Atmospheric & Oceanic Science
Permanent URI for this communityhttp://hdl.handle.net/1903/2264
Formerly known as the Department of Meteorology.
Browse
190 results
Search Results
Item Vertical Column Densities of LNOx*(2024) Seiler, Madilynn; Bucsela, Eric; Pickering, KennethThis dataset was created by Eric Bucsela and contains vertical column densities of LNOx before background contribution was removed (LNOx*). There are 6 values for these column amounts, one for each method used to retrieve vertical column densities. The method used in Seiler et al., (2025) is that of VLNOxhi_cld. There are files for three case studies: June 11th, 2012, August 5th, 2007, August 6th, 2006.Item OMI Satellite LNOx* VCD June 11, 2012(2024) Seiler, Madilynn; Buscela, Eric; Pickering, Kenneth1ºx1º gridded OMI Vertical Column Densities of Lightning NOx without background subtraction for June 11th, 2012Item OCEAN HEAT CONTENT CALCULATION IMPROVEMENTS FOR EARTH’S ENERGY IMBALANCE QUANTIFICATION(2024) Boyer, Tim; Carton, James; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Earth’s Energy Imbalance, the difference between incoming and outgoing radiation at the top of the atmosphere, is stored in the atmosphere, land surface, cryosphere, and ocean, but is stored overwhelmingly (~90%) in the ocean on interannual and longer time scales. This imbalance, which is reflected in ocean heat uptake, is a primary indicator of the magnitude of change in energy the Earth’s system as well as an essential variable for understanding short-term variations and their effects on long-term regional and global climate change. The primary methods for calculating ocean heat content all depend on situ measurements of ocean subsurface temperature. The ocean subsurface temperature observing system as it is currently configured, with a substantial but not exclusive contribution from autonomous Argo profiling floats, is shown here to allow estimation of annual global ocean heat uptake with an uncertainty well below that possible with earlier ocean observing systems. It is also shown that maintenance and improvement of a global best quality ocean temperature profile database will lower uncertainty, both historically and for the current observing system and compensate to some extent for areas of sparse data in both direct calculation from observation and in data assimilation models. It is also shown that improvements to the methods used for mapping the inhomogeneous and anisotropic observations onto a regular grid spanning the global ocean will reduce uncertainty historically, currently, and into the future. On shorter monthly timescales regional changes in the Earth’s Energy Imbalance requires tracking the storage within the atmosphere, land, and cryosphere, and the heat transport within the ocean especially to depths where the energy is stored on longer time scales, in addition to ocean heat uptake. Monthly heat uptake estimates discussed here can be utilized with additional terms from atmosphere/land and ocean/sea ice reanalyses to provide Earth's Energy Imbalance estimates on these shorter time-scales in the future.Item UNDERSTANDING CLIMATIC FACTORS DRIVING WILDFIRES IN THE WESTERN U.S.(2024) ZHANG, LEI; Li, zhanqing; Meteorology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Wildfires have profound and catastrophic impacts on landscapes and human society and act as important agents in the transformation of ecosystems. Over the past decades, the western United States (WUS) has experienced a significant increase of large wildfires, with substantial rise of the economic and ecological costs. Considerable research efforts towards understanding climate change as a primary driver of larger and more severe wildfires, which exacerbates summer drought, reduces spring snowpack, etc. However, the physical relationships among wildfires in North America (NA) and regional feedback processes to changes in the large-scale circulation, global dryness, and linkages to global warming are still poorly understood. Our observational analyses of wildfire-climate relationships in North America were conducted using diverse independent observations and reanalysis data sets for the period 1984–2014. Results show that the WUS has experienced the most robust increase in burned area. In addition to warming, the WUS has been under the influence of multi-decadal trends in tropospheric relative humidity deficit, reduced cloudiness, increased surface net insolation, and enhanced adiabatic warming and drying from increased tropospheric subsidence, as well as drying from enhanced offshore low-level flow. These trends are found to be associated with a widening of the descending branch of the Hadley circulation, consistent with climate model projections under greenhouse gases warming. This work sheds new light on the underlying regional climate processes affecting wildfire trends in NA and linkages with climate change under global warming. My second work focuses on analyzing the causes of the exceptional 2020 fire season in the WUS. Our comprehensive examination shows this extraordinary year for fires in the WUS is the results of “perfect storm”, a combination of multiple climate and weather extremes events. Extreme fuel aridity in September serves as a compelling example of the critical significance of tropospheric subsidence to the surface and atmospheric RH deficit. The third study evaluates performance of the Canada and US fire indices over the various ecoregions of the WUS. My study also finds Haines index combined with current index further improves the performance of conditional frequency distribution and predictive skill of large fires, suggesting the importance and merit of input from atmosphere dryness and stability into current fire indices.Item 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.Item Understanding the Effects of Aerosols on Electrification and Lightning Polarity in an Idealized Supercell Thunderstorm via Model Emulation(Wiley, 2023-12-29) Sun, Mengyu; Li, Zhanqing; Wang, Tao; Mansell, Edward R.; Qie, Xiushu; Shan, Siyu; Liu, Dongxia; Cribb, MaureenAerosol effects on the lightning intensity and polarity of a continental supercell storm were investigated using a three-dimensional lightning scheme within the Weather Research and Forecasting model. We find that both intra-cloud (IC) and cloud-to-ground (CG) flashes are enhanced by the increasing number of cloud condensation nuclei (CCN), especially the percentage of positive CG (+CG) strokes peaking at 42%. Electrical characteristics of the storm varied in different aerosol scenarios through microphysical processes. Added aerosols increase the number of cloud droplets and ice-phase hydrometeors. The greater ice-crystal concentration and larger graupel size ensure sufficient charge separation, leading to higher charge density and more lightning discharges. In addition, an inverted polarity charge structure with a strong positive-charge region in the mid-levels was formed mainly due to the positively charged graupel in the presence of higher supercooled cloud water content. Positive lightning channels originating from this positive-charge region propagated to the ground, producing more +CG strokes. When the aerosol concentration was low, the charge density in the upper positive-charge region was much lower due to smaller ice-particle content. Consequently, there were barely any +CG strokes. Most of the negative CG flashes deposited positive charge in the lower negative-charge region.Item On the Role of Indian Ocean SST in Influencing the Differences in Atmospheric Variability Between 2020–2021 and 2021–2022 La Niña Boreal Winters(Wiley, 2024-03-10) Zhang, Tao; Kumar, ArunThe difference in observed atmospheric anomalies over the Northern Hemisphere winter between 2021–22 and 2020–21 La Niña years indicated a tripole pattern consisting of a Japan cyclone, a Bering Sea anticyclone, and a cyclone over the North American continent. This feature, however, was not replicated in the North American Multi-Model Ensemble (NMME) forecasts. A set of model sensitivity experiments was performed to better understand the cause of this discrepancy. The results revealed the possible role of the influence of sea surface temperature (SST) anomalies, particularly over the Indian Ocean, on the observed circulation differences that was further modulated by internal atmospheric variability. The failure in predicting circulation changes in NMME was next attributed to the errors in SST predictions over the Indian Ocean and highlights the need for improvements in SST forecasts over this region.Item Evaluation of Stratocumulus Evolution Under Contrasting Temperature Advections in CESM2 Through a Lagrangian Framework(Wiley, 2024-02-16) Zhang, Haipeng; Zheng, Youtong; Li, ZhanqingThis 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.Item On the use of consistent bias corrections to enhance the impact of Aeolus Level-2B Rayleigh winds on National Oceanic and Atmospheric Administration global forecast skill(Wiley, 2023-10-14) Liu, Hui; Garrett, Kevin; Ide, Kayo; Hoffman, Ross N.The operational Aeolus Level-2B (L2B) horizontal line-of-sight (HLOS) retrieved Rayleigh winds, produced by the European Space Agency (ESA), utilize European Centre for Medium-Range Weather Forecasts (ECMWF) short-term forecasts of temperature, pressure, and horizontal winds in the Rayleigh–Brillouin and M1 correction procedures. These model fields or backgrounds can contain ECMWF model-specific errors, which may propagate to the retrieved Rayleigh winds. This study examines the sensitivity of the retrieved Rayleigh winds to the changes in the model backgrounds, and the potential benefit of using the same system, in this case the National Oceanic and Atmospheric Administration's Finite-Volume Cubed Sphere Global Forecast System (FV3GFS), for both the corrections and the data assimilation and forecast procedures. It is shown that the differences in the model backgrounds (FV3GFS minus ECMWF) can propagate through the Level-2B horizontal line-of-sight Rayleigh wind retrieval process, mainly the M1 correction, resulting in differences in the retrieved Rayleigh winds with mean and standard deviation of magnitude as large as 0.2 m·s−1. The differences reach up to 0.4, 0.6, and 0.7 m·s−1 for the 95th, 99th, and 99.5th percentiles of the sample distribution with maxima of ∼1.4 m·s−1. The numbers of the large differences for the combined lower and upper 5th, 1st, and 0.5th percentile pairs are ∼6,100, 1,220, and 610 between 2.5 and 25 km height globally per day respectively. The ESA-disseminated Rayleigh wind product (based on the ECMWF corrections) already shows a significant positive impact on the FV3GFS global forecasts. In the observing system experiments performed, compared with the ESA Rayleigh winds, the use of the FV3GFS-corrected Rayleigh winds lead to ∼0.5% more Rayleigh winds assimilated in the lower troposphere and show enhanced positive impact on FV3GFS forecasts at the day 1–10 range but limited to the Southern Hemisphere.Item DEVELOPMENT AND APPLICATION OF PROPINQUITY MODELING FRAMEWORK FOR IDENTIFICATION AND ANALYSIS OF EXTREME EVENT PATTERNS(2024) kholodovsky, vitaly; Liang, Xin-Zhong; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Extreme weather and climate events such as floods, droughts, and heat waves can cause extensive societal damage. While various statistical and climate models have been developed for the purpose of simulating extremes, a consistent definition of extreme events is still lacking. Furthermore, to better assess the performance of the climate models, a variety of spatial forecast verification measures have been developed. However, in most cases, the spatial verification measures that are widely used to compare mean states do not have sufficient theoretical justification to benchmark extreme events. In order to alleviate inconsistencies when defining extreme events within different scientific communities, we propose a new generalized Spatio-Temporal Threshold Clustering method for the identification of extreme event episodes, which uses machine learning techniques to couple existing pattern recognition indices with high or low threshold choices. The method consists of five main steps: construction of essential field quantities, dimension reduction, spatial domain mapping, time series clustering, and threshold selection. We develop and apply this method using a gridded daily precipitation dataset derived from rain gauge stations over the contiguous United States. We observe changes in the distribution of conditional frequency of extreme precipitation from large-scale, well-connected spatial patterns to smaller-scale, more isolated rainfall clusters, possibly leading to more localized droughts and heatwaves, especially during the summer months. Additionally, we compare empirical and statistical probabilities and intensities obtained through the Conventional Location Specific methods, which are deficient in geometric interconnectivity between individual spatial pixels and independent in time, with a new Propinquity modeling framework. We integrate the Spatio-Temporal Threshold Clustering algorithm and the conditional semi-parametric Heffernan and Tawn (2004) model into the Propinquity modeling framework to separate classes of models that can calculate process level dependence of large-scale extreme processes, primarily through the overall extreme spatial field. Our findings reveal significant differences between Propinquity and Conventional Location Specific methods, in both empirical and statistical approaches in shape and trend direction. We also find that the process of aggregating model results without considering interconnectivity between individual grid cells for trend construction can lead to significant variations in the overall trend pattern and direction compared with models that do account for interconnectivity. Based on these results, we recommend avoiding such practices and instead adopting the Propinquity modeling framework or other spatial EVA models that take into account the interconnectivity between individual grid cells. Our aim for the final application is to establish a connection between extreme essential field quantity intensity fields and large-scale circulation patterns. However, the Conventional Location Specific Threshold methods are not appropriate for this purpose as they are memoryless in time and not able to identify individual extreme episodes. To overcome this, we developed the Feature Finding Decomposition algorithm and used it in combination with the Propinquity modeling framework. The algorithm consists of the following three steps: feature finding, image decomposition, and large-scale circulation patterns connection. Our findings suggest that the Western Pacific Index, particularly its 5th percentile and 5th mode of decomposition, is the most significant teleconnection pattern that explains the variation in the trend pattern of the largest feature intensity.