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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
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
Corey K. Potvin
,
Montgomery L. Flora
,
Patrick S. Skinner
,
Anthony E. Reinhart
, and
Brian C. Matilla

Abstract

Forecasters routinely calibrate their confidence in model forecasts. Ensembles inherently estimate forecast confidence but are often underdispersive, and ensemble spread does not strongly correlate with ensemble-mean error. The misalignment between ensemble spread and skill motivates new methods for “forecasting forecast skill” so that forecasters can better utilize ensemble guidance. We have trained logistic regression and random forest models to predict the skill of composite reflectivity forecasts from the NSSL Warn-on-Forecast System (WoFS), a 3-km ensemble that generates rapidly updating forecast guidance for 0–6-h lead times. The forecast skill predictions are valid at 1-, 2-, or 3-h lead times within localized regions determined by the observed storm locations at analysis time. We use WoFS analysis and forecast output and NSSL Multi-Radar/Multi-Sensor composite reflectivity for 106 cases from the 2017 to 2021 NOAA Hazardous Weather Testbed Spring Forecasting Experiments. We frame the prediction task as a multiclassification problem, where the forecast skill labels are determined by averaging the extended fraction skill scores (eFSSs) for several reflectivity thresholds and verification neighborhoods and then converting to one of three classes based on where the average eFSS ranks within the entire dataset: POOR (bottom 20%), FAIR (middle 60%), or GOOD (top 20%). Initial machine learning (ML) models are trained on 323 predictors; reducing to 10 or 15 predictors in the final models only modestly reduces skill. The final models substantially outperform carefully developed persistence- and spread-based models and are reasonably explainable. The results suggest that ML can be a valuable tool for guiding user confidence in convection-allowing (and larger-scale) ensemble forecasts.

Significance Statement

Some numerical weather prediction (NWP) forecasts are more likely to verify than others. Forecasters often recognize situations where NWP output should be trusted more or less than usual, but objective methods for “forecasting forecast skill” are notably lacking for thunderstorm-scale models. Better estimates of forecast skill can benefit society through more accurate forecasts of high-impact weather. Machine learning (ML) provides a powerful framework for relating forecast skill to the characteristics of model forecasts and available observations over many previous cases. ML models can leverage these relationships to predict forecast skill for new cases in real time. We demonstrate the effectiveness of this approach to forecasting forecast skill using a cutting-edge thunderstorm prediction system and logistic regression and random forest models. Based on this success, we recommend the adoption of similar ML-based methods for other prediction models.

Open access
Daniel Whitesel
,
Rezaul Mahmood
,
Christopher Phillips
,
Joshua Roundy
,
Eric Rappin
,
Paul Flanagan
,
Joseph A. Santanello Jr.
,
Udaysankar Nair
, and
Roger Pielke Sr.

Abstract

Land-use land-cover change affects weather and climate. This paper quantifies land–atmosphere interactions over irrigated and nonirrigated land uses during the Great Plains Irrigation Experiment (GRAINEX). Three coupling metrics were used to quantify land–atmosphere interactions as they relate to convection. They include the convective triggering potential (CTP), the low-level humidity index (HIlow), and the lifting condensation level (LCL) deficit. These metrics were calculated from the rawinsonde data obtained from the Integrated Sounding Systems (ISSs) for Rogers Farm and York Airport along with soundings launched from the three Doppler on Wheels (DOW) sites. Each metric was categorized by intensive observation period (IOP), cloud cover, and time of day. Results show that with higher CTP, lower HIlow, and lower LCL deficit, conditions were more favorable for convective development over irrigated land use. When metrics were grouped and analyzed by IOP, compared to nonirrigated land use, HIlow was found to be lower for irrigated land use, suggesting favorable conditions for convective development. Furthermore, when metrics were grouped and analyzed by clear and nonclear days, CTP values were higher over irrigated cropland than nonirrigated land use. In addition, compared to nonirrigated land use, the LCL deficit during the peak growing season was lower over irrigated land use, suggesting a favorable condition for convection. It is found that with the transition from the early summer to the mid/peak summer and increased irrigation, the environment became more favorable for convective development over irrigated land use. Finally, it was found that regardless of background atmospheric conditions, irrigated land use provided a favorable environment for convective development.

Restricted access
Junkai Qian
,
Qiang Wang
,
Peng Liang
,
Suqi Peng
,
Huizan Wang
, and
Yanling Wu

Abstract

The Kuroshio intrusion (KI) into the South China Sea (SCS) significantly affects the environment, ecology, and climate change of the SCS. However, due to the nonlinearity of KI, its numerical prediction often requires a large ensemble size to measure prediction uncertainty. The huge computational costs of large numbers of members and high-resolution numerical models pose significant challenges for KI prediction. We, therefore, construct a Kuroshio ensemble deep learning prediction system (KurNet) by taking different values of parameters to predict KI paths because the deep learning models have high prediction skills and low computational cost. The KurNet containing 64 ensemble members not only can output ensemble mean forecast results of the Kuroshio path but also can estimate probability density functions for the path types. The KurNet illustrates a high predictive ability for the KI with the mean classification accuracy of 71.9% and root-mean-square error of 0.913 on the testing set, which is superior to the single control prediction by ∼1.0%–2.9%, although the control prediction model is selected as one of the ensemble members with the best predictive ability on the validation set. Furthermore, the predictability analysis of 10 KI events indicates that when the lead time is 3 days, the most important areas are in the east of Luzon Island due to the upstream Kuroshio transport. As the lead time increases, the most important area is in the Luzon Strait due to the eddy activity. Observing system simulation experiments reveal that the KI forecast skill can be enhanced by ∼12%–18% when uncertainties of the input data in these important regions are removed.

Restricted access
Dongxue Mo
,
Po Hu
,
Jian Li
,
Yijun Hou
, and
Shuiqing Li

Abstract

The wave effect is crucial to coastal ocean dynamics, but the roles of the associated wave-dependent mechanisms, such as the wave-enhanced surface stress, wave-enhanced bottom stress, and three-dimensional wave force, are not yet fully understood. In addition, the parameterizations of each mechanism vary and need to be assessed. In this study, a coupled wave–current model based on the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model system was established to identify the effect of the wave-dependent mechanism on storm surges and currents during three typical extreme weather systems, i.e., cold wave, extratropical cyclone, and typhoon systems, in a semienclosed sea. The effects of the three coupled mechanisms on the surface or bottom stress, in terms of both magnitude and direction, were investigated and quantified separately based on numerical sensitivity analysis. A total of seven parameterizations is used to evaluate these mechanisms, resulting in significant variations in the storm surge and current vectors. The similarities and differences of the wave-induced surge and wave-induced current among the various mechanisms were summarized. The change in the surface stress and bottom stress and the excessive momentum flux due to waves were found to mainly occur in shallow nearshore regions. Optimal choice of the combination of parameterization schemes was obtained through comparison with measured data. The wave-induced current in the open waters with a deep-water depth and complex terrain could generate cyclonic or anticyclonic current vorticities, the number and intensity of which always increased with the enhanced strength and rotation of the wind field increased.

Significance Statement

Waves induced by extreme weather systems can significantly modulate the storm surge and current field. Previous studies have developed different parameterizations for each physical mechanism. In this study, we aimed to separate and quantify the contribution of the wave-dependent mechanisms with typical parameterizations for storm surges and currents during three types of weather systems. The prediction of wave-induced surges in nearshore regions is critical especially during extreme weather systems and has diverse practical applications in ocean engineering. Through comparison with measured data, the best combination of parameterizations was identified, which could be helpful for regional disaster warning and management.

Restricted access
J. Anselin
,
P. R. Holland
,
A. Jenkins
, and
J. R. Taylor

Abstract

Efforts to parameterize ice shelf basal melting within climate models are limited by an incomplete understanding of the influence of ice base slope on the turbulent ice shelf–ocean boundary current (ISOBC). Here, we examine the relationship between ice base slope, boundary current dynamics, and melt rate using 3D, turbulence-permitting large-eddy simulations (LESs) of an idealized ice shelf–ocean boundary current forced solely by melt-induced buoyancy. The range of simulated slopes (3%–10%) is appropriate to the grounding zone of small Antarctic ice shelves and to the flanks of relatively wide ice base channels, and the initial conditions are representative of warm-cavity ocean conditions. In line with previous studies, the simulations feature the development of an Ekman boundary layer adjacent to the ice, overlaying a broad pycnocline. The time-averaged flow within the pycnocline is in thermal wind balance, with a mean shear that is only weakly dependent on the ice base slope angle α, resulting in a mean gradient Richardson number 〈Rig〉 that decreases approximately linearly with sinα. Combining this inverse relationship with a linear approximation to the density profile, we derive formulations for the friction velocity, thermal forcing, and melt rate in terms of slope angle and total buoyancy input. This theory predicts that melt rate varies like the square root of slope, which is consistent with the LES results and differs from a previously proposed linear trend. The derived scalings provide a potential framework for incorporating slope dependence into parameterizations of mixing and melting at the base of ice shelves.

Significance Statement

The majority of Antarctica’s contribution to sea level rise can be attributed to changes in ocean-driven melting at the base of ice shelves (the floating extensions of the Antarctic ice sheet). Turbulent ocean currents and melting are strongest where the ice base is steeply sloped, but few studies have systematically examined this effect. We use an idealized ice shelf–ocean model to examine how variations in ice base slope influence ocean mixing and ice melting. We derive a formula predicting that melting varies like the square root of the ice base slope, and this scaling is supported by the simulations. These results provide a potential framework for improving the representation of ice shelf melting in climate models.

Open access
Florence L. Beaudry
,
Stéphane Bélair
,
Julie M. Thériault
,
Dikra Khedhaouiria
,
Franck Lespinas
,
Daniel Michelson
,
Pei-Ning Feng
, and
Catherine Aubry

Abstract

The Canadian Precipitation Analysis (CaPA) system provides near-real-time precipitation analyses over Canada by combining observations with short-term numerical weather prediction forecasts. CaPA’s snowfall estimates suffer from the lack of accurate solid precipitation measurements to correct the first-guess estimate. Weather radars have the potential to add precipitation measurements to CaPA in all seasons but are not assimilated in winter due to radar snowfall estimate imprecision and lack of precipitation gauges for calibration. The main objective of this study is to assess the impact of assimilating Canadian dual-polarized radar-based snowfall data in CaPA to improve precipitation estimates. Two sets of experiments were conducted to evaluate the impact of including radar snowfall retrievals, one set using the high-resolution CaPA (HRDPA) with the currently operational quality control configuration and another increasing the number of assimilated surface observations by relaxing quality control. Experiments spanned two winter seasons (2021 and 2022) in central Canada, covering part of the entire CaPA domain. The results showed that the assimilation of radar-based snowfall data improved CaPA’s precipitation estimates 81.75% of the time for 0.5-mm precipitation thresholds. An increase in the probability of detection together with a decrease in the false alarm ratio suggested an improvement of the precipitation spatial distribution and estimation accuracy. Additionally, the results showed improvements for both precipitation mass and frequency biases for low precipitation amounts. For larger thresholds, the frequency bias was degraded. The results also indicated that the assimilation of dual-polarization radar data is beneficial for the two CaPA configurations tested in this study.

Open access