Atmospheric & Oceanic Science Research Works

Permanent URI for this collectionhttp://hdl.handle.net/1903/1596

Formerly known as the Department of Meteorology.

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A 7-Year Climatology of Warm-Sector Heavy Rainfall over South China during the Pre-Summer Months
(MDPI, 2021-07-15) Chen, Tao; Zhang, Da-Lin
In view of the limited predictability of heavy rainfall (HR) events and the limited understanding of the physical mechanisms governing the initiation and organization of the associated mesoscale convective systems (MCSs), a composite analysis of 58 HR events over the warm sector (i.e., far ahead of the surface cold front), referred to as WSHR events, over South China during the months of April to June 2008~2014 is performed in terms of precipitation, large-scale circulations, pre-storm environmental conditions, and MCS types. Results show that the large-scale circulations of the WSHR events can be categorized into pre-frontal, southwesterly warm and moist ascending airflow, and low-level vortex types, with higher frequency occurrences of the former two types. Their pre-storm environments are characterized by a deep moist layer with >50 mm column-integrated precipitable water, high convective available potential energy with the equivalent potential temperature of ≥340 K at 850 hPa, weak vertical wind shear below 400 hPa, and a low-level jet near 925 hPa with weak warm advection, based on atmospheric parameter composite. Three classes of the corresponding MCSs, exhibiting peak convective activity in the afternoon and the early morning hours, can be identified as linear-shaped, a leading convective line adjoined with trailing stratiform rainfall, and comma-shaped, respectively. It is found that many linear-shaped MCSs in coastal regions are triggered by local topography, enhanced by sea breezes, whereas the latter two classes of MCSs experience isentropic lifting in the southwesterly warm and moist flows. They all develop in large-scale environments with favorable quasi-geostrophic forcing, albeit weak. Conceptual models are finally developed to facilitate our understanding and prediction of the WSHR events over South China.
A Century of Observed Temperature Change in the Indian Ocean
(Wiley, 2022-06-25) Wenegrat, J. O.; Bonanno, E.; Rack, U.; Gebbie, G.
The Indian Ocean is warming rapidly, with widespread effects on regional weather and global climate. Sea-surface temperature records indicate this warming trend extends back to the beginning of the 20th century, however the lack of a similarly long instrumental record of interior ocean temperatures leaves uncertainty around the subsurface trends. Here we utilize unique temperature observations from three historical German oceanographic expeditions of the late 19th and early 20th centuries: SMS Gazelle (1874–1876), Valdivia (1898–1899), and SMS Planet (1906–1907). These observations reveal a mean 20th century ocean warming that extends over the upper 750 m, and a spatial pattern of subsurface warming and cooling consistent with a 1°–2° southward shift of the southern subtropical gyre. These interior changes occurred largely over the last half of the 20th century, providing observational evidence for the acceleration of a multidecadal trend in subsurface Indian Ocean temperature.
A CloudSat and CALIPSO-based evaluation of the effects of thermodynamic instability and aerosol loading on Amazon Basin deep convection and lightning
(2023-08-14) Allen, Dale
The Amazon Basin, which plays an important role in the carbon and water cycle, is under stress due to changes in climate, agricultural practices, and deforestation. The Basin includes a rainforest in the northwest and a mix of deforested areas, savannah-type vegetation, and agriculture in the southeast. The effects of instability and aerosol loading on thunderstorms in the Basin (75-45° W, 0-15° S) were examined during mid-August through mid-December, a period with large variations in aerosols, intense convective storms, and plentiful flashes. The analysis used measurements of radar reflectivity, ice water content (IWC), and aerosol type from instruments aboard the CloudSat and CALIPSO satellites, flash rates from the ground-based STARNET network, and aerosol optical depth (AOD) from a surface network and a meteorological re-analysis. After controlling for convective available potential energy (CAPE), a measure of instability, it was found that thunderstorms that developed under dirty (high-AOD) conditions were approximately 1.5 km deeper, had 50% more IWC, and more than two times as many flashes as storms that developed under clean (low-AOD) conditions. Flash rates were also found to be larger during periods when smoke rather than dust was common in the lower troposphere, likely because these periods were less stable.
A Neural-Network Based MPAS—Shallow Water Model and Its 4D-Var Data Assimilation System
(MDPI, 2023-01-10) Tian, Xiaoxu; Conibear, Luke; Steward, Jeffrey
The technique of machine learning has been increasingly applied in numerical weather predictions. The aim of this study is to explore the application of a neural network in data assimilation by making use of the convenience in obtaining the tangent linear and adjoint (TL/AD) of a neural network (NN) and formulating a NN-based four-dimensional variational (4D-Var) DA system. A NN-based shallow water (SW) model is developed in this study. The NN model consists of three layers. The weights and biases in the NN-based SW model are trained with 60 years of hourly ERA5 geopotentials and wind field at 500 hPa as initial conditions and the corresponding 12-h forecasts by Model for Prediction Across Scales (MPAS)-SW, in total of 534,697 sets of samples. The 12-h forecasts from independent dates made by NN-based SW prove to closely emulate the simulations by the actual MPAS-SW model. This study further shows that the TL/AD of an NN model can be easily developed and validated. The ease of obtaining the TL/AD makes NN conveniently applicable in various aspects within a data assimilation (DA) system. To demonstrate such, a continuous 4D-Var DA system is also developed with the forward NN and its adjoint. To demonstrate the functionality of the NN-based 4D-Var DA system, the results from a higher resolution simulation will be treated as observations and assimilated to analyze the low resolution initial conditions. The forecasts starting from the analyzed initial conditions will be compared with those without assimilation to demonstrate improvements.
A Study of the Oklahoma City Urban Heat Island Effect Using a WRF/Single-Layer Urban Canopy Model, a Joint Urban 2003 Field Campaign, and MODIS Satellite Observations
(MDPI, 2017-09-07) Zhang, Hengyue; Jin, Menglin S.; Leach, Martin
The urban heat island effect (UHI) for inner land regions was investigated using satellite data, ground observations, and simulations with an Single-Layer Urban Canopy Parameterization (SLUCP) coupled into the regional Weather Research Forecasting model (WRF, http://wrf-model.org/index.php). Specifically, using the satellite-observed surface skin temperatures (Tskin), the intensity of the UHI was first compared for two inland cities (Xi’an City, China, and Oklahoma City (OKC)), which have different city populations and building densities. The larger population density and larger building density in Xi’an lead to a stronger skin-level UHI by 2 °C. However, the ground observed 2 m surface air temperature (Tair) observations showed an urban cooling island effect (UCI) over the downtown region in OKC during the daytime of 19 July 2003, from a DOE field campaign (Joint Urban 2003). To understand this contrast between satellite-based Tskin and ground-based Tair, a sensitivity study using WRF/SLUCP was analyzed. The model reproduced a UCI in OKC. Furthermore, WRF/Noah/SLUCM simulations were also compared with the Joint Urban 2003 ground observations, including wind speeds, wind directions, and energy fluxes. Although the WRF/SLUCM model failed to simulate these variables accurately, it reproduced the diurnal variations of surface temperatures, wind speeds, wind directions, and energy fluxes reasonably well.
Aircraft Observations of Dust and Pollutants over NE China: Insight into the Meteorological Mechanisms of Long-Range Transport
(2006) Dickerson, Russell; Li, Can; Li, Zhanqing; Marufu, Lackson; Stehr, Jeffrey; Chen, H.; Wang, P; Xia, X.; Ban, X.; Gong, F.; Yaun, J.; Yang, J.
Substantial concentrations of trace gases and aerosols are lofted and carried from Asia over the Pacific producing an inter-hemispheric impact on atmospheric chemistry and climate. The meteorological mechanism leading to this large-scale transport of dust and pollutants remains a major uncertainty in quantifying the global effects of emissions from the developing world. Models and downwind measurements have identified isentropic advection associated with wave cyclones (warm conveyor belt circulation) as an important mechanism. We present data from a case study conducted over Shenyang in NE China as part of EAST-AIRE in April 2005 in which upstream convection, rather than WCB lofting appears to dominate. Observations from instrumented aircraft flights, back trajectories, and satellite images of clouds (GOES) and aerosols (MODIS) are analyzed. In this heavily industrialized and populated region, the warm-sector PBL air ahead of a cold front was highly polluted. In the free troposphere, between ~1000 and 4000 m altitude, concentrations of trace gases and aerosols were lower, but well above background; we measured ~70 ppb O3, ~300 ppb CO, ~2 ppb SO2, and ~ 8x10-5 m-1 aerosol scattering. These observations show that dry (non-precipitating) convection can be an important mechanism for converting local air pollution problems into regional or global atmospheric chemistry problems. Climatological data indicate that spring (MAM) precipitation over NE China is low, about 90 mm compared to 290 mm over the NE US. Cloud cover, however, is similar with cumulus clouds reported about 7% of the time over NE China and about 9% of the time over the NE US suggesting that lofting in dry convective events may be common over NE Asia. Evaluation of models’ convective schemes and further observations near the source regions are called for.
Analysis of Summertime PM2.5 and haze in the Mid-Atlantic Region
(Air & Waste Management Association (A&WMA), 2003) Chen, L.-W. Antony; Chow, Judith C.; Doddridge, Bruce G.; Dickerson, Russell R.; Ryan, William F.; Mueller, Peter K.
Observations of the mass and chemical composition of particles less than 2.5 µm in aerodynamic diameter (PM2.5), light extinction, and meteorology in the urban Baltimore-Washington corridor during July 1999 and July 2000 are presented and analyzed to study summertime haze formation in the mid-Atlantic region. The mass fraction of ammoniated sulfate (SO4^2-) and carbonaceous material in PM2.5 were each ~50% for cleaner air (PM2.5 < 10 µg/m3) but changed to ~60% and ~20%, respectively, for more polluted air (PM2.5 > 30 µg/m3). This signifies the role of SO4^2- in haze formation. Comparisons of data from this study with the Interagency Monitoring of Protected Visual Environments network suggest that SO4^2- is more regional than carbonaceous material and originates in part from upwind source regions. The light extinction coefficient is well correlated to PM2.5 mass plus water associated with inorganic salt, leading to a mass extinction efficiency of 7.6 ± 1.7 m2/g for hydrated aerosol. The most serious haze episode occurring between July 15 and 19, 1999, was characterized by westerly transport and recirculation slowing removal of pollutants. At the peak of this episode, 1-hr PM2.5 concentration reached ~45 µg/m3, visual range dropped to ~5 km, and aerosol water likely contributed to ~40% of the light extinction coefficient.
Assessing Coastal SMAP Surface Salinity Accuracy and Its Application to Monitoring Gulf of Maine Circulation Dynamics
(MDPI, 2018-08-06) Grodsky, Semyon A.; Vandemark, Douglas; Feng, Hui
Monitoring the cold and productive waters of the Gulf of Maine and their interactions with the nearby northwestern (NW) Atlantic shelf is important but challenging. Although remotely sensed sea surface temperature (SST), ocean color, and sea level have become routine, much of the water exchange physics is reflected in salinity fields. The recent invention of satellite salinity sensors, including the Soil Moisture Active Passive (SMAP) radiometer, opens new prospects in regional shelf studies. However, local sea surface salinity (SSS) retrieval is challenging due to both cold SST limiting salinity sensor sensitivity and proximity to land. For the NW Atlantic, our analysis shows that SMAP SSS is subject to an SST-dependent bias that is negative and amplifies in winter and early spring due to the SST-related drop in SMAP sensor sensitivity. On top of that, SMAP SSS is subject to a land contamination bias. The latter bias becomes noticeable and negative when the antenna land contamination factor (LC) exceeds 0.2%, and attains maximum negative values at LC = 0.4%. Coastward of LC = 0.5%, a significant positive land contamination bias in absolute SMAP SSS is evident. SST and land contamination bias components are seasonally dependent due to seasonal changes in SST/winds and terrestrial microwave properties. Fortunately, it is shown that SSS anomalies computed relative to a satellite SSS climatology can effectively remove such seasonal biases along with the real seasonal cycle. SMAP monthly SSS anomalies have sufficient accuracy and applicability to extend nearer to the coasts. They are used to examine the Gulf of Maine water inflow, which displayed important water intrusions in between Georges Banks and Nova Scotia in the winters of 2016/17 and 2017/18. Water intrusion patterns observed by SMAP are generally consistent with independent measurements from the European Soil Moisture Ocean Salinity (SMOS) mission. Circulation dynamics related to the 2016/2017 period and enhanced wind-driven Scotian Shelf transport into the Gulf of Maine are discussed.
Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF-Hydro System
(Wiley, 2022-03-14) Lahmers, Timothy M.; Kumar, Sujay V.; Rosen, Daniel; Dugger, Aubrey; Gochis, David J.; Santanello, Joseph A.; Gangodagamage, Chandana; Dunlap, Rocky
The NASA LIS/WRF-Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi-scale surface hydrological modeling capabilities of the WRF-Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub-surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF-Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open-loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF-Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications.
Climate change quadruples flood-causing extreme monsoon rainfall events in Bangladesh and northeast India
(Wiley, 2023-12-22) Fahad, Abdullah A.; Hasan, Mahdi; Sharmili, Noshin; Islam, Shammunul; Swenson, Erik T.; Roxy, M. K.
Bangladesh and northeast India are the most densely populated regions in the world where severe floods as a result of extreme rainfall events kill hundreds of people and cause socio-economic losses regularly. Owing to local high topography, the moisture-carrying monsoon winds converge near southeast Bangladesh (SEB) and northeast Bangladesh and India (NEBI), which produces significant extreme rainfall events from May to October. Using observed data, we find an increasing trend of 1-day extreme event (>150 mm/day-1) frequency during 1950–2021. The extreme rainfall events quadrupled over western Meghalaya (affecting NEBI) and coastal SEB during this period. Composite analysis indicates that warm Bay of Bengal sea-surface temperature intensifies the lower tropospheric moisture transport and flux through the low-level jet (LLJ) to inland, where mountain-forced moisture converges and precipitates as rainfall during extreme events. To understand the role of climate change, we use high-resolution downscaled models from Coupled Model Intercomparison Project phase 6 (CMIP6). We find that the monsoon extreme event increase is ongoing and the region of quadrupled events further extends over the NEBI and SEB in the future (2050–2079) compared with historical simulations (1950–1979). A quadrupling of the intense daily moisture transport episodes due to increased LLJ instability, a northward shift of LLJ, and increased moisture contribute to the increased future extreme events. This dynamic process causes moisture to be transported to the NEBI from the southern Bay of Bengal, and the local thermodynamic response to climate change contributes to the increased extreme rainfall events. The CMIP6 projection indicates that more devastating flood-causing extreme rainfall events will become more frequent in the future.
Climate model shows large-scale wind and solar farms in the Sahara increase rain and vegetation
(Science, 2018-09-07) Li, Yan; Kalnay, Eugenia; Motesharrei, Safa; Rivas, Jorge; Kucharski, Fred; Kirk-Davidoff, Daniel; Bach, Eviatar; Zeng, Ning
Wind and solar farms offer a major pathway to clean, renewable energies. However, these farms would significantly change land surface properties, and, if sufficiently large, the farms may lead to unintended climate consequences. In this study, we used a climate model with dynamic vegetation to show that large-scale installations of wind and solar farms covering the Sahara lead to a local temperature increase and more than a twofold precipitation increase, especially in the Sahel, through increased surface friction and reduced albedo. The resulting increase in vegetation further enhances precipitation, creating a positive albedo–precipitation–vegetation feedback that contributes ~80% of the precipitation increase for wind farms. This local enhancement is scale dependent and is particular to the Sahara, with small impacts in other deserts.
Differentiating the Contributions of Particle Concentration, Humidity, and Hygroscopicity to Aerosol Light Scattering at Three Sites in China
(Wiley, 2022-11-23) Jin, Xiaoai; Li, Zhanqing; Wu, Tong; Wang, Yuying; Su, Tianning; Ren, Rongmin; Wu, Hao; Zhang, Dongmei; Li, Shangze; Cribb, Maureen
The scattering of light by aerosol particles dictates atmospheric visibility, which is a straightforward indicator of air quality. It is affected by numerous factors, such as particle number size distribution, particle mass concentration (PM2.5), ambient relative humidity (RH), and chemical composition. The latter two factors jointly influence the aerosol liquid water content (ALWC). Here, the particle backscattering coefficient (βp) under ambient RH conditions is investigated to differentiate and quantify the contributions of aerosol properties and meteorology using comprehensive observational datasets acquired at three megacities in China, that is, Beijing (BJ), Nanjing (NJ), and Guangzhou (GZ). Overall, the temporal variations in βp under ambient RH conditions are consistent with those in ALWC at the three sites. The PM2.5 in BJ is systematically higher than in NJ and GZ, while ambient RH and aerosol hygroscopicity in NJ are much higher than in BJ and GZ. Notable differences in the variations of βp with related factors at the three sites are demonstrated. βp is more sensitive to particle hygroscopicity and mass in NJ and ambient RH in BJ. The relative contributions of these factors to βp at the three sites under different pollution conditions are differentiated and quantified. The factor with the largest impact on the variability in βp shifts from particle mass to ambient RH as air quality deteriorated to heavy pollution in BJ. The opposite is true in NJ. In GZ, the contributions of these factors to changes in βp under different pollution conditions are similar, both dominated by PM2.5.
Diurnal Variability of Surface Temperature over Lakes: Case Study for Lake Huron
(MDPI, 2021-02-13) Chen, Wen; Pinker, Rachel T.; Rivera, Gerardo; Hook, Simon
The significance of the diurnal variability of Lake Surface Temperature (LST) has been recognized; yet, its magnitude in terms of spatial and temporal variability is not well known. Attempts have been made to derive such information from satellites at a high spatial resolution; however, most have been made from polar orbiting satellites that sample only twice per day. We have developed an approach to derive such information from geostationary satellites at an hourly time scale and at a spatial resolution of about 5 km. The approach to derive LST uses the Radiative Transfer for TIROS Operational Vertical Sounder (TOVS) (RTTOV) model driven by the Modern-Era Retrospective analysis for Research and Applications (MERRA)-2 information. The methodology has been implemented over Lake Huron for about six years. We present the results of the evaluation against various independent satellite products and demonstrate that there is a strong diurnal variability in the skin temperature over the lake and that the lowest and highest values, as derived twice per day from polar orbiting satellites, may not represent the magnitude of the Diurnal Temperature Range (DTR).
Effect of Urban Built-Up Area Expansion on the Urban Heat Islands in Different Seasons in 34 Metropolitan Regions across China
(MDPI, 2022-12-31) Han, Wenchao; Tao, Zhuolin; Li, Zhangqing; Chengg, Miaomiao; Fan, Hao; Cribb, Maureen; Wang, Qi
The urban heat island (𝑈𝐻𝐼) refers to the land surface temperature (LST) difference between urban areas and their undeveloped or underdeveloped surroundings. It is a measure of the thermal influence of the urban built-up area expansion (𝑈𝐵𝐴𝐸), a topic that has been extensively studied. However, the impact of 𝑈𝐵𝐴𝐸 on the LST differences between urban areas and rural areas (𝑈𝐻𝐼𝑈−𝑅) and between urban areas and emerging urban areas (𝑈𝐻𝐼𝑈−𝑆) in different seasons has seldom been investigated. Here, the 𝑈𝐻𝐼𝑈−𝑆 and 𝑈𝐻𝐼𝑈−𝑅 in 34 major metropolitan regions across China, and their spatiotemporal variations based on long-term space-borne observations during the period 2001–2020 were analyzed. The 𝑈𝐵𝐴𝐸 quantified by the difference in landscape metrics of built-up areas between 2020 and 2000 and their impact on 𝑈𝐻𝐼 was further analyzed. The 𝑈𝐵𝐴𝐸 is impacted by the level of economic development and topography. The 𝑈𝐵𝐴𝐸 of cities located in more developed regions was more significant than that in less developed regions. Coastal cities experienced the most obvious 𝑈𝐵𝐴𝐸, followed by plain and hilly cities. The 𝑈𝐵𝐴𝐸 in mountainous regions was the weakest. On an annual basis, 𝑈𝐻𝐼𝑈−𝑅 was larger than 𝑈𝐻𝐼𝑈−𝑆, decreasing more slowly with 𝑈𝐵𝐴𝐸 than 𝑈𝐻𝐼𝑈−𝑆. In different seasons, the 𝑈𝐻𝐼𝑈−𝑆 and 𝑈𝐻𝐼𝑈−𝑅 were larger, more clearly varying temporally with 𝑈𝐵𝐴𝐸 in summer than in winter, and their temporal variations were significantly correlated with 𝑈𝐵𝐴𝐸 in summer but not in winter. The seasonal difference in 𝑈𝐻𝐼𝑈−𝑅 was larger than that of 𝑈𝐻𝐼𝑈−𝑆. Both the 𝑈𝐻𝐼𝑈−𝑆 and 𝑈𝐻𝐼𝑈−𝑅 in coastal cities were the lowest in summer, decreasing the fastest with 𝑈𝐵𝐴𝐸, while those in mountain cities decreased the slowest. The change in the density of built-up lands was the primary driver affecting the temporal variations in 𝑈𝐻𝐼𝑈−𝑆 and 𝑈𝐻𝐼𝑈−𝑅 during 𝑈𝐵𝐴𝐸, followed by changes in proportion and shape, while the impact of the speed of expansion was the smallest, all of which were more obvious in summer than in winter. The decreased density of built-up lands can reduce 𝑈𝐻𝐼. These findings provide a new perspective for a deeper understanding of the effect of urban expansion on LST in different seasons.
Evaluation of Stratocumulus Evolution Under Contrasting Temperature Advections in CESM2 Through a Lagrangian Framework
(Wiley, 2024-02-16) Zhang, Haipeng; Zheng, Youtong; Li, Zhanqing
This study leveraged a Lagrangian framework to examine the evolution of stratocumulus clouds under cold and warm advections (CADV and WADV) in the Community Earth System Model 2 (CESM2) against observations. We found that CESM2 simulates a too rapid decline in low-cloud fraction (LCF) and cloud liquid water path (CLWP) under CADV conditions, while it better aligns closely with observed LCF under WADV conditions but overestimates the increase in CLWP. Employing an explainable machine learning approach, we found that too rapid decreases in LCF and CLWP under CADV conditions are related to overestimated drying effects induced by sea surface temperature, whereas the substantial increase in CLWP under WADV conditions is associated with the overestimated moistening effects due to free-tropospheric moisture and surface winds. Our findings suggest that overestimated drying effects of sea surface temperature on cloud properties might be one of crucial causes for the high equilibrium climate sensitivity in CESM2.
Flood Impacts on Net Ecosystem Exchange in the Midwestern and Southern United States in 2019
(Wiley, 2023-09-06) Balashov, Nikolay V.; Ott, Lesley E.; Weir, Brad; Basu, Sourish; Davis, Kenneth J.; Miles, Natasha L.; Thompson, Anne M.; Stauffer, Ryan M.
Climate extremes such as droughts, floods, heatwaves, frosts, and windstorms add considerable variability to the global year-to-year increase in atmospheric CO2 through their influence on terrestrial ecosystems. While the impact of droughts on terrestrial ecosystems has received considerable attention, the response to flooding is not well understood. To improve upon this knowledge, the impact of the 2019 anomalously wet conditions over the Midwest and Southern US on CO2 vegetation fluxes is examined in the context of 2017–2018 when such precipitation anomalies were not observed. CO2 is simulated with NASA's Global Earth Observing System (GEOS) combined with the Low-order Flux Inversion, where fluxes of CO2 are estimated using a suite of remote sensing measurements including greenness, night lights, and fire radiative power as well as with a bias correction based on insitu observations. Net ecosystem exchange CO2 tracers are separated into the three regions covering the Midwest, South, and Eastern Texas and adjusted to match CO2 observations from towers located in Iowa, Mississippi, and Texas. Results indicate that for the Midwestern region consisting primarily of corn and soybeans crops, flooding contributes to a 15%–25% reduction of annual net carbon uptake in 2019 in comparison to 2017 and 2018. These results are supported by independent reports of changes in agricultural activity. For the Southern region, comprised mainly of non-crop vegetation, annual net carbon uptake is enhanced in 2019 by about 10%–20% in comparison to 2017 and 2018. These outcomes show the heterogeneity in effects that excess wetness can bring to diverse ecosystems.
Full Results Accompanying A Machine Learning Examination of Hydroxyl Radical Differences Among Model Simulations for CCMI-1
(2020) Nicely, Julie M.; Duncan, Bryan N.; Hanisco, Thomas F.; Wolfe, Glenn M.; Salawitch, Ross J.; Deushi, Makoto; Haslerud, Amund S.; Jöckel, Patrick; Josse, Béatrice; Kinnison, Douglas E.; Klekociuk, Andrew; Manyin, Michael E.; Marécal, Virginie; Morgenstern, Olaf; Murray, Lee T.; Myhre, Gunnar; Oman, Luke D.; Pitari, Giovanni; Pozzer, Andrea; Quaglia, Ilaria; Revell, Laura E.; Rozanov, Eugene; Stenke, Andrea; Stone, Kane; Strahan, Susan; Tilmes, Simone; Tost, Holger; Westervelt, Daniel M.; Zeng, Guang
The hydroxyl radical (OH) plays critical roles within the troposphere, such as determining the lifetime of methane (CH4), yet is challenging to model due to its fast cycling and dependence on a multitude of sources and sinks. As a result, the reasons for variations in OH and the resulting methane lifetime, both between models and in time, are difficult to diagnose. We apply a neural network (NN) approach to address this issue within a group of models that participated in the Chemistry-Climate Model Initiative (CCMI). Analysis of the historical specified dynamics simulations performed for CCMI indicates that the primary drivers of methane lifetime differences among ten models are the flux of UV light to the troposphere (indicated by the photolysis frequency JO1D), the mixing ratio of tropospheric ozone (O3), the abundance of nitrogen oxides (NOx=NO+NO2), and details of the various chemical mechanisms that drive OH. Water vapor, carbon monoxide (CO), the ratio of NO:NOx, and formaldehyde (HCHO) explain moderate differences in methane lifetime, while isoprene, methane, the photolysis frequency of NO2 by visible light (JNO2), overhead ozone column, and temperature account for little-to-no model variation in methane lifetime. We also apply the NNs to analysis of temporal trends in OH from 1980 to 2015. All models that participated in the specified dynamics historical simulation for CCMI demonstrate a decline in methane lifetime during the analysed timeframe. The significant contributors to this trend, in order of importance, are tropospheric O3, JO1D, NOx, and H2O, with CO also causing substantial interannual variability in OH burden. Finally, the identified trends in methane lifetime are compared to calculated trends in the tropospheric mean OH concentration from previous work, based on analysis of observations. The comparison reveals a robust result for the effect of rising water vapor on OH and methane lifetime, imparting an increasing and decreasing trend of about 0.5 % per decade, respectively. The responses due to NOx, ozone column, and temperature are also in reasonably good agreement between the two studies.
Hygroscopicity of Different Types of Aerosol Particles: Case Studies Using Multi-Instrument Data in Megacity Beijing, China
(MDPI, 2020-03-01) Wu, Tong; Li, Zhanqing; Chen, Jun; Wang, Yuying; Wu, Hao; Jin, Xiao'ai; Liang, Chen; Li, Shangze; Wang, Wei; Cribb, Maureen
Water uptake by aerosol particles alters its light-scattering characteristics significantly. However, the hygroscopicities of different aerosol particles are not the same due to their different chemical and physical properties. Such differences are explored by making use of extensive measurements concerning aerosol optical and microphysical properties made during a field experiment from December 2018 to March 2019 in Beijing. The aerosol hygroscopic growth was captured by the aerosol optical characteristics obtained from micropulse lidar, aerosol chemical composition, and aerosol particle size distribution information from ground monitoring, together with conventional meteorological measurements. Aerosol hygroscopicity behaves rather distinctly for mineral dust coarse-mode aerosol (Case I) and non-dust fine-mode aerosol (Case II) in terms of the hygroscopic enhancement factor, 𝑓𝛽(𝑅𝐻,𝜆532), calculated for the same humidity range. The two types of aerosols were identified by applying the polarization lidar photometer networking method (POLIPHON). The hygroscopicity for non-dust aerosol was much higher than that for dust conditions with the 𝑓𝛽(𝑅𝐻,𝜆532) being around 1.4 and 3.1, respectively, at the relative humidity of 86% for the two cases identified in this study. To study the effect of dust particles on the hygroscopicity of the overall atmospheric aerosol, the two types of aerosols were identified and separated by applying the polarization lidar photometer networking method in Case I. The hygroscopic enhancement factor of separated non-dust fine-mode particles in Case I had been significantly strengthened, getting closer to that of the total aerosol in Case II. These results were verified by the hygroscopicity parameter, κ (Case I non-dust particles: 0.357 ± 0.024; Case II total: 0.344 ± 0.026), based on the chemical components obtained by an aerosol chemical speciation instrument, both of which showed strong hygroscopicity. It was found that non-dust fine-mode aerosol contributes more during hygroscopic growth and that non-hygroscopic mineral dust aerosol may reduce the total hygroscopicity per unit volume in Beijing.
Impact of Fire Emissions on U.S. Air Quality from 1997 to 2016–A Modeling Study in the Satellite Era
(MDPI, 2020-03-12) Tao, Zhining; He, Hao; Sun, Chao; Tong, Daniel; Liang, Xin-Zhong
A regional modeling system that integrates the state-of-the-art emissions processing (SMOKE), climate (CWRF), and air quality (CMAQ) models has been combined with satellite measurements of fire activities to assess the impact of fire emissions on the contiguous United States (CONUS) air quality during 1997–2016. The system realistically reproduced the spatiotemporal distributions of the observed meteorology and surface air quality, with a slight overestimate of surface ozone (O3) by ~4% and underestimate of surface PM2.5 by ~10%. The system simulation showed that the fire impacts on primary pollutants such as CO were generally confined to the fire source areas but its effects on secondary pollutants like O3 spread more broadly. The fire contribution to air quality varied greatly during 1997-2016 and occasionally accounted for more than 100 ppbv of monthly mean surface CO and over 20 µg m−3 of monthly mean PM2.5 in the Northwest U.S. and Northern California, two regions susceptible to frequent fires. Fire emissions also had implications on air quality compliance. From 1997 to 2016, fire emissions increased surface 8-hour O3 standard exceedances by 10% and 24-hour PM2.5 exceedances by 33% over CONUS.
Implementation of a Discrete Dipole Approximation Scattering Database Into Community Radiative Transfer Model
(Wiley, 2022-12-07) Moradi, Isaac; Stegmann, Patrick; Johnson, Benjamin; Barlakas, Vasileios; Eriksson, Patrick; Geer, Alan; Gelaro, Ronald; Kalluri, Satya; Kleist, Daryl; Liu, Quanhua; Mccarty, Will
The Community Radiative Transfer Model (CRTM) is a fast model that requires bulk optical properties of hydrometeors in the form of lookup tables to simulate all-sky satellite radiances. Current cloud scattering lookup tables of CRTM were generated using the Mie-Lorenz theory thus assuming spherical shapes for all frozen habits, while actual clouds contain frozen hydrometeors with different shapes. The Discrete Dipole Approximation (DDA) technique is an effective technique for simulating the optical properties of non-spherical hydrometeors in the microwave region. This paper discusses the implementation and validation of a comprehensive DDA cloud scattering database into CRTM for the microwave frequencies. The original DDA database assumes total random orientation in the calculation of single scattering properties. The mass scattering parameters required by CRTM were then computed from single scattering properties and water content dependent particle size distributions. The new lookup tables eliminate the requirement for providing the effective radius as input to CRTM by using the cloud water content for the mass dimension. A collocated dataset of short-term forecasts from Integrated Forecast System of the European Center for Medium-Range Weather Forecasts and satellite microwave data was used for the evaluation of results. The results overall showed that the DDA lookup tables, in comparison with the Mie tables, greatly reduce the differences among simulated and observed values. The Mie lookup tables especially introduce excessive scattering for the channels operating below 90 GHz and low scattering for the channels above 90 GHz.