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Interannual variability of Indonesian rainfall related to the tropical Indo-Pacific climate modes and its future projections in CMIP6

Nur'utami Murni Ngestu 東北大学

2021.09.24

概要

Understanding rainfall variability is very important for tropical countries, especially for Indonesia. Climatic variations in rainfall anomalies can cause severe hydrological disasters and cause socioeconomic problems. This study analyzes the Indonesian rainfall variability in the past- current and the future projection associated with the interannual/interdecadal climate modes for the dry season from June to November (JJASON) by using observation and simulation model data.

The interannual and interdecadal variabilities of Indonesian rainfall for the past-current condition are investigated by using two types of rainfall data input sources, i.e., the Climate Research Unit (CRU) from 1901 to 2016 and the Global Precipitation Climatology Project (GPCP) from 1979 to 2016. We apply the Empirical Orthogonal Function (EOF) to these data. The first principal component (PC1) of both CRU and GPCP data shows that the canonical El Niño Southern Oscillation (canonical ENSO), ENSO Modoki, and Indian Ocean Dipole (IOD) are the major climate modes influencing the interannual variability of rainfall in Indonesia. In addition, the Interdecadal Pacific Oscillation (IPO) is the major decadal phenomenon affecting the decadal variability of rainfall. Three phases of the IPO have been identified, strongly related to Indonesian rainfall as determined by the PC1 of the CRU, i.e., two negative phases in 1939–1978 and 1998– 2016, and a positive phase in 1979–1997. The impact of IPO modulation on Indonesian rainfall response to canonical ENSO and ENSO Modoki is not significant. However, recently, the response of Indonesian rainfall to ENSO Modoki is more significant than canonical ENSO. Meanwhile, the IPO plays a role in modulating the impact of IOD on Indonesian rainfall, where the impact is weakened during the positive IPO phase and strengthened during the negative IPO phase.

Understanding the performance of the climate model in simulating the historical condition of Indonesian rainfall variability is needed before further analyzing the projection models. The sixth phase of Coupled Model Intercomparison Project (CMIP6) models are used. The performance of the 14 CMIP6 models for historical period is evaluated by observing the climatological conditions, the main pattern on rainfall variability, and the rainfall response to the interannual climate modes, compared to the observation from 1979 to 2014 for JJASON. The seasonality of Indonesian rainfall is successfully represented by CMIP6 models, although most models show larger rainfall than the observations. The regional differences vary among the CMIP6 models relative to the observation rainfall, especially over northern and eastern Indonesia. For the leading mode of Indonesian rainfall, several models show a similar condition as the observations, especially for the southern part of Indonesia. However, the difference is quite significant for the northern and northeastern parts of Indonesia. In evaluating Indonesian rainfall variability related to the interannual climate modes, each model has its limitations. For canonical ENSO, most CMIP6 models are difficult to distinguish its impacts on Indonesian rainfall. In contrast, some CMIP6 models can simulate the response of both rainfall and SST to ENSO Modoki and IOD as the observation. Based on the performance of　CMIP6 in the three aspects, we found that the MPI-ESM1-2-HR and GFDL-ESM4 models have a better performance to simulate the Indonesian rainfall.

Simulation of Indonesian rainfall variability is performed by applying four future shared socio-economic pathway (SSP) radiative forcing scenarios, namely SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Future simulation is examined by focusing on MPI-ESM1-2-HR and GFDL-ESM4 models for two period times analysis, namely the near-term from 2029 to 2064 and the long-term from 2065 to 2100, for the dry season, JJASON. The simulation models are analyzed on the main pattern of rainfall variability and the rainfall response to the interannual climate modes. The MPI- ESM1-2-HR and GFDL-ESM4 models show a similar leading mode pattern of Indonesian rainfall variability as their historical result on each scenario and each period analysis, except for the MPI- ESM1-2-HR model with SSP5-8.5 in the long-term period. The changes of rainfall anomaly are enhanced for the future warming scenario, especially for SSP3-7.0 and SSP5-8.5 in each model. For the warmest scenario, the composite of rainfall anomaly and SST anomaly (SSTA) for the positive and negative phase PC1 of MPI-ESM1-2-HR rainfall model on both near-term and long-term periods shows that the positive IOD event might occur more frequently than the negative IOD. In addition, Indonesian rainfall variability is also influenced by climate variability over the Indian Ocean and the tropical central Pacific Ocean.

The response of Indonesian rainfall to the interannual climate modes in both the MPI- ESM1-2-HR and GFDL-ESM4 model shows a difficulty to distinguish the impacts of canonical ENSO. In contrast, the impacts area is clearly shown for ENSO Modoki and IOD. For ENSO Modoki, the MPI-ESM1-2-HR and GFDL-ESM4 models show a similar pattern for each scenario as shown in the observation for both near-term and long-term periods. During El Niño Modoki (La Niña Modoki), decreased (increased) rainfall over central-eastern Indonesia is strengthened in the future warming scenarios. The SSTA over the central Pacific shows a higher positive anomaly and narrower area than the observation. For IOD, the Indonesian rainfall responses in the MPI-ESM1- 2-HR and GFDL-ESM4 models show a different pattern to each other. During positive IOD (negative IOD), decreased (increased) rainfall in the MPI-ESM1-2-HR model for each scenario occurs over southwestern Indonesia, similar to the recent term. In addition, in some scenarios, a decreased rainfall occurs over central Indonesia but shows no consistency between near-term and long-term periods. For the GFDL-ESM4 model, decreased rainfall area expands over central Indonesia, accompanied by a positive SSTA in the central-eastern Pacific Ocean.

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