TYPES OF CLIMATE MODELS.
Climate scientists often talk about “layers” or
“spectrums” of climate models, from simple to
complex. The complexity of climate models
increases as follows. The number of spatial
dimensions represented. Its dimension is the
representation resolution. The scope of the
components and processes of the climate system
'contained' in the model. And the degree to which
the process is given as a realistic rather than a
simplified representation. As a result, more complex
models tend to be computationally more expensive.
The following description introduces several types
of models and represents different levels of
complexity (for other types, see, for example,
Stocker 2011; McGuffie & HendersonSellars 2014).
The types described here are physically based in the
sense that they are based to a considerable degree
on physical theory. Data-driven or "empirical"
climate models have also been developed for some
purpose, but they are not generic and are only
mentioned when passing.
, Some of the simplest climate models are the
Energy Balance Model (EBM), which is designed to
very intensively represent the Earth's surface
energy balance (McGuffie & HendersonSellars 2014:
Ch.3). These models are built using both physical
theories (such as the StefanBoltzmann equation)
and empirical parameters (such as the Earth's
albedo and atmospheric emissivity) that can have
surface temperature as the only dependent
variable. A zero-dimensional EBM represents the
entire climate system as a single point. You can use
them to manually calculate an estimate of the
global average surface temperature when the
system is in radial equilibrium. One-dimensional
(1D) and two-dimensional (2D) EBMs employ the
same energy budgeting scheme, but represent
average temperatures at different latitudes and/or
longitudes and outline heat transport between
them (see Sellers 1969 for an early example) ).
Equations in 1D and 2DEBM are usually solved using
a digital computer. [1]
Climate scientists often talk about “layers” or
“spectrums” of climate models, from simple to
complex. The complexity of climate models
increases as follows. The number of spatial
dimensions represented. Its dimension is the
representation resolution. The scope of the
components and processes of the climate system
'contained' in the model. And the degree to which
the process is given as a realistic rather than a
simplified representation. As a result, more complex
models tend to be computationally more expensive.
The following description introduces several types
of models and represents different levels of
complexity (for other types, see, for example,
Stocker 2011; McGuffie & HendersonSellars 2014).
The types described here are physically based in the
sense that they are based to a considerable degree
on physical theory. Data-driven or "empirical"
climate models have also been developed for some
purpose, but they are not generic and are only
mentioned when passing.
, Some of the simplest climate models are the
Energy Balance Model (EBM), which is designed to
very intensively represent the Earth's surface
energy balance (McGuffie & HendersonSellars 2014:
Ch.3). These models are built using both physical
theories (such as the StefanBoltzmann equation)
and empirical parameters (such as the Earth's
albedo and atmospheric emissivity) that can have
surface temperature as the only dependent
variable. A zero-dimensional EBM represents the
entire climate system as a single point. You can use
them to manually calculate an estimate of the
global average surface temperature when the
system is in radial equilibrium. One-dimensional
(1D) and two-dimensional (2D) EBMs employ the
same energy budgeting scheme, but represent
average temperatures at different latitudes and/or
longitudes and outline heat transport between
them (see Sellers 1969 for an early example) ).
Equations in 1D and 2DEBM are usually solved using
a digital computer. [1]
