M. Levent Kurnaz | Searcharxiv

arXiv2026

Import-Dependent Grain Processing Hubs: The Case of Türkiye's Flour Sector

International commerce has long been seen as a key way to keep the global food system stable, allowing agricultural surpluses in some areas to compensate for shortages in others. This strategy has led to the rise of highly specialised processing hubs that combine significant industrial capacity with agricultural inputs sourced from throughout the world. Türkiye's flour sector -- currently the largest wheat flour exporter in the world -- represents one of the most prominent examples of this model. However, increasing climate variability and geopolitical fragmentation raise important questions regarding the long-term resilience of food systems that rely heavily on imported biological inputs. Recent research shows the growing probability of synchronised crop failures across multiple agricultural regions due to atmospheric circulation anomalies and climate-induced extreme weather events. The assumption that global markets can consistently rebalance supply disruptions through trade is challenged by such events. Using the flour industry of Türkiye as a case study, this paper investigates the susceptibility of globally integrated grain processing centres. In order to assess the correlation between the scope of industrial processing and the capacity of domestic agricultural production, we introduce the Biophysical Autonomy Ratio~(BAR). The analysis demonstrates that Türkiye's BAR has declined consistently over time, suggesting that its processing sector has expanded beyond the domestic production base. The results suggest that in order to enhance the resilience of the food system in the future, it may be necessary to establish a more precise alignment between biological production systems and industrial food infrastructure. The paper concludes by addressing the policy implications for national food security governance in the context of escalating climate instability.

physics.ao-ph↗

arXiv2023

Detecting Extreme Temperature Events Using Gaussian Mixture Models

Extreme temperature events have traditionally been detected assuming a unimodal distribution of temperature data. We found that surface temperature data can be described more accurately with a multimodal rather than a unimodal distribution. Here, we applied Gaussian Mixture Models (GMM) to daily near-surface maximum air temperature data from the historical and future Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations for 46 land regions defined by the Intergovernmental Panel on Climate Change (IPCC). Using the multimodal distribution, we found that temperature extremes, defined based on daily data in the warmest mode of the GMM distributions, are getting more frequent in all regions. Globally, a 10-year extreme temperature event relative to 1985-2014 conditions will occur 13.6 times more frequently in the future under 3.0°C of Global Warming Levels (GWL). The frequency increase can be even higher in tropical regions, such that 10-year extreme temperature events will occur almost twice a week. Additionally, we analysed the change in future temperature distributions under different GWL and found that the hot temperatures are increasing faster than cold temperatures in low latitudes, while the cold temperatures are increasing faster than the hot temperatures in high latitudes. The smallest changes in temperature distribution can be found in tropical regions, where the annual temperature range is small. Our method captures the differences in geographical regions and shows that the frequency of extreme events will be even higher than reported in previous studies.

physics.ao-ph↗