Journal of Geophysical Research: Atmospheres最新文献

The sub-grid turbulent orographic form drag (STOFD) significantly affects the regional circulation and precipitation. This study explores the local and remote effects of the STOFD on the summer monsoon precipitation across Eastern China using the Regional Climate Model Version 4 adopting a STOFD scheme. Results indicate that the local and remote effects of the STOFD primarily influence the improvement of summer precipitation simulation in the Southeastern and Northern China, respectively. The local effects of the STOFD can lead to 37.1% and 10.7% reduction of the absolute error and the root mean square error (RMSE) of simulated summer precipitation in the Southeastern China with complex sub-grid terrains. The remote effects of the STOFD within the Indochina Peninsula and Yunnan-Guizhou Plateau result in the absolute error and RMSE of simulated summer precipitation in the Northern China with mild sub-grid terrain decreased by 90.1% and 32.9%, respectively. Moreover, the remote effects of the STOFD within the Tianshan Mountains and Tibetan Plateau can clearly improve the simulated precipitation in both the Southeastern and Northern China. The disturbances generated by the local effects of the STOFD are more locally concentrated than those produced by the STOFD remote effects, leading to a more significant improvement of precipitation simulation in the Southeastern China. While the disturbances resulted from the remote effects of the STOFD affect the summer precipitation in both the Southeastern and Northern China obviously. This study highlights the significance of the remote effects of the STOFD in improving the summer precipitation simulation in Eastern China.

Stratospheric volcanic aerosols can affect the global radiative balance and stratospheric composition. In this study, we analyze ensemble experiments with an interactive stratospheric aerosol microphysical general circulation model, designed to assess the climate forcing from large-magnitude explosive eruptions in the tropics and northern extratropics. Previous studies have generally identified a lower radiative forcing from extratropical eruptions from the shorter stratospheric lifetime of volcanic sulfate aerosols. However, our study finds that both the shorter lifetime and lower effective radiative forcing (ERF) efficacy contribute to the lower ERF in the northern extratropical eruptions. The simulated 2-year averaged ERF efficacy in northern extratropical eruptions is 22% lower than that in the equatorial eruptions due to the seasonal mismatch of peak stratospheric aerosol optical depth and solar radiation in the first year after a northern extratropical volcanic eruption. Additionally, equatorial eruptions accelerate the Brewer-Dobson circulation (BDC), while northern hemispheric (NH) extratropical eruptions decelerate the BDC branch in NH and accelerate the BDC branch in southern hemisphere (SH), leading to different spatio-temporal pattern of ozone anomalies. Consequently, dominated by the dynamical processes, equatorial eruption leads to ozone loss in tropics and increase in midlatitudes, while both northern extratropical summer and winter eruptions trigger ozone decrease in NH and increase in SH.

Cloud feedback is the largest source of uncertainty in climate sensitivity. This feedback is generally discussed in terms of radiative flux at the top of the atmosphere, but the vertical distribution of the contribution of cloud changes to the top-of-atmosphere cloud feedback should be discussed to understand the feedback in greater detail. We have developed a simple analysis method called “vertical profile analysis of cloud feedback,” which simply uses radiative flux data from each level of the model. The analysis is applied for typical cloud regimes and the results are discussed together with cloud fraction change profiles. The advantages and disadvantages of the vertical profile analysis, compared with the commonly used International Satellite Cloud Climatology Project (ISCCP) histogram kernel method, are discussed. Our analysis has the advantage of resolving the detailed vertical profiles of the contribution to the top-of-atmosphere cloud feedback from the changes in cloud regimes associated with warming, such as reduced cloud cover and upward shifts of the cloud layer in subtropical low-cloud regions as well as the increased height of the melting layer in tropical deep convection. The main disadvantage is that the vertical profile analysis cannot represent the feedback components sorted by cloud optical thickness as in the ISCCP histogram kernel method.

Atmospheric radiative heating rate, which manifests radiative energy convergence in the atmosphere, is a fundamental factor shaping the Earth's climate and driving climate change. Compared to the radiative energy budget at the top of atmosphere or surface, the atmospheric energy budget and heating rate are less studied due to a lack of observational constraints and diagnostic tools. Motivated by growing interest in atmospheric energy budget and to facilitate the heating rate analysis, we innovate a set of radiative kernels, which quantitatively measure the sensitivity of atmospheric heating rate to different geophysical variables. When multiplied with the changes in these geophysical variables, these kernels quantify their contributions to the heating rate change. A climate change experiment of Global Climate Models (GCMs) is used to test the application of heating rate kernels. The results indicate the radiative heating rate change simulated by GCMs can be well reproduced by the kernels, validating the kernel method. The decomposition of the heating rate changes reveals the contributing mechanisms. For example, in the tropical upper troposphere, the negative heating anomaly in a warmer climate is dominated by atmospheric temperature and water vapor. Increases in both variables intensify atmospheric thermal radiation to space, partially offset by a positive heating anomaly caused by the lifting high-cloud tops. Moreover, compared to the results corrected using the kernels, the cloud effect inferred from the radiative heating difference between clear- and all-skies (“cloud radiative heating”) has a non-negligible bias, necessitating the use of kernels to quantify the cloud-induced heating rate changes.

K-changes are observed as step-like increases in the thundercloud electric fields. The K-changes occur in the late part of intra-cloud lightning or during negative cloud-to-ground lightning between return strokes. It has been shown that the processes leading to K-changes initiate in the decayed part of a positive leader channel and propagate toward the flash origin. They are often accompanied by microsecond-scale electric field pulses. We introduce a new model to simulate processes leading to the K-changes in cloud-to-ground lightning. Our method is based on the full solution of Maxwell's equations coupled to Poisson's equation for the thundercloud charge structure. To model the K-changes, we gradually increase the decayed channel conductivity. The modeled current wavefront propagates due to the K-processes downward along a vertical channel and completely attenuates before reaching the ground. We derive the evolution of the linear charge densities and the scalar electric potential along the channel leading to K-changes. We model electrostatic step-like changes in the measured electric field together with the approximate rates and amplitudes of the microsecond scale pulses. Step-like changes increase their amplitudes with the length of the simulated channel and with a higher conductivity of the channel. The microsecond-scale pulse waveshapes depend mainly on the propagation velocity of the current wave, and the time scale of the conductivity increase. We show that our modeled waveforms are in a good agreement with observations conducted in Florida.

Nitrogen oxides (NO_x = NO + NO₂) are of great concern due to their impact on human health and the environment. In recent years, machine learning (ML) techniques have been widely used for surface NO₂ estimation with rapid developments in computational power and big data. However, the uncertainties inherent to such retrievals are rarely studied. In this study, a novel ML framework has been developed, enhanced with uncertainty quantification techniques, to estimate surface NO₂ and provide corresponding data-induced uncertainty. We apply the Boosting Ensemble Conformal Quantile Estimator (BEnCQE) model to infer surface NO₂ concentrations over Western Europe at the daily scale and 1 km spatial resolution from May 2018 to December 2021. High NO₂ mainly appears in urban areas, industrial areas, and roads. The space-based cross-validation shows that our model achieves accurate point estimates (r = 0.8, R² = 0.64, root mean square error = 8.08 μg/m³) and reliable prediction intervals (coverage probability, PI-50%: 51.0%, PI-90%: 90.5%). Also, the model result agrees with the Copernicus Atmosphere Monitoring Service (CAMS) model. The quantile regression in our model enables us to understand the importance of predictors for different NO₂ level estimations. Additionally, the uncertainty information reveals the extra potential exceedance of the World Health Organization (WHO) 2021 limit in some locations, which is undetectable by only point estimates. Meanwhile, the uncertainty quantification allows assessment of the model's robustness outside existing in-situ station measurements. It reveals challenges of NO₂ estimation over urban and mountainous areas where NO₂ is highly variable and heterogeneously distributed.