Abstract

Abstract. Possible future climate change effects on dryness conditions in Poland are estimated for six climate projections using the standardized precipitation index (SPI). The time series of precipitation represent six different climate model runs under the selected emission scenario for the period 1971–2099. Monthly precipitation values were used to estimate the SPI for multiple timescales (1, 3, 6, 12, and 24 months) for a spatial resolution of 25 km for the whole country. Trends in the SPI were analysed using the Mann–Kendall test with Sen's slope estimator for each grid cell for each climate model projection and aggregation scale, and results obtained for uncorrected precipitation and bias corrected precipitation were compared. Bias correction was achieved using a distribution-based quantile mapping (QM) method in which the climate model precipitation series were adjusted relative to gridded precipitation data for Poland. The results show that the spatial pattern of the trend depends on the climate model, the timescale considered and on the bias correction. The effect of change on the projected trend due to bias correction is small compared to the variability among climate models. We also summarize the mechanisms underlying the influence of bias correction on trends in precipitation and the SPI using a simple example of a linear bias correction procedure. In both cases, the bias correction by QM does not change the direction of changes but can change the slope of trend, and the influence of bias correction on SPI is much reduced. We also have noticed that the results for the same global climate model, driving different regional climate model, are characterized by a similar pattern of changes, although this behaviour is not seen at all timescales and seasons.

Highlights

  • Drought is an extreme event that can produce significant deleterious effects under both present and future climatic conditions according to the recent Special Report by the Intergovernmental Panel on Climate Change (IPCC) on Managing the Risk of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX).The assessment of future drought scenarios is crucial for many aspects of the national economy, including agriculture, energy, biodiversity, forestry, and the health and water sectors (Jenkins and Warren, 2015)

  • The bias corrected precipitation values are characterized by a similar seasonal pattern to that of the observed values, with a slight underestimation of monthly precipitation values relative to observed values

  • In addition, it must be remembered that bias correction is performed on individual daily precipitation values, rather than monthly totals

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Summary

Introduction

Drought is an extreme event that can produce significant deleterious effects under both present and future climatic conditions according to the recent Special Report by the Intergovernmental Panel on Climate Change (IPCC) on Managing the Risk of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX).The assessment of future drought scenarios is crucial for many aspects of the national economy, including agriculture, energy, biodiversity, forestry, and the health and water sectors (Jenkins and Warren, 2015). Recent IPCC reports and scientific articles indicate that drought events have been increasing in frequency and intensity in some regions over the last part of the 20th century as a result of climate change (Kaczmarek et al, 1996; Alexander et al, 2006; Bartholy and Pongracz, 2007; Brázdil et al, 2009; Kiktev et al, 2009; Somorowska, 2009; Dai, 2011; KLIMADA, 2012; Seneviratne et al, 2012). Climate projections suggest that drought is likely to increase (at a medium level of confidence) and may become more intense in some regions, including central Europe (IPCC, 2012), especially in areas with dry conditions in today’s climate (IPCC, 2014, AR5). Published analyses of drought in Poland have mainly been concerned with the classification of drought types and the development of drought indices (Łabedzki, 2007; Łabedzki and KaneckaGeszke, 2009; Tokarczyk, 2013), monitoring of drought conditions

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