PAGES Magazine articles

Weitzel N, Brierley C, Bühler J, Chevalier M, Ellerhoff B, Skiba V and Rehfeld K
PAGES Magazine articles
Past Global Changes Magazine

Heidelberg, Germany, and online, 5-7 July 2021

A key question of the paleoclimate community is how paleoclimate data can be used to evaluate long-term predictability in climate models. How can we improve estimates of past climate variability and our understanding of the state and timescale dependency of Earth's climate? "Ping pong" serves as a metaphor to describe the back-and-forth in comparing paleoclimate data with model simulations. This is a core challenge in climate research, which requires a better understanding of proxies as well as the consequences of neglected or poorly simulated processes in climate models.

To address this question, this Climate Variability Across Scales (CVAS; workshop ( brought together a diverse pool of ∼60 scientists, ranging from early-career scientists to experienced experts from various fields and different working groups, including CVAS, Speleothem Isotopes Synthesis and AnaLysis (SISAL;, 2k Network (, and the PAGES-endorsed Paleoclimate Modelling Intercomparison Project (PMIP; One half participated online and the other half gathered at the "Internationales Wissenschaftsforum" in Heidelberg, Germany. Keynote talks focused on climate variability on different temporal and spatial scales, best practices for the joint use of models and proxies, the role of paleoclimate in future predictions, and the state of the art in the analysis and interpretation of various proxy types. Discussions resulting from these presentations continued in three working groups.

The first group discussed philosophical questions regarding the design and impact of data–model comparison studies, summarized in Figure 1. Among these were what insights into climate can be gained from data–model comparison, and how both sources of information can be leveraged. The importance of formulating a clear hypothesis prior to the comparison was stressed, as well as the need to explain experimental choices and assumptions such that the scope and limitations of the respective analysis are clearly defined. Consequently, techniques for a rigorous treatment of uncertainties are required and due to various uncertainties on both sides, data and models are not necessarily expected to "agree".

Figure 1: Key components and challenges of data–model comparison. The relevant tools, variables, intercomparison projects, and challenges (in orange) are illustrated with respect to the targeted time ranges. The workshop specifically addressed the overarching question of how paleoclimatology can contribute to solving research questions on future climate scenarios.

The second group formulated a research project based on the recommendations from the first group. Inspired by the keynote talks, the group decided to study the temperature–hydroclimate relationship in the tropics and test the hypothesis of positive covariability between the two. The participants identified suitable databases (e.g. PAGES2k Consortium 2017; Konecky et al. 2019; Comas-Bru et al. 2020) for a multi-archive and multi-proxy approach. Comparison against isotope-enabled simulations (e.g. Bühler et al. 2021) is planned. Key questions that the group identified include whether emerging isotope-enabled simulations facilitate more robust data–model comparisons, and how multiple archives and simulations can be used to understand the underlying mechanisms controlling the covariability of hydroclimate and temperature.

The third group started with the conundrum of reported agreement of global mean temperature variability in models and proxies on decadal-to-centennial scales (e.g. Neukom et al. 2019), whereas reconstructed local surface temperature variability is higher than in simulations (e.g. Laepple and Huybers 2014). The group reviewed the literature, with a focus on the spatial and temporal scales of interest. Finally, the group collected and assessed potential reasons to explain the conundrum, including effects from an overestimation of spatially uncorrelated variability in temperature reconstructions, misspecification of the spatial correlation structures in models, and the suppression of variability by climate field reconstruction methods. The group plans to expand the literature review and develop research protocols to quantify the contributions of identified potential explanations.

For most participants, the workshop was the first experience with a hybrid conference format, and the feedback was quite positive. We emphasize the importance of an appropriate technical infrastructure on site and the prior set-up of a clear workshop structure. The use of a virtual communication platform and shared working documents helped to connect virtual and on-site participants.

Loutre M-F, Gell PA, Meissner KJ and Vannière B
PAGES Magazine articles
Past Global Changes Magazine

Online, 15 June 2021

PAGES organized a session during the hybrid Sustainability Research & Innovation Congress 2021 (SRI2021;, a joint initiative of Future Earth and the Belmont Forum, which was led by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and Future Earth Australia.

International reports on climate, biodiversity and ecosystems, such as the Intergovernmental Panel on Climate Change (IPCC) and Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES), describe the unprecedented changes that the Earth system has experienced over the last few decades. While the data are clear, it is difficult for a human being to identify these changes and their potential consequences—partly because our individual memory is short, and partly because on a daily or annual basis we experience much larger changes and quickly adapt and get used to new conditions. In other words, we struggle to identify the baselines of changes and how these baselines shift with time. Therefore, it is important to identify reference points for the most recent changes, or at least to be aware of how humans are shaping the climate and ecosystems.

Human-induced climate change is altering some of the processes that underlie modes of climate variability that can have major impacts on societies. Short observational records make it difficult to understand the full range of natural climate variability and to robustly detect recent changes in these modes of variability. Nerilie Abram reviewed paleoclimate evidence for changes in the El Niño-Southern Oscillation, Indian Ocean Dipole and Southern Annular Mode during the last millennium. She discussed how unusual some of these phenomena have been over recent years and gave perspectives on the likely future of these systems (Fig. 1a).

Figure 1: Illustration of the four topics discussed during this session. (A) Modes of climate variability; (B) human impacts on aquatic ecosystems; (C) flood and (D) fire hazards.

Image credits: (A) Australian Government, Bureau of Meteorology; (B) Peter Gell; (C) Meteoschweiz (Gasthaus zur Krone, Basel; Staatsarchiv Basel-Stadt, BILD13, 323); (D) Matthew Abbot (Lake Conjola, 31 Dec 2019;

Michael Reid described the deep history of human impacts on aquatic ecosystems. He highlighted how paleoecological reconstructions can provide avenues to determine impacts on ecosystems caused by human activities, including activities that took place long before the establishment of systematic ecological monitoring. He stressed the importance of understanding the relative and interactive effects of multiple stressors and showed, with an interesting example from the Murray-Darling Basin in Australia, the need to address all stressors, not just the most recent ones (Fig. 1b).

Threats to property and people linked to floods are becoming more and more important, in particular because of increasing population densities in areas prone to flooding. Furthermore, the magnitude, frequency, and timing are changing, thus exacerbating the issue. Extreme flooding can be documented through historical, botanical, and geological records. Bruno Wilhelm reviewed how these archives record floods and the information they can provide (Fig. 1c). He then showed how the flood risk assessment can be improved with these records and discussed avenues for improvement. Flood management and mitigation plans should take this information into account.

Simon G. Haberle provided a long-term perspective on the 2019-2020 Australian Bushfire Crisis. The paleoecological records from pollen and charcoal show an increase in fire activity during times of past climate change – particularly during transitions from cooler to warmer climates. The presence of humans on the Australian landscape for at least the last 65,000 years appears to have mediated past climate impacts on fire regimes and fire sensitive ecosystems through Aboriginal fire management practices. The cessation of these landscape management practices across much of Australia with the start of European colonization more than 200 years ago induce a shift from low but persistent levels of burning to a much more variable fire regime pattern (Fig. 1d).

The session concluded with a fruitful panel discussion. The importance of past climate and ecosystem reconstructions was discussed, especially in light of their capacity to improve risk assessments related to current and future changes. The need to improve communication channels between past global change experts and policy makers or other end users, such as insurance companies, was also highlighted. Overall, this session successfully brought together experts from different areas of the climate system and ecosystems to discuss changes, transitions, and resilience of vital systems upon which our well-being depends.

Franco-Gaviria F and Amador-Jiménez M
PAGES Magazine articles
Past Global Changes Magazine

Online, 2-3 June 2021

This workshop on socio-environmental histories of the Colombian tropical Andean forests ( fostered relevant academic and institutional exchanges between social and environmental disciplines. The workshop included eight invited panelists, two main speakers, eight working group moderators, eight poster presenters, and over 120 attendees each day. On average, 60 attendees participated in the breakout activities, which focused on understanding past human and landscape interactions in the tropical Andes of Colombia.

The workshop was a multidisciplinary event with 40% of academics and students from the natural sciences and 60% from the social sciences and the arts. The workshop followed a hybrid methodological concept, designed to promote interdisciplinary discussions. Over the course of two five-hour days, we held open conferences, panel discussions with experts and small discussion groups. In order to participate, it was compulsory to read articles, in advance, on environmental change issues in the Andes from journals and prestigious authors from the natural and social sciences. The breakout activity led to discussions within groups of eight people. This setting allowed for better interaction among participants, moderators, and panelists.

During the event we also experimented with "real-time illustrations" to summarize the discussions (Fig. 1). These six illustrations were widely distributed and shared among participants and the general public.

Figure 1: A real-time illustration made at the meeting following working group discussions on integration frameworks that could bring social and natural sciences together.

On the first day, the central theme was socio-environmental history, with the aim of understanding change and transformation in the Colombian Andes, thereby fostering a dialog between biophysical and social sciences. Introductory talks by Dunia Urrego and Henry Hooghiemstra, and the panel discussion moderated by Monica Amador, led the audience to reflect on the importance of temporal and spatial scales in studying the past. Some observations and comments led to the identification of pathways for integrating disciplines of the social and biophysical sciences into socio-environmental history. These discussions during the plenary session sparked a debate within the working groups where experts and attendees addressed possibilities of integrating knowledge, but also some of the assumptions that are made within the various disciplines regarding our interpretation of the interactions between humans and non-humans in the past (Fig. 1).

Following the same methodology, the second day was dedicated to addressing the relationships between socio-environmental history and public policy. This session began with Naomi Millner, who spoke on social aspects of socio-environmental history within the BioResilience research project (, followed by Sonia Archila who presented a multi-species perspective on socio-environmental history. The panel of experts moderated by Nicolás Loaiza and Mónica Amador encouraged the interdisciplinary discussion on how a placed social and environmental history could be a tool to manage natural resources and integrate policymakers and stakeholders. For the plenary session with experts, the discussions highlighted the importance of integrating knowledge between different academic disciplines and local communities to improve our understanding of the long history of the territories. Extending historical projections of the landscapes could allow us to determine when laws were formulated and understand the public policy of each territory. All the presentations and discussions were recorded and transcribed, and will be synthesized into a document that condenses the meeting's outcomes. This will form the basis for an academic article on interdisciplinarity in socio-environmental reconstruction and history in the Andes.

After the workshop, discussions have continued between organizers and national institutions, such as the National Institute of Anthropology (ICANH), to give continuity to these working groups in socio-environmental history. The organizing committee is aiming for a second integration meeting, in person, in 2022. The planned outcomes after the first workshop are to publish (1) video recordings for the two sessions, (2) graphic summaries created in real-time, and (3) an interdisciplinary paper gathered from the different workshop discussions.

Gayo EM, Lima M, Freeman J, Robinson E and Latorre C
PAGES Magazine articles
Past Global Changes Magazine

Online, 25-26 March 2021

The PEOPLE 3000 ( working group aims to understand how the interplay between human population growth, upscaling in social complexity, and climate variability might have driven resilience or collapse of socio-ecological systems during the Holocene. By integrating long-term timeseries for ecological, climatic, and demographic trends under common mathematical frameworks, we formally evaluate convergences/divergences in feedback loops between biophysical and social systems in different regions of the globe. We are thereby interested in exploring explanations beyond statistical correlations between human population change, climate variability, and anthropogenic land use, based on modeling theoretical climate–ecosystem–population feedback relationships.

One of the modeling approaches that we have developed to explain the trajectory of Holocene SES is based on the Population Dynamic Theory (PDT), which allows us to test empirically for the interplay between climate, ecosystem and population processes. PDT proposes that the impact of climate variability on human populations cannot be evaluated independently of demographic levels. Simply put, climatic conditions affect food production, which in turn set the carrying capacity (k). Clearly, unfavorable conditions necessarily affect k through resource limitation, but if the population/food production ratio is defining the per-capita share of resources as well as competition strength, the availability of a limiting factor will decrease regardless of the effect of climate on food production. This implies that even small changes in a relevant climate variable might trigger disproportionate impacts in population growth rates. We have begun to explore this kind of dynamic in several past populations from the Americas and Polynesia (Bird et al. 2020; Lima et al. 2020).

Figure 1: (A) Paleo-population levels inferred for the Atacama Desert from the summed probability distribution (SPD) of radiocarbon dates. (B) Population growth rates during the period 3300-2150 cal yr BP.

Our last workshop before the final PEOPLE 3000 synthesis, "Understanding long-term human-environment feedback loops through the integration of archaeology, paleoclimate and ecological models" ( was held virtually due to restrictions of the ongoing COVID-19 pandemic. This activity aimed to expand our number of case studies by establishing collaborative strategies to integrate ecological, climate and demographic proxy data into a PDT framework for addressing the question: Does human population size and/or rates of change better correlate with climate driven changes in ecosystem structure, diversity or functionality?

Through keynote and flash-poster presentations, we discussed approaches for extracting refined paleodemographic signals from archaeological radiocarbon timeseries as well as trends for anthropogenic land-use changes from paleoecological archives. We established synergies with researchers from other complementary initiatives, e.g. LandCover6k (, Paleoclimate Modelling Intercomparison Project (PMIP; and Humans on Planet Earth (HOPE; for assessing human-environment feedback loops based on the integration of our PEOPLE 3000 radiocarbon database (Bird et al. 2021) and global paleoenvironmental datasets collated previously by our collaborators.

A manuscript has been outlined that will allow us to explore relationships between technological innovations and population growth in past agrarian societies from around the world. For the case presented in Figure 1, accelerated growth rates occurred as population sizes increased in the inland Atacama Desert and the adoption of agriculture spurred on new forms of cooperation among individuals (i.e. pottery, metal-working, irrigation, pastoralism). Nevertheless, as population sizes approached the higher k set by agriculture, growth rates started to decrease due to the interplay between population levels and increased competition strength.

Breakout sessions on the second day were dedicated to discussing the future of PEOPLE 3000. Directions for a new Phase 2, which launched in August 2021, focus on exploring coevolutionary relationships between social organization, changes in human populations, and disturbance regimes over the Holocene. Phase 2 will broaden our number of case studies by including early-career researchers from traditionally under-represented regions in paleoscience that attended the workshop, and delve further into new computational tools available for the paleoenvironmental record (e.g. Neotoma). New collaborations with other initiatives such as LandCover6k, PMIP and HOPE will help develop novel synergies to examine mechanisms proposed to explain the variable deep histories of resilience (or lack thereof) observed in past socio-ecological systems.

Hoggarth JA, Latorre C, Freeman J, Robinson E, Gayo E and Bird D
PAGES Magazine articles
Past Global Changes Magazine

Understanding what makes some socio-ecological systems (SES) more resilient to changing disturbance regimes (e.g. the length of fire season caused by abrupt climate change) is integral to explain long-term patterns in the development of human societies over the past 3,000 years. The PAGES PalEOclimate and the PeopLing of Earth (PEOPLE 3000; working group explores this question by integrating archaeological data, paleoecological data, and dynamic modeling (Fig. 1) to identify regionally comparative case studies across the world.

Humans adapt to natural conditions, including variability in climate systems, over time with each generation inheriting ecological and cultural knowledge. Drawing from niche construction theory, we further argue that humans modify selection pressures in their environments that affect both humans and other species (Odling-Smee et al. 2003). As disturbances may change over time, an SES may become vulnerable when those changes extend outside of the range held within the cultural and ecological memory of a society.

More flexible systems may improve the resilience of SESs over even highly productive but rigid systems. Using radiocarbon datasets as proxies of populations, Freeman et al. (2021) explored whether systems with greater variability in production developed differing population stability patterns than those with more landscape engineering over time. They found that agricultural societies that relied on landscape engineering to intensify production and control variability of production experienced the most stability and the least severe population declines during times of environmental stress.

Figure 1: Conceptual diagram of PEOPLE 3000, showing the three areas of archaeology, dynamic models, and paleoecology that are integrated in the project.

Scientific goals and activities

The PEOPLE 3000 working group has three primary goals: (1) to develop low- and high-precision coupled records of paleoclimate, human population, and human institutions over the last 3000 years; (2) compare changes in population, paleoclimate, and institutions from region to region; and (3) identify regionally comparative patterns to explain relationships between variation in ecosystem change, subsistence and social diversity, and the severity of social-ecological reorganization. To explore these questions, we are building a global data infrastructure for comparing patterns of human population ecology. To date, in Phase 1 of PEOPLE 3000, this has taken the form of a global radiocarbon database, compiling, and curation of over 150,000 radiocarbon dates from around the world and developing protocols for using those data (Bird et al. 2021).

PEOPLE 3000's goals in Phase 2 of the project are to integrate radiocarbon and paleoecological/ paleoenvironmental data, as well as information on social institutions, to develop coevolutionary models on carrying capacity, social integration, and data from paleoclimate and paleoecology. To date we have collected data from 17 core case study regions and will work on issues of quality control and integrating data from each region.

PEOPLE 3000 is currently seeking members from around the world. We will hold open online meetings each May to present research findings from individual members and to recruit new case studies. Visit the PEOPLE 3000 website at and sign up to the mailing list to be contacted by us on our upcoming meetings and activities.

Upcoming activities

We held a hybrid meeting to wrap up Phase 1 of the project in November 2021 (, with separate working groups meeting in individual countries and communicating over Zoom. Our first online meeting of Phase 2 will take place in May 2022, open to all PEOPLE 3000 members and interested researchers.

Monchamp M-E, Armbrecht L, Capo E, Coolen MJL, Cordier T, Domaizon I, Epp LS, Giguet-Covex C, Gregory-Eaves I, Herzschuh U, Parducci L, Stoof-Leichsenring KR and Williams JW
PAGES Magazine articles
Past Global Changes Magazine

PaleoEcoGen is a new working group that was launched with the aim of bringing together scientists from around the world who use ancient environmental DNA (ancient eDNA) as a novel proxy to examine the response of past biological communities to environmental changes ( We are particularly interested in exploiting the added value of these emerging ancient eDNA tools to advance our knowledge of critical transitions in Earth's Quaternary history. To this end, we aim to stimulate and enhance international ancient eDNA research by organizing topical workshops to discuss new methodologies in the field (including synthetic analyses and modeling approaches), and to coordinate research efforts for bigger picture analyses that, ultimately, will help to inform conservation efforts and future biodiversity assessments.

Changes in ecosystem dynamics can occur gradually over centuries to millennia, or abruptly (i.e. at decadal to annual timescales). Rapid changes may challenge the fitness and survival of organisms, including those that are essential for ecosystem maintenance. Even small disturbances may weaken the stability and resilience of an ecosystem (Fig. 1), and ultimately lead to "critical transitions" where the system is pushed from one equilibrium state to another (Taranu et al. 2018). These "tipping points" are often hard to predict because of the complexity of the interactions between organisms and their environment, and they imply prolonged ecosystem consequences that may not be reversible.

Figure 1: (Left) Schematic representation of a critical transition between two states triggered by a small forcing. (Right) Simplified workflow of the proposed approach to identify past critical transitions and evaluate subsequent biological changes based on ancient environmental DNA timeseries.

Critical transitions have been documented for terrestrial and aquatic ecosystems, as well as social-ecological systems, and they have been studied across many scientific disciplines (Scheffer et al. 2009; 2012). In the context of global changes, especially the "Great Acceleration" (Steffen et al. 2015), studying critical transitions has been identified as a priority in paleoecological research by the scientific community (Seddon et al. 2014). With new methodological approaches on the rise in the paleosciences, we now have the opportunity to describe past critical transitions and their effects on biological communities (Taranu et al. 2018; Capo et al. 2021).

Our working group is motivated to address two key questions:

  • How can we use (sedimentary) ancient eDNA timeseries to better identify and characterize past critical transitions?
  • What are the subsequent evolutionary and ecological trajectories, and which projections for future biodiversity and ecosystem change can be drawn from past critical transitions during the Quaternary?

The detailed study of critical transitions in paleoecology requires the generation of the most comprehensive view possible—of an ecosystem, its drivers, and their interactions. To meet this challenge, stratigraphic analysis of ancient eDNA is a key analytical approach because of its potential to provide new insights into: (1) the composition of biological communities across multiple trophic levels including organisms that do not fossilize; (2) species interactions within communities; and (3) the response of organisms, from individual taxa to communities, to past environmental changes (Coolen et al. 2013; Domaizon et al. 2017; Schulte et al. 2021; Liu et al. 2021). Like any other proxy, ancient eDNA has its limitations (Capo et al. 2021), but the field is now sufficiently mature to offer exciting new opportunities to expand our knowledge using paleoenvironmental data.

Upcoming activities

Our first online workshop will be in 2022 in collaboration with the sedaDNA scientific society ( The workshop will be dedicated to improving inclusion of African ancient eDNA researchers by offering a collaborative platform and training opportunities in molecular techniques applied to sedimentary ancient eDNA. A second workshop (in-person or online, depending on COVID-19 pandemic regulations) will focus on developing a multivariate modeling approach based on ancient eDNA temporal data (Taranu et al. 2018) to investigate the timing and magnitude of shifts in paleoenvironmental records.

Visit our website ( and register to our mailing list to keep up to date with our activities and find out how to get involved. PaleoEcoGen is also on Twitter: @PaleoEcoGen

Hargreaves JC
PAGES Magazine articles
Past Global Changes Magazine

The Past to Future Working Group enables paleoclimate information from both PMIP models and climate proxies to be used to better constrain predictions of future climate change.


The Past to Future Working Group (P2F; was formed to enable paleoclimate information from both PMIP models and climate proxies to be used to better constrain predictions of future climate change. The remit of the group is wide; in principle any spatial and temporal scales of change and any metric of the climate system may be considered. Here we mostly focus on the equilibrium response of the climate system. In this context, the most significant progress over the last few years has been made in better defining and constraining climate sensitivity. Related work focusing on variability is reported elsewhere in this issue. Paleoclimate model simulations were included in the fifth iteration of the Climate Model Intercomparison Project (CMIP5), making this the first time that ensembles of historical, paleo, and future projections were run with the same model versions. In anticipation of this, the P2F group was initiated at the 2012 PMIP3 workshop in Crewe, UK (Crucifix et al. 2012). The overarching purpose of the group was to encourage the use of paleoclimatic information to improve predictions of climate change.

As a cross-cutting group, the main focus of P2F was to facilitate research activities. The working group website was used to improve accessibility to outputs from model simulations and data from climate proxies, and to highlight relevant publications in the field. The main meeting point has been at the European Geophysical Unuin General Assembly, where there have been a variety of EGU sessions with a focus on Past to Future activities. There have also been several workshops with a significant Past to Future element. Connections between the Cloud Feedback Model Intercomparison Project (CFMIP; and PMIP have strengthened through the activities of the working group. Joint experiments have been planned; scientists from CFMIP have given keynote presentations at PMIP (and vice-versa); and there are a number of scientists who are active in both MIPs.

The status of early research focused on combining models and data from paleoclimates to constrain predictions of future climate change is well described by Schmidt et al. (2014). This paper outlines various methodologies, illustrated by examples, each of which "uses a specific target (or targets) from a palaeo-climate reconstruction of change that is within the scope of the modelled system, defines a metric of skill that quantifies the accuracy of the modelled changes and assesses the connection to a future prediction". The P2F group promoted a very similar methodology for using the CMIP ensemble, and additionally, highlighted a number of research targets, including:

  • Estimating future climate by exploring information from multiple PMIP intervals;
  • Exploring divergent estimates of climate sensitivity—towards reconciliation; and
  • Predicting regional climate change—going beyond climate sensitivity.


Climate sensitivity is the equilibrium global temperature change resulting from a doubling of the atmospheric carbon dioxide concentration. Estimation of climate sensitivity has been the main focus for quantitative P2F research using statistical methods. The recent community assessment of climate sensitivity by Sherwood et al. (2020) included a substantial paleoclimate component and involved several P2F members working alongside researchers with primary expertise in the interpretation of paleodata. Results from PMIP activities influenced almost every aspect of the paleoclimate component of the assessment.

Figure 1: (A) Reconstruction of Last Glacial Maximum surface air temperature anomaly (ºC) based on multi-model regression. Proxy data are represented as colored dots. (B) Uncertainty in Last Glacial Maximum surface air temperature anomaly (ºC) from bootstrap resampling. Results presented as half-width of 95% confidence interval (Fig. 1 and 3 from Annan and Hargreaves 2013).

For example, in order to estimate climate sensitivity using paleoclimates, an estimate of the large scale temperature changes for paleoclimates relative to modern is required. Figure 1 shows a reconstruction of the Last Glacial Maximum temperature anomaly that was included in the evidence for the assessment. This estimate (from Annan and Hargreaves 2013) was a result of the P2F working group activities, and combined information from the PMIP2 ensemble and from climate proxy compilations (MARGO Project Members 2009; Bartlein et al. 2011; Schmittner et al. 2011). Uncertainty estimates are critical to these kinds of assessments, and the estimated uncertainties in this reconstruction are shown in the lower sub-figure. Figure 2 shows the baseline result from Sherwood et al. (2020), and the result that would be obtained if paleoclimate information was, instead, entirely ignored. It is clear that the paleoclimatic component significantly constrains the high end of the assessed range for climate sensitivity.

The assessment also made significant progress on one of the other goals of the working group: reconciling the previously divergent estimates of climate sensitivity from different constraints, which were found to be due in part to the pattern effect (Andrews et al. 2018) and some differences in the precise definitions of climate sensitivity that had been used. An emergent constraint is the term used to describe model variables for which measurements are available which may, through use of the multi-model ensemble, be used to refine probabilistic estimates of future climate change given certain emissions forcings. Members of the working group have further developed the use of emergent constraints to estimate climate sensitivity. Initial work focused on analyzing correlations between the Last Glacial Maximum (LGM) temperature anomaly and climate sensitivity, with the mid-Pliocene considered more recently (e.g. Hargreaves et al. 2012; Hopcroft and Valdes 2015; Hargreaves and Annan 2016). Recently Renoult et al. (2020) presented a Bayesian framework for combining emergent constraints from different periods, potentially including non-paleo emergent constraints. This method makes all assumptions explicit and enables emergent constraints to be incorporated into assessments of climate sensitivity, although some caveats remain.

Figure 2: Estimated PDFs for climate sensitivity (S) with and without using paleo information, based on the values estimated by Sherwood et al. (2020). The baseline 66% range not including paleo information was 2.6–4.6 K. Including the paleoclimatic constraint, the range tightens to 2.6–3.9 K.

Progress in regional climate change has usually been more qualitative and process based (e.g. Schmidt et al. 2014; Koh and Brierley 2015; Seth et al. 2019). Large-scale changes such as Arctic amplification and land–ocean contrast may be expected to be useful for constraining future climate but direct temperature comparisons have not so far indicated robust past–future relationships in the ensembles. However, detailed analyses of the processes involved in Arctic amplification may enable particular seasons for particular paleoclimate intervals to be used as constraints (LGM: Laîné et al. 2016; mid-Holocene: Yoshimori and Suzuki 2019). There is also potential for Arctic ice extent to be used to constrain likely future Arctic sea-ice changes (Last Interglacial: Kageyama et al. 2021). The merger of P2F with Paleovar (to make P2Fvar) in PMIP4 has increased the range of spatial and temporal scales being studied (Rehfeld et al. 2020; Brown et al. 2020; D'Agostino et al. 2020), a trend which we hope to see continue.

In addition to developing the more regional focus, the simulations of the last deglaciation which have been performed within PMIP have the potential to develop towards a new P2F activity of directly paleoclimate-constrained projections, as modelers extend the deglaciation runs into the next centuries (Fieg et al. 2021).

In summary, even with relatively few scientists working primarily in this area, the group is able to use the resources of PMIP, CFMIP, and CMIP, and has been very successful in raising the profile of paleoclimate as a topic of increasing interest to a wide range of researchers.

Rehfeld K and Brown J
PAGES Magazine articles
Past Global Changes Magazine

Here, we outline recent insights into interannual to decadal variability of Earth's surface climate based on PMIP experiments and comparison with future climate simulations. These studies have provided new perspectives on large-scale changes of surface climate, low- and high-latitude modes of variability, and internally versus externally forced variability.

Climate variability in past, present and future

Interannual to decadal variability of surface climate variables arises through internal dynamics in the atmosphere-hydrosphere-biosphere-cryosphere system driven by the incoming solar radiation. External forcing impacts the climate system on multiple timescales. Constant adjustment of the Earth's energy budget through feedbacks and dynamics lead to changes in the global mean temperature, regional patterns, and fluctuations around the regional mean—in other words, to climate variability. Variations in the Earth's orbit (104–103 years) change the seasonal distribution of insolation quasi-periodically, while changes in solar luminosity (103–101 years) modulate the overall energy input to the system. At the same time, explosive volcanic eruptions stochastically perturb the system on seasonal to interannual timescales.

Equilibrium simulations from PMIP3 and PMIP4 allow us to examine the response of the climate system to different orbital insolation, topography, ice-sheet configurations and greenhouse-gas concentrations (Braconnot et al. 2012; Kageyama et al. 2018). Simulations for the last (pre-industrial) millennium (Jungclaus et al. 2017) have been used frequently to test the impact of solar and volcanic forcing on climate at interannual to centennial timescales. The PMIP4 working group "Past2Future: Insights from a constantly varying past" ( aims to improve our general understanding of climate stability through multi-model analyses of a range of climate states, focusing on large-scale patterns of change and internal modes of variability. It builds on the efforts of the PMIP3 working groups "Past to future" and "Paleovar".

Large-scale changes in simulated climate variability

The PMIP last millennium experiments have been key to improving our understanding of the role of volcanism and solar variability in driving climate variations. Studies incorporating both proxy and model data to assess mechanisms, blended reconstructions, and impacts on society draw on the considerable overlap between observations, and the dense proxy networks fostered by different PAGES working groups.

One key insight is that current climate models show too little variability locally, at individual observation locations. This was shown by Laepple and Huybers (2014), who investigated ocean surface temperature variability in CMIP5/PMIP3 last millennium simulations. At interannual timescales (2–5 years), no systematic offset between simulated regional temperature variability and observations could be found. However, on decadal to multicentennial timescales they observed a progressive increase in the underestimation of variability in gridbox-scale model surface ocean temperatures. On the other hand, the global mean simulated and reconstructed/observed temperature variability on interannual to multidecadal timescales are of similar magnitude over the last millennium (Laepple and Huybers 2014; Parsons et al. 2020) and the Common Era (PAGES 2k Consortium 2019). This consistency of simulated and reconstructed variability at the global scale, despite the lack of modeled regional variability at decadal and longer timescales, remains unexplained.

PMIP experiments targeting time periods prior to the late Holocene include a range of boundary conditions such as the land–sea mask, orbital parameters, greenhouse gas concentrations, and ice-sheet distribution. These experiments generally do not consider forcing on interannual to centennial timescales by changes in solar luminosity or explosive volcanism, as proxy-based reconstructions do not yet exist (and may not be possible given archive and proxy uncertainties). This implies that changes in interannual to multidecadal variability in these equilibrium simulations reflect the internal dynamical response of the climate system to the boundary conditions.

Figure 1: Temperature and precipitation variance change systematically with global mean temperature. Variance ratios (R = σscenpi where σpi is the standard deviation of the preindustrial control simulation and σscen is the variance of {LGM,4xCO2}) were calculated from each CMIP5/CMIP6/PMIP3 model on (A) the last 50 years of the LGM simulations and (B) years 101–150 of the 1pctCO2 increase scenarios and compared to the final 50 years of the pre-industrial simulations. All simulations were linearly detrended and variance ratios were averaged. Colors classify regions with concurrent changes in temperature (RT) and precipitation (RP) variability. Changes of less than 5% are masked as white. Black shading indicates an increase in total precipitation by more than 0.4 mm/day in the annual mean. Visualization by J. Bühler based on data from Rehfeld et al. (2020).

Interannual to multidecadal variability changes systematically across equilibrium simulations for the LGM, the mid-Holocene, and for idealized warming scenarios (abrupt4xCO2 and 1pctCO2; Taylor et al. 2012). Figure 1 illustrates the large-scale and mirroring changes between interannual to decadal temperature and precipitation variability in the LGM and 1pctCO2 cases. At the global scale, the colder climate is characterized by more variability in temperature and less in precipitation in most regions. The warming scenario is associated with increasing temperature and precipitation variability across the tropics and subtropics. Changes follow a contrasting land–sea pattern. Spectral analysis showed that these general patterns hold from seasonal to multidecadal timescales across the PMIP3/CMIP5/CMIP6 model ensemble (Rehfeld et al. 2020).

Changes in modes of climate variability

Many studies have investigated changes in the El Niño Southern Oscillation (ENSO) characteristics in PMIP experiments. Zheng et al. (2008) compared multiple models for paleoclimate (PMIP2 LGM, mid-Holocene experiments) and future simulations, finding relationships between the tropical Pacific mean state and ENSO amplitude, as well as a reduced mid-Holocene ENSO variability. An and Choi (2014) compared ENSO in PMIP2 and PMIP3 mid-Holocene experiments and found a significant reduction in ENSO amplitude for PMIP2 models but only a marginal reduction in PMIP3 models due to competing processes, with weakened air–sea coupling leading to suppressed ENSO but weakening of the annual cycle over the tropical eastern Pacific supporting intensified ENSO.

Using an ensemble of PMIP3 and PMIP4 mid-Holocene experiments, Brown et al. (2020) found a consistent reduction in ENSO amplitude of 7% for PMIP3 models and 10% for PMIP4 models relative to pre-industrial ENSO amplitude. Comparison of mid-Holocene proxy records and PMIP3 simulations showed that models underestimated the reduction in ENSO amplitude compared with proxy reconstructions (Emile-Geay et al. 2016). Investigation of PMIP4 last interglacial experiments showed a stronger reduction in ENSO amplitude of around 20%, consistent with the larger seasonal insolation anomalies than the mid-Holocene experiments (Brown et al. 2020).

Figure 2: Changes in SST variability associated with ENSO for PMIP and CMIP experiments. The ensemble mean difference between the SST composites in each model during El Niño minus La Niña (defined as ±1 standard deviation) in the (A) midHolocene, (B) lgm, (C) lig127k, (D) 1pctCO2 and (E) abrupt4xCO2 experiments minus the same pattern for the piControl simulations is shown. The ensemble mean ENSO SST patterns in the piControl simulations are shown as black contours. Stippling indicates that more than two-thirds of the ensemble members agree on the sign of the change. Modified from Brown et al. (2020).

Simulations of ENSO in PMIP2 and PMIP3 LGM experiments showed a range of changes in ENSO characteristics (Masson-Delmotte et al. 2013). PMIP4 LGM simulations show no significant change in ENSO amplitude but reduced variability in the western Pacific SST variability, indicating a spatial shift in the ENSO pattern (Brown et al. 2020). Examination of ENSO in past cold and warm climates (as shown in Fig. 2) can provide insights into the relationship between changes in the mean state and ENSO variability (Saint-Lu et al. 2015), and may assist in constraining projections of future ENSO change.

Across the PMIP3/CMIP5/CMIP6 ensemble, ENSO indices showed increasing variability with warming, but the changes were not significant given the large intermodel spread (Rehfeld et al. 2020). Similarly, some other interannual to multidecadal modes of variability showed systematic, but weak, changes in variability with increasing global mean temperature across the PMIP3 ensemble. This included the boreal winter North Atlantic Oscillation and the Northern Annular Mode (weakly positive), amongst others. The Atlantic meridional and zonal modes showed decreasing variability with warming. For many of the other proposed modes of variability the integration length (typically 50–100 years) was insufficient to assess whether or not systematic changes are expected to occur with regional or global warming.

This general perspective of reduced temperature variability with warming at the global scale is consistent with the direction of temperature variability changes from a multi-proxy study targeting multicentennial to millennial timescales (Rehfeld et al. 2018). Proxy-based confirmation on shorter timescales will, however, require reduced age uncertainties, removal of confounding effects due to other climate variables, the environment, or the archive structure, and an expansion of the high-resolution proxy network.

Challenges and Perspectives

A series of interconnected challenges need to be tackled regarding the paleoclimate record and paleoclimate modeling, in order to further enhance our understanding of changes in interannual to multidecadal variability. Firstly, this entails testing the impact of centennial- to millennial-scale variations in the mean state on variability at these shorter timescales. On the modeling side this requires the incorporation of nonstationary elements such as ice sheets, biogeochemistry, and land surface processes, but also a reasonable understanding of the nature of the variability of the glacial ocean circulation. This could be facilitated by considering an ensemble of models of different complexity together to assess stabilizing and destabilizing feedbacks of low frequency changes on interannual variability.

Spatio-temporal shifts in modes operating on interannual to decadal timescales can be expected to occur with warming. This has been extensively studied for ENSO, where shifts in the frequency of different "flavors" or spatial patterns of ENSO may occur. Examination of PMIP mid-Holocene simulations suggested changes in the occurrence of Central Pacific versus Eastern Pacific events (e.g. An and Choi 2014; Emile-Geay et al. 2016). Shifts in the spatial pattern of ENSO in past climates therefore need to be considered when carrying out model–proxy comparisons. To evaluate the simulated variability, especially in pre-Holocene time periods, the proxy network needs further consolidation in time and space, in order to assess signal-to-noise ratios and distinguish model deficiencies (e.g. underestimated SST variability) from archive noise (e.g. from bioturbation, intermittency, or aliasing). The comparability of modeled and reconstructed signals could further be improved by forward modeling of tracer species (e.g. water isotopologs) in collaboration with PMIP/CMIP experiments, longer model integrations, and the inclusion of solar and volcanic forcings in experiments.

Liu J, Ning L, Yan M, Sun W, Chen K and Qin Y
PAGES Magazine articles
Past Global Changes Magazine

The recent progress on paleomonsoon modeling on various timescales within the five typical periods included in PMIP4 simulations are summarized in this paper. The remaining controversial issues and potential directions of the paleomonsoon modeling for future studies are also discussed.

During recent decades, remarkable progress has been made on monsoon variability and physical mechanisms, both by the modern and paleoclimatic communities (Fig. 1; Wang et al. 2017). However, paleomonsoon variability on different spatiotemporal scales is a complex topic requiring more studies from both proxy reconstructions and numerical modeling simulations. Phase 4 of the Paleoclimate Modelling Intercomparison Project (PMIP4) provides unique opportunities to better understand the past changes and corresponding physical mechanisms of the paleomonsoon on different timescales using updated models, more comprehensive boundary conditions, and improved experimental protocols compared with previous PMIP phases (Kageyama et al. 2018).

Figure 1: Mechanisms of global monsoon variability and the timescales (Fig. 6 from Wang et al. 2017).

Last millennium

Previous paleomonsoon studies focusing on centennial changes during the last millennium (LM) have mainly compared the monsoon variability during the typical climatic periods within the LM, i.e. the Medieval Climate Anomaly (MCA) and the Little Ice Age (LIA; e.g. Jungclaus et al. 2017). Results indicate stronger (weaker) East Asian summer monsoon (EASM) and Indian summer monsoon during the MCA (LIA) due to the changes in the land–sea thermal contrast and atmospheric circulation, which were induced by total solar irradiance and volcanic eruptions. The coherent responses of monsoon systems to centennial-scale modulation of the total solar irradiance and volcanic eruptions found in both model simulations and proxy reconstructions indicate the robustness of the physical mechanisms behind this variability. However, despite the remarkable progress, it is not easy to quantitatively separate the contributions from external forcings and internal variability (Wang et al. 2017).

On the decadal timescale, monsoon variability is primarily associated with internal variability, e.g. Pacific decadal oscillation and Atlantic multi-decadal oscillation. Meanwhile, external forcings, such as volcanic eruptions, solar radiation, and land-use and land-cover change, could also influence the monsoon variability through regulating the land–sea thermal contrast, cycle of surface water, and energy balance (e.g. Qin et al. 2020). Although transient model simulations cannot exactly match the decadal monsoon phases with those found in the proxy reconstructions because models have their own initial conditions, specific events relevant to external forcings could still be reproduced through design of sensitivity experiments. Moreover, in addition to the experiments with default forcings, further experiments with different combinations of external forcings will focus on either specific periods during LM or the last two millennia in PMIP4. These will contribute to the exploration of the forcing uncertainties and the model–data comparisons on multi-scale monsoon variability (Jungclaus et al. 2017).

Mid-Holocene and last interglacial

The impacts of orbital forcing on paleomonsoon variability are examined through two groups of equilibrium simulations covering two interglacial epochs with greenhouse gas (GHG) levels similar to the pre-industrial (PI) period and the continental configurations almost identical to modern period, i.e. the mid-Holocene (MH), approximately 6 kyr BP, and the last interglacial (LIG; Otto-Bliesner et al. 2017), approximately 127 kyr BP. In general, the multi-model ensemble mean results show an enhanced Northern Hemisphere monsoon and reduced Southern Hemisphere monsoon, especially for the enhanced North African and Asian monsoons, and a weaker Australian monsoon both during the MH and LIG (Fig. 2; Brierley et al. 2020; Otto-Bliesner et al. 2021). Meanwhile, because of the larger insolation anomalies during the LIG compared to MH simulations, the changes of individual monsoon systems (e.g. areal extents and total amount of precipitation) during the LIG have a greater magnitude than those in the MH (Fig. 2).

Figure 2: Multi-model ensemble mean of (A) relative changes of areal extents and (B) relative changes in total amount of precipitation in individual monsoons from PMIP4 MH and LIG simulations. The abbreviations used to identify each regional monsoon are as follows: North America monsoon system (NAMS), North Africa (NAF), South Asia (SAS), and East Asia summer (EAS) monsoon in the Northern Hemisphere and South American Monsoon System (SAMS), southern Africa (SAF), and Australian-maritime continent (AUSMC) monsoon in the Southern Hemisphere (adapted by Anni Zhao from Fig. 7 from Brierley et al. 2020 and Fig. 16 from Otto-Bliesner et al. 2021).

In addition to the aforementioned equilibrium simulations, several groups of transient simulations covering the entire Holocene have been carried out (e.g. Braconnot et al. 2019; Bader et al. 2020). These provide opportunities to investigate the characteristics and mechanisms behind multi-scale monsoon variability during the Holocene. These studies have suggested that the interannual- to millennial-scale monsoon variability could be influenced by internal variability, e.g. El Niño-like SST modes, Indian Ocean Dipole (IOD)-like SST modes, the AMOC, and external forcings, e.g. orbital parameters and GHGs (Braconnot et al. 2019; Crétat et al. 2020; Bader et al. 2020). For example, Braconnot et al. (2019) and Crétat et al. (2020) found that changes in orbital parameters cause long-term drying trends in the Indian and West African monsoons, but the Indian monsoon is more sensitive to anthropogenic GHGs, while ENSO and the IOD modulate the interannual-to-decadal Indian monsoon variability. Moreover, some extreme droughts that have been strongly associated with monsoon weakening, e.g. the 4.2 kyr BP event, may have been caused by both long-term drying trends (due to orbital forcing) and low-frequency fluctuations (due to internal variability).

Last glacial maximum

The last glacial maximum (LGM), approximately 21 kyr BP, provides an opportunity to investigate the impacts of changes in ice-sheet and continent extent on paleomonsoon variability (Kageyama et al. 2018). During the LGM, the reduction of summer monsoon precipitation in the Northern Hemisphere was twice as large as in the Southern Hemisphere. This asymmetric response is mainly caused by the moisture convergence feedback induced by the continental ice-sheet forcing rather than the reduction of moisture content (Cao et al. 2019). The multi-model ensemble mean results suggest that the lowered sea level may lead to expanded land area and thus an enhanced land–sea thermal contrast; this can further lead to a strengthened Australian summer monsoon in contrary to the weakened global monsoon during the LGM (Yan et al. 2018).


Generally, a robust enhancement of the West African, Indian, and East Asian summer monsoons was found during the mid-Pliocene (MP), approximately 3.2 million years ago, relative to the PI, consistent with geological reconstructions (Haywood et al. 2021). When the 11 model simulations are compared with geological records, a northwestward shift in the EASM's northern edge is captured, which is influenced by the intensification and westward extension of western Pacific subtropical high (Huang et al. 2019). Berntell et al. (2021) found that the multi-model ensemble mean in PlioMIP2 simulations shows a significant strengthening of the West African monsoon during the MP, with increased precipitation over the Sahel and Southern Sahara associated with the deepening of the Sahara Heat Low induced by GHG forcing.

Remaining controversial issues and potential directions

The progress made during PMIP4 discussed above has largely improved our understanding of paleomonsoon variability and the relevant physical mechanisms on various timescales. However, many specific issues remain poorly understood and could benefit from developments of the paleomonsoon modeling and model–data comparisons and syntheses, such as the relative influences of the polar ice-sheet development and the oceanic warm pool on global monsoon variability, further reduction of uncertainty for proxies already in use, and the development of new types of proxies that could identify certain features of the monsoon more accurately (Wang et al. 2017).

Such questions have motivated climatologists around the world and have given rise to strong collaborations between the paleoclimate reconstruction and modeling communities on future studies, on aspects of comparisons, synthesis, and fusion between proxy reconstructions and modeling simulations, through applications of isotope-enabled paleoclimate modeling and paleoclimate data assimilation, as well as other topics. Meanwhile, the paleomonsoon modeling community could also progress further with the help from the developments of the new generation of Earth system models, such as higher-resolution models and improved physical parameterizations, as well as incorporation of new findings from the paleomonsoon reconstruction communities, such as improved reconstructions of external forcings.

Lunt DJ, Huber M, Otto-Bliesner BL, Chan WL, Hutchinson DK, Ladant JB, Morozova P, Niezgodzki I, Steinig S, Zhang Z and Zhu J
PAGES Magazine articles
Past Global Changes Magazine

DeepMIP has brought together the modeling and proxy communities, with an initial focus on the early Eocene climatic optimum, ∼50 million years ago. In addition to evaluating global-scale metrics such as GMST and polar amplification, mechanisms of warmth are also being interrogated.

CO2 reconstructions indicate that the closest analogs to potential 22nd-century CO2 concentrations under mid-to-low-mitigation scenarios existed tens of millions of years ago, in "deep-time". The Deep-Time Model Intercomparison Project (DeepMIP; is dedicated to conceiving, designing, carrying out, analyzing, and disseminating the results of an international effort to improve our understanding of these deep-time climates. Here, deep-time climates are defined as time periods prior to the Pliocene, ∼5 million years ago. At its heart, DeepMIP aims to foster closer links between the paleoclimate modeling and data communities, grow communities of practice, develop and disseminate best practices, and to use this model–data synergy to:

  • design, carry out, and analyze appropriate model simulations;
  • create, collate, and synthesize proxy datasets; and
  • evaluate model simulations, with a dual aim of learning about the past and informing the future.

History of DeepMIP

Prior to becoming part of PMIP, initial work was kick-started by the publication of several studies which independently modeled the early Eocene (∼50 million years ago), a time period characterized by CO2 concentrations ∼1200–2500 ppmv, global mean temperatures ∼23–30ºC, and the complete absence of ice sheets. The temperature response to the high CO2 concentrations and modified boundary conditions in the models were compared within the framework of an ad-hoc "ensemble of opportunity" (Lunt et al. 2012). Following on from this, several studies explored other aspects of these simulations, including the hydrology (Carmichael et al. 2016), implications for glaciation (Gasson et al. 2014), or the modification of model parameters (Lunt et al. 2013). However, there was a growing realization that for further progress to be made, a more formal, consistent experimental design and model intercomparison was necessary.

In 2015, in a meeting at NCAR, funded by NERC in the framework of an "International Opportunities Fund" project, the community came together to discuss such a formalization. DeepMIP was founded, and became part of PMIP. DeepMIP now has a membership of 200 scientists, with representation from the modeling as well as the marine and terrestrial proxy communities (; there have been a total of six meetings, with the most recent being online (

DeepMIP activities and results so far

The first DeepMIP activity was to formally define a model experimental design for the time periods of interest. These were chosen to be the early Eocene climatic optimum (EECO), the Paleocene-Eocene Thermal Maximum (PETM), and the latest Paleocene. This experimental design was published as part of the PMIP4/CMIP6 Special Issue in GMD (Lunt et al. 2017). Following this, the time periods were more formally defined, guidelines and principles for the synthesis of proxy data and the strengths and weaknesses of various proxies were laid out, and the first version of the DeepMIP proxy database was also published (Hollis et al. 2019).

This proxy database was used to characterize the best estimates of global mean temperature in the three time periods of interest, and their uncertainties (Inglis et al. 2020). A variety of methods was applied to convert the relatively sparse proxy data into global means, ranging from a simple latitudinal-banded average, to Gaussian process regression. These different methods were compared and combined, resulting in estimates for the latest Paleocene, PETM, and EECO, of 21–29ºC, 26–36ºC, and 22–31ºC, respectively (90% confidence interval). This study also used the temperature estimates and the best existing CO2 estimates to provide a quantification of equilibrium climate sensitivity (ECS) based on Eocene data. This resulted in ECS estimates of 1.6–8.0ºC, 1.9–5.2ºC, and 1.3–5.0ºC for the same three time periods.

Figure 1: Modeled near-surface annual mean air temperature (ºC) from eight models in the DeepMIP-Eocene (early Eocene) model ensemble, and proxy reconstructions from Hollis et al. (2019). The simulations shown here were carried out at a range of CO2 concentrations from 840 (3x pre-industrial) to 1680 (6x pre-industrial) ppmv. Also shown is pre-industrial temperature from the CESM1.2_CAM5 model. The full model results are described in Lunt et al. (2021). The model data can be obtained from the DeepMIP model output database; see here for more info:

The proxy database was also used to evaluate the DeepMIP model simulations, which were presented, and their large-scale features discussed, in Lunt et al. (2021; see Fig. 1). The work showed that compared with results from previous studies of the Eocene, the DeepMIP simulations show a smaller ensemble spread in the global mean surface temperature response for a given atmospheric CO2 concentration—this may result from the standardised experimental design and topographic/bathymetric boundary conditions.

These simulations also revealed a relatively high Eocene climate sensitivity (ECS) on average (average of 4.5ºC per CO2 doubling), compared to previous work (average of 3.3ºC per CO2 doubling). An energy balance analysis of the model ensemble indicated that global mean warming in the Eocene compared with the preindustrial period mostly arises from decreases in emissivity due to the elevated CO2 concentration (and associated water vapor and long-wave cloud feedbacks), whereas the reduction in the Eocene in terms of the meridional temperature gradient is primarily due to emissivity and albedo changes owing to the non-CO2 boundary conditions (i.e. the removal of the Antarctic ice sheet and changes in vegetation).

In contrast with previous work, three of the eight models examined showed results that are consistent with the proxies in terms of the global mean temperature, meridional SST gradient, and CO2. However, at a more regional scale, the models lack skill. In particular, the modeled anomalies are substantially lower than those indicated by the proxies in the southwest Pacific (Fig. 1, lower panels); here, modeled continental surface air temperature anomalies are more consistent with surface air temperature proxies, implying a possible inconsistency between marine and terrestrial temperatures in either the proxies or models in this region.

Figure 2: Modeled global-mean near-surface annual mean air temperature (ºC) from those model simulations in the DeepMIP-Eocene (early Eocene) model ensemble that carried out simulations at 4x or 6x pre-industrial (consistent with proxy CO2 estimates of Anagnostou et al. 2020), plus the 3x pre-industrial simulation using CESM2.1 (Zhu et al. 2020). Also shown is the proxy-based estimate of global mean temperature from Inglis et al. (2020).

The results from Lunt et al. (2021) and Inglis et al. (2020) have contributed to the recently published 6th assessment report of the Intergovernmental Panel on Climate Change (IPCC AR6). Individual models that have used the DeepMIP boundary conditions have also contributed exciting results, including the finding that one of the high-ECS CMIP6 models, CESM2.1, produces a climate that is substantially warmer than indicated by the paleoproxies (Zhu et al. 2020; Fig. 2). This result is partly due to the response to the non-CO2 forcings, which suggests that more attention is due on that subject. In addition, one of the low-ECS CMIP6 models, INMCM4-8, produces results at the low end of the proxy temperature estimates (Fig. 2). This indicates that the early Eocene may be a potentially useful tuning target for Earth system model (ESM) development, in particular if tighter constraints can be placed on the CO2 concentration.

Ongoing and future DeepMIP activities

It is currently a very busy time for DeepMIP scientists, as several papers exploring the model ensemble and proxy data are currently in various stages of preparation. This includes studies focusing on ocean circulation (Zhang et al. submitted), Arctic sea ice (Niezgodzki et al. submitted), and the African monsoon (Williams et al. in prep), and other papers listed here:; of these, several studies are proposing to explore the role of paleogeography and ocean gateways on regional climate. It is anticipated that many of these papers will be published in a Special Issue of Paleoceanography and Paleoclimatology, "DeepMIP in the Hothouse Earth: late Paleocene – early Eocene Climates and their lessons for the future", which is being organized by Margot Cramwinckel, Michael Henehan, and Jean-Baptiste Ladant. This work will be aided greatly by the existence of the DeepMIP model outputs database, which contains the model outputs from all eight Eocene models; see here for access:

It may be that DeepMIP will explore more time periods over the next few years. Based on experience, we expect that new time periods are best explored initially with ad-hoc ensembles of opportunity. In this regard, there has already been progress on the Eocene-Oligocene Transition (EOT; Hutchinson et al. 2021) and the Miocene (Burls et al. 2021). As such, we have already created three sub-groups within DeepMIP; DeepMIP-Eocene, DeepMIP-EOT, and DeepMIP-Miocene.

In any case, whatever happens, we will always expect DeepMIP to have a focus on integration of models and proxies, and work to bringing the modeling and data communities ever closer.