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. We have developed a new statistical approach (M3Fusion) for combiningsurface ozone observations from thousands of monitoring sites around theworld with the output from multiple atmospheric chemistry models to produce aglobal surface ozone distribution with greater accuracy than can be providedby any individual model. The ozone observations from 4766 monitoring siteswere provided by the Tropospheric Ozone Assessment Report (TOAR) surfaceozone database, which contains the world's largest collection of surfaceozone metrics. Output from six models was provided by the participants of theChemistry-Climate Model Initiative (CCMI) and NASA's Global Modeling andAssimilation Office (GMAO). We analyze the 6-month maximum of the maximumdaily 8 h average ozone value (DMA8) for relevance to ozone health impacts.We interpolate the irregularly spaced observations onto a fine-resolutiongrid by using integrated nested Laplace approximations and compare the ozonefield to each model in each world region. This method allows us to produce aglobal surface ozone field based on TOAR observations, which we then use toselect the combination of global models with the greatest skill in each ofeight world regions; models with greater skill in a particular region aregiven higher weight. This blended model product is bias corrected within2∘ of observation locations to produce the final fused surface ozoneproduct. We show that our fused product has an improved mean squared errorcompared to the simple multi-model ensemble mean, which is biased high inmost regions of the world.

geoscientific model development

A new method (M&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt;Fusion v1) for combining observations and multiple model output for an improved estimate of the global surface ozone distribution