Paleoclimate Simulations
The paleoclimate simulations used here come from the HadCM3 version of the UK Met Office Unified Model General Circulation Model (GCM) and the Community Climate System Model (CCSM) of the National Center for Atmospheric Research (NCAR). These are well-established coupled ocean atmosphere climate model, having contributed to the last three Intergovernmental Panel on Climate Change (IPCC) Assessment Reports (AR3, AR4 and AR5), and used to simulate climate for nearly 20 years. The atmospheric component has a time-step of 30 minutes, and is coupled to the ocean every day. Typically, the climatology is output every month, and the mean annual and monthly climate are calculated from these data. As the name GCM suggests, this class of climate model is able to reproduce the major circulations in the both the atmosphere and ocean, as well as major drivers of inter-annual variability. The resolution also allows for synoptic weather patterns to be simulated, along with key climate oscillations, but may not simulate well local extremes or regions with high gradients (e.g. extreme convective events). HadCM3 is in the middle of the range of overall climate sensitivities exhibited by the IPCC-class climate models, whereas CCSM models are in the lower overall range of climate sensitivities.
The paleoclimate simulations are mathematical models of the general circulation of a planetary atmosphere or ocean. They are massively complex simulations of weather on Earth during as the result of global conditions at that time period, including: location and shape of terrestrial landmasses, CO2/N2O/CH4 levels, and orbital parameters
If GCM simulations represent a composite of a continuous period (i.e. the CCSM data from PaleoView), mean/min/max climate values were calculated in 50 year window increments, which were subsequently processed for the whole focal time period (e.g. for the Heinrich Stadial 1, which occurred from 17.0-14.7 ka, all 50 yr composites within this period were averaged).
Details of Paleoclimate Simulations
| time period (bp) |
GCM |
Citations |
| Pleistocene: late-Holocene, Meghalayan (4.2-0.3 ka) |
CCSM | Fordham et al., 2017 |
| Pleistocene: mid-Holocene, Northgrippian (8.326-4.2 ka) |
CCSM | Fordham et al., 2017 |
| Pleistocene: early-Holocene, Greenlandian (11.7-8.326 ka) |
CCSM | Fordham et al., 2017 |
| Pleistocene: Younger Dryas Stadial (12.9-11.7 ka) |
CCSM | Fordham et al., 2017 |
| Pleistocene: Bølling-Allerød ( 14.7-12.9 ka) |
CCSM | Fordham et al., 2017 |
| Pleistocene: Heinrich Stadial 1 (17.0-14.7 ka) |
CCSM | Fordham et al., 2017 |
| Pleistocene: Last Interglacial (ca. 130 ka) |
CCSM | Otto-Bliesner… 2006 |
| Pleistocene: MIS19 (ca. 787 ka) |
HadCM3 | Brown et al., 2018 |
| Pliocene: mid-Pliocene warm period (3.205 Ma) | HadCM3 | Hill 2015 |
| Pliocene: M2 (ca. 3.3 Ma) | HadCM3 | Dolan et al., 2015 |
Statistical Downscaling:
We employed the Change-Factor method to downscale the paleoclimatic climatologies. This approach creates high-resolution layers by quantifying the differences between the paleo and current (control) climatologies for each raw variable, at the native model-specific spatial resolution. This functions as a calibration step to measure the raw climate anomalies at the coarser spatial scale climate model. Once this step is completed, the difference layers (commonly called delta layers, change-factor differences, or climate change anomalies) are downscaled to high-resolutions (here 5-20km) and summed to a matching high-resolution current climate variable. This method is relatively quick, requiring less than a day of computational time per raster layer, and can be efficiently applied to global datasets. A major benefit of the Change-Factor method relative to other methods of downscaling is its ability to incorporate small-scale topographic nuances in regional climatologies that are often not captured in climate models, but present in the high-resolution current datasets. Examples include climatic differences in mountainous regions such as differences between valleys, mid-elevation ranges, and their peaks.
Here, we created global delta layers by subtracting the raw temperature and precipitation values of each snapshot paleoclimatic simulation from corresponding HadCM3 control simulations that represent the pre-industrial era. The delta layer represents the pixel-by-pixel changes from pre-industrial conditions, within the constraints of each snapshot climate simulation. The delta layers were downscaled 60 fold from 2.5 arc-degrees to 2.5 arc-minutes (ca. 5km) using a tensioned spline in ArcGIS 10.5 (sampling=12 nearest observations to a focal point, weight of 0.1, ESRI 2018). A spline is a deterministic interpolation method that ihas been commonly considered as appropriate for interpolation environmental variables. We used a tensioned spline (instead of a regularized spline) to avoid extraneous inflection points, and more generally to preserve shape properties, such as monotonicity and convexity, of a set of data points – and to do so without sacrificing smoothness . Spline approaches are based on requirement that the interpolation function passes through the data points, but also yield the smoothest transition as possible.
The high-resolution delta layers were then summed to a corresponding current monthly temperature or precipitation climate layers from the Climatologies at High-Resolution for the Earth’s Land Surface Areas (CHELSA) database, at the same resolution (see download links here). Though rare in our analyses, negative precipitation values were converted to zero. To reduce pixel-depth and file sizes of final products, all monthly temperature raster layers were multiplied by 10 and converted to integers. Prior to the creation of bioclimate layers, final monthly layers were adjusted to the mean sea-level of paleoclimatic period, based on adjustments to a contemporary bathymetry dataset.
Downscaling creates high resolution datasets useful for biological modeling from the coarse data output from paleoclimatic simulations
Bioclimatic parameters:
From the high-resolution monthly temperature and precipitation values, we calculated a set of derived parameters broadly used in ecological applications. These bioclimatic variables are derived from the monthly mean temperature (or minimum and maximum temperature, depending on their availability) and precipitation values. They are specifically developed for species distribution modelling and related ecological applications . The procedure for generating bioclimatic variables followed WorldClim and used the ‘biovars’ function of the R package dismo. Output bioclimate layers were saved as individual GeoTiffs (*tif) and projected in the WGS 1984 projection.
Bio_1=Annual Mean Temperature [°C*10]
Bio_2=Mean Diurnal Range [°C]
Bio_3=Isothermality [Bio_2/Bio_7]
Bio_4=Temperature Seasonality [standard deviation*100]
Bio_5=Max Temperature of Warmest Month [°C*10]
Bio_6=Min Temperature of Coldest Month [°C*10]
Bio_7=Temperature Annual Range [°C*10]
Bio_8=Mean Temperature of Wettest Quarter [°C*10]
Bio_9=Mean Temperature of Driest Quarter [°C*10]
Bio_10=Mean Temperature of Warmest Quarter [°C*10]
Bio_11=Mean Temperature of Coldest Quarter [°C*10]
Bio_12=Annual Precipitation [mm/year]
Bio_13=Precipitation of Wettest Month [mm/month]
Bio_14=Precipitation of Driest Month [mm/month]
Bio_15=Precipitation Seasonality [coefficient of variation]
Bio_16=Precipitation of Wettest Quarter [mm/quarter]
Bio_17=Precipitation of Driest Quarter [mm/quarter]
Bio_18=Precipitation of Warmest Quarter [mm/quarter]
Bio_19=Precipitation of Coldest Quarter [mm/quarter]
Bioclimatic variables, or bioclims, are derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables
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