Uncertainty in the Representation of Orography in Weather and Climate Models and Implications for Parameterized Drag

The representation of orographic drag remains a major source of uncertainty for numerical weather prediction (NWP) and climate models. Its accuracy depends on contributions from both the model grid-scale orography and the subgrid-scale orography (SSO). Different models use different source orography data sets and different methodologies to derive these orography fields. This study presents the first comparison of orography fields across several operational global NWP models. It also investigates the sensitivity of an orographic drag parameterization to the intermodel spread in SSO fields and the resulting implications for representing the Northern Hemisphere winter circulation in a NWP model. The intermodel spread in both the grid-scale orography and the SSO fields is found to be considerable. This is due to differences in the underlying source data set employed and in the manner in which this data set is processed (in particular how it is smoothed and interpolated) to generate the model fields. The sensitivity of parameterized orographic drag to the intermodel variability in SSO fields is shown to be considerable and dominated by the influence of two SSO fields: the standard deviation and the mean gradient of the SSO. NWP model sensitivity experiments demonstrate that the intermodel spread in these fields is of first-order importance to the intermodel spread in parameterized surface stress, and to current known systematic model biases. The revealed importance of the SSO fields supports careful reconsideration of how these fields are generated, guiding future development of orographic drag parameterizations and reevaluation of the resolved impacts of orography on the flow. Plain Language Summary Mountains play a governing role in global atmospheric circulation via the aerodynamic drag they exert on the atmosphere. At smaller scales they influence winds and weather, for example, instigating damaging downslope windstorms in their lee; generating winds which power onshore wind farms; and causing clear-air turbulence, which affects commercial aviation. Consequently, it is important that mountains (or "orography") and their effects are represented accurately in global weather and climate models. While broad mountains are well resolved by these models, smaller mountains and steep slopes are poorly resolved or unresolved. To approximate the drag exerted on the atmosphere by this "subgrid-scale" orography (SSO), "missing" hills or mountains are assumed in each grid box, whose height, steepness, and shape are defined by data fields derived from the SSO. In this study, it is found that both model grid-scale orography and SSO fields vary significantly across currently operational models. These differences have a profound effect on the resultant drag, and consequently on the atmospheric circulation. The implication of these results is that changes in how orography is represented in our models have the capacity to bring significant improvements in our ability to model atmospheric circulations across a range of spatial and temporal scales.