Abstract

Using data from 33 models from the CMIP5 historical and AMIP5 simulations, we have carried out a systematic analysis of biases in total precipitation and its convective and large-scale components over the south Asian region. We have used 23 years (1983–2005) of data, and have computed model biases with respect to the PERSIANN-CDR precipitation (with convective/large-scale ratio derived from TRMM 3A12). A clustering algorithm was applied on the total, convective, and large-scale precipitation biases seen in CMIP5 models to group them based on the degree of similarity in the global bias patterns. Subsequently, AMIP5 models were analyzed to conclude if the biases were primarily due to the atmospheric component or due to the oceanic component of individual models. Our analysis shows that the set of individual models falling in a given group is somewhat sensitive to the variable (total/convective/large-scale precipitation) used for clustering. Over the south Asian region, some of the convective and large-scale precipitation biases are common across groups, emphasizing that although on a global scale the bias patterns may be sufficiently different to cluster the models into different groups, regionally, it may not be true. In general, models tend to overestimate the convective component and underestimate the large-scale component over the south Asian region, although with spatially varying magnitudes depending on the model group. We find that the convective precipitation biases are largely governed by the closure and trigger assumptions used in the convection parameterization schemes used in these models, and to a lesser extent on details of the individual cloud models. Using two different methods: (i) clustering, (ii) comparing the bias patterns of models from CMIP5 with their AMIP5 counterparts, we find that, in general, the atmospheric component (and not the oceanic component through biases in SSTs and atmosphere-ocean feedbacks) plays a major role in deciding the convective and large-scale precipitation biases. However, the oceanic component has been found important for one of the convective groups in deciding the convective precipitation biases (over the maritime continent).

Highlights

  • Sharing of components between various model versions from the same center may sound obvious, even different modeling centers share large fraction of the model code

  • The level of similarity between ACCESS and HadGEM is not very different than that between: (i) MIROC-ESM-CHEM and MIROC-ESM, (ii) NorESM1-ME and NorESM1-M, (iii) GISS-E2R-CC and GISS-E2R, (iv) HadGEM2-ES and HadGEM2-CC, and (v) GFDL-ESM2G and GFDL-ESM2M

  • We have carried out a systematic analysis of the structure of precipitation biases in 33 CMIP5 and AMIP5 models, and have grouped them based on the correlation of their biases in total, convective and large-scale precipitation on global scale

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Summary

Introduction

Sharing of components between various model versions from the same center may sound obvious, even different modeling centers share large fraction of the model code. We have carried out a systematic analysis of similarity and dissimilarity in bias structures of CMIP5 and AMIP5 models in simulating the partitioning of precipitation between the convective and large-scale components.

Results
Conclusion

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