Quantifying internal variability in a regional climate model: a case study for Southern Africa

Crétat, Julien; Macron, Clémence; Pohl, Benjamin; Richard, Yves

doi:10.1007/s00382-011-1021-5

Cited by 49 publications

(45 citation statements)

References 49 publications

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“…The spatial structures in Fig. 7 show strong similarities to previous studies of RIV over the North America domain (Lucas-Picher et al 2008a;Separovic et al 2008), and South Africa domain (Crétat et al 2011). However, the values of the internal variability obtained for the Mediterranean domain are lower than in other domains, probably because of a smaller domain size.…”

Section: Spatial Distribution Of the Internal Variabilitysupporting

confidence: 82%

“…7a, b) shows the weakest values of RIV, with maxima not exceeding 10 % (in summer) of the total variability over the easternmost areas. As pointed out by Crétat et al (2011), the chaotic variability is smaller for those variables connected to large-scale variability, as geopotential height or sea level pressure. T2 m (Fig.…”

Section: Spatial Distribution Of the Internal Variabilitymentioning

confidence: 89%

“…Others studies, as Crétat et al (2011) or Nikiema and Laprise (2011a, b), found out that for RCM domains located at high latitudes, as the Arctic where the IV is very strong, more than 20 members are needed to avoid biases in the estimation of the inter-member spread.…”

Section: Discussionmentioning

confidence: 99%

“…Concerning the seasonality, the IV presents larger amplitudes in summer in the Northern Hemisphere, due to stronger local processes (such as convection), combined with weaker LBC control (Caya and Biner 2004;Alexandru et al 2007;Lucas-Picher et al 2008a, b). The variable the most affected by IV in RCMs is the precipitation, rather than temperature or large-scale variables such as sea level pressure, since precipitation is associated with convective scale processes (Crétat et al 2011). All the mentioned studies reveal that, in regional modelling, it is highly advisable to adopt a probabilistic approach (ensemble) to take into account IV effects, in particular when the focus is weather processes or case-study analysis.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Spatial Distribution Of the Internal Variabilitysupporting

confidence: 82%

Section: Spatial Distribution Of the Internal Variabilitymentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…For regional-scale modeling, case studies are commonly used to highlight the regional spatiotemporal feature (Pitman and Narisma 2005;Crétat et al 2011). The year 1998 has a typical weather event with a large summer flood in the Yangtze River Valley over EAMR (Huang et al 1998;Tao et al 1998).…”

mentioning

confidence: 99%

The improvement of a regional climate model by coupling a land surface model with eco-physiological processes: A case study in 1998

Cao

Rong

2013

Climatic Change

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The Atmospheric-Vegetation Interaction Model (AVIM) is coupled with a Regional Integrated Environment Modeling System (RIEMS) to improve the regional simulation of climate variables. A case study in 1998 is implemented to study the improvement mechanism through land-air interaction in East Asia, especially in Asian summer monsoon regions. The coupled model reduces the warming bias in July in East China through the surface heat fluxes changes. Compared to the original model of RIEMS, the strong precipitation of eastern China in July is weakened by coupling of the interactive vegetation. The surface heat flux in uncoupled model is remarkably overestimated in these regions, and the enhanced heating from land surface, particularly with latent heat flux in July, will produce the overestimated temperature and precipitation in East China. Through coupling AVIM with RIEMS, the simulated area-averaged latent heat flux of two key regions decreases (e.g. from 132.36 to 103.13 W/m 2 over the region 1 between 105-125°E and 20-40°N) in July, which makes the overestimated temperature and precipitation declined, respectively.

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Internal variability of North Atlantic tropical cyclones

Done

Bruyère

et al. 2014

JGR Atmospheres

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Using a regional model initial condition ensemble, this study quantifies the magnitude of internal variability of North Atlantic tropical cyclone frequency for a case study year and identifies potential physical sources. For tropical cyclone formations from easterly waves, the simulated internal variability of tropical cyclone frequency for 1998 is approximately two fifths of the total (externally forced and internal) variability of observed tropical cyclone frequency. The simulated internal variability of tropical cyclone frequency is found to arise in approximately equal measure from variability of easterly wave occurrence and development and variability of the transition from incipient warm cores to tropical cyclones. Variable interaction between developing tropical cyclones and vertical wind shear associated with upper level cyclones is identified as a potentially important contributing factor to tropical cyclone internal variability.

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Quantifying internal variability in a regional climate model: a case study for Southern Africa

Cited by 49 publications

References 49 publications

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

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

The improvement of a regional climate model by coupling a land surface model with eco-physiological processes: A case study in 1998

Internal variability of North Atlantic tropical cyclones

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