Related Experiment Video
Updated: Jul 9, 2025
11:19
Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses
Published on: October 21, 2016
12.0K
State dependence of CO2 forcing and its implications for climate sensitivity
Haozhe He1, Ryan J Kramer2,3, Brian J Soden1
1Rosenstiel School of Marine, Atmospheric and Earth Science, University of Miami, Miami, FL, USA.
Science (New York, N.Y.)
|November 30, 2023
Summary
Instantaneous radiative forcing from carbon dioxide (CO2) doubling is not constant. Its dependence on the climate base state, particularly stratospheric cooling, impacts climate sensitivity and model discrepancies.
Area of Science:
- Climate science
- Atmospheric physics
- Radiative transfer
Background:
- Current climate models assume constant instantaneous radiative forcing (IRF) for CO2 doubling.
- Variations in climate sensitivity are attributed to radiative feedbacks and their base-state dependence.
Purpose of the Study:
- To investigate the assumption of constant IRF.
- To determine the impact of base-state dependence on IRF and climate sensitivity.
- To explain intermodel spread in climate sensitivity.
Main Methods:
- Analysis of radiative forcing calculations.
- Examination of climate model outputs.
- Assessment of base-state effects on IRF.
Main Results:
- IRF is not constant; it depends on the climatological base state.
- IRF increases by ~25% per CO2 doubling.
- IRF has risen ~10% since the preindustrial era due to stratospheric cooling.
- Base-state dependence explains ~50% of intermodel spread in IRF.
Conclusions:
- The non-constant nature of IRF challenges existing climate sensitivity assumptions.
- Stratospheric cooling is a key factor influencing IRF and climate sensitivity.
- Addressing base-state dependence is crucial for reducing climate model uncertainty.
Related Concept Videos
01:50Global Climate Change
24.4K
Throughout its ~4.5 billion year history, the Earth has experienced periods of warming and cooling. However, the current drastic increase in global temperatures is well outside of the Earth’s cyclic norms, and evidence for human-caused global climate change is compelling. Paleoclimatology, the study of ancient climate conditions, provides ample evidence for human-caused global climate change by comparing recent conditions with those in the past.
24.4K
01:31Chemical Factors Affecting Respiration Centers
1.1K
Chemical factors such as changing CO2, O2, and H+ levels in arterial blood play a critical role in influencing respiration depth and rates. These variations are detected by chemoreceptors—specialized sensors located in two primary body areas. Central chemoreceptors are found throughout the brain stem, including the ventrolateral medulla, while peripheral chemoreceptors are located in the aortic arch and carotid arteries.
CO2 has a potent influence on respiration and is strictly regulated....
CO2 has a potent influence on respiration and is strictly regulated....
1.1K
02:39Phase Diagrams
41.1K
A phase diagram combines plots of pressure versus temperature for the liquid-gas, solid-liquid, and solid-gas phase-transition equilibria of a substance. These diagrams indicate the physical states that exist under specific conditions of pressure and temperature and also provide the pressure dependence of the phase-transition temperatures (melting points, sublimation points, boiling points). Regions or areas labeled solid, liquid, and gas represent single phases, while lines or curves represent...
41.1K
01:16What is Climate?
18.6K
Climate refers to the prevailing weather conditions in a specific area over an extended period. As the saying goes, “Climate is what you expect. Weather is what you get.” Climate is influenced by geographic factors, such as latitude, terrain, and proximity to bodies of water.
18.6K
01:11Calculating and Interpreting the Linear Correlation Coefficient
6.0K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
6.0K
01:11Regression Analysis
5.7K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.7K