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Ke Xu
and
Riyu Lu

Abstract

The break events of the western North Pacific summer monsoon vary significantly in duration, ranging from a few days to more than two weeks. In this study, we classify the monsoon break events into short-lived (≤8 days) and long-lived (>8 days) events, which account for 78% and 22% of the total events during 1979–2020, respectively. The results show that convection suppression is stronger and broader for long-lived events than for short-lived events. In addition, the temporal distributions of the two break categories are distinct: short-lived events present a roughly even distribution from late July to late September, while long-lived events are highly concentrated, with a striking frequency peak around early September.

The mechanisms responsible for break events are investigated. Results indicate that both break categories are co-contributed by 10–25-day and 30–60-day oscillations. Short-lived events result from a phase lock of the two oscillations, which explain 54% and 35% of the convection suppression, respectively. By contrast, long-lived events are initiated by both oscillations but maintained only by 30–60-day oscillations. In addition, 30–60-day oscillations reach the peak intensity after the monsoon onset due to seasonal background changes, which is critical for forming the frequency peak of long-lived events around early September. Furthermore, it is found that long-lived events prefer to occur in the developing phase of positive SST anomalies in the tropical central Pacific, when 30–60-day oscillations are abnormally enhanced over the western North Pacific.

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Cory L. Armstrong
and
Anna Grace Usery

Abstract

When a tornado hits, there is little time to think through mental checklists for needed items. This study attempted to understand what information sources those in the path of tornados utilized for preparation and how those sources influence people to act. Results from the study indicate that television and radio are the top two information sources, and that some visual graphics—gauged via heat maps to understand higher levels of severe weather preparation—were reported as useful. Contrary to meteorological intentions, results showed that participants were less likely to prepare for impending weather when radar displayed tornado locations and intensity. In addition, those who identified as having more interest in weather-related information in the study were significantly more likely to prepare, along with those who fear future tornadoes. Each variable explored is underpinned by the theory of planned behavior and the risk information seeking and processing (RISP) model to better understand behavioral intentions and actions. This study offers two new concepts of general weather that have not previously been explored: interest and general versus specific storm preparation.

Significance Statement

The purpose of this work is to learn more about how individuals gather information and obtain weather warnings, primarily during tornado events. In particular, the study seeks to understand how individuals view and interpret visual graphics with information about the location and details about the event. Further, results suggest some differences between those who generally prepare for storm season versus those who only prepare for a specific event. Researchers may also be interested to know how weather enthusiasts may differ in their preparatory activities in comparison with nonweather enthusiasts. All of this information will help meteorologists and media professionals to better target their messages during severe weather.

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Zoey Rosen

Abstract

The narratives of emerging adults, such as university students, can reveal aspects of their professional and academic identities that explain their career paths. While narrative has been studied as a tool in the meteorological classroom, narrative has not been used to study why students choose to become meteorologists. This study aims to identify the narrative features about what draws students to pursue meteorology as a career, and reflect upon how the telling of these narratives can help career counselors and other stakeholders, like universities, to understand this discipline of students. This study is a qualitative textual analysis of N = 34 video clips of meteorology students from around the United States submitted for the 2020 AMS Student Conference welcome video, #MyFieldMyStory campaign. The findings show that formative experiences like early childhood memories, mediated experiences with the weather, and family interactions were major life themes in the students’ stories. Other reasons students chose this career path were concerns over local climatic effects, a desire to control their course of study, curiosity stemming from internships and research opportunities, confidence from their personal math/science propensity in school, and a commitment to do work that can mitigate the effects of severe weather or inform people of impending threats. The students’ narratives also showed optimism around future jobs and graduate school, as well as an exploration of their identity through finding their passion in this career path. This study is an interesting initial delve into narratively analyzing stories from emerging meteorologists.

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Hong-Li Ren
,
Yuntao Wei
, and
Shuo Zhao

Abstract

The real-time multivariate Madden-Julian Oscillation (MJO) (RMM) index has now been widely applied as a standard in operational sub-seasonal prediction and monitoring. Its calculation procedures involve the extraction of major intraseasonal variability (ISV) by subtracting the prior 120-day mean. However, this study uncovers that such a real-time strategy artificially creates unwanted low-frequency variability (LFVartificial) that might cause non-negligible influences on the RMM amplitude and phase. Compared to the real LFV, the LFVartificial explains more (~70% in boreal summer) of the residual LFV (LFVresidual) in the RMM index. It occupies 33% of all days that the LFVresidual explains more than one-half of total RMM amplitude, 19% that the LFV contribution exceeds ISV, and 10% that the LFVartificial-associated RMM amplitude surpasses 0.8. The RMM-defined “MJO” is obscured by the LFVresidual in such a way that the eastward-propagating mode is stronger and bigger with a slower phase speed, as compared with the “true” MJO derived from the 20–100-day filtered data. The interference effects of LFVresidual on the MJO might be particularly strong when the background state is changing rapidly with time. However, these issues can be well avoided when one chooses to remove the centered 120-day mean, as evidenced by the largely reduced three percentages (17%, 8%, and 1%) mentioned above in the so-derived index. These results give us a reminder that more attention should be paid to monitoring or predicting an MJO using the RMM index in a rapidly changing low-frequency background or the boreal summer.

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Martin Janssens
,
Jordi Vilà-Guerau de Arellano
,
Chiel C. van Heerwaarden
,
Stephan R. de Roode
,
A. Pier Siebesma
, and
Franziska Glassmeier

Abstract

Condensation in cumulus clouds plays a key role in structuring the mean, non-precipitating trade-wind boundary layer. Here, we summarise how this role also explains the spontaneous growth of mesoscale (> О(10) km) fluctuations in clouds and moisture around the mean state in a minimal-physics, large-eddy simulation of the undisturbed period during BOMEX on a large (О(100) km) domain. Small, spatial anomalies in latent heating in cumulus clouds, which form on top of small moisture fluctuations, give rise to circulations that transport moisture, but not heat, from dry to moist regions, and thus reinforce the latent heating anomaly. We frame this positive feedback as a linear instability in mesoscale moisture fluctuations, whose time scale depends only on i) a vertical velocity scale and ii) the mean environment’s vertical structure. In our minimal-physics setting, we show both ingredients are provided by the shallow cumulus convection itself: It is intrinsically unstable to length scale growth. The upshot is that energy released by clouds at kilometre scales may play a more profound and direct role in shaping the mesoscale trade-wind environment than is generally appreciated, motivating further research into the mechanism’s relevance.

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Rosimar Rios-Berrios
,
Naoko Sakaeda
,
Héctor J. Jimenez-González
,
Angelie Nieves-Jimenez
,
Yidiana Zayas
,
Elinor Martin
,
Shun-Nan Wu
,
Cameron R. Homeyer
, and
Ernesto Rodríguez

Abstract

The diurnal cycle of coastal rainfall over western Puerto Rico was studied with high-frequency radiosondes launched by undergraduate students at the University of Puerto Rico at Mayagüez (UPRM). Thirty radiosondes were launched during a three-week period as part of NASA’s Convective Processes Experiment—Aerosols and Winds (CPEX-AW) field project. The objective of the radiosonde launches over Puerto Rico was to understand the evolution of coastal convective systems that are often challenging to predict. Four different events were sampled: (1) a short-lived rainfall event during a Saharan-air dust outbreak, (2) a two-day period of limited rainfall activity under northeasterly wind conditions, (3) a two-day period of heavy rainfall over land, and (4) a two-day period of long-lived rainfall events that initiated over land and propagated offshore during the evening hours. The radiosondes captured the sea-breeze onset during the mid-morning hours, an erosion of lower-tropospheric inversions, and substantial differences in column humidity between the four events. All radiosondes were launched by volunteer undergraduate students who were able to participate in-person, while the coordination was done virtually with lead scientists located in Puerto Rico, Oklahoma, and St. Croix. Overall, this initiative highlighted the importance of student-scientist collaboration in collecting critical observations to better understand complex atmospheric processes.

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Xiaomin Chen
,
Andrew Hazelton
,
Frank D. Marks
,
Ghassan J. Alaka Jr
, and
Chunxi Zhang

Abstract

Continuous development and evaluation of planetary boundary layer (PBL) parameterizations in hurricane conditions are crucial for improving tropical cyclone (TC) forecasts. A turbulence kinetic energy (TKE)-based eddy-diffusivity mass-flux (EDMF-TKE) PBL scheme, implemented in NOAA’s Hurricane Analysis and Forecast System (HAFS), was recently improved in hurricane conditions using large-eddy simulations. This study evaluates the performance of HAFS TC forecasts with the original (experiment HAFA) and modified EDMF-TKE (experiment HAFY) based on a large sample of cases during the 2021 North Atlantic hurricane season. Results indicate that intensity and structure forecast skill was better overall in HAFY than in HAFA, including during rapid intensification. Composite analyses demonstrate that HAFY produces shallower and stronger boundary layer inflow, especially within 1–3 times the radius of maximum wind (RMW). Stronger inflow and more moisture in the boundary layer contribute to stronger moisture convergence near the RMW. These boundary layer characteristics are consistent with stronger, deeper, and more compact TC vortices in HAFY than in HAFA. Nevertheless, track skill in HAFY is slightly reduced, which is in part attributable to the cross-track error from a few early cycles of Hurricane Henri that exhibited ~400 n mi track error at longer lead times. Sensitivity experiments based on HAFY demonstrate that turning off cumulus schemes notably reduces the track errors of Henri while turning off the deep cumulus scheme reduces the intensity errors. This finding hints at the necessity of unifying the mass fluxes in PBL and cumulus schemes in future model physics development.

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Divya Upadhyay
,
Sudhanshu Dixit
, and
Udit Bhatia

Abstract

Quantifying uncertainties in estimating future hydropower production directly or indirectly affects India’s energy security, planning, and management. The chaotic and nonlinear nature of atmospheric processes results in considerable Internal ClimateVariability (ICV) for future projections of climate variables. Multiple Initial Condition ensembles (MICE) and Multi-Model ensembles (MME) are often used to analyze the role of ICV and model uncertainty in precipitation and temperature. However, there are limited studies focusing on quantifying the role of internal variability on impact variables, including hydropower production. In this study, we analyze the role of ICV and model uncertainty on three prominent hydropower plants of India using MICE of EC-Earth3 and MME from CMIP6. We estimate the streamflow projections for all ensemble members using the Variable Infiltration Capacity hydrological model. We estimate maximum hydropower production generated using monthly release and hydraulic head available at the reservoir. We also analyzed the role of bias correction in hydropower production. The results show that ICV plays a significant role in estimating streamflow and hydropower potential for monsoon and throughout the year, respectively. Model uncertainty contributes more to total uncertainty than ICV in estimating the streamflow and potential hydropower. However, ICV is increasing towards the far-term (2075-2100). We also show that bias correction does not preserve ICV in estimating the streamflow. Although there is an increase in uncertainty for estimated streamflow, mean hydropower shows a decrease towards the far-term for February to May, more prominent for MICE than MME. The results suggest a need to incorporate uncertainty due to internal variability for addressing power security in changing climate scenarios.

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A. Bodas-Salcedo
,
J. M. Gregory
,
D. M. H. Sexton
, and
C. P. Morice

Abstract

We develop a statistical method to assess CMIP6 simulations of large-scale surface temperature change during the historical period (1850-2014), considering all timescales, allowing for the different unforced variability of each model and the observations, observational uncertainty, and applicable to ensembles of any size. The generality of this method, and the fact that it incorporates information about the unforced variability, makes it a useful model assessment tool. We apply this method to the historical simulations of the CMIP6 multi-model ensemble. We use three indices which measure different aspects of large-scale surface-air temperature change: global-mean, hemispheric gradient, and a recently-developed index that captures the sea-surface temperature (SST) pattern in the tropics (SST#; Fueglistaler and Silvers, 2021). We use the following observations: HadCRUT5 for the first two indices, and AMIPII and ERSSTv5 for SST#. In each case, we test the hypothesis that the model's forced response is compatible with the observations, accounting for unforced variability in both models and observations as well as measurement uncertainty. This hypothesis is accepted more often (75% of the models) for the hemispheric gradient than for the global mean, for which half of the models fail the test. The tropical SST pattern is poorly simulated in all models. Given that the tropical SST pattern can strongly modulate the relationship between energy imbalance and global-mean surface temperature anomalies on annual to decadal time scales (short-term feedback parameter), we suggest this should be a focus area for future improvements due to its potential implications for the global-mean temperature evolution in decadal time scales.

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G Chagnaud
,
G Panthou
,
T Vischel
, and
T Lebel

Abstract

Rainfall in the Sahel is extremely variable on daily to multi-decadal timescales, challenging climate models to realistically simulate its past and future evolution and questioning their relevance for defining suitable climate change adaptation strategies. Improving confidence in climate models may be achieved by i) evaluating their capacity for reproducing observed climatic evolution and ii) attributing these evolution. Moreover, there is a need to consider relevant climatic indicators, from an end-user point of view. Fully-coupled (CMIP6-AOGCM) models with idealized detection and attribution forcings (DAMIP) as well as atmosphere-only simulations (AMIP) are used to investigate the respective roles of external forcing factors and internal climate variability.

We show that CMIP6 models contain signs of the intensification of the rainfall regime as detected over the past 35 years from a regional daily observations network. Both the increase in intensity and occurrence of wet days, as well as that of extreme daily rainfall, are remarkably well reproduced by historical simulations incorporating anthropogenic forcing factors, aerosols contributing the largest share of this trend. Though more strongly affected by model structure uncertainty, the greenhouse gases forcing also displays noticeably robust features. Models are shown to fail at simulating realistic dry extreme evolution.

These findings give incentive for further investigating the underlying physical mechanisms that drive the sahelian rainfall regime evolution at regional to sub-regional scales. Furthermore, future hydro-climatic trajectories in the Sahel should be explored, though particular caution is required as to which rainfall indicator to consider.

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