Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDisparity of tropospheric and surface temperature trends: New evidence
95
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Observations suggest that the earths surface has been warming relative to the troposphere for the last 25 years; this is not only difficult to explain but also contrary to the results of climate models. We provide new evidence that the disparity is real. Introducing an additional data set, R2 2 meter temperatures, a diagnostic variable related to tropospheric temperature profiles, we find trends derived from it to be in close agreement with satellite measurements of tropospheric temperature. This suggests that the disparity likely is a result of near-surface processes. We find that the disparity does not occur uniformly across the globe, but is primarily confined to tropical regions which are primarily oceanic. Since the ocean measurements are sea surface temperatures, we suggest that the disparity is probably associated with processes at the ocean-atmosphere interface. Our study thus makes unlikely some of the explanations advanced to account for the disparity; it also demonstrates the importance of distinguishing between land, sea and air measurements
rate research
Read More
As a consequence of greenhouse forcing, all state of the art general circulation models predict a positive temperature trend that is greater for the troposphere than the surface. This predicted positive trend increases in value with altitude until it reaches a maximum ratio with respect to the surface of as much as 1.5 to 2.0 at about 200 to 400 hPa. However, the temperature trends from several independent observational data sets show decreasing as well as mostly negative values. This disparity indicates that the three models examined here fail to account for the effects of greenhouse forcings.
This paper illustrates an application of machine learning (ML) within a complex system that performs grade estimation. In surface mining, assay measurements taken from production drilling often provide useful information that allows initially inaccurate surfaces created using sparse exploration data to be revised and subsequently improved. Recently, a Bayesian warping technique has been proposed to reshape modeled surfaces using geochemical and spatial constraints imposed by newly acquired blasthole data. This paper focuses on incorporating machine learning into this warping framework to make the likelihood computation generalizable. The technique works by adjusting the position of vertices on the surface to maximize the integrity of modeled geological boundaries with respect to sparse geochemical observations. Its foundation is laid by a Bayesian derivation in which the geological domain likelihood given the chemistry, p(g|c), plays a similar role to p(y(c)|g). This observation allows a manually calibrated process centered around the latter to be automated since ML techniques may be used to estimate the former in a data-driven way. Machine learning performance is evaluated for gradient boosting, neural network, random forest and other classifiers in a binary and multi-class context using precision and recall rates. Once ML likelihood estimators are integrated in the surface warping framework, surface shaping performance is evaluated using unseen data by examining the categorical distribution of test samples located above and below the warped surface. Large-scale validation experiments are performed to assess the overall efficacy of ML assisted surface warping as a fully integrated component within an ore grade estimation system where the posterior mean is obtained via Gaussian Process inference with a Matern 3/2 kernel.
The variation with time from 1956-2002 of the globally averaged rate of ionization produced by cosmic rays in the atmosphere is deduced and shown to have a cyclic component of period roughly twice the 11 year solar cycle period. Long term variations in the global average surface temperature as a function of time since 1956 are found to have a similar cyclic component. The cyclic variations are also observed in the solar irradiance and in the mean daily sun spot number. The cyclic variation in the cosmic ray rate is observed to be delayed by 2-4 years relative to the temperature, the solar irradiance and daily sun spot variations suggesting that the origin of the correlation is more likely to be direct solar activity than cosmic rays. Assuming that the correlation is caused by such solar activity, we deduce that the maximum recent increase in the mean surface temperature of the Earth which can be ascribed to this activity is $lesssim14%$ of the observed global warming.
The importance of snow cover and ice extent in the Northern Hemisphere was recognized by various authors leading to a positive feedback of surface reflectivity on climate. In fact, the retreat of Arctic sea ice is accompanied by enhanced solar input in the Arctic region, i.e. a decrease of the terrestrial albedo. We have studied this effect for the past six decades and estimate the corresponding global warming in the northern hemisphere. A simple 1-dimensional model is used that includes the simultaneous increase of the greenhouse gases. Our results indicate that the latter directly cause a temperature rise of only 0.2 K in 1955 to 2015, while a notably larger effect 0.7 +/- 0.2 K is found for the loss of Arctic sea ice in the same time. These numbers comprise most of the reported mean temperature rise of 1.2 +/- 0.2 K of the northern hemisphere. The origin of the sea-ice retreat is discussed, e.g. internal variability or feedback by the CO2 concentration increase. Our data also suggest a delayed response of the global surface temperature rise to the loss of sea ice with a time constant of approximately 10 to 20 years.
Nowadays, electrochemical reduction of CO$_2$ has been considered as an effective method to solve the problem of global warming. The primary challenge in studying the mechanism is to determine the adsorption states of CO$_2$, since complicated metal surfaces often result in many different adsorption sites. Based on the density functional theory (DFT) calculations, we performed a theoretical study on the adsorption of CO$_2$ on the Ag electrode surface. The results show that the adsorption populations of CO$_2$ are extremely sensitive to the adsorption sites. Importantly, we found that the preferable adsorption positions are the terrace sites, rather than the previous reported step sites. The adsorption populations were found with the order of (211) > (110) > (111) > (100). Subsequently, the adsorption characteristics were correlated with the d-band theory and the charge transfers between Ag surfaces and CO$_2$.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
