Decadal trends in climate over India

Decadal variations of meteorological parameters, vig, temperature (surface air maximum temperature, minimum temperature and upper air up to middle troposphere), station level pressure and seasonal and annual rainfall are studied for the period 1901 to 1986 (upper air data available from 1951 onwards), Tests of significance applied to data series (stationwise as well as country as a whole) show that the temperatures are showing a decreasing trend in almost all the northern parts of the country (north of 23" N) and a rising trend in southern parts (south of 23"N), For the country as a whole, however, there is a small warming trend Atmospheric pressure shows a fall between second and third decades but does not indicate any significant change after 1930, Decadal analysis of seasonal (Jun-Sep) and annual rainfall indicates that the variations in rainfall are within the statistical limits.

Population Dynamics of Rice Insect Pests through Light Trap Catches and Its Relation to Abiotic Factors in High Altitude Tribal Zone of Seethampeta in Andhra Pradesh, India

Studies were carried out at Agricultural Research Station, Seethampeta in Andhra Pradesh during the kharif season for three consecutive years starting from 2017 to 2019 on the population dynamics of insect pests occurring in paddy and also to assess the influence of weather parameters on insect pests. The insect pests observed in the light trap catches were, Yellow stem borer, Gall midge, Leaf folder, Green leafhopper, Plant hopper (BPH/WBPH) and Grasshoppers. The light trap catches of rice insect pests were recorded at weekly interval during 32nd Standard Meteorological Week (SMW) to 52nd Standard Meteorological Week (SMW) and the data were correlated with the weather parameters. The results revealed that more number of adults of Yellow stem borer were noticed during the year 2019 from 45th to 48th SMW and the correlation studies revealed that that maximum temperature, minimum and maximum relative humidities had significant positive influence and regression value of R2 (743, 638 and 726 during 2017, 2018 & 2019). The population of gall midge was negligible during the year 2017 and 2018. Whereas, in 2019 peak was notice during 38th SMW (15 No’s) maximum relative humidity has positive relation with increase in the gall midge population. Leaf folder adults were trapped more in the light traps during the year 2019 with peak catches of 11.00 No’s on 42nd SMW and were positively correlated with maximum temperature, minimum and maximum relative humidities. Similarly, leafhopper, brown leaf hopper, grass hoppers were positively correlated with the relative humidity.

Modeling of climate change in cold arid regions of north western Himalayas using multiple linear regression (MLR)

The present study aimed at modeling the impacts of climate change on precipitation and temperature and its trend in the context of changing climate in cold arid regions of north western Himalayas using multiple linear regression (MLR) model. The study was carried out in three different time slices viz., near future (2017-2045), mid future (2046-2072) and far future (2073-2099). The study includes the calibration of the observed climate data (maximum temperature, minimum temperature and precipitation) for fourteen years (2002-2015) and the outputs of downscaled scenario A2 of the Global Climate Model (GCM) data of Hadley Centre Coupled Model, (HadCM3) was used for validation, for the future. Daily climate (maximum temperature, minimum temperature and precipitation) scenarios were generated from 1961 to 2099 under A2 defined by Intergovernmental Panel on Climate Change (IPCC). During calibration, the maximum temperature, minimum temperature and precipitation showed decreasing trend. During validation, the maximum temperature showed an increasing trend in near future (2017- 2045) and decreasing trend in mid (2046-2072) and far future (2073-2099). While as, the minimum temperature and precipitation showed an increasing trend and decreasing trend respectively, in three futuristic phases. After validation, on comparison with the measured data, the variation in maximum temperature was found -2.59 oC in near future, -3.17 oC in mid future and -3.41 oC in far future. Similarly, for minimum temperature and precipitation, the variations with observed data were found 0.91 oC and -32.2 mm, respectively in near future, 2.01 oC and -34.6 mm, respectively in mid future, 4.08 oC and -3.4 mm, respectively in far future. These changes may be found due to global warming which lead to decrease in average annual precipitation and increase in average minimum temperatures causing the melting of glaciers.

Delineation of maize growing areas vis-à-vis climate and technology using statistical and geospatial techniques in Punjab

In the present investigation, maize growing areas in Punjab were delineated with respect toclimate and technology variables using statistical and geospatial techniques. The effect of the climate(maximum temperature, minimum temperature and rainfall) and technology variables (fertilizers, irrigation)on maize yield was studied in spatio-temporal domain in maize growing areas of Punjab. Long-termdata on climate and technology variables as well as maize productivity was collected for maize growingdistricts of the state. The maximum temperature during maize growing season was highest (35.7°C) inAmritsar, the minimum temperature was highest (25.3°C) in Ludhiana, whereas rainfall was highest (765.4 mm) in Gurdaspur. The results of Mann-Kendall test showed significant increase in maximum temperature @ 0.03°C year-1 in Hoshiarpur, Kapurthala, Patiala and Roopnagar and minimum temperature @ 0.04°C year-1in Gurdaspur, Hoshiarpur, Kapurthala and Roopnagar, @ 0.03°C year-1 in Jalandhar and @ 0.05°C year-1 in Ludhiana and Patiala districts. Analysis indicated that the maize yield was significantly higher in Ludhiana than other districts. Spatial variability in maize yield, climate and technology was studied using Geographic Information System (GIS). The integration of the layers of climate parameters with yield in GIS demarcated four major maize growing zones in Punjab.

Increase of Temperature and Salinity in the Active Layer of the North-Eastern Black Sea from 2010 to 2020

Purpose. The paper is aimed at assessing salinity and temperature variability in the upper 300-meter layer of the northeastern part of the Black Sea based on the analysis of the archival and modern expeditionary data. Methods and Results. The data on the cross-sections “coast – sea center” (with the length of 70–110 nautical miles) performed from 1999 to 2009, as well as the results of regular ship monitoring in the shelf-slope zone of the northeastern part of the Black Sea carried out in 2010–2020 were used. It was found that salinity was progressively increasing in the upper 200-meter layer during the last decade. Salinity increase, on the average, constituted annually about 0.05–0.06 PSU. An increase of temperature was also observed below the layer of temperature minimum (core of the cold intermediate layer). In particular, the lower 8.7 °C isotherm rose annually, on the average, by 11 m from its annual average depth 242 m in 2010 up to 121 m in 2020. Salinity growth led to the corresponding changes in water density that resulted in elevation of the lower boundary of the oxygen-containing layer (potential density is 15.8) from the depth of 143 m in 2010 to 124 m in 2020. Conclusions. Climatic changes have led to a noticeable salinity increase in the upper 200-meter layer of the northeastern Black Sea, as well as to a temperature increase in the layers situated below the temperature minimum layer. Though the measurements were carried out in a certain area of the shelf-slope zone, there are reasons to assume that the observed dynamics can be attributed to the entire Black Sea. Physical reasons for the observed changes require a detailed research.

Increase of Temperature and Salinity in the Active Layer of the North-Eastern Black Sea from 2010 to 2020

Purpose. The paper is aimed at assessing salinity and temperature variability in the upper 300-meter layer of the northeastern part of the Black Sea based on the analysis of the archival and modern expeditionary data. Methods and Results. The data on the cross-sections “coast – sea center” (with the length of 70–110 nautical miles) performed from 1999 to 2009, as well as the results of regular ship monitoring in the shelf-slope zone of the northeastern part of the Black Sea carried out in 2010–2020 were used. It was found that salinity was progressively increasing in the upper 200-meter layer during the last decade. Salinity increase, on the average, constituted annually about 0.05–0.06 PSU. An increase of temperature was also observed below the layer of temperature minimum (core of the cold intermediate layer). In particular, the lower 8.7 °C isotherm rose annually, on the average, by 11 m from its annual average depth 242 m in 2010 up to 121 m in 2020. Salinity growth led to the corresponding changes in water density that resulted in elevation of the lower boundary of oxygen-containing layer (potential density 15.8) from the depth 143 m in 2010 to 124 m in 2020. Conclusions. Climatic changes have led to a noticeable salinity increase in the upper 200-meter layer of the northeastern Black Sea, as well as to a temperature increase in the layers situated below the temperature minimum layer. Though the measurements were carried out in a certain area of the shelfslope zone, there are the reasons to assume that the observed dynamics can be attributed to the entire Black Sea. Physical reasons for the observed changes require a detailed research

IS CLIMATE INDICATOR AFFECTS TRANSMISSION OF COVID-19 PANDEMIC: CLIMATIC IMPACTS ON COVID-19 TRANSMISSION

The climate variations have lot of the financial, medical and economic consequences. Studies showed that climate plays the vital role in virus transmission. This study analyzed the impact of climate indicators on COVID-19 concerning Pakistan. The secondary data is used for analysis as obtained from world health organization, ministry of health Pakistan and Pakistan meteorological department. The results show that all the research variables like temperature maximum, temperature minimum, humidity, and wind flow are positively and significantly correlated to COVID-19. Findings show that temperature maximum, temperature minimum, and wind flow have the positive and significant association with COVID-19, while the humidity has a positive impact on COVID-19 transmission. The study show that minimum temperature is favorable for the virus transmission. Thus, the study provides significant results in reaching the decision and concluding the study.

Evidence of chromospheric molecular hydrogen emission in a solar flare observed by the IRIS satellite

We have carried out the first comprehensive investigation of enhanced line emission from molecular hydrogen, H2 at 1333.79 Å, observed at flare ribbons in SOL2014-04-18T13:03. The cool H2 emission is known to be fluorescently excited by Si iv 1402.77 Å UV radiation and provides a unique view of the temperature minimum region (TMR). Strong H2 emission was observed when the Si iv 1402.77 Å emission was bright during the flare impulsive phase and gradual decay phase, but it dimmed during the GOES peak. H2 line broadening showed non-thermal speeds in the range 7-18 $\rm {km~s}^{-1}$, possibly corresponding to turbulent plasma flows. Small red (blue) shifts, up to 1.8 (4.9) $\rm {km~s}^{-1}$ were measured. The intensity ratio of Si iv 1393.76 Å and Si iv 1402.77 Å confirmed that plasma was optically thin to Si iv (where the ratio = 2) during the impulsive phase of the flare in locations where strong H2 emission was observed. In contrast, the ratio differs from optically thin value of 2 in parts of ribbons, indicating a role for opacity effects. A strong spatial and temporal correlation between H2 and Si iv emission was evident supporting the notion that fluorescent excitation is responsible.

Relationship Between Climate Change and Rice Production of Bangladesh: A data analysis using MCMC method

In this study, we analyzed publicly available agricultural data on rice production in Bangladesh between 2008 to 2017 to address the relationship between climate changes and rice production in Bangladesh by estimating predictor variables, i.e., average rainfall and maximum temperature, minimum temperature, and humidity. A generalized linear regression model sets up for each rice (Aush, Aman, Boro) with the climate variables (average rainfall, maximum temperature, minimum temperature, and humidity). We used Markov-Chain-Monte-Carlo's (MCMC)'s Gibbs sampling on the collected data to approximate marginal posterior distribution from the prior distribution to see the profound relationship between those predictor variables and the predicted variables (Aush, Aman, Boro). We also saw whether any storm's impact could modify the relationship between climate change variables and rice production in Bangladesh.

Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model

With the rapid spread of the pandemic due to the coronavirus disease 2019 (COVID-19), the virus has already led to considerable mortality and morbidity worldwide, as well as having a severe impact on economic development. In this article, we analyze the state-level correlation between COVID-19 risk and weather/climate factors in the USA. For this purpose, we consider a spatio-temporal multivariate time series model under a hierarchical framework, which is especially suitable for envisioning the virus transmission tendency across a geographic area over time. Briefly, our model decomposes the COVID-19 risk into: (i) an autoregressive component that describes the within-state COVID-19 risk effect; (ii) a spatiotemporal component that describes the across-state COVID-19 risk effect; (iii) an exogenous component that includes other factors (e.g., weather/climate) that could envision future epidemic development risk; and (iv) an endemic component that captures the function of time and other predictors mainly for individual states. Our results indicate that maximum temperature, minimum temperature, humidity, the percentage of cloud coverage, and the columnar density of total atmospheric ozone have a strong association with the COVID-19 pandemic in many states. In particular, the maximum temperature, minimum temperature, and the columnar density of total atmospheric ozone demonstrate statistically significant associations with the tendency of COVID-19 spreading in almost all states. Furthermore, our results from transmission tendency analysis suggest that the community-level transmission has been relatively mitigated in the USA, and the daily confirmed cases within a state are predominated by the earlier daily confirmed cases within that state compared to other factors, which implies that states such as Texas, California, and Florida with a large number of confirmed cases still need strategies like stay-at-home orders to prevent another outbreak.