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Andrea W. Harris
and
Jennifer Albrecht

Abstract

Temperature-related illness (TRI) encompasses heat-related illness, such as heat exhaustion and heatstroke, and cold-related illness, such as frostbite and hypothermia. TRI is typically the result of exposure to ambient weather conditions; because of this, unhoused individuals are hypothesized to have higher risk of TRI. However, no national epidemiological studies have been completed to determine this risk. The objective of this study was to determine the association between homelessness and emergency department (ED) diagnosis of TRI in the United States. We conducted a cross-sectional study of adult ED visits in the U.S. from 2005 through 2020 using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a nationally representative sample of non-federal ED patient visits. Housing status (housed vs. unhoused) was measured using NHAMCS patient residence category, with blank responses excluded. TRI was defined as ED clinician diagnosis of heat- or cold-related illness using ICD-9 and ICD-10 codes. Multivariable logistic regression was used to determine adjusted odds of TRI by housing status. There were 323,606 non-pediatric ED visits in the NHAMCS sample. TRI diagnosis was present in 288 (0.09%) visits. 4099 visits (0.9%) were categorized as unhoused. After adjusting for sex, mental health diagnosis, and alcohol or substance use or use disorder, the odds of TRI diagnosis in unhoused individuals was 4.08 (95% CI 2.09,7.95) compared to housed individuals. We found a higher adjusted odds of TRI diagnosis at an ED visit among unhoused individuals compared with housed individuals.

Restricted access
ShaoPeng Che
,
Kai Kuang
, and
Shujun Liu

Abstract

Nongovernmental organizations (NGOs) have increasingly played pivotal roles in shaping climate agendas and mobilizing individuals to engage in environmental initiatives. However, the nature of NGOs’ online interaction with users, especially in developing countries, remains largely unexplored. This study focused on the dynamics of engagement between a Chinese NGO, Chinese Weather Enthusiasts (CWE), and Chinese youth on the social media platform of Bilibili. The research comprised two main components. First, named entity recognition was employed to analyze weather-related terms in CWE’s posts on Bilibili and dynamic topic modeling was utilized to uncover shifts in thematic focus. Subsequently, descriptive analysis and negative binomial regression were employed to investigate the correlation between weather types and user engagement metrics. The study unveiled two noteworthy findings: first, CWE posts are closely linked to short-term weather, providing timely content that may meet the public’s demand for climate information. Second, the engagement of Chinese youth users is not affected by extreme weather types. Future research should continue to elucidate strategies that NGOs can employ to enhance online engagement among youth users.

Significance Statement

This study seeks to contribute to the current literature of climate communication by investigating how NGOs engage with Chinese youth on social media, an area that has received scant attention thus far. Focusing on an influential Chinese climate NGO, CWE, and its interactions with Chinese youth on the social media platform of Bilibili, this research sheds lights on strategies to communicate information related to extreme weather to this demographic. Examining factors that influence online user engagement offers both theoretical insights about the mechanisms of climate communication and practical implications for NGOs and policymakers to mobilize youth for environmental initiatives. The findings also underscore the importance of tailoring climate communication to align with the daily experiences of the target audience and public-centric approaches in climate communication strategies.

Restricted access
Iréne Lake
and
Melissa S. Bukovsky
Open access
Ruth Mottram
,
Michiel van den Broeke
,
Andrew Meijers
,
Christian Rodehacke
,
Rebecca L. Dell
,
Anna E. Hogg
,
Benjamin J. Davison
,
Stef Lhermitte
,
Nicolaj Hansen
,
Jose Abraham Torres Alavez
, and
Martin Olesen
Open access
Ryan A. Sobash
and
David A. Ahijevych

Abstract

The High Resolution Rapid Refresh (HRRR) model provides hourly-updating forecasts of convective-scale phenomena, which can be used to infer the potential for convective hazards (e.g., tornadoes, hail, and wind gusts), across the United States. We used deterministic 2019–2020 HRRR version 4 (HRRRv4) forecasts to train neural networks (NNs) to generate 4-hourly probabilistic convective hazard forecasts (NNPFs) for HRRRv4 initializations in 2021, using storm reports as ground truth. The NNPFs were compared to the skill of a smoothed updraft helicity (UH) baseline to quantify the benefit of the NNs. NNPF skill varied by initialization time and time of day, but were all superior to the UH forecast. NNPFs valid at hours between 18 UTC – 00 UTC were most skillful in aggregate, significantly exceeding the baseline forecast skill. Overnight NNPFs (i.e., valid 06–12 UTC) were least skillful, indicating a diurnal cycle in hazard predictability that was present across all HRRRv4 initializations. We explored the sensitivity of HRRRv4 NNPF skill to NN training choices. Including an additional year of 2021 HRRRv4 forecasts for training slightly improved skill for 2022 HRRRv4 NNPFs, while reducing the training dataset size by 40% using only forecasts with storm reports was not detrimental to forecast skill. Finally, NNs trained with 2018–2020 HRRRv3 forecasts led to a reduction in NNPF skill when applied to 2021 HRRRv4 forecasts. In addition to documenting practical predictability challenges with convective hazard prediction, these findings reinforce the need for a consistent model configuration for optimal results when training NNs and provide best practices when constructing a training dataset with operational convection-allowing model forecasts.

Restricted access
Marina Baldissera Pacchetti
,
Julie Jebeile
, and
Erica Thompson

Abstract

The continued development of general circulation models (GCMs) toward increasing resolution and complexity is a predominantly chosen strategy to advance climate science, resulting in channeling of research and funding to meet this aspiration. Yet many other modeling strategies have also been developed and can be used to understand past and present climates, to project future climates, and ultimately to support decision-making. We argue that a plurality of climate modeling strategies and an equitable distribution of funding among them would be an improvement on the current predominant strategy for informing policymaking. To support our claim, we use a philosophy of science approach to compare the increasing resolution and complexity of general circulation models with three alternate examples: the use of machine learning techniques, the physical climate storyline approach, and Earth system models of intermediate complexity. We show that each of these strategies prioritizes a particular set of methodological aims, among which are empirical agreement, realism, comprehensiveness, diversity of process representations, inclusion of the human dimension, reduction of computational expense, and intelligibility. Thus, each strategy may provide adequate information to support different specific kinds of research and decision questions. We conclude that, because climate decision-making consists of different kinds of questions, many modeling strategies are all potentially useful and can be used in a complementary way.

Open access
Daniel M. Gilford
,
Andrew J. Pershing
,
Joseph Giguere
, and
Friederike E. L. Otto
Open access
Zheng Liu
and
Axel Schweiger

Abstract

The effect of leads in Arctic sea ice on clouds is a potentially important climate feedback. We use observations of clouds and leads from the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) to study the effects of leads on clouds. Both leads and clouds are strongly forced by synoptic weather conditions, with more clouds over both leads and sea ice at lower sea level pressure. Contrary to previous studies, we find that the overall lead effect on low-level cloud cover is −0.02, a weak cloud dissipating effect in cold months, after the synoptic forcing influence is removed. This is due to compensating contributions from the cloud dissipating effect by newly frozen leads under high pressure systems and the cloud enhancing effect by newly open leads under low pressure system. The lack of proper representation of lead effect on clouds in current climate models and reanalyses may impact their performance in winter months, such as in sea ice growth and Arctic cyclone development.

Restricted access
Manho Park
,
Zhonghua Zheng
,
Nicole Riemer
, and
Christopher W. Tessum

Abstract

We developed and applied a machine-learned discretization for one-dimensional (1D) horizontal passive scalar advection, which is an operator component common to all chemical transport models (CTMs). Our learned advection scheme resembles a second-order accurate, three-stencil numerical solver but differs from a traditional solver in that coefficients for each equation term are output by a neural network rather than being theoretically derived constants. We subsampled higher-resolution simulation results—resulting in up to 16× larger grid size and 64× larger time step—and trained our neural-network-based scheme to match the subsampled integration data. In this way, we created an operator that has low resolution (in time or space) but can reproduce the behavior of a high-resolution traditional solver. Our model shows high fidelity in reproducing its training dataset (a single 10-day 1D simulation) and is similarly accurate in simulations with unseen initial conditions, wind fields, and grid spacing. In many cases, our learned solver is more accurate than a low-resolution version of the reference solver, but the low-resolution reference solver achieves greater computational speedup (500× acceleration) over the high-resolution simulation than the learned solver is able to (18× acceleration). Surprisingly, our learned 1D scheme—when combined with a splitting technique—can be used to predict 2D advection and is in some cases more stable and accurate than the low-resolution reference solver in 2D. Overall, our results suggest that learned advection operators may offer a higher-accuracy method for accelerating CTM simulations as compared to simply running a traditional integrator at low resolution.

Significance Statement

Chemical transport modeling (CTM) is an essential tool for studying air pollution. CTM simulations take a long computing time. Modeling pollutant transport (advection) is the second most computationally intensive part of the model. Decreasing the resolution not only reduces the advection computing time but also decreases accuracy. We employed machine learning to reduce the resolution of advection while keeping the accuracy. We verified the robustness of our solver with several generalization testing scenarios. In our 2D simulation, our solver showed up to 100 times faster simulation with fair accuracy. Integrating our approach to existing CTMs will allow broadened participation in the study of air pollution and related solutions.

Open access
Free access