Improving GCM-based decadal ocean carbon flux predictions using
observationally-constrained statistical models
Abstract
Initialized climate model simulations have proven skillful for near-term
predictability of the key physical climate variables. By comparison,
predictions of biogeochemical fields like ocean carbon flux, are still
emerging. Initial studies indicate skillful predictions are possible for
lead-times up to six years at global scale for some CMIP6 models.
However, unlike core physical variables, biogeochemical variables are
not directly initialized in existing decadal preciction systems, and
extensive empirical parametrization of ocean-biogeochemistry in Earth
System Models introduces a significant source of uncertainty. Here, we
propose a new approach for improving the skill of decadal ocean carbon
flux predictions using observationally-constrained statistical models,
as alternatives to the ocean-biogeochemistry models. We use observations
to train multi-linear and neural-network models to predict the ocean
carbon flux. To account for observational uncertainties, we train using
six different observational estimates of the flux. We then apply these
trained statistical models using input predictors from the Canadian
Earth System Model (CanESM5) decadal prediction system to produce new
decadal predictions. Our hybrid GCM-statistical approach significantly
improves prediction skill, relative to the raw CanESM5 hindcast
predictions over 1990-2019. Our hybrid-model skill is also larger than
that obtained by any available CMIP6 model. Using bias-corrected CanESM5
predictors, we make forecasts for ocean carbon flux over 2020-2029. Both
statistical models predict increases in the ocean carbon flux larger
than the changes predicted from CanESM5 forecasts. Our work highlights
the ability to improve decadal ocean carbon flux predictions by using
observationally-trained statistical models together with robust input
predictors from GCM-based decadal predictions.