Investigation of regional climate models’ internal variability with a ten-member ensemble of 10-year simulations over a large domain

Lucas‐Picher, Philippe; Caya, Daniel; Elía, Ramón de; Laprise, René

doi:10.1007/s00382-008-0384-8

Cited by 107 publications

(141 citation statements)

References 18 publications

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“…The results indicate that IV contributions are smallest in winter and more important in spring and summer, and to a lesser extent in fall, confirming earlier results (e.g., Alexandru et al 2007;Lucas-Picher et al 2004, 2008a. It is noteworthy that the contributions from the EM fields shown earlier in Fig.…”

Section: Available Enthalpy and Kinetic Energy Duesupporting

confidence: 90%

“…It is also well documented that IV greatly varies with weather regime, season, domain size and model configuration. For instance, over mid-latitudes such as North America regional domain, it was found that IV increases with domain size and, for precipitation and geopotential height, stronger IV occurs in summer as a result of the combination of slower upper tropospheric winds, implying longer residence time within the regional domain and reduced control exerted by LBC on simulations, and stronger local forcing processes such as convection (Alexandru et al 2007;Lucas-Picher et al 2004, 2008a. Alexandru et al (2007) were amongst the first to study the IV climatology by using a large ensemble of RCM simulations with 20 members.…”

Section: Introductionmentioning

confidence: 99%

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Energy cycle associated with inter-member variability in a large ensemble of simulations with the Canadian RCM (CRCM5)

Nikiéma¹,

Laprise²

2015

Clim Dyn

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Section: Available Enthalpy and Kinetic Energy Duesupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Energy cycle associated with inter-member variability in a large ensemble of simulations with the Canadian RCM (CRCM5)

Nikiéma¹,

Laprise²

2015

Clim Dyn

Self Cite

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“…The TV can be considered as the internal variability of a GCM. The intermember spread generated by the same RCM driven by the same LBCs is expected to be smaller than TV (Alexandru Lucas-Picher et al 2008a;Laprise et al 2008).…”

Section: Statistics To Estimate Internal Variabilitymentioning

confidence: 98%

“…The IV generated by the ALADIN ensemble is estimated by using the same statistics introduced in Alexandru et al (2007) and Lucas-Picher et al (2008a). Basically, the IV is defined as the inter-member spread, computed from daily data for each grid point and for each day, as:…”

Section: Statistics To Estimate Internal Variabilitymentioning

confidence: 99%

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Impact of the internal variability on the cyclone tracks simulated by a regional climate model over the Med-CORDEX domain

Sánchez-Gómez

Somot

2016

Clim Dyn

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Internal variability and model uncertainty components in future hydrometeorological projections: The Alpine Durance basin

Lafaysse

Hingray

Mezghani

et al. 2014

Water Resources Research

116

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A multireplicate multimodel ensemble of hydrological simulations covering the 1860-2099 period has been produced for the Upper Durance River basin (French Alps). An original quasi-ergodic analysis of variance was applied to quantify uncertainties related to General Circulation Models (GCMs), Statistical Downscaling Models (SDMs) and the internal variability of each GCM/SDM simulation chain. For temperature, GCM uncertainty prevails and SDM uncertainty is nonnegligible. Significant warming and in turn significant changes are predicted for evaporation, snow cover and seasonality of discharges. For precipitation, GCM and SDM uncertainty components are of the same order. A high contribution of the large and smallscale components of internal variability is also obtained, inherited, respectively, from the GCMs and the different replicates of a given SDM. The same applies for annual discharge. The uncertainty in values that could be experienced for any given future period is therefore very high. For both discharge and precipitation, even the sign of future realizations is uncertain at a 90% confidence level. These findings have important implications. Similarly to GCM uncertainty, SDM uncertainty cannot be neglected. The same applies for both components of internal variability. Climate change impact studies based on a single SDM realization are likely to be no more relevant than those based on a single GCM run. They may lead to poor decisions for climate change adaptation.

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Investigation of regional climate models’ internal variability with a ten-member ensemble of 10-year simulations over a large domain

Cited by 107 publications

References 18 publications

Energy cycle associated with inter-member variability in a large ensemble of simulations with the Canadian RCM (CRCM5)

Energy cycle associated with inter-member variability in a large ensemble of simulations with the Canadian RCM (CRCM5)

Impact of the internal variability on the cyclone tracks simulated by a regional climate model over the Med-CORDEX domain

Internal variability and model uncertainty components in future hydrometeorological projections: The Alpine Durance basin

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