RegCM4: Model description and preliminary tests over multiple CORDEX domains

Climate Research (Impact Factor: 2.68). 01/2011; 936:577X.
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    ABSTRACT: The skill of a regional climate model (RegCM4) in capturing the mean patterns, interannual variability and extreme statistics of daily-scale temperature and precipitation events over Mexico is assessed through a comparison of observations and a 27-year long simulation driven by reanalyses of observations covering the Central America CORDEX domain. The analysis also includes the simulation of tropical cyclones. It is found that RegCM4 reproduces adequately the mean spatial patterns of seasonal precipitation and temperature, along with the associated interannual variability characteristics. The main model bias is an overestimation of precipitation in mountainous regions. The 5 and 95 percentiles of daily temperature, as well as the maximum dry spell length are realistically simulated. The simulated distribution of precipitation events as well as the 95 percentile of precipitation shows a wet bias in topographically complex regions. Based on a simple detection method, the model produces realistic tropical cyclone distributions even at its relatively coarse resolution (dx = 50 km), although the number of cyclone days is underestimated over the Pacific and somewhat overestimated over the Atlantic and Caribbean basins. Overall, it is assessed that the performance of RegCM4 over Mexico is of sufficient quality to study not only mean precipitation and temperature patterns, but also higher order climate statistics.
    Climate Dynamics 02/2013; · 4.23 Impact Factor
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    ABSTRACT: We present a validation analysis of a regional climate model coupled to a distributed one dimensional (1D) lake model for the Caspian Sea Basin. Two model grid spacings are tested, 50 and 20 km, the simulation period is 1989–2008 and the lateral boundary conditions are from the ERA-Interim reanalysis of observations. The model is validated against atmospheric as well as lake variables. The model performance in reproducing precipitation and temperature mean seasonal climatology, seasonal cycles and interannual variability is generally good, with the model results being mostly within the observational uncertainty range. The model appears to overestimate cloudiness and underestimate surface radiation, although a large observational uncertainty is found in these variables. The 1D distributed lake model (run at each grid point of the lake area) reproduces the observed lake-average sea surface temperature (SST), although differences compared to observations are found in the spatial structure of the SST, most likely as a result of the absence of 3 dimensional lake water circulations. The evolution of lake ice cover and near surface wind over the lake area is also reproduced by the model reasonably well. Improvements resulting from the increase of resolution from 50 to 20 km are most significant in the lake model. Overall the performance of the coupled regional climate—1D lake model system appears to be of sufficient quality for application to climate change scenario simulations over the Caspian Sea Basin.
    Climate Dynamics 10/2013; · 4.23 Impact Factor
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    ABSTRACT: Adoption of conservation agriculture (CA) is increasingly being promoted as a way of adapting agricultural systems to increasing climate variability, especially for areas such as southern Africa where rainfall is projected to decrease. The DSSAT crop simulation models can be a valuable tool in evaluating the effects of CA which are viable both economically and environmentally. Our objectives were: (1) to evaluate the ability of DSSAT to predict continuous maize (Zea mays L.) yield for conventional tillage (CT) and CA systems as well as maize yield for a CA maize–cowpea (Vigna unguiculata) rotation on an Oxic rhodustalf (2) to use DSSAT to project weather effect of climate change on yield, economic returns and risk in CT and CA systems. The DSSAT model was calibrated using data from 2007–2008 season and validated against independent data sets of yield of 2008–2009 to 2011–2012 seasons. Simulations of maize yields were conducted on projected future weather data from 2010 to 2030 that was generated by RegCM4 using the A1B scenario. The DSSAT model calibration and validation showed that it could be used for decision-making to choose specific CA practices especially for no-till and crop residue retention. Long term simulations showed that maize–cowpea rotation gave 451 kg ha�1 and 1.62 kg mm�1 rain more maize grain yield and rain water productivity, respectively compared with CT. On the other hand, CT (3131–5023 kg ha�1) showed larger variation in yield than both CA systems (3863 kg ha�1 and 4905 kg ha�1). CT and CA systems gave 50% and 10% cumulative probability of obtaining yield below the minimum acceptable limit of 4000 kg ha�1 respectively suggesting that CA has lower probability of low yield than CT, thus could be preferred by risk-averse farmers in uncertain climatic conditions. Using similar reasoning, Mean-Gini Dominance analysis showed the dominancy of maize–cowpea rotation and indicated it as the most efficient management system. This study therefore suggests that CA, especially when all three principles are practiced by smallholders in the medium altitude of Lilongwe and similar areas, has the potential to adapt the maize based systems to climate change. Use of DSSAT simulation of the effects of CA was successful for no-till and crop residue retention, but poor for crop rotation. Refinement of crop rotation algorithm in DSSAT is recommended.
    Soil and Tillage Research 05/2014; 143. · 2.37 Impact Factor


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Jun 2, 2014