摘要：A method is presented to estimate the probability distributions of
climate system properties based on a hierarchical Bayesian model. At the base
of the model, we use simulations of a climate model in which the outputs depend
on the climate system properties and can also be compared with observations.
The degree to which the model outputs are \consistent" with the observations is
used to obtain the likelihood for the climate system properties. We de ne the
climate system properties as those properties of the climate model that control
the large-scale response of the climate system to external forcings. In this paper,
we use the MIT 2D climate model (MIT2DCM) to provide simulations of ocean,
surface and upper atmospheric temperature behavior over zones de ned by lati-
tude bands. In the MIT2DCM, the climate system properties can be set via three
parameters: Climate sensitivity (the equilibrium surface temperature change in
response to a doubling of CO2 concentrations), the rate of deep-ocean heat uptake
(as set by the di
usion of temperature anomalies into the deep-ocean below the
climatological mixed layer), and net strength of the anthropogenic aerosol forcings.
In this work, we use output from MIT2DCM coupled with historical temperature
records to make inference about these climate system properties. Even though the
MIT2DCM is far less computationally demanding than a full 3D climate model,
the task of running the model for each combination of the climate parameters and
processing its output is computationally demanding. Thus, a statistical model is
required to approximate the model output. We obtain results that are critical
for understanding uncertainty in future climate change and provide an indepen-
dent check that the information contained in recent climate change is robust to
statistical treatment.
