Abstract

In an ensemble of Regional Climate Model (RCM) simulations where different members are initialised at different times but driven by identical lateral boundary conditions, the individual members provide different, but equally acceptable, weather sequences. In others words, RCM simulations exhibit the phenomenon of Internal Variability (or inter-member variability—IV), defined as the spread between members in an ensemble of simulations. Our recent studies reveal that RCM’s IV is associated with energy conversions similar to those taking place in weather systems. By analogy with the classical work on global energetics of weather systems, a formulation of an energy cycle for IV has been developed that is applicable over limited-area domains. Prognostic equations for ensemble-mean kinetic energy and available enthalpy are decomposed into contributions due to ensemble-mean variables and those due to deviations from the ensemble mean (IV). Together these equations constitute an energy cycle for IV in ensemble simulations of an RCM. A 50-member ensemble of 1-year simulations that differ only in their initial conditions was performed with the fifth-generation Canadian RCM (CRCM5) over an eastern North America domain. The various energy reservoirs of IV and exchange terms between reservoirs were evaluated; the results show a remarkably close parallel between the energy conversions associated with IV in ensemble simulations of RCM and the energy conversions taking place in weather systems in the real atmosphere.

Highlights

  • In climate modelling, ensemble simulations have become a standard approach to filter out the unpredictable component of the Earth system, to provide estimates of the uncertainties associated with climate projections and to improve the determination of rare events such as climate extremes (IPCC 2013)

  • Regional Climate Models (RCM) are integrated on a limited-area domain from initial conditions (IC) and lateral boundary conditions (LBC) provided either by an archived simulation of a driving Global Climate Model (GCM) or by gridded analyses of observations

  • Based on the 50 1-year simulations that differ only by their starting date, the available enthalpy and kinetic energy associated with the ensemble-mean state, AEM and KEM, were computed following Eqs. (3) and (4)

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Summary

Inter-member variability

KEM Kinetic energy of ensemble-mean n Index number of the simulation in the ensemble. N Total number of simulations ps, pT Pressure at bottom and top of atmosphere p Pressure pr Reference value of pressure p00 Standard value of pressure Q Total diabatic heating rate. R Gas constant for air S Entropy Sr Reference entropy T Temperature −→ Tr Reference temperature value V (u, v) Horizontal wind vector z Altitude α Specific volume ω Vertical movement in pressure coordinate (dp/dt) Φ Geopotential height φ Latitude θ Potential temperature ψ General atmospheric parameter Ensemble-mean operator ()′ Deviation from EM ()∗ Deviation from Tr ()× Deviation from horizontal average along isobaric surfaces () Horizontal average along isobaric surfaces

Introduction
Model description and simulation design
Methodology: inter‐member variability energy cycle
Available enthalpy and kinetic energy of ensemble‐mean state
Available enthalpy and kinetic energy due to inter‐member variability
Inter‐member variability energy cycle in the ensemble of simulations
Energy exchanges between inter‐member variability reservoirs
Energy exchanges between ensemble‐mean reservoirs
Decomposition on isobaric surface and deviation thereof
Summary and conclusion
Full Text
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