Atmosphere (Aug 2018)

Evaluation of NESMv3 and CMIP5 Models’ Performance on Simulation of Asian-Australian Monsoon

  • Juan Li,
  • Young-Min Yang,
  • Bin Wang

DOI
https://doi.org/10.3390/atmos9090327
Journal volume & issue
Vol. 9, no. 9
p. 327

Abstract

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The Asian-Australian monsoon (AAM) has far-reaching impacts on global and local climate. Accurate simulations of AAM precipitation and its variabilities are of scientific and social importance, yet remain a great challenge in climate modeling. The present study assesses the performance of the newly developed Nanjing University of Information Science and Technology Earth System Model version 3 (NESMv3), together with that of 20 Coupled Model Intercomparison Project phase 5 (CMIP5) models, in the simulation of AAM climatology, its major modes of variability, and their relationships with El Nino-Southern Oscillation (ENSO). It is concluded that NESMv3 (1) reproduces, well, the observed features of AAM annual mean precipitation; (2) captures the solstice mode (the first annual cycle mode) of AAM realistically, but has difficulty in simulating the equinox mode (the second annual cycle mode) of AAM; (3) underestimates the monsoon precipitation intensity over the East Asian subtropical frontal zone, but overestimates that over the tropical western North Pacific; (4) faithfully reproduces the first season-reliant empirical orthogonal function (SEOF) mode of AAM precipitation and the associated circulation anomalies, as well as its relationship with ENSO turnabout, although the correlation is underestimated. Precipitation anomaly patterns of the second SEOF mode and its relationship with El Nino are poorly simulated by NESMv3 and most of the CMIP5 models as well, indicating that the monsoon variability prior to the ENSO onset is difficult to reproduce. In general, NESMv3’s performance in simulating AAM precipitation ranks among the top or above-average compared with the 20 CMIP5 models. Better simulation of East Asian summer monsoon and western Pacific subtropical high remains a major target for future improvement, in order to provide a reliable tool to understand and predict AAM precipitation.

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