Category: New Publications Page 2 of 4

Our new paper, “The water balance representation in Urban-PLUMBER land surface models“, is published in Journal of Advances in Modeling Earth Systems (IF: 4.4).

The paper can be downloaded at https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2024MS004231.

Authors: H. J. Jongen, M. Lipson, A. J. Teuling, S. Grimmond, J.-J. Baik, M. Best, M. Demuzere, K. Fortuniak, Y. Huang, M. G. De Kauwe, R. Li, J. McNorton, N. Meili, K. Oleson, S.-B. Park, T. Sun, A. Tsiringakis, M. Varentsov, C. Wang, Z.-H. Wang, G. J. Steeneveld

Abstract: Urban Land Surface Models (ULSMs) simulate energy and water exchanges between the urban surface and atmosphere. However, earlier systematic ULSM comparison projects assessed the energy balance but ignored the water balance, which is coupled to the energy balance. Here, we analyze the water balance representation in 19 ULSMs participating in the Urban-PLUMBER project using results for 20 sites spread across a range of climates and urban form characteristics. As observations for most water fluxes are unavailable, we examine the water balance closure, flux timing, and magnitude with a score derived from seven indicators expecting better scoring models to capture the latent heat flux more accurately. We find that the water budget is only closed in 57% of the model-site combinations assuming closure when annual total incoming fluxes (precipitation and irrigation) are within 3% of the outgoing (all other) fluxes. Results show the timing is better captured than magnitude. No ULSM has passed all water balance indicators for any site. Models passing more indicators do not capture the latent heat flux more accurately refuting our hypothesis. While output reporting inconsistencies may have negatively affected model performance, our results indicate models could be improved by explicitly verifying water balance closure and revising runoff parameterizations. By expanding ULSM evaluation to the water balance and related to latent heat flux performance, we demonstrate the benefits of evaluating processes with direct feedback mechanisms to the processes of interest.

DOI: https://doi.org/10.1029/2024MS004231

Our new paper, “South America is becoming warmer, drier, and more flammable“, is published in Communications Earth & Environment (IF: 8.1).

The paper can be downloaded at https://www.nature.com/articles/s43247-024-01654-7.

Authors: Sarah Feron, Raúl R. Cordero, Alessandro Damiani, Shelley MacDonell, Jaime Pizarro, Katerina Goubanova, Raúl Valenzuela, Chenghao Wang, Lena Rester, Anne Beaulieu

Abstract: South America is experiencing severe impacts from climate change. Although the warming of the subcontinent closely follows the global path, the rise of temperatures has been more pronounced in some regions, which have also seen a parallel increment in the occurrence of droughts and weather conditions associated with enhanced fire risk. Here, we use reanalysis datasets to analyze the progression of the concurring warm, dry, and high fire risk conditions (i.e., dry compounds) since 1971. We show that the frequency of these compound extremes has surged in key South American regions including the northern Amazon, which have seen a 3-fold increase in the number of days per year with extreme fire weather conditions (including high temperatures, dryness, and low humidity). Our results also suggest that the surface temperature of the tropical Pacific Ocean modulates the interannual variability of dry compounds in South America. While El Niño enhances the fire risk in the northern Amazon, dry extremes in the Gran Chaco region appear to be more responsive to La Niña.

DOI: https://doi.org/10.1038/s43247-024-01654-7

Our new paper, “Unraveling the discrepancies between Eulerian and Lagrangian moisture tracking models in monsoon- and westerly-dominated basins of the Tibetan Plateau“, is published in Atmospheric Chemistry and Physics (IF: 5.2).

The paper can be downloaded at https://acp.copernicus.org/articles/24/10741/2024/.

Authors: Ying Li, Chenghao Wang, Qiuhong Tang, Shibo Yao, Bo Sun, Hui Peng, Shangbin Xiao

Abstract: Eulerian and Lagrangian numerical moisture tracking models, which are primarily used to quantify moisture contributions from global sources to specific regions, play a crucial role in hydrology and (paleo)climatology studies on the Tibetan Plateau (TP). Despite their widespread applications in the TP region, potential discrepancies in their moisture tracking results and their underlying causes remain unexplored. In this study, we compare the most widely used Eulerian and Lagrangian moisture tracking models over the TP, i.e., WAM2layers (the Water Accounting Model – 2 layers) and FLEXPART-WaterSip (the FLEXible PARTicle dispersion model coupled with the “WaterSip” moisture source diagnostic method), specifically focusing on a basin governed by the Indian summer monsoon (Yarlung Zangbo River basin, YB) and a westerly-dominated basin (upper Tarim River basin, UTB). Compared to the bias-corrected FLEXPART-WaterSip, WAM2layers generally estimates higher moisture contributions from westerly-dominated and distant sources but lower contributions from local recycling and nearby sources downwind of the westerlies. These differences become smaller with higher spatial and temporal resolutions of forcing data in WAM2layers. A notable advantage of WAM2layers over FLEXPART-WaterSip is its closer alignment of estimated moisture sources with actual evaporation, particularly in source regions with complex land–sea distributions. However, the evaporation biases in FLEXPART-WaterSip can be partly corrected through calibration with actual surface fluxes. For moisture tracking over the TP, we recommend using high-resolution forcing datasets, prioritizing temporal resolution over spatial resolution for WAM2layers, while for FLEXPART-WaterSip, we suggest applying bias corrections to optimize the filtering of precipitation particles and adjust evaporation estimates.

DOI: https://doi.org/10.5194/acp-24-10741-2024

Our new paper, “Improving estimation of diurnal land surface temperatures by integrating weather modeling with satellite observations“, is published in Remote Sensing of Environment (IF: 11.1).

The paper can be downloaded at https://www.sciencedirect.com/science/article/pii/S003442572400419X.

Authors: Wei Chen, Yuyu Zhou, Ulrike Passe, Tao Zhang, Chenghao Wang, Ghassem R. Asrar, Qi Li, Huidong Li

Abstract: Land surface temperature (LST) derived from satellite observations and weather modeling has been widely used for investigating Earth surface-atmosphere energy exchange and radiation budget. However, satellite-derived LST has a trade-off between spatial and temporal resolutions and missing observations caused by clouds, while there are limitations such as potential bias and expensive computation in model calibration and simulation for weather modeling. To mitigate those limitations, we proposed a WRFM framework to estimate LST at a spatial resolution of 1 km and temporal resolution of an hour by integrating the Weather Research and Forecasting (WRF) model and MODIS satellite data using the morphing technique. We tested the framework in eight counties, Iowa, USA, including urban and rural areas, to generate hourly LSTs from June 1st to August 31st, 2019, at a 1 km resolution. Upon evaluation with in-situ LST measurements, our WRFM framework has demonstrated its ability to capture hourly LSTs under both clear and cloudy conditions, with a root mean square error (RMSE) of 2.63 K and 3.75 K, respectively. Additionally, the assessment with satellite LST observations has shown that the WRFM framework can effectively reduce the bias magnitude in LST from the WRF simulation, resulting in a reduction of the average RMSE over the study area from 4.34 K (daytime) and 4.12 K (nighttime) to 2.89 K (daytime) and 2.75 K (nighttime), respectively, while still capturing the hourly patterns of LST. Overall, the WRFM is effective in integrating the complementary advantages of satellite observations and weather modeling and can generate LSTs with high spatiotemporal resolutions in areas with complex landscapes (e.g., urban).

DOI: https://doi.org/10.1016/j.rse.2024.114393

Our new paper, “Methane dynamics
altered by reservoir operations in a typical tributary of the Three Gorges Reservoir“, is published in Water Research (IF: 11.4).

The paper and its supplement can be downloaded at https://www.sciencedirect.com/science/article/pii/S0043135424010625.

Authors: Jia Liu, Fei Xue, Xiaojuan Guo, Zhengjian Yang, Manchun Kang, Min Chen, Daobin Ji, Defu Liu, Shangbin Xiao, and Chenghao Wang

Abstract: Substantial nutrient inputs from reservoir impoundment typically increase sedimentation rate and primary production. This can greatly enhance methane (CH₄) production, making reservoirs potentially significant sources of atmospheric CH₄. Consequently, elucidating CH₄ emissions from reservoirs is crucial for assessing their role in the global methane budget. Reservoir operations can also influence hydrodynamic and biogeochemical processes, potentially leading to pronounced spatiotemporal heterogeneity, especially in reservoirs with complex tributaries, such as the Three Gorges Reservoir (TGR). Although several studies have investigated the spatial and temporal variations in CH₄ emissions in the TGR and its tributaries, considerable uncertainties remain regarding the impact of reservoir operations on CH₄ dynamics. These uncertainties primarily arise from the limited spatial and temporal resolutions of previous measurements and the complex underlying mechanisms of CH₄ dynamics in reservoirs. In this study, we employed a fast-response automated gas equilibrator to measure the spatial distribution and seasonal variations of dissolved CH₄ concentrations in XXB, a representative area significantly impacted by TGR operations and known for severe algal blooms. Additionally, we measured CH₄ production rates in sediments and diffusive CH₄ flux in the surface water. Our multiple campaigns suggest substantial spatial and temporal variability in CH₄ concentrations across XXB. Specifically, dissolved CH₄ concentrations were generally higher upstream than downstream and exhibited a vertical stratification, with greater concentrations in bottom water compared to surface water. The peak dissolved CH₄ concentration was observed in May during the drained period. Our results suggest that the interplay between aquatic organic matter, which promotes CH₄ production, and the dilution process caused by intrusion flows from the mainstream primarily drives this spatiotemporal variability. Importantly, our study indicates the feasibility of using strategic reservoir operations to regulate these factors and mitigate CH₄ emissions. This eco-environmental approach could also be a pivotal management strategy to reduce greenhouse gas emissions from other reservoirs.

DOI: https://doi.org/10.1016/j.watres.2024.122163

Our new paper, “A modeling framework to assess fenceline monitoring and self-reported upset emissions of benzene from multiple oil refineries in Texas“, is published in Atmospheric Environment X (IF: 3.8).

The paper and its supplement can be downloaded at https://www.sciencedirect.com/science/article/pii/S2590162124000480.

Authors: Qi Li, Lauren Padilla, Tammy Thompson, Shuolin Xiao, Elizabeth Mohr, Xiaohe Zhou, Nino Kacharava, Yuanfeng Cui, and Chenghao Wang

Abstract: Benzene as one type of hazardous air pollutants (HAPs) is produced by industrial production processes and/or emitted during upset events caused by man-made or natural accidents. Although upset emissions of benzene can be a significant contributor to the total emission, it is still challenging to quantify. This study first develops a fast modeling framework using obstacle-resolving computational fluid dynamics modeling to compare the modeled within-facility-scale passive pollutant dispersion with the observed levels based on self-reported emissions for fourteen facilities in Texas, United States. Results of numerical simulations demonstrate that neglecting the obstacle effect can underpredict (overpredict) the near-(far-)field concentrations for a low source. For a source located above obstacles, underprediction occurs at all distances. The diagnostic framework is applied to 107 self-reported upset emission events for fourteen petroleum refineries in Texas from year 2019–2022. Considering different metrics across all events, it can be concluded that the modeled concentrations based on self-reported emissions likely underpredict the observed concentration increments. Depending on the possible source height, the median factor of underprediction ranges from 3 to 95 based on the average-plume metric. The agreement between model and observation is better for events characterized by high emission amounts and rates, which also correspond to high observed concentration increments. Overall, the research highlights the importance of considering obstacles and demonstrates the potential application of the current approach as an efficient diagnostic method for self-reported upset emissions using fenceline observations of HAPs.

DOI: https://doi.org/10.1016/j.aeaoa.2024.100281

The School of Meteorology and the Center for Analysis and Prediction of Storms at the University of Oklahoma invite applications for two fully funded full-time Postdoctoral Researcher positions focused on the modeling of air quality and/or greenhouse gases (GHGs) in the urban environment. Areas of focus include but are not limited to:

• Development of new or improved numerical parameterization schemes
• Spatial and temporal characterization of air pollution (e.g., ozone and particulate matter) and GHGs
• Assessment of mitigation strategies to reduce air pollutants and GHG emissions

Both positions are based in Norman, OK, and will be working with Dr. Chenghao Wang and Dr. Xiao-Ming Hu.

Salary will be commensurate with the applicant’s experience. Full-time employment comes with OU research staff benefits, including generous paid leave, health insurance, and retirement savings plans. The successful candidates will work in the National Weather Center, with numerous opportunities to collaborate with world-leading academic and operational partners both on and off campus, such as the Center for Analysis and Prediction of Storms (CAPS) and National Center for Atmospheric Research (NCAR). The University of Oklahoma and City of Norman offer a vibrant college town atmosphere with numerous recreational and cultural activities. Norman is just 20 miles away from Oklahoma City, which provides all the amenities of a larger city. Norman also has a low cost of living compared to most cities in the U.S.

Qualifications: Applicants must have earned a Ph.D. in Atmospheric Sciences, Engineering, Earth Science, Computer Science, or a closely related field by the time of appointment. Candidates should have demonstrated experience with numerical 3D air quality models, such as WRF-Chem, CMAQ, HYSPLIT, GEOS-Chem, and LES models, be proficient in programming languages commonly used in models (Fortran) and data analytics (MATLAB, Python, R, or NCL), and possess strong oral and written communication skills, evidenced by their publication record and presentations at scientific meetings.

Application Instructions

Applicants are encouraged to apply as soon as possible. To apply, interested individuals should submit electronically:

Please submit your application through http://apply.interfolio.com/150611 by Sep 30, 2024. Applications will be reviewed as received and will continue until the positions are filled. For questions regarding these two positions, please contact Dr. Chenghao Wang (chenghao.wang@ou.edu) or Dr. Xiao-Ming Hu (xhu@ou.edu).

Dr. Wang was recently awarded the NASA Early Career Investigator Grant titled “Compound Heat and Ozone Pollution Episodes in the Urban Environment: Dynamics, Mechanism, and Mitigation with Nature-Based Solutions”.

See OU News here: https://www.ou.edu/news/articles/2024/july/researcher-receives-nasa-funding-to-study-ozone-pollution.

Our new paper, “The potential of urban irrigation for counteracting carbon-climate feedback“, is published in Nature Communications (IF: 16.6).

The paper and its supplement can be downloaded at https://www.nature.com/articles/s41467-024-46826-3.

Authors: Peiyuan Li, Zhi-Hua Wang, and Chenghao Wang

Abstract: Global climate changes, especially the rise of global mean temperature due to the increased carbon dioxide (CO2) concentration, can, in turn, result in higher anthropogenic and biogenic greenhouse gas emissions. This potentially leads to a positive loop of climate–carbon feedback in the Earth’s climate system, which calls for sustainable environmental strategies that can mitigate both heat and carbon emissions, such as urban greening. In this study, we investigate the impact of urban irrigation over green spaces on ambient temperatures and CO2 exchange across major cities in the contiguous United States. Our modeling results indicate that the carbon release from urban ecosystem respiration is reduced by evaporative cooling in humid climate, but promoted in arid/semi-arid regions due to increased soil moisture. The irrigation-induced environmental co-benefit in heat and carbon mitigation is, in general, positively correlated with urban greening fraction and has the potential to help counteract climate–carbon feedback in the built environment.

DOI: https://doi.org/10.1038/s41467-024-46826-3

Our new paper, “Enhanced observations from an optimized soil-canopy-photosynthesis and energy flux model revealed evapotranspiration-shading cooling dynamics of urban vegetation during extreme heat“, is published in Remote Sensing of Environment (IF: 13.5).

The paper and its supplement can be downloaded at https://www.sciencedirect.com/science/article/pii/S0034425724001093. The Share Link to download a copy is https://authors.elsevier.com/c/1ikad7qzT3Dj5 (valid through Apr 30, 2024).

Authors: Zhaowu Yu, Jiaqi Chen, Jike Chen, Wenfeng Zhan, Chenghao Wang, Wenjuan Ma, Xihan Yao, Siqi Zhou, Kai Zhu, and Ranhao Sun

Abstract: Previous studies on the cooling of urban vegetation mainly focused on its transpiration or shading effect separately, neglecting to explore the combined evapotranspiration-shading cooling. Further, accurate quantification of evapotranspiration-shading cooling remains challenging due to heterogeneity of urban landscapes, which limits understanding of its high-resolution spatiotemporal patterns. Here, we integrate high-precision remote sensing data and the Soil-Canopy-Observations of Photosynthesis and Energy Fluxes (SCOPE) model to propose an optimized quantitative approach. The approach was used to investigate changes in evapotranspiration-shade cooling during extreme heat. Taking Shanghai metropolitan as case, the results show: (1) The cooling capacity of urban vegetation in nighttime (18:00–6:00) is enhanced during extreme heat, which is attributed to accumulated effect of shading and enhanced evapotranspiration due to elevated vapor-pressure deficit. (2) In densely built-up areas with limited vegetation, there is a significant lack of thermal regulation, especially in the early morning (7:00) and late evening (17:00), thus exacerbating thermal stress. (3) At midday (11:00–13:00) there was a slight decrease in evaporative cooling, probably caused by the behaviour of the stomatal closure at high temperatures. Concurrently, high radiation causes the shading effect of vegetation to become more prominent, amplifying the cooling contrast between areas with dense and sparse vegetation cover. Moreover, the study also highlights that grassland with >50% cover can provide cooling effects similar to that of forest land. Overall, our study not only enhances the understanding of urban vegetation’s cooling effects but also underscores the importance of strategic urban vegetation planning in mitigating urban heat, particularly under the escalating frequency and intensity of heat waves.

DOI: https://doi.org/10.1016/j.rse.2024.114098