GPP 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) of 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 on (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, and soil moisture in IPSL-CM6A-LR and MPI-ESM-LR, revealing underlying differences in the source of GPP IAV among ESMs. Between 13 % and 24 % of the GPP IAV is predictable one year ahead, with four out of six ESMs between 19 % and 24 %. Up to 32 % of the GPP IAV induced by soil moisture is predictable, while only 7 % to 13 % of the GPP IAV induced by radiation. The results show that while ESMs are fairly similar in their ability to predict themselves, their predicted contribution to the atmospheric CO₂ variability originates from different regions and is caused by different drivers. A higher coherence in atmospheric CO₂ predictability could be achieved by reducing uncertainties of GPP sensitivity to soil moisture, and by accurate observational products for GPP IAV.