Analysis of the Radiation Fluxes over Complex Surfaces on the Tibetan Plateau

Analysis of long-term, ground-based observation data on the Tibetan Plateau help to enhance our understanding of land-atmosphere interactions and their influence on weather and climate in this region. In this paper, the daily, monthly, and annual averages of radiative fluxes, surface albedo, surface temperature, and air temperature were calculated for the period of 2006 to 2019 at six research stations on the Tibetan Plateau. The surface energy balance characteristics of these six stations, which include alpine meadow, alpine desert, and alpine steppe, were then compared. The downward shortwave radiation at stations BJ, QOMS, and NAMORS was found to decrease during the study period, due to increasing cloudiness. Meanwhile, the upward shortwave radiation and surface albedo at all stations were found to have decreased overall. Downward longwave radiation, upward longwave radiation, net radiation, surface temperature, and air temperature showed increasing trends on inter-annual time scales at most stations. Downward shortwave radiation was maximum in spring at BJ, QOMS, NADORS, and NAMORS, due to the influence of the summer monsoon. Upward shortwave radiation peaked in October and November due to the greater snow cover. BJ, QOMS, NADORS, and NAMORS showed strong sensible heat fluxes in the spring while MAWORS showed strong sensible heat fluxes in the summer. The monthly and diurnal variations of surface albedo at each station were “U” shaped. The diurnal variability of downward longwave radiation at each station was small, ranging from 220 to 295 W·m−2.The diurnal variation in surface temperature at each station slightly lagged behind changes in downward shortwave radiation, and the air temperature, in turn, slightly lagged behind the surface temperature.

Impacts of aerosol-photolysis interaction and aerosol-radiation feedback on surface-layer ozone in North China during a multi-pollutant air pollution episode

We examined the impacts of aerosol-radiation interactions, including the effects of aerosol-photolysis interaction (API) and aerosol-radiation feedback (ARF), on surface-layer ozone (O3) concentrations during one multi-pollutant air pollution episode characterized by high O3 and PM2.5 levels from 28 July to 3 August 2014 in North China, by using the Weather Research and Forecasting with Chemistry (WRF-Chem) model embedded with an integrated process analysis scheme. Our results show that aerosol-radiation interactions decrease the daytime downward shortwave radiation at surface, 2 m temperature, 10 m wind speed, planetary boundary layer height, photolysis rates J[NO2] and J[O1D] by 115.8 W m−2, 0.56 °C, 0.12 m s−1, 129 m, 1.8 × 10−3 s−1 and 6.1 × 10−6 s−1, and increase relative humidity at 2 m and downward shortwave radiation in the atmosphere by 2.4 % and 72.8 W m−2. The weakened photolysis rates and changed meteorological conditions reduce surface-layer O3 concentrations by up to 11.4 ppb (13.5 %), with API and ARF contributing 74.6 % and 25.4 % of the O3 decrease, respectively. The combined impacts of API and ARF on surface O3 are further quantitatively characterized by the ratio of changed O3 concentration to local PM2.5 level. The ratio is calculated to be −0.14 ppb (µg m−3)−1 averaged over the multi-pollutant air pollution area in North China. Process analysis indicates that the weakened O3 chemical production makes the greatest contribution to API effect while the reduced vertical mixing is the key process for ARF effect. This study implies that future PM2.5 reductions will lead to O3 increases due to weakened aerosol-radiation interactions. Therefore, tighter controls of O3 precursors are needed to offset O3 increases caused by weakened aerosol-radiation interactions in the future.

Short-term forecasting of regional biospheric CO₂ fluxes in Europe using a light-use-efficiency model (VPRM, MPI-BGC version 1.2)

Forecasting atmospheric CO2 concentrations on synoptic timescales (∼ days) can benefit the planning of field campaigns by better predicting the location of important gradients. One aspect of this, accurately predicting the day-to-day variation in biospheric fluxes, poses a major challenge. This study aims to investigate the feasibility of using a diagnostic light-use-efficiency model, the Vegetation Photosynthesis Respiration Model (VPRM), to forecast biospheric CO2 fluxes on the timescale of a few days. As input, the VPRM model requires downward shortwave radiation, 2 m temperature, and enhanced vegetation index (EVI) and land surface water index (LSWI), both of which are calculated from MODIS reflectance measurements. Flux forecasts were performed by extrapolating the model input into the future, i.e., using downward shortwave radiation and temperature from a numerical weather prediction (NWP) model, as well as extrapolating the MODIS indices to calculate future biospheric CO2 fluxes with VPRM. A hindcast for biospheric CO2 fluxes in Europe in 2014 has been done and compared to eddy covariance flux measurements to assess the uncertainty from different aspects of the forecasting system. In total the range-normalized mean absolute error (normalized) of the 5 d flux forecast at daily timescales is 7.1 %, while the error for the model itself is 15.9 %. The largest forecast error source comes from the meteorological data, in which error from shortwave radiation contributes slightly more than the error from air temperature. The error contribution from all error sources is similar at each flux observation site and is not significantly dependent on vegetation type.

Sensitivity of simulated temperature, precipitation, and global radiation to different WRF configurations over the Carpathian Basin for regional climate applications

In this study, the Weather Research and Forecasting (WRF) model is used to produce short-term regional climate simulations with several configurations for the Carpathian Basin region. The goal is to evaluate the performance of the model and analyze its sensitivity to different physical and dynamical settings, and input data. Fifteen experiments were conducted with WRF at 10 km resolution for the year 2013. The simulations differ in terms of configuration options such as the parameterization schemes, the hydrostatic and non-hydrostatic dynamical cores, the initial and boundary conditions (ERA5 and ERA-Interim reanalyses), the number of vertical levels, and the length of the spin-up period. E-OBS dataset 2 m temperature, total precipitation, and global radiation are used for validation. Temperature underestimation reaches 4–7 °C for some experiments and can be reduced by certain physics scheme combinations. The cold bias in winter and spring is mainly caused by excessive snowfall and too persistent snow cover, as revealed by comparison with satellite-based observations and a test simulation without snow on the surface. Annual precipitation is overestimated by 0.6–3.8 mm day−1, with biases mainly accumulating in the period driven by large-scale weather processes. Downward shortwave radiation is underestimated all year except in the months dominated by locally forced phenomena (May to August) when a positive bias prevails. The incorporation of downward shortwave radiation to the validation variables increased the understanding of underlying problems with the parameterization schemes and highlighted false model error compensations.