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Baoqiang Xiang, Lucas Harris, Thomas L. Delworth, Bin Wang, Guosen Chen, Jan-Huey Chen, Spencer K. Clark, William F. Cooke, Kun Gao, J. Jacob Huff, Liwei Jia, Nathaniel C. Johnson, Sarah B. Kapnick, Feiyu Lu, Colleen McHugh, Yongqiang Sun, Mingjing Tong, Xiaosong Yang, Fanrong Zeng, Ming Zhao, Linjiong Zhou, and Xiaqiong Zhou

Abstract

A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL SPEAR global coupled model. Based on 20-year hindcast results (2000-2019), the boreal wintertime (November-April) Madden-Julian Oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (15 days). The slow-propagating MJO detours southward when traversing the maritime continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases.

The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.

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Chris C. Funk, Pete Peterson, George J. Huffman, Martin Francis Landsfeld, Christa Peters-Lidard, Frank Davenport, Shraddhanand Shukla, Seth Peterson, Diego H. Pedreros, Alex C. Ruane, Carolyn Mutter, Will Turner, Laura Harrison, Austin Sonnier, Juliet Way-Henthorne, and Gregory J. Husak

Abstract

As human exposure to hydro-climatic extremes and the number of in situ precipitation observations declines, precipitation estimates, such as the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG), provide a critical source of information. Here, we present a new gauge-enhanced data set (CHIMES) designed to support global crop and hydrologic modeling and monitoring. CHIMES enhances the IMERG Late Run product using an updated Climate Hazards Center’s (CHC) high-resolution climatology (CHPclim) and low-latency rain-gauge observations. CHPclim differs from other products because it incorporates long-term averages of satellite precipitation, which increases CHPclim’s fidelity in data-sparse areas with complex terrain. This fidelity translates into performance increases in unbiased IMERGlate data, which we refer to as CHIME. This is augmented with gauge observations to produce CHIMES.

The CHC’s curated rain-gauge archive contains valuable contributions from many countries. There are two versions of CHIMES: preliminary and final. The final product has more copious and better-curated station data. Every pentad and month, bias-adjusted IMERG late fields are combined with gauge observations to create pentadal and monthly CHIMESprelim and CHIMESfinal. Comparisons with pentadal, high-quality gridded station data show that IMERG late performs well (r=0.75), but has some systematic biases which can be reduced. Monthly cross-validation results indicate that unbiasing increases the variance explained from 50 to 63 percent and decreases the mean absolute error from 48 to 39 mm month−1. Gauge enhancement then increases the variance explained to 75 percent, reducing the mean absolute error to 27 mm month−1.

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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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Louise Crochemore, Carolina Cantone, Ilias G. Pechlivanidis, and Christiana S. Photiadou

Abstract

In a context that fosters the evolution of hydroclimate services, it is crucial to support and train users in making the best possible forecast-based decisions. Here, we analyze how decision-making is influenced by the seasonal forecast performance based on the Call For Water serious game in which participants manage a water supply reservoir. The aim is twofold: 1) train participants in the concepts of forecast sharpness and reliability, and 2) collect participants’ decisions to investigate the levels of forecast sharpness and reliability needed to make informed decisions. In the first game round, participants are provided with forecasts of varying reliability and sharpness, while in the second round, they have the possibility to pay for systematically reliable and sharp forecasts (improved forecasts). Exploitable answers were collected from 367 participants, predominantly researchers, forecasters, and consultants in the water resources and energy sectors. Results show that improved forecasts led to better decisions, enabling participants to step out of purely conservative strategies and successfully take risks. Reliability levels of 60% are necessary for decision-making while both reliability levels above 70% and sharpness are required for informed risk-prone strategies. Improved forecasts are judged more valuable in extreme years, for instance, when hedging against water shortage risks. Additionally, participants working in the energy, air quality, and agriculture sectors, as well as traders, decision-makers, and forecasters, invested the most in forecasts. Finally, we discuss the potential of serious games to foster capacity development in hydroclimate services and provide recommendations for forecast-based service development.

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