Evaluating WorldClim Version 1 (1961–1990) as the Baseline for Sustainable Use of Forest and Environmental Resources in a Changing Climate

Abstract

WorldClim version 1 is a high-resolution, global climate gridded dataset covering 1961–1990; a “normal” climate. It has been widely used for ecological studies thanks to its free availability and global coverage. This study aims to evaluate the quality of WorldClim data by quantifying any discrepancies by comparison with an independent dataset of measured temperature and precipitation records across Europe. BIO1 (mean annual temperature, MAT) and BIO12 (mean total annual precipitation, MAP) were used as proxies to evaluate the spatial accuracy of the WorldClim grids. While good representativeness was detected for MAT, the study demonstrated a bias with respect to MAP. The average difference between WorldClim predictions and climate observations was around +0.2 °C for MAT and −48.7 mm for MAP, with large variability. The regression analysis revealed a good correlation and adequate proportion of explained variance for MAT (adjusted R2 = 0.856) but results for MAP were poor, with just 64% of the variance explained (adjusted R2 = 0.642). Moreover no spatial structure was found across Europe, nor any statistical relationship with elevation, latitude, or longitude, the environmental predictors used to generate climate surfaces. A detectable spatial autocorrelation was only detectable for the two most thoroughly sampled countries (Germany and Sweden). Although further adjustments might be evaluated by means of geostatistical methods (i.e., kriging), the huge environmental variability of the European environment deeply stressed the WorldClim database. Overall, these results show the importance of an adequate spatial structure of meteorological stations as fundamental to improve the reliability of climate surfaces and derived products of the research (i.e., statistical models, future projections).

Potential Impact of Climate Change on the Forest Coverage and the Spatial Distribution of 19 Key Forest Tree Species in Italy under RCP4.5 IPCC Trajectory for 2050s

“…This aspect can represent a fair improvement especially for mountainous areas where the use of coarse data can only partially capture the effect of orography [74]. The results we obtained also highlighted the difference in the use of GCMs versus RCMs which are probably optimized scenarios for local areas but very complex and whose calculation is time consuming [61,75,76]. Unfortunately, the use of local data is not still very common and ensemble models are lacking in literature [77].…”

“…This step was done to compare the current climate condition with six GCMs we downloaded from the WorldClim website with 30 arc-sec of spatial resolution. The selected GCMs are those elaborated by the fourth version of Community Climate System (CCSM) here and for the following models CC, the Hadley Centre Global Environment Model version 2 family (HADGEM2 2-AO, 2-CC, 2-ES), respectively, HD, HE, and HG, the Max Planck Institute for Meteorology Earth System Model (MPI-ESM-LR) hereafter MP and the Meteorological Research Institute climate model (MRI-CGCM3) MG. To avoid potential biases that originated from different climate data sources (i.e., WorldClim portal and E-OBS data), the WorldClim future projections were recalculated as anomalies from the 1961-1990 climatic normal period, currently distributed as WorldClim version 1.4 [60,61]. Once anomalies were calculated, these were added to the same climatic normal period we obtained from E-OBS for Italy , using spatial reprojection to realign the two grids.…”

Potential impact of climate change on the spatial distribution of key forest tree species in Italy under RCP4.5 for 2050s

“…The use different climatic data can lead to very different SDM projections with potential impacts on SFM decision (Beaumont et al 2008;Harris et al 2014); this aspect can represent a problem especially for mountainous areas where the use of coarse data can only partially capture the effect of orography (Buras and Menzel 2019). The results we obtained also highlighted the difference on the use of GCMs versus RCMs which are probably optimized scenarios for local areas but very complex and whose calculation is time consuming (Giannakopoulos et al 2009;Lelieveld et al 2012;Marchi et al 2019). Unfortunately the use of local data is not still very common and ensemble models are lacking in literature (Liu et al 2014).…”

“…This step was done to compare the current climate condition with 6 GCMs we downloaded from the WorldClim website with 30 arc-sec of spatial resolution. The selected GCMs are those elaborated by the fourth version of Community Climate System (CCSM) here and for the following models CC, the Hadley Centre Global Environment Model version 2 family (HADGEM2 2-AO, 2-CC, 2-ES) respectively HD, HE and HG, the Max Planck Institute for Meteorology Earth System Model (MPI-ESM-LR) hereafter MP and the Meteorological Research Institute climate model (MRI-CGCM3) MG. To avoid potential biases originated from different climate data sources (i.e WorldClim portal and E-OBS data), the WorldClim future projections were recalculated as anomalies from the 1961-1990 climatic normal period, currently distributed as WorldClim version 1.4 (Hijmans et al 2005;Marchi et al 2019). Once anomalies were calculated these were added to the same climatic normal period we obtained from E-OBS for Italy, using spatial reprojection to realign the two grids.…”

Exploring Nonlinear Intra-Annual Growth Dynamics in Fagus sylvatica L. Trees at the Italian ICP-Forests Level II Network

“…Climate change can reduce, or at least alter, quality and quantity of the ecosystem services that forests provide, thus affecting resilience and cultural identity of local communities [4][5][6]. It is, therefore, of primary importance to develop and use reliable and adequate climatic surfaces for the purpose [7][8][9]. This would impact on the possibility to derive useful instruments for policymakers able to anticipate changes in forest ecosystems' functionality and their capacity to deliver services [10].…”