Leveraging higher-order time integration methods for improved computational efficiency in a rainshaft model

TL;DR

This paper proposes higher-order Runge-Kutta time integration methods with adaptive stepping for rain microphysics in climate models, significantly improving accuracy and efficiency over traditional first-order schemes.

Contribution

It introduces higher-order Runge-Kutta integrators with adaptive time stepping for rain microphysics, reducing computational costs and improving solution accuracy in climate models.

Findings

Higher-order integrators achieve 10x faster results than P3.

Adaptive time stepping eliminates need for substepping procedures.

Microphysical process analysis informs optimal process grouping.

Abstract

Cloud and precipitation microphysics packages in atmospheric general circulation models typically use first-order time integration methods with a large time step, requiring ad hoc limiters and substepping of the sedimentation scheme to prevent solutions from becoming unstable. We show that in the latest version of Energy Exascale Earth System Model, E3SMv3, the rain microphysics provided by the Predicted Particle Properties (P3) scheme is underresolved in time at the model's default 300s time step. The P3 scheme requires limiters to guarantee stability, but those limiters make large discretization errors more difficult to detect. When the time step of the P3 scheme is reduced to sufficiently capture correct microphysics behavior, wall clock time of the simulation is increased by nearly a factor of 40. Instead of reducing the microphysics time step, we recommend using higher-order time integrators based on Runge-Kutta methods, which offer improved solution accuracy at comparable computational costs. A key to obtaining computationally efficient microphysics results is the use of adaptive time stepping, which also eliminates the need for specialized substepping procedures in the sedimentation process. We also analyze individual microphysical processes by extracting inverse timescales from Jacobians of the process rates, which gives insight about the maximum time step each process is able to take while maintaining stability and accuracy, and about how individual processes should be grouped together for most efficient results. The proposed integrators can achieve the accuracy level required to correctly model rain microphysics parameterizations more than 10x faster than the P3 scheme.