5:15 PM - 6:45 PM
[ACG37-P01] Reconstruction of the land carbon cycle from 1948 to 2015 using the dynamical vegetation model MRI-LPJ
Keywords:vegetation, carbon cycle
Land surface exchanges water, carbon dioxide (CO2), and momentum with the atmosphere, and it also affects the radiation via changing the surface albedo. This tight coupling between land and atmosphere would be critical in constraining future climate change and the carbon budget (Friedlingstein et al., 2022). However, the uncertainty associated with the estimated land carbon budget is much larger than that of the oceanic carbon budget (Arora et al., 2020). It has been discussed that the response of the land carbon budget and atmospheric pCO2 to the interannual climate variability related to the El Niño-Southern Oscillation would help to constrain the response of the land carbon cycle to the future climate change (Cox et al., 2013), providing a fundamental insight of the behavior of the land carbon budget under the warming climate. For a better constraint on the land carbon cycle in model intercomparison projects, we developed an offline dynamical global vegetation model MRI-LPJ, which is developed based on the vegetation model of the LPJ-LMfire model (Pfeiffer et al., 2013) with an improvement in the representation of leaf phenology. The MRI-LPJ is driven by daily meteorological data, using state-of-the-art reanalysis data Japanese Reanalysis for Three Quarters of a Century (JRA-3Q; Kosaka et al., 2024).
Our model demonstrates that the gross primary productivity (GPP) and net primary productivity (NPP) have increased by ~15–20% between 1948 and 2015. The global annual-mean GPP values at 1948 and 2015 are ~98 and ~117 GtC yr–1, respectively. These values are within the uncertainty range of other models, reconstructions based on satellite observations, and eddy covariance observation sites (Zhao and Running et al., 2010; Beer et al., 2010; Jung et al., 2011; Cai et al. 2014; Anav et al., 2015). The global annual-mean NPP values in 1948 and 2015 are 53 and 63 GtC yr–1, respectively, which are also comparable to the estimates using observation data and models (Cramer et al., 2001; Zhao and Running 2010; Rafique et al., 2016; Wang et al., 2021). The GPP, NPP, and the CO2 flux from the atmosphere to the land exhibit interannual variations similar to the reconstructions of the past CO2 flux and other model-based reconstructions (Le Quéré et al., 2009; Friedlingstein et al., 2022). The MRI-LPJ model has been coupled to the Meteorological Research Institute Earth-system model MRI-ESM 2.0 (Yukimoto et al., 2019), so that the MRI-LPJ receives the daily changes of the long-wave and short-wave radiations, precipitation rates, surface and soil temperatures. In this presentation, we would also discuss the performances of the MRI-LPJ model coupled to the ESM.
Our model demonstrates that the gross primary productivity (GPP) and net primary productivity (NPP) have increased by ~15–20% between 1948 and 2015. The global annual-mean GPP values at 1948 and 2015 are ~98 and ~117 GtC yr–1, respectively. These values are within the uncertainty range of other models, reconstructions based on satellite observations, and eddy covariance observation sites (Zhao and Running et al., 2010; Beer et al., 2010; Jung et al., 2011; Cai et al. 2014; Anav et al., 2015). The global annual-mean NPP values in 1948 and 2015 are 53 and 63 GtC yr–1, respectively, which are also comparable to the estimates using observation data and models (Cramer et al., 2001; Zhao and Running 2010; Rafique et al., 2016; Wang et al., 2021). The GPP, NPP, and the CO2 flux from the atmosphere to the land exhibit interannual variations similar to the reconstructions of the past CO2 flux and other model-based reconstructions (Le Quéré et al., 2009; Friedlingstein et al., 2022). The MRI-LPJ model has been coupled to the Meteorological Research Institute Earth-system model MRI-ESM 2.0 (Yukimoto et al., 2019), so that the MRI-LPJ receives the daily changes of the long-wave and short-wave radiations, precipitation rates, surface and soil temperatures. In this presentation, we would also discuss the performances of the MRI-LPJ model coupled to the ESM.