|Title||Using NWP to assess climate models.|
|Year of Publication||2007|
|Authors||Rodwell, M, Palmer, TN|
|Secondary Title||Technical Memorandum|
Estimates of climate change remain uncertain - hampering strategic decision making in many sectors. In large part this uncertainty arises from uncertainty in the computational representation of known physical processes and uncertainty in unrepresented processes. For example, perturbed model experiments have shown climate sensitivities to a doubling of CO2 of between 2 and 12°C. These experiments assess the likelihood of each perturbed model's climate prediction based on how well it simulates present-day climate. Here we demonstrate a different method that harnesses the power of the data assimilation system to directly assess the perturbed physics of a model. The method used here quantifies systematic initial tendencies in the first few timesteps of a model forecast. After suitable temporal averaging, these initial tendencies imply systematic imbalances in the physical processes associated with model error. We show how these tendencies can be used to produce probability weightings for each model that could be used in the construction of p.d.f.s of climate change. The approach typically costs 5% of the cost of a 100-year coupled model present-day-climate assessment. Importantly, since the approach is amenable to linear analysis, it could further reduce the cost of model assessment by several orders of magnitude: making the exercise computationally feasible. The initial tendency approach only assesses "fast physics" perturbations, i.e. perturbations that have an impact on weather forecasts as well as climate. However, recent publications suggest that the majority of present model parameter uncertainty is associated with fast physics. If such a test were adopted, the assessment of the ability to simulate present-day climate would then only be required for models that "pass" the fast physics test. The study highlights the advantages of a more seamless approach to forecasting that combines numerical weather prediction, climate forecasting and all scales in-between.