DOI: 10.5061/dryad.fttdz08qt
title: Evaluating multiple historical climate products in ecological models under current and projected temperatures

description: 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.
description: 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).
description: 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<br> transect: Temperature transect code:
 HEA = Mt. Healy; IGLOO = Igloo Creek; SPR = Spruce Ridge; NAB = Nabesna;
 FUN = Funnel Creek.<br> plot_ID: Individual sampling plot
 name<br> tmean_obs (°C): Estimated temperature at each sensor.
 Estimation employed approach described in Roland et al. (2019).<br>
 tmean_chelsa (°C): Mean July temperature extracted from CHELSA data
 (1981-2010).<br> tmean_daymet (°C): Mean July temperature extracted
 from Daymet data (1981-2010).<br> tmean_prism1 (°C): Mean July
 temperature extracted from PRISM data (1971-2000).<br> tmean_prism2
 (°C): Mean July temperature extracted from PRISM data
 (1981-2010).<br> tmean_snap (°C): Mean July temperature extracted
 from SNAP data (1981-2010).<br> tmean_wc1 (°C): Mean July
 temperature extracted from WorldClim data (1960-1990).<br> tmean_wc2
 (°C): Mean July temperature extracted from WorldClim data
 (1970-2000).<br> ifsar_elev (km): High-resolution (5 m) elevation
 data extracted from IfSAR data (from
 http://ifsar.gina.alaska.edu/).<br> chelsa_elev (km): Lower
 resolution elevation data extract from CHELSA data.<br> daymet_elev
 (km): Lower resolution elevation data extract from Daymet data.<br>
 prism_elev (km): Lower resolution elevation data extract from PRISM
 data.<br> snap_elev (km): Lower resolution elevation data extract
 from SNAP data.<br> wc_elev (km): Lower resolution elevation data
 extract from WorldClim data.<br> elevdiff_chelsa (km): Difference
 between IfSAR elevation and CHELSA elevation.<br> elevdiff_daymet
 (km): Difference between IfSAR elevation and Daymet elevation.<br>
 elevdiff_prism (km): Difference between IfSAR elevation and PRISM
 elevation.<br> elevdiff_snap (km): Difference between IfSAR
 elevation and SNAP elevation.<br> elevdiff_wc (km): Difference
 between IfSAR elevation and WorldClim elevation.<br> plot_slope (°):
 Slope of the sampling plot, measure from IfSAR.<br> plot_EQ (°):
 Equivalent latitude of the plot (Lee 1962), calculated from slope, aspect,
 and latitude data.<br> soil_depth (cm): Average of deepest 4 field
 measurements out of 16 using a metal probe.<br> 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.<br> 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.