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.