Agromet http://jurnal.ipb.ac.id/index.php/agromet <p>Agromet publishes original research articles or reviews that have not been published elsewhere. The scope of publication includes agricultural meteorology/climatology (the relationships between a wide range of agriculture and meteorology/climatology aspects). Articles related to meteorology/climatology and environment (pollution and atmospheric conditions) may be selectively accepted for publication. This journal is published twice a year by the Indonesian Association of Agricultural Meteorology (PERHIMPI) in collaboration with the Department of Geophysics and Meteorology, Faculty of Mathematics and Natural Sciences, IPB University.<br><br><br><br></p> PERHIMPI (Indonesian Association of Agricultural Meteorology) en-US Agromet 0126-3633 Spatial and Temporal Analysis of El Niño Impact on Land and Forest Fire in Kalimantan and Sumatra http://jurnal.ipb.ac.id/index.php/agromet/article/view/31804 <p>Land and forest fires in Kalimantan and Sumatra, Indonesia occurred annually at different magnitude and duration. Climate and sea interaction, like El Niño, influences the severity of dry seasons preceding the fires. However, research on the influence of El Niño intensity to fire regime in Kalimantan and Sumatra is limited. Therefore, this study aims to analyze the spatial and temporal patterns of the effects of El Niño intensity on land and forest fires in fire-prone provinces in Indonesia. Here, we applied the empirical orthogonal function analysis based on singular value decomposition to determine the dominant patterns of hotspots and rainfall data that evolve spatially and temporally. For analysis, the study required the following data: fire hotspots, dry-spell, and rainfall for period 2001-2019. This study revealed that El Niño intensity had a different impacts for each province. Generally, El Niño will influence the severity of forest fire events in Indonesia. However, we found that the impact of El Niño intensity varied for Kalimantan, South Sumatra, and Riau Province. Kalimantan was the most sensitive province to the El Niño event. The duration and number of hotspots in Kalimantan increased significantly even in moderate El Niño event. This was different for South Sumatra, where the duration and number of hotspots only increased significantly when a strong El Niño event occurred.</p> Sri Nurdiati Ardhasena Sopaheluwakan Pandu Septiawan Copyright (c) 2021 Sri Nurdiati, Ardhasena Sopaheluwakan, Pandu Septiawan https://creativecommons.org/licenses/by-nc/4.0 2021-01-25 2021-01-25 35 1 1 10 10.29244/j.agromet.35.1.1-10 Identification of Global Warming Contribution to the El Niño Phenomenon Using Empirical Orthogonal Function Analysis http://jurnal.ipb.ac.id/index.php/agromet/article/view/32038 <p>Sea surface temperature (SST) is identified as one of the essential climate/ocean variables. The increased SST levels worldwide is associated with global warming which is due to excessive amounts of greenhouse gases being released into the atmosphere causing the multi-decadal tendency to warmer SST. Moreover, global warming has caused more frequent extreme El Niño Southern Oscillation (ENSO) events, which are the most dominant mode in the coupled ocean-atmosphere system on an interannual time scale. The objective of this research is to calculate the contribution of global warming to the ENSO phenomenon.&nbsp; SST anomalies (SSTA) variability rosed from several mechanisms with differing timescales. Therefore, the Empirical Orthogonal Function in this study was used to analyze the data of Pacific Ocean sea surface temperature anomaly. By using EOF analysis, the pattern in data such as precipitation and drought pattern can be obtained. The result of this research showed that the most dominant EOF mode reveals the time series pattern of global warming, while the second most dominant EOF mode reveals the El Niño Southern Oscillation (ENSO). The modes from this EOF method have good performance with 95.8% accuracy rate.</p> Mochamad Tito Julianto Septian Dhimas Ardhasena Sopaheluwakan Sri Nurdiati Pandu Septiawan Copyright (c) 2021 Mochamad Tito Julianto, Septian Dhimas, Ardhasena Sopaheluwakan, Sri Nurdiati, Pandu Septiawan https://creativecommons.org/licenses/by-nc/4.0 2021-02-19 2021-02-19 35 1 11 19 10.29244/j.agromet.35.1.11-19