Recent and Future Changes in Spatiotemporal Variability of Precipitation and Temperature and the Impacts on Selected Precipitation-Temperature Indices

While much effort has been devoted to analyzing long-term changes of temperature and precipitation in mean values and extremes, studies on changes in variability have been rather scarce. Trends in variability are, however, important, among others because their interaction with trends in mean values determines the degree with which extremes would change. The knowledge of long-term changes in temporal variability is essential for assessments of climate change impacts on various sectors, including hydrology (floods and droughts), agriculture, health, and energy demand and production.

SPAGETTA is a stochastic spatial daily weather generator (WG), which uses first-order multivariate (dimension = the product of the number of variables and the number of gridpoints) autoregressive model to represent the spatial and temporal variability of surface weather variables (including precipitation and temperature). The generator is a suitable tool for assessing changes in the spatial and temporal variability of the weather series because of following reasons: (A) The inter-gridpoint lag-0 and lag-1(day) correlations of both precipitation and temperature included in a set of WG parameters are good representatives for spatial and temporal variability of the two weather variables. (B) Statistical significance of changes in the lag-0 and lag-1 correlations derived from the calibration series may be easily assessed by comparing the changes with a variability (related to the stochasticity of WG) of the lag-0 and lag-1 correlations across multiple realizations of synthetic weather series. (C) Separate effects of changes in various WG parameters (derived, e.g., from RCM-simulated daily series representing the future climate) on any climatic characteristic may be easily assessed by modifying only selected WG parameter(s).

In the first part of the contribution, we employ SPAGETTA generator to analyze recent and future changes in spatiotemporal variability of precipitation and temperature in 8 European regions; these regions are defined in Dubrovsky et al 2020 (Theor. Appl. Climatol.). The recent changes are derived from gridded observational E-OBS time series, and the future changes (2070-2099 vs 1971-2000) are based on multiple RCM-simulated surface weather series. In the second part, we assess separate effects of changes in the means, variability and lag-0 & lag-1 correlations of temperature and precipitation. The changes in WG parameters are derived from a set of 19 RCM simulations, the changes are used to modify corresponding WG parameters calibrated from the E-OBS data, the synthetic series are generated, and the impacts on a set of compound temperature-precipitation characteristics representing “Days” with spatially significant extent of significantly non-normal weather (e.g. hot-dry spells), and the “Spells” of such days, are analyzed.