Liu, Y. X., L. J. Cheng, Y. Y. Pan, Z. T. Tan, J. Abraham, B. Zhang, J. Zhu, and J. Q. Song, 2022: How well do CMIP6 and CMIP5 models simulate the climatological seasonal variations of ocean salinity? Adv. Atmos. Sci., 39(10), 1650−1672, https://doi.org/10.1007/s00376-022-1381-2.
Citation: Liu, Y. X., L. J. Cheng, Y. Y. Pan, Z. T. Tan, J. Abraham, B. Zhang, J. Zhu, and J. Q. Song, 2022: How well do CMIP6 and CMIP5 models simulate the climatological seasonal variations of ocean salinity? Adv. Atmos. Sci., 39(10), 1650−1672, https://doi.org/10.1007/s00376-022-1381-2.

How Well Do CMIP6 and CMIP5 Models Simulate the Climatological Seasonal Variations in Ocean Salinity?

  • This paper includes a comprehensive assessment of 40 models from the Coupled Model Intercomparison Project phase 5 (CMIP5) and 33 models from the CMIP phase 6 (CMIP6) to determine the climatological and seasonal variation of ocean salinity from the surface to 2000 m. The general pattern of the ocean salinity climatology can be simulated by both the CMIP5 and CMIP6 models from the surface to 2000-m depth. However, this study shows an increased fresh bias in the surface and subsurface salinity in the CMIP6 multimodel mean, with a global average of −0.44 g kg−1 for the sea surface salinity (SSS) and −0.26 g kg−1 for the 0–1000-m averaged salinity (S1000) compared with the CMIP5 multimodel mean (−0.25 g kg−1 for the SSS and −0.07 g kg−1 for the S1000). In terms of the seasonal variation, both CMIP6 and CMIP5 models show positive (negative) anomalies in the first (second) half of the year in the global average SSS and S1000. The model-simulated variation in SSS is consistent with the observations, but not for S1000, suggesting a substantial uncertainty in simulating and understanding the seasonal variation in subsurface salinity. The CMIP5 and CMIP6 models overestimate the magnitude of the seasonal variation of the SSS in the tropics in the region 20°S–20°N but underestimate the magnitude of the seasonal change in S1000 in the Atlantic and Indian oceans. These assessments show new features of the model errors in simulating ocean salinity and support further studies of the global hydrological cycle.
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