A comparison of two numerical weather prediction methods for diagnosing fast‐physics errors in climate models

Abstract An important aspect of the assessment of climate models is the representation of fast physical processes, which have a strong impact on climate projections. Since these fast processes also have a strong impact on weather forecasts, there is potential in using numerical weather prediction (NWP)‐based methodologies in climate model assessments. Two NWP‐based methods have been proposed in the literature. The first is referred to as the ‘initial tendency’ approach and assesses the systematic departure in the first few hours of short climate model resolution forecasts from the weather trajectory. For this method, the forecast model needs data assimilation capabilities to create initial conditions consistent with the model physics. The second approach is referred to as the ‘transpose‐AMIP’ method. This makes use of analyses produced with a different forecast model to initiate weather forecasts with a climate model. It is generally recommended that errors are diagnosed after a few forecast days to minimise the impact of the non‐native initial conditions. Here we compare these two methods in one NWP model over the subtropical South Pacific, which encompasses climatologically important regimes and transitions between them. The impact of the non‐native initial conditions is compared with the impact of an error introduced in the convection scheme. Results show that care is needed in the design and evaluation of transpose‐AMIP‐style experiments if model errors are to be disentangled from, at short lead‐times, the initial shock of using non‐native initial conditions and, at longer lead‐times, the complicating effects of interactions and the growth of chaos. On the other hand, the initial tendency approach is able to identify the introduced model error. This provides some evidence that it might be worthwhile for climate centres to explore the use of data assimilation.