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Izuru Takayabu, Roy Rasmussen, Eiichi Nakakita, Andreas Prein, Hiroaki Kawase, ShunIchi Watanabe, Sachiho A. Adachi, Tetsuya Takemi, Kosei Yamaguchi, Yukari Osakada, and Ying-Hsin Wu
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John Kochendorfer, Michael Earle, Roy Rasmussen, Craig Smith, Daqing Yang, Samuel Morin, Eva Mekis, Samuel Buisan, Yves-Alain Roulet, Scott Landolt, Mareile Wolff, Jeffery Hoover, Julie M. Thériault, Gyuwon Lee, Bruce Baker, Rodica Nitu, Luca Lanza, Matteo Colli, and Tilden Meyers

Abstract

Accurate snowfall measurements are necessary for meteorology, hydrology, and climate research. Typical uses include creating and calibrating gridded precipitation products, the verification of model simulations, driving hydrologic models, input into aircraft deicing processes, and estimating streamflow runoff in the spring. These applications are significantly impacted by errors in solid precipitation measurements. The recent WMO Solid Precipitation Intercomparison Experiment (SPICE) attempted to characterize and reduce some of the measurement uncertainties through an international effort involving 15 countries utilizing over 20 types and models of precipitation gauges from various manufacturers. Key results from WMO-SPICE are presented herein. Recent work and future research opportunities that build on the results of WMO-SPICE are also highlighted.

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Jakob Zscheischler and Flavio Lehner

Abstract

Extreme event attribution answers the question whether and by how much anthropogenic climate change has contributed to the occurrence or magnitude of an extreme weather event. It is also used to link extreme event impacts to climate change. Impacts, however, are often related to multiple compounding climate drivers. Because extreme event attribution typically focuses on univariate assessments, these assessments might only provide a partial answer to the question of anthropogenic influence to a high-impact event. We present a theoretical extension to classical extreme event attribution for certain types of compound events. Based on synthetic data we illustrate how the bivariate fraction of attributable risk (FAR) differs from the univariate FAR depending on the extremeness of the event as well as the trends in and dependence between the contributing variables. Overall, the bivariate FAR is similar in magnitude or smaller than the univariate FAR if the trend in the second variable is comparably weak and the dependence between both variables is moderate or high, a typical situation for temporally co-occurring heatwaves and droughts. If both variables have similarly large trends or the dependence between both variables is weak, bivariate FARs are larger and are likely to provide a more adequate quantification of the anthropogenic influence. Using multiple climate model large ensembles, we apply the framework to two case studies, a recent sequence of hot and dry years in the Western Cape region of South Africa and two spatially co-occurring droughts in crop-producing regions in South Africa and Lesotho.

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Sean R. Scott, Jason P. Dunion, Mark L. Olson, and David A. Gay

Abstract

Atmospheric dust is an important mass transfer and nutrient supply process in Earth surface ecosystems. For decades, Saharan Dust has been hypothesized as a supplier of nutrients to the Amazon Rain Forest and Eastern North America. However, isotope studies aimed at detecting Saharan dust in the American sedimentary record have been ambiguous. A large Saharan dust storm emerged off the coast of Africa in June 2020 and extended into southeastern United States. This storm provided a means to evaluate the influence of Saharan dust in North America confirmed by independent satellite and ground observations. Precipitation samples from 17 sites within the National Atmospheric Deposition Program (NADP) were obtained from throughout the southeastern United States prior to, during, and after the arrival of Saharan dust. Precipitation samples were measured for their lead (Pb) isotopic composition, total Pb content, and 210Pb activity using multi-collector inductively coupled plasma mass spectrometry. We measured a significant isotopic shift (approximately 0.7 % in the 208Pb/206Pb relative to the 207Pb/206Pb) in precipitation that peaked in late June 2020 when the dust blanketed the southeastern US. However, the magnitude and short time period of the isotopic shift would make it difficult to detect in sedimentary records.

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Forrest M. Mims III

Abstract

A 30-year time series (4 Feb 1990 to 4 Feb 2020) of aerosol optical depth of the atmosphere (AOD), total precipitable water (TPW) and total column ozone has been conducted in Central Texas using simple, highly stable instruments. All three parameters in this ongoing measurement series exhibited robust annual cycles. They also responded to many atmospheric events, including the historic volcanic eruption of Mount Pinatubo (1991), a record El Niño (1998), an unprecedented biomass smoke event (1998) and the La Niña that caused the driest drought in recorded Texas history (2011). Reduced air pollution caused mean AOD to decline from 0.175 to 0.14. The AOD trend measured for 30 years by an LED sun photometer, the first of its kind, parallels the trend from 20 years of measurements by a modified Microtops II. While TPW responded to El Niño-Southern Oscillation conditions, TPW exhibited no trend over the 30 years. The TPW data compare favorably with 4.5 years of simultaneous measurements by a nearby NOAA GPS (r2 = 0.78). The 30 years of ozone measurements compare favorably with those from a series of NASA ozone satellites (r2 = 0.78). In 2016, 194 comparisons of Microtops II and world standard ozone instrument Dobson 83 at the Mauna Loa Observatory agreed within 1.9% (r2 = 0.81). The paper concludes by observing that students and citizen scientists can collect scientifically useful atmospheric data with simple sun photometers that use one or more LEDs as spectrally selective photodiodes.

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William E. Foust

Abstract

Weather, climate, and other Earth system models are growing in complexity as computing resources and technologies continue to evolve with time. Thus, models are and will remain a vital tool for scientific research. Exposure and education on the workings of such models can generate interest towards atmospheric science, and it can increase scientific literacy amongst the general public. Additionally, studies have suggested that early exposure to these models can affect the career trajectory of students. However, gaining exposure and experience remains difficult outside of internships, research settings, and other professional endeavors. Some of these barriers can include hardware and computing costs, curriculum structure, and access to instructors. As a means of addressing these barriers, the goal of this work is to utilize low-cost hardware and abstract away some of the complexities of running a numerical weather model without sacrificing fidelity. The approach is to create a graphical user interface (GUI) where users can quickly configure the model, run it, and analyze the output without knowledge of model configuration, system architecture, or navigation via a command line interface. The Pi-WRF application is packaged such that users can download and run the model within a matter of minutes. The application is designed to promote informal learning through hands-on experience. It is targeted towards lower secondary level students, but it can scale across grade levels, and it can be adapted for general audiences.

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Yunxia Zhao, Hamid Norouzi, Marzi Azarderakhsh, and Amir AghaKouchak

ABSTRACT

Most previous studies of extreme temperatures have primarily focused on atmospheric temperatures. Using 18 years of the latest version of the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data, we globally investigate the spatial patterns of hot and cold extremes as well as diurnal temperature range (DTR). We show that the world’s highest LST of 80.8°C, observed in the Lut Desert in Iran and the Sonoran Desert in Mexico, is over 10°C above the previous global record of 70.7°C observed in 2005. The coldest place on Earth is Antarctica with the record low temperature of −110.9°C. The world’s maximum DTR of 81.8°C is observed in a desert environment in China. We see strong latitudinal patterns in hot and cold extremes as well as DTR. Biomes worldwide are faced with different levels of temperature extremes and DTR: we observe the highest zonal average maximum LST of 61.1° ± 5.3°C in the deserts and xeric shrublands; the lowest zonal average minimum LST of −66.6° ± 14.8°C in the tundra; and the highest zonal average maximum DTR of 43.5° ± 9.9°C in the montane grasslands and shrublands. This global exploration of extreme LST and DTR across different biomes sheds light on the type of extremes different ecosystems are faced with.

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Jonathan D. W. Kahl, Brandon R. Selbig, and Austin R. Harris

ABSTRACT

Wind gusts are common to everyday life and affect a wide range of interests including wind energy, structural design, forestry, and fire danger. Strong gusts are a common environmental hazard that can damage buildings, bridges, aircraft, and trains, and interrupt electric power distribution, air traffic, waterways transport, and port operations. Despite representing the component of wind most likely to be associated with serious and costly hazards, reliable forecasts of peak wind gusts have remained elusive. A project at the University of Wisconsin–Milwaukee is addressing the need for improved peak gust forecasts with the development of the meteorologically stratified gust factor (MSGF) model. The MSGF model combines gust factors (the ratio of peak wind gust to average wind speed) with wind speed and direction forecasts to predict hourly peak wind gusts. The MSGF method thus represents a simple, viable option for the operational prediction of peak wind gusts. Here we describe the results of a project designed to provide the site-specific gust factors necessary for operational use of the MSGF model at a large number of locations across the United States. Gust web diagrams depicting the wind speed– and wind direction–stratified gust factors, as well as peak gust climatologies, are presented for all sites analyzed.

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I.-I. Lin, Robert F. Rogers, Hsiao-Ching Huang, Yi-Chun Liao, Derrick Herndon, Jin-Yi Yu, Ya-Ting Chang, Jun A. Zhang, Christina M. Patricola, Iam-Fei Pun, and Chun-Chi Lien

Abstract

Devastating Japan in October 2019, Supertyphoon (STY) Hagibis was an important typhoon in the history of the Pacific. A striking feature of Hagibis was its explosive rapid intensification (RI). In 24 h, Hagibis intensified by 100 knots (kt; 1 kt ≈ 0.51 m s−1), making it one of the fastest-intensifying typhoons ever observed. After RI, Hagibis’s intensification stalled. Using the current typhoon intensity record holder, i.e., STY Haiyan (2013), as a benchmark, this work explores the intensity evolution differences of these two high-impact STYs. We found that the extremely high prestorm sea surface temperature reaching 30.5°C, deep/warm prestorm ocean heat content reaching 160 kJ cm−2, fast forward storm motion of ∼8 m s−1, small during-storm ocean cooling effect of ∼0.5°C, significant thunderstorm activity at its center, and rapid eyewall contraction were all important contributors to Hagibis’s impressive intensification. There was 36% more air–sea flux for Hagibis’s RI than for Haiyan’s. After its spectacular RI, Hagibis’s intensification stopped, despite favorable environments. Haiyan, by contrast, continued to intensify, reaching its record-breaking intensity of 170 kt. A key finding here is the multiple pathways that storm size affected the intensity evolution for both typhoons. After RI, Hagibis experienced a major size expansion, becoming the largest typhoon on record in the Pacific. This size enlargement, combined with a reduction in storm translational speed, induced stronger ocean cooling that reduced ocean flux and hindered intensification. The large storm size also contributed to slower eyewall replacement cycles (ERCs), which prolonged the negative impact of the ERC on intensification.

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Noah Dormady, Anthony Fasano, Alfredo Roa-Henriquez, Drew Flanagan, William Welch, and Dylan Wood

Abstract

This study reports on two experiments to investigate the informational determinants of hurricane evacuation decisions (temporal and spatial). Whereas most observational and experimental studies in this domain address the public’s response to forecast information, this study addresses emergency management decisions. Using a subject sample of emergency managers and other public safety leaders, contrasted with a more typical university subject pool, this study presents an experimental design that overcomes the counterfactual problem present in all prior published experiments, by relying on an actual storm (Hurricane Rita) with a known outcome. Several methodological advancements are presented, including the use of an established numeracy instrument, integration of advanced hydrodynamic forecasts, and use of a loss aversion frame to improve generalizability. Results indicate that the availability of additional forecast information (e.g., wind speed, forecast tracks) significantly increases the probability and improves the timing of early voluntary evacuation. However, we observe that more numerate subjects are less likely to avoid relying upon forecast information that is characterized by probability (e.g., the uncertainty in the forecast track, sometimes referred to as the “cone of uncertainty”). Consequently, more numerate emergency managers are almost twice as likely as less numerate ones to provide additional evacuation time to their coastal communities, and they do so by longer than a typical workday (8.8 hours). Results also indicate that subjects knowingly over-evacuate large populations when making spatial mandatory evacuation orders. However, results indicate that numeracy mitigates this effect by more than half in terms of the population subject to mandatory evacuation.

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