Abstract: Radiative forces of greenhouse gases (GHG) increase the temperature of the Earth's surface, more on land, and less in oceans, due to their thermal capacities. Given this inertia, the temperature increase is delayed over time. Air temperature, however, is not delayed as air thermal capacity is much lower. In this study, through analysis and synthesis of multidisciplinary science and data, an estimate of atmospheric temperature increase is made. Then, this estimate is used to shed light on current observations of ice and snow loss, desertification and forest fires, and increased extreme air disturbances. The reason for this inquiry is due to the author’s skepticism that current changes cannot be explained by a "~1 oC" global average surface temperature rise within the last 50-60 years. The only other plausible cause to explore for understanding is that of atmospheric temperature rise. The study utilizes an analysis of air temperature rise from three different scientific disciplines: thermodynamics, climate science experiments, and climactic historical studies. The results coming from these diverse disciplines are nearly the same, within ± 1.6%. The direct radiative force of GHGs with a high level of scientific understanding is near 4.7 W/m2 on average over the Earth’s entire surface in 2018, as compared to one in pre-Industrial time in the mid-1700s. The additional radiative force of fast feedbacks coming from various forms of water gives approximately an additional ~15 W/m2. In 2018, these radiative forces heated the atmosphere by approximately 5.1 oC, which will create a thermal equilibrium average ground surface temperature increase of 4.6 oC to 4.8 oC by the end of this century. After 2018, the temperature will continue to rise without any additional increases in the concentration of the GHGs, primarily of carbon dioxide and methane. These findings of the radiative force of GHGs in 2018 were applied to estimates of effects on major Earth ecosystems. This additional force of nearly 20 W/m2 causes an increase in ice melting by an additional rate of over 90 cm/year, green leaves temperature increase by nearly 5 oC, and a work energy increase of air by approximately 40 Joules/mole. This explains the observed high rates of ice melting at all altitudes and latitudes, the spread of deserts and increases in forest fires, as well as increased energy of tornadoes, typhoons, hurricanes, and extreme weather, much more plausibly than the 1.5 oC increase in average global surface temperature in the same time interval. Planned mitigation and adaptation measures might prove to be much more effective when directed toward the reduction of existing GHGs in the atmosphere.
Abstract: Due to climate warming and consequently due to ice and snow melting of the Arctic Ocean, the highly biologically active ocean surface area has been expanding quickly making possible longer marine biota growth seasons during polar summers. That increase the probability of the remote marine environment secondary contribution, especially secondary organic contribution, to the particle production and particle growth events and particle properties, consequently effecting on the open ocean, pack ice and ground based regions radiation budget and thus on the feedbacks between arctic biota, particles, clouds, and climate.
Abstract: Ice cover County has a significant impact on rivers as it affects with the ice melting capacity which results in flooding, restrict navigation, modify the ecosystem and microclimate. River ices are made up of different ice types with varying ice thickness, so surveillance of river ice plays an important role. River ice types are captured using infrared imaging camera which captures the images even during the night times. In this paper the river ice infrared texture images are analysed using first-order statistical methods and secondorder statistical methods. The second order statistical methods considered are spatial gray level dependence method, gray level run length method and gray level difference method. The performance of the feature extraction methods are evaluated by using Probabilistic Neural Network classifier and it is found that the first-order statistical method and second-order statistical method yields low accuracy. So the features extracted from the first-order statistical method and second-order statistical method are combined and it is observed that the result of these combined features (First order statistical method + gray level run length method) provides higher accuracy when compared with the features from the first-order statistical method and second-order statistical method alone.
Abstract: In this paper, the ice melting in rectangular,
cylindrical and conical forms, which are erected vertically against air
flow, are experimentally studied in the free convection regime.The
results obtained are: Nusslet Number, heat transfer coefficient
andGrashof Number, and the variations of the said numbers in
relation to the time. The variations of ice slab area and volume are
measured, too.
Abstract: A method to determine experimentally the melting
rate, rm, and the heat transfer coefficients, αv (W/(m3K)), at
convective melting in a fixed bed of particles under adiabatic regime
is established in this paper. The method lies in the determining of the
melting rate by measuring the fixed bed height in time. Experimental
values of rm, α and α v were determined using cylindrical particles of
ice (d = 6.8 mm, h = 5.5 mm) and, as a melting agent, aqueous NaCl
solution with a temperature of 283 K at different values of the liquid
flow rate (11.63·10-6, 28.83·10-6, 38.83·10-6 m3/s).
Our experimental results were compared with those existing in
literature being noticed a good agreement for Re values higher than
50.
Abstract: Climate change leading to global warming affects the
earth through many different ways such as weather (temperature, precipitation, humidity and the other parameters of weather), snow coverage and ice melting, sea level rise, hydrological cycles, quality of water, agriculture, forests, ecosystems and health. One of the most
affected areas by climate change is hydrology and water resources.
Regions where majority of runoff consists of snow melt are more
sensitive to climate change. The first step of climate change studies
is to establish trends of significant climate variables including precipitation,
temperature and flow data to detect any potential climate
change impacts already happened. Two popular non-parametric trend
analysis methods, Mann-Kendal and Spearman-s Rho were applied
to Upper Euphrates Basin (Turkey) to detect trends of precipitation,
temperatures (maximum, minimum and average) and streamflow.