Simulations of the global oceanic latent heat flux (LHF) in the CMIP5 multimodel ensemble (MME) were evaluated in comparison with 11 LHF products. The results show that the mean state of LHF in the MME coincides well with that in the observations, except for a slight overestimation in the tropical regions. The reproduction of the seasonal cycle of LHF in the MME is in good agreement with that in the observations. However, biases are relatively obvious in the coastal regions. A prominent upward trend in global-mean LHF is confirmed with all of the LHF products during the period of 1979–2005. Despite the consistent increase of LHF in CMIP5 models, the rates of increase are much weaker than those in the observations, with an average of approximately one-ninth that in the observations. The findings show that the rate of increase of near-surface specific humidity qa in MME is nearly 6 times that in the observations, while the rate of increase of the near-surface wind speed U is less than one-half that in the observations. The faster increase of qa and the slower increase of U could both suppress evaporation, and thus latent heat released by the ocean, which may be one of the reasons that the upward trend of LHF in the MME is nearly one order of magnitude lower than that in the observations.
Five latent heat flux (LHF) products are evaluated based on in situ observations in the South China Sea (SCS), including the ECWMF ERA‐Interim (ERA‐I), the NCEP2, the Objectively Analyzed air‐sea Fluxes (OAFlux), the Japanese 55 year Reanalysis (JRA55), and the TropFlux data sets. The results show that there are good correlations between the LHF products and observations, ranging from 0.68 to 0.74. However, mean biases of −8 to 40 W m−2 exist in the LHF products with respect to the observations. For root‐mean‐square errors, the OAFlux data set is the closest to the observations, followed by ERA‐I and TropFlux, while the NCEP2 data set shows significant overestimation. It is found that the biases in the near‐surface‐specific humidity are most correlated with the biases in the LHF products, followed by the biases in the near‐surface wind speed, air temperature, and sea surface temperature. The biases in the LHF products have a prominent seasonal variation that is 25 W m−2 higher in boreal winter than in summer. Using the thermal equation, it is shown that the tendency errors of the mixed‐layer temperature estimated by the biases in the LHF products vary from −2.0 to 3.5°C/month in the SCS. When all of the products are averaged, the errors are reduced to a range of −0.7 to 1.5°C/month. It is noteworthy that the errors in summer are more obvious than those in winter, since a thinner mixed layer in the summer can amplify the effect of even a small bias in the LHF.
Effects caused by precipitation on the measurements of three-dimensional sonic anemometer are analyzed based on a field observational experiment conducted in Maoming, Guangdong Province, China. Obvious fluctuations induced by precipitation are observed for the outputs of sonic anemometer-derived temperature and wind velocity components. A technique of turbulence spectra and cospectra normalized in the framework of similarity theory is utilized to validate the measured variables and calculated fluxes. It is found that the sensitivity of sonic anemometer-derived temperature to precipitation is significant, compared with that of the wind velocity components. The spectra of wind velocity and cospectra of momentum flux resemble the standard universal shape with the slopes of the spectra and cospectra at the inertial subrange, following the −2/3 and −4/3 power law, respectively, even under the condition of heavy rain. Contaminated by precipitation, however, the spectra of temperature and cospectra of sensible heat flux do not exhibit a universal shape and have obvious frequency loss at the inertial subrange. From the physical structure and working principle of sonic anemometer, a possible explanation is proposed to describe this difference, which is found to be related to the variations of precipitation particles. Corrections for errors of sonic anemometer-derived temperature under precipitation is needed, which is still under exploration.
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