Will a perfect model agree with perfect observations? The impact of spatial sampling

Abstract

Abstract. The spatial resolution of global climate models with interactive aerosol and the observations used to evaluate them is very different. Current models use grid spacings of ∼ 200 km, while satellite observations of aerosol use so-called pixels of ∼ 10 km. Ground site or airborne observations relate to even smaller spatial scales. We study the errors incurred due to different resolutions by aggregating high-resolution simulations (10 km grid spacing) over either the large areas of global model grid boxes ("perfect" model data) or small areas corresponding to the pixels of satellite measurements or the field of view of ground sites ("perfect" observations). Our analysis suggests that instantaneous rootmean-square (RMS) differences of perfect observations from perfect global models can easily amount to 30-160 %, for a range of observables like AOT (aerosol optical thickness), extinction, black carbon mass concentrations, PM 2.5 , number densities and CCN (cloud condensation nuclei). These differences, due entirely to different spatial sampling of models and observations, are often larger than measurement errors in real observations. Temporal averaging over a month of data reduces these differences more strongly for some observables (e.g. a threefold reduction for AOT), than for others (e.g. a twofold reduction for surface black carbon concentrations), but significant RMS differences remain (10-75 %). Note that this study ignores the issue of temporal sampling of real observations, which is likely to affect our present monthly error estimates. We examine several other strategies (e.g. spatial aggregation of observations, interpolation of model data) for reducing these differences and show their effectiveness. Finally, we examine consequences for the use of flight campaign data in global model evaluation and show that significant biases may be introduced depending on the flight strategy used.

Factors controlling black carbon distribution in the Arctic

“…The arithmetic mean of observations is 17.4 ng g −1 , larger than the geometric mean of 12.7 ng g −1 and the median of 11.8 ng g −1 (see the vertical lines in Fig. 2 and Table 1 This discrepancy is acceptable for global models because it has been suggested that the error due to different spatial sampling of global models (∼ 200 km) and point observations was up to 160 % (Schutgens et al, 2016). In addition, BC air at the surface and in the free troposphere is sensitive to the above three processes in the Arctic, particularly during winter and spring (see Sect.…”

Unveiling aerosol–cloud interactions – Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate

“…It is noteworthy that the actual number of degrees of freedom may be somewhat smaller, however, due to high spatial autocorrelation between sampled aerosol in each region as identified in many studies (e.g. Anderson et al, 2003;Schutgens et al, 2016;Kovacs, 2006;Santese et al, 2007;Shinozuka and Redemann, 2011). Nevertheless, the strength of the aerosol indirect forcing estimate Table 2.…”

Jury is still out on the radiative forcing by black carbon

“…Such timescales are not small compared with the BC atmospheric lifetime of 3-5 d, especially considering that dilution effects may lengthen the aging timescale in the real atmosphere compared with the static chamber measurements performed by Peng et al It is unclear how representative these measurements are for global and annual averages, but we know that generalizations can introduce serious errors due to spatial and temporal sampling issues (9). This implies that a simple scaling of the BC RF by the absorption enhancement factor measured by Peng et al-as performed by the authors in their table S1 and figure 4, and extended in the commentary (10)-is overly simplistic.…”