Diagnosing Mechanisms for a Spatio-Temporally Varying Tropical Land Rainfall Response to Transient El Niño Warming And Development of a Prognostic Climate Risk Management Framework

• Main Author: Parhi, Pradipta
• Format: Thesis
• Language: unknown
• Published: Columbia University 2020
• Subjects: Environmental engineering; FOS Environmental engineering; Climatic changes; Climatic changes > Risk assessment; Climatic changes > Risk management; Climatic changes > Economic aspects; Climatic changes > Social aspects; Rain and rainfall; Indian; North Atlantic
• Online Access: https://dx.doi.org/10.7916/d8-x3p5-t040; https://academiccommons.columbia.edu/doi/10.7916/d8-x3p5-t040

Assessing and managing risks posed by climate variability and change is challenging in the tropics, from both a scientific and a socio-economic perspective. While our understanding of the tropical land rainfall variability and its future projection is highly uncertain, most of the vulnerable countries with a limited adaptation capability are within the tropical band. This dissertation combines a process-based physical understanding with observational analysis to characterize the spatio-temporal changes in the tropical land rainfall during a transient El Niño evolution, with an emphasis on the risk management of the dry and wet extremes. The broad objectives are two-fold: 1) To make better sense of the higher uncertainty in the tropical rainfall response to warming and 2) to improve climate risk management strategies in the tropical developing countries. An ENSO teleconnection mechanism, referred to as the tropical tropospheric temperature or TTT mechanism provides a theoretical framework to study the remote tropical land rainfall behavior during a transient El Niño warming. The TTT mechanism postulates that the tropic-wide free tropospheric warming interacts locally with the deep convection to modulate remote tropical climate. During the growth phase, anomalous free tropospheric temperature causes direct and fast atmospheric adjustments leading to tropospheric stability to deep moist convection and a drier response. Subsequently, during mature phase, a recovery of the initial rainfall deficit follows due to indirect and slower adjustments in surface temperature and humidity fields. In chapter 2 and 3 of this dissertation, the changes in the observed tropical land rainfall characteristics and other climate fields conditional on the growth and mature phase of El Niño warming are investigated and the role of dynamical and thermodynamic mechanisms as hypothesized by the TTT mechanism are elucidated. In chapter 4, an El Niño forecast based early action investment strategy is developed to reduce the socio-economic impacts of rainfall extremes at sub-seasonal to inter-annual lead time scales. In the part I (chapter 2), the analysis is conducted at a regional scale over the tropical Africa. Using the TTT mechanism, a physical explanation is provided for the contrasting rainfall response over the Western Sahel and tropical Eastern Africa during an El Niño. The study finds that the Western Sahel’s main rainy season (July-September) is affected by the growth phase of El Niño through (i) a lack of neighboring North Atlantic sea surface warming, (ii) an absence of an atmospheric column water vapor anomaly over the North Atlantic and Western Sahel, and (iii) higher atmospheric vertical stability over the Western Sahel, resulting in the suppression of mean seasonal rainfall as well as number of wet days. In contrast, the short rainy season (October-December) of tropical Eastern Africa is impacted by the mature phase of El Niño through (i) neighboring Indian Ocean sea surface warming, (ii) positive column water vapor anomalies over the Indian Ocean and tropical Eastern Africa, and (iii) higher atmospheric vertical instability over tropical Eastern Africa, leading to an increase in mean seasonal rainfall as well as in the number of wet days. While the modulation of the frequency of wet days and seasonal mean accumulation is statistically significant, daily rainfall intensity (for days with rainfall >1 mm/day), whether mean, median, or extreme, does not show a significant response in either region. Hence, the variability in seasonal mean rainfall that can be attributed to the El Niño–Southern Oscillation phenomenon in both regions is likely due to changes in the frequency of rainfall. These observed changes agree with the predictions of the TTT mechanism. In the part II (chapter 3), a global scale analysis is performed to more generally characterize the spatio-temporal differences in remote tropical land rainfall response to El Niño warming. The principal conclusions are: 1) during the El Niño growth phase relative to the neutral phase, rainfall decreases. A significant decrease in mean accumulation can be attributed to a significant increase in proportion of dry days and decrease in median and extreme intensity. A significant descent anomaly confirms the vertical stabilization and dominance of dynamical processes. 2) During the mature phase relative to the growth phase, rainfall increases, signifying a recovery from the suppression of deep moist convection. A significant increase in mean accumulation is accompanied by a decrease in proportion of dry days and by an increase in median and extreme intensity characteristics. The significant rise in the moisture field corroborates the dominance of thermodynamic processes. These findings are expected from the TTT mechanism and generalizes the findings of part I to the global scale. In the part III (chapter 4), an El Niño forecast based index insurance policy is developed that can be used as an early action investment instrument. The forecast insurance (FI) design framework is illustrated with an application to El Niño associated flood hazard during the January-February-March-April (JFMA) season over Piura region of Peru. In order to determine the economic utility of the system, a simple cost-loss decision model, incorporating the insurance cost, is developed. The main conclusion is that the proposed El Niño forecast insurance policy with the pre-event Niño1.2 index based trigger has significant reliability and substantial utility for a wide range of policy parameters considered. Relative to a no early action strategy, the advantage of the system generally increases with i) shortening in the lead time from 9 to 1 month, ii) increase in El Niño severity level from 10 to 50 year return period and iii) increase in avoidable loss to cost ratio (LCR) ratio from 1 to 1000. These results and the forecast insurance modeling and utility evaluation frameworks have implications for designing optimal contingent financial instruments for disaster risk reduction and climate change adaptation.