Heterogeneous Correlation Map Between Estimated ENSO And IOD From ERA5 And Hotspot In Indonesia

Nurdiati, Sri; Bukhari, Fahren; Julianto, Mochamad Tito; Najib, Mohamad Khoirun; Nazria, Nuzhatun

doi:10.34312/jgeosrev.v3i2.10443

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…For estimating fire risk using Canadian Forest FWI, we used the European Centre for Medium range Weather Forecasts Reanalysis (ERA5) daily reanalysis data (Hersbach et al, 2020) including rainfall, air temperature at 2 m, relative humidity at 1000 hPa as well as zonal wind and meridional wind at 10 m, thanks to the availability of these variables at a high resolution of 0.25° online. We also used the ERA5 data for estimating zonal winds over equatorial Western Pacific, as it has a good track record of capturing ENSO and its related teleconnections as in previous studies despite being a reanalysis product (Capotondi & Ricciardulli, 2021; Nikonovas et al, 2022; Nurdiati et al, 2021). For sea surface temperature over eastern North Pacific, we chose monthly HadISST2.0 as it is embedded with satellite measurements (Kennedy et al, 2013).…”

Section: Study Area and Datasetsmentioning

confidence: 99%

Quantifying causal teleconnections to drought and fire risks in Indonesian Borneo

Lam,

Catto,

Barciela

et al. 2024

Intl Journal of Climatology

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Fires occurring over the peatlands in Indonesian Borneo accompanied by droughts have posed devastating impacts on human health, livelihoods, economy and the natural environment, and their prevention requires comprehensive understanding of climate‐associated risk. Although it is widely known that the droughts are associated with El Niño events, the onset process of El Niño and thus the drought precursors and their possible changes under the future climate are not clearly understood. Here, we use a causal network approach to quantify the strength of teleconnections to droughts at a seasonal timescale shown in observations and climate models. We portray two drivers of June‐July‐August (JJA) droughts identified through literature review and causal analysis, namely Niño 3.4 sea surface temperature (SST) in JJA (El Niño Southern Oscillation [abbreviated as ENSO]) and SST anomaly over the eastern North Pacific to the east of the Hawaiian Islands (abbreviated as Pacific SST) in March‐April‐May (MAM) period. We argue that the droughts are strongly linked to ENSO variability, with drier years corresponding to El Niño conditions. The droughts can be predicted with a lead time of 3 months based on their associations with Pacific SST, with higher SST preceding drier conditions. We find that under the SSP585 scenario, the Coupled Model Intercomparison Phase 6 (CMIP6) multi‐model ensembles show significant increase in both the maximum number of consecutive dry days in the Indonesian Borneo region in JJA (p = 0.006) and its linear association with Pacific SST in MAM (p = 0.001) from year 2061 to 2100 compared with the historical baseline. Some models are showing unrealistic amounts of JJA rainfall and underestimate drought risks in Indonesian Borneo and their teleconnections, owing to the underestimation of ENSO amplitude and overestimation of local convections. Our study strengthens the possibility of early warning triggers of fires and stresses the need for taking enhanced climate risk into consideration when formulating long‐term policies to eliminate fires.

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Section: Study Area and Datasetsmentioning

confidence: 99%

Quantifying causal teleconnections to drought and fire risks in Indonesian Borneo

Lam,

Catto,

Barciela

et al. 2024

Intl Journal of Climatology

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“…It has been previously researched that fires in Indonesia are strongly influenced by local climatic conditions (such as precipitation and dry spells) and regional climatic conditions such as the Indian Ocean Dipole (IOD) and El Nino-Southern Oscillation (ENSO) [34], [35]. Based on the systematic literature review description, several research opportunities related to the copula model in wildfires analysis in Indonesia can be proposed with a research roadmap, as shown in Figure 3.…”

Section: Development Opportunities Of the Copula Model In Wildfires In Indonesiamentioning

confidence: 99%

Copula in Wildfire Analysis: A Systematic Literature Review

Najib¹,

Nurdiati²,

Sopaheluwakan

2021

InPrime:Ind.Jour.Pure.Applied.Math

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Copula model is a method that can be implemented in various study fields, including analyzing wildfires. The copula distribution function gives a simple way to define joint distribution between two or more random variables. This study aims to review the application of copula in the analysis of wildfires using a Systematic Literature Review (SLR) and provide insight into research opportunities related to the application in Indonesia. The results show there are very few articles using the copula model in the analysis of wildfires. However, the increasing number of article citations each year shows the importance of such article research and has contributed to wildfire analysis development. In that article, 50% of studies applied the copula model to direct wildfire analysis (using fire data) in Canada, Portugal, and the US. Meanwhile, the other 50% use the copula model for indirect wildfire analysis (not using fire data) in Canada and the European region. The outcome of the presented review will provide the latest research positions and future research opportunities on the application of copula in the analysis of wildfires in Indonesia.Keywords: copula; wildfire; systematic literature review. AbstrakModel copula merupakan metode yang dapat diimplementasikan pada berbagai bidang penelitian, salah satunya pada analisis kebakaran hutan. Fungsi sebaran copula memberikan cara yang mudah untuk mendefinisikan sebaran peluang bersama antara dua peubah acak atau lebih. Tujuan penelitian ini mengulas penerapan model copula tersebut pada analisis kebakaran hutan dalam studi literatur menggunakan Systematic Literature Review (SLR) serta memberikan peluang riset ke depan terkait implementasinya pada analisis kebakaran hutan di Indonesia. Hasil penelitian menunjukkan bahwa model copula pada analisis kebakaran hutan masih sangat sedikit. Namun, peningkatan jumlah sitasi artikel tiap tahun menunjukkan pentingnya penelitian tersebut dan memiliki kontribusi pada perkembangan analisis kebakaran hutan. Pada artikel tersebut, sebanyak 50% penelitian menerapkan model copula pada analisis kebakaran secara langsung (menggunakan data kebakaran) di Kanada, Portugal, dan Amerika. Sementara, sebanyak 50% lainnya menerapkan model copula pada analisis kebakaran secara tak langsung (tidak menggunakan data kebakaran), yaitu di Kanada dan kawasan Eropa. Hasil tinjauan memberikan posisi riset terkini serta usulan riset ke depan mengenai penerapan model copula untuk analisis kebakaran hutan dan lahan di Indonesia.Kata kunci: copula; kebakaran hutan; studi literatur sistematik.

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“…Previous research has found that forest fires in Kalimantan are more sensitive to ENSO than IOD. Meanwhile, the effect of IOD on forest fires was more pronounced in the southern part of Sumatra because it is located near the Indian Ocean (Nurdiati et al, 2021). Moreover, although it has been mentioned that the lack of precipitation can be a triggering factor for forest fires, the total precipitation is insufficient to characterize forest fires in Indonesia.…”

Section: Introductionmentioning

confidence: 99%

Joint Distribution and Coincidence Probability of the Number of Dry Days and the Total Amount of Precipitation in Southern Sumatra Fire-Prone Area

NURDIATI¹,

NAJIB²,

THALIB³

2022

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El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) can affect the increase in rainfall intensity and the number of dry days, also known as dry spells that can cause drought and increase the potential for forest fires. This study examines the effect of ENSO and IOD conditions on the joint distribution of the number of dry days and total precipitation in a fire-prone area in southern Sumatra, Indonesia. The joint distribution is constructed using rotated copulas from several families, including Gaussian, student's t, Clayton, Gumbel, Frank, Joe, Galambos, BB1, BB6, BB7, and BB8. Fire-prone areas are defined using k-mean clustering, while the copula parameters are estimated using the inference of function for margins (IFM) method. Based on the peak of joint probability density functions (PDFs), ENSO and IOD conditions had a significant effect in the dry season but had no significant effect in the rainy season. The peak of joint PDFs is getting to the dry-dry conditions when the ENSO and IOD indexes increase in the dry season. However, based on coincidence probability, ENSO conditions still influence the joint distribution between the number of dry days and total precipitation during the rainy season but not with IOD conditions. The lower the ENSO index, the higher the probability of wet conditions co-occurring in the number of dry days and total precipitation. Meanwhile, ENSO and IOD conditions significantly affect the coincidence probability between the number of dry days and total precipitation. Moderate-Strong El Niño has the most considerable coincidence probability of 68.5%, followed by Positive IOD with 62.6%. The two conditions had similar effects on the joint distribution of the number of dry days and total precipitation. Moreover, the association between the number of dry days and the total precipitation was stronger in the dry season than in the rainy season.

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Heterogeneous Correlation Map Between Estimated ENSO And IOD From ERA5 And Hotspot In Indonesia

Cited by 10 publications

References 16 publications

Quantifying causal teleconnections to drought and fire risks in Indonesian Borneo

Quantifying causal teleconnections to drought and fire risks in Indonesian Borneo

Copula in Wildfire Analysis: A Systematic Literature Review

Joint Distribution and Coincidence Probability of the Number of Dry Days and the Total Amount of Precipitation in Southern Sumatra Fire-Prone Area

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