Using a new local high resolution daily gridded dataset for Attica to statistically downscale climate projections

Abstract

In this study we present a methodological framework to obtain statistically downscaled high resolution climate projections over the Attica region in Greece. The framework relies on the construction of a local daily gridded dataset for temperature variables (maximum, minimum and mean daily temperatures) and daily precipitation sums. To this aim, a mosaic of data that includes observations derived from ground stations and a high resolution simulation, performed by the Weather Research and Forecasting (WRF) model, for 1 year (1995) are blended using various gridding techniques to produce a 1 km 1 km high resolution daily gridded dataset for the period 1981–2000. The comparison of the gridded dataset against the observations reveals that the produced dataset maintains the observed long term statistical properties over the period 1981–2000 for both temperature and precipitation variables. Consequently, the produced dataset is used to statistically downscale Regional Climate Model simulations from the EURO-CORDEX initiative for the present (1981–2000) and the future climate (2081–2100) under the Representative Concentration Pathway (RCP) 8.5 climate scenario using two different bias adjustment techniques. The results indicate that the selection of the bias adjustment method is important and can affect the simulated climate change signals in a different way. Thus bias adjustment should be performed with caution and examined thoroughly before any such downscaled climate change projection dataset reach decision and policy makers in order to plan climate change related adaptation strategies.

Modification of heat-related effects on mortality by air pollution concentration, at small-area level, in the Attica prefecture, Greece

“…Subsequently, the six-hourly air temperature at 2 m were post-processed to obtain the daily means. The methodology followed is similar to that reported in Varotsos et al (2023) [ 30 ] where the authors transferred the spatial variability of Weather Research and Forecasting (WRF) Model over Attica to an observational daily gridded data set. In particular, in the present study the following steps have been implemented:…”

Seasonal fire danger forecasts for supporting fire prevention management in an eastern Mediterranean environment: the case of Attica, Greece

“…In particular, the seasonal forecast meteorological variables used to calculate the FWI were initially regridded to the ERA5-Land grid by means of bilinear interpolation, and next, bias correction was applied using empirical quantile mapping (EQM). This two-step approach is the reversed order of the framework of bias correction and spatial disaggregation, which has been previously used to statistically downscale global and/or regional models for both climate change and seasonal forecast studies (Lorenz et al, 2021;Marcos et al, 2018;Varotsos et al, 2022). Regarding the bias correction method, EQM works by adjusting the 1st-99th percentiles of the predicted empirical probability density function (PDF) based on the observed empirical PDF, while for lower or higher values falling outside this range, a constant extrapolation is applied using the correction obtained for the 1st or 99th percentile, respectively.…”

Diurnal Temperature Range and Its Response to Heat Waves in 16 European Cities—Current and Future Trends

“…The empirical quantile mapping (EQM) technique was used for the statistical bias correction of daily maximum air temperature, daily minimum air temperature and diurnal temperature range. When using EQM, the observed empirical probability density function (PDF) is used to correct the 1st to 99th percentiles of the predicted empirical probability density function (PDF), while a constant extrapolation is used for lower or higher values falling outside this range [47][48][49][50][51]. This technique is capable of correcting the discrepancies in the distribution of the simulated parameters against the observed ones.…”