Sensitivity of Distributions of Climate System Properties to Surface Temperature Datasets
Open Access
- Author:
- Libardoni, Alex Gordon
- Graduate Program:
- Meteorology
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- None
- Committee Members:
- Chris Eliot Forest, Thesis Advisor/Co-Advisor
Chris Eliot Forest, Thesis Advisor/Co-Advisor - Keywords:
- Likelihood estimation
Climate variability
Climate sensitivity
Climate models
Climate model evaluation
Climate model calibration
Climate data records
Climate change observations
Climate
Bayesian statistics
Net aerosol forcing
Temperature records
Transient climate response
Uncertainty quantification - Abstract:
- Predictions of climate change depend strongly on the accurate implementation and parameterization of climate system properties, processes, and feedbacks. In this study, surface temperature, upper-air temperature, and ocean heat content data are used to constrain the distributions of the parameters that define three climate system properties: climate sensitivity, the rate of ocean heat uptake into the deep ocean, and net anthropogenic aerosol forcing. Climate sensitivity is diagnosed by changing the strength of cloud feedback, the rate of deep-ocean heat uptake is determined by varying the effective vertical diffusivity of heat anomalies in the ocean, and the net anthropogenic aerosol forcing is controlled by scaling the spatial and temporal pattern of sulfate aerosol loadings by the model-defined global value in the 1980s. Running a climate model of intermediate complexity forced by historical forcing patterns over a range of these parameter values allows for the derivation of probability distribution functions for the model parameter values corresponding to the climate system properties. Using five different surface temperature datasets, this study explores the sensitivity of the parameter distributions to the choice of surface temperature data used to evaluate the model output. Differences in estimates of climate sensitivity mode and mean are as great as 1 K between the datasets. Ocean effective diffusivity is poorly constrained using all datasets and the shape of the distribution differs greatly depending on which surface dataset is used. Distributions for anthropogenic aerosol forcing cluster into two groups. While each group has the same general shape, the location of the mode and confidence intervals differ by approximately 0.1 watts per square meter between the two clusters. This difference is small compared to other uncertainties in climate forcings. Transient climate response derived from these distributions ranges anywhere between 1 and 3 K and the shape of the distribution of these possible values is surface dataset dependent. Some distributions are tall and narrow, while other distributions are short and broad. Understanding the differences in parameter distributions and predicted warming is critical to understanding the full range of uncertainty involved in climate model calibration studies.