The 2016 southeastern US drought: an extreme departure from centennial wetting and cooling

Monitoring the impact of climate extremes and COVID-19 on statewise sentiment alterations in water pollution complaints

The COVID-19 pandemic and associated prevention policies can directly or indirectly alter the sentiment of individuals while registering water pollution complaints, but observational evidence remains limited. Here, we conducted a sentiment analysis on over 10,000 water pollution complaints from residents in Alabama, USA (2012-2021) to better understand how and to what extent COVID-19 has altered emotion (polarity score-based) and attitude (subjectivity) of water pollution complaints. We found that the 2017 state-wise drought significantly increased the percentage of negative water pollution complaints by +35%, with no significant alternation in attitude before the COVID-19 pandemic. Since COVID-19, the percentage of negative and subjective water pollution complaints significantly decreased and increased by -30 and +20%, respectively, and these sentiment alternations were maintained by 2021. This study provides a new direction for environmental governance and management, requiring a timely response to changes in the public's emotions and attitudes during the next climate extremes and pandemics.

Is there a dry season in the Southeast US?

Fill et al. (Global Change Biology, 25, 3562-3569, 2019) reported significant increases in dry season length over the past 120 years in the Southeast US, suggesting increased wildfire risk in a region associated with a frequent fire regime. We identified two flaws that call into question the findings and their relevance to regional wildfire risk. First, with the exception of Florida, there is little evidence for a climatologically meaningful 'dry season' in the Southeast because most areas experience relatively evenly distributed monthly precipitation. Second, the sampling method used to derive Cumulative Rainfall Anomalies does not appear to actually reflect a bootstrap sample as described.

First Report of Armillaria Root Disease Pathogen, Armillaria gallica, on Rhododendron and Quercus rubra in Georgia, USA

Armillaria root and butt diseases, which are a global issue, can be influenced by changing environmental conditions. Armillaria gallica is a well-known pathogen of diverse trees worldwide (Brazee and Wick 2009). Besides A. gallica causing root rot of Hemerocallis sp. and Cornus sp. in South Carolina (Schnabel et al. 2005), little is reported on the distribution and host range of A. gallica in the southeastern USA. In July 2017, three Armillaria isolates were obtained from two naturally occurring hosts in Georgia, USA and cultured on malt extract medium (3% malt extract, 3% dextrose, 1% peptone, and 1.5% agar). One isolate (GA3) was obtained in Unicoi State Park near Helen, Georgia (Lat. 34.712275, Long. -83.727765, elev. 498 m) from the basal portion of Rhododendron sp. with extensive root/butt decay, but no crown symptoms were evident (Supplementary Figure 1). GA4 and GA5 (Lat. 33.902433, Long. -83.382453, elev. 215 m) were isolated from wind-felled Quercus rubra (red oak) with root disease at the State Botanical Gardens in Athens, Georgia. GA4 was associated with a large root ball (ca. 4-m diameter) (Supplementary Figure 2), and GA5 was obtained from a mature tree with infected roots, with characteristic spongy rot of Armillaria root disease. Crown symptoms could not be evaluated because the crowns had been removed before the collections. Several other oaks with Armillaria root disease were noted throughout the State Botanical Gardens. Pairing tests reduced these three isolates (whiteish mycelia with a dark, brownish crust and rhizomorphs), to two genets with GA4 = GA5. Both genets (GA3 and GA4) were identified as A. gallica using translation elongation factor 1α (tef1) sequences (Genbank Nos. MT761697 and MT761698, respectively) that showed ≥ 97% identity (≥ 98% coverage) with A. gallica sequences (KF156772, KF156775). Also, nine replications of somatic pairing tests showed 33 - 67% compatibility with A. gallica (occurs in southeastern USA), compared with 0 - 22% for A. mexicana, A. mellea (occurs in southeastern USA), A. solidipes, and Desarmillaria tabescens (occurs in southeastern USA). To our knowledge, this note represents the first report of A. gallica on Rhododendron and Q. rubra in Georgia, USA, which has experienced severe drought in recent decades (e.g., Park Williams et al. 2017) that could predispose trees to Armillaria infection (e.g., Wargo 1996). Quercus rubra was previously reported as a host of A. gallica in Arkansas (Kelley et al. 2009) and Massachusetts (Brazee and Wick 2009), USA. In Missouri, USA, A. gallica has been reported as a weak pathogen with potential biological control against A. mellea (Bruhn et al. 2000). Other reports from several regions on various hosts suggest pathogenicity of A. gallica is associated with changing climate (Nelson et al. 2013, Kim et al. 2017, Kubiak et al. 2017). Wide genetic variation and/or cryptic speciation within A. gallica may account for differences in ecological behavior (Klopfenstein et al. 2017), but this is difficult to evaluate because Armillaria pathogenicity tests cannot be used on most forest tree seedlings. This study suggests that A. gallica is more widespread than previously known and its adverse impacts on woody plants may intensify over time, depending on the environmental conditions. Further studies are needed to determine environmental influences on A. gallica, the full distribution of A. gallica, and its effects in forests of the southeastern USA.

Biomass combustion produces ice-active minerals in biomass-burning aerosol and bottom ash

Ice nucleation and the resulting cloud glaciation are significant atmospheric processes that affect the evolution of clouds and their properties including radiative forcing and precipitation, yet the sources and properties of atmospheric ice nucleants are poorly constrained. Heterogeneous ice nucleation caused by ice-nucleating particles (INPs) enables cloud glaciation at temperatures above the homogeneous freezing regime that starts near -35 °C. Biomass burning is a significant global source of atmospheric particles and a highly variable and poorly understood source of INPs. The nature of these INPs and how they relate to the fuel composition and its combustion are critical gaps in our understanding of the effects of biomass burning on the environment and climate. Here we show that the combustion process transforms inorganic elements naturally present in the biomass (not soil or dust) to form potentially ice-active minerals in both the bottom ash and emitted aerosol particles. These particles possess ice-nucleation activities high enough to be relevant to mixed-phase clouds and are active over a wide temperature range, nucleating ice at up to -13 °C. Certain inorganic elements can thus serve as indicators to predict the production of ice nucleants from the fuel. Combustion-derived minerals are an important but understudied source of INPs in natural biomass-burning aerosol emissions in addition to lofted primary soil and dust particles. These discoveries and insights should advance the realistic incorporation of biomass-burning INPs into atmospheric cloud and climate models. These mineral components produced in biomass-burning aerosol should also be studied in relation to other atmospheric chemistry processes, such as facilitating multiphase chemical reactions and nutrient availability.

In the Line of Fire: Consequences of Human-Ignited Wildfires to Homes in the U.S. (1992–2015)

With climate-driven increases in wildfires in the western U.S., it is imperative to understand how the risk to homes is also changing nationwide. Here, we quantify the number of homes threatened, suppression costs, and ignition sources for 1.6 million wildfires in the United States (U.S.; 1992–2015). Human-caused wildfires accounted for 97% of the residential homes threatened (within 1 km of a wildfire) and nearly a third of suppression costs. This study illustrates how the wildland-urban interface (WUI), which accounts for only a small portion of U.S. land area (10%), acts as a major source of fires, almost exclusively human-started. Cumulatively (1992–2015), just over one million homes were within human-caused wildfire perimeters in the WUI, where communities are built within flammable vegetation. An additional 58.8 million homes were within one kilometer across the 24-year record. On an annual basis in the WUI (1999–2014), an average of 2.5 million homes (2.2–2.8 million, 95% confidence interval) were threatened by human-started wildfires (within the perimeter and up to 1-km away). The number of residential homes in the WUI grew by 32 million from 1990–2015. The convergence of warmer, drier conditions and greater development into flammable landscapes is leaving many communities vulnerable to human-caused wildfires. These areas are a high priority for policy and management efforts that aim to reduce human ignitions and promote resilience to future fires, particularly as the number of residential homes in the WUI grew across this record and are expected to continue to grow in coming years.

TROPOMI NO2 Tropospheric Column Data: Regridding to 1 km Grid-Resolution and Assessment of their Consistency with In Situ Surface Observations

This work presents a regridding procedure applied to the nitrogen dioxide (NO2) tropospheric column data, derived from the Copernicus Sentinel 5 Precursor Tropospheric Monitoring Instrument (S5P/TROPOMI). The regridding has been performed to provide a better comparison with punctual surface observations. It will be demonstrated that TROPOMI NO2 tropospheric column data show improved consistency with in situ surface measurements once the satellite retrievals are scaled to 1 km spatial sampling. A geostatistical technique, i.e., the ordinary kriging, has been applied to improve the spatial distribution of Level 2 TROPOMI NO2 data, which is originally sparse and uneven because of gaps introduced by clouds, to a final spatial, regular, sampling of 1 km × 1 km. The analysis has been performed for two study areas, one in the North and the other in the South of Italy, and for May 2018-April 2020, which also covers the period January 2020-April 2020 of COVID-19 diffusion over the Po Valley. The higher spatial sampling NO2 dataset indicated as Level 3 data, allowed us to explore spatial and seasonal data variability, obtaining better information on NO2 sources. In this respect, it will be shown that NO2 concentrations in March 2020 have likely decreased as a consequence of the lockdown because of COVID-19, although the far warmest winter season ever recorded over Europe in 2020 has favored a general NO2 decrease in comparison to the 2019 winter. Moreover, the comparison between NO2 concentrations related to weekdays and weekend days allowed us to show the strong correlation of NO2 emissions with traffic and industrial activities. To assess the quality and capability of TROPOMI NO2 observations, we have studied their relationship and correlation with in situ NO2 concentrations measured at air quality monitoring stations. We have found that the correlation increases when we pass from Level 2 to Level 3 data, showing the importance of regridding the satellite data. In particular, correlation coefficients of Level 3 data, which range between 0.50–0.90 have been found with higher correlation applying to urban, polluted locations and/or cities.

Impact of wildfire on particulate matter in the southeastern United States in November 2016

In November 2016, a large area of wildfire occurred in the southeastern United States, concomitant with the occurrence of severe drought during the same period. Whereas the previous studies on biomass burning over this region mainly focused on the prescribed fire, this study investigated the impact of wildfire using the two-way-coupled Weather Research and Forecasting model and Community Multiscale Air Quality model. Two episodic wildfire burning events (November 6 to 9 and November 13 to 16, 2016) were selected, and the mean contribution to fine particulate matter (PM_2.5) in the southeastern United States from wildfires reached 9.6 to 42.5 μg m^-3 and 10.9 to 26.1 μg m^-3, with mean relative contributions of 41% and 49%, respectively, during these two events. The effect of wildfire propagates along the path of the smoke plume, which is determined by the wind speed and direction. For instance, during the first event, the dominant low-altitude wind vector displayed an anticyclonic-type flow with low wind speed, resulting in relatively localized influence and high intensity. In contrast, during the second event, relatively fast eastward wind, particularly over the latter part of the event, strengthened the diffusion and affected larger areas in comparison with the first event. Moreover, differently from the previous studies, this study took a further step to reveal the mechanism of the aerosol direct effect on the deterioration of air quality during wildfire, mainly through the modulation of reduction in surface downward shortwave radiation, planetary boundary layer height and wind speed, subsequently, facilitating pollution accumulation. Quantification analysis showed an average of 10% to 14% extra enhancement of PM_2.5 during the November 6 to 8 episode. Considering that more frequent drought is projected to occur in the southeastern United States, wildfire may play an even more important role in modulating the air quality in this region.

Past Variance and Future Projections of the Environmental Conditions Driving Western U.S. Summertime Wildfire Burn Area

Increases in vapor pressure deficit (VPD) have been hypothesized as the primary driver of future fire changes. The Coupled Model Intercomparison Project Phase 5 (CMIP5) models agree that western U.S. surface temperatures and associated dryness of air as defined by the VPD will increase in the 21st century for Representative Concentration Pathways (RCPs) 4.5 and 8.5. However, we find that averaged over seasonal and regional scales, other environmental variables demonstrated to be relevant to flammability, moisture abundances, and aridity-such as precipitation, evaporation, relative humidity, root zone soil moisture, and wind speed-can be used to explain observed variance in wildfire burn area as well or better than VPD. However, the magnitude and sign of the change of these variables in the 21st century are less certain than the predicted changes in VPD. Our work demonstrates that when objectively selecting environmental variables to maximize predictive skill of linear regressions (minimize square error on unseen data) VPD is not always selected and when it is not, the magnitude of future increases in burn area becomes less certain. Hence, this work shows that future burn area predictions are sensitive to what environmental predictors are chosen to drive burn area.

The Impacts of Prescribed Fire on PM2.5 Air Quality and Human Health: Application to Asthma-Related Emergency Room Visits in Georgia, USA

Short-term exposure to fire smoke, especially particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5), is associated with adverse health effects. In order to quantify the impact of prescribed burning on human health, a general health impact function was used with exposure fields of PM2.5 from prescribed burning in Georgia, USA, during the burn seasons of 2015 to 2018, generated using a data fusion method. A method was developed to identify the days and areas when and where the prescribed burning had a major impact on local air quality to explore the relationship between prescribed burning and acute health effects. The results showed strong spatial and temporal variations in prescribed burning impacts. April 2018 exhibited a larger estimated daily health impact with more burned areas compared to Aprils in previous years, likely due to an extended burn season resulting from the need to burn more areas in Georgia. There were an estimated 145 emergency room (ER) visits in Georgia for asthma due to prescribed burning impacts in 2015 during the burn season, and this number increased by about 18% in 2018. Although southwestern, central, and east-central Georgia had large fire impacts on air quality, the absolute number of estimated ER asthma visits resulting from burn impacts was small in these regions compared to metropolitan areas where the population density is higher. Metro-Atlanta had the largest estimated prescribed burn-related asthma ER visits in Georgia, with an average of about 66 during the reporting years.

Seasonal temperatures and hydrological conditions improve the prediction of West Nile virus infection rates in Culex mosquitoes and human case counts in New York and Connecticut

West Nile virus (WNV; Flaviviridae: Flavivirus) is a widely distributed arthropod-borne virus that has negatively affected human health and animal populations. WNV infection rates of mosquitoes and human cases have been shown to be correlated with climate. However, previous studies have been conducted at a variety of spatial and temporal scales, and the scale-dependence of these relationships has been understudied. We tested the hypothesis that climate variables are important to understand these relationships at all spatial scales. We analyzed the influence of climate on WNV infection rate of mosquitoes and number of human cases in New York and Connecticut using Random Forests, a machine learning technique. During model development, 66 climate-related variables based on temperature, precipitation and soil moisture were tested for predictive skill. We also included 20-21 non-climatic variables to account for known environmental effects (e.g., land cover and human population), surveillance related information (e.g., relative mosquito abundance), and to assess the potential explanatory power of other relevant factors (e.g., presence of wastewater treatment plants). Random forest models were used to identify the most important climate variables for explaining spatial-temporal variation in mosquito infection rates (abbreviated as MLE). The results of the cross-validation support our hypothesis that climate variables improve the predictive skill for MLE at county- and trap-scales and for human cases at the county-scale. Of the climate-related variables selected, mean minimum temperature from July-September was selected in all analyses, and soil moisture was selected for the mosquito county-scale analysis. Models demonstrated predictive skill, but still over- and under-estimated WNV MLE and numbers of human cases. Models at fine spatial scales had lower absolute errors but had greater errors relative to the mean infection rates.

Investigating the causes of increased 20th-century fall precipitation over the southeastern United States

Much of the eastern United States (US) experienced increased precipitation over the 20^th century. Characterizing these trends and their causes is critical for assessing future hydroclimate risks. Here, US precipitation trends are analyzed during 1895-2016, revealing that fall precipitation in the southeastern region north of the Gulf of Mexico (SE-Gulf) increased by nearly 40%, primarily increasing after the mid-1900s. As fall is the climatological dry season in the SE-Gulf and precipitation in other seasons changed insignificantly, the seasonal precipitation cycle diminished substantially. The increase in SE-Gulf fall precipitation was caused by increased southerly moisture transport from the Gulf of Mexico, which was almost entirely driven by stronger winds associated with enhanced anticyclonic circulation west of the North Atlantic Subtropical High (NASH) and not by increases in specific humidity. Atmospheric models forced by observed SSTs and fully-coupled models forced by historical anthropogenic forcing do not robustly simulate 20^th-century fall wetting in the SE-Gulf. SST-forced atmospheric models do simulate an intensified anticyclonic low-level circulation around the NASH, but the modeled intensification occurred farther west than observed. CMIP5 analyses suggest an increased likelihood of positive SE-Gulf fall precipitation trends given historical and future GHG forcing. Nevertheless, individual model simulations (both SST-forced and fully-coupled) only very rarely produce the observed magnitude of the SE-Gulf fall precipitation trend. Further research into model representation of the western ridge of the fall NASH is needed, which will help us better predict whether 20^th-century increases in SE-Gulf fall precipitation will persist into the future.

Forcing Single-Column Models Using High-Resolution Model Simulations

To use single-column models (SCMs) as a research tool for parameterization development and process studies, the SCM must be supplied with realistic initial profiles, forcing fields, and boundary conditions. We propose a new technique for deriving these required profiles, motivated by the increase in number and scale of high-resolution convection-permitting simulations. We suggest that these high-resolution simulations be coarse grained to the required resolution of an SCM, and thereby be used as a proxy for the true atmosphere. This paper describes the implementation of such a technique. We test the proposed methodology using high-resolution data from the UK Met Office's Unified Model, with a resolution of 4 km, covering a large tropical domain. These data are coarse grained and used to drive the European Centre for Medium-Range Weather Forecast's Integrated Forecasting System (IFS) SCM. The proposed method is evaluated by deriving IFS SCM forcing profiles from a consistent T639 IFS simulation. The SCM simulations track the global model, indicating a consistency between the estimated forcing fields and the true dynamical forcing in the global model. We demonstrate the benefits of selecting SCM forcing profiles from across a large domain, namely, robust statistics, and the ability to test the SCM over a range of boundary conditions. We also compare driving the SCM with the coarse-grained data set to driving it using the European Centre for Medium-Range Weather Forecast operational analysis. We conclude by highlighting the importance of understanding biases in the high-resolution data set and suggest that our approach be used in combination with observationally derived forcing data sets.

Human-Related Ignitions Increase the Number of Large Wildfires across U.S. Ecoregions

Large fires account for the majority of burned area and are an important focus of fire management. However, ‘large’ is typically defined by a fire size threshold, minimizing the importance of proportionally large fires in less fire-prone ecoregions. Here, we defined ‘large fires’ as the largest 10% of wildfires by ecoregion (n = 175,222 wildfires from 1992 to 2015) across the United States (U.S.). Across ecoregions, we compared fire size, seasonality, and environmental conditions (e.g., wind speed, fuel moisture, biomass, vegetation type) of large human- and lighting-started fires that required a suppression response. Mean large fire size varied by three orders of magnitude: from 1 to 10 ha in the Northeast vs. >1000 ha in the West. Humans ignited four times as many large fires as lightning, and were the dominant source of large fires in the eastern and western U.S. (starting 92% and 65% of fires, respectively). Humans started 80,896 large fires in seasons when lightning-ignited fires were rare. Large human-started fires occurred in locations and months of significantly higher fuel moisture and wind speed than large lightning-started fires. National-scale fire policy should consider risks to ecosystems and economies by these proportionally large fires and include human drivers in large fire risk assessment.