Investigating forest management's impact on local climate in Fennoscandia through statistical and dynamical modeling

The forest plays a crucial role in the land ecosystem, impacting local climates through various biophysical mechanisms. While numerous observational and modeling studies have explored the distinctions between forested and non-forested areas, the impact of forest management on surface temperature has been relatively understudied. This limited attention is attributed to the inherent challenges associated with adapting climate models to effectively account for the complexities of forest structure parameters. Employing a combination of machine learning-based statistical analysis and a regional climate model, along with high-resolution maps detailing various forest compositions and structures, we explore the connection between specific forest management strategies and local temperature variations. The findings reveal a tendency for more developed forests to contribute to higher land surface temperatures compared to younger or less developed ones. Relative to the present state of Fennoscandian forests, an ideal scenario with fully developed forests is found to an annual mean warming of 0.26 ℃ in statistical models, with a range of 0.03 to 0.69 ℃ (5th to 95th percentile). However, the dynamical model indicates an annual average cooling effect of -0.25 °C, ranging from -0.42 to -0.10 °C (5th to 95th percentiles), attributing this difference to the dynamical model's inability to accurately simulate winter warming. Both models project a cooling effect in summer, with statistical and dynamical models showing -0.03 ± 0.22 ℃ and -0.53 ± 0.20 ℃, respectively. Conversely, scenarios involving undeveloped forests result in an annual average cooling of -0.29 ℃ in statistical models, with a range of -0.61 to -0.01 ℃, a slight summer warming of 0.03 ± 0.16 ℃, and a winter cooling of -0.69 ± 0.47 ℃. The dynamical model, however, predicts an annual average warming of 0.28 ± 0.18 °C, a summer warming of 0.53 ± 0.15 °C (mainly driven by increased sensible heat fluxes), and a winter cooling of -0.29 ± 0.25 °C. This study deepens our understanding of how alterations in vegetation impact climate patterns. While our findings shed light on the intricate connections between forest composition and surface temperatures, there's a clear need to refine how regional climate models capture the intricate biophysical mechanisms within forest dynamics. Enhancements in this representation will be crucial for establishing a comprehensive understanding of how forest management practices specifically influence local climate regulation services.