1

Erik Bigras

Rensselaer Polytechnic Institute

Can the Model Teach? Environmental Governance Through Technical Practice

Abstract

The increased availability of large data sets have foregrounded data models as viable

ways in which knowledge can be produced within the arena of environmental governance. Early

models in the 1970s and 1980s have been successfully deployed to understand the regional

effects of pollutants on air quality. However, the CAA Amendment of 1990 presented a new

challenge for air quality scientists: how could air quality be evaluated and predicted over areas

larger than the regional scale? This became increasingly important in the face of heavy attacks

against the knowledge claims of air climate scientists.

One such model, CMAQ, was released in 1998 by the EPA. Designed as a for-policy

model, CMAQ enables data modelers to create politically timely scientific knowledge. However,

how do the knowledge claims emanating from CMAQ come to make sense to politicians? How

is the model able to speak across disciplinary boundaries? Taking its inspiration from the work of

Gayatri Spivak, this article argues that, whether or not it can speak, CMAQ can nevertheless

teach particular situated stakeholders. Based on participant observation conducted at annual

modeling conferences and during CMAQ training courses, and interviews conducted at the

Environmental Protection Agency‟s Office of Research and Development, this article examines

how CMAQ creates micro-hegemonic language spaces within which it puts its stakeholders into

a particular double bind where legibility is created through one‟s internalization of CMAQ‟s

micro-hegemonic discourse. However, CMAQ stakeholders negotiate this double bind by

learning to learn in the ways in which CMAQ is able to teach.

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Introduction

My only friend is a flickering screen. At least, that is how things appear to be as I am

sitting in front of an IBM laptop computer, surrounded by other students. Despite theirs and the

instructor's presence, my attention is focused on the machine on the desk in front of me. It is able

to focus my attention on itself in ways that, Joseph Dumit (1995) argues, allows one's goal to be

appropriated through the act of interaction with particular technologies. This was my first hands-

on experience with the Environmental Protection Agency's (EPA) Community Multi-Scale Air

Quality (CMAQ) modeling system. For the next two days, I was going to learn how to become a

modeler.

CMAQ modeling, it turns out, required some of the skills that social scientists already

possess. Primarily, modeling with CMAQ involved reading. In order to effectively use CMAQ, I

read the instruction manual that the instructor gave me, the data tables in order to ensure that

they were formatted properly, and the code in order to modify it in ways that would enable

CMAQ to perform the operations I needed it to perform. However, reading the code also

involved reading comments, tips and hints left by other CMAQ users. Therefore, using CMAQ

goes beyond sitting in front of a flickering screen and typing commands into a Linux terminal.

Using the model involves interaction with what CMAQ pioneer Dr. Daewon Byun (Byun and

Ching, 1999) called a living documentation, namely an ever evolving and community-updated

documentation that defines how CMAQ ultimately will interact with the world of environmental

governance and political decision-making.

Studying CMAQ, therefore, involves understanding how data models produce particular

forms of legibility and expertise that can cross epistemic boundaries (Mauz and Granjou, 2013).

As such, this article interacts with several ongoing conversations within anthropology and

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science and technology studies (STS). For example, because CMAQ is understood as a player in

environmental governance, it becomes a device through which neoliberal policies can be

contested (Keane, 2008; Mitchell, 2005; Reno, 2011) or through which particular forms of civic

action can be deployed (Anand, 2011). Second, as Timothy Mitchell (2002) reminds us, the

social sciences offer „little room to examine the ways [humans and nonhumans] emerge together

in a variety of combinations, or how so-called human agency draws its force by attempting to

divert or attach itself to other kinds of energy or logic‟ (Mitchell, 2002, p. 29). Studying CMAQ

involves understanding how particular marginalities are made legible across epistemic and

discursive boundaries (Haraway, 1997; Latour, 1988). Finally, the last two decades have seen an

increased anthropological interest in the ways that digital media can shape and reshape social

meanings (Coleman, 2010; Escobar, 1994; Wilson and Peterson, 2002). Within this particular

area, there is a focus on the ways in which code is deployed as a means to produce particular

forms of social organization (Coleman, 2009; Kelty, 2008; Mackenzie, 2006). For CMAQ, this

area of inquiry becomes important because understanding the code – and by extension the living

documentation – allows analysts to understand the ways in which CMAQ creates meanings that

are understandable across boundaries.

Air Quality as a Site for Ethnographic Inquiry

In September 2011, I visited the EPA's Office of Research and Development (ORD) in

order to conduct interviews with bench scientists and administrators. During an interview I

conducted with S. Trivikrama Rao1, he revealed that 1990 was a watershed year for climate

science in general and air quality science2 in particular because it was the year that the Clean Air

Act (CAA) receive its last major amendment. Specifically, this amendment targeted four key

areas: acid rain, urban air pollution, toxic air emissions, and stratospheric ozone depletion (EPA,

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2011). For the first time, air chemistry was able to concentrate on specific substances, namely

ozone, carbon monoxide, and particulate matter (EPA, 2008).

Because the EPA is itself a governmental agency, it is necessarily involved in the politics

of air quality. As such, the policy decision-making process will use the EPA's air quality science.

However, how could such a feat be accomplished? Scientific research is a slow and costly

process, so much so that scientific results can only be used in the policy decision-making process

with great difficulty. In fact, Harry Collins and Robert Evans (2002) famously argued for a

separation of technical knowledge production from political decision-making because the two

existed in such different time-scapes that reconciliation was difficult at most, futile at best.

However, the continued existence of the EPA was contingent on being able to produce politically

timely science.

In order to fulfill this mandate, the EPA turned to modeling. Modeling already had a

strong presence in the air quality science community. Models such as urban airshed models in the

1970s and regional oxidant and acid deposition models in the 1980s have been successfully

deployed to understand the local effects of pollutants on air quality (Schere, 2003). However, the

CAA Amendment of 1990 presented a new challenge for the EPA air quality scientists: how

could air quality be evaluated and predicted over areas larger than the regional scale? The EPA's

answer was the development of a multiscale model in the 1990s. In 1998, under the direction of

Dr. Daewon Byun, the EPA released its first multiscale model, CMAQ, to the public.

The stakes were high both for the EPA and the general public. To the EPA, CMAQ can be

understood as representing an effort to remain relevant in the fast moving world of policy

decision-making. To the general public, CMAQ represents a way to ensure a higher quality of

life in years to come. As an anthropologist studying models, the stakes are also very high. To me,

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CMAQ is an effort to produce scientific knowledge that is politically defensible and can be the

basis for progressive policy decisions in the realm of air quality (Edwards, 1999). A tall order to

say the least, especially given the heavy attacks (Dessler and Parson, 2006) aimed at any kind of

climate science that can potentially introduce new economic costs. As such, studying models in

general and CMAQ in particular is important because the scientific knowledge models produce

is the knowledge immediately available to policy makers. It is because of these high stakes that I

have turned my ethnographic attention towards the field of air quality data modeling.

Speech, Modeling and the Social Sciences

The framing for this essay, like most essays, comes after the fact. In October 2011, I did

fieldwork at the annual Community Modeling and Analysis System (CMAS) conference in

Chapel Hill, North Carolina, in order to learn about – and be trained to use – CMAQ. At this

conference, I gathered hours of audio that will make up a large part of the ethnographic material

present in this essay. The first part of the conference was made up of presentations by CMAQ

users from around the world. I was interested in the motivations for using CMAQ, their

frustrations, and the narrative styles in which these things coalesced. I also was interested in

styles of interaction amongst CMAQ users and developers. The second part of the conference

was a training session in which I would participate as an ethnographer. What the conference did

not provide me was a framing device. I knew that the material I gathered could be divided into

important themes such as credibility, collaboration, research choice, public good, and data.

However, I remained without a way to make sense of these themes in way that were faithful to

my experiences at the conference.

It was not until a month later that such a frame would become apparent to me. In

November 2011, I was at the Greyhound bus station in downtown Albany, New York, waiting for

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the scheduled bus that would take me to the annual American Anthropological Association

(AAA) conference in Montreal, Canada. By chance, also waiting in Albany for a bus going to the

same conference was a fellow anthropologist from Cornell University whom I had met at

previous conferences. As academics often do, we discussed our existing projects during the four

hour trip to Montreal.

It was during this discussion that my colleague asked me the following question: „How

would you say that the model speaks?‟ As anthropologists specializing in STS, we were both

familiar with the ways in which artifacts can substitute for particular human functions. For

example, Bruno Latour's (1992) classic essay on the door closer speaks of a delegation of tasks

to mundane artifacts in ways that allows them to substitute for human actors. Similarly, Gary

Downey's (1998) study of computer assisted design (CAD) and computer assisted manufacturing

(CAM) reveals how the adoption and use of particular technologies create dominant images that

guide the realm of what counts as possible interactions. Therefore, I was not flustered when my

colleague asked me how the model could speak.

It is important to clarify that when I say that the model can speak, I do not mean that it

can do so in an anthropomorphic way. It is not a question of assigning intentionality or free will

to artifacts (Downey et al., 1995; Latour, 1992). Rather, it is a matter of understanding how

technology can possess agency, namely the capacity to influence the actions of other social

actors. That technology can do so should be no surprise to students of sociotechnical phenomena.

For example, Karl Marx (1978) described how particular technologies of production are able to

create certain forms of social arrangement. Similarly, Lewis Mumford (1934) famously

associated the scheduling possibilities that come with the clock with certain authoritarian

patterns of social organization. Within the field of design studies, this notion is also present. For

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example, Victor Margolin (1995) argues that the social world within which one operates will

influence the final outcome of the design process. Similarly, Christopher Kelty (2008) and Tom

Boellstorff (2008) both reveal that the barrier between the online and offline is porous and that

events happening in one arena will invariably affect the other. Asking how a model can speak

asks how a particular model is able to organize communication, coordination, and collaboration.

It is asking how the characteristics and situatedness of a model allow it to interact with, and

influence, the social world within which it exists.

This particular way of thinking about models is inspired by Gayatri Spivak's (1988)

famous essay „Can the Subaltern Speak?‟ in which she argues that subaltern groups cannot speak

because they must utilize the hegemonic language in order to be understood. However, in this

essay I will argue something slightly different. Rather than ask if the model can speak, I will ask

if the model can teach. In Spivak's essay, the subaltern might not be able to speak, but he or she

can nonetheless teach something to a plethora of actors, be they politicians, citizens, or social

scientists, as long as said actors as situated in ways that allow them to be receptive to said

teachings. Spivak's subalterns are caught within a deutero-learning double bind (Bateson, 1972;

Visser, 2003): their lessons can only be taught by the deployment of a micro-hegemonic space

within which their audience is willing and able to learn how to learn as subalterns do. In this

essay, I will argue that the various actors interacting with CMAQ are in a similar position.

Through the deployment of a complex assemblage of technical practices, CMAQ can speak to

policy decision makers because it creates a micro-hegemonic space of language within which it

teaches actors – politicians, modelers, programmers, anthropologists – how to speak. In effect,

CMAQ enacts a double bind where actors must learn how to learn in the ways that CMAQ

teaches in order for speech to be enabled. In this sense, CMAQ moves beyond being simply a

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mediating technology and becomes an important actor in the world of environmental governance

and political decision making.

Tropes of models as mediators and autonomous agents (Morrison and Morgan, 1999;

Morrison, 1999) are plentiful in the academic literature where they often are described as

possessing some kind of independence from human agents that allows them to 'speak for

themselves.' However, the human element is never completely abstracted. For example, Sharon

Traweek's (1988) ethnographic work on with particle physicists demonstrates how scientific

laboratory instruments are constructed in such a way that allows them to act as material

representations of particular disciplinary theoretical foundations.

All of these issues were at the back of my mind during the 2011 CMAS Conference. As

will become quickly apparent in my following account, I did not encounter the model itself until

after the conference. Rather, conference presentations were composed of human speakers

displaying graphs and slides. Nevertheless, I had the distinct feeling that the model was speaking

to me and teaching me. So much so that when a colleague later asked me if I had found the

model convincing and believable, I could answer nothing other than “Yes.” So if the model was

not present at the conference, how was it speaking to me so clearly and loudly? How was it able

to teach me? I will attempt to answer these questions in the following pages.

What is CMAQ?

Though I have described CMAQ as a model, it is more accurate to think of it as a

modeling system in that it negotiates the representations created by multiple simulation models.

CMAQ is part of the EPA's Models-3 and “One Atmosphere” framework that attempts to

understand climatology using a holistic3 perspective (Byun and Ching, 1999). Within this

framework, CMAQ originally relied on two other models to accomplish its tasks: the Mesoscale

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and Microscale Model version 5 (MM5) for meteorological data, and the Models-3 Emissions

Processing and Projection System (MEPPS) for pollution emissions data (Byun and Ching,

1999). However, CMAQ can be configured to function with a number of different models. For

instance, in his presentation, Jonathan Pleim4 mentioned that the newest version of CMAQ –

version 5 – was optimized to function with the Weather and Research Forecasting (WRF)

meteorology model, a fact emphasized by Rohit Mahur5 and David Wong

6, whose individual

presentations dealt with the coupling of CMAQ and WRF. Similarly, the conference attendees

appeared to favor the Sparse Matrix Operator Kernel Emissions (SMOKE) model, currently

developed jointly by the US EPA and the University of North Carolina at Chapel Hill's Institute

for the Environment (UNCIE).

Interviews I conducted with Rao in September 2011 revealed three distinctive

characteristics of CMAQ that differentiate it from other models. First, CMAQ is an open source

model which „[e]nable[s] no-cost distribution and application by the modeling community‟

(CMAS Center, n.d.). This strategy appears to be successful. For example, Rao mentions that

CMAQ outreach is now global and that the United Kingdom had recently decided to use CMAQ

for regulatory purposes. This helps create a particular social ecology in which CMAQ makes

sense. A language – a micro-hegemonic space – has been developed through which users from

multiple disciplinary and professional backgrounds can effectively communicate and coordinate

their practices. Second, CMAQ is peer reviewed by a community of scientists. As Rao notes,

We usually have about seven people that are experts in their fields

because a modeling system is multidisciplinary. We bring the

science from different disciplines and integrate them in one

conceptual computational model, and no one person, even the

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developers of the CMAQ model, has expertise in all the areas that

the modeling system, of all the relevant processes in the model. So

we assemble this team of experts from multiple disciplines, about

seven people. In choosing the panel, the peer-review panel, we

usually have good representation from academia, you know for the

scientific aspect, and then industry, because, after all, the model is

and will be used for policy-making. […] And then the third group

that we bring are the users, like State or regional organizations.

Third, CMAQ was created as a for-policy model (Brunner, n.d.; Edwards, 1999). This means that

the science reflected in the model must be relevant to the policy-making process. In fact, when

asked how the CMAQ team decides when it is appropriate to update the model, Rao replied,

This is an issue we wrestle with all the time. In fact, we're trying to

make sure that every scientist in the division thinks like this

because scientists like to play with all kinds of things. So, which

ones are you going to go after? That's the big decision that we have

to make because of time and resources. So we are going to be

primarily guided by the question „Does it make a difference for a

policy decision? If we were to add some new thing in the model,

would a policy-maker make a different decision?‟

Using CMAQ

CMAQ is also a distributed model (CMAS Center, 2010). As I previously stated, it

requires meteorological and emissions data from other models. These data are then converted to

a format that is readable by CMAQ through a sub-routine called the Meteorology-Chemistry

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Interface Processor (MCIP). Once this step is accomplished, several other sub-routines must be

compiled and executed: ICON sets the initial conditions in which the simulation will take place,

BCON sets the conditions at the geographical boundary of the simulation, and JPROC processes

the photolysis7 rates of the various chemicals. All of these processes must be individually

programmed, compiled, and executed by the person wishing to use CMAQ. In effect, CMAQ

modelers must have a good knowledge of computer programming8 in order to be able to

effectively use CMAQ. As such, the multidisciplinary aspect of CMAQ present in the model's

peer review process as described by Rao returns to haunt9 CMAQ users who must embrace it

despite their own disciplinary affiliations.

Only once these operations are performed can the modeler finally meet what usually is

imagined as being CMAQ. It requires the input of various other models and sub-routines

because, at its core, CMAQ is a chemical transportation model, meaning that it deals exclusively

with the ways in which chemicals travel through the atmosphere. In fact, the CMAQ Chemical

Transport Model (CCTM), is the following equation (CMAS Center, 2010):

𝜕𝐶i

𝜕t+𝜕(uCi)

𝜕x+𝜕(vCi)

𝜕y+𝜕(wCi)

𝜕z=

𝜕

𝜕𝑥(𝐾H

𝜕𝐶i

𝑑𝑥) +

𝜕

𝜕𝑦(𝐾H

𝜕𝐶i

𝑑𝑦) +

𝜕

𝜕𝑦(𝐾V

𝜕𝐶i

𝑑𝑦) + 𝑅i+ 𝑆 + 𝐿i

FIGURE 1. The CMAQ Atmospheric Diffusion Equation.

The Atmospheric Diffusion Equation (ADE) is a complex mass balance equation where each side

of the equation represents a given state of the atmosphere. As such, the ADE is a marker of both

continuity, in that the total chemical mass remains constant, and difference, in that it represents

the change in the composition of the atmosphere at a given moment. Also, much like Traweek's

(1988) detectors, the ADE acts as a materialized form of systemic knowledge. For example,

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Pleim noted that the CMAQ model represents a form of „consensus knowledge on atmospheric

kinetics.‟ Similarly, Jesse Bash10

remarked that the latest version of CMAQ is a „better

representation of state-of-the-science [air chemistry].‟

Also, CMAQ is an important actor in the creation of particular subject positions. Again

CMAQ plays with both continuity and difference. For example, operating CMAQ requires that

users pay attention to certain things: what mechanisms for aqueous chemistry should I use?

Should I worry about advection? Did I set my boundary conditions properly? In effect, CMAQ

forces its users to become modelers, even if temporarily. It forces them into a double bind where

they must transform their own subjectivity in order to communicate through the model: doing

otherwise effectively terminates the possibilities for collaboration and communication. CMAQ

enables continuity by focusing the modelers' attention towards similar worries and concerns.

Within the discipline of design studies, Nigel Cross (2006) argues for the existence of a

„designerly‟ way of knowing that is unique to design. Similarly, CMAQ creates a unique way of

knowing that allows CMAQ users to understand one another by teaching how CMAQ modelers

learn. However, the fact that CMAQ forces its users to become modelers also introduces

difference. Few, if any, CMAQ users begin their career as modelers. Rather, they often identify

with such disciplines as climate science, air chemistry, or even anthropology. However, using

CMAQ requires that users become modelers. Suddenly, they are introduced to a different set of

epistemological concerns. For example, as an anthropologist of technoscience, I am worried

deeply about the ways in which my interpretations will impact the everyday life of my

interlocutors. However, this particular worry is displaced when I become a CMAQ modeler.

Rather than being interpretive, my worries become technical: I am now worried about the proper

placement of semi-colons within the JAVA code I am writing. By displacing what I should be

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worried about, CMAQ positions me within a particular system of knowledge production with

which I was not previously entangled.

Through this brief description, it is possible to come to some preliminary conclusions as

to what CMAQ does. Beyond its technical capabilities – accepting inputs, producing data,

enabling visualizations – CMAQ acts as a social agent. First, CMAQ is a player in disciplinary

differentiation. The play of continuity and difference continues to be present in CMAQ even in

the ways in which it interacts with the social world. As such, CMAQ can be understood as an

experimental system (Rheinberger, 1997) where particular scientific disciplines renegotiate their

own subjectivities. CMAQ is an epistemic thing (Rheinberger, 1997) in that it acts as a place

where various actors engage new questions11

. However, the focus of inquiry can be different. For

example, Mahur noted that CMAQ provides an opportunity to study the assessment of

directionality and magnitude when it comes to integrating feedback mechanisms, which is

something he says is not well studied in the modeling community. On the other hand, an

anthropologist might understand CMAQ as a conceptual object through which one can study

scientific practice. CMAQ unites the computer scientist and the anthropologist through the

imaginary (Fortun and Fortun, 2005; Fujimura, 2003; Marcus, 1995; Traweek, 1995) – CMAQ –

within which we experiment, yet divides us by enabling different epistemological inquiries

within the shared space.

2011 CMAS Conference

In their opening presentations at the 2011 Annual CMAS Conference at the CMAS

Center in Chapel Hill, North Carolina, Adel Hanna12

and Teichman said that this year was special

for several reasons. First, Hanna noted that 2011 was the tenth anniversary of CMAS, which

„marks a significant landmark of a dream that really started more than 20 years ago with the

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development of a new program for air quality models which was the Models-3 program.‟

Second, Teichman commented on the growth of the conference. In 2001, the conference was

attended by seventy participants who represented four countries and gave twenty-one

presentations. In 2011, the year of my attendance, the numbers had grown to 275 participants

representing thirty countries and giving 150 presentations.

I attended the conference with a colleague who studies CMAQ from a different angle – as

a form of environmental communication. When I arrived to the conference grounds on the first

day, I could not help but be reminded of several small social scientific conferences I had attended

as an anthropologist and STS scholar. Upon entering the building, I was greeted by a long central

hall. I made my way to the back of the hall after being informed that the CMAS conference was

located there13

. The conference obligatorily opened with the ritual obtaining of the conference

badge and bag, followed by a quick visit to the breakfast table setup in the lobby. This feeling of

familiarity would remain with me throughout the conference: during intermissions, participants

gathered in the lobby and divided into small groups engaging in animated conversation;

presentations were split between two rooms, thereby forcing us to move from one to the other;

when wandering the hall during breaks, one could easily notice a mix of students and professors,

discussing topics ranging from personal matters to the particular methodologies used when

calculating water coverage over a geographical area.

However, it quickly became evident that difference also was rearing its head. I first

encountered a sense of dislocation during the early presentations. Very quickly, it became

apparent that the content of the presentations would be quite technical in nature, which surprised

us considering our familiarity with anthropological conferences where the content of

presentation is, more often than not, non-technical in nature. For example, words such as

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photolysis rates, negative bias, adjoint modeling, advection, PBL height, and surface forcing

made recurring appearances. At first, I was at a loss when attempting to comprehend the content

of the presentations. However, comprehension very quickly emerged. Despite the fact that the

vocabulary was almost never defined, my colleague and I nevertheless were able to rapidly

understand the technical content of the presentations. In fact, we effectively began predicting the

content of presentations, and we were correct more often than not. This facility to assimilate and

utilize the technical jargon points to CMAQs ability to create a particular micro-hegemonic space

of language in which one can easily become interpolated. Through these presentations, my

colleague and I were beginning to learn how to learn in the ways necessary for CMAQ to

communicate with us.

Another aspect of the presentation content was also a marker of difference. Because

cultural anthropology and STS are interpretive disciplines, interpretation plays a key part in

anthropological and STS conference presentations. Often, the whole point of presenting in the

first place is to offer one's interpretation of the data in order to obtain valuable feedback and

critique. However, CMAQ presentations did not engage in interpretation. Rather, content-wise,

the presentations I attended almost all reported simulation results. This was very different from

all conferences that I had previously attended. Retrospectively, it is also puzzling when

juxtaposed to a statement made by Zachary Adelman14

, my CMAQ instructor. As he was

discussing CMAQ and modeling with another student, he mentioned that „modeling is the easy

part; it's the interpretation that's difficult.‟ If this is true, then why was interpretation absent from

the conference presentations? If interpretation is where experience and expertise come into play,

why not offer it in order to obtain feedback and critique, and display one's own mastery of the

subject matter in order to accumulate social and cultural capital (Bourdieu, 2004)? This would

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remain a point of inquiry for me, and I would not begin to formulate an answer until weeks later,

when my Cornell colleague provided me with a framework through which I would analyze the

conference presentations.

The conference itself lasted for the typical duration of three days, from Monday to

Wednesday. Thursday marked the first day that my colleague and I would not be jointly

conducting participant observation. On that day, my colleague attended a by-invitation-only

session on model evaluation while I headed to downtown Chapel Hill to begin my technical

training with CMAQ. After three days listening to modelers reporting on what CMAQ could

accomplish, I finally was going to meet the model itself! So I eagerly rode the bus to downtown

Chapel Hill, walked from the terminus to the UNCIE building on Franklin Street, made my way

down a narrow corridor leading to a claustrophobically small elevator tucked into a remote

corner of the building, navigated narrower corridors upon exiting the elevator before finally

arriving to the training room where I was greeted by a small confectionery table, a smartboard,

and twelve IBM laptop computers.

Learning from CMAQ: Day One

The course began with the distribution of the training manual followed by introductions

and by a seventy-five minute lecture on the basic knowledge we would need to learn CMAQ. I

was the only social scientist undergoing training. It quickly became apparent that knowledge of

chemistry and meteorology would not be required. In fact, my interrupted training as a computer

engineer during the dotcom boom of the late 1990s would serve me well because what was

required were programming skills as well as a basic knowledge of the Linux operating system;

CMAQ does not possess a graphical interface and operating it requires writing code and

navigating Linux folder structures. As a student computer engineer, I had learned C++ and DOS,

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which were close enough structurally to the JAVA, FORTRAN and Linux of CMAQ that I did

not have many difficulties operating the program.

The first day of training involved learning how to build, modify, and run all the sub-

routines – MCIP, BCON, ICON, JPROC, and CCTM – required to properly use CMAQ. It was

during this first day that I experienced the feeling of being disciplined (Foucault, 1977) into a

particular subject position. As I related previously in this essay, I was no longer worried by my

usual social scientific matters. Rather, all my attention was focused on ensuring the good

functioning of the model. This became evident by the amount of time I spent debugging the code

I had written or modified15

. The model was now the focus of all my attention. This focus

continued throughout the second day of training, which was mostly concerned with the

application of the previous day's knowledge to the execution of particular problems.

After the opening lecture on the first day, the instructor left us alone with the training

manual, his role now largely one of troubleshooting and question answering. The course itself

was designed as an exercise in self-instruction. I was in possession of the instruction manual –

which contained both questions and answers – and I was expected to work my way through it,

gradually acquiring the skills necessary to operate CMAQ. As previously mentioned, the first

day of training focused largely on learning to operate and configure all the sub-routines

previously enumerated that are necessary to properly run CMAQ. The first day's exercises all

followed a similar pattern. First, I was tasked to read a brief introduction that detailed what was

to be accomplished during this phase of the training. Second, I was given instructions as to the

proper steps required to configure the programs that would create the necessary CMAQ sub-

routines. Third, I was taught how to configure the sub-routines themselves so that they would

produce the results using the parameters and data that were available to me. Fourth, I was tasked

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to examine and learn the format of the data input files that would be used by the sub-routines to

model the data. Fifth, I ran the sub-routines in order to create output files that would

subsequently be used during the final steps of the modeling process. Lastly, I was required to

examine the output files themselves in order to understand how the data was arranged and

displayed.

In effect, the first day of the training was designed to provide me with the necessary

knowledge to properly use CMAQ. It created the proper micro-hegemonic space within which

communication becomes possible. However, it did not yet make me into a modeler. So far, I had

done little more than type in Linux commands, write lines of JAVA and FORTRAN code, read

tables containing meteorological and chemical data, and sat back as my code compiled. I was,

nevertheless, on my way to becoming a modeler. Through this technical training, I was learning

the epistemological foundations through which air climate modeling knowledge is produced. I

was learning to learn as CMAQ does, and it was teaching me that, in the realm of modeling, the

technical is epistemological.

During the first day of the training – and also during the second day – I found myself

constantly referring back to the work I had previously done. Had I forgotten the Linux command

allowing me to edit the sub-routine's configuration files? I could go back to the manual and re-

learn it. What were the proper parameters to use when creating a sub-routine? I could simply

open up the configuration files of a previously created sub-routine and confirm the proper

entries. CMAQ was a patient teacher in that it contained in itself the capacity to teach – or re-

teach – me what I should know to make it work. CMAQ was an important social actor in the

creation of a micro-hegemonic space of language that made CMAQ legible. This would become

very apparent to me during the second day of the training.

19

By the end of the day, I was able to successfully execute all the necessary sub-routines

that would enable CMAQ to do the work for which it was designed. As a final exercise, I was

required to input the CCTM output file into a visualization program. So far, I had encountered

only text and numbers. However, now I was going to see a visual representation of my data for

the first time. Upon entering the proper command, I was rewarded with a map of Colorado, upon

which was located a small plume of ozone. The plume even moved and changed as I moved the

image forward in time. At last, I thought, I was a modeler! Or was I? As the second day would

remind me, there is more to modeling than simply coding.

Learning from CMAQ: Day Two

The second day began much as the first had. I was one of the first students to arrive and I

was greeted by the same plate of confectionery as the previous day. After a brief period, the

instructor gave a second lecture before allowing us to resume our apprenticeship with CMAQ.

However, this day's lessons would be different. Rather than learning all the commands necessary

to use CMAQ, we would be expected to apply our existing knowledge to solve various problems.

Today's challenges would include nested simulations, multi-day simulations, and a case study

involving East Asia. I powered up CMAQ and set myself to work.

Up to now, I had had very little interaction with the instructor. After all, most of my

training so far had involved coding, a task at which I already was experienced. It was during the

problem solving exercises that I would encounter my first moments of doubt and uncertainty

operating CMAQ. As he was concluding the lecture, the instructor warned us that he had set a

few traps for us in order to test out abilities. During the day, I avoided several, fell into one, and,

with CMAQ's help, successfully navigated another.

The first trap into which I fell involved a misnamed file. In order to properly operate

20

CMAQ, one must be very diligent of the way files are named and configured. I was quickly

running through the multi-day simulation exercise when I realized that I could not continue. The

exercise called for evaporation data at a certain date, but I could find no such file in my folders.

It was then that I called for the instructor, who listened to my problem, and afterwards informed

me that the file in question was present, only misnamed. If I had opened the existing evaporation

data files, I would have seen that the content of the file – which I had learned to read the

previous day – did not match the file's name. After a quick correction, I was able to resume my

learning.

The second trap, the one I successfully navigated, involved photolysis rates, and

illustrates CMAQ‟s capacity to deploy a micro-hegemonic space of language within which one is

able to successfully operate. I was now concentrating on the case study exercise when I noticed

that I possessed no photolysis data file and could therefore not properly run JPROC. At first I

found this puzzling, but quickly recovered and copied the photolysis data file from a previous

exercise. Photolysis rates are constant, after all, so one file was as good as another. But wait, how

did I know this? Before attending the conference, I had no idea that such a thing as photolysis

even existed. So how could I be so certain that photolysis rates were constant? After a few

moments, I realized that CMAQ had taught that fact to me. During the first day's exercises, I was

required to open and read all of the input data files for all the sub-routines. As such, I opened

multiple photolysis rate data files and learned that the entries were identical across files and

exercises. The successful deployment of CMAQ‟s micro-hegemonic space of language enabled

me to interpret the data so that I would be able to make sense of the world I was currently

experiencing.

The training course made evident the facility CMAQ possesses to cross epistemic

21

communities and cultures (Knorr Cetina, 1999). I was the only social scientist undergoing

training, but the other trainees represented disciplines as varied as environmental science,

chemistry, and geology. However, we all shared a common goal. We were all present in the

classroom on that morning in order to learn CMAQ. Disciplinary worries were cast aside as we

all turned our attention towards learning to operate the model. Therefore, despite these different

disciplinary identifications, CMAQ allowed us to communicate effectively. We were able to

discuss what we were hoping to gain through the training, as well as why we were undergoing

the training in the first place. My neighbor, a geologist, was curious as to why an anthropologist

would learn to use CMAQ, and she appeared to be quite interested and intrigued by the notion

that social scientists have long taken the natural sciences as an object of study. CMAQ acted as a

fulcrum through which we were able to communicate despite our differences.

‘How Would You Say that the Model Speaks?’

When framing an issue using the metaphor of speech, it is important to attempt to

understand who it is that is speaking at any given moment. When were modelers speaking?

When was the model? When was I speaking? As Spivak (1988) argues in her essay, the capacity

to speak is intimately linked to the deployment of particular subject positions. In fact, Spivak

argues that her subjects could not speak because they existed within a system of deployed

dominant imperial subjectivities and were unable to assume their own.

In the previous sections of this essay, I mentioned that the model was not present at the

conference, that I had not really encountered it until I began the technical training. However, that

is an error born from misunderstanding the kind of subjectivities created by CMAQ. Saying that

the model was absent is similar to saying that the model cannot speak, in Spivak's sense, because

it is enmeshed in a particular system of subjectivities where speech is dependent on presence.

22

However, CMAQ operates differently. The reason I was able to respond that I had found the

model convincing and believable is because it had been speaking to me all along during the

conference. CMAQ's speech was enabled through the deployment of a micro-hegemonic space

of language in which users, developers, and conference attendees are interpolated.

CMAQ is described as a modeling system, and it is important to take this description

seriously because with CMAQ, the notion of the system creates continuity beyond the artifact. In

her study of quantum physics, Karen Barad (2007) argues that it is impossible to separate a

phenomenon from the way said phenomenon was observed and measured. For example, a

microbe cannot be understood as being independent from the microscope used to view it or of

the scientific knowledge inherent in the practice of using said microscope. Microbe, microscope,

and scientist are intra-acting, by which Barad means that they exist in a state of entangled

agencies. For example, I was able to quickly predict the content of subsequent presentations at

the conference because the modelers were playing within the micro-hegemonic space of

language created by CMAQ. The presenters were actively involved in creating and shifting the

boundaries of this micro-hegemony by experimenting with, and discussing, what CMAQ could

become in the future. CMAQ was teaching me, speaking to me, through the presenter. In this

sense, I was not predicting the content of the presentations so much as I was learning the

limitations and affordances of CMAQ itself. The presenters were teaching me, speaking to me,

through CMAQ.

Does CMAQ speak in the same way when I am listening to a presentation as when

I am actively engaging in CMAQ‟s operation? No. Here, intra-action (Barad, 2007) becomes

recursive. Within the social sciences, recursivity was made popular by the works of Kelty.

Through his study of the Free Software movement, Kelty (2008) argues for the existence of

23

recursive publics, namely publics deeply concerned with the conditions of that which makes

them a public in the first place. Recursive intra-action acts in a similar manner in that recursive

intra-acting actors are deeply entangled with that which makes them actors in the first place. For

example, when I was learning CMAQ, I had to learn to run the routines that would eventually

build CMAQ. I had to construct each sub-routine from scratch, run it, and then incorporate it into

the CCTM in order to obtain mappable results. In effect, I was a computer program involved in

bringing CMAQ into existence. It shifted my worries from interpretive to technical ones so that I

would focus all my attention on the model itself as opposed to the meaning of the result. CMAQ

was making me into a modeler because a language and community of speakers has been created

wherein CMAQ makes sense.

Conclusion

Attacks against scientific knowledge pointing at a need for comprehensive air quality

regulation remain strong, so much so that multiple books have been published in recent years

that attempt to understand the stakes of the controversy (Hamilton, 2010; Hulme, 2009; Oreskes

and Conway, 2010). However, despite this situation, CMAQ continues to thrive. Its capacity to

produce timely scientific knowledge that can be used in the policy decision-making process

continues to be acknowledged by scientists and politicians alike. The version 5 of CMAQ was

publicly released in 2012 and incorporates the latest scientific developments in air quality

science and multi-processor computing. Also, Rao revealed that as of 2012, England had decided

to use CMAQ as part of its air quality policy decision-making process, thereby joining the

United States and Canada who also utilize the EPA's model as a basis for policy-making.

I made my way back to my own academic institution on the Friday night after the

conference ended. As I was sitting in my airplane seat, flying over the Eastern United States of

24

America, I became aware that, somehow, I had been delegated a task by CMAQ much in the

same way humans delegate certain functions to door closers (Latour, 1992). By undergoing

technical training and attending the conference, I had, in effect, become one of CMAQ's voices. I

had effectively negotiated the double bind by learning to learn as it does, as it required. I was

able to reproduce its micro-hegemonic space of language at other times and places. It was as if I

had a duty to enable it to speak by pushing outward the boundaries of the micro-hegemonic

space of language that CMAQ helped create and that makes it legible and able to speak and

teach.

In fact, before leaving the classroom, I had one last conversation with CMAQ. During

this conversation, my instructor gave me my very own micro-hegemonic object, which

symbolizes the small piece of epistemic authority (Gieryn, 1999) I have acquired that enables

CMAQ to speak and teach. This object is located between the epistemic communities of

modeling and anthropology, and will, undoubtedly, be the focus of many interpretive worries as I

continue writing my dissertation and continue learning about CMAQ. I now share this object

with you in the hopes that you, too, can acquire and perform some of the interpretive worries that

allow CMAQ‟s micro-hegemonic space of language to move across boundaries, and that allows

its interlocutors' subjectivities to be transformed in ways that allows them to learn as CMAQ

does, to speak with and through it, and to be able to negotiate the knowledge that CMAQ is able

to teach.

25

FIGURE 2. The Introduction to CMAQ Certificate of Completion.

26

Notes

1 Director, EPA Atmospheric Modeling and Analysis Division (AMAD).

2 Air quality science is used here as an umbrella term aimed as encompassing the

interdisciplinarity of the project that includes such disciplines as diverse as computer

science, air gas chemistry, and meteorology.

3 Kevin Teichman, the Office of Research and Development (ORD) Deputy Assistant

Administrator for Science defined a holistic perspective as a way to understand how impacts

in one particular medium also impact other media. Historically, air chemistry models have

tended to isolate geographical areas from each other.

4 Research physical scientist, EPA AMAD.

5 Chief, EPA AMAD.

6 Computational scientist, EPA AMAD.

7 For the purpose of this essay, photolysis is the rate at which chemicals decay under natural

sunlight.

8 The JAVA programming language in this case.

9 Here, haunting is used in the Derridean (Derrida, 1994) sense that all discourses contain that

which may undo them. I began my academic career as a computer engineer, but quickly

decided that it was not something that I enjoyed. However, anthropology and STS asked the

kind of epistemological questions that I preferred asking. Since then, both anthropology and

STS have provided me with a good academic home. However, CMAQ forces me to interact

with, and reinteriorize, a moment of my past that I wish I could forget.

10 Physical scientist, EPA.

11 CMAQ also embodies the second aspect of experimental systems, the technical object that

27

permits new questions to be asked. To EPA computer scientists, it is a piece of software that

must be coded in order to be able to investigate particular hypotheses. To me, it is a

computer program that I must learn to operate in order to answer my dissertation research

questions.

12 CMAS Director.

13 Like many small conferences, we shared the space with another group also holding a

conference.

14 Research associate, Center for Environmental Modeling for Policy Development, UNC-

Chapel Hill.

15 My previous experience with programming became very useful when the compiler's

debugging function kept telling me that I had an extra curly bracket. Had I not known that

curly brackets are commonly used to delineate functions, I would not have been able to

understand that I was, in fact, missing a curly bracket despite the error message being

communicated to me. Once I closed the offending function, the code compiled without any

difficulty.

28

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