Evaluating the global ocean biogeochemistry models used in the Global Carbon Budget with the International Ocean benchmarking (IOMB) System

The ocean carbon cycle plays a key role in the uptake and storage of atmospheric carbon dioxide. Each year, the Global Carbon Budget (GCB) estimates the oceanic sink of CO₂ that amounted to 2.8 ± 0.4 Gt C yr^-1 in the year 2022 (Friedlingstein et al., 2023). However, the estimates by global ocean biogeochemistry models (GOBMs, 2.5 ± 0.4 Gt C yr^-1 ) are substantially lower than the estimates based on upscaled observations (3.1 Gt C yr^-1 [2.5,3.3]) and the range of the sink estimates covered by the GOBMs is substantial (1.1 Gt C yr-1 ). Biases and uncertainties in the GOBM estimates of the ocean carbon sink may be due to imperfections in the representation of physical (e.g., transport, mixing) and biogeochemical processes, as well as in the forcing fields. In this study, we use the International Ocean Model Benchmarking (IOMB) system to evaluate the performance of GCB models against state-of-the-art observations. IOMB is a Python-based open-source software package that is used to evaluate the performance of CMIP models and the counterpart to ILAMB that is used to evaluate the dynamic vegetation models for the land sink in the GCB. Using IOMB, we analyze the physical (upper ocean temperature, vertical temperature gradient, mixed layer depth, salinity) and biogeochemical (nutrients, chlorophyll, oxygen, total alkalinity, dissolved inorganic carbon (DIC), anthropogenic DIC) variables of interest. Further, the addition of new targeted metrics (AMOC, stratification indices, CFCs) will be discussed. IOMB evaluates the model performances using statistical measures such as bias, root mean square error (RMSE), annual cycle phase, spatial distribution, interannual variability and a score will be estimated for each model, providing a benchmark for the current state of GCB models and highlighting the areas for further model development.