We propose a non-steady state model of the global temperature change. The model describes Earths surface temperature dynamics under main climate forcing. The equations were derived from basic physical relationships and detailed assessment of the numeric parameters used in the model. It shows an accurate fit with observed changes in the surface mean annual temperature (MAT) for the past 116 years. Using our model, we analyze the future global temperature change under scenarios of drastic reductions of COtextsubscript{2}. The presence of non-linear feed-backs in the model indicates on the possibility of exceeding two degrees threshold even under the carbon dioxide drastic reduction scenario. We discuss the risks associated with such warming and evaluate possible benefits of developing COtextsubscript{2}-absorbing deciduous tree plantations in the boreal zone of Northern Hemisphere.
The annual temperature cycle of the earth closely follows the annual cycle of solar flux. At temperate latitudes, both driving and response cycles are well described by a strong annual sinusoidal component and a non-vanishing semiannual component. A new analysis of historical weather station records in the United States determines persistent annual and semiannual variation with high precision. Historical annual temperature ranges are consistent with prior studies. Semiannual temperature cycles were much stronger than expected based on the semiannual solar driving. Instead, these cycles were consistent with multiplicative effects of two annual cycles. Our methods provide a quantitative window into the climates nonlinear response to solar driving, which is of potential value in testing climate models.
The $rmLambda$CDM cosmological model is remarkable: with just 6 parameters it describes the evolution of the Universe from a very early time when all structures were quantum fluctuations on subatomic scales to the present, and it is consistent with a wealth of high-precision data, both laboratory measurements and astronomical observations. However, the foundation of $rmLambda$CDM involves physics beyond the standard model of particle physics: particle dark matter, dark energy and cosmic inflation. Until this `new physics is clarified, $rmLambda$CDM is at best incomplete and at worst a phenomenological construct that accommodates the data. I discuss the path forward, which involves both discovery and disruption, some grand challenges and finally the limits of scientific cosmology.
In this paper, starting from the updated time series of global temperature anomalies, Ta, we show how the solar component affects the observed behavior using, as an indicator of solar activity, the Solar Sunspot Number SSN. The results that are found clearly show that the solar component has an important role and affects significantly the current observed stationary behavior of global temperature anomalies. The solar activity behavior and its future role will therefore be decisive in determining whether or not the restart of the increase of temperature anomalies observed since 1975 will occur.
What we expect from radiology AI algorithms will shape the selection and implementation of AI in the radiologic practice. In this paper I consider prevailing expectations of AI and compare them to expectations that we have of human readers. I observe that the expectations from AI and radiologists are fundamentally different. The expectations of AI are based on a strong and justified mistrust about the way that AI makes decisions. Because AI decisions are not well understood, it is difficult to know how the algorithms will behave in new, unexpected situations. However, this mistrust is not mirrored in our expectations of human readers. Despite well-proven idiosyncrasies and biases in human decision making, we take comfort from the assumption that others make decisions in a way as we do, and we trust our own decision making. Despite poor ability to explain decision making processes in humans, we accept explanations of decisions given by other humans. Because the goal of radiology is the most accurate radiologic interpretation, our expectations of radiologists and AI should be similar, and both should reflect a healthy mistrust of complicated and partially opaque decision processes undergoing in computer algorithms and human brains. This is generally not the case now.
The famous two-fold cost of sex is really the cost of anisogamy -- why should females mate with males who do not contribute resources to offspring, rather than isogamous partners who contribute equally? In typical anisogamous populations, a single very fit male can have an enormous number of offspring, far larger than is possible for any female or isogamous individual. If the sexual selection on males aligns with the natural selection on females, anisogamy thus allows much more rapid adaptation via super-successful males. We show via simulations that this effect can be sufficient to overcome the two-fold cost and maintain anisogamy against isogamy in populations adapting to environmental change. The key quantity is the variance in male fitness -- if this exceeds what is possible in an isogamous population, anisogamous populations can win out in direct competition by adapting faster.
Alexei V. Karnaukhov
,Elena V. Karnaukhova
,Elena P. Popova
.
(2021)
.
"Non-steady state model of global temperature change: Can we keep temperature from rising more than on two degrees?"
.
Sergei Lyuksyutov F
هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا