Advances in Atmospheric Sciences最新文献

This study quantified the regional damages resulting from temperature and sea level changes using the Regional Integrated of Climate and Economy (RICE) model, as well as the effects of enabling and disabling the climate impact module on future emission pathways. Results highlight varied damages depending on regional economic development and locations. Specifically, China and Africa could suffer the most serious comprehensive damages caused by temperature change and sea level rise, followed by India, other developing Asian countries (OthAsia), and other high-income countries (OHI). The comprehensive damage fractions for China and Africa are projected to be 15.1% and 12.5% of gross domestic product (GDP) in 2195, with corresponding cumulative damages of 124.0 trillion and 87.3 trillion United States dollars (USD) from 2005 to 2195, respectively. Meanwhile, the comprehensive damage fractions in Japan, Eurasia, and Russia are smaller and projected to be lower than 5.6% of GDP in 2195, with cumulative damages of 6.8 trillion, 4.2 trillion, and 3.3 trillion USD, respectively. Additionally, coastal regions like Africa, the European Union (EU), and OHI show comparable damages for sea level rise and temperature change. In China, however, sea level-induced damages are projected to exceed those from temperature changes. Moreover, this study indicates that switching the damage modules on or off affects the regional and global emission trajectories, but the magnitude is relatively small. By 2195, global emissions under the experiments with all of the damage modules switched off, only the sea level damage module switched on, and only the temperature damage module switched on, were 3.5%, 2.3% and 1.2% higher than those with all of the damage modules switched on, respectively.

In order to quantify the influence of external forcings on the predictability limit using observational data, the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent (CNLLE) method. The effectiveness of this algorithm is validated and compared with the nonlinear local Lyapunov exponent (NLLE) and signal-to-noise ratio methods using a coupled Lorenz model. The results show that the CNLLE method is able to capture the slow error growth constrained by external forcings, therefore, it can quantify the predictability limit induced by the external forcings. On this basis, a preliminary attempt was made to apply this method to measure the influence of ENSO on the predictability limit for both atmospheric and oceanic variable fields. The spatial distribution of the predictability limit induced by ENSO is similar to that arising from the initial conditions calculated by the NLLE method. This similarity supports ENSO as the major predictable signal for weather and climate prediction. In addition, a ratio of predictability limit (RPL) calculated by the CNLLE method to that calculated by the NLLE method was proposed. The RPL larger than 1 indicates that the external forcings can significantly benefit the long-term predictability limit. For instance, ENSO can effectively extend the predictability limit arising from the initial conditions of sea surface temperature over the tropical Indian Ocean by approximately four months, as well as the predictability limit of sea level pressure over the eastern and western Pacific Ocean. Moreover, the impact of ENSO on the geopotential height predictability limit is primarily confined to the troposphere.

Arctic sea ice has undergone a significant decline in the Barents–Kara Sea (BKS) since the late 1990s. Previous studies have shown that the decrease in sea ice caused by increased poleward moisture transport is modulated by tropical sea temperature changes (mainly referring to La Niña events). The occurrence of multi-year La Niña (MYLA) events has increased significantly in recent decades, and their impact on Arctic sea ice needs to be further explored. In this study, we investigate the relationship between sea-ice variation and different atmospheric diagnostics during MYLA and other La Niña (OTLA) years. The decline in BKS sea ice during MYLA winters is significantly stronger than that during OTLA years. This is because MYLA events tend to be accompanied by a warm Arctic–cold continent pattern with a barotropic high pressure blocked over the Urals region. Consequently, more frequent northward atmospheric rivers intrude into the BKS, intensifying longwave radiation downward to the underlying surface and melting the BKS sea ice. However, in the early winter of OTLA years, a negative North Atlantic Oscillation presents in the high latitudes of the Northern Hemisphere, which obstructs the atmospheric rivers to the south of Iceland. We infer that such a different response of BKS sea-ice decline to different La Niña events is related to stratospheric processes. Considering the rapid climate changes in the past, more frequent MYLA events may account for the substantial Arctic sea-ice loss in recent decades.

Raindrop size distribution (DSD) plays a crucial role in enhancing the accuracy of radar quantitative precipitation estimates in the Tibetan Plateau (TP). However, there is a notable scarcity of long-term, high-resolution observations in this region. To address this issue, long-term observations from a two-dimensional video disdrometer (2DVD) were leveraged to refine the radar and satellite-based algorithms for quantifying precipitation in the hinterland of the TP. It was observed that weak precipitation (R<1, mm h⁻¹) accounts for 86% of the total precipitation time, while small raindrops (D<2 mm) comprise 99% of the total raindrop count. Furthermore, the average spectral width of the DSD increases with increasing rain rate. The DSD characteristics of convective and stratiform precipitation were discussed across five different rain rates, revealing that convective precipitation in Yangbajain (YBJ) exhibits characteristics similar to maritime-like precipitation. The constrained relationships between the slope Λ and shape μ, D_m and N_w of gamma DSDs were derived. Additionally, we established a correlation between the equivalent diameter and drop axis ratio and found that raindrops on the TP attain a nearly spherical shape. Consequently, the application of the rainfall retrieval algorithms of the dual-frequency precipitation radar in the TP is improved based on the statistical results of the DSD.

While being successful in the detection and attribution of climate change, the optimal fingerprinting method (OFM) may have some limitations from a physics-and-dynamics-based viewpoint. Here, an analysis is made on the linearity, non-interaction, and stationary-variability assumptions adopted by OFM. It is suggested that furthering OFM needs a viewpoint beyond statistical science, and the method should be combined with theoretical tools in the dynamics and physics of the Earth system, so as to be applied for the detection and attribution of nonlinear climate change including tipping elements within the Earth system.

An unstably stratified flow entering into a stably stratified flow is referred to as penetrative convection, which is crucial to many physical processes and has been thought of as a key factor for extreme weather conditions. Past theoretical, numerical, and experimental studies on penetrative convection are reviewed, along with field studies providing insights into turbulence modeling. The physical factors that initiate penetrative convection, including internal heat sources, nonlinear constitutive relationships, centrifugal forces and other complicated factors are summarized. Cutting-edge methods for understanding transport mechanisms and statistical properties of penetrative turbulence are also documented, e.g., the variational approach and quasilinear approach, which derive scaling laws embedded in penetrative turbulence. Exploring these scaling laws in penetrative convection can improve our understanding of large-scale geophysical and astrophysical motions. To better the model of penetrative turbulence towards a practical situation, new directions, e.g., penetrative convection in spheres, and radiation-forced convection, are proposed.

This study assesses the capability of a coarse-resolution ocean model to replicate the response of the Southern Ocean Meridional Overturning Circulation (MOC) to intensified westerlies, focusing on the role of the eddy transfer coefficient (κ). κ is a parameter commonly used to represent the velocities induced by unresolved eddies. Our findings reveal that a stratification-dependent κ, incorporating spatiotemporal variability, leads to the most robust eddy-induced MOC response, capturing 82% of the reference eddy-resolving simulation. Decomposing the eddy-induced velocity into its vertical variation (VV) and spatial structure (SS) components unveils that the enhanced eddy compensation response primarily stems from an augmented SS term, while the introduced VV term weakens the response. Furthermore, the temporal variability of the stratification-dependent κ emerges as a key factor in enhancing the eddy compensation response to intensified westerlies. The experiment with stratification-dependent κ exhibits a more potent eddy compensation response compared to the constant κ, attributed to the structure of κ and the vertical variation of the density slope. These results underscore the critical role of accurately representing κ in capturing the response of the Southern Ocean MOC and emphasize the significance of the isopycnal slope in modulating the eddy compensation mechanism.

Nitrous oxide (N₂O) is a long-lived greenhouse gas that mainly originates from agricultural soils. More and more studies have explored the sources, influencing factors and effective mitigation measures of N₂O in recent decades. However, the hierarchy of factors influencing N₂O emissions from agricultural soils at the global scale remains unclear. In this study, we carry out correlation and structural equation modeling analysis on a global N₂O emission dataset to explore the hierarchy of influencing factors affecting N₂O emissions from the nitrogen (N) and non-N fertilized upland farming systems, in terms of climatic factors, soil properties, and agricultural practices. Our results show that the average N₂O emission intensity in the N fertilized soils (17.83 g N ha⁻¹ d⁻¹) was significantly greater than that in the non-N fertilized soils (5.34 g N ha⁻¹ d⁻¹) (p< 0.001). Climate factors and agricultural practices are the most important influencing factors on N₂O emission in non-N and N fertilized upland soils, respectively. For different climatic zones, without fertilizer, the primary influence factors on soil N₂O emissions are soil physical properties in subtropical monsoon zone, whereas climatic factors are key in the temperate zones. With fertilizer, the primary influence factors for subtropical monsoon and temperate continental zones are soil physical properties, while agricultural measures are the main factors in the temperate monsoon zone. Deploying enhanced agricultural practices, such as reduced N fertilizer rate combined with the addition of nitrification and urease inhibitors can potentially mitigate N₂O emissions by more than 60% in upland farming systems.

The Yangtze River basin (YRB) experienced a record-breaking mei-yu season in June–July 2020. This unique long-lasting extreme event and its origin have attracted considerable attention. Previous studies have suggested that the Indian Ocean (IO) SST forcing and soil moisture anomaly over the Indochina Peninsula (ICP) were responsible for this unexpected event. However, the relative contributions of IO SST and ICP soil moisture to the 2020 mei-yu rainfall event, especially their linkage with atmospheric circulation changes, remain unclear. By using observations and numerical simulations, this study examines the synergistic impacts of IO SST and ICP soil moisture on the extreme mei-yu in 2020. Results show that the prolonged dry soil moisture led to a warmer surface over the ICP in May under strong IO SST backgrounds. The intensification of the warm condition further magnified the land thermal effects, which in turn facilitated the westward extension of the western North Pacific subtropical high (WNPSH) in June–July. The intensified WNPSH amplified the water vapor convergence and ascending motion over the YRB, thereby contributing to the 2020 mei-yu. In contrast, the land thermal anomalies diminish during normal IO SST backgrounds due to the limited persistence of soil moisture. The roles of IO SST and ICP soil moisture are verified and quantified using the Community Earth System Model. Their synergistic impacts yield a notable 32% increase in YRB precipitation. Our findings provide evidence for the combined influences of IO SST forcing and ICP soil moisture variability on the occurrence of the 2020 super mei-yu.

Warming-induced carbon loss via ecosystem respiration (R_e) is probably intensifying in the alpine grassland ecosystem of the Tibetan Plateau owing to more accelerated warming and the higher temperature sensitivity of R_e (Q₁₀). However-little is known about the patterns and controlling factors of Q₁₀ on the plateau, impeding the comprehension of the intensity of terrestrial carbon–climate feedbacks for these sensitive and vulnerable ecosystems. Here, we synthesized and analyzed multiyear observations from 14 sites to systematically compare the spatiotemporal variations of Q₁₀ values in diverse climate zones and ecosystems, and further explore the relationships between Q₁₀ and environmental factors. Moreover-structural equation modeling was utilized to identify the direct and indirect factors predicting Q₁₀ values during the annual-growing, and non-growing seasons. The results indicated that the estimated Q₁₀ values were strongly dependent on temperature- generally, with the average Q₁₀ during different time periods increasing with air temperature and soil temperature at different measurement depths (5 cm, 10 cm, 20 cm). The Q₁₀ values differentiated among ecosystems and climatic zones, with warming-induced Q₁₀ declines being stronger in colder regions than elsewhere based on spatial patterns. NDVI was the most cardinal factor in predicting annual Q₁₀ values, significantly and positively correlated with Q₁₀. Soil temperature (T_s) was identified as the other powerful predictor for Q₁₀, and the negative Q₁₀–T_s relationship demonstrates a larger terrestrial carbon loss potentiality in colder than in warmer regions in response to global warming. Note that the interpretations of the effect of soil moisture on Q₁₀ were complicated, reflected in a significant positive relationship between Q₁₀ and soil moisture during the growing season and a strong quadratic correlation between the two during the annual and non-growing season. These findings are conducive to improving our understanding of alpine grassland ecosystem carbon–climate feedbacks under warming climates.