Table 1
Overview of the climatic models presented in this paper, emphasising the parameters, spatial and temporal resolution and time reach of the models. The column to the far right shows the sources of information for the model.
| CLIMATIC PARAMETERS | SPATIAL RESOLUTION (LATITUDE × LONGITUDE) | TEMPORAL RESOLUTION | PERIOD REACH | SOURCE | AVAILABLE SITE | |
|---|---|---|---|---|---|---|
| CESM2 | Atmosphere, ocean, land, river run-off, land ice, sea ice | 1° × 1° or 2° × 2° | 1 kyr | – | Bacmeister et al. (2020), Rocha (2023), Cesm.ucar.edu | Community Earth System Model 2 (CESM2) | Community Earth System Model |
| HadCM3 | Atmosphere and ocean temperature, sea ice quantity | 2.5° × 3.75° | 1 kyr | 800 ka | Gordon et al. (2000), Krapp et al. (2021) | BRIDGE — Paper: Valdes et al 2017 |
| Paleo-PGEM | Ocean-atmospheric model (AOGCM) & ice sheet quantity | 1° × 1° | 1 kyr | 5 Ma | Holden et al. (2016, 2019), Barreto et al. (2023) | PALEO-PGEM-Series: a spatial time series of the global climate over the last 5 million years (Plio-Pleistocene) |
| Paleo-Clim | Surface temperature & precipitation estimates (AOGCM) | 0.04° × 0.04° | Snapshots | 130 ka, 787 ka, 3.264–3.025 Ma, 3.3 Ma | Brown et al. (2018) | PaleoClim.org |
| Oscillayers | 19 bio-climatic parameters of mean and extreme temperatures and precipitation | 0.04° × 0.04° | 10 kyr | 5.4 Ma | Gamisch (2019) | Dryad | Data -- Oscillayers: a data set for the study of climatic oscillations over Plio-Pleistocene time scales at high spatial-temporal resolution |
| PMIP4 | Air temperature, precipitation, sea surface temperature, ocean heat, snow cover & depth, soil moisture, ice sheet extent, vegetation fraction | 0.5° × 0.5° to 2.5° × 2.5° | Snapshots | Last millennium, LGM, mid-Holocene, last glacial, mid-Pliocene | Taylor et al. (2012), Kageyama et al. (2018) | Model database | PMIP |
Table 2
Overview of the environmental reconstructions presented in this paper, emphasising the parameters, spatial and temporal resolution, and chronological range of the models. The column to the far right shows the sources used for the reconstructions.
| CLIMATIC PARAMETERS | OUTPUTS | SPATIAL RESOLUTION | TEMPORAL RESOLUTION | PERIOD REACH | SOURCE | AVAILABLE SITE | |
|---|---|---|---|---|---|---|---|
| LOVECLIM/iLOVECLIM | Ocean general circulation model (AOGCM) & thermodynamic sea ice model | Vegetation cover following climatic variables | 1° × 1° | 1-year | 125 ka | Goosse et al. (2010), Roche et al. (2014) | Loveclim 784K | Climate Data Overview – iLOVECLIM ESM |
| PRISM4 | Palaeogeography, sea level, ocean temperature, land and sea ice | Terrestrial vegetation, soil, lakes | 0.25° × 0.25° | Marine isotope stages | 2.6–3.6 Ma | Dowsett et al. 2016 | PRISM4: Data |
| BIOME4 | Atmospheric CO2 (CESM) | Vegetation cover, divided into 28 biomes from PFTs | 0.5° × 0.5° | 1000-year | LGM | Harrison & Prentice (2003), Zeller et al. (2023) | GitHub – jedokaplan/BIOME4: The BIOME4 equilibrium global vegetation model |
| CARAIB | Air temperature, precipitation, sunshine hours, air humidity, wind speed | Soil hydrology, surface energy budget, GPP, NPP, LAI, biomass, BAGs cover fraction, fire module | 1° × 1° | 1-year | LGM, Mid Holocene, Miocene | François et al. (1998), Dury et al. (2011) | UMCCB: Research: Projects: BIOSERF |
| ORCHIDEE | Atmospheric CO2, air temperature, humidity | Soil carbon, soil temperature and hydrology, vegetation divided into 13 PFT groups, river and floodplain scheme | 1° × 1° | 1-year | Input dependent | Krinner et al. (2005, Guimberteau et al. (2018) | Documentation/Forcings – ORCHIDEE |
Figure 1
Flow diagram displaying the workflow for producing a raster layer applicable to a hominin dispersal simulation.
Table 3
List of climatic emulators and reconstructions addressed in this paper, advantages and limitations for investigating global hominin dispersal patterns during the Early Pleistocene and validation methods.
| EMULATOR | ADVANTAGES | LIMITATIONS | VALIDATION |
|---|---|---|---|
| CESM2 | High spatial resolution High temporal resolution | Lack of Pleistocene studies/data | No, but comes with control simulations |
| HadCM3 | High temporal resolution | Lower spatial resolution Timescale limited to 800 ka | Observed ocean data |
| Paleo-PGEM | High spatial resolution High temporal resolution Time reach: 5 Ma | Computing limitations, accuracy of represented elements/dynamics in the downscaling | Observed temperature reconstructions & proxy data |
| Paleo-Clim | Very high spatial resolution | Snapshot approach | Inter-model comparison |
| Oscillayers | Very high spatial resolution Bioclimatic parameters Time reach: 5.4 Ma | Low temporal resolution | Inter-model comparison |
| PMIP4 | Very high spatial resolution Detailed output | Snapshot approach Requires detailed input data | Proxy reconstructions, control simulations, benchmark tools |
Table 4
List of environmental emulators and reconstructions addressed in this paper, advantages and limitations for investigating global hominin dispersal patterns during the Early Pleistocene and validation methods.
| EMULATOR | ADVANTAGES | LIMITATIONS | VALIDATION |
|---|---|---|---|
| LOVECLIM/ iLOVECLIM | High spatial resolution High temporal resolution Vegetation cover Sea Ice | So far only tested back to 125 ka | Proxy data & inter-model comparison |
| PRISM4 | Very high spatial resolution Vegetation cover Land and sea ice Lake element Time reach: 2.6–3.6 Ma | Low temporal resolution | No, functions as a conceptual model |
| BIOME4 | Very high spatial resolution High temporal resolution Biome-divided vegetation cover | Limited to the LGM | Proxy data divided into PFT groups |
| CARAIB | Very high spatial resolution Variety of output data: soil hydrology, biomass, GPP, NPP, LAI & BAGs | Input data requirements | Inter-model comparisons, CO2 observations |
| ORCHIDEE | High spatial resolution Very high temporal resolution PFT-class divided vegetation cover, including GPP and NPP values River and floodplain scheme Applicable to any climate forcing | Very detailed, heavy to run Based on present measurements | Observed temperature gradients, basin-scale averages, topsoil moisture models, forest data |