Predicting “Unprecedented change”: A crisis for analogs

Abstract

Earth System Science (ESS) and climate change research largely rely on predictive computer models. Models represent Earth’s processes and aim to provide policy-relevant information. Despite models’ dominance today, there were other ways to envisage the future. This article examines another method that scientists proposed to foresee the effects of anthropogenic climate change: analogs. An analog is a state of the Earth that is compositionally similar to a poorly understood past or future Earth state. The analog method uses the properties of the better-known state to fill in information about the poorly known state. During the late 1980s and the early 2000s, ecologists promoted the analog method as an alternative to the reductionism of models, especially for understanding the effects of human-caused climate change. Ecologists worried that models omitted and oversimplified biological phenomena whereas analogs captured ecological complexity. Yet promoters of the analog technique faced an epistemological crisis with anthropogenic climate change, which scientists increasingly framed as “unprecedented.” This formulation meant that there would be no analogs in the future. Promoters of analogs thus worked to demarcate how analogs could still be relevant to ESS and predictive climate models.

Earth System Science (ESS) became a dominant interdisciplinary environmental research program in the 1980s. ESS aimed to understand the structure and functioning of the Earth as a complex, adaptive system, driven by the diverse interactions between physical, chemical, and biological processes. Advocates and practitioners of ESS see the emergence and evolution of ESS as a logical outgrowth of Cold War concerns about the Earth’s resilience as well as new technologies such as Earth-observing satellites and computer models (Steffen et al., 2020; Uhrqvist and Linnér, 2015). Historians have complicated this picture, suggesting historical contingencies and additional factors that explain ESS’s ascendence (Barton, 2020, 2023; Coen and Jonsson, 2022; Rispoli, 2020, 2022).

What is consistent across these narratives is an understanding that computer models were a key technique in ESS. Models aimed to elucidate important processes, features, and feedbacks within the Earth’s system to make predictions about future states at policy-relevant timescales. Modeling was particularly important because ESS arose when there was increasing scientific concern over human-caused climate change. Scientists hoped that the outputs of models would help guide efforts to shape international policy on climate change (Heymann and Dahan Dalmedico, 2019).

Edwards (2010, 2011) and others have ably described the history of models, showing how climate models were built from the same techniques as numerical weather prediction (Washington et al., 2009). During the Cold War, scientists with expertise in theoretical meteorology and computer programing working at a handful of institutions, including RAND, developed models to simulate global processes (Edwards, 1996; Turnbull, 2023). From there, scientists could experiment, determining how increasing carbon dioxide levels, for example, affect the climate. General circulation models (GCMs) were among the most important. These numerical models first represented processes in the atmosphere at resolutions of hundreds of kilometers, then coupled large-scale atmospheric processes with the oceans and, increasingly, represented the entire Earth system on timescales relevant to policy decisions (Dahan, 2010).

Yet, the dominance of models cannot be taken for granted. As the historiography shows, models are the product of particular circumstances and social contexts, and their dominance was not foretold (Beck and Mahony, 2018; Martin-Nielsen, 2015). There were, and remain, many possible ways to study the climate and Earth’s systems more broadly. And there were other ways to make quantitative projections about the future (Coen and Jonsson, 2022; Kwa, 2005; Rispoli, 2020). Among them were techniques from ecology.

This paper analyzes the emergence of the modern analog technique during the 1980s through the early 2000s and examines attempts by ecologists to use the analog technique to foresee the effects of anthropogenic climate change. An analog is a state of the earth, such as climatic conditions or an ecological community, that is compositionally similar as a past or future Earth state. The analog method uses the properties of the better-known state to infer properties about the poorly known state. To foresee climate change’s impacts, ecologists proposed filling in details about the future based on past states of the Earth that were similar to those predicted due to climate change.

Between the late 1980s and the early 2000s, ecologists, particularly American paleoecologists involved in global ecology, promoted the analog method as a technique to supplement the reductionism of GCMs. While both ecology and climate science reasoned by analogy and employed models (Rosol, 2015, 2017), paleoecologists were proposing the modern analog technique as a particular method to foresee the effects of climate change. These scientists advanced the analog technique in leading journals and in key positions in organizations studying global change.

Yet these paleoecologists faced an epistemological challenge: to help policymakers and the public understand that anthropogenic climate change was not part of climate’s natural variation, scientists had presented human-caused climate change as unprecedented. This message of unprecedented change stuck; scientific papers and newspapers routinely framed climate change as a rupture with the past. When the future was thought to break with the past, analogs could not be used to characterize future change in the ways that paleoecologists had hoped.

I argue that framing climate change as unprecedented led to an epistemological crisis that forced scientists to demarcate how analogs could still be relevant to climate change research. Ultimately, using analogs to foresee in the way paleoecologists once preferred fell out of favor. Instead, scientists claimed that analogs should be used to support models by setting initial parameters and verifying outputs. In this function, analogs helped predictive modeling cement its hegemonic status.

This episode shows that framing the future as discontinuous from the past helped to ensure the dominance of models and the worlds that they represented. Models focused on the physics of the atmosphere and ocean circulation, rather than describing terrestrial ecosystems. These models largely omitted and oversimplified small-scale processes and biological and human dimensions of environmental change in favor of the large-scale physical climate system. ¹

The rise of the analog method

The analog method was especially popular among American ecologists who used pollen analysis to study vegetation’s responses to climatic change. Pollen analysis studies the different pollen types present in different layers of sediment cores to reconstruct the history of vegetation and how related environmental factors, including climate, change over time. The analog method aimed to fill in details about former vegetation communities that were not immediately discernable from pollen profiles. The pollen types in sediment cores are useful for reconstructing the vegetation present in the past, but they can say little about properties such as vegetation structure. The analog technique would provide details about those properties by identifying a well-studied modern community with a similar pollen profile to a past community. Scientists would then use the properties of the well-understood community to infer details about the past community.

Margaret B. Davis, a paleoecologist and palynologist then working as a research associate at the University of Michigan, proposed the analog method in 1963 (Davis, 1963). To illustrate the power of the analog technique, Davis followed up with a 1969 paper that reconstructed the vegetation around New Haven, CT. At different depths in her pollen core, she found pollen profiles that resembled the pollen profiles of several present-day communities in Canada. To Davis, the similarities suggested that, 8,000–9,500 years ago, New Haven probably resembled the present-day mixed coniferous-deciduous forests of northern Ontario. Twelve thousand years ago, New Haven was characterized by boreal forest of open-lichen woodland akin to the forest presently found in northern Quebec. Davis (1969: 331) then explained that the climates that currently characterize these locations could give, “by analogy, a general idea of ancient climate” of present-day Connecticut.

Davis’ method offered a tangible sense of the vegetation, ecology, and climate of southern New England as they changed over time. Her reconstructions corresponded to actual places people could study. By drawing on the present to make inferences about the past, the modern analog technique was meant to overcome the main challenges of paleoecology. As two paleoecologists later explained, paleoecologists, unlike real-time ecologists, did not have the “the luxury of observing and measuring in whatever detail is permitted by time and resources.” Instead, “paleoecological observations are restricted to whatever material evidence has been left in sediments, tree-rings, rodent middens, or other geohistorical archives.” This feature of paleoecological work meant paleoecologists eventually would “run up against hard limits to detail” (Jackson and Blois, 2015: 4915). Paleoecologists could know which vegetation had been present in the past, but not details such as how it was arranged. The geological record did not contain these details. But the analog technique promised to overcome this limitation by providing information that was otherwise unavailable in the geological archives. By having modern communities stand in for past ones, paleoecologists, too, would have the same luxury of observation as ecologists.

The analog technique became a common practice in paleoecology; many scientists described how their study site was once like some other present-day location that people could directly investigate. In this way, pollen workers were re-orienting past climates away from the abstract; instead, they were reconstructing past environments that people could investigate in analogous ways to modern ones, thereby capturing more of the ecological and climatic complexity of former environments.

Refining the analog technique

Although Davis was expounding the benefits of the analog technique, the technique was still crude, even in the late 1960s. Scientists used informal and subjective ways to determine if past and present pollen profiles matched. They might just look at two pollen profiles and claim they were analogs. ² Davis, for instance, did not offer many insights into why past profiles from southern New England were analogs with a profile from present-day Ontario, rather than some slightly different profile from elsewhere.

Recognizing that subjective inferences based on visual confirmation characterized the analog technique, in the 1970s, pollen analysts began to use a variety of statistical methods to determine whether two profiles were analogs (Webb and McAndrews, 1976). The introduction of computerized algorithms for multivariate analysis aided scientists in these efforts. Computer algorithms meant that, by the early 1980s, pollen workers had formal, numerical matching tools to statistically determine the similarity of fossil and modern pollen profiles. They called this statistical approach the modern analog technique (Jackson and Williams, 2004).

The first publication widely cited for demonstrating the power of the modern analog technique appeared in 1985. Jonathan Overpeck, along with his PhD advisor Thompson Webb III (who was Davis’ former postdoc) and British paleoecologist Iain Colin Prentice, examined 1618 modern pollen profiles from eastern North America. They performed multivariate statistical analyses on several coefficients that assessed common, rare, and intermediate pollen genera in samples from various ecosystems, such as tundra, boreal forest, mixed forest, and deciduous forest. Doing so allowed them to define critical values that each coefficient could not exceed if two pollen samples originated from the same ecosystem. Overpeck and his colleagues were providing a “quantitative aid” to determine when the past vegetation could be said to be an analog for certain modern ecosystems, and when they could not (Overpeck et al., 1985: 87).

This technique helped fill in many details about the past. Scientists had long struggled to determine properties like vegetation structure and composition as well as forcing variables like climate for past environments. There was little evidence of these properties in the fossil record. But when the analog technique indicated that a modern pollen sample was an analog for a fossil pollen sample, scientists could assume that the properties of the former ecosystem were “like those now associated” with its modern analog (Overpeck et al., 1985: 87). As Overpeck et al. (1985: 88) wrote, “we found modern samples so similar to their fossil counterparts that we could ‘reconstruct’” former environments. With the modern analog technique, scientists could infer more details about the past than ever before.

The no-analog situation and complex vegetation dynamics

While the modern analog technique was useful for reconstructing many past environments, some pollen profiles had no modern analog. When compared to every pollen profile in databases, these past pollen profiles exceeded the critical values. Their pollen profiles were too different from any modern profile for analogical reasoning. The 1985 paper on the statistical technique identified two no-analog situations. One occurred in present-day Minnesota 9,300 years ago and the other in Michigan 11,000 years ago.

No-analog situations led to important insights about ecological dynamics. In a 1989 Presidential Address to the Ecological Society of America that closed a scientific meeting on global change, Davis promoted the analog technique by explaining how the similarities between analogs were useful for reconstructing the past. But she also noted that no-analog situations made clear that ecological change was complex. Davis (1989a: 225) stated that “the message from paleoecology is clear.” Species respond individualistically to change. Biological responses to climate change are often accompanied by time lags. Further, multiple factors interact with one another to produce the vegetation patterns seen in the fossil record (see also, Davis, 1984, 1989b). Sometimes, these complex interactions would result in no-analog situations.

Davis used these insights to critique models. She warned that the practice of building models that predict the future by shifting existing communities around the globe must stop. These models were oversimplifying the interactions between different components of the global system. Instead, Davis (1989a: 225) argued that scientists needed a “functional understanding of the responses of individual species to multiple impacts. Larger scale models will have to build on the reactions of individual species to make accurate predictions of communities, ecosystems, and finally landscape-scale biological systems.”

Davis’ critique centered on scale. ³ The vast scales of computer models were oversimplifying complex ecological processes at the level of individuals, communities, ecosystems, and landscapes. To overcome this problem, Davis (1990) recommended that scientists begin modeling small-scale, individualistic processes before scaling upwards.

Davis’ concerns also reflected a return to an old question in ecology about how to divide the natural world. Since the turn of the 20th century, the community had been an important unit in ecology. Due to the influence of Clements (1916), a community was understood to comprise different species that were tightly integrated and interdependent in ways akin to a complex organism. This view was challenged by ecologists such as Gleason (1926) who argued that there were no discrete community types because species respond individualistically to environmental factors. For Gleason, the result was continuously varying distributions and abundances of individuals and populations rather than internally integrated communities (Kingsland, 1991, 2005; McIntosh, 1985).

While Clements initially carried the day, in the second half of the 20th century, ecologists were once again concerned about individual responses to outside factors, such as climate change, and whether these dynamics meant that the community concept appropriately described the world (Jackson and Blois, 2015). In the more recent questioning of the community concept, ecologists started from the premise that the climate was in constant flux, rather than the stable backdrop for communities. With scientists showing that the Quaternary climate constantly varied, ecologists were now primed to focus on how these fluctuations influenced individual species to respond at various rates. Given their findings about complex, individual responses, these ecologists described environmental change as a kaleidoscope of different arrangements coming in and out of being at different moments of time. To understand the effects of climate change, it was important to study these processes and their effects on individual species and their interactions before trying to scale up to regional or subcontinental vegetation patterns.

The rise of models

Davis was proposing a different way to model, one that did not begin with the global and scaled down but one that scaled upwards from individualistic processes to create a regional picture of vegetation. Ecologists hoped that these models could portray the effects of anthropogenic climate change on vegetation patterns. For this work, the record of the past, including its no analogs, offered insights for building the models: they taught scientists about the complex vegetation dynamics that the models would have to capture. The geological record was also useful because it enabled scientists to test how well the models simulated past patterns and rates of vegetation change. If a simulation of the past was successful, the likely model would likely do well simulating the future (Overpeck et al., 1992). Models thus promised to be useful in ecology for understanding future change, although scientists had developed few of these models by the turn of the 20th century.

Modeling was also common in climate science, but it was a very different kind of modeling. ⁴ Models in the earth and atmospheric sciences typically started at huge scales and attempted to scale downwards. Given the limited computing power at the time, Earth System models could not incorporate the small-scale or they would become intractable (Edwards, 2010; Schmidt and Moyer, 2008). Further contributing to their omission, many of the ecological processes that Davis said needed to be incorporated into models were not well understood compared to the large-scale planetary dynamics. The dynamics of individual organisms, as well as their interactions, could not been subsumed under tractable general laws in the same way as atmospheric dynamics. Only the latter could be modeled.

Models of atmospheric, rather than ecological, processes thus took center stage. The Intergovernmental Panel on Climate Change (IPCC), which privileged the earth sciences as most authors came from these disciplines (Bjurström and Polk, 2011), saw models as the “best tool” for studying how climate could change because of human influences (McG et al., 1990: xx). Predictive global climate modeling became the IPCC’s main epistemic strategy (Borie et al., 2021; Guillemot, 2023; Hulme and Mahony, 2010; Shackley, 1997; Shackley et al., 1998). By employing climate models, the IPCC could increase the credibility of the narrative that it promoted, namely that Earth’s climate system should be treated as a unitary system that spans the entire globe. Doing so helped to establish climate change as a shared concern for human groups and created a framework for policy action that used the conventions of international law to mitigate the effects of climate change (Miller, 2007).

The IPCC’s structure and epistemic strategy left little room for ecological perspectives. The IPCC’s first assessment report, published in 1990, relied heavily on atmospheric models with little about vegetation. Working Group I’s summary for policymakers had less than a page on the effects on ecosystems and used part of the space to acknowledge that current models could do little to understand the localized effects of climate change on ecosystems. The longer chapter on ecosystems focused on vegetation’s role in the carbon cycle rather than the effects of climate change on the plant communities (Melillo et al., 1990). Working Group II’s mandate was to examine the impacts on ecosystems and societies. They did not accomplish this directive because of the difficulty of determining the effects on Earth. Working Group II explained that “comprehensive estimates of the physical and biological effects of climate change at the regional level are difficult.” They made clear that there were significant “scientific uncertainties regarding the relationship between climate change and biological effects and between these effects and socioeconomic consequences” (McG et al., 1990: 1). What was better understood was the close coupling between greenhouse gases and temperature. Models of these parameters took center stage. In so doing, the future was handed over to predictive, global models (Bjurström and Polk, 2011; Carey et al., 2014; Hulme, 2011). The “climate” that most people concentrated on was characterized most strongly by global mean temperature and greenhouse gas concentrations, rather than climate’s differential impacts on specific lives and livelihoods. ⁵

The global, model-based view only hardened in the face of climate change denialists. To combat climate skeptics, the scientists operating under the framework of the IPCC emphasized scientific consensus-building and doubled-down on the “mechanically objectivity” of mathematical models of well-understood processes, especially changes in global mean surface temperate. Consensus-building minimized the diversity of techniques and understandings of climate (Brysse et al., 2013; Hulme, 2010; Oreskes and Conway, 2010; Schwartz, 2017). The result was a global understanding of climate, a 10,000-foot view from nowhere produced mostly by white, male, earth and atmospheric scientists in the global north (Corbera et al., 2016; Ho-Lem et al., 2011; Hulme and Mahony, 2010; Liverman et al., 2022; Morrison, 2018; Yamineva, 2017). But this science-first strategy, as Deborah Coen and others have argued, does not offer the flexibility to confront new evidence and leaves out many aspects of environmental and social change related to climate. Instead, Coen (2018) contends, we get bland statements about a narrow set of questions. Ecologists hoped to broaden the questions to include how climate change would influence organic life. They proposed the analog method as way to do so.

The initial promise of modern analog technique to look forward

The analog method, as applied to future climatic change, was similar to reconstructions of past communities. The technique would use the properties of a better-known past or present community to fill in information about an analogous future community. ⁶ The past or present analogs were communities that experienced similar climatic conditions—such as higher temperature, elevated greenhouse gas concentrations, or periods of rapid climatic change—as those anticipated in the future. ⁷

For example, scientists used the communities that resulted from higher temperatures during the middle Holocene warm period as analogs for vegetation patterns that might result in a warmer world. Or they employed periods of rapid warming such as those experienced at the end of the Younger Dryas, the glacial period that occurred 12,900–11,700 years before present, as an analog for the rapid warming likely because of human actions (Webb, 1993). Or scientists utilized the high carbon dioxide concentrations during the early Pliocene, about 5 million years ago, as an analog for forecasted atmospheric conditions. No matter whether scientists selected variables related to periods of rapid change, times with high greenhouse gas concentrations, or periods with elevated temperature, scientists thought that the analog technique was a promising method to understand climate’s likely effects on ecosystems.

The goal of analogs was not to forecast per se. Analogs were meant to help scientists identify potential responses of vegetation to changing conditions. Analogs were thus a first approximation of how individual species and vegetation assemblages might respond to climate change. By filling in details about future responses, these approximations served as heuristic devices that presented complex and intangible futures as part of a cohesive narrative of change at scales and resolutions useful for human planning (Meyer et al., 1998). The goal was to help policymakers and non-specialists act given the potential impacts of climate change. The analog technique was thus a promising tool for understanding how ecosystems would respond to anthropogenic climate change.

Dismissing analogs and reaffirming models

Although those working with pollen had hoped to understand the impacts of anthropogenic climate change using the analog method, the past seemed a poor guide to the future given humans’ tremendous power to radically alter the global climate. Anthropogenic climate change was regularly framed as “geologically unprecedented” (Williams et al., 2007). This framing meant that the past and present were poor analogs for the future. The IPCC had considered analogs as a “potentially useful way of predicting patterns of future climate” but had written that “analogues of future greenhouse-gas-changed climates have not been found” (IPCC Working Group 1, 1990: xxiv). Scientists would not expect analogs because the “combustion of large amounts of fossil fuel and emission of CO₂ into the atmosphere is an unprecedented geophysical event” with no equivalent in the past or present to serve as an analog (MacCracken, 1985: 17).

The language of rupture was a common trope in discussions of anthropogenic climate change. Thomas Wigley, who proposed the analog technique in a Department of Energy report vying to set American priorities and methods given climate change, told a U.S. senate subcommittee in 1987 that, within the next 50 years, Earth’s temperatures would be warmer than they had been in 2 million years and “mankind will be living in a world whose climate differs radically from anything in human history” (Wigley quoted in Detjen, 1987). At the same hearing, a University of Chicago atmospheric scientist said that changes in Earth’s temperatures were occurring with “unprecedented rapidity” (Ramanathan quoted in Detjen, 1987). The popular press adopted this language. In 1983, the New York Times wrote of a “strange new world in our future” with the “climate expected by the end of the 21st century [to] resemble none that man has ever experienced” (Oppenheimer, 1983: A27). In 1990, after the IPCC issued its first assessment report, The Guardian reported “the world’s temperature is set to rise at an unprecedented rate” (Williams, 1990: 3). The Washington Post put things more bluntly, warning that top scientists “are telling us that they fear a dramatic, unprecedented and perhaps catastrophic warming. . . we are gambling with the climatic underpinnings upon which all societies depend for survival” (Wirth, 1990: A21).

This language of rupture was meant to spur action to prevent future change. Those wanting to maintain their profits and lifestyles had increasingly questioned the evidence for anthropogenic climate change (Oreskes and Conway, 2010). In response, scientists claimed that anthropogenic climate change was fundamentally different than changes in the past, which incentivized immediate action to prevent further catastrophe (Watkins, 2023). Yet one of the effects of this framing was a sense that the past was disconnected from the future (Simon, 2017, 2019). Past analogs were no longer useful analogs for an unprecedented future. As a group of prominent scientists admitted after a 1992 workshop on analogs, “the past is a, but not necessarily the, key to understanding the present and the future” (Webb, 1993: italics in original).

The lack of available analogs became more acute as scientists promoting the analog method searched for appropriate analogs. In 1990, Thomas Crowley, who had first studied marine cores before becoming an expert in multi-proxy analyses of past climates, concluded that there were no satisfactory analogs for future greenhouse warming. To make this pronouncement, Crowley had reviewed the most promising potential analogs given anthropogenic climate change. Potential analogs included the Holocene warm period and the rapid changes during the last interglacial (120,000 years before present), as well as pre-Pleistocene warm periods such as the Pliocene (about 3–4 million years ago), the Eocene (about 50 million years ago), and the mid-Cretaceous (about 100 million years ago). Crowley found each of these proposed analogs wanting. In some past periods, such as the Holocene and the last interglacial, the warming was more regional than global. Given that anthropogenic change was understood as global, using these past periods seemed inappropriate. Further, the Earth’s geography in the deep-time potential analogs was quite different. Millions of years ago, there were significant differences in the arrangements of the continents and Earth’s ice cover. These geographic features play important roles in the climate system, such as Earth’s albedo and atmospheric and ocean circulation. Given the dissimilarities, Crowley (1990) explained that none of the oft-cited potential analogs was an appropriate analog for the future.

Scientists’ characterizations of future no-analogs were quite different from how they had described no-analog situations in the past. These differences reflected a shift in thinking about the continuity of history (Simon, 2015, 2019). ⁸ When scientists recognized that past communities with no modern analogs resulted from how individual vegetation responded to change, they recognized that a lack of analogs was a normal part of environmental processes in a dynamic world. Yet, when scientists described future no-analog situations that they thought would result from human-caused climate change, they did not portray future no-analogs as normal. There was something unique about them. They called future no-analogs “emerging” and “novel,” words which signaled discontinuity from that which came before, rather than part of the normal kaleidoscope of change (Williams and Jackson, 2007: 477).

Without analogs and with models poorly suited to smaller-scale vegetation change, ecological futures became all but unknowable (Brooks et al., 2009; Williams and Jackson, 2007). Scientists wanted to look forward, but they found that the problem that they hoped to understand limited their foresight. These scientists often described their lack of knowledge about the future using the nautical metaphor of sailing into uncharted waters, which emphasized how little they could know about what lay ahead (Crowley, 1990). They wanted to navigate these unknown seas but had no data to guide them. As paleoecologists Williams and Jackson, 2007: 480 stated:

As we sail into the future, we need to forecast what lies ahead. However, novel climates represent uncharted portions of climate space, where we have no observational data to parameterize and validate ecological forecasts. They are the climatic equivalent of uncharted regions of the world, to which early European cartographers supposedly applied the label, ‘Here there be dragons.’

With these metaphors, scientists were expressing a need to know the future, along with a frustration that foreknowledge was becoming increasingly impossible in a no-analog future.

The desire to know the future but lack of knowledge of it was unsettling for proxy scientists descended from a lineage of scientists who had thought the past provided guidance for the future (Charenko, 2020). Given anthropogenic climate change, these scientists recognized that, “at best, we may only be able to predict that many novel communities will emerge, and surprises will occur” (Williams and Jackson, 2007: 479). Perhaps these novel communities would not reveal themselves to be the feared dragon, but there was little way to know what lay ahead given the perceived break between past and future.

Could analogs still be used in an unprecedented future?

While scientists saw analogs as poor guides to a historically unprecedented future, the epistemic crisis brought about by this framing did lead to discussions about when analogs could and could not be used. Scientists proposed several ways that analogs could contribute to understanding futures altered by anthropogenic climate change. Many of the proposed uses for analogs revolved around models, Models could be run under conditions representing unprecedented disturbances, such as uncharted greenhouse gas. But these models could be improved with analogs. Analogs could help constrain models, helping scientists understand key processes that needed to be built into the models. A different set of analogs could help verify model outputs, testing the models to see how well they could simulate observed changes in the past.

There was also a sense that analogs could provide “cautionary tales” about the changes likely because of climate change (Jackson and Overpeck, 2000: 213). While analogs could not provide “specific images of future climate and vegetation change,” no analog futures helped to show that the magnitude and rates of future changes would likely exceed the largest changes of the past. Would the biosphere and humanity be ready for such significant changes? Could plants, animals and people survive these challenges?

In reformulating the usefulness of analogs in a no analog world, scientists presented models as the key method to know the future. Rather than providing specific images of the future that began with small-scale ecological processes, analogs would serve these models. The top-down, rather than bottom-up image from the earth and atmospheric sciences would prevail, even if the complexity that ecologists preferred would not.

ESS inherited a tendency to model to scale downwards, rather than an inclination try to capture the complexity of ecological processes with the analog technique and scale upwards. After the 1990s, scientists increasingly incorporated vegetation and economic and social processes into integrated assessment models (IAMs). They had found ways to better include more of the interactions and feedbacks of the atmosphere, hydrosphere, cryosphere, biosphere, and humans. But it was models that scaled down, not up, that reigned because models claimed to be able to foresee unprecedented changes in ways that analogs could not. These models continue to struggle to incorporate ecological, economic, and social components of the earth system and only focus on a narrow range of futures (Bonan et al., 2024; Mann, 2002; Steffen et al., 2020). With models, we have lost the true complexity of the earth system. We cannot see the differential impacts of climate change on individuals. And, with the analog technique now in the service of models from the physical geosciences, we do not have many techniques that might allow us to challenge the epistemic authority of models and to create new ways to live in the world. ⁹