Theses and Dissertations from UMD
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Item No change in ENSO hydroclimate variability after the industrial revolution as recorded in ?18O of Tectona grandis L.F. from Southeast Sulawesi, Indonesia(2023) Herho, Sandy Hardian Susanto; Evans, Michael MNE; Geology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)El Niño-Southern Oscillation (ENSO) is a quasi-periodic interannual oscillation of the ocean-atmosphere system in the tropical Pacific which greatly influences global climate variability. However, the long-term response to greenhouse gas forcing is still controversial. In this study, we measured the oxygen isotopic composition of ?-cellulose samples at intraannual resolution from independently crossdated teak cores (Tectona grandis L. f.) collected at Muna, Indonesia (5.3ºS, 123ºE, elev. 10m). The site and observation has been previously shown to provide an indirect measure of ENSO activity via local precipitation amount variations associated with ENSO. We created an ensembled composite of the interannual variability for the period 1680-2005 (316 years) using empirical high pass filtering and random sampling of intra-annual resolution measurements. In processing this time series composite, we used Singular Spectrum Analysis (SSA) to high pass filter the data for the interannual variability associated with ENSO. The annually-resolved composite time series of ?18O that we constructed has a higher resolution than other studies that have been conducted to reconstruct ENSO-hydroclimate activities in the western tropical Pacific region over this period. Using this ?18O composite, we compared the distribution of events in the period before and after the industrial revolution using the two-sample Kolmogorov-Smirnov(KS) test. We found no statistically significant change in the distribution of ?18O anomalies. The same statistical test was applied to the Niño 3.4 reconstruction from the Last Millennium Reanalysis (LMR). The results of this study suggest that if there is indeed a forced response of ENSO, it is as yet indetectable. This may be because the forcing is not yet large enough or the forced response is small relative to the unforced variability. Additional factors that might explain this result in the ?18O composite include its observational and interpretational uncertainty, and in the LMR reconstruction, the scarcity of tropical observational constraints and systematic error in the representation of ENSO in climate simulations.Item Outgoing Longwave Radiation at the Top of Atmosphere: Algorithm Development, Comprehensive Evaluation, and Case Studies(2019) Zhou, Yuan; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Outgoing longwave radiation (OLR) at the top of the atmosphere (TOA) represents the total outgoing radiative flux emitted from the Earth’s surface and atmosphere in the thermal-infrared wavelength range. It plays a role as a powerful diagnostic of Earth’s climate system response to absorbed incoming solar radiation (ASR). Long-term measurements of OLR are essential for quantitatively understanding the climate system and its variability. However, inconsistencies and uncertainties have been always existing in OLR estimation among different datasets and algorithms. The objective of this dissertation is to carry out a comprehensive investigation on OLR with three specific questions: 1) How large are the discrepancies in estimates from various OLR products and what are their spatial and temporal patterns? 2) How to generate more accurate and more useful OLR estimates from multi-spectral satellite observations? 3) How does OLR respond to extreme climate and geological events such as El Niño/Southern Oscillation (ENSO) and giant earthquakes, and does the newly developed OLR products have any advantage to predict such events? To address those questions, this dissertation 1) conducts comprehensive evaluations on multiple OLR datasets by performing inter-comparisons among different satellite retrieved OLR products and different reanalysis OLR datasets, respectively; 2) develops an algorithm framework for estimating OLR from multi-spectral satellite observations based on radiative transfer simulations and statistical approaches; 3) investigates the correlation between OLR anomalies and historical ENSO events and a typical giant earthquake, and makes an attempt to predict ENSO and earthquake through OLR variations. Results indicate that 1) obvious discrepancies exist among different OLR datasets, with the two Japanese Meteorological Agency’s (JMA) Japanese Reanalysis project (JRA) OLRs displays the largest differences with others. However, all OLR products and datasets have comparable magnitude of inter-annual variability and monthly/seasonally anomaly, resulting in similar capability to capture the tropical expansion and ENSO events; 2) the developed OLR algorithm framework can generate reliable OLR estimates from multi-spectral remotely sensed data including Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR); 3) OLR has a potential to predict ENSO events through traditional statistical approach and machine learning methods, and it has slight advantage over the sea-surface-temperature (SST) as a metric for this purpose. The developed high resolution AVHRR OLR performs better than High-Resolution Infrared Radiation Sounder (HIRS) and NOAA interpolated AVHRR OLR in predicting ENSO. In addition, the singularities in OLR spatial anomalies around the giant earthquake epicenter starting three days prior to the earthquake days also suggests the OLR as an effective precursor of such an event, and the developed AVHRR OLR showed much stronger sensitivity to the coming earthquake than the existing NOAA interpolated AVHRR OLR, suggesting that the former one as a better indicator for the earthquake prediction. In this dissertation, the in-depth inter-comparisons among various OLR datasets will contribute as a reference for peers in the climate community who use OLR as one of inputs in their climate models or other diagnostic purpose. The developed OLR algorithm framework could be utilized to estimate OLR from future multi-spectral satellite data. This study also demonstrates that OLR is a promising indicator to predict ENSO and testifies that it is a precursor of giant earthquakes, which has implications for decision making aimed at alleviating the impacts on life and property from these extreme climate variations through some preventive measures such as releasing weather alert and conducting evacuations.