Hristo Chervenkov and Kiril Slavov
National Institute of Meteorology and Hydrology (NIMH), 1784 Sofia, Bulgaria
Chervenkov, H. & Slavov, K. (2022). Assessment of the future thermal conditions over Europe based on CMIP5 ensemble of agro-meteorological indices. Bulg. J. Agric. Sci., 28 (6), 972–984
Agriculture and forestry are arguably the sectors most dependent on climate and the ongoing and expected future climate changes have essential importance for both of them. Based on the availability of reliable sources of information, which represent CMIP5 global climate change simulations, we present an updated assessment of projected future climate over Europe. The study exploits a set of 5 climate indices with primary relevance for the agriculture and forestry, with special focus on the onset, termination and length of the growing season.
The indices are calculated in consistent manner in the frames of the Global Agriculture project, stored in the database of the Inter Sectoral Impact Model Intercomparison Project and are available on the Copernicus Data Store. As a part of the present study they are systematically analyzed for the near past climate (1981–2010) as well as for the projected future climate up to the end of the 21st century. The projected future climate is evaluated by purpose-build multimodel ensemble from all available models within the project CMIP5 and is performed for all four RCP scenarios.
First of all, the study demonstrates distinct warming expressed in the spatial patterns and the temporal evolution of all considered indicators. In particular, in the scenario with the strongest forcing (RCP8.5) the multiyear mean of the onset of the growing season over Central Europe for the period 2070–2099 becomes 20 days earlier and the termination - 20 days later in comparison to the baseline period, which results in prolongation of the growing season with more than a month. The warming dominates practically over the whole domain, intensifies gradually with the increasing radiative forcing and is statistically significant over its essential part in the most cases. The proposed and applied novel approach for estimation of the timing of the growing season does not reveal statistically significant long-term seasonal shift.