## 1. Introduction



The field of climate science emerged in the second half of the
twentieth century. Though it is sometimes also referred to as
"climatology", it differs markedly from the field of
climatology that came before. That climatology, which existed from the
late-nineteenth century (if not earlier), was an inductive science, in
many ways more akin to geography than to physics; it developed systems
for classifying climates based on empirical criteria and, by the
mid-twentieth century, was increasingly focused on the calculation of
statistics from weather observations (Nebeker 1995; Edwards 2010;
Weart 2008 [2017, Other Internet Resources]; Heymann & Achermann
forthcoming). Climate science, by contrast, aims to explain and
predict the workings of a global climate system--encompassing the
atmosphere, oceans, land surface, ice sheets and more--and it
makes extensive use of both theoretical knowledge and mathematical
modeling. In fact, the emergence of climate science is closely linked
to the rise of digital computing, which made it possible to simulate
the large-scale motions of the atmosphere and oceans using fluid
dynamical equations that were otherwise intractable; these motions
transport mass, heat, moisture and other quantities that shape
paradigmatic climate variables, such as average surface temperature
and rainfall. Today, complex computer models that represent a wide
range of climate system processes are a mainstay of climate
research.



The emergence of climate science is also linked to the issue of
anthropogenic climate change. In recent decades, growing concern about
climate change has brought a substantial influx of funding for climate
research. It is a misconception, however, that climate science
*just is* the study of anthropogenic climate change. On the
contrary, there has been, and continues to be, a significant body of
research within climate science that addresses fundamental questions
about the workings of the climate system. This includes questions
about how energy flows in the system, about the roles of particular
physical processes in shaping climates, about the interactions that
occur among climate system components, about natural oscillations
within the system, about climate system feedbacks, about the
predictability of the climate system, and much more.



This entry provides an overview of some of the core concepts and
practices of contemporary climate science, along with philosophical
work that engages with them. Thus far, most of this philosophical work
has focused on the epistemology of climate modeling and
closely-related topics.
 Section 2
 introduces a few basic concepts: climate system, climate and climate
change.
 Section 3
 turns to climate data, highlighting some of the complexities and
challenges that arise when producing three important types of climate
dataset.
 Section 4
 focuses on climate modeling, briefly describing some of the main types
of climate model employed today and then considering in more detail
the construction, use and evaluation of complex global climate models.
Finally,
 Section 5
 discusses research in climate science that addresses the issue of
anthropogenic climate change, as well as several recent controversies
related to this research; it also provides some pointers to the large
literature that has emerged on ethics and climate change.


## 2. Basic Concepts



On a standard characterization, Earth's *climate system*
is the complex, interactive system consisting of the atmosphere,
hydrosphere, cryosphere, lithosphere and biosphere (IPCC-glossary:
1451). Some definitions appear to be narrower, limiting the scope of
the climate system to those aspects of the atmosphere, hydrosphere,
etc. that jointly determine the values of paradigmatic climate
variables, such as average surface temperature and rainfall, in
response to external influences (see, e.g., American Meteorological
Society 2017a). In either case, it would seem that some human
activities, including those that release greenhouse gases to the
atmosphere and those that change the land surface, are part of the
climate system. In practice, however, climate scientists often
classify such human activities as external influences, along with
volcanic eruptions (which eject reflective aerosols and other material
into the atmosphere) and solar output (which is the primary source of
energy for the system) (see IPCC-glossary: 1454). Classifying human
activities as external to the climate system seems to be a pragmatic
choice--it is easier, and a reasonable first approximation, to
represent anthropogenic greenhouse gas emissions as exogenous
variables in climate models--though it may also reflect a deeper
ambivalence about whether humans are part of nature. 



A *climate* is a property of a climate system, but there are
different views on what sort of property it is. According to what might be
called *actualist* views, a climate is defined by actual
conditions in the climate system. Narrower actualist definitions,
which have their roots in climatology (see
 Section 1),
 reference weather conditions only. For example, the climate of a
region is often defined as its average weather conditions, or the
statistical distribution of those conditions, when long time periods
are considered (Houghton 2015: 2; IPCC-glossary: 1450). The standard
period of analysis, set by the World Meteorological Organization, is
30 years, defining a "climate normal" for a region. Given
recent rates of change in climate system conditions, however, some
climate scientists suggest that climate normals should now be defined
over much shorter periods (Arguez & Vose 2011). Broader actualist
definitions of climate reference conditions throughout the climate
system. The Intergovernmental Panel on Climate Change (IPCC), for
example, gives a broader definition of climate as "the state,
including a statistical description, of the climate system"
(IPCC-glossary: 1450). These broader definitions emerged in the second
half of the twentieth century in connection with increased efforts to
understand, with the help of physical theory, how climates in the
narrower sense are maintained and changed via atmospheric, oceanic and
other processes. These efforts in effect marked the emergence of
climate science itself (see
 Section 1).



A number of other definitions of "climate" are what Werndl
(2016) calls *model-immanent*: they reference conditions
indicated by a model of the climate system, rather than actual
conditions. For example, taking a dynamical systems perspective,
climate is often identified with an attractor of the climate system,
that is, with the conditions represented by an attractor of a
(perfect) mathematical model of the climate system (Palmer 1999; Smith
2002). Roughly speaking, an attractor is a set of points to which the
values of variables in a dynamical model tend to evolve from a wide
range of starting values. Actual conditions in the climate system over
a finite period are then understood to be a single realization from
the set of possibilities represented by the attractor. A practical
drawback of this way of thinking about climate is that the
distribution of conditions in a given finite time period need not
resemble those represented by an (infinite-time) attractor (Smith
2002; Werndl 2016). More fundamentally, standard (autonomous)
dynamical systems theory does not apply to models of the climate
system in which external factors like solar output and greenhouse gas
concentrations are changing significantly with time, as they are for
the real climate system; how to apply the resources of non-autonomous
dynamical systems theory to the climate system is a current area of
research (e.g., Chekroun et al. 2011; Drotos et al. 2015).



Werndl (2016) provides a critical, philosophical examination of
several definitions of climate and proposes a novel, model-immanent
definition on which a climate is a distribution of values of climate
variables over a suitably-long but finite time period, under a regime
of external conditions and starting from a particular initial
state. Roughly speaking, a regime is a pattern of variation in
external conditions that has an approximately constant mean value over
different sub-periods of a chosen time period. This definition is
model-immanent because it refers to the distribution of conditions
that would obtain (i.e., the conditions that a perfect model of the
climate system would indicate) if the climate system were subjected to
a regime for a suitably-long time period; this distribution defines
the climate of periods in which that regime actually obtains, even if
those periods are relatively short. Werndl's definition avoids
many of the problems that she finds with other definitions, but still
some questions remain, including how to employ the regime concept when
external conditions are changing rapidly.



Definitions of *climate* *change* are closely related to
definitions of climate. For instance, scientists subscribing to the
climate-as-attractor view might characterize climate change as the
difference between two attractors, one associated with a set of
external conditions obtaining at an earlier time and one associated
with a different set obtaining at a later time. By contrast, a
definition of climate change associated with a narrower, actualist
view is: 



> 
> 
> any systematic change in the long-term statistics of climate elements
> (such as temperature, pressure, or winds) sustained over several
> decades or longer. (American Meteorological Society 2017b) 
> 
> 
> 



The latter definition, unlike the former, allows that climate change
might occur even in the absence of any changes in external conditions,
as a result of natural processes internal to the climate system (e.g.,
slowly-evolving ocean circulations). That is, the latter definition
allows that climate change can be a manifestation of *internal
variability* in the climate system.



The notion of internal variability, and various other concepts in
climate science, also raise interesting questions and problems, both
conceptual and empirical (Katzav and Parker forthcoming). Thus far, however, the foundations of
climate science have remained largely unexplored by philosophers.


## 3. Climate Data



The sources and types of observational data employed in climate
science are tremendously varied. Data are collected not only at
land-based stations, but also on ships and buoys in the ocean, on
airplanes, on satellites that orbit the earth, by drilling into
ancient ice at earth's poles, by examining tree rings and ocean
sediments, and in other ways. Many challenges arise as climate
scientists attempt to use these varied data to answer questions about
climate and climate change. These challenges stem both from the
character of the data--they are gappy in space and time, and they
are obtained from instruments and other sources that have limited
lifespans and that vary in quality and resolution--and from the
nature of the questions that climate scientists seek to address, which
include questions about long-term changes on a global scale.



To try to overcome these challenges, climate scientists employ a rich
collection of *data modeling* practices (Edwards 2010; Frigg et
al. 2015b). These are practices that take available observations and
apply various procedures for quality control, correction, synthesis
and transformation. Some of these practices will be highlighted below,
in the course of introducing three important types of climate dataset:
station-based datasets
 (Section 3.1),
 reanalyses
 (Section 3.2),
 and paleoclimate reconstructions
 (Section 3.3).
 Because of the extensive data modeling involved in their production,
these datasets are often referred to as *data products.* As the
discussion below will suggest, an interesting feature of data modeling
practices in climate science is that they tend to be dynamic and
iterative: data models for nearly the same set of historical
observations are further and further refined, to address limitations
of previous efforts.


### 3.1 Station-Based Datasets



The weather and climate conditions that matter most immediately to
people are those near the earth's surface, where they live.
Coordinated networks of land-based observing stations--measuring
near-surface temperature, pressure, precipitation, humidity and
sometimes other variables--began to emerge in the mid-nineteenth
century and expanded rapidly in the twentieth century (Fleming 1998:
Ch.3). Today, there are thousands of stations around the world making
daily observations of these conditions, often overseen by national
meteorological services. In recent decades, there have been major
efforts to bring together records of past surface observations in
order to produce long-term global datasets that are useful for climate
change research (e.g., Menne et al. 2012; Rennie et al. 2014). These
ongoing efforts involve international cooperation as well as
significant "data rescue" activities, including imaging
and digitizing of paper records, in some cases with the help of the
public.



Obtaining digitized station data, however, is just the first step. As
Edwards (2010: 321) emphasizes, "...if you want global
data, you have to make them". To construct global temperature
datasets that are useful for climate research, thousands of station
records, amounting to millions of individual observational records,
are merged, subjected to quality control, homogenized and transformed
to a grid. Records come from multiple sources, and *merging*
aims to avoid redundancies while maximizing comprehensiveness in
station coverage (Rennie et al. 2014). Procedures for *quality
control* seek to identify and remove erroneous data. For example,
production of the Global Historical Climate Network--Daily
(GCHN-Daily) database involves 19 automated quality assurance tests
designed to detect duplicate data, climatological outliers and
spatial, temporal and internal inconsistencies (Durre et al. 2010).
*Homogenization* attempts to remove jumps and trends in station
time series that are due to non-climatic factors, e.g., because an
instrument is replaced with a new one, a building is constructed
nearby, or the timing of observations changes. Homogenization methods
rely on station metadata, when it is available, as well as physical
understanding and statistical techniques, such as change-point
analysis (Costa & Soares 2009). Finally, for many purposes, it is
useful to have datasets that are *gridded*, providing
temperature values at points on a grid, where each point is associated
with a spatial region (e.g., 2deg latitude x 2deg
longitude). Transforming from a collection of stations to a grid
involves further methodological choices: which stations should
influence the value assigned to a given grid point, what to do if the
associated region contains no reporting stations over a period, etc.
In practice, scientific groups make different methodological choices
(Hartmann et al. 2013).



Gridded station-based datasets for temperature, precipitation and
other variables have been developed (e.g., Harris et al. 2014).
Surface temperature datasets have attracted particular attention,
because of their role in efforts to quantify the extent of recent
global warming. Three prominent sources for such temperature datasets
are NASA's Goddard Institute for Space Studies (GISS), the
University of East Anglia's Climatic Research Unit (CRU) and the
U.S. National Centers for Environmental Information (NCEI).
Periodically, these groups develop new versions of their datasets
reflecting both the acquisition of additional data as well as
methodological innovation, e.g., addressing additional sources of
inhomogeneity (see Hansen et al. 2010; Jones et al. 2012; Lawrimore et
al. 2011). Despite the many different methodological choices involved
in producing these datasets, there is good agreement among analyses of
twentieth century global land surface temperature changes derived from
them, especially for the second half of the twentieth century.
Nevertheless, climate contrarians have expressed concern that these
analyses exaggerate late twentieth-century warming. This recently
motivated a fourth, independent analysis by the non-governmental
organization Berkeley Earth; drawing on a much larger set of station
records and using a quite different geostatistical methodology for
handling inhomogeneities, their analysis confirmed the twentieth
century global warming seen in other datasets (Rohde et al. 2013).


### 3.2 Reanalyses



*In situ* observations of conditions away from earth's
surface are much less plentiful than surface observations.
Radiosondes, which are balloon-borne instrument packages that ascend
through the atmosphere measuring pressure, temperature, humidity and
other variables, are now launched twice daily at hundreds of sites
around the world, but they do not provide even, global coverage
(Ingleby et al. 2016). Satellite-borne instruments can provide global
coverage--and are of significant value in the study of weather
and climate--but they do not directly measure vertical profiles
of key climatological variables like temperature; approximate values
of these variables must be inferred in a complex way from radiance
measurements. A recently-established system of several thousand ocean
floats, known as *Argo*, takes local profiles of temperature
and salinity in the top 2000 meters of the ocean roughly every 10 days
(Riser et al. 2016). None of these data sources existed a century ago,
though observations of conditions away from earth's surface were
sometimes made (e.g., using thermometers attached to kites).



One way to remedy spatial and temporal gaps in observations is to
perform statistical interpolation. Beginning in the 1990s, however,
climate scientists also began to employ a different type of
methodology, known as *data assimilation* (Kalnay 2003). First
developed in the context of weather forecasting, data assimilation
estimates the three-dimensional state of the atmosphere or ocean using
not only available observations but also one or more forecasts from a
physics-based simulation model. The forecast constitutes a first-guess
estimate of the atmosphere or ocean state at the time of interest;
this is updated in light of observational data collected around that
time. In daily weather forecasting, the resulting best estimate is
known as the *analysis*, and it provides initial conditions for
weather prediction models. To produce long-term datasets for climate
research, data assimilation is performed iteratively for a sequence of
past times (e.g., every 12 hours over several decades), producing a
retrospective analysis or *reanalysis* for each time in the
sequence (Bengtsson & Shukla 1988; Edwards 2010).



Atmospheric reanalysis datasets are in heavy use in climate research,
because they provide complete gridded data at regular time steps over
long time periods, both at the surface and above, for a wide range of
variables, including ones that are difficult to measure with
instruments. Many reanalysis datasets cover a few decades of the
twentieth century and are produced using as much of the available
observational data as is feasible (see Dee et al. 2016 for an
overview); when satellite data are available, this can amount to
millions of observations for each analysis period in the sequence. An
interesting exception is NOAA's 20th Century
Reanalysis (20CR) project: it covers the entire twentieth century and
assimilates only surface pressure observations and observed monthly
sea surface temperatures and sea ice distribution (Compo et al. 2011).
20CR is not informed by any temperature observations over land, yet
its results indicate twentieth century global warming over land
similar to that seen in station-based temperature datasets.



20CR has been described as providing independent
"observational" confirmation of the twentieth century
warming seen in station-based datasets (Compo et al. 2013). More generally, despite the
central role of computer forecasts in reanalysis, many climate
scientists refer to reanalysis data as "observations" and
use them as such: to investigate climate system dynamics, to evaluate
climate models, to find evidence of the causes of recent climate
change, and so on. Other climate scientists, however, emphasize that
reanalyses should not be confused with "real" observations
(e.g., Schmidt 2011). Parker (2017) argues that differences between
data assimilation and traditional observation and measurement are not
as great as one might think; she suggests that data assimilation can
be understood as a complex measuring procedure that is still under
development.


### 3.3 Paleoclimate Reconstructions



Climate scientists are also interested in climates of the distant
past, before the advent of meteorological instruments. These
paleoclimatic investigations rely on *proxies*: aspects of the
natural environment that are "interpreted, using physical and
biophysical principles, to represent some combination of
climate-related variations back in time" (IPCC-glossary: 1460).
For example, variations in the ratio of different isotopes of oxygen
in deep ice cores and in the fossilized shells of tiny animals serve
as proxies for variations in temperature. Additional proxies for
climate-related variables come via tree rings, corals, lake sediments,
boreholes and other sources (PAGES-2K-Consortium 2013; Masson-Delmotte
et al. 2013).



Producing proxy-based paleoclimate reconstructions, especially on
hemispheric or global scales, involves a host of methodological
challenges. Just a few will be mentioned here. First, as the
characterization above suggests, proxies often reflect the influence
of multiple environmental factors at once. For example, tree rings can
be influenced not only by temperature but also by precipitation, soil
quality, cloud cover, etc., which makes it more difficult to
confidently infer a single variable of interest, such as temperature.
Second, proxies often must be calibrated using recent observations
made with meteorological instruments, but the instrumental record
covers only a very short period in earth's history, and factors
shaping the proxy in the distant past may be somewhat different.
Third, proxies of a given type can have limited geographical coverage:
ice cores are found only at the poles, tree rings are absent in
locations where there is no distinct growing season, and so on.
Fourth, the temporal resolution of different types of proxy can differ
markedly--from a single year to a century or more--adding a
layer of complexity when attempting to use multiple proxies together.
For these reasons and others, quantifying the uncertainties associated
with proxy reconstructions is also a significant challenge (Frank et
al. 2010).



Despite these challenges, climate scientists have produced
paleoclimatic reconstructions covering various regions, time periods
and climate-related variables, including temperature, precipitation,
streamflow, vegetation and more. Temperature reconstructions in
particular have been a source of controversy (see
 Section 5.3),
 in part because they underwrite conclusions like this one, from the
Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment
Report: the period 1983-2012 was *very likely* (i.e.,
probability >0.95) the warmest 30-year period of the last 800 years
(Masson-Delmotte et al. 2013: 386). Vezer (2016a) suggests that
inferences to such conclusions can be characterized as a form of
variety-of-evidence reasoning, insofar as they involve multiple
temperature reconstructions informed by different proxies,
methodological assumptions and statistical techniques (see also
Oreskes 2007).


## 4. Climate Modeling



Models of the climate system, especially computer simulation models,
have come to occupy a central place in both theoretical and applied
research in climate science. After providing an overview of some
common types of climate model
 (Section 4.1),
 this section focuses primarily on atmosphere-ocean general
circulation models and earth system models, discussing some noteworthy
features of their construction
 (Section 4.2),
 some of their most important uses
 (Section 4.3),
 and how they are evaluated
 (Section 4.4).
 The focus is on these complex climate models both because they have
attracted the most attention from philosophers and, relatedly, because
they play particularly important roles in climate research today,
including climate change research.


### 4.1 Types of Climate Model



Climate scientists often speak of a "hierarchy" or
"spectrum" of climate models, ranging from simple to
complex. The complexity of climate models increases with: the number
of spatial dimensions represented; the resolution at which those
dimensions are represented; the range of climate system components and
processes "included" in the model; and the extent to which
those processes are given realistic rather than simplified
representations. As a consequence, more complex models tend to be more
computationally demanding as well. The discussion below introduces a
few types of model, illustrating different levels of complexity (for
other types see, e.g., Stocker 2011; McGuffie & Henderson-Sellars
2014). The types discussed here are physics-based, in the sense that
they are grounded to a significant extent in physical theory.
Data-driven or "empirical" climate models also have been
developed for some purposes but are less common and will be mentioned
only in passing.



Among the simplest climate models are *energy balance models*
(EBMs), designed to represent earth's surface energy budget in a
highly aggregate way (McGuffie & Henderson-Sellars 2014: Ch.3).
These models, which often have surface temperature as their sole
dependent variable, are constructed using both physical theory (e.g.,
the Stefan-Boltzmann equation) and empirical parameters (e.g.,
representing the albedo of the earth and the emissivity of the
atmosphere). Zero-dimensional EBMs represent the entire climate system
as a single point. They can be used to calculate by hand an estimate
of the globally-averaged surface temperature when the system is in
radiative equilibrium. One-dimensional (1-D) and two-dimensional (2-D)
EBMs take a similar energy-budget approach but represent average
temperature at different latitudes and/or longitudes and account in a
rough way for the transport of heat between them (see Sellers 1969 for
an early example). The equations of 1-D and 2-D EBMs are usually
solved with the help of digital
 computers.[1]



*Earth system models of intermediate complexity* (EMICs) come
in a range of forms but tend to be both comprehensive and highly
idealized (Claussen et al. 2002). An EMIC might incorporate not only representations of the atmosphere, ocean, land surface and sea ice, but also representations
of some biospheric processes, ice sheets and ocean sediment
processes. These representations, however, are often relatively simple or coarse. The atmosphere component of an EMIC, for
instance, might be a two-dimensional enhanced version of an EBM known
as an energy moisture balance model. The ocean might be simulated
explicitly in three dimensions using fluid dynamical equations, but
with low spatiotemporal resolution. (See Flato et al. 2013: Table 9.2
and 9.A.2 for examples and further details.) The relative simplicity
and coarseness of EMICs makes them computationally efficient, however, which in
turn makes it possible to use them to simulate the climate system on
millennial time scales.



At the complex end of the spectrum, coupled ocean-atmosphere
*general circulation models* (GCMs) simulate atmospheric and
oceanic motions in three spatial dimensions, often at as high a
resolution as available supercomputing power allows. They also
incorporate representations of the land surface and sea ice, and they
attempt to account for important interactions among all of these
components. GCMs evolved from atmosphere-only general circulation
models, which in turn were inspired by early weather forecasting
models (Edwards 2000; Weart 2010). The latest generation of climate
models, *earth system models* (ESMs), extend GCMs by
incorporating additional model components related to atmospheric
chemistry, aerosols and/or ocean biogeochemistry (Flato et al. 2013:
747). In both GCMs and ESMs, numerical methods are used to estimate
solutions to discretized versions of fluid dynamical equations at a
set of points on a three-dimensional
 grid.[2]
 With increased computing power in recent decades, the horizontal
spacing of these grid points for the atmosphere has reduced from
several hundred kilometres to ~100 km, and the number of vertical
layers has increased from ~10 to ~50, with a time step of 10-30
minutes (McGuffie & Henderson-Sellers 2014: 282). Despite this
increased resolution, many important processes--including the
formation of clouds and precipitation, the transfer of radiation, and
chemical reactions--still occur at sub-grid scales; accounting
for the effects of these processes is a major challenge, as discussed
in the next section.



Another important type of climate model is the *regional climate
model* (RCM). Like GCMs and ESMs, RCMs are designed to be
comprehensive and to incorporate realistic, theory-based
representations of climate system processes. Unlike GCMs and ESMs,
however, RCMs represent only a portion of the globe (e.g., a continent
or country), which allows them to have a higher spatiotemporal
resolution without exceeding available computing power. With this
higher resolution, RCMs can explicitly simulate smaller-scale
processes, and they also have the potential to reveal spatial
variations in conditions--due to complex topography, for
instance--that cannot be resolved by GCMs/ESMs. These features
make RCMs an attractive tool for studies of regional climate change
(e.g., Mearns et al. 2013). A challenge, however, is specifying
conditions at the horizontal boundaries of the modelled region in a
physically-consistent way; in practice, these boundary conditions
often are informed by results from GCM/ESM simulations.


### 4.2 Constructing Climate Models



Currently, there are a few dozen state-of-the-art GCMs/ESMs housed at
modeling centers around the world (see Flato et al. 2013: Table 9.1).
These are huge models, in some cases involving more than a million
lines of computer code; it takes considerable time to produce simulations
of interest with them, even on today's supercomputers. To construct a GCM/ESM
from scratch requires a vast range of knowledge and expertise (see
Lahsen 2005). Consequently, many of today's GCMs and ESMs have
been built upon the foundations of earlier generations of models
(Knutti et al. 2013). Such models often have a layered history, with
some parts of their code originally developed years or even decades
ago--sometimes by scientists who are no longer working at the
modeling center--and other parts just added or upgraded. Even
models at different modeling centers sometimes have pieces of computer
code in common, whether shared directly or borrowed independently from
an earlier model.



Many of today's GCMs/ESMs have an ostensibly modular design:
they consist of several component models corresponding to different
parts of the climate system--atmosphere, ocean, land surface,
etc.--as well as a "coupler" that passes information
between them where the spatial boundaries of the component systems meet and
that must account for any differences in the component models'
spatiotemporal resolution (Alexander & Easterbrook 2015). Often
these component models are modified versions of named, stand-alone
models. For example, the Community Earth System Model version 1.2
(CESM1.2) housed at the U.S. National Center for Atmospheric Research
incorporates the Community Atmosphere Model (CAM) and an extension of
the Parallel Ocean Program (POP). Such modularity is intended to allow
for easier reconfiguration of a GCM/ESM for different modeling
studies, e.g., replacing the ocean model with a simpler version when
this will suffice. Lenhard and Winsberg (2010), however, argue that
complex climate models in fact exhibit only a "fuzzy"
modularity: in order for the model as a whole to work well, some of
the details of the component models will be adjusted to mesh with the
particular features of other components and to try to compensate for
their particular limitations.



Each component model in a GCM/ESM in turn incorporates representations
of a number of important processes operating within that part of the
climate system. For atmosphere models, for example, it is common to
distinguish the "dynamics" and the "physics"
(see, e.g., Neale et al. 2010). The dynamics (or "dynamical
core") is a set of fluid dynamical and thermodynamic equations
reflecting conservation of momentum, mass and energy, as well as an
equation of state. These equations are used to simulate large-scale
atmospheric motions, which transport heat, mass and moisture; the
scale of the motions that can be resolved depends on the grid mesh of
the model. The "physics" encompasses representations of
sub-grid processes that impact grid-scale conditions in a significant
way: radiative transfer, cloud formation, precipitation and more.
These processes are *parameterized*, i.e., represented as a
function of the grid-scale variables that are calculated explicitly in
the dynamical core (e.g., temperature, pressure, winds, humidity).
Parameterization is necessary in other components of climate models as
well, whenever sub-grid processes significantly influence
resolved-scale variables.



Constructing a parameterization is akin to an engineering problem,
where the goal is to find an "adequate substitute" (Katzav
2013a) for the explicit simulation of a sub-grid process, using a
limited set of ingredients. This is a challenging task. For given
values of the grid-scale variables, there are usually many possible
realizations of the sub-grid conditions. The standard approach to
parameterization has been a deterministic one, aiming to estimate the
contribution that the sub-grid process would make on average, over
many possible realizations that are physically consistent with a given
set of grid-scale conditions (McFarlane 2011). An alternative approach
gaining popularity is *stochastic* *parameterization*,
which aims to estimate the contribution made by a single,
randomly-selected member of the set of realizations physically
consistent with the grid-scale conditions (Berner et al. 2017).
Typically, parameterizations of either type are informed by physical
understanding but also incorporate elements derived at least in part
from observations (see also Sundberg 2007; Guillemot 2010); this is
why parameterizations are often described as
"semi-empirical". Because climate models incorporate both
accepted physical theory and such semi-empirical, engineered elements,
they are sometimes described as having a hybrid
realist-instrumentalist status (Parker 2006; Katzav 2013a; see also
Goodwin 2015).



Parameterization also gives rise to fuzzy modularity *within*
component models of GCMs/ESMs: which parameterizations work best with
a given climate model depends to some extent on how other sub-grid
processes have already been represented and the errors that those other
representations introduce (see also Lenhard & Winsberg 2010 on
"generative entrenchment"). Even the best-available
parameterizations, however, usually have significant limitations.
Indeed, uncertainty about how to adequately parameterize sub-grid
processes remains a major source of uncertainty in climate modeling.
Differences in parameterizations--especially for cloud
processes--account for much of the spread among simulations of
the climate system's response to increasing greenhouse gas
concentrations (Flato et al. 2013: 743). This is one motivation for a
new approach to parameterization: *super-parameterization*
involves explicitly simulating a sub-grid process in a simplified way,
by coupling a 1-D or 2-D model of the process (e.g., cloud formation)
to each GCM/ESM grid point (Randall et al. 2013; Gramelsberger 2010).
This is a multi-scale modeling approach that requires significant
additional computing power, relative to traditional
parameterization.



Construction of climate models also inevitably involves some
*tuning*, or *calibration*, which involves the (often ad
hoc) adjustment of parameter values or other elements of the model in
order to improve model performance, usually measured by fit with
observations. The observations that are targeted vary from case to
case; they might relate to individual processes or to aggregate,
system-level variables, such as global mean surface temperature (see
Mauritsen et al. 2012; Hourdin et al. 2017). Which parameter values are
adjusted depends on the observations with which a better fit is
sought, but often adjustments are made within parameterizations, to
parameters whose best values are significantly
uncertain. The extent to which a model is tuned is usually not
documented or reported in detail (but see Mauritsen et al. 2012 for an
instructive example). Recently, there have been calls for more
transparency in reporting tuning strategies and targets (Hourdin et
al. 2017), in part because information about tuning is relevant to
model evaluation (see
 Section 4.4).
 An interesting question, however, is exactly what counts as tuning
since, even when formal quantitative comparison with a set of
observational data is not performed, modelers can be familiar with
those data and may well make choices in model
development--choices which could reasonably have been somewhat
different--with the expectation that they will improve the
model's performance with respect to those already-seen data.



A further issue concerns the role of social and ethical values in
climate model construction. Winsberg (2010, 2012; Biddle &
Winsberg 2009) argues that such values influence climate model
construction (and thereby results) both by shaping priorities in model
development and via inductive risk considerations, e.g., when deciding
to represent a climate system process in one way rather than another,
in order to reduce the risk that the model's results err in a
way that would have particularly negative non-epistemic consequences.
He contends that social and ethical values operate in climate model
construction whenever there are no decisive, purely epistemic grounds
for considering one model-building option to be the best available.
Parker (2014a) disputes this, pointing to the importance of pragmatic
factors, such as ease of implementation, local expertise and
computational demands. Intemann (2015) argues that social and ethical
values can legitimately influence climate model construction,
including via the routes identified by Winsberg, when this promotes
democratically-endorsed social and epistemic aims of research. 


### 4.3 Uses of Climate Models



Climate models are used for many types of purpose; just a few will be
mentioned here (see also Petersen 2012: Ch.5). One important use is in
*characterizing* features of the climate system that are
difficult to learn about via available observations. For example, the
internal variability of the climate system is often estimated from the
variability seen in long GCM/ESM simulations in which external
conditions are held constant at pre-industrial levels (Bindoff et al.
2013). Internal variability is difficult to estimate from the
instrumental record, because the record is relatively short and
reflects the influence not just of natural internal processes but also
of changing external conditions, such as rising greenhouse gas
concentrations. Estimates of internal variability in turn play an
important role in studies that seek to detect climate change in
observations (see
 Section 5.1).



Climate models are also used as scientists seek *explanations*
and *understanding*. Typically, explanations sought in climate
science are causal; climate scientists seek accurate accounts of how
combinations of climate system processes, conditions and features
together bring about climate phenomena of interest, including climate
change. Parker (2014b) identifies several ways in which climate models
have facilitated such explanation: by serving as a surrogate for
observations, the analysis of which can suggest explanatory hypotheses
and help to fill in gaps in how-possibly/plausibly explanations; by
allowing scientists to test hypotheses relevant to explanation, e.g.,
that a set of causal factors represented in the model is sufficient to
produce a phenomenon (see also Lorenz 1970); and by serving as
experimental systems that can be manipulated and studied in order to
gain insight into their workings, which can inform thinking about how
the climate system itself works. In connection with the latter, Held
(2005) calls for increased efforts to develop and systematically study
"hierarchies of lasting value"--sets of models,
ranging from the highly idealized to the very complex, that are
constructed such that they stand in known relations to one another so
that the sources of differences in their behavior can be more readily
diagnosed; he contends that the study of such hierarchies is essential
to the development of climate theory in the twenty-first century.



In addition, climate models are used to make *predictions*. It
might be thought that, due to chaos, climate prediction is impossible.
But chaos is an obstacle to precisely predicting the trajectory of a
system--the time-ordered sequence of its states over a
period--not (necessarily) for predicting the *statistical
properties* of one or more trajectories; climate prediction is
concerned with the latter. Short-term predictions of climate for
periods ranging from a year to a decade or so are made with both
physics-based and empirical models (e.g., Meehl et al. 2014; Krueger
& von Storch 2011). For these predictions, assumptions are made
about what external conditions actually will be like over the forecast
period, and the forecasts begin from an observationally-based estimate
of the recent state of the climate system. Better known, however, is
the use of climate models to make longer-term conditional predictions,
known as *projections*. These are predictions of what future
climate would be like under particular external condition
*scenarios*, without assuming that any of those scenarios will
actually occur, and they are often launched from an initial state that
is meant to be representative of the climate at the start of the
simulated period, though not estimated directly from observations (see
 Section 4.4).
 Climate change projections have become a major focus of climate
research and will be discussed further in
 Section 5.2
 below.



Recently there have been calls for discipline-level changes in the
practice of climate modeling, in order to better serve some of the
purposes just mentioned. To improve climate change projections, for
instance, Shukla et al. (2009) call for a "revolution" in
climate modeling, such that both expertise and computing power are
consolidated in a few international modeling centers, allowing for
much-higher-resolution simulations. Katzav and Parker (2015)
critically examine this and other leading proposals and reflect on
their potential benefits and costs.


### 4.4 Evaluating Climate Models



Evaluation of climate models occurs throughout the model development
process. Parameterizations and component models are often tested
individually "off-line" to see how they perform (Randall
et al. 2007), and adjustments are made to try to improve them when
they fall short of expectations. Likewise, there is testing and
adjustment as the different model components are coupled. This
evaluation can have both qualitative and quantitative dimensions. For
instance, it might be checked that the model's climate is
basically realistic-looking, with salient features (e.g., of the
atmospheric circulation) in roughly the right place. Quantitative
evaluation produces scores on *performance metrics*. These can
include measures of conservation and stability--how close does
the model come to achieving a top-of-atmosphere energy
balance?--as well measures of fit between model output and
observational data. The evaluation that occurs in the course of model
development, however, is rarely reported in publications. What is
reported instead are features of the model, including how it performs,
once it is fully constructed, tuned and ready to be released for
scientific use.



Climate model evaluation can have many different goals. A basic
motivation is simply to learn about how climate models relate to the
climate system. Borrowing the language of Giere (2004), we might say
that a goal is to learn about the respects in which and degrees to
which climate models are *similar to the climate system* (Lloyd
2010). A climate scientist might investigate, for example, whether a
particular GCM exhibits a seasonal cycle in temperature for North
America that roughly matches that which currently occurs in the real
system. Evaluation efforts can also be directed at understanding
similarities at the level of the processes and mechanisms underlying
behaviors. In this regard, Lloyd (2009, 2010, 2015) points to fit
between model results and observations on a variety of metrics,
independent support for model components, and robustness of findings
as considerations that are relevant. A second common goal of climate
model evaluation, closely related to the first, is to learn whether a
model is *adequate for particular purposes* (Parker 2009; Baumberger et al. 2017). As discussed in
 Section 4.3,
 scientists use climate models for various purposes: to characterize
climate system properties (e.g., internal variability), to make
projections of future climate change under particular scenarios, and
so on. Many evaluation activities are intended to inform conclusions
about climate models' adequacy for these purposes
and, ipso facto, about the levels of confidence that can be had in the
models' estimates of internal variability, their projections of
future climate change, etc.



What is known about the ingredients of a climate model--e.g.,
that they closely approximate accepted theory or involve massive
simplifications--can provide reasons to think that the model is
similar (or not) to the climate system in particular ways or adequate
(or not) for particular purposes (Baumberger et al. 2017). But climate scientists also seek
evidence regarding similarity and adequacy by comparing model results
to observations (see also Guillemot 2010). Such comparisons can be
qualitative or quantitative, as noted above. In some cases, it is
relatively straightforward to gauge whether model-data comparisons
provide evidence for or against hypotheses about model-system
similarities or about the adequacy of climate models for
particular purposes. Prime candidates include cases in which what is
hypothesized entails just a rough fit between model results and
well-observed climate system behaviors. Thus, for example, the
Intergovernmental Panel on Climate Change (IPCC) is able to report




> 
> 
> very high confidence that models reproduce the general features of the
> global-scale annual mean surface temperature increase over the
> historical period, including the more rapid warming in the second half
> of the 20th century, and the cooling immediately following
> large volcanic eruptions. (Flato et al. 2013: 743) 
> 
> 
> 



In many other cases, however, it is more difficult to gauge the extent
to which model-data comparisons provide evidence of a model's similarity to the climate system or its
adequacy for particular purposes. There are a number of complicating
factors. 



First, differences between modeling results and observational data
might not be due to shortcomings of the climate model. Climate
datasets can contain errors or have uncertainties that are
underestimated or not reported. Indeed, in more than one
case, it has turned out that model-data conflicts were resolved
largely in favor of the models (see, e.g., Lloyd 2012). There also
have been cases where a poor fit between modeling results and data was judged to
be due in part to errors in assumptions about the external conditions
(e.g., aerosol concentrations) that obtained during the simulated
period (e.g., Santer et al. 2014, 2017). More fundamental is an issue
related to the initial conditions of climate simulations (see also
Schmidt & Sherwood 2015). Oftentimes, a simulation for a
particular period is launched from a representative state that is
arrived at after letting the climate model run for a while under
external conditions like those assumed to hold at the start of the
period of interest; this "spin up" of the model is needed to
allow its various components to come into balance with one another.
Since this representative state inevitably differs somewhat from the
actual conditions at the start of the period of interest (e.g., on Jan
1, 1900), and since the climate system is believed to be chaotic, the
simulation cannot be expected to closely track observations throughout
the period, even if the model perfectly represents all climate system
processes; the goal is for differences between climate simulations and
observations to be no larger than would result when running a perfect
model from other initial states that are consistent with the external
conditions at the start of the period of interest (i.e., no larger than
differences stemming from internal variability).



Interpretation can be difficult even when there is relatively good fit
between modeling results and observational data. One reason is that,
to varying degrees, today's climate models have been tuned to
twentieth century observations. Tuning sometimes delivers an improved
score on a performance metric by compensating for errors elsewhere in
the model (Petersen 2000), and the stability of errors is not
guaranteed as external conditions change, e.g., as greenhouse gas
concentrations continue to rise (Stainforth et al. 2007; Knutti 2008).
Some climate scientists suggest that data used in tuning cannot be
used subsequently in model evaluation (e.g., Randall et al. 2007: 596;
Flato et al. 2013: 750). Steele and Werndl (2013, 2016), however,
argue that in some circumstances it is legitimate to use data for both
calibration (i.e., tuning) and confirmation, something they illustrate
with both Bayesian and frequentist methodologies. Frisch (2015)
advocates what he calls a *moderate predictivism* in this
context: if a climate model is tuned to achieve a good fit with data
for a particular variable, this provides less support for the
hypothesis that the model can deliver the same performance with
respect to other predictions for that variable (e.g., future values),
compared to the case when the good fit is achieved without tuning (see
also Stainforth et al. 2007; Katzav et al. 2012; Mauritsen et al.
2012; Schmidt & Sherwood 2015). Frisch defends this view on the
grounds that successful simulation without tuning gives us more reason
to think that a climate model has accurately represented key processes
shaping the variable of interest; if we were already confident of the
latter, then successful simulation without tuning would not have such
an advantage. In any case, to take account of tuning in model
evaluation, it is important to know what tuning has been done, and
this information is often not readily available (see
 Section 4.2).



A further complication is that, in many cases, the
"observations" used in climate model evaluation are
reanalysis datasets whose contents are determined in part by model-based
weather forecasts (Edwards 1999,
2010; Flato et al. 2013: Table 9.3; see also
 Section 3.2
 above). Since weather forecasting models and climate models can
incorporate some similar idealizations and simplifications, the worry
arises that fit between reanalyses and climate modeling results might
be artificially inflated in some respects, due to shared imperfections. Identifying such artificial inflation of fit can be
challenging, given limited availability of independent observations,
i.e., ones not used in producing the reanalysis. Leuschner (2015:
370), however, suggests that a focus on the shared assumptions
themselves might be a promising way forward; the adequacy of shared
assumptions about the radiative effects of aerosols, for instance,
might be established via comparison with the results of laboratory
experiments (see also Frigg et al. 2015b: 957).



Lastly, when it comes to evaluating the adequacy of climate models for
some purposes, especially predictive purposes, it can be difficult to
know what sort of fit with past climate conditions would even count as
evidence of a climate model's adequacy-for-purpose (Parker
2009). How accurately should a climate model simulate various features
of past climate if it is adequate for, say, predicting future changes
in global mean surface temperature to within some specified margin of
error? This can be very difficult to answer, not only because there is
more than one way to achieve such predictive accuracy (e.g., by
distributing error differently across contributing variables), but
also because simplifications and idealizations in the model, as well
as tuned components, might work better under some external conditions
than others, e.g., under those of the recent past rather than those
assumed in future scenarios (see ibid.). Katzav (2014) suggests that,
in practice, attempts to determine what sort of fit with past
conditions would count as evidence of a climate model's
adequacy-for-purpose will often rely on results from climate models
themselves, in a way that is question-begging.



Given these challenges, climate model evaluation today often focuses
primarily on describing the extent to which modeling results fit with
observations and on characterizing how that fit has improved from one
generation of models to the next; it tends to reach only relatively
modest conclusions about the adequacy of climate models for
particular purposes. For example, recent Intergovernmental Panel on
Climate Change (IPCC) reports have concluded that today's
climate models provide "credible quantitative estimates"
of climate system conditions in support of climate change detection,
attribution and projection (see
 Section 5),
 where this credibility is underwritten both by how the models are
constructed--i.e., their grounding in basic physical
principles--and by how they perform on a wide range of metrics
(see Randall et al. 2007; Flato et al. 2013; see also Knutti 2008).
The credibility of results is judged to be greater for larger spatial
scales (i.e., for quantities averaged over global or continental
scales) and for variables that are less spatially heterogeneous (e.g.,
for temperature and pressure as opposed to precipitation), but the
expected accuracy of modeling results used in detection, attribution
and projection studies is rarely explicitly quantified. To begin to move the discussion forward, Baumberger et al. (2017) draw on both philosophical and scientific resources to outline a conceptual framework intended to aid evaluation of the adequacy of climate models for particular purposes; they point to empirical accuracy, robustness, and coherence with background knowledge as relevant considerations (see also Knutti (2018: 11.4), highlighting the importance of "process understanding").



Finally, it is worth noting that a significant motivation for model
evaluation studies is model improvement: model evaluation activities
can be directed toward understanding why a model's results
exhibit particular errors, in the hope of improving the model in
future. Lenhard and Winsberg (2010), however, suggest that such
"analytical understanding" of climate model performance is
largely out of reach. They argue that features of climate models,
including their complexity, their fuzzy modularity (see
 Section 4.2)
 and their incorporation of "kludges"--unprincipled
fixes applied in model development to make the model as a whole work
better--will often make it very difficult to apportion blame for
poor simulation performance to different parts of climate models;
climate modeling, they argue, faces a particularly challenging variety
of *confirmational holism* (see also Petersen 2000). As
empirical support, they point to the limited success of model
intercomparison projects (e.g., Taylor et al. 2012) in identifying the
sources of disagreement in climate model simulations. However, there
have been some notable successes in localizing sources of error and
disagreement in climate simulations. For example, differences in the
representation of cloud processes have been identified as a major
source of disagreement among estimates of climate sensitivity (Flato
et al. 2013: 743). Likewise, a growing body of work on "emergent
constraints" has uncovered sources of spread in various aspects
of climate change projections (see, e.g., Qu & Hall 2007 and
subsequent work; Frigg et al. 2015c). Thus, while Lenhard and Winsberg
are surely right that it is difficult to achieve analytical
understanding of climate model performance, just how limited the
prospects are seems an open question.


## 5. Anthropogenic Climate Change



The idea that humans could change Earth's climate by emitting
large quantities of carbon dioxide and other gases is not a new one
(Fleming 1998; Weart 2008 [2017, Other Internet Resources]). In the
late nineteenth century, Swedish chemist Svante Arrhenius calculated
that doubling the levels of carbonic acid (i.e., carbon dioxide) in
the atmosphere would warm Earth's average surface temperature by
several degrees Celsius (Arrhenius 1896). By the mid-twentieth
century, oceanographer Roger Revelle and colleagues concluded: 



> 
> 
> By the year 2000, the increase in atmospheric CO2 ...
> may be sufficient to produce measurable and perhaps marked change in
> climate. (quoted in Oreskes 2007: 83-84) 
> 
> 
> 



In 1988, climate scientist James Hansen famously testified to the U.S.
Congress that global warming was already happening. The same year, the
World Meteorological Organization and the United Nations Environment
Program established the Intergovernmental Panel on Climate Change
(IPCC) 



> 
> 
> to provide policymakers with regular assessments of the scientific
> basis of climate change, its impacts and future risks, and options for
> adaptation and mitigation. (IPCC 2013a) 
> 
> 
> 



Drawing on the expertise of the international climate science
community, the IPCC has delivered assessment reports roughly every
five years since
 1990.[3]



Because IPCC reports usefully synthesize a huge body of scientific
research, they will be referenced frequently in what follows. The discussion
will focus primarily on questions about the physical climate system,
rather than questions about the impacts of climate change on societies
or about climate policy options; the latter, which are also addressed
by the IPCC, call for expertise that extends well beyond climate
science (see IPCC 2014).
 Section 5.1
 discusses detection and attribution research, which is concerned with
the following questions: In what ways has Earth's climate
changed since pre-industrial times? How much have human activities,
especially human emissions of greenhouse gases, contributed to the
changes?
 Section 5.2
 considers the projection of future climate change, with special
attention to debates about the interpretation of model-based
projections.
 Section 5.3
 provides a brief overview of several recent controversies related to
the methods and conclusions of climate change research. Finally,
 Section 5.4
 notes some of the ethical questions and challenges raised by the
issue of anthropogenic climate change.


### 5.1 Detection and Attribution



The IPCC defines *detection* of climate change as 



> 
> 
> the process of demonstrating that climate or a system affected by
> climate has changed in some defined statistical sense, without
> providing a reason for that change. (IPCC-glossary: 1452) 
> 
> 
> 



They imply that they are interested in climate change due to external
factors, rather than natural processes internal to the climate system,
when they add: 



> 
> 
> An identified change is detected in observations if the likelihood of
> occurrence by chance due to internal variability alone is determined
> to be small, for example <10%. (ibid.) 
> 
> 
> 



Detection thus requires a statistical estimate of how much a quantity
or field of interest might fluctuate due to internal variability. It
is challenging to estimate this from the instrumental record.
Estimates have been extracted from paleoclimate data for some
quantities (Schurer et al. 2013) but, as noted in
 Section 4.3,
 often they are obtained from long GCM/ESM simulations in which
external conditions are held constant.



In its periodic assessments, the IPCC has reached increasingly strong
conclusions about the detection of climate change in observations. The
IPCC's Fifth Assessment Report concluded that it is
"virtually certain" (i.e., probability >0.99) that the
increase in global mean surface temperature seen since 1950 is not due
to internal variability alone (Bindoff et al. 2013: 885). That is, the
probability that the warming is due to internal variability alone was
assessed by the scientists, on the basis of available evidence and
expert judgment, to be less than
 1%.[4]
 Indeed, it was noted that, even if internal variability were three
times larger than estimates from simulations, a change would still be
detected (ibid.: 881, citing Knutson et al. 2013). Changes have been
formally detected in many other aspects of the climate system as well,
not just in the atmosphere but also in the oceans and the
cryosphere.



The process of *attribution* seeks to identify the cause(s) of
an observed change in climate. It employs both basic physical
reasoning--observed changes are qualitatively consistent (or not)
with the expected effects of a potential cause--as well as
quantitative studies. The latter include fingerprint studies, in which
climate models are used to simulate what would occur over a period if
a particular causal factor (or set of factors) were changing, while
other potential causal factors were held constant; the resulting
simulated pattern of change in the target variable or
field--often a spatiotemporal pattern--is the
"fingerprint" of that factor (or set of factors).
Scientists then perform a regression-style analysis, looking for the
linear combination of the fingerprints that best fits the observations
and checking whether the residual is statistically consistent with
internal variability (see, e.g., Bindoff et al. 2013: Box 10.1). If
so, then an estimate of the contributions of the different causal
factors that were considered can be extracted from the analysis.
Because of uncertainties associated with observational data, the
response patterns and various methodological choices, attribution
studies produce a range of estimated contributions for each
 factor.[5]



As with detection, IPCC conclusions related to attribution have grown
stronger over time and have come to encompass more variables and
fields. In their Fifth Assessment Report, the IPCC concluded that it
is "extremely likely" (i.e., probability >0.95) that
more than half of the increase in global mean surface temperature
since 1950 was due to anthropogenic greenhouse gas emissions and other
anthropogenic forcings (Bindoff et al. 2013: 869). This conclusion was
informed by multiple fingerprint studies, as well as physical
reasoning and expert judgment. The latter underwrite key assumptions
of the attribution exercise (e.g., that no significant causes have
been overlooked) and, relatedly, play a role in deciding the extent to
which face-value probabilities produced in fingerprint studies should
be downgraded (e.g., from "virtually certain" to
"extremely likely") in light of remaining uncertainties
and recognized limitations of those studies. For various other
changes, including increases in global mean sea level since the 1970s
and retreat of glaciers since the 1960s, the IPCC concludes that it is
at least "likely" (i.e., probability >0.66) that human
influence played a role (ibid.: 869-871).



These conclusions about detection and attribution align with, if not
constitute, the "consensus" position regarding the reality
and causes of recent climate change. The extent to which there is a
scientific consensus on these matters, however, has itself become a
topic of debate. It is often reported, for example, that 97% (or more)
of climate scientists agree that anthropogenic emissions of greenhouse
gases are causing global climate change (Cook et al. 2016). Analyses
of the scientific literature, as well as surveys of scientists, have
been conducted to demonstrate (or challenge) the existence of this
consensus. This focus on consensus can seem misguided: what matters is
that there is good evidence for a scientific claim, not merely that
some percentage of scientists endorses it (see also Intemann 2017).
Yet consensus among experts can serve as indirect evidence for a
scientific claim--an indication that the scientific evidence
favors or even strongly supports the claim--at least if the
consensus is "produced in the right manner" (Odenbaugh
2012). A consensus emerging from a process that gives free rein to
shared bias or groupthink (Ranalli 2012), or that does not require
that those surveyed have relevant expertise, might have little
evidential value. By contrast, a "hard won"
consensus--one that emerges despite the reluctance of
independent-minded and critical parties, after "vigorous debate
and a thorough examination of the range of alternative
explanations" (ibid.: 187) might be significant; the indirect
evidence provided by such a consensus could be useful to non-experts
who are not able to evaluate the scientific evidence themselves.
Ranalli (2012) suggests that parties on opposite sides of debates
about the reality and causes of climate change generally hold similar
views about what makes for a reliable scientific consensus, but
disagree over the extent to which the climate consensus meets the
standard.



In support of the consensus position regarding detection and
attribution, Oreskes (2007) argues that research underpinning it
accords with various models of scientific reliability: it has a strong
inductive base, it has made nontrivial correct predictions, it has
survived persistent efforts at falsification, it has uncovered a body
of evidence that is consilient, and--in light of all of
this--climate scientists have concluded that the best explanation
of recent observed warming includes a significant anthropogenic
component. She also emphasizes that the core idea that increased
atmospheric concentrations of carbon dioxide would warm the climate
emerged first from basic physical understanding, not from computer
models of the climate system. In a complementary analysis, Lloyd
(2010, 2013) offers a defense of the use of climate models in
attribution research and argues that studies involving multiple
climate models--which incorporate similar assumptions about the
radiative properties of greenhouse gases but differ somewhat in their
empirically-supported representations of other climate system
processes--provide enhanced support for the conclusion that
greenhouse gases are (part of) a good explanation of observed recent
warming; she argues that such studies illustrate a distinctive kind of
*model robustness*, which incorporates variety-of-evidence
considerations, and that such robustness is a "confirmatory
virtue" (see also Vezer 2016b). Robustness and
variety-of-evidence considerations are also highlighted by Parker
(2010), in a different way; she suggests that they have been
important for justifying detection and attribution claims, given that
formal studies, such as fingerprint studies, often rely on contentious
or false assumptions. Winsberg (2018: Ch.11&12) critically
examines and synthesizes much of this recent work on robustness in
climate science, drawing on Schupbach's (2016) account of
robustness analysis as explanatory reasoning.



Katzav (2013b), however, considers another perspective on
evidence--Mayo's (1996) severe testing framework--and
argues that the claim that more than half of the increase in global
mean surface temperature since 1950 was due to anthropogenic forcing
has not passed a severe test in Mayo's sense, even when
variety-of-evidence considerations are factored in; from a severe
testing perspective, he contends, we still lack good evidence for this
conclusion. He suggests that some weaker attribution claims--such
as the claim that anthropogenic forcing had *some* role in
post-1950 warming--might be ones for which there is good evidence
in Mayo's demanding sense. 


### 5.2 Projecting Future Climate Change



Climate scientists also seek to understand how climate would change in
the future if greenhouse gas emissions and other anthropogenic forcing
factors were to evolve in particular ways. Such information can be of
significant interest to policy makers, who might choose to implement
policies that push toward some future scenarios and away from others,
and to many other decision makers as well (e.g., insurance companies
deciding which policies to offer). Climate models have emerged as an
important tool for making projections of future conditions. For a
given scenario, it is common to make an *ensemble* of
projections using different climate models or model versions; this is
motivated in part by uncertainty about how to construct a climate
model that can deliver highly accurate projections (Parker 2006; Betz 2009). This uncertainty in turn stems both from limited theoretical
understanding of some processes operating within the climate system as
well as from limited computing power, which constrains how existing
knowledge can be implemented in models (see the discussion of
parameterization in
 Section 4.2).



Different types of ensemble study explore the consequences of
different sources of uncertainty in modeling. A
*multi-model ensemble* (MME) study probes *structural
uncertainty*, which is uncertainty about the form that modeling
equations should take as well as how those equations should be solved.
An example of an MME study is the Coupled Model Intercomparison
Project Version 5 (CMIP5), which produced projections using a few
dozen climate models developed at different modeling centers around
the world (see Flato et al. 2013: Table 9.1). A *perturbed-physics
ensemble* (PPE) consists of multiple versions of the same climate
model, where the versions differ in the numerical values assigned to
one or more parameters within the model. Examples of studies employing
PPEs include the climateprediction.net project (Stainforth et al.
2005) and the UK Climate Projections (UKCP09) project (Murphy et al.
2009). PPEs probe *parameter uncertainty*, i.e., uncertainty
about the numerical values that should be assigned to parameters
within a given model structure. In an *initial condition
ensemble* (ICE), the same model or model version is assigned
different initial conditions. ICEs can probe *initial condition
uncertainty*, which is uncertainty about the initial model state
from which simulations should be launched, and they are often used in
combination with one of the other two types of ensemble (see Werndl forthcoming for further discussion).



When it comes to interpreting ensemble projections, a range of views
and methodologies have emerged. At one end of the spectrum, there are
statistical methodologies used to infer probabilities (of future
changes in climate) from ensemble results. For instance, some MME
studies have adopted what amounts to a "truth-plus-error"
framework: probabilities are estimated by treating ensemble members as
random draws from a distribution of possible models centered on truth
(e.g., Tebaldi et al. 2005). The performance of today's MMEs on
past data, however, suggests that they are not centered on truth
(Knutti et al. 2010a). An alternative
"statistically-indistinguishable" framework allows for the
estimation of probabilities by assuming that ensemble members and
truth (i.e., perfect observations of future conditions) are drawn from
the same statistical distribution. Examining past data, Annan and
Hargreaves (2010) find this assumption to roughly hold for one
prominent MME (the CMIP3 ensemble), at least for some variables.
Bishop and Abramowitz (2013), however, argue that results from
today's MMEs lack some statistical properties that should be
present if the assumption of indistinguishability is accurate. They
propose an alternative "replicate-Earth" framework, which
involves post-processing MME projections so that they display those
desired statistical properties; Bishop and Abramowitz do not seem to
insist, however, that the post-processed distribution be considered a
probability distribution. Bayesian methodologies have also been
developed. The UKCP09 project, for example, used a Bayesian PPE
methodology to produce probabilistic climate change projections out to
2100 at a fine spatiotemporal resolution for the United Kingdom
(Murphy et al. 2009). That methodology treated parameter uncertainty
as uncertainty about which model was the best (on given metrics of
performance) in a given model class, and attempted to account for the
difference between the best model and a perfect model with the help of
results from an MME (Rougier 2007; Sexton et al. 2012). 



Scientists and philosophers have raised a number of criticisms of
methods that infer probabilities from ensemble results. As implied
above, some lines of criticism cite evidence that the statistical
assumptions underlying the studies are not met. Others argue that
uncertainty about future climate change is deeper than precise
probabilities imply, either because of challenges associated with
quantifying structural model uncertainty (e.g., Stainforth et al.
2007; 
Hillerbrand 2014; Parker 2014a) or, relatedly, because the possible future states of
the climate system (i.e., the outcome space) is itself uncertain
(Katzav et al. 2012). In other words, these critics contend that
probabilistic uncertainty estimates have a false precision and, in that sense, are misleading about the actual
state of knowledge. Frigg et al. (2013, 2014, 2015a) argue that, for
PPE studies like UKCP09, such probabilities are also likely to be
misleading in a different sense, namely, because they differ markedly
from the probabilities that would be obtained from a model with no
structural error. They illustrate that, for nonlinear systems, even a
small amount of structural model error can give rise to very
misleading probabilities--a consequence they dub the
"hawkmoth effect" (taking inspiration from the
"butterfly effect", which relates to initial condition
error; see also Mayo-Wilson 2015, Lawhead forthcoming). Winsberg and
Goodwin (2016), however, contend that more work is needed to establish
that the hawkmoth effect is as devastating for probabilistic climate
projection as Frigg et al. claim (see also Goodwin & Winsberg
2016).



Some of these critics of probabilistic approaches advocate a very
different interpretation of results from today's ensemble
projection studies, according to which the projections indicate
changes that are (merely) *plausible* or *possible* in
light of current understanding. Stainforth et al. (2007), for example,
suggest that the range of projections produced using state-of-the-art
climate models constitutes a "non-discountable envelope"
of future changes, a set of possible changes that should not be
disregarded; this does not imply that changes outside the envelope are
discountable. Likewise, Katzav (2014) argues that climate change
projections can often be interpreted as indicating *real*
*possibilities*: 



> 
> 
> a state of affairs in a target domain is...a real possibility
> relative to time *t* if and only if (a) its realisation is
> compatible with the basic way things are in the target domain over the
> period during which it might be realised and (b) our knowledge at
> *t* does not exclude its realisation over that period. (2014:
> 236) 
> 
> 
> 



Betz (2015), however, argues that even a possibilistic interpretation
of ensemble results may be hard to justify, since it is difficult to
show that contrary-to-fact assumptions employed in climate models do
not lead to projections that are inconsistent with background
knowledge; on his view, though not on Katzav's, this consistency
is necessary for projections to indicate serious possibilities. Part
of the disagreement here thus hinges on what it takes for something to
be a serious/real possibility.



There are also intermediate views. One such view sees today's
ensemble projection studies as imperfect investigations of
uncertainty, whose results should be considered by experts alongside
all other available information when reaching conclusions about future
climate change; those conclusions might take various forms, depending
on the variable under consideration (see, e.g., Kandlikar et al. 2005;
Mastrandrea et al. 2010). For example, in its recent
assessment reports, the IPCC relied on ensemble modeling results,
background knowledge and expert judgment to arrive at
"likely" ranges for changes in near-surface global mean
temperature under different scenarios; the experts judged it to be
*likely* (i.e., probability >0.66) that the temperature
change would fall within the 5% to 95% range of the CMIP5 ensemble
results (Collins et al. 2013: 1031). For the moderate emission
scenario known as RCP6.0, for instance, the conclusion was that the
global mean temperature during 2081-2100 would *likely*
be between 1.4 and 3.1 degrees Celsius warmer than during
1986-2005 (ibid.). The IPCC thus asserted more than that the
changes projected by the CMIP5 ensemble were possibilities, but they
did not go so far as to assign a single, full probability density
function over temperature change values.



Intertwined with the issue of ensemble interpretation is the issue of weighting, i.e., 
whether to assign greater weight to some projections than others.
This is part and parcel of some methods, but in MME studies it has
been common to give equal weight to participating models, as the IPCC approach
illustrates. This is sometimes referred to as "model
democracy" or "one-model one-vote" (Knutti 2010).
Part of the original motivation for model democracy was the difficulty
of determining which of the state-of-the-art models included in MMEs
would give the most accurate projections of future conditions (see
 Section 4.4).
 This difficulty notwithstanding, recently weighting has been
advocated on the grounds that, for at least some projected variables,
there is good reason to think that some models are more skillful than
others and, moreover, the one-model one-vote approach fails to take
account of dependence among models, i.e., of the fact that an MME can
include several models that are variants of one another (Knutti et al.
2017). A key challenge, however, is to select and combine
relevant metrics of performance and other criteria to assign appropriate
weights for a given projected variable (Gleckler et al. 2008; Knutti et al. 2010b; Weigel et al. 2010). The basic
issue of interpretation discussed above also remains, i.e., how to
interpret the weighted ensemble results.



There are many other philosophically-interesting questions related to
ensemble climate projection (Frame et al. 2007); just a few will be
mentioned here. First, how can ensemble studies be designed so that
they probe uncertainty in desired ways? Today's MME studies do
not probe structural uncertainty systematically, and it is unclear
what it would mean to do so; there are questions about how to define
the space of relevant models and how to sample it (Murphy et al. 2007). Second, how significant are robust results? Climate
scientists often assume that agreement among projections has special
epistemic significance--e.g., that we can be substantially more
confident in those projected changes (Pirtle et al. 2010)--but
Parker (2011) argues that it is difficult to make the case for this.
There are also questions about how to quantify the extent of agreement
or robustness (see Collins et al. 2013: Box 12.1) and about what it
means for ensemble members to be independent, an important factor in
interpreting the significance of robustness (Annan and Hargreaves
2017). Finally, to what extent do non-epistemic values influence
ensemble results? Winsberg (2010, 2012; see also Biddle & Winsberg
2009) identifies pathways of influence via model construction (see
 Section 3.2),
 which in turn affect probabilistic uncertainty estimates produced
from ensemble results, though not necessarily in a way that raises
concerns about "wishful thinking". Parker (2014a) suggests that this influence
might be dampened by representing uncertainty in coarser ways that
also better reflect the extent of actual uncertainty, e.g., by using
the IPCC imprecise probability intervals; the extent to which the value influence is escapable in practice, however, remains unclear.


### 5.3 Recent Controversies



Climate "contrarians" challenge key conclusions of
"mainstream" climate science, such as those of the IPCC,
in a host of public venues: blogs, newspaper op-ed pieces, television
and radio interviews, Congressional hearings and, occasionally,
scientific journals. Those considered contrarians come in many
varieties, from climate scientists who consider headline attribution
claims to be insufficiently justified--they are usually described
as climate "skeptics"--to individuals and groups from
outside of climate science whose primary motivation is to block
climate policy, in some cases by "manufacturing doubt"
about the reality or seriousness of anthropogenic climate change; they
are commonly labelled climate "deniers" (see, e.g.,
Oreskes & Conway 2010; Ranalli 2012). (The use of these labels
varies, however.) Contrarians have played a role in creating or
sustaining a number of public controversies related to climate
science. Four of these are discussed very briefly below, along with
some recent philosophical work reflecting on the impact of contrarian
dissent.



*The Tropospheric Temperature Controversy*. Climate model
simulations indicate that rising greenhouse gas concentrations will
induce warming not only at earth's surface but also in the layer
of atmosphere extending 8-12 km above the surface, known as the
*troposphere*. Satellites and radiosondes are the primary means
of monitoring temperatures in this layer. Analyses of these data in
the early 1990s, when only about a decade of satellite data was
available, indicated a lack of warming in the troposphere (Spencer
& Christy 1990). Prima facie, this presented a challenge to
climate science, and it became a key piece of evidence in contrarian
dismissals of the threat of anthropogenic climate change. Over time,
additional research uncovered numerous problems with the satellite and
radiosonde data used to estimate tropospheric temperature trends, with
many of these problems related to homogenization (NRC 2000; Karl et
al. 2006). More recent observational estimates agree that the
troposphere has been warming and, although observed trends still tend
to be somewhat smaller than those in simulations, the mainstream view
is that there is "no fundamental discrepancy" between
observations and models, given the significant uncertainties involved
(Thorne et al. 2011). Nevertheless, the debate continues (see, e.g.,
Christy et al. 2010; Santer et al. 2017). Lloyd (2012) suggests that
this controversy in part involves a conflict between a "direct
empiricist" view that sees observational data as a naked
reflection of reality, taking priority over models, and a more nuanced
"complex empiricist" view (see also Edwards 2010:
417).



*The Hockey Stick Controversy*. The hockey stick controversy
focused on some of the first millennial-scale paleoclimate
reconstructions of the evolution of Northern Hemisphere mean
near-surface temperature. These reconstructions, when joined with the
instrumental temperature record, indicate a long, slow decline in
temperature followed by a sharp rise beginning around 1900; their
shape is reminiscent of a hockey stick (e.g., Mann et al. 1998, 1999).
Such reconstructions featured prominently in the IPCC's Third
Assessment Report (Albritton et al. 2001) and constituted part of the
evidence for the IPCC conclusion that "the 1990s are likely to
have been the warmest decade of the millennium" (Folland et al.
2001: 102). Contrarian criticism in the published literature followed
two main lines. One argued that there were problems with the data and
statistical methods used in producing the reconstructions (McIntyre
& McKitrick 2003, 2005), while a second appealed to additional
proxy data and alternative methods for their interpretation in order
to challenge the uniqueness of late-twentieth century warming (Soon et
al. 2003). Mainstream climate scientists offered direct replies to
these challenges (e.g., Mann et al. 2003; Wahl & Ammann 2007), and
additional research has since produced new and longer reconstructions
using a variety of proxies, which also support the conclusion that the
late twentieth century was anomalously warm (Masson-Delmotte et al.
2013).
 [6]
 Contrarians, however, continue to criticize temperature
reconstructions and the conclusions drawn from them, on various
grounds; a recent target was Marcott et al. 2013. Book length accounts
of the controversy from opposing sides include Montford 2010 and Mann
2012.



*The Climategate Controversy*. In 2009, a large number of
emails were taken from the University of East Anglia's Climatic
Research Unit and made public without authorization. Authors of the
emails included climate scientists at a variety of institutions around
the world. Focusing primarily on a few passages, contrarians claimed
that the emails revealed that climate scientists had manipulated data
to support the consensus position on anthropogenic climate change and
had suppressed legitimate dissenting research in various ways (e.g.,
by preventing its publication or by refusing to share data). A number
of independent investigations were subsequently conducted, all
exonerating climate scientists of the charges of scientific fraud and
misconduct that contrarians had alleged (e.g., Russell et al. 2010).
Some of the investigations, however, did find that climate scientists
had failed to be sufficiently transparent, especially in their response to
contrarian requests for station data used to estimate changes in
global temperature (ibid.: 11). Despite the exonerations,
surveys found that in some countries this "Climategate"
episode--as it became known in popular media--significantly
reduced public trust in climate science (Leiserowitz et al. 2013).



*The Hiatus Controversy*. Global mean near-surface temperature
increased significantly during the 1990s but then showed little
increase between the late 1990s and the early 2010s. By the mid-2000s,
contrarians began to claim that global warming had stopped and that
climate models (and climate science) were thus fundamentally flawed,
since they had projected more warming. Part of the problem here was
communication: graphs shared with policymakers and the public often
highlighted the average of climate model projections, which smoothed
out the significant variability seen in individual simulations and
suggested a relatively steady warming; in fact, the observed rate of
warming was not so different from that seen in some of the model
projections (see Schmidt 2012; Risbey et al. 2014). Moreover,
significant variations in temperature on decadal time scales, akin to
that seen in the observations, were not entirely unexpected, given
internal variability in the climate system (Easterling & Wehner
2009). Nevertheless, by the early 2010s, pressure was mounting for
climate scientists to give a detailed explanation of the apparent
slowdown in surface warming, by then referred to as the
"hiatus" or "pause", and to explain why most
models projected more warming. A preliminary analysis was given in the
IPCC Fifth Assessment Report (see Flato et al. 2013: Box 9.2). A host
of potential explanatory factors were identified--related to
external forcing, internal variability, ocean heat uptake and errors in
observational data--which contrarians portrayed as excuses.
Subsequent investigation found evidence for actual contributions from
most of the hypothesized factors, though discussion of their relative
importance continues (see Medhaug et al. 2017 for an overview;
a large literature on the topic has emerged). In the meantime, since
2014, global mean temperature has once again shown a sharp increase,
in part due to a strong El Nino event in 2015/16.



Contrarian dissent has impacted the practice of climate science in
various ways. Most obviously, research is sometimes directed (at least in part) at rebutting contrarian claims and arguments. For example, a recent research
paper related to tropospheric temperature trends (Santer et al. 2017)
is explicitly framed as a response to contrarian claims made in the
course of testimony to the U.S. Senate (see also Lewandowsky et al. 2015 on "seepage" and the hiatus controversy). In addition, Brysse et al.
(2013) contend that pressure from contrarians and the risk of being
accused of alarmism may be part of the explanation for climate
scientists' tendency to err on the side of caution in their
predictions related to climate change. Drawing these threads together,
Biddle and Leuschner (2015) suggest that contrarian dissent has
impeded scientific progress in at least two ways: 



> 
> 
> by (1) forcing scientists to respond to a seemingly endless wave of
> unnecessary and unhelpful objections and demands and (2) creating an
> atmosphere in which scientists fear to address certain topics and/or
> to defend hypotheses as forcefully as they believe is appropriate.
> (Biddle & Leuschner 2015: 269) 
> 
> 
> 



They argue that, while dissent is science is often epistemically
fruitful, the dissent expressed by climate contrarians has tended to
be epistemically detrimental (see also Biddle et al. 2017; Leuschner
2018).


### 5.4 Ethics and Climate Change



The issue of anthropogenic climate change raises a host of challenging
ethical questions. Most of these are beyond the scope of this entry on
climate science. A very brief discussion is provided here
nevertheless, because the questions are important and because a full
entry on the topic is not yet available. 



The basic ethical question is: What ought to be done about
anthropogenic climate change, and by whom? The question arises because
there is good evidence that climate change is already having harmful
impacts on both humans and non-human nature, and because continued
high rates of greenhouse gas emission can be expected to bring
additional and more devastating harms in the future (Field et al.
2014). Attempting to address this basic ethical question, however,
leads to further, complex questions of global and intergenerational
justice, as well as to questions regarding our ethical obligations to
non-human nature. Here are just a few examples: Do some nations,
including those that have emitted large quantities of greenhouse gases
in the past, have an obligation to bear more of the costs of climate
change mitigation and adaptation than other nations? (See, e.g., Baer
et al. 2009; Caney 2011; Singer 2016: Ch.2.) When considering actions
to mitigate climate change, how should the harms and benefits to
future generations be weighed against those affecting people today?
(See, e.g., Broome 2008; Gardiner 2006.) How should impacts of climate
change on non-human nature, including loss of biodiversity, be taken
into account? (See, e.g., Palmer 2011.) Are there circumstances in
which proposed geoengineering solutions--such as injecting
sulfate aerosols into the stratosphere or seeding the oceans with
carbon-absorbing phytoplankton--are ethically acceptable? (See,
e.g., Gardiner 2010; Preston 2013.) There is a large and growing
philosophical literature engaging with these and related questions;
some anthologies and book-length works include Arnold 2011; Broome
2012; Gardiner 2011; Gardiner et al. 2010; Garvey 2008; Maltais and
McKinnon 2015, Preston 2016; Shue 2014; Singer 2016.