Summer monsoon rainfall over Bangladesh in relation to Multivariate ENSO Index

The relationship between monthly rainfall over Bangladesh during monsoon season and bi-monthly Multivariate ENSO Index (MEI) pertaining to the period from first week of previous month to first week of the month under consideration, has been investigated. The MEI is calculated as the first Principal Component (PC) of six variables over the tropical Pacific, viz., sea surface temperature, sea level pressure, zonal and meridional components of the surface wind, surface air temperature and total cloudiness fraction of the sky. The MEI values for prognostic purposes are available by the first week of every month. MEI is better for monitoring ENSO than other indices like Southern Oscillation Index (SOI) or various SST indices as it integrates complete information on ENSO and reflects the nature of complete ocean atmosphere system. Positive values of MEI indicate warm ENSO phase (EI-Nino) and negative ones represent cold phase (La-Nina). The results of the present study show that June rainfall of Bangladesh is adversely affected by the ENSO. But interestingly Bangladesh seems to receive more than normal rainfall during August of ENSO years. ENSO does not seem to have any significant adverse impact on July and September rainfall of Bangladesh. The results of the study may find applications in foreshadowing monsoon rainfall over Bangladesh on a monthly scale.

Study of rainfall departure over catchments of Bihar plains

Station wise daily rainfall data of sixty years is used to study rainfall departure and variability in Kosi, Kamala/Bagmati/Adhwara and Gandak/Burhi Gandak catchments during monsoon season. Station and catchment wise rainfall time series have been made to compute rainfall departure and Coefficient of Variation (CV). Southern Oscillation Index (SOI), Multivariate ENSO Index (MEI) and ENSO strength based on percentile analysis are used to ascertain their impact on rainfall distribution in the category as excess, normal, deficient and scanty. Results indicate that the variability is greater over Kosi as compared to the other catchments. Probability of normal rainfall is found 0.75 and there is no possibility of scanty rain over the catchments during El Nino and La Nina year. Similarly probabilities of normal, deficient, excess rainfall are found as 0.67, 0.18 and 0.15 respectively during mixed year. SOI has emerged as principal parameter which modifies the departure during El Nino and La Nina year. MEI along with ENSO strength are more prominent during mixed year particularly to ascertain deficient and excess rain in weak and strong- moderate La Nina respectively .

Does ENSO significantly affect rice production in Indonesia? A preliminary study using computational time-series approach

ENSO is a phenomenon that is suspected to influence rice production in Indonesia. In this study, we try to find a direct correlation between ENSO and rice production in this region by using various latest computational time series methods, such as Dynamic Time Warping, Wavelet Coherence, and Bayesian Structural Time Series to quantify the statistical relationship between the Multivariate ENSO Index on annual rice production in 1961 - 2019. We did not find a direct correlation between these two variables, which may be due to the local influence of ENSO on different rice production areas in Indonesia.

Vertical crustal deformations and climate variability through PCA and SVD

<p>We investigated a large network of permanent GPS stations to identify and analyse common patterns in the series of the GPS height, environmental parameters, and climate indexes.</p><p>The study is confined to Europe, the Mediterranean, and the North-eastern Atlantic area, where 114 GPS stations were selected from the Nevada Geodetic Laboratory (NGL) archive. The GPS time series were selected on the basis of the completeness and the length of the series.</p><p>In addition to the GPS height, the parameters analysed in this study are the atmospheric surface pressure (SP), the terrestrial water storage (TWS), and a few climate indexes, such as MEI (Multivariate ENSO Index). The Principal Component Analysis (PCA) is the methodology adopted to extract the main patterns of space/time variability of the parameters.</p><p>Moreover, the coupled modes of space/time interannual variability between pairs of variables was investigated. The methodology adopted is the Singular Value Decomposition (SVD).</p><p>Over the study area, main modes of variability in the time series of the GPS height, SP and TWS were identified. For each parameter, the main modes of variability are the first four. In particular, the first mode explains about 30% of the variance for GPS height and TWS and about 46% for SP. The relevant spatial patterns are coherent over the entire study area in all three cases.</p><p>The SVD analysis of coupled parameters, namely H-AP and H-TWS, shows that most of the common variability is explained by the first 3 modes, which account for almost 80% and 45% of the covariance, respectively.</p><p>Finally, we investigated the relation between the GPS height and a few climate indexes. Significant correlations, up to 50%, were found between the MEI (Multivariate Enso Index) and about half of the stations in the network.</p>

Changes in precipitation amounts and extremes across Xinjiang (northwest China) and their connection to climate indices

Xinjiang is a major part of China’s arid region and its water resource is extremely scarcity. The change in precipitation amounts and extremes is of significant importance for the reliable management of regional water resources in this region. Thus, this study explored the spatiotemporal changes in extreme precipitation using the Mann–Kendall (M–K) trend analysis, mutation test, and probability distribution functions, based on the observed daily precipitation data from 89 weather stations in Xinjiang, China during 1961–2018. We also examined the correlations between extreme precipitation and climate indices using the cross-wavelet analysis. The results indicated that the climate in Xinjiang is becoming wetter and the intensity and frequency of extreme precipitation has begun to strengthen, with these trends being more obvious after the 1990s. Extreme precipitation trends displayed spatial heterogeneity in Xinjiang. Extreme precipitation was mainly concentrated in mountainous areas, northern Xinjiang, and western Xinjiang. The significant increasing trend of extreme precipitation was also concentrated in the Tianshan Mountains and in northern Xinjiang. In addition, the climate indices, North Atlantic Oscillation, Atlantic Multidecadal Oscillation, Multivariate ENSO Index and Indian Ocean Dipole Index had obvious relationships with extreme precipitation in Xinjiang. The relationships between the extreme precipitation and climate indices were not clearly positive or negative, with many correlations advanced or delayed in phase. At the same time, extreme precipitation displayed periodic changes, with a frequency of approximately 1–3 or 4–7 years. These periodic changes were more obvious after the 1990s; however, the exact mechanisms involved in this require further study.

A Recent Study of the Relationship between the Precipitation Rate over Saudi Arabia in the Fall and Climatic Indices

This paper presents a recent study of the relationship between precipitation rate (PR) over Saudi Arabia (SA) within the months of the fall season and climatic indices. The fall monthly PR data spanning the study period between 1948 and 2018 is considered. In addition, the monthly climatic index records (arctic oscillation (AO), global surface air temperature (GSAT), multivariate ENSO index (MEI), North Atlantic Oscillation (NAO) index, Nino 3.4 index, and Southern Oscillation Index (SOI)) for the fall months were also considered. The statistical trend, anomaly, and correlation analyses are applied in this study. The results reveal that the sweeping changes in PR show generally positive trends throughout the fall seasons of the past decades. Moreover, the climatic indices have an effect on the PR over SA within the fall months and season. During the study period, the most substantial relationship recorded, with an inverse correlation of −0.7, is between the PR over SA and the climatic index of GSAT for September and October. Moreover, there is a clear correlation of +0.5 between the PR over SA and the ENSO and Nino 3.4 index for October and November.

Thirty Years of Atmospheric CO2 Observations at the Plateau Rosa Station, Italy

The atmospheric background CO2 concentration is a key quantity for the analysis and evaluation of the ongoing climate change. Long-term CO2 observations have been carried out at the high Plateau Rosa mountain station, in the north-western Alps since 1989. The complete time series covers thirty years, and it is suitable for climatological analysis. The continuous CO2 measurements, collected since 1993, were selected, by means of a BaDS (Background Data Selection) filter, to obtain the hourly background data. The monthly background data series was analysed in order to individuate the parameters that characterise the seasonal cycle and the long-term trend. The growth rate was found to be 2.05 ± 0.03 ppm/year, which is in agreement with the global trend. The increased background CO2 concentration at the Plateau Rosa site is the consequence of global anthropic emissions, whereas the natural variability of the climatic system taken from the SOI (South Oscillation Index) and MEI (Multivariate ENSO Index) signals was detected in the inter-annual changes of the Plateau Rosa growth rate.

Seasonal Prediction of North Atlantic Accumulated Cyclone Energy and Major Hurricane Activity

Building upon our previous seasonal hurricane prediction model, here we develop two statistical models to predict the number of major hurricanes (MHs) and accumulated cyclone energy (ACE) in the North Atlantic basin using monthly data from March to May for an early June forecast. The input data include zonal pseudo–wind stress to the 3/2 power, sea surface temperature in the North Atlantic, and, depending on the magnitude of the Atlantic multidecadal oscillation index, the multivariate ENSO index. From 1968 to 2017, these models have a mean absolute error of 0.96 storms for MHs and 30 units for ACE. When tested over an independent period from 1958 to 1967, the models show a 22% improvement for MHs and 16% for ACE over a no-skill metric based on a 5-yr running average. Both the MH and ACE results show consistent improvements over those produced by three other centers using statistical–dynamical hybrid models and a 5-yr running average prediction over the period 2000–17 for MHs (2003–17 for ACE) in a simulated real-time prediction. These improvements vary from 25% to 37% for MHs and from 15% to 37% for ACE. While most forecasting centers called for a slightly above-average hurricane season in May/June 2017, our models predicted in June 2017 a very active season, in much better agreement with observations.