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Related Concept Videos

Contaminants and Errors01:16

Contaminants and Errors

Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.

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Quantifying X-Ray Fluorescence Data Using MAPS
14:58

Quantifying X-Ray Fluorescence Data Using MAPS

Published on: February 17, 2018

Quantifying errors in trace species transport modeling.

Michael J Prather1, Xin Zhu, Susan E Strahan

  • 1Earth System Science Department, University of California, Irvine, CA 92697, USA. mprather@uci.edu

Proceedings of the National Academy of Sciences of the United States of America
|December 11, 2008
PubMed
Summary
This summary is machine-generated.

Numerical errors in Earth system models can lead to incorrect scientific conclusions. This study demonstrates a method to quantify advection errors in carbon dioxide transport models by increasing resolution.

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Area of Science:

  • Earth System Science
  • Atmospheric Chemistry
  • Computational Modeling

Background:

  • Earth system models are expected to converge to a single correct solution with appropriate approximations and numerical methods.
  • Accurate simulation of trace species transport, such as carbon dioxide (CO2), is crucial for understanding climate dynamics.
  • Previous studies have highlighted the importance of numerical methods in tracer transport but lacked a systematic approach to quantify errors.

Purpose of the Study:

  • To investigate the impact of numerical errors on the atmospheric transport of CO2 using established tracer transport models.
  • To assess the convergence of different models under varying resolutions and identify potential discrepancies in scientific conclusions.
  • To develop a practical approach for quantifying advection errors in Earth system models under realistic conditions.

Main Methods:

  • Controlled numerical experiments were conducted using two well-validated atmospheric transport models for CO2.
  • Model resolution was systematically doubled to reduce numerical errors and observe convergence patterns.
  • Comparison of simulation results under different resolutions to identify and quantify advection errors.

Main Results:

  • Significant and unexpected differences were observed between the two models' simulations of CO2 transport.
  • Doubling the model resolution led to partial convergence of results, indicating the influence of numerical error.
  • The study identified that lack of knowledge regarding numerical errors can lead to erroneous scientific conclusions.

Conclusions:

  • Numerical errors in tracer transport models can significantly impact scientific findings derived from Earth system models.
  • A practical methodology for quantifying advection errors by systematically increasing model resolution was demonstrated.
  • This approach enables more reliable scientific conclusions by accounting for and reducing numerical uncertainties in climate modeling.