Scale space methods for climate model analysis

Abstract

[1] In this paper, we introduce methods for evaluating climate model performance across spatial scales. These techniques are based on the "scale space" framework widely used in the image processing and computer vision communities. We discuss why the diffusion equation on the sphere provides a particularly attractive means of smoothing two-dimensional maps of global climate data. We establish that no structure is introduced into a map as an artifact of the smoothing procedure. This allows for the comparison of models and observations at multiple scales. As a test case for these methods, we compare the ability of high-and low-resolution versions of the Community Climate System Model (CCSM) to simulate the seasonal climatologies of surface air temperature (TAS), sea level pressure (PSL), and total precipitation rate (PR). For TAS, we find that the high-resolution model is better able to capture the boreal summer (JJA) climatological pattern at fine scales, although there is no such improvement in winter (DJF). We find the performances of the high-and low-resolution models to be similarly capable of capturing the summertime sea level pressure climatology at all scales. However, the high-resolution model PSL climatology is degraded for DJF, especially at larger scales. For both JJA and DJF precipitation climatologies, we find larger precipitation errors in the high-resolution model at the finest scales; however, performance at larger scales is improved.

Identifying external influences on global precipitation

“…In this paper, we focus on boreal winter (December-February; hereafter DJF). We begin by smoothing observed DJF seasonal precipitation climatologies so that spatial structure on scales less than 5°is removed (18). Fig.…”

Evaluation of the added value of a high‐resolution regional climate model simulation of the South Asian summer monsoon climatology

“…Consequently, they generally better reproduce observed climatology, such as mean and extreme precipitation, for almost all regions and seasons but especially in heterogeneous regions such as the mountains (Prein et al , ). Moreover, the large‐scale dynamics of the climate systems are partially determined by nonlinear processes, whose characteristic scales might be unresolved at coarse resolutions (Marvel et al , ) but captured at higher resolution. With these improvements, high resolution RCMs are expected to simulate local and regional weather phenomena more accurately (Marvel et al , ) leading to improved simulation of the distribution of events at finer temporal scales, e.g.…”

Technical note: Simultaneous fully dynamic characterization of multiple input–output relationships in climate models

“…We note that, because the {z A i } are uniform across each region, there will be discontinuities at the region boundaries, which could pose problems, particularly for spectral dynamical cores. Further research will need to be undertaken to reveal how this can best be handled; one possibility could be scale space smoothing methods (Marvel et al, 2013).…”