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Downward long-wave irradiance (DnLWI) is a variable that directly influences the surface net radiation, which in turn affects weather and climate. Due to the lack of abundant DnLWI observations, numerical weather prediction (NWP) models provide a very effective way to yield the DnLWI. Nevertheless, the reliability of the DnLWI predicted by the NWP models needs the evaluation based on the observations or accurate radiative transfer models. In this study, the DnLWI product of the China Meteorological Administration mesoscale model (CMA-MESO) was extensively validated using long-term high time-resolution (1 min) DnLWI measurements carried out at 42 sites in China. The results showed that the DnLWIs predicted by the CMA-MESO model generally agreed well with the observations, i.e., with a relative mean bias error (rMBE) of –2.0 %, but overestimated them under overcast (3.1 %) and underestimated them under dry (–5.3 %) and cloudless (–5.2 %) conditions. It is also found that the discrepancies in the DnLWI predicted by the CMA-MESO model exhibited spatial and diurnal variations, e.g., the discrepancies were significantly smaller at night than those during the day due to the stable nocturnal boundary layer. According to the results of the partial least squares analysis, the high cloud cover, medium cloud cover, planetary boundary layer height, and integrated cloud ice were the most important factors affecting the accuracy of the DnLWIs predicted by the CMA-MESO model under all sky conditions. By comparing the outputs of the CMA-MESO model and the MODerate resolution atmospheric TRANsmission (MODTRAN) model, it was found that the uncertainties in the DnLWI predicted by the CMA-MESO model mainly arose from an inappropriate consideration of the high and medium clouds under all sky conditions as well as shortcomings in the radiation scheme under cold dry cloudless conditions. [ABSTRACT FROM AUTHOR]

The New Baseline Surface Radiation (NBSR) system was established at the Shangdianzi (SDZ) regional Global Atmosphere Watch (GAW) station in 2013 to observe nine broadband radiation components, i.e. the global, direct, diffuse, and upwelling shortwave irradiance (GSWI, DSWI, DifSWI, and UpSWI); the photosynthetically active radiation (PAR); the ultraviolet irradiance (UVAI and UVBI); and the down- and upwelling longwave irradiance (DnLWI and UpLWI). To test the 1 min raw radiometric data, a Hybrid Algorithm for Radiation Data Quality Control (HARDQC) is presented in this study based on well-established methods, together with the solar irradiance dataset and the spectral features of the instrument bands. Subsequently, a NBSR dataset, which consists of radiation data at multiple timescales (i.e. 1 min, hourly, daily, monthly, monthly average hourly, and monthly average daily) over 2013–2022, is established and evaluated. Results show that more than 98.7 % of all radiation components passed the physical possibility test. The percentages of those that passed the extremely rare test are greater than 98.6 % for all radiation components except for the DnLWI (97.1 %). The percentages of those that passed the comparison test are greater than 83.3 % (GSWI), 78.3 % (DSWI), 81.7 % (DifSWI), 93.1 % (UpSWI), 88.9 % (PAR), 95.6 % (UVAI), 96.3 % (UVBI), 99.8 % (DnLWI), and 99.7 % (UpLWI), respectively. Due to data logger faults, removal of the instruments for calibration, and lightning strikes, some apparent data gaps in the upwelling radiation components (January 2015–August 2017) and all radiation components (December 2018; July to September 2021) were detected. Despite the existence of a few imperfections in the NBSR dataset, it is still reliable to apply it in many fields such as the validation of satellite products and numerical models, the investigation of relationships between radiation and atmospheric composition, and the detection of changes in the surface fluxes. The dataset described in this paper is available at 10.1594/PANGAEA.963330 (Quan et al., 2023b). [ABSTRACT FROM AUTHOR]

Dust storms have large impacts on air quality and meteorological elements; however, their relationships with atmospheric greenhouse gases (e.g., CO2) and radiation components remain uncertain. In this study, the co-variation of dust and CO2 concentrations and its possible influencing mechanism are examined using observations at the Shangdianzi (SDZ) regional Global Atmosphere Watch (GAW) station along with simulations of the Vegetation Photosynthesis and Respiration Model coupled with the Weather Research and Forecasting model (WRF-VPRM), during two dust storm events on 15 and 28 March 2021. During these events, hourly CO2 concentrations decreased by 40–50 ppm at SDZ while dust concentrations increased to 1240.6 and 712.4 µg m−3. The elevated dust increased diffusive shortwave irradiance by 50–60% and decreased direct shortwave irradiance by ~60% along with clouds. The dust events were attributed to the passages of two cold front systems over northern China. At SDZ, during the frontal passages, wind speed increased by 3–6 m s−1, and relative humidity decreased by 50–60%. The CO2 variations associated with the frontal systems were captured by the WRF-VPRM despite the overestimated surface CO2 level at SDZ. Biogenic CO2 flux plays an indistinctive role in the large CO2 variation at SDZ, as it is weak during the non-growing season. The cold fronts pushed polluted air southeastward over the North China Plain and replaced it with low-CO2 air from Northwest China, leading to the decline in CO2. These findings demonstrate that mesoscale synoptic conditions significantly affect the regional transport and dispersion of CO2, which can influence the prediction of terrestrial carbon balance on a regional scale. [ABSTRACT FROM AUTHOR]

Downward long-wave radiation (DLR) affects energy exchange between the land surface and the atmosphere and plays an important role in weather forecasting, agricultural activities, and the development of climate models. Because DLR is seldom observed at conventional radiation stations, numerous empirical parameterizations have been presented to estimate DLR from screen-level meteorological variables. The reliability and representativeness of parameterization depend on the coefficients regressed from the simultaneous observations of DLR and meteorological variables. Only a few previous studies have attempted to build parameterizations over regions in China such as the Tibetan Plateau and East China. In this study, a long-term (2011–2022) hourly dataset of DLR and meteorological elements, obtained from seven stations of the China Baseline Surface Radiation Network, was used to recalculate the coefficients of the Brunt and Weng models and to develop a new model. Results showed that the mean bias error (MBE) and relative MBE (rMBE) between the measured clear-sky DLR and that estimated using the Brunt, Weng, and new models were - 4.3, - 5.1, and 3.7 W m -2 and - 1.5 %, - 1.8 %, and 1.3 %, respectively. The root mean square errors (RMSEs) were in the range of 13.8–14.3 W m -2 , and the relative RMSEs (rRMSEs) were approximately 5.0 %. The MBEs (rMBEs) of the Brunt, Weng, and new models under all-sky conditions were - 2.8 W m -2 (- 1.0 %), - 6.1 W m -2 (- 2.1 %), and - 1.5 W m -2 (- 0.5 %), respectively. The RMSE (rRMSE) of the parameterization models in retrieving all-sky DLR was ∼ 17.5 W m -2 (∼ 6.1 %). Therefore, the models are considered suitable for retrieval of DLR over China. [ABSTRACT FROM AUTHOR]