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1. Emerging Anthropogenic Influences on the Southcentral Alaska Temperature and Precipitation Extremes and Related Fires in 2019

   https://doi.org/10.3390/land10010082  

   Bhatt, Uma S.; Lader, Rick T.; Walsh, John E.; Bieniek, Peter A.; Thoman, Richard; Berman, Matthew; Borries-Strigle, Cecilia; Bulock, Kristi; Chriest, Jonathan; Hahn, Micah; et al (January 2021, Land)

   null (Ed.)

   The late-season extreme fire activity in Southcentral Alaska during 2019 was highly unusual and consequential. Firefighting operations had to be extended by a month in 2019 due to the extreme conditions of hot summer temperature and prolonged drought. The ongoing fires created poor air quality in the region containing most of Alaska’s population, leading to substantial impacts to public health. Suppression costs totaled over $70 million for Southcentral Alaska. This study’s main goals are to place the 2019 season into historical context, provide an attribution analysis, and assess future changes in wildfire risk in the region. The primary tools are meteorological observations and climate model simulations from the NCAR CESM Large Ensemble (LENS). The 2019 fire season in Southcentral Alaska included the hottest and driest June–August season over the 1979–2019 period. The LENS simulation analysis suggests that the anthropogenic signal of increased fire risk had not yet emerged in 2019 because of the CESM’s internal variability, but that the anthropogenic signal will emerge by the 2040–2080 period. The effect of warming temperatures dominates the effect of enhanced precipitation in the trend towards increased fire risk. 

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2. Arctic-associated increased fluctuations of midlatitude winter temperature in the 1.5° and 2.0° warmer world

   https://doi.org/10.1038/s41612-023-00345-y  

   Hong, Yungi; Wang, S.-Y. Simon; Son, Seok-Woo; Jeong, Jee-Hoon; Kim, Sang-Woo; Kim, Baekmin; Kim, Hyungjun; Yoon, Jin-Ho (December 2023, npj Climate and Atmospheric Science)

   Abstract In recent decades, the interior regions of Eurasia and North America have experienced several unprecedentedly cold winters despite the global surface air temperature increases. One possible explanation of these increasing extreme cold winters comes from the so-called Warm Arctic Cold Continent (WACC) pattern, reflecting the effects of the amplified Arctic warming in driving the circulation change over surrounding continents. This study analyzed reanalysis data and model experiments forced by different levels of anthropogenic forcing. It is found that WACC exists on synoptic scales in observations, model’s historical and even future runs. In the future, the analysis suggests a continued presence of WACC but with a slightly weakened cold extreme due to the overall warming. Warm Arctic events under the warmer climate will be associated with not only a colder continent in East Asia but also a warmer continent, depending on the teleconnection process that is also complicated by the warmer Arctic. Such an increasingly association suggests a reduction in potential predictability of the midlatitude winter anomalies. 

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3. Measuring "weather whiplash" events in North America: a new large-scale regime approach

   https://doi.org/10.1029/2022JD036717  

   Francis, J. (October 2022, Journal of Geological Research)

   Fu, R. (Ed.)

   The term “weather whiplash” was recently coined to describe abrupt swings in weather conditions from one extreme to another, such as from a prolonged, frigid cold spell to anomalous warmth or from drought to heavy precipitation. These events are often highly disruptive to agriculture, ecosystems, and daily activities. In this study we propose and demonstrate a novel metric to identify weather whiplash events (WWEs) and track their frequency over time. We define a WWE as a transition from one persistent large-scale circulation regime to another distinctly different pattern, as determined using an objective pattern cluster analysis called self-organizing maps (SOMs). We focus on the domain spanning North America and the eastern N. Pacific Ocean. A matrix of representative atmospheric patterns in 500-hPa geopotential height anomalies is created from daily fields. We analyze the occurrence of WWEs originating with long-duration events (defined as lasting 4 or more days) in each pattern, as well as the associated extremes in temperature and precipitation. A WWE is detected when the pattern two days following a long-duration event is substantially different, measured using internal matrix distances and thresholds. Changes in WWE frequency are assessed objectively based on reanalysis and historical climate model simulations, and in the future using climate model projections. Temporal changes in the future under RCP 8.5 forcing are more robust than those in recent decades. We find consistent increases in WWEs originating in patterns with an anomalously warm Arctic and decreases in cold-Arctic patterns. 

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4. Socio‐Environmental Determinants of Mental and Behavioral Disorders in Youth: A Machine Learning Approach

   https://doi.org/10.1029/2023GH000839  

   Wertis, Luke; Sugg, Margaret M.; Runkle, Jennifer D.; Rao, Douglas (September 2023, GeoHealth)

   Abstract Growing evidence indicates that extreme environmental conditions in summer months have an adverse impact on mental and behavioral disorders (MBD), but there is limited research looking at youth populations. The objective of this study was to apply machine learning approaches to identify key variables that predict MBD‐related emergency room (ER) visits in youths in select North Carolina cities among adolescent populations. Daily MBD‐related ER visits, which totaled over 42,000 records, were paired with daily environmental conditions, as well as sociodemographic variables to determine if certain conditions lead to higher vulnerability to exacerbated mental health disorders. Four machine learning models (i.e., generalized linear model, generalized additive model, extreme gradient boosting, random forest) were used to assess the predictive performance of multiple environmental and sociodemographic variables on MBD‐related ER visits for all cities. The best‐performing machine learning model was then applied to each of the six individual cities. As a subanalysis, a distributed lag nonlinear model was used to confirm results. In the all cities scenario, sociodemographic variables contributed the greatest to the overall MBD prediction. In the individual cities scenario, four cities had a 24‐hr difference in the maximum temperature, and two of the cities had a 24‐hr difference in the minimum temperature, maximum temperature, or Normalized Difference Vegetation Index as a leading predictor of MBD ER visits. Results can inform the use of machine learning models for predicting MBD during high‐temperature events and identify variables that affect youth MBD responses during these events. 

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5. Impacts of Projected Arctic Sea Ice Loss on Daily Weather Patterns over North America

   https://doi.org/10.1175/JCLI-D-23-0389.1  

   Gervais, Melissa; Sun, Lantao; Deser, Clara (February 2024, Journal of Climate)

   Abstract Future Arctic sea ice loss has a known impact on Arctic amplification (AA) and mean atmospheric circulation. Furthermore, several studies have shown it leads to a decreased variance in temperature over North America. In this study, we analyze results from two fully coupled Community Earth System Model (CESM) Whole Atmosphere Community Climate Model (WACCM4) simulations with sea ice nudged to either the ensemble mean of WACCM historical runs averaged over the 1980–99 period for the control (CTL) or projected RCP8.5 values over the 2080–99 period for the experiment (EXP). Dominant large-scale meteorological patterns (LSMPs) are then identified using self-organizing maps applied to winter daily 500-hPa geopotential height anomalies () over North America. We investigate how sea ice loss (EXP − CTL) impacts the frequency of these LSMPs and, through composite analysis, the sensible weather associated with them. We find differences in LSMP frequency but no change in residency time, indicating there is no stagnation of the flow with sea ice loss. Sea ice loss also acts to de-amplify and/or shift thethat characterize these LSMPs and their associated anomalies in potential temperature at 850 hPa. Impacts on precipitation anomalies are more localized and consistent with changes in anomalous sea level pressure. With this LSMP framework we provide new mechanistic insights, demonstrating a role for thermodynamic, dynamic, and diabatic processes in sea ice impacts on atmospheric variability. Understanding these processes from a synoptic perspective is critical as some LSMPs play an outsized role in producing the mean response to Arctic sea ice loss. Significance StatementThe goal of this study is to understand how future Arctic sea ice loss might impact daily weather patterns over North America. We use a global climate model to produce one set of simulations where sea ice is similar to present conditions and another that represents conditions at the end of the twenty-first century. Daily patterns in large-scale circulation at roughly 5.5 km in altitude are then identified using a machine learning method. We find that sea ice loss tends to de-amplify these patterns and their associated impacts on temperature nearer the surface. Our methodology allows us to probe more deeply into the mechanisms responsible for these changes, which provides a new way to understand how sea ice loss can impact the daily weather we experience. 

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