Quantum Atmospheric Biophysics: A Comparison of Four Weather Stations in India on Average Monthly Temperatures Since 1892 and Forecasts to 2150

The identification method revealed asymmetric wavelets of dynamics, as fractal quanta of the behavior of the surface air layer at a height of 2 m, according to the average monthly temperature at four weather stations in India (Srinagar, Jolhpur, New Delhi and Guvahati). For Srinagar station, the maximum for all years is observed in July, for Jolhpur and New Delhi stations it shifts to June, and for Guvahati it shifts to August. With a high correlation coefficient of 0.9659, 0.8640 and 0.8687, a three-factor model of the form was obtained. The altitude, longitude and latitude of the station are given sequentially. The hottest month for Srinagar over a period of 130 years is in July. At the same time, the temperature increased from 23.4 °C to 24.2 °C (by 3.31%). A noticeable decrease in the intensity of heat flows in June occurred at Jolhpur (over 125 years, a decrease from 36.2 °C to 33.3 °C, or by 8.71%) and New Delhi (over 90 years, a decrease from 35.1 °C to 32.4 °C, or by 7.69%). For almost 120 years, Guvahati has experienced complex climate changes: In 1902, the hottest month was July, but in 2021 it has shifted to August. The increase in temperature at various stations is considered. At Srinagar station in 2021, compared to 1892, temperatures increased in June, September and October. Guvahati has a 120-year increase in December, January, March and April. Temperatures have risen in February, March and April at Jolhpur in 125 years, but have risen in February and March at New Delhi Station in 90 years. Despite the presence of tropical evergreen forests, the area around Guvahati Station is expected to experience strong warming.

Characteristics of Spatial and Temporal Variations of Monthly Mean Surface Air Temperature over Qinghai-Tibet Plateau

The recorded meteorological data of monthly mean surface air temperature from 72 meteorological stations over the Qinghal-Tibet Plateau in the period of 1960-2003 have been analyzed by using Empirical Orthogonal Function （EOF） method, to understand the detailed features of its temporal and spatial variations. The results show that there was a high consistency of the monthly mean surface air temperature, with a secondarily different variation between the north and the south of the plateau. Warming trend has existed at all stations since the 1960s, while the warming rates were different in various zones. The source regions of big rivers had intense warming tendency. June, November and December were the top three fast-warming months since the 1960s; while April, July and September presented dramatic warming tendency during the last decade.

Monthly Mean Temperature Prediction Based on a Multi－level Mapping Model of Neural Network BP Type

In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level model of this type with ixj=3x2,k=l,and the 1980 monthly mean temperture predichon on a long-t6rm basis were prepared by steadily modifying the weighting coefficient,making for the correlation coefficient of 97% with the measurements.Furthermore,the weighhng parameter was modified for each month of 1980 by means of observations,therefore constrcuhng monthly mean temperature forecasts from January to December of the year,reaching the correlation of 99.9% with the measurements.Likewise,the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlahon of 98% and the month-tO month forecasts of 99.4%.

Further-Adjusted Long-Term Temperature Series in China Based on MASH

A set of homogenized monthly mean surface air temperature （SAT） series at 32 stations in China back to the 19th century had previously been developed based on the RHtest method by Cao et al., but some inhomogeneities remained in the dataset. The present study produces a further-adjusted and updated dataset based on the Multiple Analysis of Series for Homogenization （MASH） method. The MASH procedure detects 33 monthly temperature records as erroneous outliers and 152 meaningful break points in the monthly SAT series since 1924 at 28 stations. The inhomogeneous parts are then adjusted relative to the latest homogeneous part of the series. The new data show significant warming trends during 1924-2016 at all the stations, ranging from 0.48 to 3.57℃ （100 yr）^-1, with a regional mean trend of 1.65℃ （100 yr）^-1 ; whereas, the previous results ranged from a slight cooling at two stations to considerable warming, up to 4.5℃ （100 yr）^-1. It is suggested that the further-adjusted data are a better representation of the large-scale pattern of climate change in the region for the past century. The new data axe available online at http：//www.dx.doi.org/10.11922/sciencedb.516.

Wavelet Analysis of Average

The identification method in the CurveExpert-1.40 software environment revealed asymmetric wavelets of changes in the average monthly temperature of New Delhi from 1931 to 2021.The maximum increment for 80 years of the average monthly temperature of 5.1℃was in March 2010.An analysis of the wave patterns of the dynamics of the average monthly temperature up to 2110 was carried out.For forecasting,formulas were adopted containing four components,among which the second component is the critical heat wave of India.The first component is the Mandelbrot law(in physics).It shows the natural trend of decreasing temperature.The second component increases according to the critical law.The third component with a correlation coefficient of 0.9522 has an annual fluctuation cycle.The fourth component with a semi-annual cycle shows the influence of vegetation cover.The warming level of 2010 will repeat again in 2035-2040.From 2040 the temperature will rise steadily.June is the hottest month.At the same time,the maximum temperature of 35.1℃in 2010 in June will again reach by 2076.But according to the second component of the heat wave,the temperature will rise from 0.54℃to 16.29°C.The annual and semi-annual cycles had an insignificant effect on the June temperature dynamics.Thus,the identification method on the example of meteorological observations in New Delhi made it possible to obtain summary models containing a different number of components.The temperature at a height of 2 m is insufficient.On the surface,according to space measurements,the temperature reaches 55°C.As a result,in order to identify more accurate asymmetric wavelets for forecasting,the results of satellite measurements of the surface temperature of India at various geographical locations of meteorological stations are additionally required.

Preliminary Study of Reconstruction of a Dynamic System Using an One－Dimensional Time Series

This paper concerns the reconstruction of a dynamic system based on phase space continuation of monthly meantemperature iD time series and the assumption that the equation for the time-varying evolution of phase space statevariables contains linear and nonlinear quadratic terms. followed by the fitting of the dataset subjected to continuation so as to get, by the least square method. the coefficients of the terms, of which those of greater variance contribution are retained for use. Results show that the obtained low-order system may be used to describe nonlinear properties of the short range climate variation shown by monthly mean temperature series.

Climatic Change in Qinghai-Tibet Plateau and the Surrounding Areas in 130 Years

[ Objective] The research aimed to study climatic change in Qinghai -Tibet Plateau and the surrounding areas from 1880 to 2011. [ Method] Based on GISS temperature grid data, by using change rules of the annual and monthly anomaly temperatures, sliding t-test and wavelet analysis, periodicity and tendency of the atmospheric temperature change in Qinghai -Tibet Plateau and the surrounding areas were analyzed. [ Re- sult] Both annual and monthly anomaly temperatures in Qinghai -Tibet Plateau in recent 130 years presented rise tendency. Since the 1990s, tem- perature rose evidently, and it presented temperature-rise tendency of winter 〉 autumn 〉 spring 〉 summer. Rise velocity of the temperature had spatial difference. Rise velocity of the temperature in west Inner Mongolia was the highest, followed by west Sichuan and east Tibet. Rise velocity of the temperature in some areas of Xinjiang was the slowest. Abrupt change of the temperature happened in the 1930s, and main period of the wavelet analysis was 10 years. [ Conclusion] The research could lay foundation for discussinq Qlobal climate change.