Bias correction for integrated climate projection modeling

Abstract

Projections of future conditions from Earth systems models (ESMs) are necessary to understand and predict effects of changing environmental conditions on biological systems. Such projections suffer from biases, or mismatches between model output and observations. While adjusting or bias-correcting model output is common, many methods exist with little understanding of their effects on forecasts of biological change. Here, we explore the bias-correction process and its effects on downstream predictive biological models. As an example, we use the Bering 10K, a downscaled ESM for a productive and economically important subarctic ecosystem. We first characterize existing biases for three categories of variables exhibiting different scales and challenges: bottom temperature, sea ice, and net primary production. We then apply eight bias-correction approaches to six indices generated from the three categories and quantify sources of uncertainty in the trajectories of these ecosystem variables. Finally, we demonstrate how different bias-correction approaches affect downstream biological models using three case studies: 1) fish thermal spawning habitat suitability, (2) predicted zooplankton abundance, and (3) match-mismatch of phytoplankton and zooplankton bloom timing. We find that biases manifest in absolute values over time and in the timing of seasonal events. Time series of all six indices differed depending on bias-correction method, differences that were propagated to downstream biological models. For a given year and simulation, depending on method, thermal spawning habitat suitability and zooplankton abundance differed up to 149% and 151%, and match-mismatch increased or did not change. Our work highlights that bias correction reduces mismatches between observations and model output but choosing an approach requires careful consideration as to not amplify and propagate bias in downstream biological models. To that end, we identify best practices for bias correcting global or regional ESMs, including a decision tree to help improve forecasts of the effects of climate change on biological systems.