Choice of reference climate conditions matters in impact studies: Case of bias‐corrected CORDEX data set

Abstract

Climate models have provided driving data for impact studies for decades. However, the uncertainties related to the use of such data have typically not been sufficiently considered. We investigate how CORDEX climate simulations, which were corrected for bias based on MESAN reanalysis data for the period of 1989-2010, match the gridded observational data set E-OBS. Furthermore, we investigate whether the bias-corrected simulations contain significant residual bias (RB), which we defined as the bias exceeding the range of the observational uncertainty (U obs ) that emerges from differences between the two data sets MESAN and E-OBS. Because the reference period selected in climate change impact studies often differs from the period used for bias correction, we further investigated whether the RB and other performance metrics of the periods 1989-2010 and 1961-1990 differ. We conducted this assessment for whole Europe and for biogeographical zones. Most of the used performance metrics show a good match of the simulations with MESAN in the period of 1989-2010. The comparison against E-OBS yields worse results, indicating a significant difference between the two observational data sets. Minor bias exceeding the range of U obs (RB) occurred over large land areas of Europe in this period. Based on the analysis conducted for the period of 1961-1990, the RB is several times larger than that of the period of 1989-2010; the other metrics also show worse performances. Our findings imply that both the selection of the reference climate data set and reference period warrant greater attention in impact studies. In particular, we recommend researchers to use a bias correction period as reference period in their studies. Alternatively, a new bias correction should be applied if any different period is to be used as a reference.

Fine-scale variation in projected climate change presents opportunities for biodiversity conservation in Europe

“…The simulations were driven by two Representative Concentration Pathway scenarios: RCP4.5 and RCP8.5, producing 10 climate projections in total (5 RCMs × 2 RCPs). The testing of bias-corrected temperature and precipitation model results against the observation-based E-OBS data set 56 was conducted by 57 . The dataset is available at 10.5281/zenodo.1204351.…”

Linking Global to Regional Climate Change

“…Observational uncertainties and internal variability introduce uncertainty in the estimation of biases and thus in the calibration of bias-adjustment methods. Dobor and Hlásny (2019) found a considerable influence of the choice of the observational dataset and calibration period on the adjustment for some regions. RCM biases are typically larger than observational uncertainties, but in some regions, and in particular for wet-day frequencies, spatial patterns and the intensity distribution of daily precipitation, the situation may reverse (Kotlarski et al, 2019).…”

“…The choice of baseline provides a source of uncertainty for the assessment of climate impacts (e.g., for the response of bird species in Africa; Baker et al, 2016). Besides, a range of different baselines may need to be considered to satisfy a variety of users, since this choice affects the perceived result (Dobor and Hlásny, 2019). The influence of the different baseline periods can be explored using the Interactive Atlas where different baselines are available, for example, 1986-2005 (according to AR5), 1995(this Report), and both 1961-1990and 1981.…”

Fine-scale variation in projected climate change presents opportunities for biodiversity conservation in Europe

“…We considered nine candidate bias-corrected regional climate models (RCM) for Europe, accessible in the EURO-CORDEX database and covering the period 1951-2100. We selected five climate models results, representing all combinations of global and regional climate models included in the original dataset (Table S1; see 53 for more details on the selection criteria). The models were driven by two Representative Concentration Pathway scenarios: RCP4.5 and RCP8.5 54 , giving us 10 climate projections in total (5 RCMs × 2 RCPs).…”