Internal climate variability plays an important role in the abundance and distribution of phytoplankton in the global ocean. Previous studies using large ensembles of Earth system models (ESMs) have demonstrated their utility in the study of marine phytoplankton variability. These ESM large ensembles simulate the evolution of multiple alternate realities, each with a different phasing of internal climate variability. However, ESMs may not accurately represent real world variability as recorded via satellite and in situ observations of ocean chlorophyll over the past few decades. Observational records of surface ocean chlorophyll equate to a single ensemble member in the large ensemble framework, and this can cloud the interpretation of long-term trends: are they externally forced, caused by the phasing of internal variability, or both? Here, we use a novel statistical emulation technique to place the observational record of surface ocean chlorophyll into the large ensemble framework. Much like a large initial condition ensemble generated with an ESM, the resulting synthetic ensemble represents multiple possible evolutions of ocean chlorophyll concentration, each with a different phasing of internal climate variability. We further demonstrate the validity of our statistical approach by recreating a ESM ensemble of chlorophyll using only a single ESM ensemble member. We use the synthetic ensemble to explore the interpretation of long-term trends in the presence of internal variability. Our results suggest the potential to explore this approach for other ocean biogeochemical variables.

Ocean primary production constitutes approximately half of global biospheric production, affecting both fisheries productivity and biogeochemical cycling. Although climate change is predicted to affect the ocean's biological productivity, the extent of the global impact is poorly quantified. Assessing changes in the ocean biosphere using remote sensing data is challenged by the relatively short length of the observational record, restricting our ability to disentangle fluctuations in internal variability from forced anthropogenic trends. Additionally, the majority of ocean circulation models with embedded biogeochemistry do not skillfully predict observational records of ocean chlorophyll at ocean time series locations. To overcome these limitations, we have constructed a synthetic ensemble of global ocean chlorophyll concentration. By employing statistical resampling methods to the SeaWiFS and MODIS ocean color datasets and creating surrogate climate modes of ENSO and PDO, we quantify the range of internal climate variability in the 20 year observational record and create multiple alternate realities for the possible evolution of the ocean biosphere over time. Our synthetic ensemble can be used for a variety of purposes, including diagnosing patterns of internal variability and emergence of anthropogenic trends in observed chlorophyll, and validating Earth system model representation of such variability.