Dos usos exploratorios
for General Circulation
Models in Climate Science
Joseph Wilson
University of Colorado at Boulder
In this paper I present two ways in which climate modelers use general circulation
models for exploratory purposes. The complexity of Earth’s climate system makes it
difficult to predict precisely how lower-order climate dynamics will interact over
time to drive higher-order dynamics. The same issues arise for complex models built
to simulate climate behavior like the Community Earth Systems Model (CESM).
I argue that as a result of system complexity, climate modelers use general circu-
lation models to perform model dynamic exploration (MDE) and climate dynamic
exploration (CDE). MDE and CDE help climate modelers to better understand
the dynamic structure of the general circulation model system and the actual cli-
mate system, respectivamente.
Introducción
Climatologists understand many things about the behavior of Earth’s climate.
Sin embargo, the complexity of the climate system makes it difficult to know
with precision how it will evolve over time. Earth’s climate consists of incred-
ibly complex causal interactions occurring at global scales over long periods of
tiempo. To better understand the operations of the climate system, climate mod-
elers run simulations of the climate using general circulation models (GCMs).
en este documento, I argue that because there is no general theory that explains how
specified climate conditions will evolve over time, modelers rely on GCMs to
perform a dual exploratory function. Namely, GCMs are used to explore their
own dynamic structure as well as the dynamic structure of the climate system.
De este modo, I argue that there are at least two specific ways that general circula-
tion models are used for exploratory purposes. The first of these exploratory
techniques I call model dynamic exploration (MDE). MDE often takes the
Perspectives on Science 2021, volumen. 29, No. 4
© 2021 by The Massachusetts Institute of Technology
https://doi.org/10.1162/posc_a_00380
493
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General Circulation Models in Climate Science
form of sensitivity tests, a process whereby climate modelers examine how a
model responds to variations in controlled parameters. The models used to
simulate the climate system are themselves highly complex systems, as they
must be to simulate real-world climate behavior. Tal como, it is often unclear
how the behavior of lower-order model components over time will drive the
system as a whole. The second exploratory technique I call climate dynamic
exploration (CDE). CDE involves utilizing GCMs to better understand the
structure of the climate system itself. En particular, climate modelers observe
the behavior of appropriately crafted GCMs to explore how Earth’s climate
system will evolve under specific conditions. A well-known instance of
CDE is the modeling of different emissions scenarios considered by the Inter-
governmental Panel on Climate Change (IPCC AR5, 2014). By specifying
different rates of carbon dioxide emission across otherwise similar models, cli-
mate modelers explore the impact that differing dynamic conditions have on
the evolution of the global climate. In utilizing MDE and CDE climate mod-
elers use GCMs to explore the higher-order behavior of a complex system that
emerges from specified lower-order dynamics.
En la sección 1, I lay out the structure, and comment on the history, del
Community Earth Systems Model (CESM), a fully-coupled global climate
model created by the National Center for Atmospheric Research (NCAR).1
With an understanding of how climate models like CESM are structured,
I discuss the exploration of model structure with MDE in Section 2. In Sec-
ción 3, I consider how modelers use GCMs to explore the dynamics of Earth’s
climate itself with CDE.
The Community Earth Systems Model
1.
Background theory from various scientific fields comes to bear on the climatol-
ogists’ understanding of Earth’s climate. Sin embargo, it is often the case that these
theoretical models can only capture the behavior of such diverse phenomena by
simplifying and idealizing the target system. In being broadly applicable, phys-
ical theory must misrepresent the real-world target by restricting focus to the
causal interactions of a small handful of factors while omitting facts that have a
marginal, albeit non-zero, influence on the target system. This motivation is
discussed in Weisberg’s account of minimal model idealization, where he argues
that such minimal models are constructed to include only the causal factors that
“make a difference” (Weisberg 2007, pag. 642; emphasis original). By focusing on
the causally relevant factors, minimal models play a crucial role in facilitating
scientific explanation. En efecto, while focusing on specific causal patterns within
1.
I would like to give special thanks the organizers and co-participants at the 2018
Community Earth Systems Model Tutorial at NCAR for such enlightening discussions on
the structure and function of the CESM.
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Perspectives on Science
495
the target system misrepresents that system, in doing so these idealizations im-
prove the scientists’ general understanding of the system (Potochnik, 2017,
ch. 4). While representing the evolution of Earth’s climate over time will nec-
essarily require some forms of simplification and idealization, it does require
that the causal contribution of significant climate features be taken into ac-
count. Tal como, climatologists need a tool that can calculate a large number
of interactions occurring over a number of timesteps. The Community Earth
Systems Model is an example of such a tool.
The Community Earth Systems Model (CESM) created by the National Cen-
ter for Atmospheric Research (NCAR) for the wider climate research commu-
nity began as the Community Climate Model (CCM) en 1983. As a program
federally funded by the National Science Foundation, NCAR was created by the
US Congress in 1960 to bring together specialists from the numerous fields per-
taining to Earth science, from oceanographers to volcanologists, to facilitate col-
laboration towards an improved understanding of the atmosphere. Desde 1983,
NCAR has worked to provide researchers the necessary resources for model con-
estructura. Their primary organizational mission: “to serve the U.S. academic cli-
mate research community through provision, desarrollo, and testing of the
Community Climate Model (CCM)" (Shackley 2001, pag. 124).
Simon Shackley (2001) argues that climate researchers at NCAR are a par-
adigm example of the epistemic lifestyle commonly found within American cli-
mate modeling. Epistemic lifestyles comprise the “set of intellectual questions
and problems, and the accompanying set of practices, that provide a sense of
purpose, logro, and ambition to a scientist’s work life” (Shackley
2001, pag. 114). According to Shackley, climate modelers in the United States
differ from climate modelers in the United Kingdom with respect to their ep-
istemic lifestyle. En particular, climate modelers in the UK tend to be “climate
seers,” emphasizing the sensitivity of the climate system to changing variables
and processes. Por otro lado, climate modelers in the United States tend to
be “climate model constructors,” striving to capture the full complexity of the
climate system in their climate models. Shackley notes that NCAR, an exemplar
of the epistemic lifestyle predominantly found in the United States, houses a
small team of three or four scientists operating as climate seers but otherwise
consists of researchers dedicated to testing and improving the CESM. Con
an overwhelming majority of NCAR researchers endorsing the climate model
constructor lifestyle, the organizational structure at NCAR has pushed the de-
velopment of the CESM towards higher spatial resolution and more complex
modeling of dynamic processes.
The CESM has thus developed steadily over the past several decades, incor-
porating a greater number of climate-relevant features in increasingly greater
detail. The current version, CESM 2.11, consists of several submodels, each ded-
icated to representing a particular domain in the global climate regime. Estos
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General Circulation Models in Climate Science
domain-specific submodels include a land model, an atmosphere model, un
ocean model, a land ice model, a sea ice model, and models representing river
runoff and surface waves. Each submodel is modular, operating independently
from the others, but interaction across models is possible through communica-
tion with a centralized coupler. The atmosphere model and ocean model are
built around a dynamical core that solves the relevant fluid and thermodynamic
equations on resolved scales, simulating the general circulation in each domain.
These fluid and thermodynamic equations are derived applications of Newton’s
second law, ensuring that mass and energy are conserved as they flow through-
out the system.
En el momento de escribir, the atmospheric model of CESM is the Community
Atmospheric Model 6.0 (CAM6). CAM arose in 2004 from prior generations of
the NCAR CCM, with the first CAM (CAM3) comprising the fifth generation
of the NCAR atmospheric model. The name was changed to CAM to reflect the
role of the model in a fully coupled earth systems model (es decir., the CESM). Como
semejante, the development of CAM over the last fifteen years, from CAM3 to
CAM6, has included previously neglected dynamic processes (p.ej. explicit rep-
resentations of fractional land and sea-ice coverage in CAM3, the simulation of
full aerosol cloud interactions included in CAM5). CAM5 also introduced an
interactive chemistry model (CAM-CHEM) capable of simulating biogenic
emissions and the deposition of aerosols to snow, ice, ocean, and vegetation.
The most recent version of the atmospheric model, CAM6, introduces substan-
tial modifications to the parameterizations for nearly all atmospheric processes,
replacing the model dynamics for parameterized processes like cloud macrophy-
sics, shallow convection, and boundary layer turbulence.
CAM6 typically runs at approximately 100-kilometer horizontal resolution,
sufficient to capture much of the relevant large-scale climate phenomena. Rel-
evant processes that occur on scales too small to be resolved are parameterized as
subgrid models in order to capture the relevant contribution of the process in
each grid cell. While some of these parameterizations are run by CAM6 or
POP2, the ocean circulation model, many of them are offloaded to other sub-
modelos. Por ejemplo, the type of land cover in a grid cell will determine how
much of the incoming solar radiation is absorbed and converted into heat
energía, and how much is reflected back into space. But the surface of the
Earth can vary drastically across a single 100x100km grid cell, significado
that land surface variations must be parameterized if we are to adequately
approximate surface reflectance. The Community Land Model (CLM) en
the CESM accounts for this surface heterogeneity by representing the al-
bedo (light reflected by a body or surface) of the grid cell as a proportional
average of the surfaces present in that cell. The CLM begins by representing
how much of the surface within a cell is composed of vegetation, city, lake,
crop, and glacial surfaces. Once we know the surface composition of a cell,
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the CLM can calculate its total albedo as a proportional average these surface
components’ albedo. The CLM does not spatially represent the diverse surface
types in a cell but can functionally approximate the influence that surface di-
versity within each cell would have on local solar reflectance. The functional
approximation of parameterization thus collapses the complex dynamic inter-
actions of land surface processes into a small number of parameter values.
As briefly stated before, each of the domain-specific submodels comprising
the CESM communicates with the others through a coupler. This communica-
tion allows the behavior of the different submodels to influence each other. Como
semejante, the centralized coupler functions as a central node of communication for
the various submodels of CESM. The coupler controls the flow of time for each of
the submodels, synchronizing their execution and ensuring that fluxed quanti-
ties are conserved throughout their interaction. It is this interaction facilitated
by the coupler that enables climate models like the CESM to best simulate the
variety of diverse interactions influencing the state of Earth’s climate. Para examen-
por ejemplo, the CLM will need to share the calculated albedo values of each cell with the
atmospheric model, through interfacing with the coupler. The CLM may in
turn receive temperature and precipitation data from the atmospheric model
(via the coupler) in order to update the surface properties of each cell. O, para
ejemplo, ocean modelers may want an active atmospheric model to constrain
temperatures and wind speeds in the lower atmosphere for the purposes of better
representing sea surface temperature. En este caso, the atmospheric model will
send the relevant lower atmospheric output to the coupler at desired intervals,
where it will share the output with the ocean model to use in its own calcula-
ciones. A CESM run that wishes to take advantage of all the modules will have an
atmosphere model, an ocean model, a land ice model, a sea ice model, a river
modelo, and a surface wave model all interacting through a central coupler. Este
will be computationally expensive, taking a larger amount of time and re-
sources, but may be required for some purposes. Por otro lado, an atmo-
spheric modeler may only need an active atmosphere model for the purpose at
mano, replacing any necessary input from other models with a static dataset.
This will be computationally cheap and require relatively less time to perform.
We now have a general understanding of how general circulation models like
CESM are structured so as to simulate the behavior of the climate system. El
behavior of a general circulation model is governed by a combination of physical
dynamics that are explicitly resolved by the simulation and those that are un-
resolved but approximated by parameterization. The resolved flow of mass and
energy throughout the atmosphere and ocean are governed by fluid dynamic
equations derived from Newton’s second law, making up the dynamical core
of atmosphere and ocean models. Unresolved processes occurring at subgrid
scales are parameterized as subgrid models. These subgrid models approximate
the influence of subgrid phenomena on resolved behavior, altering the properties
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General Circulation Models in Climate Science
of relevant grid cells. While modular, the various submodels of the CESM may
communicate via a centralized coupler, allowing every part of every submodel to
interact with each other. This coupler functions as an intermediary between the
submodels, synchronizing their computation while taking relevant outputs
from each submodel and providing it as an input for other submodels in calcu-
lating their dynamics over time. Coupled climate models like the CESM are,
de este modo, complex model systems designed to represent the complex climate system.
With this complexity in mind, we can now consider two ways in which GCMs
are used for exploratory purposes.
Exploring the Dynamics of Model Systems
2.
Climate models like the CESM are complex structures, made up of numerous
components interacting in intricate ways over a large number of timesteps. Este
complexity bestows climate models with what Johannes Lenhard and Eric
Winsberg (Lenhard and Winsberg 2010) call “analytic impenetrability.” Ana-
lytic impenetrability precludes a modeler from attributing causal responsibility
for some behavior or output to a particular model assumption. The interconnec-
tedness of model components, and the resulting causal complexity, makes it im-
possible to analytically tease out the particular causal influence that each
component has on model performance. En efecto, the complexity of the model
system arguably makes the entire question of individual component contribu-
tion unintelligible. The attribution of causal responsibility for model perfor-
mance must be a holistic attribution to the model assumptions taken together.
The analytic impenetrability of complex simulation models is distinct from
what Humphreys calls epistemic opacity (Humphreys 2004, 2009). According
to Humphreys “a process is essentially epistemically opaque to [cognitive agent]
X if and only if it is impossible, given the nature of X, for X to know all of the
epistemically relevant elements of the process” (Humphreys 2009, pag. 618). A
computer simulation is thus epistemically opaque insofar as features of the sim-
ulation that are important to its justification are inaccessible to researchers.
When computer simulations are epistemically opaque, there are good reasons
to reject those simulations as reliable sources of novel information about the
world (Guala 2002; Parker 2009a). Por otro lado, computer simulations
are not epistemically opaque to the extent that epistemically relevant processes
are surveyable. To be surveyable is simply to be not epistemically opaque—for
all epistemic features important to justification to be accessible to researchers.
The surveyability of proofs and calculations, Durán argues, is crucial to the con-
fidence of mathematicians in computational methods (cf. Durán 2018, ch. 4). En
distinguishing between different kinds of epistemic opacity, Andreas Kaminski
refers to the specific inability of a cognitive agent to survey a computer simula-
tion due to the complexity of its mathematical operations as “internal mathe-
matical opacity” (Kaminski et al. 2018). General circulation models are thus
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Perspectives on Science
499
internally mathematically opaque to the extent that researchers are unable to
survey the application of the physical dynamics at every timestep within each
submodel and between each submodel and the centralized coupler.
Related to internal mathematical opacity, the complexity of a general circu-
lation model will also make it impossible for human cognitive agents to infer
how the model system will evolve over time from the initial conditions alone.
There are too many deeply interconnected processes running in a full-scale earth
systems model for modelers to predict the evolution of the simulation before
actually running the simulation. A modeler cannot infer how a model system
will develop over time—with its specific initial conditions and dynamics—from
an assessment of those initial conditions and dynamics alone. Even for experi-
enced modelers it is not uncommon to discover that a seemingly unimportant
adjustment has led the model to behave unexpectedly. De este modo, a modeler will
need to observe and manipulate model performance in order to learn about
how the components of the model interact in guiding the behavior of the system
con el tiempo. Lenhard (2007) argues that by introducing “a degree of contradic-
tion” into the dynamics of a GCM, Akio Arakawa (1966) was able to provide the
model with a long-term stability that previously seemed impossible. Eso
es, the introduction of contradictory foundational dynamics did not desta-
bilize the GCM, but rather stabilized its performance. De este modo, I argue that
the causal complexity of a climate model produces two related issues: el
analytic impenetrability of the model precludes an inference from effect to
causa, and inferential impenetrability of the model precludes an inference
from initial model conditions to an understanding of model evolution.
Without a general theory relating lower-order phenomena and system-wide
climate behavior, climate modelers are left to explore these relations with the use
of GCMs. Axel Gelfert (2016, ch. 4.5) describes four ways in which models are
used by scientists to perform exploratory functions. Exploratory models can be
(1) useful starting points for later inquiry, (2) proof-of-principle demonstrations,
(3) possible explanations, y (4) assessments of target suitability. These four
forms of exploration can promote, among other things, a tacit model-based un-
derstanding “sometimes loosely described as developing ‘a feel for’ the model
(y, por extensión, for the behavior of its target system)" (Gelfert 2016, pag. 73).
General circulation models have certainly been used for the four exploratory
purposes that Gelfert presents, helping climate modelers to develop better
model-based understanding. Sin embargo, I argue that models with the struc-
tural complexity of modern GCMs can in addition be used to explore the
relationship between lower-order model features and higher-order phenomena.
In the absence of a theory linking lower-order dynamics and higher-order
dinámica, climate modelers may yet develop a feel for the dynamic relations
present in some GCM, and by extension an understanding of the dynamic
relations in the target system. While I don’t take an improved understanding
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General Circulation Models in Climate Science
of dynamic relations to be limited to a single kind of understanding, it does
promote the “grasping of causal patterns” (Potochnik 2017). In the absence of
a general theory bridging lower-order and higher-order climate phenomena,
GCMs help researchers to grasp the causal patterns relating the phenomena.
This grasping of causal patterns can serve the additional function of helping
researchers develop a feel for the target system. Tal como, GCMs facilitate an
additional kind of exploration in line with Gelfert’s original set of exploratory
purposes. Complex simulation models like GCMs allow researchers to explore
what higher-order phenomena emerge from lower-order dynamics. De este modo, I sug-
gest a novel exploratory use for complex models that target complex systems.
In the face of model complexity, modelers must explore a model’s behavior
under a variety of specific conditions to develop an understanding of the model. I
call this “model dynamic exploration” (MDE). MDE is required early in the life
of a model when the parameters of the model have yet to be calibrated. Modelo
parameters will be calibrated in response to the exploration of how particular
parameterization schemas cause the model to behave. MDE is thus a crucial as-
pect of tuning complex simulation models. Sin embargo, MDE is not limited to the
early life of a model. In developing GCMs MDE can often take the form of a
sensitivity test, where a modeler takes interest in how some feature or features of
the model will respond to variations in some other feature. Por ejemplo, un
ocean modeler may want to know how sensitive sea surface temperatures are
to changes in insolation (eso es, incoming solar radiation) within a particular
run of the CESM. Tal como, the modeler can run a coupled ocean-atmosphere
model under a wide range of insolation conditions. Incoming solar radiation
currently averages about 340 W/m2, but the modeler may choose to run an en-
semble of models with radiation values between 250–500 W/m2. The modeler
may then observe how sea surface temperatures represented in the model vary in
response to insolation change.
It’s important to note that parameter variations need not fall into the range of
plausible, or even nomologically possible, values for the sensitivity test to be
útil. Modelers performing sensitivity tests are not initially interested in accu-
rately representing the target climate system. A modeler can vary insolation
values between 250 y 500 W/m2 even if many of the values in that range
are implausible for contemporary or historical purposes. The modeler is primar-
ily exploring the dynamics of the model system in order to better understand
how specific features of the model are related, not for the immediate purpose of
accurately representing some climate system. This exploration helps modelers to
better understand the models they are using and the contexts in which a par-
ticular model will and will not be useful.
In our example, the modeler is specifically interested in exploring how sea
surface temperatures depicted in the model vary in response to changes in inso-
lación. Upon inspection of the ensemble results, the modeler may discover that
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sea surface temperatures respond too much or too little to varying insolation.
Background climatological theory may suggest a range of plausible responses
that the model does not reproduce, implying that some of the model dynamics
are inadequately represented. Por otro lado, model performance may fall
within the expected range for a subset of insolation values, suggesting that the
GCM adequately approximates the influence of solar insolation on sea surface
temperatures when insolation values are within that subset.
Observing model performance is our best means for developing a deeper un-
derstanding of the models we use to simulate the climate. De este modo, it is often
worthwhile to run a GCM under a range of similar yet diverse conditions in
order to explore the interdependencies present in the model. An assessment
of the underlying equations and initial conditions of the model will not reveal
these interdependencies, leaving modelers with no alternative but to explore
model performance.
Exploring the Dynamics of Climate Systems
3.
Por supuesto, climate modelers are not merely interested in exploring the structure
of their own complicated research tools. While it is often necessary to investigate
the behavior of a GCM to better understand the model, this is ultimately so that
the model may be later used to investigate the actual climate. This brings us
to the second novel form of exploration in which general circulation models are
útil: climate dynamic exploration (CDE). CDE is a process by which climate
modelers utilize GCMs to explore the way that the elements of the climate sys-
tem interact over time, driving the higher-order behavior of the climate system.2
For a climatologist to use GCMs to explore the dynamics of the actual
climate system, the GCM must bear the right relationship to the target
sistema. In line with a view she previously defends regarding evolutionary
theory (lloyd 1987), Elisabeth Lloyd argues that climate modelers, in ac-
cordance with the semantic view of scientific theory, utilize sets of models
to explain empirical phenomena.3 Under the semantic view of theories,
models and their target systems are homomorphic, sharing a common
estructura. De este modo, for a GCM to tell us something about the climate system,
it must be structured to capture the behavior of the climate system. De
2. While the kind of exploration depicted in CDE is not likely to be limited to cli-
mate science, and it is most likely that CDE is a particular application of a more general
target dynamic exploration to climate science, I will restrict my discussion in this paper to
the particular application.
3. Elisabeth Lloyd’s 1987 work on evolutionary models articulates in detail how the
semantic view accounts for the use of ecological and evolutionary models. I would like to
thank her for bringing to light the connection between her work on climate modelling and
her work on ecological and evolutionary modelling (Elisabeth Lloyd, personal communica-
ción, Agosto 10, 2018).
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General Circulation Models in Climate Science
curso, given that parameterizations are known to misrepresent the target
system in important respects, we should not expect GCMs to accurately
represent all aspects of the climate’s behavior (Parker 2009b, 2020). Bastante,
as Parker argues, GCMs can sacrifice representational accuracy in parame-
terizing their dynamics insofar as they are still adequate for their intended
purpose.
Still, GCMs must bear some structural similarities to their target cli-
mate system for them to be useful for climate modeling purposes, y
Lloyd presents four ways in which GCMs can be shown to bear such sim-
ilaridades. Lloyd argues that there are four methods for confirming GCM
homomorphism: Model Fit, Variety of Evidence, Robustez, and Indepen-
dent Support (2009). A climate model is confirmed when it accurately pre-
dicts or retrodicts observational data (Model Fit), and further confirmed
when it accurately predicts or retrodicts multiple independent observations
(Variety of Evidence). Aspects of a model can also be confirmed when mul-
tiple, heterogeneous models with a common structure produce the same
robust result (Robustez), or when independent evidence exists for the
reliability of parameters, parameterizations, and general theory (Indepen-
dent Support). Lloyd’s four methods of confirmation are important across
model purposes, exploratory and otherwise, for supporting the homomor-
phism of the GCM and the climate system.
With regard to CDE the adequacy of the GCM may be assessed in light
of model fit and variety of evidence when the GCM aims to represent the
actual climate during times for which we have observational evidence.
Sin embargo, when CDE is used to consider climate dynamics for counterfac-
tual conditions, or during unobserved future times, climatologists may
only have access to the confirmation methods of robustness and independent
apoyo. Por ejemplo, when GCMs are used to model counterfactual nat-
ural (non-anthropogenic) forcings for comparison with models that include
anthropogenic forcings, real world observations (IPCC 2019) and the variety
of the evidence are of no help in assessing the model fit to the counterfactual
target system. Bastante, independent understanding of the climate system is
required to constrain the values for non-anthropogenic forcings.
Performing CDE requires that modelers represent the foundational
physical equations guiding the evolution of the climate system at resolved
escamas, important dynamic features occurring at unresolved scales as param-
eterizations, and the relevant boundary conditions. These physical dynam-
circuitos integrados, parameterizations, and relevant boundary conditions are independently
supported applications by a combination model-independent theory and
empirical observation.
The fluid and thermodynamic equations composing the dynamical core
of the atmospheric and ocean models are derived from Newton’s second
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503
law, which has enjoyed a large share of empirical support in numerous and
diverse thermodynamic and fluid dynamic contexts. These equations con-
serve mass and energy across the system in accordance with well-supported
physical principles. Tal como, the adequacy of Newtonian mechanics for
modeling fluid and thermodynamics provides independent support for
their implementation in GCMs. Sin embargo, Newtonian equations derived
to the continuous Navier-Stokes equations must be discretized to be useful
for modeling purposes. Discretizing techniques, like the finite-volume dis-
cretization method (Herrington et al. 2019) adopted by the CESM, allow
for the tractability of the otherwise unsolvable equations. Such methods are
supported insofar as they satisfactorily approximate the output of contin-
uous equations. Eso es, through model fit with observation.
Parameterizations represent a host of important but unresolved climate-
relevant processes. While submodel parameterizations can be developed
with an eye to more sophisticated theoretical models, their adequacy as
simplifications will require adequate performance in light of relevant ob-
servaciones. Many parameterization submodels are thus calibrated to ap-
proximate the influence of subgrid processes in accordance with
empirically observed relations. Por ejemplo, a land model may represent
the albedo of a grid-cell as the proportional average of the albedo of its
constituent land-types. Doing so requires that climatologists empirically
calibrate the albedo of various land types in accordance with measured
valores. Once these values are empirically derived from observation they
can be incorporated into the land-model for the purpose of better approx-
imating a grid cell’s surface reflectance. De este modo, submodel parameterizations
are supported by model-independent theory and their ability to reproduce
model-independent observation.
Mientras tanto, boundary conditions for GCM simulations of the twentieth
and twenty-first centuries are supported by independent empirical ob-
servicio. Satellites, radiosondes, simple thermometers, barometers,
udometers, and the like are used to measure a variety of features of Earth’s
clima, going back as far as the 1800s. Sea surface temperature boundary
conditions in CAM between 1850 a 1981 (Hurrell et al. 2008), for ex-
amplio, are derived from adjusted and gridded in situ temperature measure-
ments of sea surface water collected in buckets behind moving ships
(Folland and Parker 1995). Empirical observations constituting the instru-
ment record thus constrain the boundary conditions for contemporary cli-
mate simulation.
Por otro lado, GCMs used for paleoclimate purposes rely on proxy
measures to get a handle on what the relevant boundary conditions were in
the distant past. Popular proxy climate measures like tree rings, oxygen
isotopes, biomarkers, and pollen are particularly useful when instrumental
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General Circulation Models in Climate Science
data is lacking for the relevant time period. Since the growth of a tree dis-
plays clearly discernible annual patterns, and this growth is sensitive to
temperature and moisture conditions, the analysis of tree rings can provide
a dendroecological account of past climate trends. It has been shown that
the isotopic composition of the shells of planktonic foraminifera reliably
varies with temperature, making the oxygen isotopes preserved in those
shells a useful proxy for sea surface temperatures. The kinds of biochemi-
cals produced by such organisms are also susceptible to environmental con-
ditions, making the preserved chemical fossils they leave behind an
indicator of what those ocean conditions were like. Similarmente, the pollen
of land flora is well preserved in the sediment record, making pollen that
can be reliably tied to a particular kind of plant very useful for constrain-
ing the local climate at the time of deposition.
The fundamental dynamic equations, parameterizations, and boundary con-
ditions make up the model assumptions necessary to capture the relevant fea-
tures of the climate system. If a GCM does not adequately capture the relevant
features of the climate, then it is not adequate for climate dynamic exploration in
that case. This is primarily what distinguishes climate dynamic exploration
from model dynamic exploration: ensuring the external validity of model as-
sumptions.4 Climate dynamic exploration requires that the model be validated,
such that researchers establish the adequacy of the relationship between the sim-
ulation model and the empirical world (Oberkampf et al. 2003). If the model
assumptions do not adequately capture the features relevant to the purpose at
mano, then the model cannot reliably be used to study the target climate system
(Parker 2009b, 2020). If a GCM does capture the relevant structure of the cli-
mate system, then we can use its performance to explore the dynamics of the
actual climate. Eso es, we can explore the dynamics of the climate system by
observing the dynamics of the model system once the GCM has been validated,
or shown to be externally valid, for its intended purpose; this process requires
establishing that the model bears the relevant structural similarities to the target
system via Lloyd’s methods of confirmation. Only by establishing the external
validity of a GCM can an exploration of the model permit an exploration of the
target climate system.
It may be apparent by now that climate dynamic exploration is always a kind
of model dynamic exploration. Climate modelers explore the target climate sys-
tem by way of exploring how the lower-order dynamics of the general circula-
tion model govern its higher-order behavior. CDE is an instance of MDE in as
much as it utilizes the evolution of the general circulation model to explore the
4. Eric Winsberg (2010) argues that it is the emphasis on establishing the external
validity of simulation that distinguishes model simulation from traditional experiment,
which focuses on establishing the internal validity of the experiment.
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Perspectives on Science
505
evolution of its target climate system. That the model must bear the right sim-
ilarities to the target climate system means, sin embargo, that one is not free to ex-
plore the GCM by way of adjusting the parameters in any which way. We may
thus distinguish between MDE in cases where the model is the target of explo-
ration, with less concern for precisely how the model might later be used to
represent some real-world target, and cases of MDE where the exploratory
choices are guided by some specific application to the world.
After implementing the requisite dynamic equations, parameteriza-
ciones, and boundary conditions, modelers can run the model to explore
how these features of climate system behave. While many assumptions
have been made about the structure of the climate system in generating
el modelo, novel higher-order patterns and behaviors will emerge during
model performance. As a result of inferential impenetrability some of these
patterns and behaviors will be unanticipated. It is these emergent climate
phenomena and their influence that modelers are exploring when they run
general circulation models. Climate modelers are investigating how spec-
ified lower-order dynamics interact at global scales over significant periods
of time to influence system wide behavior. En efecto, that the use of general
circulation models in climate science largely consists of CDE suggests that
exploration is not always merely an intermediary stage in scientific model-
En g. Exploration can be introduced early in model development and persist
as a distinct mode of research.
Como ejemplo, suppose that we are interested in the global distribution
of a particular species of planktonic foraminifera. The global distribution
of a particular foraminifera (single cell organisms with shells) species may
be one of many higher-order features we consider when modeling the
Earth’s climate. Suppose that we are particularly interested in how that
species will respond to a 100ppm increase in atmospheric CO2. Nosotros estafamos-
struct a general circulation model to address our question, knowing that it
must incorporate the ways in which atmospheric CO2 influences the sur-
face water conditions, including temperature, salinity, the prevalence of
predators, and nutrient availability. To adequately model the relevant fac-
tors will at least require coupled atmospheric, oceanic, river runoff, land ice
and sea ice models. The atmospheric model will model the influence of the
carbon-infused atmosphere, feeding insolation and temperature values to
ocean and land models. The ocean model will represent ocean circulation
and the distribution of temperature, salinity, and relevant biochemistry.
Our species of interest is expected to occupy ocean surfaces of within a
particular temperature-salinity range, in some proportion to nutrient avail-
capacidad. Sin embargo, changes in salinity, temperature and nutrient availability
will also result from river runoff and the melting of land and sea ice, so it
will be important to model their contribution to surface water conditions.
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General Circulation Models in Climate Science
Tal como, the global distribution of our target foraminifera is a higher-order
pattern emerging from the interaction of a variety of lower-order dynamics
across various submodels, the interaction of which we explore with our cli-
mate simulations.
Climate modelers may also choose to explore the effect that higher-order
phenomena have on more localized aspect of the climate system. por ejemplo-
amplio, because the presence of ice cover in the Arctic is so significant to
global climate sensitivity, climate modelers regularly explore the influence
that twenty-first century warming will have specifically on the Arctic (p.ej.
Wunderling et al. 2020). En este caso, modelers implement the requisite
dinámica, parameterizations, and boundary conditions, then run the model.
The model produces higher-order patterns as usual, and these higher-order pat-
terns are inferentially impenetrable from initial conditions. Being interested
specifically in the condition of the Arctic during the duration of the simulation,
the modeler will focus on how these higher-order patterns (emerging from
lower-order dynamics) effect ice-loss in the Arctic. De este modo, climate modelers
may also explore the effects of higher-order climate dynamics on a more local
aspect of the climate system.
CDE is a process whereby modelers explore the higher-order climate behav-
ior that arises from specified lower-order dynamics. As a result of inferential
impenetrability, running general circulation models is the best means avail-
able for exploring emergent climate patterns and behaviors. For this explora-
tion to be successful, sin embargo, model dynamics, parameterizations, y
boundary conditions must adequately represent the relevant features of the
climate system. To learn about the real-world climate system by assessing
GCM performance, the GCM must capture the relevant independently sup-
ported lower-order dynamics.
Conclusión
4.
In this paper I have argued that climate modelers take advantage of two ex-
ploratory practices in studying complex systems, model dynamic exploration
(MDE) and climate dynamic exploration (CDE). MDE allows climate modelers
to better understand the relationship between established lower-level dynamics
and the evolution of the model system over time. CDE helps modelers to un-
derstand the complicated dynamics of the real-world climate by way of asses-
sing general circulation models. It is likely that modelers working with
models of comparable causal and spatiotemporal complexity outside of clima-
tology take advantage of similar forms of model exploration, but I leave this for
future discussion. Sin embargo, it is certain that both MDE and CDE are impor-
tant forms of exploratory model use for climate modelers working to develop
an understanding of the dynamics governing Earth’s climate system into the
twenty-first century.
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