Comment on esd-2022-14

Abstract

Climate model output emulation has long been attempted to support impact research, mainly to fill-in gaps in the scenario space. Given the computational cost of running coupled Earth System Models (ESMs) an effective emulator would be used to create climatic impact-driver information under scenarios that could not be run by ESMs. Lately, the necessity of accounting for internal variability has also made the availability of initial condition ensembles important, increasing further the computational demand. However, at least so far, emulators have always been limited to simplified ESM output, either seasonal, annual or decadal averages, and/or basic quantities, like temperature and precipitation, often emulated independently of one another. With this work, we propose a more comprehensive solution to climate model output emulation. Our emulator, STITCHES, uses existing archives of Earth System Models' (ESMs) scenario experiments to construct new scenarios, or enrich existing initial condition ensembles, which is what other emulators do. Importantly, its output has the same characteristics of the ESM output it set out to emulate: multivariate, spatially resolved and high frequency as the original ESM output is. STITCHES extends the idea of time-sampling – by which climate outcomes are stratified by the global warming level at which they occur, irrespective of the scenario and time associated to them – to the construction of a continuous Global Surface Air Temperature (GSAT) trajectory over the whole 21st century that replicates a target trajectory to be emulated. STITCHES does so by stitching together decade-long windows within a model simulation when GSAT has similar characteristics to the target GSAT trajectory, but in doing so STITCHES creates a series of pointers to a sequence of decades within existing scenarios in the ESM archived output, and the emulator can thus recover any type of output, at any frequency and spatial scale available from the original ESM's experiment that produced each decade. We show that the stitching does not introduce artifacts, in the great majority of cases, even when the criteria for the identification of the decades to be stitched together are not strictly tailored to the specific ESM emulated. We show this is the case for the variable that we expect to be smoother and less noisy than many variables commonly used for impact analysis, annual GSAT. Our results also suggest that most other surface atmospheric variables commonly used for impact analysis would be similarly unaffected by the stitching procedure. We successfully test the method's performance over many CMIP6/ScenarioMIP-participating ESMs and experiments. Only a few exceptions surface, but these less-than-optimal outcomes are always associated with a scarcity of the archived simulations from which to gather the decade-long windows that form the emulated GSAT trajectory. In the great majority of cases, STITCHES performance remains satisfactory according to metrics that reward consistency in trends, interannual and inter-ensemble variance, and autocorrelation structure of the time series stitched together. The method therefore can be used to create new scenarios with different GSAT pathways than existing simulations, and to increase the size of existing initial condition ensembles. There are aspects of our emulator that will immediately disqualify it for specific applications, like when climate information is needed whose characteristics result from accumulated quantities over windows of times longer than those used as building blocks by STITCHES. But for many applications, we argue that a stitched product can satisfy the needs of impact researchers. Thus, we think it could open up the possibility of designing the next scenario experiments within CMIP7 according to new principles, relieved of the need to produce a number of similar trajectories that vary only in radiative forcing strength.