Accounting for Satellite Sampling Bias in the Validation of CESM2 Sea Surface Temperature and Chlorophyll

Satellite observations of sea surface temperature (SST) and ocean chlorophyll are critical for validating Earth system models (ESMs). However, missing satellite data due to cloud cover, sea ice, and low solar angle can introduce sampling bias that distorts model–observation comparisons. Here, we quantify satellite sampling bias in Moderate Resolution Imaging Spectroradiometer (MODIS) SST and chlorophyll and demonstrate how accounting for this bias changes our estimates of model performance. We apply realistic MODIS sampling to modeled SST and chlorophyll from an ocean-only hindcast simulation (2003–2016) of the Community Earth System Model. These model outputs are compared to real-world MODIS observations to examine how selective sampling affects the magnitude and spatial patterns of the apparent model bias. We find that model bias generally exceeds sampling bias, though the relative importance of the two depends on the spatial and temporal scale. Sampling bias is most pronounced at high-latitudes and in persistently cloudy regions, where it can impact annual means and apparent long-term trends. Accounting for sampling bias reduces model–observation differences in the multi-year means: the root mean square error decreases from 0.976 to 0.792°C for SST and from 0.635 to 0.624 (log-transformed units) for chlorophyll. However, in some regions, correcting for satellite sampling bias increases model bias. These results demonstrate that while sampling bias is generally a small uncertainty compared to model bias, it can meaningfully influence model evaluation and should be considered in assessments of ESM performance for SST and chlorophyll.