How essential of the balance between large and small scale features to reproduce precipitation during a sudden sharp turn from ... View Full Text


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Article Info

DATE

2019-04

AUTHORS

Yuanyuan Ma, Yi Yang, Chenghai Wang

ABSTRACT

Sudden turn from drought to flood (STDF) is a unique representation of intra-seasonal extreme events and occurs frequently. However, it is notoriously difficult to represent in climate simulations due to the accumulation of model errors. This study uses a regional climate model (RCM) with different initialization and nudging schemes to explore effective approaches for capturing a STDF event. Results show that the conventional continuous integration with single initialization cannot reproduce the STDF event, while nudging or re-initialization can. Furthermore, spectral nudging and re-initialization outperform the conventional continuous simulation in reproducing precipitation features, but grid nudging induces the largest biases for precipitation though it has the smallest biases for other meteorological elements. Scale separation analysis shows that the large-scale features of the conventional continuous simulation drift far from the actual fields and force erroneous small-scale features, whereas the nudging and re-initialization successfully prevent the model from drifting away from the forcing fields at large-scales. The different performance for simulating precipitation among spectral nudging, re-initialization and grid nudging can be attributed to that the former two methods generate their own small-scale information via the RCM, while grid nudging over-suppresses the small-scale information while retaining the large-scale features. The difference in small-scale features affects the simulation of different moisture fluxes and convergences, as well as clouds, and then results in diverse precipitation. These results illustrate that both the consistency with large-scale features and the local variability from small-scale features are both robust factors for reproducing precipitation features during extreme events using RCMs. More... »

PAGES

5013-5029

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    DOI

    http://dx.doi.org/10.1007/s00382-018-4445-3

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