K day−1 in February, due to local heating by smoke absorption of solar radiation. This heating resulted in a vertical temperature anomaly dipole, a positive potential vorticity (PV) anomaly, and anticyclonic circulation. We call this dynamical component of the smoke plume “smoke with induced rotation and lofting” (SWIRL). This study uses Navy Global Environmental Model (NAVGEM) analyses to detail the SWIRL structure over 2 months. The main diagnostic tool is an anticyclone edge calculation based on the scalar Q diagnostic. This provides the framework for calculating the time evolution of various SWIRL properties: PV anomaly, streamfunction, horizontal size, vertical thickness, flow speed, and tilt. In addition, we examine the temperature anomaly dipole, the SWIRL interaction with the large-scale wind shear, and the ozone anomaly associated with lofting of air from the lower to the middle stratosphere.

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David A. Peterson, Edward J. Hyer, James R. Campbell, Jeremy E. Solbrig, and Michael D. Fromm

Abstract

The first observationally based conceptual model for intense pyrocumulonimbus (pyroCb) development is described by applying reanalyzed meteorological model output to an inventory of 26 intense pyroCb events from June to August 2013 and a control inventory of intense fire activity without pyroCb. Results are based on 88 intense wildfires observed within the western United States and Canada. While surface-based fire weather indices are a useful indicator of intense fire activity, they are not a skillful predictor of intense pyroCb. Development occurs when a layer of increased moisture content and instability is advected over a dry, deep, and unstable mixed layer, typically along the leading edge of an approaching disturbance or under the influence of a monsoonal anticyclone. Upper-tropospheric dynamics are conducive to rising motion and vertical convective development. Mid- and upper-tropospheric conditions therefore resemble those that produce traditional dry thunderstorms. The specific quantity of midlevel moisture and instability required is shown to be strongly dependent on the surface elevation of the contributing fire. Increased thermal buoyancy from large and intense wildfires can serve as a potential trigger, implying that pyroCb occasionally develop in the absence of traditional meteorological triggering mechanisms. This conceptual model suggests that meteorological conditions favorable for pyroCb are observed regularly in western North America. PyroCb and ensuing stratospheric smoke injection are therefore likely to be significant and endemic features of summer climate. Results from this study provide a major step toward improved detection, monitoring, and prediction of pyroCb, which will ultimately enable improved understanding of the role of this phenomenon in the climate system.

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David A. Peterson, Michael D. Fromm, Jeremy E. Solbrig, Edward J. Hyer, Melinda L. Surratt, and James R. Campbell

Abstract

Intense wildfires occasionally generate fire-triggered storms, known as pyrocumulonimbus (pyroCb), that can inject smoke particles and trace gases into the upper troposphere and lower stratosphere (UTLS). This study develops the first pyroCb detection algorithm using three infrared (IR) channels from the imager on board GOES-West (GOES-15). The algorithm first identifies deep convection near active fires via the longwave IR brightness temperature, distinguishing between midtropospheric and UTLS injections. During daytime, unique pyroCb microphysical properties are characterized by a medium-wave brightness temperature that is significantly larger than that in the longwave, allowing for separation of pyroCb from other deep convection. A cloud-opacity test reduces potential false detections. Application of this algorithm to 88 intense wildfires observed during the 2013 fire season in western North America resulted in successful detection of individual intense events, pyroCb embedded within traditional convection, and multiple, short-lived pulses of pyroconvective activity. Comparisons with a community inventory indicate that this algorithm captures the majority of pyroCb. The primary limitation is that pyroCb anvils can be small relative to GOES-West pixel size, especially in regions with large viewing angles. The algorithm is also sensitive to some false positives from traditional convection that either ingests smoke or exhibits extreme updraft velocities. A total of 26 pyroCb events are inventoried, including 31 individual pulses, all of which can inject smoke into the UTLS. Six of the inventoried intense pyroCb were not previously documented. Near-real-time application of this algorithm can be extended to other regions and to next-generation geostationary sensors, which offer significant advantages for pyroCb and fire detection.

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David A. Peterson, Edward J. Hyer, James R. Campbell, Michael D. Fromm, Johnathan W. Hair, Carolyn F. Butler, and Marta A. Fenn

Abstract

The 2013 Rim Fire, which burned over 104,000 ha, was one of the most severe fire events in California’s history, in terms of its rapid growth, intensity, overall size, and persistent smoke plume. At least two large pyrocumulonimbus (pyroCb) events were observed, allowing smoke particles to extend through the upper troposphere over a large portion of the Pacific Northwest. However, the most extreme fire spread was observed on days without pyroCb activity or significant regional convection. A diverse archive of ground, airborne, and satellite data collected during the Rim Fire provides a unique opportunity to examine the conditions required for both extreme spread events and pyroCb development. Results highlight the importance of upper-level and nocturnal meteorology, as well as the limitations of traditional fire weather indices. The Rim Fire dataset also allows for a detailed examination of conflicting hypotheses surrounding the primary source of moisture during pyroCb development. All pyroCbs were associated with conditions very similar to those that produce dry thunderstorms. The current suite of automated forecasting applications predict only general trends in fire behavior, and specifically do not predict 1) extreme fire spread events and 2) injection of smoke to high altitudes. While these two exceptions are related, analysis of the Rim Fire shows that they are not predicted by the same set of conditions and variables. The combination of numerical weather prediction data and satellite observations exhibits great potential for improving automated regional-scale forecasts of fire behavior and smoke emissions.

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