The Impact of a Stochastic Parameterization Scheme on Climate Sensitivity in EC‐Earth

Stochastic schemes, designed to represent unresolved subgrid‐scale variability, are frequently used in short and medium‐range weather forecasts, where they are found to improve several aspects of the model. In recent years, the impact of stochastic physics has also been found to be beneficial for the model's long‐term climate. In this paper, we demonstrate for the first time that the inclusion of a stochastic physics scheme can notably affect a model's projection of global warming, as well as its historical climatological global temperature. Specifically, we find that when including the “stochastically perturbed parametrization tendencies” (SPPT) scheme in the fully coupled climate model EC‐Earth v3.1, the predicted level of global warming between 1850 and 2100 is reduced by 10% under an RCP8.5 forcing scenario. We link this reduction in climate sensitivity to a change in the cloud feedbacks with SPPT. In particular, the scheme appears to reduce the positive low cloud cover feedback and increase the negative cloud optical feedback. A key role is played by a robust, rapid increase in cloud liquid water with SPPT, which we speculate is due to the scheme's nonlinear interaction with condensation. Plain Language Summary Accurate estimates of the extent of global warming by 2100 are crucial for determining effective climate policies. However, large uncertainties still remain, due in large part to uncertainties in the response of clouds to global warming. Model uncertainties arise largely from the need to crudely estimate the effects of processes too small to represent perfectly (given the limits of current computing power). “Stochastic schemes” aim to represent such errors, thereby, potentially, improving climate models. Indeed, many such improvements have been found over the last decade. In this paper, we show that the inclusion of such a scheme in one particular climate model has the effect of reducing the projected global warming by 10%. We link this to the scheme having changed the response of clouds to global warming. A potential consequence of this result is that carefully calibrated stochastic schemes could, by representing model errors, improve the accuracy of global warming projections. Key Points The inclusion of a stochastic scheme reduces climate sensitivity in a general circulation model This reduction, of around 10%, is linked to changes in cloud cover and cloud optical depth feedbacks Well‐calibrated stochastic schemes may give more accurate glob