Spurious seasonality of Earth observation LAI across three northern evergreen needleleaf forests: Implications for analyses of the carbon cycle

Abstract. Leaf area index (LAI) is a key biophysical variable which quantifies the surface area for light capture and photosynthetic activity per unit ground area, giving a first order constraint on potential photosynthesis. LAI is tightly coupled to the carbon, energy, and water cycles of the global terrestrial system. Numerous Earth observation (EO) products provide estimates of LAI over time (LAI_EO), delivering valuable information on plant phenology and canopy dynamics. Widely-used LAI_EO, however, consistently exhibit unrealistic seasonality in evergreen needleleaf forests at the northern latitudes. Taking a model-data fusion approach, we show that naïvely assimilating biased, whole-year LAI_EO (i.e., the business-as-usual (BAU) approach) at three well-studied evergreen needleleaf forests in Fennoscandia implies an ecosystem carbon cycle which is unrealistic and inconsistent with independent lines of evidence. We further demonstrate that the model-data fusion framework, CARDAMOM, is capable of diagnosing realistic seasonal amplitudes of LAI by assimilating localised information on leaf lifespan coupled with summer-only LAI_EO (i.e., the alternative (ALT) approach). Important differences arise from the BAU and ALT experiments. The BAU experiment showed highly seasonal canopy dynamics and diagnostic leaf traits erroneously consistent with deciduous species. Conversely, the ALT experiment displayed canopy dynamics and functional characteristics more reflective of evergreen needleleaf species. For BAU, biases in LAI_EO propagated throughout the carbon cycle, especially in the southern, more productive sites. This investigation highlights the need for improved LAI_EO estimates in northern evergreen forests to enhance understanding of carbon cycle processes in this region of rapid warming and large carbon stores, and provides a mechanism for improvement using independent leaf trait data.