Project code: PN-III-P1-1.1-PD-2019-0469
Contract No: PD 63
Project title: ” Looking into the past to anticipate the future: Extreme climate variability in Dobrogea Region from a paleo perspective based on stable oxygen isotope in tree rings”
The acronym of the project: DobrEx
Abstract:
Reconstruction of droughts and heat waves events are necessary for a better understanding of their dynamics and to improve projections associated with future extreme events, especially in convergent climatic areas such as Dobrogea. The oak ring width from the low altitude regions has, in general, a low correlation with the main climatic parameters, but the stable isotopes in these areas can provide additional information about past climatic variability. As such, the specific objectives of this project are manifold: 1) High-resolution records of past climate extremes from δ18O chronology in tree ring cellulose from the Dobrogea region; 2) Reconstruction of the climatic (e.g. heat waves) and hydroclimatic (e.g. droughts) extreme events from the Dobrogea region over the last centuries (~300 years) and 3) Interpretation of the present climatic and hydroclimatic extremes in the context of the long-term variability, by using proxy-based data (δ18O chronology in tree ring cellulose), observational records and paleoclimate model simulations (available from the Coupled Model Intercomparing Project Phase 5).
The expected results of this project are to reconstruct the past climatic variability based on the new paleo reconstruction made using stable isotopic composition in tree-ring cellulose, at local and large scale, and to analyze the past climatic and hydroclimatic extreme events in the context of the long-term variability. Thus, the results obtained throughout the current project can be regarded as a direct contribution to the improvement of the National Strategy on Climate Change of Romania, which a special focus on the measures needed to adapt and manage the desertification of the south-eastern part of Romania, by improving the climatic models and therefore improving their accuracy for future projection of extreme events and their intensity.