The extensive global climate observing system (GCOS) reference upper-air network (GRUAN) datasets provide a chance to validate newly released Atmospheric Infrared Sounder (AIRS) version 7 (v7) products over the Arctic. This manuscript reports on the analysis performed to evaluate errors from AIRS version 6 (v6) and v7 temperature profiles and to characterize the derived low-level temperature inversion (LLI) representativeness in the Arctic region. The AIRS averaging kernel, representing the AIRS measurement sensitivity, is applied to reduce the vertical resolution of the radiosonde profiles for comparison. Due to improved retrieval algorithms, v7 produces smaller biases in the troposphere and suppresses the cold bias in v6. Nevertheless, the profile-averaged root mean square error (RMSE) increased by over 30% in v7, particularly in the winter half-year when v7 showed a larger RMSE below 800 hPa. The AIRS temperature retrieval accuracy is primarily sensitive to surface type and cloud fraction. Compared to v6, v7 has less bias over frozen land and sea ice in different cloud fraction conditions. However, the RMSEs of v7 are more sensitive to the effective cloud fraction (ECF) and are highly influenced by a more significant contribution from nonfrozen land samples. Compared to the kernel-averaged radiosonde profiles, more than 80% of the temperature profiles from v6 and v7 accurately detect LLIs. The discreteness of the AIRS’s predefined pressure level results is consistent with the radiosondes only 65% of the time for LLI depth calculation. In contrast, the AIRS can obtain LLI intensity with a relatively high correlation (>0.9). With the AIRS temperature retrieval in the boundary layer further improved, it has the potential to be used as an independent LLI detector in the Arctic region.

Remarkable climate anomalies occurred in Europe in recent years, but the reasons are not entirely disclosed. This entails further exploring the physical mechanism of anomalous climate variability over Europe on the basis of previous studies. Using NCEP-NCAR reanalysis and CMAP precipitation datasets, we investigated the relationship between the thermal condition of the Tibetan Plateau (TP) and precipitation over Europe during summer and related mechanisms behind through observational analyses and simulation experiments. The results show that the summer TP surface air temperature (SAT) is significantly correlated with the simultaneous precipitation over the region from eastern Ukraine to the North Caucasus (EUNC; 43°–51° N, 34°–46° E) during the period 1979–2017. The effect of anomalous TP heating plays, to some extent, an active role in linking the summer TP SAT with EUNC precipitation. The variability of the TP SAT is a result of the anomalous TP heating, but it can reflect the variability in temperature of a thick tropospheric air column over the TP well. Corresponding to higher (lower) TP SAT, the higher (lower) temperature anomaly appears over the TP and extends westwards through the transport of anomalous temperature fluxes from the TP to the EUNC regions in the upper troposphere, resulting in the expansion (withdrawal) of the South Asian high (SAH) and associated less (more) precipitation over the EUNC region. Numerical simulations basically reproduce the above-mentioned physical process, which confirms that, through stimulating the variability of upper-tropospheric temperatures over the TP and a larger area to its west and associated upstream large-scale atmospheric circulation, the summer TP’s thermal condition can modulate and expand the impact of the SAH to the EUNC region and therefore affect the concurrent precipitation in situ. This study implies an importance of the thermal anomaly of the TP to the variability of EUNC precipitation during summer.

The Huaihe River basin (HRB) is an important economically developed and grain production region in China, which is severely affected by rainfall anomalies, especially extreme rainfall events (EREs). It is crucial to the features of interdecadal change in EREs and the contribution of EREs to summer-mean total rainfall amount (TRA) over the HRB. Using the observational 24-h ac-cumulated rainfall and the reanalysis products from the European Center for Medium-Range Weather Forecast (ECMWF), as well as the methods of composite analysis and Mann–Kendal and running t tests, we revealed that the EREs experienced a significant interdecadal increase from the period 1990–1999 to the period 2000–2009. The EREs, particularly long persistent extreme rainfall events (LPEREs), occurred more frequently over the HRB during the latter period and dominated the interdecadal increase in the summer mean TRA. An anomalous high-pressure ridge and associated anomalous anticyclone appeared around Lake Baikal during the latter period, which led to anomalous northeasterlies along the eastern flank of the anomalous anticyclone, inducing the southward intrusion of cold air flow from higher latitudes and associated anomalous ascent and more active convection over the HRB. As such, more EREs and LPEREs occurred during the latter period. The higher pseudo-equivalent temperatures also support more active convective ascent and relevant more EREs. The results may shed light on further understanding the effect of large-scale atmospheric circulation on the interdecadal variability of EREs over the HRB, helping mitigate the disastrous impacts of EREs on local ecosystems, agriculture, soil erosion, and societies.

Temperature is a fundamental meteorological factor significantly impacting human life and socio-economic development. This study applies a multi-model fusion technique, integrating three artificial intelligence (AI) methods, to improve temperature forecast accuracy by addressing systematic errors and biases in the European Centre for Medium-Range Weather Forecasts (ECMWF) 2 m temperature predictions for Xiong’an New Area and its upstream regions. Using ECMWF forecast data from January 1, 2018, to December 31, 2021, along with ERA5 reanalysis data, we optimized a Bayesian model averaging (BMA_OP) approach, combining linear regression, LightGBM, and UNet to revise the 2 m temperature forecast. BMA_OP demonstrated improved performance, achieving an overall root-mean-square error (RMSE) of 1.15 °C, an average prediction accuracy of 73% for the ECMWF model, and an accuracy of over 91% for BMA_OP, marking a 24.7% improvement. To further assess generalization, we tested the model using full-year 2022 data, where BMA_OP outperformed the ECMWF model with an RMSE, mean absolute error (MAE), and accuracy of 1.31 °C, 1.03 °C, and 87%, respectively—exceeding the ECMWF model’s results by 16%, 13%, and 6%. These findings confirm the effectiveness of BMA_OP-based multi-model fusion technology for temperature correction.

Ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements were performed during the summer (13 June–20 August) of 2014 at a rural site in North China Plain. The vertical profiles of aerosol extinction (AE) in the lower troposphere were retrieved to analyze the temporal variations of AE profiles, near-surface AE, and aerosol optical depth (AOD). The average AOD and near-surface AE over the period of study were 0.51 ± 0.26 and 0.33 ± 0.18 km⁻¹ during the effective observation period, respectively. High AE events and elevated AE layers were identified based on the time series of hourly AE profiles, near-surface AEs and AODs. It is found that in addition to the planetary boundary layer height (PBLH) and relative humidity (RH), the variations in the wind field have large impacts on the near-surface AE, AOD, and AE profile. Among 16 wind sectors, higher AOD or AE occur mostly in the directions of the cities upstream. The diurnal variations of the AE profiles, AODs and near-surface AEs are significant and influenced mainly by the source emissions, PBLH, and RH. The AE profile shape from MAX-DOAS measurement is generally in agreement with that from light detection and ranging (lidar) observations, although the AE absolute levels are different. Overall, ground-based MAX-DOAS can serve as a supplement to measure the AE vertical profiles in the lower troposphere.