Divergent responses of evergreen needle-leaf forests in Europe to the 2020 warm winter

Abstract

Relative to drought and heat waves, the effect of winter warming on forest CO2 fluxes during the dormant season has less been investigated, despite its relevance for net CO2 uptake in colder regions with higher carbon content in soils. Our objective was to test the effect of the exceptionally warm winter in 2020 on the winter CO2 budget of cold-adapted evergreen needle-leaf forests across Europe, and identify the contribution of soil and air temperature to changes in winter CO2 fluxes in response to warming. Our hypothesis was that warming in winter leads to higher emissions across colder sites due to increased ecosystem respiration. To test this hypothesis, we used 98 site-year eddy covariance measurements across 14 evergreen needle-leaf forests (ENFs) distributed from north to south of Europe (from Sweden to Italy). We used a data-driven approach to quantify the effect of air and soil temperature on changes in net ecosystem productivity (NEP) during the warm winter of 2020. Our results showed that the impact of warming was different across sites, as in the lower altitude and lower latitude sites positive soil temperature anomalies were larger, while positive air temperature anomalies were larger in the northern latitude and high-altitude sites. Warming in winter led to a divergent response across the sites. Out of 14 sites only in 3 sites net ecosystem productivity declined in winter significantly in response to warming. In addition, we observed that in the colder sites daytime NEP (that is dominated by photosynthesis) declined with warming of the air in winter, whereas in the warmer sites daytime NEP increased with warming of the soil. This shows that warming of the air – if not translated into a direct warming of the soil– might not trigger productivity in winter if the soil within the rooting zone remains frozen. Forests within the same plant functional type category can exhibit differing reactions to winter warming and to predict their responses accurately it is crucial to account for variations in local climate, physiology, and structure simultaneously.