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Short-term time step convergence in a climate model.

Hui Wan1, Philip J Rasch1, Mark A Taylor2

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Summary
This summary is machine-generated.

This study reveals slow numerical convergence in atmospheric models, primarily due to errors in stratiform cloud schemes. Improving these parameterizations is crucial for accurate climate simulations.

Keywords:
climate modelconvergencenumerical errortime integration

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Area of Science:

  • Atmospheric Science
  • Climate Modeling
  • Numerical Analysis

Background:

  • Atmospheric general circulation models (AGCMs) are essential tools for climate research.
  • Accurate numerical simulations require understanding model convergence rates with respect to temporal resolution.
  • Parameterization of subgrid-scale physics significantly impacts model performance.

Purpose of the Study:

  • To evaluate the numerical convergence of short (1-hour) simulations using the Community Atmosphere Model version 5 (CAM5) spectral-element configuration.
  • To identify the primary sources of slow time step convergence in full-physics simulations.
  • To assess the applicability of the testing procedure to other AGCMs.

Main Methods:

  • Varied the process-coupling time step (1800s to 1s) while keeping horizontal grid spacing fixed (~110 km).
  • Conducted dynamical core tests to validate convergence assessment of adiabatic equations.
  • Performed full-physics CAM5 simulations and sensitivity experiments focusing on subgrid-scale physics.

Main Results:

  • The experimental setup accurately assessed convergence rates for adiabatic equations.
  • Full-physics CAM5 simulations exhibited a convergence rate of 0.4, significantly slower than the expected 1.0.
  • Stratiform cloud schemes were identified as the main contributors to time-stepping errors and slow convergence.

Conclusions:

  • The numerical convergence rate of CAM5 is substantially slower than theoretically expected.
  • Subgrid-scale physical parameterizations, particularly stratiform cloud schemes, introduce significant time-stepping errors.
  • The developed testing methodology can quantify time-stepping errors and identify sensitivities in AGCMs, with implications for model development.