Neutrosophic statistical analysis of resistance depending on the temperature variance of conducting material

In this work, we have proposed a neutrosophic statistical approach for the analysis of resistance of conducting material depending on the temperature variance. We have developed a neutrosophic formula and applied it to the resistance data. We also use the classical statistical approach for making a comparison between both approaches. As a result, it is observed that the neutrosophic statistical approach is more flexible and informative. Also, this work suggests that the neutrosophic statistical approach analyzes the resistance of conducting material for big data.

A permafrost implementation in the simple carbon–climate model Hector v.2.3pf

Permafrost currently stores more than a fourth of global soil carbon. A warming climate makes this carbon increasingly vulnerable to decomposition and release into the atmosphere in the form of greenhouse gases. The resulting climate feedback can be estimated using land surface models, but the high complexity and computational cost of these models make it challenging to use them for estimating uncertainty, exploring novel scenarios, and coupling with other models. We have added a representation of permafrost to the simple, open-source global carbon–climate model Hector, calibrated to be consistent with both historical data and 21st century Earth system model projections of permafrost thaw. We include permafrost as a separate land carbon pool that becomes available for decomposition into both methane (CH4) and carbon dioxide (CO2) once thawed; the thaw rate is controlled by region-specific air temperature increases from a preindustrial baseline. We found that by 2100 thawed permafrost carbon emissions increased Hector’s atmospheric CO2 concentration by 5 %–7 % and the atmospheric CH4 concentration by 7 %–12 %, depending on the future scenario, resulting in 0.2–0.25 ∘C of additional warming over the 21st century. The fraction of thawed permafrost carbon available for decomposition was the most significant parameter controlling the end-of-century temperature change in the model, explaining around 70 % of the temperature variance, and was distantly followed by the initial stock of permafrost carbon, which contributed to about 10 % of the temperature variance. The addition of permafrost in Hector provides a basis for the exploration of a suite of science questions, as Hector can be cheaply run over a wide range of parameter values to explore uncertainty and can be easily coupled with integrated assessment and other human system models to explore the economic consequences of warming from this feedback.

Study on Temperature Variance for SimCLR based Activity Recognition

Human Activity Recognition (HAR) is a process to automatically detect human activities based on stream data generated from various sensors, including inertial sensors, physiological sensors, location sensors, cameras, time, and many others. In this paper, we propose a robust SimCLR model for human activity recognition with a temperature variance study. In this work, SimCLR, a contrasting learning technique is optimized via regulating the temperature for visual representations, is incorporated for improving the HAR performance in healthcare.

Evaluating and Improving Parameterizations of the Variance of Temperature Fluctuations Over Heterogeneous Landscapes for Surface Boundary Conditions in Atmospheric Models

<p>The implementation of higher-order turbulence closure schemes in Earth system models (e.g., the Cloud Layers Unified by Binormals; CLUBB) aims to improve the modeling of convection and radiative transfer in numerical weather prediction and climate models. However, the added value of these schemes is constrained by the specification of boundary conditions on higher-order statistics. At the land surface, many of the higher order turbulence statistics that are required as boundary conditions are parameterized using formulations more appropriate for stationary and planar-homogeneous flow in the absence of subsidence. A case in point is the variance of the potential temperature fluctuations.  Because of the additive nature of variances arising from non-uniformity in surface heating, current parameterizations are not readily generalizable. The current scheme used in CLUBB, as well as other models, relies on limited studies over uniform terrain, with the variance entirely determined by local sensible heat flux, friction velocity, and the Obukhov stability parameter without regard to local site characteristics. This presentation aims to address this weakness by leveraging the National Ecological Observation Network (NEON) network of eddy covariance towers to validate the current parameterization scheme for potential temperature variance, as well as propose improvements for more heterogeneous terrain.</p><p>The turbulence fluctuations of temperature at 39 NEON sites are processed and quality controlled, removing points occurring at night, while precipitation is falling, and with sub-zero temperatures. Results overall indicate the current scheme performs well, especially over flat homogeneous terrain where local flux relationships dominate. When there is sufficiently heterogeneous, rough terrain or non-closure of the local energy balance, however, existing schemes fail to accurately estimate the variances in temperature. In these cases, the parameterization needs to be modified, and initial results suggest simple adjustments can yield improvements and reduce error close to that of the uniform sites with local energy balance closure. The successful improvement of the temperature variance parameterization scheme implies high potential for similar, new, empirically derived parameterizations for the surface boundaries for other higher order turbulent statistics (e.g. temperature skewness) in atmospheric turbulence models.</p>

Drivers of atmospheric and oceanic surface temperature variance: a frequency domain approach

Ocean-atmosphere coupling modifies the variability of Earth’s climate over a wide range of timescales. However, attribution of the processes that generate this variability remains an outstanding problem. In this manuscript, air-sea coupling is investigated in an eddy-resolving, medium-complexity, idealized, ocean-atmosphere model. The model is run in three configurations: fully coupled, partially coupled (where the effect of the ocean geostrophic velocity on the sea surface temperature field is minimal), and atmosphere-only. A surface boundary layer temperature variance budget analysis computed in the frequency domain is shown to be a powerful tool for studying air-sea interactions, as it differentiates the relative contributions to the variability in the temperature field from each process across a range of timescales (from daily to multidecadal). This method compares terms in the ocean and atmosphere across the different model configurations to infer the underlying mechanisms driving temperature variability. Horizontal advection plays a dominant role in driving temperature variance in both the ocean and atmosphere, particularly at timescales shorter than annual. At longer timescales, the temperature variance is dominated by strong coupling between atmosphere and ocean. Furthermore, the Ekman transport contribution to the ocean’s horizontal advection is found to underlie the low-frequency behavior in the atmosphere. The ocean geostrophic eddy field is an important driver of ocean variability across all frequencies and is reflected in the atmospheric variability in the western boundary current separation region at longer timescales.

Direct Temporal Cascade of Temperature Variance in Eddy-Permitting Simulations of Multidecadal Variability

The North Atlantic is characterized by basin-scale multidecadal fluctuations of the sea surface temperature with periods ranging from 20 to 70 years. One candidate for such a variability is a large-scale baroclinic instability of the temperature gradients across the Atlantic associated with the North Atlantic Current. Because of the long time scales involved, most of the studies devoted to this problem are based on low-resolution numerical models leaving aside the effect of explicit mesoscale eddies. How high-frequency motions associated with the mesoscale eddy field affect the basin-scale low-frequency variability is the central question of this study. This issue is addressed using an idealized configuration of an ocean general circulation model at eddy-permitting resolution (20 km). A new diagnostic allowing the calculation of nonlinear fluxes of temperature variance in frequency space is presented. Using this diagnostic, we show that the primary effect of mesoscale eddies is to damp low-frequency temperature variance and to transfer it to high frequencies.