Climate Models (Week 5) - Post 1
Climate Models
Climate
models are crucial tools to help people understand the complexities and dynamic
interactions within Earth's climate system. Models are built on our
understanding of basic physics and Earth processes and are grounded in
observations and measurements of the world around us. Since we only have one planet
and can only run one global climate experiment in the real world, models are
our only tools to help us peer into the future and ask "what if..."
questions. The range of probable outcomes from possible future scenarios helps
us make decisions about mitigation and adaptation.
5.1 Notes
Learning Goals
By the end of this section, you will be able to:
-Explain how modeling and observations together help us learn
about how Earth’s climate system works.
-Explain, conceptually, how a simple energy balance model
works, and some of the possible variations on an energy balance model.
Video Notes:
*Language is a model: Language is a representation of your thoughts,
but it can never really quite be what you’re thinking, though in our language
we do aim to get as close as possible to communicating what it is we are
thinking.
-Ideally climate models align with physical reality.
-(real world) Observations influence (climate) models: models
also influence observations.
-Ultimately people build models to help better understand Earth’s
climate system. It is an ongoing effort.
Key Points:
-Climate models are our attempts to represent Earth’s climate
system, so that we can better understand how it works, since we can’t conduct
whole-earth experiments.
-Climate models are grounded in physics, chemistry, and
biology.
(with climate models, we’re attempting to represent Earth’s
climate system. So it’s important to ground the model in how real world science
plays out.)
-Climate models are constrained by observation in the real world
and can also help inform further observational efforts.
-Even fairly simple climate models, like energy balance
models, can help us understand and represent important processes in Earth’s
climate system.
Readings
How do Climate Models Work
A
pixelated Earth:
-Today's most sophisticated climate
models are called GCMs, which stands for General Circulation Model or Global Climate
Model, depending on who you talk to. On average, they are about 500
000 lines of computer code long, and mainly written in Fortran, a
scientific programming language. Despite the huge jump in complexity, GCMs have
much in common with the one-line climate model above: they're just a lot of
basic physics equations put together.
-If you
look at the subject of your photo with your own eyes, it's not pixelated, no
matter how close you get - even if you look at it through a microscope. The
real world is continuous (unless you're working at the quantum level!) Similarly,
the surface of the world isn't actually split up into three-dimensional cells
(you can think of them as cubes, even though they're usually wedge-shaped)
where every climate variable - temperature, pressure, precipitation, clouds -
is exactly the same everywhere in that cell. Unfortunately, that's how
scientists have to represent the world in climate models, because that's the
only way computers work. The same strategy is used for the fourth dimension,
time, with discrete "timesteps" in the model, indicating how often
calculations are repeated.
-Despite the seemingly enormous
computer power available to us today, GCMs have always been limited by it.
Cracking
the Code:
-A climate model is actually
a collection of models - typically an atmosphere model, an
ocean model, a land model, and a sea ice model. Some GCMs split up the
sub-models (let's call them components)
-Each component represents a
staggering amount of complex, specialized processes.
-Each component is developed
independently, and as a result, they are highly encapsulated (bundled
separately in the source code). However, the real world is not encapsulated -
the land and ocean and air are very interconnected. Some central code is
necessary to tie everything together. This piece of code is called the coupler,
and it has two main purposes:
- Pass data between the components. This can get complicated if the components don't all use the same grid (system of splitting the Earth up into cells).
- Control the main loop, or "time stepping loop", which tells the components to perform their calculations in a certain order, once per time step.
Show
time:
-When it's time to run the model,
you might expect that scientists initialize the components with data collected
from the real world. Actually, it's more convenient to "spin up" the
model: start with a dark, stationary Earth, turn the Sun on, start the Earth
spinning, and wait until the atmosphere and ocean settle down into equilibrium.
The resulting data fits within the boundaries of the real climate, and could
easily pass for real weather.
-Scientists feed input files into
the model, which contain the values of certain parameters, particularly agents
that can cause climate change…Through these input files, it's possible to
recreate the climate from just about any period of the Earth's lifespan: the
Jurassic Period, the last Ice Age, the present day...and even what the future
might look like, depending on what we do (or don't do) about global warming.
-As the model runs, every cell
outputs the values of different variables (such as atmospheric pressure, ocean
salinity, or forest cover) into a file, once per time step. The model can
average these variables based on space and time, and calculate changes in the
data. When the model is finished running, visualization software converts the
rows and columns of numbers into more digestible maps and graphs.
Predicting
the Past:
-So how do we know the models are
working? Should we trust the predictions they make for the future? It's not
reasonable to wait for a hundred years to see if the predictions come true, so
scientists have come up with a different test: tell the models to predict the
past, and see if the climate it recreates matches up with observations from the
real world.
*Climate models aren't perfect,
but they are doing remarkably well. They pass the tests of predicting the past,
and go even further. For example, scientists don't know what causes El Niño, a
phenomenon in the Pacific Ocean that affects weather worldwide. There are some
hypotheses on what oceanic conditions can lead to an El Niño event, but nobody
knows what the actual trigger is. Consequently, there's no way to program El
Niños into a GCM. But they show up anyway - the models spontaneously generate
their own El Niños, somehow using the basic principles of fluid dynamics to
simulate a phenomenon that remains fundamentally mysterious to us.
-Also, history has shown us that
when climate models make mistakes, they tend to be too stable, and underestimate the
potential for abrupt changes. Take the Arctic sea ice: just a few years ago, GCMs
were predicting it would completely melt around 2100. Now, the estimate has
been revised to 2030, as the ice melts faster than anyone
anticipated:
Answering
the Big Question:
At the end of the day, GCMs are
the best prediction tools we have. If they all agree on an outcome,
it would be silly to bet against them. However, the big questions, like
"Is human activity warming the planet?", don't even require a model.
The only things you need to answer those questions are a few fundamental
physics and chemistry equations that we've known for over a century.
Climate
Models and Climate Change:
-For climate change experiments, it is important that models
capture the fundamental processes that respond to climate ‘forcing’ (e.g. the
radiation changes from changing greenhouse gases and aerosols). Consequently,
some of the important parts of a global climate model relate to:
- The response to variability of solar irradiance on a range of time scales.
- Changes to the Earth's energy balance at the surface and top of atmosphere from volcanic eruptions
- How radiation is absorbed and reflected on its way through the atmosphere but also at the surface.
- Atmosphere and ocean dynamics (and how energy and momentum is transported through the different media)
- How greenhouse gases and aerosols affect the Earth's climate and climate variability
- Sea ice and polar ice sheets
- Various climate ‘feedback’, such as the interaction of clouds and water vapour with the warming climate, and the changing absorption or emission of CO2 from the ocean and land surface.
Climate
Models and Weather Forecasts:
-While there are many similarities between models used for
daily weather forecasts and models used for climate projections, there are some
important differences. The IPCC (2013) notes:
-Unlike weather forecasts, these historical climate
simulations are not periodically adjusted with updated information about the
state of the climate to improve the forecast, they are initialized in 1850 then
loosely constrained by the prescribed forcing. Therefore, the historical
simulations are not designed (or expected) to reproduce the observed sequence
of weather and climate events during the 20th century, but they are designed to
reproduce observed multi-decadal climate statistics, such as averages.
-The 21st century simulations run from 2006-2100, driven by
prescribed anthropogenic forcings. Owing to uncertainties in the model
formulation and the initial state, any individual simulation represents only
one of the possible pathways the climate system might follow. To allow some
evaluation of these uncertainties, it is necessary to carry out a number of
simulations either with several models or by using an ensemble of simulations
with a single model, both of which increase computational cost.
5.2 Notes
Learning Goals
By the end of this section, you will be able to:
-Describe the tradeoffs among (1) model resolution in time and
space, (2) number of processes modeled, (3) time period modeled, and (4) number
of model runs.
-Define parameterization and give examples of
parameterizations in climate models.
-Describe basic categories of climate models (EBMs, EMICs,
GCMs) and their uses and limitations.
Video
Notes
*We have constraints on climate models imposed by computer
power and the speed at which we can solve the millions of equations.
Parametrization: When
modelers choose to approximate some process using reasonable assumptions and
related variables… They’re approximating aggregate effects and using those
aggregate approximations in the model. Parametrizations are reasonable and
useful approximations.
Key
Points:
-Climate modelers make choices about spatial and temporal
scales depending on the question of interest.
-Space and time scale together. Smaller spatial scales require
shorter time steps.
-Choices about what variables to include in a model, what to
explicitly model, and what to parameterize also depend on what questions the
modeler seeks to answer.
-Computer power is one of the factors that limits model
resolution, complexity, and length of virtual time that can be practically
modeled.
Reading
Energy
Balance Models:
-As indicated by their name, energy balance models estimate
the changes in the climate system from an analysis of the energy budget of the
Earth. In their simplest form, they do not include any explicit spatial
dimension, providing only globally averaged values for the computed variables.
They are thus referred to as zero-dimensional EBMs. The basis for these EBMs
was introduced by both Budyko (1969) and Sellers in (1969).
(non specific) fundamental equation:
Changes in heat storage = absorbed solar radiation - emitted
terrestrial radiation
-In order to take the geographical distribution of temperature
at the Earth’s surface into account, zero-dimensional EBMs can be extended to
include one (generally the latitude) or two horizontal. In order to represent
the net effect of heat input and output associated with horizontal transport.
Intermediate
Complexity Models:
-Like EBMs, EMICs involve some simplifications, but they
always include a representation of the Earth’s geography, i.e. they provide
more than averages over the whole Earth or large boxes. Secondly, they include
many more degrees of freedom than EBMs. As a consequence, the parameters of
EMICs cannot easily be adjusted to reproduce the observed characteristics of
the climate system, as can be done with some simpler models.
-Schematic
illustration of the structure of the climate model of intermediate complexity
MOBIDIC that includes a zonally averaged atmosphere, a 3-basin zonal oceanic
model (corresponding to the Atlantic, the Pacific and the Indian Oceans) and
simplified ice sheets.
General
Circulation Models:
-General circulation models provide the most precise and
complex description of the climate system. Currently, their grid
resolution is typically of the order of 100 to 200 km. As a consequence,
compared to EMICs (which have a grid resolution between 300 km and thousands of
kilometres), they provide much more detailed information on a regional scale. A
few years ago, GCMs only included a representation of the atmosphere, the land
surface, sometimes the ocean circulation, and a very simplified version of the
sea ice. Nowadays, GCMs take more and more components into account, and many
new models now also include sophisticated models of the sea ice, the carbon
cycle, ice sheet dynamics and even atmospheric chemistry
-Because of
the large number of processes included and their relatively high resolution,
GCM simulations require a large amount of computer time. For instance, an experiment
covering one century typically takes several weeks to run on the fastest
computers. As computing power increases, longer simulations with a higher
resolution become affordable, providing more regional details than the previous
generation of models.
Image Above: A simplified representation of part of the domain
of a general circulation model, illustrating some important components and
processes. For clarity, the curvature of the Earth has been amplified, the
horizontal and vertical coordinates are not to scale and the number of grid
points has been reduced compared to state-of-the-art models.
5.3 Notes
Learning Goals
By the end of this section, you will be able to:
-Compare model output to observations.
-Describe future temperature forecasts from climate models.
-Use a relatively simple climate model to answer questions
about stock and flow of carbon to and from the atmosphere.
-Use a relatively simple model to generate “What if…”
scenarios for the future, that keep global surface temperatures below 2°C above
preindustrial values.
Video
Notes
*Scientists use climate models to prove human involvement with
climate stocks by comparing climate models with human activity factored and not
factored, and then comparing which model more accurately follows the actual
climate observations over time.
Key
Points:
-Comparing model outputs to observations helps check how well
the models represent Earth’s climate system.
-Climate models have done pretty well modeling global
temperatures.
-In some cases, models have underestimated actual rates of
change in the climate system, notably with sea ice and sea level rise.
(underestimates are usually products of incorrect climate
theory inputs; in the Hansen 1988 model, the temperature estimates were higher
then observations even though carbon emissions were correct, this is because
Hansen used a higher climate sensitivity number then what scientists agree upon
today.)
-Model projections of future temperatures show high latitudes
and land continuing to warm faster than low latitudes and ocean.
Climate
Models
Here I played around with two
different fairly simple climate models. From the one focused on global
temperature increase I learned that if certain factors happen we can reduce our
global temperature increase from 4 degrees by 2100 to 2 degrees by 2100. These
mitigation factors include: having our carbon emissions peak in 2020, and beginning
to reduce carbon emissions by 2% annually. I am not sure how difficult this would
be in an ideally cooperative world, unfortunately, I do not believe we have the
science or policy in place to collectively do this.
Video
Notes:
*Presented by Gavin Schmidt, climatologist from NASA
Why climate models are trusted/important/used so frequently: “If
we had observations of the future, we obviously would trust them more than models,
but unfortunately…observations of the future are not available at this time.”
Important quote on climate models:
What is the use of having developed a science well enough to
make predictions if, in the end, all we’re willing to do is stand around and
wait for them to come true.” Sherwood Rowland
Great use of graphics!
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