Journal of Geophysical Research: Atmospheres最新文献

The parameterizations of air-sea turbulent heat flux are one of the major bottlenecks in atmosphere-ocean coupled model development, which play a crucial role in sea surface temperature (SST) prediction. Recently, neural networks start to be applied for the development of parameterizations of interface turbulent heat flux. However, these new parameterizations are primairily developed for specific regions and have not been tested in real atmosphere-ocean coupled models. In this study, we propose a new air-sea heat flux parameterization using a physical-informed neural network (PINN) based on multiple observational data sets worldwide. Evaluated with an independent observation data set, it is shown that the PINN can significantly reduce the RMSE of latent heat flux by at least about 48.6% compared to three traditional bulk formulas. Moreover, the PINN can be flexibly updated with new observational data by transfer learning. To test the performance of the new parameterization in realistic application, we implement the PINN into a global ocean-atmosphere coupled model and make seasonal forecasts for the first time. The PINN markedly reduce the errors of equatorial SST forecast, indicating a good performance of the PINN-based air-sea turbulent heat flux scheme. Noticeably, due to limited observational data, the NN-based parameterizations tend to underestimate heat flux at high wind speeds compared with bulk formula-based parameterizations. With more data available at extreme conditions, the PINN can be improved via transfer learning and need to be futher evaluated. This study suggests that PINN-based air-sea heat flux parameterization is promising to improve SST simulation.

As a significant macrophysical property, cloud horizontal scales play a role in cloud radiation, precipitation and vertical cloud overlap. Until now, however, the mechanisms behind the variations in cloud scale distribution have received far less attention. This study utilizes active satellite data from 2007 to 2016 to investigate the spatiotemporal distribution of cloud horizontal scales, and explains the variations through two meteorological factors: wind shear and atmospheric stability. Cloud scales exhibit a distinct power-law behavior when scale break is not considered, and the power-law exponent β is a characteristic measure of cloud scale distribution. A smaller power-law exponent β indicates a higher frequency of large clouds. During boreal summer season, the amount of large clouds is extremely large south of the 40°S but rather small between 10°S and 20°S. As wind shear decreases or atmospheric stability increases, more large clouds occur globally. The underlying mechanisms might be associated with cloud entrainment which can be promoted by wind shear but inhibited by atmospheric stability. However, our analysis of the impacts of these two factors on cloud scale distribution across different regions and heights reveals that both wind shear and atmospheric stability play dual roles on the values of the exponent β. The potential physical mechanisms, including the effects of precipitation, are further discussed. It is observed that precipitation also exerts a dual impact on the values of the exponent β. These findings underscore the significance of considering the impacts of meteorological factors on cloud scale distribution in numerical weather prediction models.

Solar climate intervention refers to a group of methods for reducing climate risks associated with anthropogenic warming by reflecting sunlight. Marine cloud brightening (MCB), one such approach, proposes to inject sea-salt aerosol into one or more regional marine boundary layer to increase marine cloud reflectivity. Here, we assess the potential influence of various MCB experiments on Africa's climate using simulations from the Community Earth System Model (CESM2) with the Community Atmosphere Model (CAM6) as its atmospheric component. We analyzed four idealized MCB experiments under a medium-range background forcing scenario (SSP2-4.5), which brighten clouds over three subtropical ocean regions: (a) Northeast Pacific (MCB_NEP); (b) Southeast Pacific (MCB_SEP); (c) Southeast Atlantic (MCB_SEA); and (d) these three regions simultaneously (MCB_ALL). Our results suggest that the climate impacts of MCB in Africa are highly sensitive to the deployment region. MCB_SEP would produce the strongest global cooling effect and thus could be the most effective in decreasing temperatures, increasing precipitation, and reducing the intensity and frequency of temperature and precipitation extremes across most parts of Africa, especially West Africa, in the future (2035–2054) compared to the historical climate (1995–2014). MCB in other regions produces less cooling and wetting despite similar radiative forcings. While the projected changes under MCB_ALL are similar to those of MCB_SEP, MCB_NEP and MCB_SEA could see more residual warming and induce a warmer future than under SSP2-4.5 in some regions across Africa. All MCB experiments are more effective in cooling maximum temperature and related extremes than minimum temperature and related extremes.

Improved urban greenhouse gas (GHG) flux estimates are crucial for informing policy and mitigation efforts. Atmospheric inversion modeling (AIM) is a widely used technique combining atmospheric measurements of trace gas, meteorological modeling, and a prior emission map to infer fluxes. Traditionally, AIM relies on mid-afternoon observations due to the well-represented atmospheric boundary layer in meteorological models. However, confining flux assessment to daytime observations is problematic for the urban scale, where air masses typically move over a city in a few hours and AIM therefore cannot provide improved constraints on emissions over the full diurnal cycle. We hypothesized that there are atmospheric conditions beyond the mid-afternoon under which meteorological models also perform well. We tested this hypothesis using tower-based measurements of CO₂ and CH₄, wind speed observations, weather model outputs from INFLUX (Indianapolis Flux Experiment), and a prior emissions map. By categorizing trace gas vertical gradients according to wind speed classes and identifying when the meteorological model satisfactorily simulates boundary layer depth (BLD), we found that non-afternoon observations can be assimilated when wind speed is >5 m/s. This condition resulted in small modeled BLD biases (<40%) when compared to calmer conditions (>100%). For Indianapolis, 37% of the GHG measurements meet this wind speed criterion, almost tripling the observations retained for AIM. Similar results are expected for windy cities like Auckland, Melbourne, and Boston, potentially allowing AIM to assimilate up to 60% of the total (24-hr) observations. Incorporating these observations in AIMs should yield a more diurnally comprehensive evaluation of urban GHG emissions.

The 72-foot sailing yacht Eugen Seibold is a new research platform for contamination-free sampling of the water column and atmosphere for biological, chemical, and physical properties, and the exchange processes between the two realms. Ultimate goal of the project is a better understanding of the modern and past ocean and climate. Operations started in 2019 in the Northeast Atlantic, and will focus on the Tropical Eastern Pacific from 2023 until 2025. Laboratories for air and seawater analyses are equipped with down-sized and automated state-of-the-art technology for a comprehensive description of the marine carbon system including CO₂ concentration in the air and sea surface, pH, macro-, and micro-nutrient concentration (e.g., Fe, Cd), trace metals, and calcareous plankton. Air samples are obtained from ca. 13 m above sea surface and analyzed for particles (incl. black carbon and aerosols) and greenhouse gases. Plankton nets and seawater probes are deployed over the custom-made A-frame at the stern of the boat. Near Real-Time Transfer of underway data via satellite connection allows dynamic expedition planning to maximize gain of information. Data and samples are analyzed in collaboration with the international expert research community. Quality controlled data are published for open access. The entire suite of data facilitates refined proxy calibration of paleoceanographic and paleoclimate archives at high temporal and spatial resolution in relation to seawater and atmospheric parameters.

Iron emissions from human activities, such as oil combustion and smelting, affect the Earth's climate and marine ecosystems. These emissions are difficult to quantify accurately due to a lack of observations, particularly in remote ocean regions. In this study, we used long-term, near-source observations in areas with a dominance of anthropogenic iron emissions in various parts of the world to better estimate the total amount of anthropogenic iron emissions. We also used a statistical source apportionment method to identify the anthropogenic components and their sub-sources from bulk aerosol observations in the United States. We find that the estimates of anthropogenic iron emissions are within a factor of 3 in most regions compared to previous inventory estimates. Under- or overestimation varied by region and depended on the number of sites, interannual variability, and the statistical filter choice. Smelting-related iron emissions are overestimated by a factor of 1.5 in East Asia compared to previous estimates. More long-term iron observations and the consideration of the influence of dust and wildfires could help reduce the uncertainty in anthropogenic iron emissions estimates.

Nighttime oxidation of monoterpenes (MT) via the nitrate radical (NO₃) and ozone (O₃) contributes to the formation of secondary organic aerosol (SOA). This study uses observations in Atlanta, Georgia from 2011 to 2022 to quantify trends in nighttime production of NO₃ (PNO₃) and O₃ concentrations and compare to model outputs from the EPA's Air QUAlity TimE Series Project (EQUATES). We present urban-suburban gradients in nighttime NO₃ and O₃ concentrations and quantify their fractional importance (F) for MT oxidation. Both observations and EQUATES show a decline in PNO₃, with modeled PNO₃ declining faster than observations. Despite decreasing PNO₃, we find that NO₃ continues to dominate nocturnal boundary layer (NBL) MT oxidation (F_NO3 = 60%) in 2017, 2021, and 2022, which is consistent with EQUATES (F_NO3 = 80%) from 2013 to 2019. This contrasts an anticipated decline in F_NO3 based on prior observations in the nighttime residual layer, where O₃ is the dominant oxidant. Using two case studies of heatwaves in summer 2022, we show that extreme heat events can increase NO₃ concentrations and F_NO3, leading to short MT lifetimes (<1 hr) and high gas-phase organic nitrate production. Regardless of the presence of heatwaves, our findings suggest sustained organic nitrate aerosol formation in the urban SE US under declining NO_x emissions, and highlight the need for improved representation of extreme heat events in chemistry-transport models and additional observations along urban to rural gradients.

Ozone-depleting substances (ODSs) are well known as primary emission from the production and consumption of traditional industrial sectors. Here, we reported the unintentional emission of ODSs from iron and steel plants as a new source, basing on real-world measurements of flue gases emitted from their major processes. The sintering was found to be the major emission process of ODSs, including chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), halons, methyl halide (CH₃Cl), methyl chloroform, carbon tetrachloride, methyl bromide and halogenated very short-lived substances. The median emission factors of CFC-113, CFC-115, HCFC-22, and CH₃Cl for typical sintering processes are 1.7, 0.7, 44.5 and 237.0 mg/t, respectively. Quantum chemical calculation figures out that the ODS species are mainly formed in the low efficiency combustion process of halogenated materials. Annual amounts of ODS and CFC-11-equivalent emissions were estimated to be 1,785 tons and 78 tons in 2019 over mainland China, respectively. Given these findings, this study provides a new prospective on searching for ODS emission sources, especially unintentional sources such as iron and steel industry and other combustion related activities.

This study examines the differences related to microphysical properties of ice in thunderstorms over the Amazon and Congo Basin using the Precipitation Feature (PF) data sets derived from passive microwave and radar observations from the Tropical Rainfall Measuring Mission and Global Precipitation Mission Core Satellites. Analysis reveals that Amazon thunderstorms are likely composed of ice crystals smaller but more numerous than those in the Congo Basin, resulting in half as many flashes per PF on average in the Amazon, for similar Ice Water Content (IWC) or Area of 30 dBZ at −10°C (A_charge). The increase of the flash count following an increase of the IWC (A_charge) is only 72% (61%) as effective in the Amazon as it would be in the Congo Basin area. PFs with similar 30 dBZ radar echo top heights exhibit lower Brightness Temperatures (TBs) in the 85/89, 165, and 183 GHz frequencies over the Amazon, indicating more numerous smaller ice particles compared to those over the Congo Basin, which tend to show colder TBs at 37 GHz, possibly due to more numerous large graupel or hail particles. Comparisons of TBs in PFs with similar 30 dBZ echo top temperature between the Amazon and 3 × 3º global grids show that the median TB in Amazon is higher than that in most oceanic areas but is comparable to areas having high oceanic lightning activity (e.g., South Pacific Convergence Zone). It suggests that systems in the Amazon have similarities with maritime precipitation systems, yet with distinct characteristics indicative of land systems.

Precipitation δ¹⁸O has offered valuable insights into the evolution of the Asian monsoon. Recent researches focusing on precipitation Δ′¹⁷O has enhanced our understanding by offering new perspectives beyond those of δ¹⁸O, revealing insights into vapor sources and continental recycling. Nevertheless, there remains a lack of interannual triple oxygen isotope data, particularly in the Asian monsoon region. In this study, we analyzed the triple oxygen isotopes and hydrogen isotopes in monthly precipitation samples collected from Chongqing in Southwest China between 2019 and 2022 A.D. Seasonal variations in δD, δ¹⁸O, δ¹⁷O, and d-excess values were observed, with lower values during the rainy season and higher values during the dry season, highlighting the impact of changes in moisture sources and local meteorological conditions on seasonal shifts in δD, δ¹⁸O, and δ¹⁷O. While, mean Δ′¹⁷O values were higher in rainy season and lower in dry season. Notably, during rainy season, there is a negative correlation between monthly Δ′¹⁷O values and the RH of the vapor source area, as well as a positive correlation with d-excess. Recalculated Δ′¹⁷O values based on RH of oceanic moisture source, are higher than the measured values for this period, indicating the contribution of terrigenous moisture to precipitation in SW China. Precipitation Δ′¹⁷O values provide a more precise reflection of changes in moisture source, continental recycling, and evapotranspiration processes that drive water cycling compared Integrating modeling works in future will facilitate the use of precipitation Δ′¹⁷O values to quantify the impact of different moisture source on precipitation.