A global long-term ocean surface daily/0.05° net radiation product from 1983–2020

The all-wave net radiation (Rn) on the ocean surface characterizes the available radiative energy balance and is important to understand the Earth’s climate system. Considering the shortcomings of available ocean surface Rn datasets (e.g., coarse spatial resolutions, discrepancy in accuracy, inconsistency, and short duration), a new long-term global daily Rn product at a spatial resolution of 0.05° from 1983 to 2020, as part of the Global High Resolution Ocean Surface Energy (GHOSE) products suite, was generated in this study by fusing several existing datasets including satellite and reanalysis products based on the comprehensive in situ measurements from 68 globally distributed moored buoy sites. Evaluation against in-situ measurements shows the root mean square difference, mean bias error and correlation coefficient squared of 23.56 Wm−2, 0.88 Wm−2 and 0.878. The global average ocean surface Rn over 1983–2020 is estimated to be 119.71 ± 2.78 Wm−2 with a significant increasing rate of 0.16 Wm−2 per year. GHOSE Rn product can be valuable for oceanic and climatic studies.

Evaluation of Surface Radiative Fluxes over the Tropical Oceans in AMIP Simulations

The performance of 20 models from the Atmospheric Model Intercomparison Project (AMIP) was evaluated concerning surface radiation over the tropical oceans (30° S–30° N) from 1979 to 2000. The model ensemble mean of the net surface shortwave radiation (QSW) was underestimated compared to the International Satellite Cloud Climatology Project (ISCCP) data by 4 W m−2. On the other hand, net longwave radiation (QLW) was overestimated by 4 W m−2, leading to an underestimation of the net surface radiation (Qrad) by 8 W m−2. The most prominent bias in the Qrad appears to be over regions of low-level clouds in the off-equatorial eastern Pacific, eastern Atlantic, and the south-eastern Indian Ocean. The root means squared error of QLW was larger than that of QSW in 17 out of 20 AMIP models. Overestimation of the total cloud cover and atmospheric humidity contributed to the underestimation of Qrad. In general, models with higher horizontal resolutions performed slightly better than those with coarser horizontal resolutions, although some systematic bias persists in all models and in all seasons, in particular, in regions of low-level clouds for QLW, and high-level clouds for QSW. The ensemble mean performed better than most models, but two high-resolution models (GFDL-HIRAM-C180 and GFDL-HIRAM-C360) outperform the model ensemble.

Evaluation of Direct Horizontal Irradiance in China Using a Physically-Based Model and Machine Learning Methods

Accurate estimation of direct horizontal irradiance (DHI) is a prerequisite for the design and location of concentrated solar power thermal systems. Previous studies have shown that DHI observation stations are too sparsely distributed to meet requirements, as a result of the high construction and maintenance costs of observation platforms. Satellite retrieval and reanalysis have been widely used for estimating DHI, but their accuracy needs to be further improved. In addition, numerous modelling techniques have been used for this purpose worldwide. In this study, we apply five machine learning methods: back propagation neural networks (BP), general regression neural networks (GRNN), genetic algorithm (Genetic), M5 model tree (M5Tree), multivariate adaptive regression splines (MARS); and a physically based model, Yang’s hybrid model (YHM). Daily meteorological variables, including air temperature (T), relative humidity (RH), surface pressure (SP), and sunshine duration (SD) were obtained from 839 China Meteorological Administration (CMA) stations in different climatic zones across China and were used as data inputs for the six models. DHI observations at 16 CMA radiation stations were used to validate their accuracy. The results indicate that the capability of M5Tree was superior to BP, GRNN, Genetic, MARS and YHM, with the lowest values of daily root mean square (RMSE) of 1.989 MJ m−2day−1, and the highest correlation coefficient (R = 0.956), respectively. Then, monthly and annual mean DHI during 1960–2016 were calculated to reveal the spatiotemporal variation of DHI across China, using daily meteorological data based on the M5tree model. The results indicated a significantly decreasing trend with a rate of −0.019 MJ m−2during 1960–2016, and the monthly and annual DHI values of the Tibetan Plateau are the highest, while whereas the lowest values occur in the southeastern part of the Yunnan−Guizhou Plateau, the Sichuan Basin and most of the southern Yangtze River Basin. The possible causes for spatiotemporal variation of DHI across China were investigated by discussing cloud and aerosol loading.