Cloud Influence on ERA5 and AMPS Surface Downwelling Longwave Radiation Biases in West Antarctica

The surface downwelling longwave radiation component (LW↓) is crucial for the determination of the surface energy budget and has significant implications for the resilience of ice surfaces in the polar regions. Accurate model evaluation of this radiation component requires knowledge about the phase, vertical distribution, and associated temperature of water in the atmosphere, all of which control the LW↓ signal measured at the surface. In this study, we examine the LW↓ model errors found in the Antarctic Mesoscale Prediction System (AMPS) operational forecast model and the ERA5 model relative to observations from the ARM West Antarctic Radiation Experiment (AWARE) campaign at McMurdo Station and the West Antarctic Ice Sheet (WAIS) Divide. The errors are calculated separately for observed clear-sky conditions, ice-cloud occurrences, and liquid-bearing cloud-layer (LBCL) occurrences. The analysis results show a tendency in both models at each site to underestimate the LW↓ during clear-sky conditions, high error variability (standard deviations > 20 W m−2) during any type of cloud occurrence, and negative LW↓ biases when LBCLs are observed (bias magnitudes >15 W m−2 in tenuous LBCL cases and >43 W m−2 in optically thick/opaque LBCLs instances). We suggest that a generally dry and liquid-deficient atmosphere responsible for the identified LW↓ biases in both models is the result of excessive ice formation and growth, which could stem from the model initial and lateral boundary conditions, microphysics scheme, aerosol representation, and/or limited vertical resolution.

Radiation Component Calculation and Energy Budget Analysis for the Korean Peninsula Region

In this study, a radiation component calculation algorithm was developed using channel data from the Himawari-8 Advanced Himawari Imager (AHI) and meteorological data from the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS). In addition, the energy budget of the Korean Peninsula region in 2016 was calculated and its regional differences were analyzed. Radiation components derived using the algorithm were calibrated using the broadband radiation component data from the Clouds and the Earth’s Radiant Energy System (CERES) to improve their accuracy. The calculated radiation components and the CERES data showed an annual mean percent bias of less than 3.5% and a high correlation coefficient of over 0.98. The energy budget of the Korean Peninsula region was −2.4 Wm−2 at the top of the atmosphere (RT), −14.5 Wm−2 at the surface (RS), and 12.1 Wm−2 in the atmosphere (RA), with regional energy budget differences. The Seoul region had a high surface temperature (289.5 K) and a RS of −33.4 Wm−2 (surface emission), whereas the Sokcho region had a low surface temperature (284.7 K) and a RS of 5.0 Wm−2 (surface absorption), for a difference of 38.5 Wm−2. In short, regions with relatively high surface temperatures tended to show energy emission, and regions with relatively low surface temperatures tended to show energy absorption. Such regional energy imbalances can cause weather and climate changes and bring about meteorological disasters, and thus research on detecting energy budget changes must be continued.

Cloudiness regulates gross primary productivity of a poplar plantation under different environmental conditions

Cloud cover regulates the gross primary productivity (GPP) of forest ecosystems by changing the radiation component and other environmental factors. In this study, we used an open-path eddy covariance system and microclimate sensors installed over a poplar plantation in northern China to measure the carbon exchange and climate variables during the mid-growing seasons (June to August) in 2014 and 2015. The results indicated that the GPP of the plantation peaked when the clearness index (CI) was between 0.45 and 0.65, at which point diffuse photosynthetically active radiation (PARdif) had reached its maximum. Cloudy skies increased the maximum ecosystem photosynthetic capacity (Pmax) by 28% compared with clear skies. PARdif and soil moisture were the most and the least crucial drivers for photosynthetic productivity of the plantation under cloudy skies, respectively. The ecosystem photosynthetic potential was higher under lower vapor pressure deficit (VPD < 1.5 kPa), lower air temperature (Ta < 30 °C), and nonstressed conditions (REW > 0.4) for cloudy skies due to effects of Ta and VPD on stoma. Overall, our research highlighted the importance of cloud-induced radiation component change and environmental variation in quantifying the GPP of forest ecosystems.