Evaluating the Sensitivity of Growing Degree Days as an Agro-Climatic Indicator of the Climate Change Impact: A Case Study of the Russian Far East

Climate is a key factor in agriculture, but we are unable to adequately predict future climates. Although some studies have addressed the short and long-run impacts of climate change on agriculture, few of them specifically focused on the analysis of its thermal component. Climatic regions with an extreme thermal range are a special case, as seasonal contrasts complicate the picture. Based on the above, the purpose of the paper is twofold. First, we review methods and indices used for the estimation of changes in the thermal component of the climate and demonstrate the usefulness of a sensitivity assessment methodology that gives some indication of the likely spatial extent of areas of high or low sensitivity to climate change and the size of the potential impact of that change, which is specifically beneficial in regions with high temperature extremes. Secondly, we constructed a composite indicator, called the Growing Degree Day Sensitivity Index (GDDSI) and defined as the percentage change in Growing Degree Day (GDD) for warming scenarios +1, +2 and +3 °C. GDDs were calculated for threshold base air temperatures of 0, 5, 10 and 15 °C, and a high-temperature limit of 30 °C. A GDD sensitivity analysis was applied to the thermally extreme climate of the Russian Far East. We analyzed the data of 50 weather stations across the study region over the period 1966–2017. The results show a strong GDDSI north-to-south gradient. In most cases, the sensitivity does not increase significantly as the warming rate increases. The higher the base threshold, the higher the sensitivity: GDDs with a threshold at 15 °C had the highest sensitivity in the far north of the study area where conditions are currently marginal for crop growth. The sensitivity analysis circumnavigates the difficulty of uncertainty in knowing what future climate to expect and informs planning decisions. The mapped results are useful for identifying areas of high sensitivity to climate change as well as the magnitude of the potential impact of that change.