scholarly journals Interpreting Observed Temperature Probability Distributions Using a Relationship between Temperature and Temperature Advection

2021 ◽  
pp. 1-53
Author(s):  
Marianna Linz ◽  
Gang Chen
Keyword(s):  
Probability Distributions ◽  
Equilibrium Temperature ◽  
Land Area ◽  
Simple Method ◽  
Conditional Mean ◽  
Temperature Distributions ◽  
Temperature Advection ◽  
Cold Events ◽  
Anomalous Wind ◽  
Spatially Variant

Abstract The non-normality of temperature probability distributions and the physics that drive it are important due to their relationships to the frequency of extreme warm and cold events. Here we use a conditional mean framework to explore how horizontal temperature advection and other physical processes work together to control the shape of daily temperature distributions during 1979-2019 in the ERA5 reanalysis for both JJA and DJF. We demonstrate that the temperature distribution in mid- and high- latitudes can largely be linearly explained by the conditional mean horizontal temperature advection with the simple treatment of other processes as a Newtonian relaxation with a spatially-variant relaxation time scale and equilibrium temperature. We analyze the role of different transient and stationary components of the horizontal temperature advection in affecting the shape of temperature distributions. The anomalous advection of the stationary temperature gradient has a dominant effect in influencing temperature variance, while both that term and the covariance between anomalous wind and anomalous temperature have significant effects on temperature skewness. While this simple method works well over most of the ocean, the advection-temperature relationship is more complicated over land. We classify land regions with different advection-temperature relationships under our framework, and find that for both seasons the aforementioned linear relationship can explain ~30% of land area, and can explain either the lower or the upper half of temperature distributions in an additional ~30% of land area. Identifying the regions where temperature advection explains shapes of temperature distributions well will help us gain more confidence in understanding the future change of temperature distributions and extreme events.

Climate Sensitivity Distributions Dependence on the Possibility that Models Share Biases

2010 ◽  
Vol 23 (16) ◽  
pp. 4395-4415 ◽  
Author(s):  
Derek M. Lemoine
Keyword(s):  
Temperature Change ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Probability Distributions ◽  
Equilibrium Temperature ◽  
Hierarchical Bayes ◽  
Lapse Rate ◽  
Posterior Distributions ◽  
Prior Beliefs ◽  
Carbon Dioxide Concentrations

Abstract Uncertainty about biases common across models and about unknown and unmodeled feedbacks is important for the tails of temperature change distributions and thus for climate risk assessments. This paper develops a hierarchical Bayes framework that explicitly represents these and other sources of uncertainty. It then uses models’ estimates of albedo, carbon cycle, cloud, and water vapor–lapse rate feedbacks to generate posterior probability distributions for feedback strength and equilibrium temperature change. The posterior distributions are especially sensitive to prior beliefs about models’ shared structural biases: nonzero probability of shared bias moves some probability mass toward lower values for climate sensitivity even as it thickens the distribution’s positive tail. Obtaining additional models of these feedbacks would not constrain the posterior distributions as much as narrowing prior beliefs about shared biases or, potentially, obtaining feedback estimates having biases uncorrelated with those impacting climate models. Carbon dioxide concentrations may need to fall below current levels to maintain only a 10% chance of exceeding official 2°C limits on global average temperature change.

Estimation of Neutral Plane Position in High Rise Buildings

1996 ◽  
Vol 14 (3) ◽  
pp. 235-248 ◽  
Author(s):  
Y. He ◽  
V. Beck
Keyword(s):  
Experimental Data ◽  
Pressure Distribution ◽  
Network Model ◽  
Neutral Plane ◽  
Computational Results ◽  
Uniform Temperature ◽  
Simple Method ◽  
High Rise ◽  
Temperature Distributions ◽  
Smoke Spread

This paper presents a simple method for calculation of the pressure distribution and the neutral plane position in a high rise building. Non-uniform temperature distributions in the stairshaft of the building and discrete door openings are taken into account. The method has been incor porated into a network model for calculating smoke spread in multi-storey buildings. Computational results are compared with experimental data ob tained by other researchers.

Confidence intervals in spectroscopic data reduction

1990 ◽  
Vol 68 (7-8) ◽  
pp. 643-648 ◽  
Author(s):  
Jean-Louis Femenias
Keyword(s):  
Confidence Intervals ◽  
Data Reduction ◽  
Spectroscopic Data ◽  
Probability Distributions ◽  
Random Variables ◽  
Confidence Probability ◽  
Correlation Effects ◽  
Simple Method ◽  
Normal Case ◽  
Standard Deviations

The calculation of reduced radii, which connect standard deviations to physical errors, is revisited. A new and simple method, which takes into account the number of random variables, the exact confidence probability, and the correlation is presented in the normal case and in the case of unknown probability distributions. It is shown that correlation effects on radii are much less important than on standard deviations.

scholarly journals Adhesive Dynamics

2014 ◽  
Vol 136 (2) ◽  
Author(s):  
Daniel A. Hammer
Keyword(s):  
Probability Distributions ◽  
Mechanical Energy ◽  
Biological Systems ◽  
Cell Interaction ◽  
Biological Interactions ◽  
Cell Capture ◽  
Simple Method ◽  
Mechanical Models ◽  
Spatio Temporal ◽  
Sampling Probability

Adhesive dynamics (AD) is a method for simulating the dynamic response of biological systems in response to force. Biological bonds are mechanical entities that exert force under strain, and applying forces to biological bonds modulates their rate of dissociation. Since small numbers of events usually control biological interactions, we developed a simple method for sampling probability distributions for the formation or failure of individual bonds. This method allows a simple coupling between force and strain and kinetics, while capturing the stochastic response of biological systems. Biological bonds are dynamically reconfigured in response to applied mechanical stresses, and a detailed spatio-temporal map of molecules and the forces they exert emerges from AD. The shape or motion of materials bearing the molecules is easily calculated from a mechanical energy balance provided the rheology of the material is known. AD was originally used to simulate the dynamics of adhesion of leukocytes under flow, but new advances have allowed the method to be extended to many other applications, including but not limited to the binding of viruses to surface, the clustering of adhesion molecules driven by stiff substrates, and the effect of cell-cell interaction on cell capture and rolling dynamics. The technique has also been applied to applications outside of biology. A particular exciting recent development is the combination of signaling with AD (so-called integrated signaling adhesive dynamics, or ISAD), which allows facile integration of signaling networks with mechanical models of cell adhesion and motility. Potential opportunities in applying AD are summarized.

Probability distributions of mechanical properties of natural aggregates using a simple method

2020 ◽  
Vol 233 ◽  
pp. 117269
Author(s):  
Sérgio C. Angulo ◽  
Natalia V. Silva ◽  
David A. Lange ◽  
Luís Marcelo Tavares
Keyword(s):  
Mechanical Properties ◽  
Probability Distributions ◽  
Simple Method ◽  
Natural Aggregates

scholarly journals Can the Delay in Antarctic Polar Vortex Breakup Explain Recent Trends in Surface Westerlies?

2014 ◽  
Vol 71 (2) ◽  
pp. 566-573 ◽  
Author(s):  
Aditi Sheshadri ◽  
R. Alan Plumb ◽  
Daniela I. V. Domeisen
Keyword(s):  
Southern Hemisphere ◽  
Seasonal Variations ◽  
Polar Vortex ◽  
Equilibrium Temperature ◽  
Temperature Distributions ◽  
Stratospheric Final Warming ◽  
Tropospheric Circulation ◽  
Recent Trends ◽  
Antarctic Polar Vortex ◽  
Surface Changes

Abstract The authors test the hypothesis that recent observed trends in surface westerlies in the Southern Hemisphere are directly consequent on observed trends in the timing of stratospheric final warming events. The analysis begins by verifying that final warming events have an impact on tropospheric circulation in a simplified GCM driven by specified equilibrium temperature distributions. Seasonal variations are imposed in the stratosphere only. The model produces qualitatively realistic final warming events whose influence extends down to the surface, much like what has been reported in observational analyses. The authors then go on to study observed trends in surface westerlies composited with respect to the date of final warming events. If the considered hypothesis were correct, these trends would appear to be much weaker when composited with respect to the date of the final warming events. The authors find that this is not the case, and accordingly they conclude that the observed surface changes cannot be attributed simply to this shift toward later final warming events.

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