Validation of IPCC AR4 models over the Iberian Peninsula

Errasti, Iñigo; Ezcurra, A.; Sáenz, Jon; Ibarra-Berastegi, Gabriel

doi:10.1007/s00704-010-0282-y

Cited by 55 publications

(40 citation statements)

References 49 publications

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“…The northern and southern sub-regions are considered, respectively, for the wet and the dry season rainfall analyses. The ability of the GCMs in simulating the interannual variability of the rainfall over the Peninsula is studied by applying widely used statistical methods (Wilks, 2006;Gleckler et al ., 2008;Errasti et al ., 2011). The intention is to judge how well the models are able to simulate the mean present rainfall climatology on the annual and on the seasonal basis in comparison with the CMP rainfall.…”

Section: Methodsmentioning

confidence: 99%

“…The coupled IPCC AR4 GCM performance has been studied over different regions of the globe. It has been found that for a particular region, the models chosen for simulating the current climate conditions perform differently (Annamalai et al ., 2007;Kripalani et al ., 2007;Carolina and Gabriel, 2009;Errasti et al ., 2011). Furthermore, different methodologies lead to differing selections of models, even for one particular region (Annamalai et al ., 2007;Kripalani et al ., 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is always deemed efficacious to validate the models on the basis of their performance in simulating the current climate conditions of the particular region under consideration. If the model biases are low and they are able to simulate well the current climate conditions of the region, then it may be assumed that their projections are also reliable for the region (Errasti et al ., 2011). Therefore, this article is focused on the evaluation of the rainfall climatology obtained from the IPCC AR4 22 GCM simulations over the Arabian Peninsula as a whole as well as over its two sub-regions (northern and southern) on the seasonal scale (wet and dry, respectively).…”

Section: Introductionmentioning

confidence: 99%

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Interannual variability of rainfall over the Arabian Peninsula using the IPCC AR4 Global Climate Models

Almazroui

Abid

Athar

et al. 2012

Intl Journal of Climatology

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ABSTRACT:The interannual rainfall variability derived from the 22 Global Climate Model (GCM) simulations of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) for the duration 1979-2000 is analysed and compared with the gridded observed dataset over the Arabian Peninsula. The annual cycle of the rainfall derived from these models is validated for the entire Arabian Peninsula, and separately for its two sub-regions, named northern and southern Arabian Peninsula. The spatial patterns of the rainfall and the mean sea level pressure are analysed to judge the ability of the models to simulate the mean climatology of the Peninsula. This analysis reveals that out of the 22 IPCC AR4 GCM multi-model datasets, only one group (composed of 5 models) is relatively better than all the others in simulating the interannual variability of the wet season rainfall for the northern sub-region, and another group (also composed of 5 models) is likewise for the dry season rainfall climatology of the southern sub-region, compared with the gridded dataset. The above two groups of models tend to fall within one-sigma standard deviation of the mean seasonal rainfall derived from the gridded dataset. Moreover, only one model [CCCMA-CGCM3 (T47) from Canada] is found to be relatively better in simulating the rainfall climatology for both the wet and the dry seasons (i.e. for the northern and the southern sub-regions) simultaneously, compared with the observed data.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interannual variability of rainfall over the Arabian Peninsula using the IPCC AR4 Global Climate Models

Almazroui

Abid

Athar

et al. 2012

Intl Journal of Climatology

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“…The performance of GCMs at the regional scale remains variable (Bollasina and Nigam 2009;Gleckler et al 2008;Randall et al 2007). Model accuracy differs with region and the type of variable simulated (Errasti et al 2010); hence, GCM models are evaluated by testing their ability to simulate the "present climate" of hydrometeorological variables (including variability and extremes). However, it should be noted that neither good performance across an arbitrary suite of measures of observed climate, nor agreement in output across a collection of models, provides a rigorous basis for assessing the accuracy of a future prediction.…”

Section: Introductionmentioning

confidence: 99%

“…Past studies have evaluated the precipitation simulated by GCMs at larger timescales (monthly, seasonal, and annual) over India region and larger spatial scales (e.g., south Asia) (Annamalai et al 2007;Bollasina and Nigam 2009;Kripalani et al 2007b;Preethi et al 2010;Rajeevan and Nanjundiah 2009). A good review of the methods available to evaluate the performance of GCMs may be found in (Errasti et al 2010;Johnson and Sharma 2009). Details of many previous studies performed at a monthly scale, such as region examined, variables evaluated, method used, and GCM model selected can be found in Table 8 in Errasti et al (2010).…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of AR4 Climate Models in simulating daily precipitation over the Indian region using skill scores

Anandhi

Nanjundiah

2014

Theor Appl Climatol

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The ability of Coupled General Circulation Models (CGCMs) participating in the Intergovernmental Panel for Climate Change's fourth assessment report (IPCC AR4) for the 20th century climate (20C3M scenario) to simulate the daily precipitation over the Indian region is explored. The skill is evaluated on a 2.5°×2.5°grid square compared with the Indian Meteorological Department's (IMD) gridded dataset, and every GCM is ranked for each of these grids based on its skill score. Skill scores (SSs) are estimated from the probability density functions (PDFs) obtained from observed IMD datasets and GCM simulations. The methodology takes into account (high) extreme precipitation events simulated by GCMs. The results are analyzed and presented for three categories and six zones. The three categories are the monsoon season (JJASO -June to October), non-monsoon season (JFMAMND -January to May, November, December) and for the entire year ("Annual"). The six precipitation zones are peninsular, west central, northwest, northeast, central northeast India, and the hilly region. Sensitivity analysis was performed for three spatial scales, 2.5°grid square, zones, and all of India, in the three categories. The models were ranked based on the SS. The category JFMAMND had a higher SS than the JJASO category. The northwest zone had higher SSs, whereas the peninsular and hilly regions had lower SS. No single GCM can be identified as the best for all categories and zones. Some models consistently outperformed the model ensemble, and one model had particularly poor performance. Results show that most models underestimated the daily precipitation rates in the 0-1 mm/day range and overestimated it in the 1-15 mm/day range.

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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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Validation of IPCC AR4 models over the Iberian Peninsula

Cited by 55 publications

References 49 publications

Interannual variability of rainfall over the Arabian Peninsula using the IPCC AR4 Global Climate Models

Interannual variability of rainfall over the Arabian Peninsula using the IPCC AR4 Global Climate Models

Performance evaluation of AR4 Climate Models in simulating daily precipitation over the Indian region using skill scores

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

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