SCISSOR: a Spectral ClImate Signal SeparatOR to assess complex climate responses to land cover changes

While large-scale afforestation and reforestation are heavily discussed as strategies for nature-based climate change mitigation and adaptation, massive deforestation is ongoing. Such widespread land use and land cover changes (LULCCs) not only alter the global climate through biomass carbon uptake or release but also through biogeophysical (BGP) processes related to changes in surface roughness, evaporation, transpiration, and albedo. These BGP effects act as local forcing to land-atmosphere interactions and lead to in situ climate responses. Caused by advection and spatio-temporal land-atmosphere-ocean interaction, they also generate nonlocal climate responses that occur remotely from the LULCC.

The non-local partition of climate response signals, and how it occurs at spatial scales different from the forcing, is still the subject of ongoing research. Here, we present a spectral perspective on climate responses to surface forcing from LULCC that aids in achieving a systematic and mechanistic understanding of the arousal and robustness of large-scale BGP effects.

We introduce spectral decomposition of forcing and response fields into a sum of signals with different wavelengths based on spherical harmonics to compare the two fields across spatial scales. Building on this approach, we define the ’cross-scale’ response signal based on the difference of response and forcing spectra. With our novel tool SCISSOR, a Spectral ClImate Signal SeparatOR, we determine the cross-scale signal of BGP-driven temperature response to deforestation, which strongly resembles the nonlocal signal as estimated by established methods such as moving window regression and checkerboard interpolation.

We further show that SCISSOR and other spectral tools can be used to analyze consistent and divergent characteristics of climate responses to LULCC between Earth System Models. We discuss the assumptions, advantages and limitations of both SCISSOR and the established signal separation methods and assess their potential use for future analysis of the complex interaction between climate and land surface changes.