Treffer: 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. Plain Language Summary: Earth system models are mathematical descriptions of the climate system that allow us to make future projections. In order to have confidence in our future projections, we first need to ensure that our models can accurately simulate the present day by comparing the model outputs to observations of key climate variables. Two of these important variables are sea surface temperature and surface ocean chlorophyll (an indicator of biological productivity), which are commonly observed from space via satellites. However, the real‐world observations are not a perfect representation of these variables, partly because measurements can only be made in certain conditions. To address the challenge of comparing imperfect observations to our model, we developed new model output that represents what a satellite would be able to see in the model‐generated world. This new model output allows us to make a more direct comparison between the model and the real‐world observations. We found that model performance may be better than previously thought in some regions but worse in others. However, the main uncertainty remains in the model itself rather than observational sampling bias. Key Points: We simulate satellite sampling of chlorophyll and sea surface temperature within the Community Earth System Model to quantify sampling biasSampling bias varies across spatial and temporal scales, with the largest impacts at high latitudes and in persistently cloudy regionsAccounting for this sampling bias alters estimates of model performance, distinguishing observational limitations from true model error [ABSTRACT FROM AUTHOR]

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