The Sources of Uncertainty in the Projection of Global Land Monsoon Precipitation - Zhou, Tianjun; Lu, Jingwen; Zhang, Wenxia; Chen, Ziming (Geophysical research letters, 2020)
Policy makers need reliable future climate projection for adaptation purposes. A clear separation of sources of uncertainty also helps narrow the projection uncertainty. However, it remains unclear for monsoon precipitation projections. Here we quantified the contributions of internal variability, model uncertainty, and scenario uncertainty to the ensemble spread of global land monsoon precipitation projections using Coupled Model Intercomparison Project Phase 5 (CMIP5) models and single‐model initial‐condition large ensembles (SMILEs). For mean precipitation, model uncertainty (contributing ~90%) dominates the projection uncertainty, while the contribution of internal variability (scenario uncertainty) decreases (increases) with time. The source of uncertainty for extreme precipitation differs from that of mean precipitation mainly in long‐term projection, with the contribution of scenario uncertainty comparable to model uncertainty. Reducing model uncertainty can effectively narrow the monsoon precipitation projection. The internal variability estimates differ slightly among models and methods, the uncertainty partitioning is robust in middle‐long term. Plain Language Summary Climate projections are subject to large uncertainty arisen from climate internal variability, model uncertainty, and scenario uncertainty. Understanding the sources of uncertainty is fundamental for narrowing the uncertainty in projections, further leading to more reliable future climate projections required for decision‐making. Focusing on the global land monsoon region, we quantified the contributions of different uncertainty components in the projections of mean and extreme precipitation. For mean precipitation, model uncertainty dominates the projection uncertainty, with a fractional contribution of ~90%, while the contribution of internal variability (scenario uncertainty) decreases (increases) with time. For extreme precipitation, the results are generally similar except that at the end of 21st century the contribution of scenario uncertainty is comparable to model uncertainty. Reductions of model uncertainty by improving model performances or employing high‐skill models can effectively narrow the uncertainty in monsoon precipitation projection. Key Points Model uncertainty (contributing ~90%) dominates the uncertainty in monsoon mean and extreme precipitation projections Reducing model uncertainty and employing high‐skill models can effectively narrow the uncertainty in monsoon precipitation projections Although internal variability estimates differ slightly among models and methods, the uncertainty partitioning is robust in middle‐long term