Monthly Latent Heat Research Articles

A daily 0.25° × 0.25° hydrologically based land surface flux dataset for conterminous China, 1961–2017

This paper describes a daily 0.25° × 0.25° terrestrial hydrological dataset (VIC-CN05.1) for China during 1961–2017. The dataset is simulated by the latest variable infiltration capacity (VIC) model and driven by pure station-based atmospheric forcings and high-resolution soil parameters based on field surveys. Multiple observational (in situ and remote sensing) products and five statistical metrics, including biases, relative error (RE), root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE), and correlation coefficient (R), are used to validate the model outputs. The results indicate that the seasonal cycles of simulated total runoff and composite runoff from the University of New Hampshire and Global Runoff Data Centre (UNH/GRDC) show good agreement in the Yangtze, southeast, and Pearl River basins, with positive REs ≤ 7% and NSEs ≥ 0.87. At the Changbaishan, Haibei Shrubland, and Qianyanzhou flux towers, VIC-CN05.1 reproduces the monthly latent heat fluxes well during 2003–2005. The monthly modeled 0–10 cm soil moisture anomalies are highly correlated to in situ measurements (438 stations) during 2003–2016, with mean R = 0.80. The spatial patterns of simulated and observed snow depth, snow water equivalent (SWE), and snow cover in winter are generally consistent in the northeastern and northwestern China. Except for the northwest basin, the simulated monthly terrestrial water storage changes in most river basins (particularly the Yangtze, Huai, and Yellow River basins) match well with observations based on the Gravity Recovery and Climate Experiment (GRACE) during 2003–2016, with positive NSEs and mean R = 0.41. This terrestrial hydrology dataset is valuable for hydroclimatic research in China, such as model evaluations and long-term trend analyses.

Averaging-Related Biases in Monthly Latent Heat Fluxes

Abstract Seasonal-to-multidecadal applications that require ocean surface energy fluxes often require accuracies of surface turbulent fluxes to be 5 W m−2 or better. While there is little doubt that uncertainties in the flux algorithms and input data can cause considerable errors, the impact of temporal averaging has been more controversial. The biases resulting from using monthly averaged winds, temperatures, and humidities in the bulk aerodynamic formula (i.e., the so-called classical method) to estimate the monthly mean latent heat fluxes are shown to be substantial and spatially varying in a manner that is consistent with most prior work. These averaging-related biases are linked to nonnegligible submonthly covariances between the wind, temperature, and humidity. To provide additional insight into the averaging-related bias, the methodology behind the third-generation Florida State University monthly mean surface flux product (FSU3) is detailed to highlight additional sources of errors in gridded datasets. The FSU3 latent heat fluxes suffer from this averaging-related bias, which can be as large as 90 W m−2 in western boundary current regions during winter and can exceed 40 W m−2 in synoptically active portions of the tropics. The regional impacts of these biases on the mixed layer temperature tendency are shown to demonstrate that the error resulting from applying the classical method is physically substantial.

Seasonal heat budget of the Mediterranean Sea

We calculate the monthly components of the Mediterranean Sea heat budget, namely the net shortwave, net longwave, latent, sensible heat fluxes, and heat storage change, for years 1984–2000. The radiative components of the seasonal heat budget are derived by a radiation transfer model, while in most other studies bulk formulae are used. A variety of data are required to run the model, among which are cloud data from International Satellite Cloud Climatology Project (ISCCP) D2 data set, aerosol data from Global Aerosol Data Set (GADS), temperature and humidity from National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) and European Centre for Medium‐range Weather Forecasts (ECMWF) Re‐Analysis (ERA‐40), and oceanographical data from Mediterranean Data Archaeology and Rescue (MEDAR) MEDATLAS database and the World Ocean Atlas 2001. We compare two methods for the estimation of the monthly latent heat flux and evaporation: the bulk aerodynamic and the heat balance. The average annual evaporation rate for the Mediterranean Sea, based on the heat balance method, is estimated at 1500 ± 190 mm yr−1. The bulk aerodynamic method produces estimates of the annual evaporation rate in the range 1060–1280 mm yr−1, depending on the source of the input data. The analysis of the heat content shows that the solar heat absorbed by the sea during summer is redistributed to winter evaporation via heat storage by the sea. Thus the peak evaporation occurs in winter and is mainly driven by energy released (100–150 Wm−2) from sea heat storage.