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Earth Systems Science Chapter 6


... (largely review from discussion in chapter 3) Global Climate Models ... v Physical Models Statistical Model Based ... a coupled AOGCM simulation ... – PowerPoint PPT presentation

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Title: Earth Systems Science Chapter 6

Earth Systems ScienceChapter 6
I. Modeling the Atmosphere-Ocean System
  1. Statistical vs physical modelsanalytical vs
    numerical modelsequilibrium vs dynamical
    modelsfinite difference vs spectral
    modelsendogenous vs exogenous variables
  2. Physically based climate models of different
    complexities (largely review from discussion in
    chapter 3)
  3. Global Climate Models (GCMs)

Statistical v Physical Models Statistical Model
Based on observations, you identify a
relationship between two variables. You do not
necessarily understand the reason why this
relationship exists. Physical Model Based on
the rules of physics, you construct a model that
describes the relationships between different
physical phenomena.
Numerical vs Analytical Models Analytical Model
Equations are solved without the aid of the
computer, resulting in one or more equations that
allow one to calculate the answer for any time,
without calculating each time step (usually using
calculus). Complicated, highly non-linear
equations often have no analytical
solutions. Numerical Model Equations are solved
at each time step, usually by computer (STELLA
solves numerical models). Can solve any set of
equations, regardless of non-linearities.
Equilibrium vs Dynamical Models Equilibrium
Model Model that provides solution only for the
equilibrium values solution does not vary as a
function of time. Dynamical Model Model that
provides solution as a function of time (STELLA
solves dynamical models).
Finite Difference vs Spectral Models Finite
Difference Model Numerical model in which the
calculations are performed in each grid
box. Spectral Model Model that uses different
mathematical techniques, and performs
calculations using wave functions.
Endogenous vs Exogenous Variables Endogenous
Variables whose values are calculated as part of
the model Exogenous Variables whose values are
specified by the modeler Excluded Variables that
are not part of the model in any way
Physically-based climate models of different
  • Many types of climate models exist. We
    discuss some of the more common types, which have
    different levels of complexity
  • Zero-dimensional radiation balance models
  • 1-dimensional radiative-convective models
  • 2-dimensional diffusive models
  • 3-dimensional Atmospheric General Circulation
    Models (AGCM)
  • 3-D coupled atmosphere ocean models (AOGCM)

Physically-based climate modelszero-dimensional
radiation balance model
Equilibrium modelTe (S/4s) (1-A) 0.25
Physically-based climate models1-dimensional
radiative-convective model
One-Layer Radiation Model
Physically-based climate models 1-dimensional
radiative-convective model
Convection, latent fluxes
Surface latent, sensible
Physically-based climate models 2-dimensional
climate model, or 2-d energy balance model (EBM)
Physically-based climate models 3-dimensional
Atmospheric General Circulation Model (AGCM)
Physically-based climate models3-D coupled
atmosphere ocean general circulation
models(AOGCM) or Global Climate Model (GCM)
I. Global Climate Models
  1. processes
  2. Climate change experiments- equilibrium-
  3. Model resolution, subgrid-scale processes
  4. Dependency on initial conditions
  5. Results from different models

Global Climate Models (GCMs) many processes
Boundary conditions (exogenous variables) vs
modeled processes (endogenous variables)
(No Transcript)
Climate Change Experiments
Typically, experiments are performed using
climate models (usually GCMs) to estimate the
effect of changing boundary conditions (e.g.
increasing carbon dioxide) on climate. Control
Run Model experiment simulating current climate
conditions using current boundary conditions
(e.g. CO2) Equilibrium experiments Model
experiment simulating the climate under changed
conditions by changing the boundary conditions to
what they might be at some future time (e.g.
doubled CO2, or 2xCO2) Transient Experiments
Model experiment simulating the gradual change
from current to future (e.g. increase in CO2 by
1 per year)
Transient Experiments
Figure 9.1 Global mean temperature change for
1/yr CO2 increase with subsequent stabilisation
at 2xCO2 and 4cCO2. The red curves are from a
coupled AOGCM simulation (GFDL_R15_a) while the
green curves are from a simple illustrative model
with no exchange of energy with the deep ocean.
The transient climate response, TCR, is the
temperature change at the time of CO2 doubling
and the equilibrium climate sensitivity, T2x,
is the temperature change after the system has
reached a new equilibrium for doubled CO2, i.e.,
after the additional warming commitment has
been realised.
Model Resolution subgrid scale processes
resolution How big are the grid boxes? The larger
they are, the less realistic the model typically
1 degree lat/lon.Bigger grid boxes lower
resolutionSmaller grid boxes higher
resolution Subgrid-scale processes many physical
processes that are important for climate occur on
very small spatial scales (e.g. cloud formation).
Since the model resolution is much larger, these
processes can not be modeled physically paramete
rization a simple method, usually a statistical
model, to account for subgrid-scale processes for
which the physically-based equations can not be
Dependency on Initial Conditions
Initial conditions the values of all stocks or
state variables (e.g. temperature, pressure,
etc) at each grid point must be specified in the
beginning of a model experimentThe transient
response of GCMs can change when initial
conditions are changed even slightly. This is
because the climate is a chaotic system. Ensemble
experiments the same model experiment is
performed a number of times with slightly
different initial conditions the results of the
ensemble members are averaged to get the
ensemble mean
Dependency on Initial Conditions
Figure 9.2 Three realisations of the
geographical distribution of temperature
differences from 1975 to 1995 to the first decade
in the 21st century made with the same model
(CCCma CGCM1) and the same IS92a greenhouse gas
and aerosol forcing but with slightly different
initial conditions a century earlier. The
ensemble mean is the average of the three
realisations. (Unit C).
Results from different models
Different GCMs have many similarities, and
therefore provide, in many ways, similar results.
However, the way the different modeling groups
choose to parameterize different processes makes
the models different. Therefore, the different
models produce somewhat different results. For
example, the models agree much more closely on
temperature than on precipitation. This is
because temperature changes are more dependent on
large scale processes, which are modeled
similarly in most models. Precipitation, however,
depends on subgrid-scale processes, which are
parameterized differently by the different
modeling groups.
Results from different models
Figure 9.5 (a) The time evolution of the
globally averaged temperature change relative to
the years (1961 to 1990) of the DDC simulations
(IS92a). G greenhouse gas only (top), GS
greenhouse gas and sulphate aerosols (bottom).
The observed temperature change (Jones, 1994) is
indicated by the black line. (Unit C).
Results from different models
Figure 9.5 (b) The time evolution of the
globally averaged precipitation change relative
to the years (1961 to 1990) of the DDC
simulations. GHG greenhouse gas only (top), GS
greenhouse gas and sulphate aerosols (bottom).
(Unit ).