southern oscillation
Recently Published Documents


TOTAL DOCUMENTS

4591
(FIVE YEARS 1475)

H-INDEX

144
(FIVE YEARS 14)

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 19-26
Author(s):  
V. GEETHALAKSHMI ◽  
S. KOKILAVANI ◽  
S.P. RAMANATHAN ◽  
GA. DHEEBAKARAN ◽  
N.K. SATHYAMOORTHY ◽  
...  

  Due to current world climate change, the accuracy of predicting rainfall is critical. This paper presents an approach using four different machine learning algorithms, viz., Decision Tree Regression (DTR), Gradient Boosting (GB), Ada Boost (AB) and Random Forest Regression (RFR) techniques to improve the rainfall forecast performance. When historical events are entered into the model and get validated to realise how well the output suits the known results referred as Hind-cast. Historical monthly weather parameters over a period of 42 years (1976 to 2017) were collected from Agro Climate Research Centre, Tamil Nadu Agricultural University. The global climate driver’s viz., Southern Oscillation Index and Indian Ocean Dipole indices were retrieved from Bureau of Meteorology, Australia. K- means algorithm was employed for centroid identification (which select the rows with unique distinguished features) at 90 per cent of the original data for the period of 42 years by eliminating the redundancy nature of the datawhich were used as training set. The result indicated the supremacy and notable strength of RFR over the other algorithms in terms of performance with 89.2 per cent. The Co-efficient of Determination (R2) for the predicted and observed values was found to be 0.8 for the monthly rainfall from 2015 to 2017.  


2022 ◽  
Author(s):  
Qing-Bin Lu

Abstract Time-series observations of global lower stratospheric temperature (GLST), global land surface air temperature (LSAT), global mean surface temperature (GMST), sea ice extent (SIE) and snow cover extent (SCE), together with observations reported in Paper I, combined with theoretical calculations of GLSTs and GMSTs, have provided strong evidence that ozone depletion and global climate changes are dominantly caused by human-made halogen-containing ozone-depleting substances (ODSs) and greenhouse gases (GHGs) respectively. Both GLST and SCE have become constant since the mid-1990s and GMST/LSAT has reached a peak since the mid-2000s, while regional continued warmings at the Arctic coasts (particularly Russia and Alaska) in winter and spring and at some areas of Antarctica are observed and can be well explained by a sea-ice-loss warming amplification mechanism. The calculated GMSTs by the parameter-free warming theory of halogenated GHGs show an excellent agreement with the observed GMSTs after the natural El Niño southern oscillation (ENSO) and volcanic effects are removed. These results provide a convincing mechanism of global climate change and will make profound changes in our understanding of atmospheric processes. This study also emphasizes the critical importance of continued international efforts in phasing out all anthropogenic halogenated ODSs and GHGs.


2022 ◽  
Author(s):  
Paul C. Rivera

An alternative physical mechanism is proposed to describe the occurrence of the episodic El Nino Southern Oscillation (ENSO) and La Nina climatic phenomena. This is based on the earthquake-perturbed obliquity change (EPOCH) model previously discovered as a major cause of the global climate change problem. Massive quakes impart a very strong oceanic force that can move the moon which in turn pulls the earth’s axis and change the planetary obliquity. Analysis of the annual geomagnetic north-pole shift and global seismic data revealed this previously undiscovered force. Using a higher obliquity in the global climate model EdGCM and constant greenhouse gas forcing showed that the seismic-induced polar motion and associated enhanced obliquity could be the major mechanism governing the mysterious climate anomalies attributed to El Nino and La Nina cycles.


MAUSAM ◽  
2022 ◽  
Vol 53 (3) ◽  
pp. 289-308
Author(s):  
D. R. KOTHAWALE ◽  
K. RUPA KUMAR

In the context of the ever increasing interest in the regional aspects of global warming, understanding the spatio-temporal variations of tropospheric temperature over India is of great importance. The present study, based on the data from 19 well distributed radiosonde stations for the period 1971-2000, examines the seasonal and annual mean temperature variations at the surface and five selected upper levels, viz., 850, 700, 500, 200 and 150 hPa. An attempt has also been made to bring out the association between tropospheric temperature variations over India and the summer monsoon variability, including the role of its major teleconnection parameter, the El Niño/Southern Oscillation (ENSO).   Seasonal and annual mean all-India temperature series are analyzed for surface and five tropospheric levels.  The mean annual cycles of temperature at different tropospheric levels indicate that the pre-monsoon season is slightly warmer than the monsoon season at the surface, 850 hPa and 150 hPa levels, while it is relatively cooler at all intermediate levels.  The mean annual temperature shows a warming of 0.18° C and 0.3° C per 10 years at the surface and 850 hPa, respectively.   Tropospheric temperature anomaly composites of excess (deficient) monsoon rainfall years show pronounced positive (negative) anomalies during the month of May, at all the levels.  The pre-monsoon pressure of Darwin has significant positive correlation with the monsoon temperature at the surface and 850 hPa.


MAUSAM ◽  
2022 ◽  
Vol 53 (3) ◽  
pp. 349-358
Author(s):  
R. P. KANE

The 12-monthly running means of CFC-11 and CFC-12 were examined for 1977-1992. As observed by earlier workers, during 1977-1988, there was a rapid, almost linear increase of these compounds, ~70% in the northern and ~77% in the southern hemisphere. From 1988 up to 1992, growth rates were slower, more so for CFC-11 in the northern hemisphere. Superposed on this pattern were QBO, QTO (Quasi-Biennial and Quasi-Triennial Oscillations). A spectral analysis of the various series indicated the following. The 50 hPa low latitude zonal wind had one prominent QBO peak at 2.58 years and much smaller peaks at 2.00 (QBO) and 5.1 years. The Southern oscillation index represented by (T-D), Tahiti minus Darwin atmospheric pressure, had a prominent peak at 4.1 years and a smaller peak at 2.31 years. CFC-11 had only one significant peak at 3.7 years in the southern hemisphere, roughly similar to the 4.1 year (T-D) peak. CFC-12 had prominent QBO (2.16-2.33 years) in both the hemispheres and a QTO (3.6 years) in the southern hemisphere. For individual locations, CFC-11 showed barely significant QBO in the range (1.95-3.07 years), while CFC 12 showed strong QBO in the range (1.86-2.38 years). The difference in the spectral characteristics of CFC-11 and CFC 12 time series is attributed to differences in their lifetimes (44 and 180 years), source emission rates and transport processes.


MAUSAM ◽  
2022 ◽  
Vol 53 (2) ◽  
pp. 165-176
Author(s):  
R. P. KANE

The time series of SOI (Southern Oscillation Index, Tahiti minus Darwin sea-level atmospheric pressure difference) was spectrally analysed by a simple method MEM-MRA, where periodicities are detected by MEM (Maximum Entropy Method) and used in MRA (Multiple Regression Analysis) to get the estimates of their amplitudes and phases. From these, the three or four most prominent ones were used for reconstruction and prediction. Using data for 1935-80 as dependent data, the reconstructed values of SOI matched well with observed values and most of the El Niños (SOI minima) and La Niñas (SOI maxima) were located correctly. But for the independent data (1980 onwards), the matching was poor. Omitting earlier data, 1945- 80, 1955-80, 1965-80 as dependent data again gave poor matching for 1980 onwards. When data for 1980 onwards only were used as dependent data, the matching was better, indicating that the spectral characteristics have changed considerably with time and recent data were more appropriate for further predictions. The 1997 El Niño was reproduced only in data for 1985 onwards. For 1990 onwards, only a single wave of 3.5 years was appropriate and explained the 1997 and 1994 events but only one (1991) of the 3 complex and quick events that occurred during 1989-95. The UCLA group of Dr. Ghil has been using the SSA (Singular Spectrum Analysis)-MEM combination for SOI analysis. For the 1980s, they got very good matching, but the 1989-95 structures were not reproduced. For recent years, their SSA-filtered SOI (used for prediction) is a simple sinusoid of ~3.5 years. It predicted the El Niño of 1997 only at its peak and even after using data up to February 1997, the abrupt commencement of the event in March 1997 and its abrupt end in June 1998 could not be predicted.   Using only a 3.5 years wave, an El Niño was expected for 2000-2001. However, a very long-lasting La Niña seems to be operative and there are no indications as yet (September of 2001) of any El Niño like conditions.


2022 ◽  
Author(s):  
Shlomi Ziskin Ziv ◽  
Chaim I. Garfinkel ◽  
Sean Davis ◽  
Antara Banerjee

Abstract. The relative importance of two processes that help control the concentrations of stratospheric water vapor, the Quasi-Biennial Oscillation (QBO) and El Nino-Southern Oscillation (ENSO), are evaluated in observations and in comprehensive coupled ocean-atmosphere-chemistry models. The possibility of nonlinear interactions between these two is evaluated both using Multiple Linear Regression (MLR) and three additional advanced machine learning techniques. The QBO is found to be more important than ENSO, however nonlinear interactions are non-negligible, and even when ENSO, the QBO, and potential nonlinearities are included the fraction of entry water vapor variability explained is still substantially less than what is accounted for by cold point temperatures. While the advanced machine learning techniques perform better than an MLR in which nonlinearities are suppressed, adding nonlinear predictors to the MLR mostly closes the gap in performance with the advanced machine learning techniques. Comprehensive models suffer from too weak a connection between entry water and the QBO, however a notable improvement is found relative to previous generations of comprehensive models. Models with a stronger QBO in the lower stratosphere systematically simulate a more realistic connection with entry water.


2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Ke Shi ◽  
Yoshiya Touge

AbstractWildfires are widespread disasters and are concurrently influenced by global climatic drivers. Due to the widespread and far-reaching influence of climatic drivers, separate regional wildfires may have similar climatic cause mechanisms. Determining a suite of global climatic drivers that explain most of the variations in different homogeneous wildfire regions will be of great significance for wildfire management, wildfire prediction, and global wildfire climatology. Therefore, this study first identified spatiotemporally homogeneous regions of burned area worldwide during 2001–2019 using a distinct empirical orthogonal function. Eight patterns with different spatiotemporal characteristics were identified. Then, the relationships between major burned area patterns and sixteen global climatic drivers were quantified based on wavelet analysis. The most significant global climatic drivers that strongly impacted each of the eight major wildfire patterns were identified. The most significant combinations of hotspots and climatic drivers were Atlantic multidecadal Oscillation-East Pacific/North Pacific Oscillation (EP/NP)-Pacific North American Pattern (PNA) with the pattern around Ukraine and Kazakhstan, El Niño/Southern Oscillation-Arctic Oscillation (AO)-East Atlantic/Western Russia Pattern (EA/WR) with the pattern in Australia, and PNA-AO-Polar/Eurasia Pattern-EA/WR with the pattern in Brazil. Overall, these results provide a reference for predicting wildfire and understanding wildfire homogeneity.


Sign in / Sign up

Export Citation Format

Share Document