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Evaluating multiple historical climate products in ecological models under current and projected temperatures

Citation

Sadoti, Giancarlo et al. (2020), Evaluating multiple historical climate products in ecological models under current and projected temperatures, Dryad, Dataset, https://doi.org/10.5061/dryad.fttdz08qt

Abstract

Gridded historical climate products (GHCPs) are employed with increasing frequency when modeling ecological phenomena across large scales and predicting ecological responses to projected climate changes. Concurrently, there is an increasing acknowledgement of the need to account for uncertainty when employing climate projections from ensembles of global circulation models (GCMs) and emissions scenarios. Despite the growing usage and documented differences among GHCPs, uncertainty characterization has primarily focused on the roles of GCM and emissions scenario choice, while the consequences of using a single GHCP to make predictions over space and time has received relatively less attention. Here we employ average July temperature data from observations and seven GHCPs to model plant canopy cover and tree basal area across central Alaska, U.S.A. We first compare fit and support of models employing raw observed or GHCP temperature values versus those with an elevation adjustment, finding (1) greater support for, and better fit using elevation-adjusted versus raw temperature models and (2) overall similar fits of elevation-adjusted models employing temperature from observations or GHCPs. Focusing on basal area, we next compare predictions generated by elevation-adjusted models employing GHCP data under current conditions and a warming scenario of current temperatures plus 2 °C, finding good agreement among GHCPs though with between-GHCP differences and variation primarily at middle elevations (~ 1,000 m). These differences were amplified under the warming scenario. Finally, using pooled indices of prediction variation and difference across GHCP models, we identify characteristics of areas most likely to exhibit prediction uncertainty under current and warming conditions. Despite (1) overall good performance of GHCP data relative to observations in models and (2) positive correlation among model predictions, variation in predictions across models—particularly in mid-elevation areas where the position of treeline may be changing—suggests researchers should exercise caution if selecting a single GHCP for use in models. We recommend the use of multiple GHCPs to provide additional uncertainty information beyond standard estimated prediction intervals, particularly when model predictions are employed in conservation planning.

Methods

This tabular dataset (in .csv format) is a combination of field-measured covariates and data extracted from gridded elevation and climate data sets. These data were those employed in modeling canopy cover presence or basal area. Climate and elevation adjustment covariates varied among models. Several data transformations were employed as described in the text of the manuscript.

Field measurements:

We employed measures of vegetation, temperature, topography, and soils (Table 1) described in Roland et al. (2019) from 83 sampling plots across five transects located within three National Park Service units in interior Alaska, U.S.A. (Figure 1). Transects included between 11 and 30 plots (mean = 16.6). Surface (~1.5 m) air temperature values were estimated from locally recorded sensor values using the approach described in Roland et al. (2019).

Climate products:

Products from which elevation and 30- or 31-year monthly July average temperature data were extracted were Climatologies at High resolution for the Earth’s Land Surface Areas (CHELSA; Karger et al. 2017), Daymet (Thornton et al. 1997), the Parameter-elevation Regression on Independent Slopes Model (PRISM; Daly et al. 1997, Daly et al. 2008), Scenarios Network for Alaska and Arctic Planning (SNAP; employing the approach of Gray et al. 2014), and WorldClim (Fick and Hijmans 2017).

Usage Notes

Field descriptions (with units):

park_code: National Park Service unit code: DENA = Denali National Park and Preserve; WRST = Wrangell-St. Elias National Park and Preserve; YUCH = Yukon-Charley Rivers National Preserve
transect: Temperature transect code: HEA = Mt. Healy; IGLOO = Igloo Creek; SPR = Spruce Ridge; NAB = Nabesna; FUN = Funnel Creek.
plot_ID: Individual sampling plot name
tmean_obs (°C): Estimated temperature at each sensor. Estimation employed approach described in Roland et al. (2019).
tmean_chelsa (°C): Mean July temperature extracted from CHELSA data (1981-2010).
tmean_daymet (°C): Mean July temperature extracted from Daymet data (1981-2010).
tmean_prism1 (°C): Mean July temperature extracted from PRISM data (1971-2000).
tmean_prism2 (°C): Mean July temperature extracted from PRISM data (1981-2010).
tmean_snap (°C): Mean July temperature extracted from SNAP data (1981-2010).
tmean_wc1 (°C): Mean July temperature extracted from WorldClim data (1960-1990).
tmean_wc2 (°C): Mean July temperature extracted from WorldClim data (1970-2000).
ifsar_elev (km): High-resolution (5 m) elevation data extracted from IfSAR data (from http://ifsar.gina.alaska.edu/).
chelsa_elev (km): Lower resolution elevation data extract from CHELSA data.
daymet_elev (km): Lower resolution elevation data extract from Daymet data.
prism_elev (km): Lower resolution elevation data extract from PRISM data.
snap_elev (km): Lower resolution elevation data extract from SNAP data.
wc_elev (km): Lower resolution elevation data extract from WorldClim data.
elevdiff_chelsa (km): Difference between IfSAR elevation and CHELSA elevation.
elevdiff_daymet (km): Difference between IfSAR elevation and Daymet elevation.
elevdiff_prism (km): Difference between IfSAR elevation and PRISM elevation.
elevdiff_snap (km): Difference between IfSAR elevation and SNAP elevation.
elevdiff_wc (km): Difference between IfSAR elevation and WorldClim elevation.
plot_slope (°): Slope of the sampling plot, measure from IfSAR.
plot_EQ (°): Equivalent latitude of the plot (Lee 1962), calculated from slope, aspect, and latitude data.
soil_depth (cm): Average of deepest 4 field measurements out of 16 using a metal probe.
Sumcover_gt_1m (%): Measured in field on two 16-m point transects using vertical strata (≥ 1 m above ground). Converted to presence and absence of canopy for models.
alltrees_BA (m2/ha): Sum of basal area for all live trees (measured at 1.37 m above ground) within 8 m of plot centers.

Funding

National Park Service