The Pliocene Model Intercomparison Project (PlioMIP) is a co-ordinated international climate modelling initiative to study and understand climate and environments of the Late Pliocene, as well as their potential relevance in the context of future climate change. PlioMIP examines the consistency of model predictions in simulating Pliocene climate and their ability to reproduce climate signals preserved by geological climate archives. Here we provide a description of the aim and objectives of the next phase of the model intercomparison project (PlioMIP Phase 2), and we present the experimental design and boundary conditions that will be utilized for climate model experiments in Phase 2.
Following on from PlioMIP Phase 1, Phase 2 will continue to be a mechanism
for sampling structural uncertainty within climate models. However, Phase 1
demonstrated the requirement to better understand boundary condition
uncertainties as well as uncertainty in the methodologies used for
data–model comparison. Therefore, our strategy for Phase 2 is to utilize
state-of-the-art boundary conditions that have emerged over the last 5
years. These include a new palaeogeographic reconstruction, detailing ocean
bathymetry and land–ice surface topography. The ice surface topography is
built upon the lessons learned from offline ice sheet modelling studies.
Land surface cover has been enhanced by recent additions of Pliocene soils
and lakes. Atmospheric reconstructions of palaeo-CO
Finally we have designed a suite of prioritized experiments that tackle issues surrounding the basic understanding of the Pliocene and its relevance in the context of future climate change in a discrete way.
The PlioMIP project was initiated in 2008 and is closely aligned with the
US Geological Survey Project known as PRISM (Pliocene Research
Interpretation and Synoptic Mapping). The PRISM project has spent more than
25 years focusing on the reconstruction and understanding of the
mid-Pliocene climate (
Phase 1 of the PlioMIP project commenced in 2008 and was concluded in 2015. In Phase 1 two mid-Pliocene experiments were performed. Experiment 1 used atmosphere-only general circulation models (GCMs) with prescribed surface boundary conditions (sea surface temperatures, sea ice and vegetation) derived from the PRISM3D data set (Dowsett et al., 2010). Land–sea distribution and topography were also prescribed from PRISM3D. Experiment 2 used coupled ocean–atmosphere GCMs where sea surface temperatures and sea ice were predicted dynamically by the models; vegetation, land–sea distribution, and topography remained fixed to PRISM3D estimates.
The scientific objectives in Phase 1 were the following:
examine large-scale features of mid-Pliocene climate that are consistent
across models determine the dominant components of mid-Pliocene warming derived from the
imposed boundary conditions examine first-order changes in ocean circulation between the mid-Pliocene
and present day examine the behaviour of the monsoons (e.g. their intensity) compare model results with proxy data to determine the performance of models
simulating a warm climate state use the mid-Pliocene as a tool to evaluate the long-term sensitivity of the
climate system to near-modern concentrations of atmospheric CO
In the context of co-ordinated international model intercomparison projects, PlioMIP achieved a number of firsts. For example, it was the first palaeoclimate modelling intercomparison project to require altered vegetation distributions to be modified in climate models, facilitating vegetation–climate feedbacks to be incorporated into the model intercomparison. It was also the first intercomparison project that required individual groups to fully document the implementation of palaeo-boundary conditions within their models, along with the basic climatological responses. This was designed to facilitate the intercomparison itself by enabling artefacts of individual methodologies of boundary condition implementation to be separated from robust model responses to imposed Pliocene boundary conditions. Through PlioMIP, a spin-off project known as PLISMIP (Pliocene Ice Sheet Model Intercomparison Project; Dolan et al., 2012) was initiated and has focused on (1) assessing ice sheet model dependency of Greenland Ice Sheet reconstructions during the Pliocene using shallow ice approximation ice sheet models (Dolan et al., 2012; Koenig et al., 2015), (2) examining the effect of different GCM climatological forcing on predicted ice sheet configurations (Dolan et al., 2015) and (3) using shallow shelf ice sheet models for Antarctica to test both ice sheet model and climate model dependency on predicted ice sheet reconstructions (de Boer et al., 2015).
Outputs from PlioMIP Phase 1 include the following:
It identified consistency in surface temperature change across models in the
tropics and a lack of consistency in the simulated temperature response at
high latitudes (Haywood et al., 2013a). Model predictions are inconsistent in terms of total precipitation rate in
the tropics (Haywood et al., 2013a). Global annual mean surface temperatures increased by 1.8 to
3.6 There was no clear indication in the model ensemble to support either
enhanced or weaker Atlantic Meridional Overturning Circulation and ocean
heat transport to the high latitudes (Z.-S. Zhang et al., 2013). Model predictions of enhanced Atlantic Meridional Overturning Circulation
and ocean heat transport to high latitudes are inconsistent, in sign as well
as strength (Z.-S. Zhang et al., 2013). Clear-sky albedo and greenhouse gas emissivity dominate polar amplification
of surface temperature warming during the Pliocene. This demonstrated the
importance of specified ice sheet and high-latitude vegetation boundary
conditions and simulated sea ice and snow albedo feedbacks. Furthermore, the
dominance of greenhouse gas emissivity in driving surface temperature
changes in the tropics was identified (Hill et al., 2014). The simulated weakened mid-Pliocene East Asian winter winds in north monsoon
China and intensified East Asian summer winds in monsoon China agreed well
with geological reconstructions (R. Zhang et al., 2013). Data–model comparison using both sea surface and surface temperature proxies
indicate that climate models potentially underestimate the magnitude of
polar amplification. However, current limitations in age control and
correlation make interpreting data–model discrepancies challenging (Dowsett
et al., 2012, 2013a; Salzmann et al., 2013). Model results indicate that longer-term climate sensitivity (Earth system
sensitivity) is greater than Charney Sensitivity (best estimate ESS
One of the key findings in PlioMIP Phase 1 was the potential underestimation of model-predicted surface temperature warming in the high latitudes. Understanding data–model discord is non-trivial and can rarely be attributed to a single factor. The complexity of understanding data–model discord is highlighted by the PMIP triangle (Fig. 1), which illustrates three possible contributions to data–model discrepancy, and it has at its vertex model physics (structural and parameter uncertainty), model boundary conditions and proxy data uncertainty.
The PMIP triangle which illustrates three possible contributions to data–model discrepancy, and it has at its vertex model physics (structural and parameter uncertainty), model boundary conditions and proxy data uncertainty (Haywood et al., 2013a).
Following on from PlioMIP Phase 1, Phase 2 will continue to be a mechanism
for sampling structural uncertainty within climate models as a suite of
different models will take part in PlioMIP. However, Phase 1 demonstrated
the requirement to better understand boundary condition uncertainties as
well as weaknesses in the methodologies used for data–model comparison, which
largely stemmed from the time-averaged nature of proxy data used in previous
data–model comparisons (Dowsett et al., 2013a; Salzmann et al., 2013).
Therefore, our strategy for Phase 2 is to utilize state-of-the-art boundary
conditions that have emerged over the last 5 years. These include a new
palaeogeography reconstruction detailing ocean bathymetry and land–ice
surface topography and new data sets describing the distribution of
Pliocene soils and lakes. The ice surface topography is built upon the
lessons learned during the PLISMIP project (Dolan et al., 2015). Land
surface cover will be enhanced by recent additions of Pliocene soils and
lakes (Pound et al., 2014). Atmospheric reconstructions of palaeo-CO
It was recognized during Phase 1 that a key influence on data–model discord stems from uncertainties associated with the derivation of the proxy data sets used to assess the climate models. Although certainty surrounding any proxy data set is limited by analytical, spatial and temporal uncertainty, Phase 1 highlighted temporal uncertainty as an important constraint on more robust methodologies for data–model comparison (DMC: Dowsett et al., 2013a; Haywood et al., 2013b; Salzmann et al., 2013). The concept of climate stability during the Pliocene is overly simplistic both in geological climate archives and climate modelling approaches.
Due to the increasing recognition of climate variability in the Pliocene, time-averaged approaches to palaeoenvironmental reconstruction have reached their ultimate potential to evaluate climate models. Therefore, enhancing the temporal resolution of data collection in order to more adequately understand climate variation in the Pliocene is required, along with developing a more strategic approach to the choice of relevant Pliocene event(s) to reconstruct and model. One of PlioMIP's guiding principles is to utilize palaeoenvironments to better inform us of likely scenarios for future global change. To this end, the event chosen for PlioMIP Phase 2 focuses on the identification of a “time slice” centred on an interglacial peak (MIS KM5c; 3.205 Ma) that has near-modern orbital forcing, and yet it retains many of the characteristics of Pliocene warmth on which we have focused in the past (Dowsett et al., 2013b; Haywood et al., 2013b; Salzmann et al., 2013; Prescott et al., 2014). Discussions surrounding potential modification of the LR04 benthic isotope stack (Lisiecki and Raymo, 2005) are currently ongoing, which may lead to a modification of the Marine Isotope Stage assigned to the astrochronological age of 3.205.
PRISM and the wider Pliocene data community are rising to the challenge to
obtain higher-resolution proxy data that will inform the models about the
chosen time slice (e.g. Dowsett et al., 2013b; see also Haywood et al.,
2013b). The key differences between the PRISM data that underpinned PlioMIP
Phase 1 and the new direction for data collection include the
following:
Expanding to a community-wide effort, new data generation will focus on key
locations and specific regions that have been identified by PlioMIP Phase 1
as important for understanding Pliocene climate variability and model
performance. In order to increase our understanding of temporal changes in Pliocene
climate, time series data are being produced as standard, which will in
essence increase previous temporal resolution by 2 orders of magnitude and
lead to enhanced methods of data–model comparison (Dowsett et al., 2013b). We will encourage the use of multi-proxy methods of data generation. This
will enable us to derive more robust and holistic palaeoenvironmental
reconstructions.
Experimental design strategy adopted for PlioMIP Phase 2. Core
experiments will be completed by all model groups. Tier 1 and Tier 2 in
either “Pliocene4Future” or “Pliocene4Pliocene” describe a series of
sensitivity tests (Tier 1 being a higher priority for completion than Tier
2). Please note that Pliocene4Future Tier 1 experiment Pre-Industrial
CO
The utilization of the mid-Pliocene as a means to understand future global change (“Pliocene4Future”) remains a priority in Phase 2. It is our intention to forge stronger links between PlioMIP, PMIP, CMIP and the next IPCC assessment. However, we recognize that many researchers are primarily interested in the Pliocene because it represents a considerable challenge to our understanding of the operation of the Earth system (“Pliocene4Pliocene”). Furthermore, a number of scientific requirements and priorities do not fit exclusively within a Pliocene4Future mandate. For example, palaeographic reconstructions are indicating more regional variations in palaeogeography than were appreciated in the past (Hill, 2015). Due to the differing requirements identified, in PlioMIP Phase 2 we have designed a portfolio of model experiments that effectively address both the “Pliocene4Future” and “Pliocene4Pliocene” agendas. This is illustrated in the following CMIP-style diagram (e.g. Taylor et al., 2012), where priorities for both agendas are highlighted, with both agendas sharing a common core experiment that will serve as the PlioMIP Phase 2 experiment within CMIP.
The experiments in PlioMIP Phase 2 have been grouped into halves “Pliocene4Pliocene” and “Pliocene4Future” and would ideally be completed by all participating groups. However, only the core experiments must be completed by all groups. Each half of the project is divided into two “tiers” (Fig. 2). After the core experiments, Tier 1 experiments are identified as a higher priority for completion than Tier 2.
We describe several model simulations, which essentially consist of various
combinations of boundary conditions associated with prescribed CO
PRISM4 palaeogeography (enhanced) including topography/bathymetry (m) over the ice sheets (left). PRISM4 topographic and bathymetric anomaly (m) from modern (ETOPO1: right). Red boxes highlight the Canadian archipelago and Bering Strait as closed in both the standard and enhanced boundary condition data sets.
Within the Pliocene4Future agenda, given the uncertainty in total greenhouse
gas forcing for the KM5c time slice, we have proposed simulations using 350
and 450 ppmv CO
Within Tier 2 we have proposed two experiments that are designed to assess
the dependence of climate sensitivity on the background climate and boundary
condition states. Here we wish to compare the response of the system to
CO
For our Pliocene4Pliocene agenda we have within Tier 1 focused on the
atmospheric CO
All required boundary conditions can be accessed from the United States
Geological Survey PlioMIP2 website (see
The experimental design for the core Pliocene KM5c time slice experiment is
summarized in Table 1 (standard and enhanced boundary conditions).
Integration length is to be set to at least 500 years in accordance with
CMIP guidelines (Coupled Model Intercomparison Project Phase) for
equilibrated coupled model experiments (Taylor et al., 2012). The
concentration of CO
Details of NetCDF data packages provided to facilitate PlioMIP Phase 2 experiments.
While Pliocene CO
The solar constant is to be specified as the same as in each participating
group's pre-industrial control run. In previous versions, the PRISM boundary
conditions (Dowsett et al., 2010) represented an average of the warm
intervals during the time slab (
The PRISM4 palaeogeography provides a consistent reconstruction of
topography, bathymetry, ice sheets and the land–sea mask that can be
implemented in PlioMIP Phase 2 models. The PRISM4 Pliocene palaeogeography
data set is provided in NetCDF format at a 1
The approach taken to derive PRISM4 ice sheets in the palaeogeography reconstruction is different to PRISM3D (Dowsett et al., 2010). The results of PLISMIP have shown that ice sheet model dependency over Greenland is low. However, the initial climatological forcing has a large impact on the predicted Greenland Ice Sheet configuration (Dolan et al., 2015; Koenig et al., 2015). Using a compilation of the results presented in Koenig et al. (2015), we have implemented an ice sheet configuration over Greenland in PRISM4 where we have the highest confidence in the possibility of ice sheet location during the warmest parts of the Late Pliocene (see Fig. 6b in Koenig et al., 2015). The reconstruction of Keonig et al. (2015) was modified by removing ice from southern Greenland. The presence of ice in that region is inconsistent with palynological studies that suggest that southern Greenland was vegetated during warm intervals of the Pliocene (e.g. de Vernal and Mudie, 1989). The PRISM4 Greenland Ice Sheet configuration is smaller than in PRISM3D, and ice is limited to high elevations in the East Greenland Mountains (Fig. 4).
Over Antarctica, work in PLISMIP is still ongoing (de Boer et al., 2015); therefore we have decided to use an ice sheet that best agrees with the available proxy data. Based on evidence from the ANDRILL core data and ice sheet modelling (Naish et al., 2009; Pollard and DeConto, 2009) that suggests that, in specific warm periods of the Late Pliocene, there was no ice present in West Antarctica, this region remains ice free in the PRISM4 palaeogeography reconstruction (Fig. 4). Over East Antarctica, Cook et al. (2014) show that the Wilkes subglacial basin may have been highly dynamic during the warmest parts of the Late Pliocene, and they infer significant potential for ice sheet retreat in this region. Additionally, Young et al. (2011) highlight the Aurora subglacial basin as an area which may have been subject to marine ice sheet instabilities in the past (potentially in the Pliocene). Therefore, over East Antarctica PlioMIP Phase 2 uses the PRISM3D ice sheet reconstruction (Hill et al., 2007; Hill, 2009; Dowsett et al., 2010), as this remains consistent with more recently available data. In this reconstruction (Fig. 4) large portions of the East Antarctic ice sheet show little change or a small increase in surface altitude with respect to modern, and significant ice sheet retreat is limited to the low-lying Wilkes and Aurora subglacial basins.
PRISM4 land–sea mask (enhanced version) showing Greenland and Antarctic ice sheet distribution. Canadian archipelago and Bering Strait closed (red boxes) in both the standard and enhanced boundary condition data sets.
For the Pliocene experiments, two versions of the palaeogeography will be
provided to climate modelling groups:
Standard: For the models where altering the LSM and bathymetry is
problematic, we provide a palaeogeography with a modern land–sea
configuration and bathymetry (apart from in the Hudson Bay, Bering Strait
and Canadian Archipelago). In this instance the Late Pliocene topographic
elevations were extended to the modern coastline, and the bathymetry
remained at modern values. Groups that are unable to change their land–sea
mask or bathymetry at all are asked to use their local modern boundary
conditions; however guidance on the implementation of Pliocene topography in
this case should be taken from the standard palaeogeography data set. Enhanced: This presents the full palaeogeographic reconstruction including
all changes to topography, bathymetry, ice sheets and the LSM.
To ensure that the climate anomalies (Pliocene minus present day) from all
PlioMIP Phase 2 climate models are directly comparable, i.e. that they
reflect differences in the models themselves rather than the differences of
modern boundary conditions, it has been decided to implement Pliocene
topography (and bathymetry) as an anomaly to whatever modern topographic
data set is used by each modelling group in their own model. To create the
Pliocene topography (and bathymetry) the difference between the PRISM4
Pliocene and PRISM4 Modern topography (bathymetry) should be calculated and
added to the modern topographic (bathymetric) data sets each participating
modelling group employs within their own pre-industrial control simulations, such that
A global data set of vegetation for the KM5c time slice is not available. A number of climate models now have the ability to simulate the type and distribution of vegetation using dynamic vegetation models. In PlioMIP Phase 2 vegetation models should be initialized with pre-industrial vegetation cover and spun up until an equilibrium condition is reached. If Pliocene vegetation cannot be predicted dynamically, modelling groups can prescribe vegetation using the Salzmann et al. (2008) PRISM3 vegetation reconstruction used within PlioMIP Phase 1 (Haywood et al., 2010, 2011) and provided as a mega biome reconstruction in the PlioMIP Phase 2 boundary condition files. An equivalent potential natural vegetation data set is also provided to guide how groups implement prescribed Pliocene vegetation. Further details on correctly approaching the implementation of prescribed Pliocene vegetation for PlioMIP Phase 2 can be found in Haywood et al. (2010: Sect. 3.5).
Due to lack of information covering the distribution of lakes and soils during PlioMIP Phase 1, lakes were absent from the land cover boundary conditions. Since PlioMIP Phase 1, the global distribution of Late Pliocene soils and lakes has been reconstructed through a synthesis of geological data (Pound et al., 2014). Initial experiments using the Hadley Centre Coupled Climate Model Version 3 (HadCM3) indicate regionally confined changes of local climate and vegetation in response to the new lake and soil boundary conditions (Pound et al., 2014). When combined (lakes plus soils), the feedbacks on climate from Late Pliocene lakes and soils improve the proxy data–model fit in western North America as well as the southern part of northern Africa (Pound et al., 2014).
In PlioMIP Phase 2 all modelling groups should implement the Pound et al. (2014) data sets for global lake (Fig. 5) and soil distribution (Fig. 6). If lake distribution is a dynamically predicted variable within a model (i.e. lake distributions can change as a result of predicted changes in climate), prescribing the Pound et al. (2014) lake data set is not necessary. The lake data set provides information on both lake size as well as the fractional coverage of lakes within model grid boxes. Figure 5 also shows how the lake distribution and sizes differ from modern, most notably the absence of post-glacial lakes in North America and the presence of large lakes in central Africa (Pound et al., 2014).
Modern and Pliocene (PRISM4) fractional lake coverage data set
(Pound et al., 2014). Modern data are based upon the FAO
The colour (for albedo) and texture translations for the nine soil orders used in the modelling of Late Pliocene soils and lakes are provided to guide the implementation of soil type and distribution in models. This translation is based upon the definition of soils with the HadCM3 (Table 2).
Groups should implement Pliocene lakes using the anomaly method (the anomaly between the provided Pliocene and modern lake data sets added to each group's local modern lake distribution data set) and ensure that minimum lake fractions do not fall below 0 and the maximum do not exceed 1 (100 %). Groups may implement the Pliocene soils using whatever method they deem most appropriate for their model. This may be by applying the provided Pliocene soil properties directly in their Pliocene simulation (i.e. as an absolute), or by calculating an anomaly from the provided modern soil data, and adding this to the local modern control soil properties. Alternatively, groups may choose to develop a regression of the provided modern soil properties with their local modern control soil properties and then apply the resulting regression formulae to the provided Pliocene soil properties.
Pound et al. (2014) data set of global modern and Pliocene soil types (shown on the enhanced PlioMIP2 land–sea mask). Modern data are based upon the FAO/UNESCO modern soil map (Version 3.6).
The colour (for albedo) and texture translations for the soil orders used in the modelling of Late Pliocene soils, based upon HadCM3 classification.
With regard to river routing the required solution is to follow modern river routes except where this would be inappropriate due to the appearance of new land grid cells in the Pliocene land–sea mask, in which case rivers should be routed to the nearest ocean grid box or most appropriate river outflow point.
Details of all experiments proposed in PlioMIP Phase 2.
Initial PRISM4 sites being investigated to generate time slice proxy data for model evaluation in PlioMIP Phase 2.
Within the Pliocene4Future agenda a pre-industrial experiment with 560
ppmv CO
For the Pliocene4Pliocene agenda we have within Tier 1 focused on the
atmospheric CO
The primary aim of the Pliocene4Pliocene Tier 2 forcing factorization
experiments is to assess the relative importance of various boundary
condition changes which contribute to Pliocene warmth. Following a similar
methodology adopted in Lunt et al. (2012) we intend to partition the total
Pliocene warming (or temperature change;
If groups do not have the computational resource to carry out the full
factorization, they may carry out a linear factorization, as follows:
Short, high-resolution time series extending from MIS M2 through KM3 will be necessary to meet the evaluation requirements of PlioMIP Phase 2. Marine sequences will depend upon chronology from the Lisiecki and Raymo 2005 (LR04) timescale and should have multiple palaeoenvironmental proxies (Dowsett et al., 2013a). Previous work from the palaeoclimate data community suggests a number of sites potentially suitable for evaluation of PlioMIP Phase 2 model outputs (e.g. Dowsett et al., 2013a, b; Fedorov et al., 2013; Salzmann et al., 2013, Brigham-Grette et al., 2013). Well-dated, high-resolution records from the continental interior are scarce, and terrestrial reconstructions will be mostly based on marine and marginal marine sequences. The primary areas of discord between simulated and estimated Pliocene palaeoclimate conditions identified in PlioMIP Phase 1 include the mid-to-high-latitude North Atlantic, tropics and upwelling regions (Dowsett et al., 2012). The PRISM4 marine and terrestrial contribution to the PlioMIP Phase 2 community evaluation data set has been initially concentrated in the North Atlantic region (Fig. 7).
If the PlioMIP Phase 2 core experiment is adopted as a CMIP6 simulation,
model data for this experiment must use the Climate Model Output Rewriter
(CMOR) format and be stored on an ESGF node (The Earth System Grid Federation).
The CMOR library has been specially developed to help meet the requirements
of the model intercomparison. Further details of CMIP6 experiments and
required outputs/CMOR file formats will be made available on the CMIP6
website (
If the PlioMIP Phase 2 core experiment is specified as a PMIP core
experiment, the same guidelines for output format and storage of data
detailed for CMIP6 apply. For PlioMIP Phase 2 experiments listed within
Tier 1 and Tier 2, more flexibility in terms of data storage and file formats is
available. PlioMIP Phase 2 has modified the established variable list
outlined by the third phase of the PMIP project. The list of required
variables can be found on the PlioMIP Phase 2 website
( The data files have to be in the (now widely used) NetCDF binary file format
and conform to the CF (Climate and Forecast) metadata convention (outlined
on the website There must be only one output variable per file. For the data that are a function of longitude and latitude, only regular
grids (grids representable as a Cartesian product of longitude and latitude
axes) are allowed. The file names have to follow the PMIP2 file name convention and be unique
(see the PMIP2 website).
A. M. Haywood, A. M. Dolan and S. J. Hunter acknowledge that the research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. 278636, as well as the EPSRC-supported Past Earth Network. U. Salzmann, A. M. Haywood and M. J. Pound acknowledge funding received from the Natural Environment Research Council (NERC Grant NE/I016287/1). A .M. Haywood and D. J. Lunt acknowledge funding received from the Natural Environment Research Council (NERC Grant NE/G009112/1). D. J. Lunt acknowledges NERC grant NE/H006273/1. H. J. Dowsett recognizes the continued support of the United States Geological Survey Climate and Land Use Change Research and Development Program. B. L. Otto-Bliesner recognizes the continued support of the National Center for Atmospheric Research, which is sponsored by the US National Science Foundation. M. A. Chandler is supported by the NASA Modeling, Analysis, and Prediction program (NASA Grant NNX14AB99A) and the NASA High-End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center. Edited by: W.-L. Chan