Gross primary productivity and the predictability of CO2: more uncertainty in what we predict than how well we predict it

Abstract. The prediction of atmospheric CO2 concentrations is limited by the high interannual variability (IAV) in terrestrial gross primary productivity (GPP). However, there are large uncertainties in the drivers of GPP IAV among Earth system models (ESMs). Here, we evaluate the impact of these uncertainties on the predictability of atmospheric CO2 in six ESMs. We use regression analysis to determine the role of environmental drivers in (i) the patterns of GPP IAV and (ii) the predictability of GPP. There are large uncertainties in the spatial distribution of GPP IAV. Although all ESMs agree on the high IAV in the tropics, several ESMs have unique hotspots of GPP IAV. The main driver of GPP IAV is temperature in the ESMs using the Community Land Model, whereas it is soil moisture in the ESM developed by the Institute Pierre Simon Laplace (IPSL-CM6A-LR) and in the low-resolution configuration of the Max Planck Earth System Model (MPI-ESM-LR), revealing underlying differences in the source of GPP IAV among ESMs. Between 13 % and 24 % of the GPP IAV is predictable 1 year ahead, with four out of six ESMs showing values of between 19 % and 24 %. Up to 32 % of the GPP IAV induced by soil moisture is predictable, whereas only 7 % to 13 % of the GPP IAV induced by radiation is predictable. The results show that, while ESMs are fairly similar in their ability to predict their own carbon flux variability, these predicted contributions to the atmospheric CO2 variability originate from different regions and are caused by different drivers. A higher coherence in atmospheric CO2 predictability could be achieved by reducing uncertainties in the GPP sensitivity to soil moisture and by accurate observational products for GPP IAV.