A test of BIOME-BGC with dendrochronology for forests along the altitudinal gradient of Mt. Changbai in northeast China. (29th July 2016) - Wu, Yulian; Wang, Xiangping; Ouyang, Shuai; Xu, Kai; Hawkins, Bradford A.; Sun, Osbert Jianxin
Abstract: Aims: Process-based models are basic tools for predicting the response of forest carbon to future climate change. The models have commonly been tested for their predictions of spatial variation in forest productivity, but much less for their ability to predict temporal variation. Here, we explored methods to test the models with tree rings, using BIOME-BGC as an example. Methods: We used net primary productivity (NPP) data and tree rings collected from five major forest types along the altitudinal gradient of Mt. Changbai, northeast China, to test local-parameterized BIOME-BGC model. We first test the model's predictions of both spatial (Test 1) and temporal changes (Test 2) in productivity. Then we test if the model can detect the climatic factors limiting forest productivity during historical climate change, as revealed by dendroclimatic analyses (Test 3). Important Findings: Our results showed that BIOME-BGC could well simulate NPP of five forest types on Mt. Changbai, with an r 2 of 0.69 between modeled and observed NPP for 17 plots along the altitudinal gradient (Test 1). Meanwhile, modeled NPP and ring-width indices were correlated and showed similar temporal trends for each forest type (Test 2). While these tests suggest that the model's predictions on spatial and temporal variation of NPP were acceptable, a further test that relate the correlations of modeled NPP with climate variables to the correlations of ring widths with climate (Test 3) showed that the model did not well identify the climatic factors limiting historical productivity dynamics for some forest types, and thus cannot reliably predict their future. Both dendrochronology and BIOME-BGC showed that forest types differed markedly in the climate factors limiting productivity because of differences in tree species and climate condition, and thus differed in responses to climate change. Our results showed that a successful prediction of spatial NPP patterns cannot assure that BIOME-BGC can well simulate historical NPP dynamics. Further, a correlation between modeled NPP and tree-ring series cannot assure that the limiting climatic factors for productivity have been correctly identified by the model. Our results suggest the necessity to test the temporal predictions of process-based models in a more robust way, and further integration of dendrochronology and biogeochemistry modeling may be helpful for this purpose.