District-level forecast of northeast monsoon rainfall over Tamilnadu using Indian Ocean dipole mode index

The northeast monsoon season (October-December) contributes a substantial percentage of annual rainfall over Tamilnadu. The present paper describes a method for prediction of northeast monsoon rainfall (NEMR) over Tamilnadu on smaller spatial scale, i.e., district-level with sufficient lead time. Tamilnadu has been divided into ten homogeneous clusters of districts and the predictions are made for each cluster with lead times of two and one months using Indian Ocean dipole mode (IODM) index. A stronger western pole of IODM during August-September is associated with enhanced northeast monsoon activity over most of the districts of Tamilnadu. The predictions on the basis of regressions developed from NEMR and IODM index data have been validated for six years from 1997-2002. For many districts the mean errors between actual (realized) and predicted rainfall are within ±10%. Hence, using IODM index, it is possible to predict the NEMR activity over most of the districts of Tamilnadu with a lead time of two months, with only exception of NEMR over Kanyakumari which is not significantly correlated to IODM phenomenon.

Relationship between Indian Ocean dipole mode and summer monsoon

Utilizing the Indian Ocean Dipole Mode (IODM) and Indian Summer Monsoon Rainfall (ISMR) data for the period 1960-2002 the relationships between the IODM and monsoon onset over Kerala and rainfall distribution over the country have been studied. It has been found that stronger/weaker western pole during April-May is associated with delayed/early monsoon onset over Kerala. Stronger eastern pole during March-April seems to be associated with enhanced seasonal (June-September) rainfall over peninsular India. The IODM index of July-August can provide good indications of summer monsoon activity over peninsular India during the withdrawal phase of the monsoon, i.e., during September.

PREFACE

The seasonal atmospheric condition over the Maritime Continent is mainly driven by the Asian-Australian Monsoon. Precipitation over the Maritime Continent is highly influenced by the intra-seasonal Madden-Julian Oscillation (MJO), also highly affected by the El-Nino Southern Oscillation (ENSO) and Indian Ocean Dipole Mode (IOD). At an interannual time scale the Maritime Continent is also crossed by Indonesia Through Flow (ITF), as the artery connecting Tropical Pacific and Indian Oceans, and acting as a crucial link of the ocean general circulation that affects not only properties of these two oceans but also global climate. This complex mixture of land and sea interaction, with various atmospheric and oceanic phenomena within, makes the Maritime Continent as a unique, enigmatic and challenging area for scientific endeavor on tropical meteorology and atmospheric sciences. Various observations and research have been coordinated, campaigned, and conducted to better understand the atmospheric and oceanic condition over the tropics, especially the Maritime Continent. Many scientific discoveries have been found to enrich the knowledge of atmospheric science on the tropics, from the International Winter Monsoon Experiment in 1978, TOGA COARE in 1993, HARIMAU that ended in 2010, to CINDY/DYNAMO in 2011. The recent Year of Maritime Continent (YMC) during 2017 - 2020 aimed to improve understanding and prediction local multi-scale variability of the Maritime Continent weather-climate system and its global impact through observations and modelling exercises, was the state-of-art for such coordinated research on the tropics. As a part of YMC program, BMKG will also be involved in Measurements and Modelling of the Indonesian Throughflow International Experiment (MINTIE) which is collaborative research among countries including Indonesia BMKG and being led by Columbia University during 2019 – 2024. LIST OF Committee, Steering Committee, Organizing Committee Leader, Leader, Secretariat & Public Relations, Treasure, Event are available in this pdf.

Influence of Terrestrial Precipitation on the Variability of Extreme Sea Levels along the Coast of Bangladesh

The coastal area of Bangladesh is highly vulnerable to extreme sea levels because of high population exposure in the low-lying deltaic coast. Since the area lies in the monsoon region, abundant precipitation and the resultant increase in river discharge have raised a flood risk for the coastal area. Although the effects of atmospheric forces have been investigated intensively, the influence of precipitation on extreme sea levels in this area remains unknown. In this study, the influence of precipitation on extreme sea levels for three different stations were investigated by multivariate regression using the meteorological drivers of precipitation, sea level pressure, and wind. The prediction of sea levels considering precipitation effects outperformed predictions without precipitation. The benefit of incorporating precipitation was greater at Cox’s Bazar than at Charchanga and Khepupara, reflecting the hilly landscape at Cox’s Bazar. The improved prediction skill was mainly confirmed during the monsoon season, when strong precipitation events occur. It was also revealed that the precipitation over the Bangladesh area is insensitive to the El Niño-Southern Oscillation and Indian Ocean Dipole mode. The precipitation over northern Bangladesh tended to be high in the year of a high sea surface temperature over the Bay of Bengal, which may have contributed to the variation in sea level. The findings suggest that the effect of precipitation plays an essential role in enhancing sea levels during many extreme events. Therefore, incorporating the effect of terrestrial precipitation is essential for the better prediction of extreme sea levels, which helps coastal management and reduction of hazards.

Why Australia was not wet during spring 2020 despite La Niña

The austral spring climate of 2020 was characterised by the occurrence of La Niña, which is the most predictable climate driver of Australian springtime rainfall. Consistent with this La Niña, the Bureau of Meteorology’s dynamical sub-seasonal to seasonal forecast system, ACCESS-S1, made highly confident predictions of wetter-than-normal conditions over central and eastern Australia for spring when initialised in July 2020 and thereafter. However, many areas of Australia received near average to severely below average rainfall, particularly during November. Possible causes of the deviation of rainfall from its historical response to La Niña and causes of the forecast error are explored with observational and reanalysis data for the period 1979–2020 and real-time forecasts of ACCESS-S1 initialised in July to November 2020. Several compounding factors were identified as key contributors to the drier-than-anticipated spring conditions. Although the ocean surface to the north of Australia was warmer than normal, which would have acted to promote rainfall over northern Australia, it was not as warm as expected from its historical relationship with La Niña and its long-term warming trend. Moreover, a negative phase of the Indian Ocean Dipole mode, which typically acts to increase spring rainfall in southern Australia, decayed earlier than normal in October. Finally, the Madden–Julian Oscillation activity over the equatorial Indian Ocean acted to suppress rainfall across northern and eastern Australia during November. While ACCESS-S1 accurately predicted the strength of La Niña over the Niño3.4 region, it over-predicted the ocean warming to the north of Australia and under-predicted the strength of the November MJO event, leading to an over-prediction of the Australian spring rainfall and especially the November-mean rainfall.

The Effect of Local Forcing on Anomalously High Rainfall during Dry Season in Java, Indonesia

During the dry season (May to October) in Java, Indonesia, anomalously high rainfall is investigated using 37-year rainfall data from the Climate Hazards Group InfraRed Precipitation with Station data. The analysis focuses on the years having high rainfall during the dry season between 1982 and 2019. It is conducted using a combination of the presence and absence of La Niña, negative Indian Ocean Dipole Mode events, and other atmospheric/oceanic parameters, such as 2-m temperature, sea surface temperature, outgoing longwave radiation, 200 mb and 850 mb wind. The results show that the presence of both La Niña and negative Indian Ocean Dipole Mode events contributes around 39% to the high rainfall during the dry season, the presence of negative Indian Ocean Dipole Mode - 22%, the absence of both events - 22%, and the presence of La Niña - 17%. The dynamics of monsoon circulation anomaly (200 mb and 850 mb) in the southern Indian Ocean off the coast of Sumatra and Java also plays a role in the increased rainfall during the dry season in Java. This anomaly occurs due to a vortex in the southern equatorial Indian Ocean around 10⁰S, triggering the formation of double Inter-tropical Convergence Zones over the area north of the equator and the southern waters of Java. The increase in rainfall due to this local factor reaches a maximum and extends in June and October, which is associated with the strengthening of circulation anomalies in southern Java, both spatially and vertically (850 and 200 mb).

Multivariate framework for the assessment of key forcing to Lake Malawi level variations in non-stationary frequency analysis

Lake Malawi in south eastern Africa is a very important freshwater system for the socio-economic development of the riparian countries and communities. The lake has however experienced considerable recession in the levels in recent years. Consequently, frequency analyses of the lake levels premised on time-invariance (or stationarity) in the parameters of the underlying probability distribution functions (pdfs) can no longer be assumed. In this study, the role of hydroclimate forcing factors (rainfall, lake evaporation, and inflowing discharge) and low frequency climate variability indicators (e.g., El Nino Southern Oscillation-ENSO and the Indian Ocean Dipole Mode-IODM) on lake level variations is investigated using a monthly mean lake level dataset from 1899 to 2017. Non-stationarity in the lake levels was tested and confirmed using the Mann-Kendall trend test (α = 0.05 level) for the first moment and the F test for the second moment (α = 0.05 level). Change points in the series were identified using the Mann-Whitney-Pettit test. The study also compared stationary and non-stationary lake level frequency during 1961 to 2004, the common period where data were available for all the forcing factors considered. Annual maximum series (AMS) and peak over threshold (POT) analysis were conducted by fitting various candidate extreme value distributions (EVD) and parameter fitting methods. The Akaike information criteria (AIC), Bayesian information criteria (BIC), deviance information criteria (DIC), and likelihood ratios (RL) served as model evaluation criteria. Under stationary conditions, the AMS when fitted to the generalized extreme value (GEV) distribution with maximum likelihood estimation (MLE) was found to be superior to POT analysis. For the non-stationary models, open water evaporation as a covariate of the lake levels with the GEV and MLE was found to have the most influence on the lake level variations as compared with rainfall, discharge, and the low frequency climatic forcing. The results are very critical in flood zoning especially with various planned infrastructural developments around the lakeshore.

RESPONS SUHU PERMUKAAN LAUT DAN KLOROFIL-A TERHADAP KEJADIAN ENSO DAN IODM DI WILAYAH INDO-PASIFIK TROPIS

Fenomena anomali laut-atmosfer antar-tahunan dari El Nino Southern Oscillation (ENSO) dan Indian Ocean Dipole Mode (IODM) di wilayah Indo-Pasifik Tropis memberikan dampak pada ekosistem laut, hidrologi dan variabilitas iklim. Penelitian ini bertujuan untuk menganalisis pola spasial dan variabilitas temporal suhu permukaan laut (SPL) dan klorofil-a permukaan (Chl-a) terkait dengan ENSO dan IODM di Indo-Pasifik Tropis. Data deret waktu bulanan dari tahun 1980-2017 (37 tahun) diperoleh dari pusat data global, dan dianalisis menggunakan metode empirical orthogonal function (EOF). Hasil penelitian menunjukkan nilai tiga terbesar pertama dari SPL (Chl-a) menjelaskan 81,7% (76%) dari total explained variance. Struktur spasial SPL mode-1 (56%) membentuk seperti dua kutub asimetris antara timur dan barat Pasifik Tropis dengan pola yang berbeda di lepas Pantai Peru. Pola ini diduga berhubungan dengan tahun normal atau La Nina. Selanjutnya, kondisi EL Nino dan IODM diduga tergambarkan oleh SPL mode-2 (19%), dengan fase negatif dominan di atas ekuator Pasifik dan menghilangnya area upwelling di lepas Pantai Peru.