Precipitation regime changes in the maritime continent

V. Kokorev*

*Corresponding author for this work

Research output: ThesisPhD Thesis - Research UT, graduation UT

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Abstract

This thesis focuses on changes in the precipitation regime in the Maritime Continent. The Maritime Continent is the region between the Indian and Pacific oceans consisting of the Indonesian and Philippine archipelagos and countless other smaller islands. The Maritime Continent connects the Indian and Pacific oceans and plays a crucial role in the world’s climate. Land-sea interactions dominate precipitation in the Maritime Continent. The precipitation is heavily influenced by seasonal monsoons and oceanic oscillations such as the El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). Climate change influences many aspects of the climate in South-East Asia. While changes in air and sea temperatures are well documented, the Maritime Continent precipitation changes are understudied. Lack of data and high temporal and spatial variability, among other factors, lead to difficulties in forming a conceptual model of historical and future change in precipitation regime in the Maritime Continent. This study contributes to filling this gap.
This study applies the precipitation regime shift – a rapid change from one stable regime to another, to explain historical variations in precipitation in the Maritime Continent. The study suggests that the interaction between the PDO and ENSO creates alternating precipitation regimes in the Maritime Continent and Indochina peninsula. The precipitation regime changes manifest differently in different parts of the Maritime Continent but can be observed almost everywhere in this region.
This thesis presents three separate lines of evidence for the regime shift's existence and its connection to the PDO. Chapters 1 and 2 present evidence of the regime shift in historical datasets. Chapter 3 looks for evidence of the regime shift in historical and future climate model simulations. Chapter 4 investigates the use of machine learning to predict long-term oscillations in the atmospheric and the ocean that lead to a precipitation regime shift.
Chapters 1 and 2 present statistical evidence for the regime shift in different datasets and using different methods. Chapter 1 uses long-period monthly data for precipitation, sea surface temperature, Pacific Decadal Oscillation, and El Niño Southern Oscillation. By separating precipitation and sea surface temperature data by different combinations of PDO and ENSO phases, the results demonstrate that statistically different regimes are formed. This part of the study also shows how changes in the PDO phase can cause rapid precipitation regime changes in the Maritime Continent.
A key piece of evidence for existence of the regime shift comes from Chapter 2, where a novel Bayesian method for detecting precipitation regime shifts is presented. The Bayesian regime detection method demonstrates that precipitation regime shift models explain historical changes better than linear change models. Moreover, the algorithm, using only precipitation data as input, can detect a regime shift at the precise time we expect it to occur – coinciding with the PDO phase change. The regime shift detection method requires a large amount of data. Hereto we used the state-of-the-art precipitation dataset of the South Asian Climate Assessment and Dataset (SACA&D). SACA&D is a daily observational dataset developed at KNMI in collaboration with Indonesian National Meteorological Service BMKG and other national meteorological services. SACA&D offers the most extensive collection of precipitation observations in the Maritime Continent available today.
Chapter 3 presents a separate line of evidence for precipitation regime shifts based on the model simulations. This study uses a simplified version of Bayesian regime shift detection to analyse precipitation regimes in the Maritime Continent simulated by CMIP6 climate models. The results show that the models that better reproduce the historical precipitation regime also show regime shifts occurring in the future simulations.
The final Chapter 4 investigates the use of machine learning in predicting long-period oscillations in the ocean and the atmosphere that govern the precipitation regime changes in the Maritime Continent. Different neural network architectures are tested in predicting Madden-Julian Oscillation and results are compared to traditional approaches. Both models trained to base prediction on spatial patters and models that use temporal patters demonstrate decent performance. The results demonstrate how modern machine learning models can improve seasonal prediction, including predicting the potential shift in precipitation regimes. In conclusion, this study presents a coherent and compelling conceptual model of historical changes in the Maritime Continent precipitation regime. The Maritime Continent's precipitation regime is governed by the Pacific Decadal Oscillation and goes through alternating drier and wetter regimes following the PDO cycle. The presence of the regime shift is demonstrated in several independent datasets and using different methods. While natural variability still plays a large role in determining day-to-day weather, the current precipitation regime shifts the precipitation probability distribution towards wetter or drier conditions.
The rapid changes in precipitation regime have far-reaching consequences for adaptation planning and understanding climate change processes in general. The rate of change often plays a more prominent role in determining impacts than the magnitude of change. The regime shift creates a period with a high rate of change followed by perceived stability. Long stable periods create the illusion of a lack of climate changes in precipitation. The periods of fast changes can lead to overestimations of the real rate of change over a more extended period. It is critical to understand the mechanism of precipitation regime shifts in the Maritime Continent and its effect on society to develop an effective adaptation strategy for future regime shifts. This study contributes to a growing body of evidence of non-linear changes in the climate system and opens a discussion about our perception of climate change in general.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Faculty of Geo-Information Science and Earth Observation
  • University of Twente
Supervisors/Advisors
  • van der Steenhoven, Gerardus, Supervisor
  • Ettema, Janneke, Co-Supervisor
  • Siegmund, Peter, Co-Supervisor, External person
Award date2 Feb 2022
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-5329-2
DOIs
Publication statusPublished - 2 Feb 2022

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