Impacts of climate change on temperature, precipitation and hydrology in Finland – studies using bias corrected Regional Climate Model data

Abstract

Abstract. Assessment of climate change impacts on climate and hydrology on catchment scale requires reliable information about the average values and climate fluctuations of the past, present and future. Regional climate models (RCMs) used in impact studies often produce biased time series of meteorological variables. In this study bias correction (BC) of RCM temperature and precipitation for Finland is carried out using different versions of the distribution based scaling (DBS) method. The DBS-adjusted RCM data are used as input of a hydrological model to simulate changes in discharges of four study catchments in different parts of Finland. The annual mean discharges and seasonal variation simulated with the DBS-adjusted temperature and precipitation data are sufficiently close to observed discharges in the control period 1961-2000 and produce more realistic projections for mean annual and seasonal changes in discharges than the uncorrected RCM data. Furthermore, with most scenarios the DBS method used preserves the temperature and precipitation trends of the uncorrected RCM data during 1961-2100. However, if the biases in the mean or the standard deviation of the uncorrected temperatures are large, significant biases after DBS adjustment may remain or temperature trends may change, increasing the uncertainty of climate change projections. The DBS method influences especially the projected seasonal changes in discharges and the use of uncorrected data can produce unrealistic seasonal discharges and changes. The projected changes in annual mean discharges are moderate or small, but seasonal distribution of discharges will change significantly.

An Uncertainty Investigation of RCM Downscaling Ratios in Nonstationary Extreme Rainfall IDF Curves

“…To further explore the scale-driven uncertainty of the IDF curves, future IDF curves from the four stations were constructed for five time scales and 11 RCM ensemble members using a distribution-based scaling (DBS) [30,31] approach to remove the bias of the RCM. A statistical downscaling method that combined nonparametric prediction models and the method of fragments framework (NPRED-MoF) [15] was used to disaggregate future daily rainfall to hourly or sub-hourly scales.…”

“…The rainfall intensities for different return periods (2, 5, 10, 25, 50, and 100 years) were then calculated, and the five types of IDF curves based on the rainfall intensity from six return periods and 24 durations could be plotted. In this study, the bias-correction procedure for the RCM precipitation and temperature predominantly depended on the DBS approach described by Yang et al [30] and Olsson et al [31]. This method adjusts the distribution of RCM data to make it consistent with the distributions of the observation data based on cumulative distribution functions (CDFs) and then determines the new RCM data distribution parameters estimated by the maximum likelihood.…”

“…Therefore, the DBS approach to correct biases in temperature depends on a Gaussian distribution. The DBS procedure for temperature was implemented using a 15-day moving window to obtain parameters [30] without separating the wet days [31].…”

“…We used a double-gamma distribution which divided the observed and RCM data into two parts based on the 95th percentile of the CDF to remove biases in the RCM precipitation data, to better capture extreme precipitation events and calculate the parameters based on a monthly segment [31].…”

A method to preserve trends in quantile mapping bias correction of climate modeled temperature

“…This may significantly change the original modeled long-term trend or other higher moments of the climate variable statistics that eventually change the long-term signal of the climate variable. In their work on distribution-based scaling (DBS) bias correction, Olsson et al (2015) showed that their methodology might alter the long-term temperature trends, attributing the phenomenon in the severity of the biases in the mean or the standard deviation between the uncorrected temperatures and the observations. Maraun (2016) discusses whether the change in the trend is a desired feature of bias correction, concluding that it is case-specific and depends on the skillfulness of the climate model to simulate the correct long-term signal.…”