In the present-day climate, cold air outbreaks occur when marine air intrudes over high-latitude continental interiors and radiatively cools, producing an abrupt drop in surface air temperature to as low as -40 C. But during the Eocene warm climate period, 55 million years ago, the presence of frost-intolerant species even at high latitudes in the Northern Hemisphere indicates that cold air outbreaks were suppressed. In projected future climate scenarios, relatively high surface temperatures at high latitudes are predicted as part of polar amplification. The lapse rate “feedback”, corresponding to enhanced warming of the lower troposphere, was found to be a major contributor [1]. The suppression of cold air in the Eocene is not well reproduced in global climate models (GCM) and the lapse rate feedback that contributes to polar amplification is still not well understood. Recent work hypothesized that the formation of low clouds as moist air flows from a warm ocean to a cold continental surface could suppress cold air outbreaks in warmer climates. Cronin and Tziperman, 2015, took a one-dimensional Lagrangian column model approach to track cloud formation and surface temperature as an air column migrates from a warm ocean surface to a cold continent [2]. Hu et al, 2018, followed up with an Eulerian analysis of GCM output over a range of cold and warm climates, looking at regional cloudiness, continental interior temperatures, and cold air extremes [3]. But neither approach is complete. The Lagrangian column model does not take into account mixing with surrounding air masses, while the Eulerian analysis does not explicitly follow the formation of clouds and their radiative impact as an air mass moves. In this work, we combine the two perspectives by studying cold air outbreaks in a variety of warm and cold climate scenarios using model output from the Community Atmosphere Model. After identifying cold air outbreaks, we backtrack trajectories for the air parcels that make up the entire cold air column. We then analyze the formation of clouds and the radiative budget to study the effects of clouds along each trajectory. Pithan, F. & Mauritsen, T. (2014). Nat Geo, 7, 181-184. Cronin, T. W. & Tziperman, E. (2015). PNAS, 112(37), 11490-11495. Hu, Z., Cronin, T. W. & Tziperman, E. (2018). JCLI, 31(23), 9625-9640.

Atmospheric nitrous oxide (N2O) is, after carbon dioxide and methane, the third most important long-lived anthropogenic greenhouse gas in terms of radiative forcing. Since preindustrial times a rising trend in the global N2O concentrations is observed. Anthropogenic emissions of N2O, mainly from agricultural activity, contribute considerably to this trend. Sparse observational constraints have made it difficult to quantify these emissions. The few studies on top-down approaches in the U.S. that exist are mainly based on Lagrangian models and ground-based measurements. They all propose a significant underestimation of anthropogenic N2O emission sources in established inventories, such as the Emissions Database for Global Atmospheric Research (EDGAR). In this study we quantify anthropogenic N2O emissions in the Midwest of the U.S., an area of high agricultural activity. In the course of the Atmospheric Carbon and Transport – America (ACT-America) campaign spanning from summer 2016 to summer 2019, an extensive dataset over four seasons has been collected including in-situ N2O aircraft based measurements in the lower and middle troposphere onboard NASA’s C-130 and B-200 aircraft. During fall 2017 and summer 2019 we conducted measurements onboard the NASA-C130 with a Quantum-Cascade-Laser-Spectrometer (QCLS) and on both aircraft over the whole campaign flask measurements (NOAA) were collected. More than 300 joint flight hours were conducted and more than 500 flask samples were collected over the U.S. Midwest. The QCLS system collected continuous N2O data for approximately 60 flight hours in this region. The Eulerian Weather Research and Forecasting model with chemistry enabled (WRF-Chem) is being used to quantify regional agricultural N2O emissions using the spatial characteristics of these atmospheric N2O mole fraction observations. The numerical simulations enable potential surface emission distributions to be compared to our airborne measurements, and source estimates can be adjusted to minimize the differences, thus quantifying N2O sources. These results are then compared to emission rates in the EDGAR inventory.

Reconnection in the magnetotail transfers magnetic energy to thermal and kinetic energy in ions and electrons. These particles are injected both Earthward and tailward from the reconnection region. The Earthward particles are transported to the inner magnetosphere where they drive the ring current and radiation belts. The injections are observed in the plasma sheet in conjunction with dipolarizations of the magnetic field. The particles have been found to travel within narrow flow channels, rather than broadly across the magnetotail, in spatially and temporally localized events known as bursty bulk flows (BBF). Simulations of such events show these narrow flow channels moving from the reconnection region to the injection region. However, global observations are needed to understand how BBFs connect the reconnection region and the inner magnetosphere during storms and substorms. Ion heating has been observed with in situ measurements at the reconnection region and within the dipolarization fronts and BBFs. Using energetic neutral atom (ENA) imaging, ion temperature maps can be calculated to provide such global observations. Regions of ion heating have been observed in these maps and comparisons with in situ measurements demonstrate that they are associated with these phenomena. An automated identification algorithm has been developed and run on our database of storm-time ion temperatures. We will present the results of case and statistical studies of the characteristics of these features.

The Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS) was launched on February 19, 2017 and began routine operation in June 2017. The first two years of SAGE III/ISS (v5.1) solar ozone data were evaluated by using correlative satellite and ground-based measurements. Among the three (MES, AO3, and MLR) SAGE III/ISS solar ozone products, AO3 ozone shows the best accuracy and precision, with mean biases less than 5% for altitudes ~15–55 km in the mid-latitudes and ~20–55 km in the tropics. In the lower stratosphere and upper troposphere, AO3 ozone shows high biases that increase with decreasing altitudes and reach ~10% near the tropopause. Preliminary studies indicate that those high biases primarily result from the contributions of the oxygen dimer (O) not being appropriately removed within the ozone channel. The precision of AO3 ozone is estimated to be ~3% for altitudes between 20 and 40 km. It degrades to ~10–15% in the lower mesosphere (~55 km), and ~20–30% near the tropopause. There could be an altitude registration error of ~100 meter in the SAGE III/ISS auxiliary temperature and pressure profiles. This, however, does not affect retrieved ozone profiles in native number density on geometric altitude coordinates. In the upper stratosphere and lower mesosphere (~40–55 km) the SAGE III/ISS (and SAGE II) sunset ozone values are systematically higher than sunrise data by ~5–8% which are almost twice larger than what observed by other satellites or model predictions. This feature needs further study.

A framework for data assimilation in climate dynamics is presented, combining aspects of quantum mechanics, Koopman operator theory, and kernel methods for machine learning. This approach adapts the formalism of quantum dynamics and measurement to perform data assimilation (filtering), using the Koopman operator governing the evolution of observables as an analog of the Heisenberg operator in quantum mechanics, and a quantum mechanical density operator as an analog of probability distributions in Bayesian data assimilation. The framework is implemented in a fully empirical, data-driven manner by representing the evolution and measurement operators via matrices in a basis of kernel eigenfunctions learned from time-ordered observations. We discuss applications to data assimilation of Indo-Pacific SST and probabilistic forecasting of the Nino 3.4 index.

Traditionally, the Saharan Air Layer (SAL) is defined as an elevated dry layer, frequently containing mineral dust transported from North Africa. The presence and characteristics of the SAL have an impact on tropical Atlantic climate and also on tropical cyclone development. However, recent observations from airborne campaigns in the Eastern Tropical Atlantic have found that under heavier dust loadings, water vapor content is increased in the SAL, rather than decreased as expected. This work will present airborne in-situ profile observations of dust loading and water vapor in the SAL from the AER-D field campaign during August 2015 in the tropical East Atlantic. The radiative impact in the shortwave and longwave spectra of the enhanced water vapor in the SAL will quantified and compared to that from mineral dust in the SAL. Trends in SAL water vapor over recent decades from satellite observations will be presented to assess the representativity of the aircraft data.

A 39 years of archived meteorological data measured at two stations located in the northern and southern parts of the Quebec, Canada is used to estimating the surface refractivity and its dry and wet components. The results of the comparison of the obtained estimates showed that for all months the values of the dry component are higher in the northern part, whereas that the values of the wet component are higher in the southern part. Due to this, for several months of the year, the values of the surface refractivity are higher in the northern part and for the remaining months in the southern part. Moreover, in both parts, August is the month where the highest values of the surface refractivity were recorded. For this month, the slope of the surface refractivity trend in the northern is several times higher than that in the southern part.

Assessment of bottom-up greenhouse gas emissions estimates through independent methods is needed to demonstrate whether reported values are accurate or if bottom-up methodologies need to be refined. Previous studies of measurements of atmospheric methane (CH4) in London revealed that inventories substantially underestimated the amount of natural gas CH4 1,2. We report atmospheric CH4 concentrations and δ13CH4 measurements from Imperial College London since early 2018 using a Picarro G2201-i analyser. Measurements from May 2019-Feb. 2020 were compared to the values simulated using the dispersion model NAME coupled with the UK national atmospheric emissions inventory, NAEI, and the global inventory, EDGAR, for emissions outside the UK. Simulations of CH4 concentration and δ13CH4 values were generated using nested NAME back-trajectories with horizontal spatial resolutions of 2 km, 10 km and 30 km. Observed concentrations were underestimated in the simulations by 12 %, and there was no correlation between the measured and simulated δ13CH4 values. CH4 from waste sources and natural gas comprised of 32.1 % and 27.5 % of the CH4 added by regional emissions. To estimate the isotopic source signatures for individual pollution events, an algorithm was created for automatically analysing measurement data by using the Keeling plot approach. Over 70 % of source signatures had values higher than -50 ‰, suggesting large amounts of natural gas CH4. The analyses based on model-data comparison of δ13CH4 and on Keeling plot source signature emission both indicate that emissions due to natural gas leaks in London are being under-reported in the NAEI. These results suggest that estimates of CH4 emissions in urban areas need to be revised in the CH4 emissions inventories. 1 Helfter, C. et al. (2016), Atmospheric Chemistry and Physics, 16(16), pp. 10543-10557 2 Zazzeri, G. et al. (2017), Scientific Reports, 7(1), pp. 1-13

Quantification of regional terrestrial carbon dioxide (CO2) fluxes is critical to our understanding of the carbon cycle. We evaluate inverse estimates of net ecosystem exchange (NEE) of CO2 fluxes in temperate North America, and their sensitivity to the observational data used to drive the inversions. Specifically, we consider the state-of-the-science CarbonTracker global inversion system, which assimilates (i) in situ measurements (’IS’), 29 (ii) the Orbiting Carbon Observatory-2 (OCO-2) v9 column CO 2 (XCO2) retrievals over land (’LNLG’), (iii) OCO-2 v9 XCO 2 retrievals over ocean (’OG’), and (iv) a combination of all these observational constraints (’LNLGOGIS’). We use independent CO2 observations from the Atmospheric Carbon and Transport (ACT)-America aircraft mission to evaluate the inversions. We diagnose errors in the flux estimates using the differences between modeled and observed biogenic CO2 mole fractions, influence functions from a Lagrangian transport model, and root-mean-square error (RMSE) and bias metrics. The IS fluxes have the smallest RMSE among the four products, followed by LNLG. Both IS and LNLG outperform the OG and LNLGOGIS inversions with regard to RMSE. Regional errors do not differ markedly across the four sets of posterior fluxes. The CarbonTracker inversions appear to overestimate the seasonal cycle of NEE in the Midwest and Western Canada, and overestimate dormant season NEE across the Central and Eastern US. The CarbonTracker inversions may overestimate annual NEE in the Central and Eastern US. The success of the LNLG inversion with respect to independent observations bodes well for satellite-based inversions in regions with more limited in situ observing networks.

The Earth Networks Total Lightning Network (ENTLN) is a global lighting detection network that has been operational since 2009. The ENTLN sensors are broadband electric field sensors that detect both intra-cloud (IC) and cloud-to-ground (CG) flashes and provide timing, location, classification, and peak current measurements. ENTLN consists of roughly 1600 wideband sensors deployed globally. Since its initial deployment, several improvements were made over the years to enhance its performance and usability. Notable ones are the addition of many new sensors each year to improve detection efficiency and extend global coverage. Firmware improvements have also been made to further increase sensitivity. A multi-parameter algorithm was incorporated to enhance IC and CG classification. To validate these improvements, Earth Networks has sponsored several studies to provide valuable feedback on performance improvements. This presentation will highlight two such studies. The first was performed at the Lightning Observatory in Gainesville (LOG), Florida using a combination of high-speed cameras and electric field sensors. For the 608 flashes in this study, a flash detection efficiency (DE) of 99% was found. Also, 97% of the flashes classified as CG by ENTLN algorithms were confirmed as CG via the measurements at LOG. The second study was performed at Langmuir Laboratory in New Mexico. In this study, 546 flashes were analyzed from three separate storms and ENTLN data was compared to simultaneously acquired interferometer (INTF) and electric field change array data (LEFA). Results show a total DE of 97.5%. Ninety one percent of flashes categorized at CG by EN were suggested to be CG by correlation of the LEFA+INTF data. Where EN determined the flash to be IC, LEFA+INTF agreed in 84% of cases.

Improved air quality has been the silver lining of the pandemic since early 2020. The air quality in northern New Jersey was continuously measured during the COVID-19 pandemic and through the three stages of recovery, i.e. the Stay-at-home Stage, Reopening Stage 1 and Reopening Stage 2. A significant change in air quality was observed during the Stay-at-home Stage (March 16 to May 16, 2020) as most people stayed home and industrial activity decreased 60%. Compared to 2019, carbon dioxide (CO2) decreased 17%, carbon monoxide (CO) decreased 7%, and nitrogen oxides (NOx) decreased 51% during the Stay-at-home Stage in 2020. However, the ground-level ozone (O3) increased in 2020 because of the reduced NOx emission and the possibly increased levels of volatile organic compounds (VOCs) due to the warmer weather. With the step-by-step reopening process, the difference in local CO2 levels between 2019 and 2020 was reduced and the NOx concentration returned to its 2019 level. The CO2 concentrations were positively correlated with CO, and the NOx concentrations were negatively correlated with O3 in 2020. However, these correlations are different from those in 2019. The impact of COVID-19 was found to influence the concentration levels of CO2, CO, NOx, and O3 beyond the effects of meteorology parameters on air quality in metropolitan New Jersey. Moreover, our findings provide a reference of air pollution reduction through replacing fossil fuels with electric or renewable energy in the transportation system and industry.

Climatological features regarding the sharp downward branch (SDB) of the Walker circulation above the Indian Ocean are comprehensively investigated. Compared to the Pacific downward branch, SDB has two distinctive features: two-peak seasonality and deep subsidence extension. The two weak phases of SDB in boreal spring and fall correspond well to the two rainy seasons at the Eastern Horn of Africa, which is not reproduced well by state-of-the-art global climate models. Unlike the Pacific counterpart, the annual-mean subsidence of SDB extends to the surface, and is supported by horizontal cold advection associated with the Asian Summer Monsoon. Two experiments using a convection-permitting atmospheric general circulation model show that mountains in East Africa, particularly the Ethiopian Highlands, is necessary for the existence of SDB. The dry and clear climate in the Northeast Africa, which is imprinted as a discontinuity of the Intertropical Convergence Zone, is sustained by the East African topography.

All LIDAR instruments are not the same, and advancement of LIDAR technology requires an ongoing interest and demand from the community to foster further development of the required components. The purpose of this paper is to make the community aware of the need for further technical development, and the potential payoff of investing experimental time, money and thought into the next generation of LIDARs. Technologies for development: We advocate for future development of LIDAR technologies to measure the polarization state of the reflected light at selected multiple wavelengths, chosen according to the species of interest (e.g., H2O and CO2 in the Martian setting). Key scientific questions: In the coming decade, dollars spent on these LIDAR technologies will go towards addressing key climate questions on Mars and other rocky bodies, particularly those with seasonally changing (i.e. insolation driven) plumes of multiple icy volatiles such as Mars, Enceladus, Triton, or Pluto, and insolation-driven dust lifting, such as cometary bodies and the Moon. We will show from examining past Martian and terrestrial lidars that orbital and landed LIDARs can be effective for producing new insights into insolation-driven processes in current planetary climate on several bodies, beyond that available to our current fleet of largely passive instruments on planetary missions.

Gridded dropsonde analyses are made using data from the OTREC (Organization of Tropical East Pacific Convection) and PREDICT (Pre-Depression Investigation of Cloud-Systems in the Tropics) projects to characterize the mesoscale properties of tropical oceanic convection in terms of selected thermodynamic parameters computable from the explicit grids of large-scale models. In particular, column relative humidity, low to mid-tropospheric moist convective instability, and convective inhibition correlate with moisture convergence, while sea surface temperature is related to the top-heaviness of mass flux profiles and the integrated entropy divergence. Local (as opposed to global) surface heat and moisture fluxes and convective available potential energy have little relation to these quantities. These results provide useful constraints for cumulus parameterizations.

Recent proceedings in the radiation belt studies have proposed new requirements for numerical methods to solve the kinetic equations involved. In this article, we present a numerical solver that can solve the general form of radiation belt Fokker-Planck equation and Boltzmann equation in arbitrarily provided coordinate systems, and with user-specified boundary geometry and boundary conditions. The solver is based upon the mathematical theory of stochastic differential equations, whose computational accuracy and efficiency are greatly enhanced by specially designed adaptive algorithms and variance reduction technique. The versatility and robustness of the solver is exhibited in three example problems. The solver applies to a wide spectrum of radiation belt modeling problems, including the ones featuring nonlinear wave-particle interactions.

The impacts of Stratospheric Aerosol Injection strategies on the Southern Annular Mode (SAM) are analysed with the Community Earth System Model (CESM). Using a set of simulations with fixed single-point SO2 injections we demonstrate the first-order dependence of the SAM response on the latitude of injection, with the northern hemispheric and equatorial injections driving a response corresponding to a positive phase of SAM and the southern hemispheric injections driving a negative phase of SAM. We further demonstrate that the results can to first order explain the differences in the SAM responses diagnosed from the two recent large ensembles of geoengineering simulations utilising more complex injection strategies - GLENS and ARISE-SAI - as driven by the differences in the simulated sulfate aerosol distributions. Our results point to the meridional extent of aerosol-induced lower stratospheric heating as an important driver of the sensitivity of the SAM response to the injection location.

Despite the necessity of Global Climate Models (GCMs) sub-selection in the dynamical downscaling experiments, an objective approach for their selection is currently lacking. Building on the previously established concepts in GCMs evaluation frameworks, we relatively rank 37 GCMs from the 6th phase of Coupled Models Intercomparison Project (CMIP6) over four regions representing the contiguous United States (CONUS). The ranking is based on their performance across 60 evaluation metrics in the historical period (1981–2014). To ensure that the outcome is not method-dependent, we employ two distinct approaches to remove the redundancy in the evaluation criteria. The first approach is a simple weighted averaging technique. Each GCM is ranked based on its weighted average performance across evaluation measures, after each metric is weighted between zero and one depending on its uniqueness. The second approach applies empirical orthogonal function analysis in which each GCM is ranked based on its sum of distances from the reference in the principal component space. The two methodologies work in contrasting ways to remove the metrics redundancy but eventually develop similar GCMs rankings. While the models from the same institute tend to display comparable skills, the high-resolution model versions distinctively perform better than their lower-resolution counterparts. The results from this study should be helpful in the selection of models for dynamical downscaling efforts, such as the COordinated Regional Downscaling Experiment (CORDEX), and in understanding the strengths and deficiencies of CMIP6 GCMs in the representation of various background climate characteristics across CONUS.

Over the last several decades, heat waves have notably increased in frequency, intensity, and duration in the United States. Studies have credited these trends to a warming climate, and therefore, it is expected that extended periods of consistent and abnormally hot temperatures will continue to occur through the 21st century. Heat waves alone can have harmful impacts on the human body, but when coupled with high humidity, these events can become especially threatening. While other studies have assessed the human health effects of extreme humid heat waves, this study aims to determine the source region and pathway of air parcels during these events, while also understanding the land-surface processes that amplify and dampen the amount of atmospheric moisture present as an air parcel reaches a target region. Through the use of the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, atmospheric moisture exchanges and concentrations are analyzed for the three days prior to a humid heat wave at Boston, MA, Burlington, VT, Albany, NY, and Philadelphia, PA, between June and August of 1980-2019. Four major source regions are identified as being largely responsible for the atmospheric moisture present during heat waves across these cities: the Atlantic Ocean, Gulf of Mexico, Great Lakes, and terrestrial evapotranspiration from the Midwest and Mid-Atlantic regions of the United States. Geographical location and proximity to water of each city has a notable influence on source region and number of humid heat waves occurring throughout the period of study. At Boston and Philadelphia, the two leading sources of moisture are the Atlantic Ocean and Gulf of Mexico, while at Burlington and Albany, the Great Lakes and terrestrial evapotranspiration are more dominant. Stark differences are also noted between the source regions and trajectories of humid versus dry heat waves at a given location. Examining the sources and paths of air parcels leading up to extreme heat events, as well as analyzing the atmospheric-land interactions that take place during that time, will provide a comprehensive understanding into the importance of a given moisture source region on a particular location, and how a warming climate may ultimately alter the degree to which a source region is responsible for atmospheric moisture in the future.

In the last decades, the Eddy Covariance (EC) technique has become a standard method to measure net ecosystem CO2 exchange (NEE), but it doesn’t let to distinguish between Gross Primary Production (GPP) and Ecosystem Respiration (Reco). Olive (Olea europea L.) is one of the most important agrosystems on the Mediterranean basin, covering 9.5Mha and accounting for 98% of olive groves global surface. In this study we analyze the EC fluxes from an olive orchard of SE Spain with two soil treatments: 1) leaving spontaneous weed cover (WC) growing on the soil, and 2) inhibiting this growth with a glyphosate-based herbicide (WF). These two different treatments provide high differences in NEE, but the contribution of each component (trees, weed and soil) in the NEE require a better understanding. In this study, we analyze Eddy Covariance fluxes from an olive orchard in SE Spain at different altitudes (above and below the olive trees). To study carbon fluxes contribution of weed in the olive orchard 4 EC towers were installed, placing them on two different areas: one area in WC treatment and the other in WF treatment. On each area, a canopy tower and a subcanopy tower were installed. After a data-filtering during the growth season in which only wind directions coming from olive orchard alleys were accepted, preliminary results from the subcanopy towers show that there are prevailing CO2 emission values from the soil in the WF area and CO2 fixation from the weed in the WC area. On the other hand, during senescence period, CO2 emission fluxes were obtained from both subcanopy towers. These results layout the relevant place of subcanopy towers to understand the role in carbon cycle of the different components in an ecosystem.

Infrasound waves can be defined as the sound waves with frequency range from 0.003 to 20 Hz. Kochi University of Technology (KUT) Infrasound Sensor Network contains 30 infrasound sensors which are distributed all over Japan, a large number of sensors are located in Shikoku Island, all infrasound stations installed with accelerometers to measure the peak ground acceleration (PGA) which can be a good detector for infrasound sources occur on or under the ground like earthquakes. Many earthquakes detected by our network after establishing of the network since 2016. In this study we will focus on all the possibilities for infrasound detection from earthquakes using KUT sensor network and International Monitoring system (IMS) stations for the earthquakes which were detected in southern of Japan during 2019. The selected events for this study are recorded in different international databases; Reviewed Event Bulletin (REB) database of International Data Center (IDC) , Japan Meteorological Agency (JMA) and United States Geological Survey (USGS). There are different scenarios for infrasound coupling from earthquakes one of these scenarios is the conversion of seismic waves to acoustic from the generated T-phases of oceanic earthquakes. On 09 of May 2019, at 23:48:00 UTC an earthquake with magnitude 6.0 mb happened in west of Kyushu Island and infrasound sensors recorded a clear P-waves, However station K53 and I30JP recorded infrasound waves at distances ranges between 850 to 870 km, In addition to T-phases well-recorded from the earthquake in H11N station near Wake island at 3750 km from the event. Progressive multi-channel cross correlation method applied on both infrasound and hydroacoustic data to identify the arrival phases and the back-azimuth of the waves from station to the source. Moreover, infrasound propagation simulation applied to the event to confirm the infrasound arrivals. Ground to Space Model (AVO-G2S) used with HWM-14 and NRL-MSISE to construct the atmospheric profile for higher altitudes up to 180 km over the event area, furthermore the 3d ray tracing process and the calculation of the transmission loss equation by normal modes and parabolic equation methods applied. In conclusion this study shows the earthquake detectability from infrasound waves using local infrasound sensors for the largest earthquakes occurred in southern of Japan during 2019. Many parameters control the generation of infrasound from earthquakes; magnitude, depth, mechanism and the topographic features. In addition to the T-phases generation through the SOFAR layer can be an evidence of seismic conversion to sound for the oceanic earthquakes as occurred on the earthquake of 09 May 2019, after applying the propagation simulation with (AVO-G2S) model on this earthquake the tropospheric arrivals confirmed and the calculated celerities well-correlated with the real detected data .

Based on the object-based tracking algorithm, the precipitation simulation ability of the convection-permitting (CP) regional climate models (RCMs) is evaluated from the viewpoints of the precipitation systems in this work. The characteristics of precipitation systems over eastern China during 1998-2007 derived from the Weather Research and Forecasting model (WRF) with the horizontal grid spacing of ~ 4 km are compared with CMORPH. On the whole, CP RCMs can capture the average duration and eccentricity of all precipitation systems reasonably. However, precipitation systems tend to be stronger but with smaller coverage area in CP RCMs, which leads to the wet biases and dry biases of accumulated precipitation amount in the longer-duration (>= 48 hr) and shorter-duration (< 48 hr) systems, respectively. Such deficiencies in accumulated precipitation amount of precipitation systems with various durations can be made up by employing spectral nudging technique in CP RCMs to a certain degree. This work further indicates that, to improve the capability of precipitation simulation in CP RCMs, the relationship between intensity and coverage area of precipitation systems should be described properly in CP RCMs, especially for those with shorter-duration.

The isotope tracer technique plays a key role in identifying the sources and atmospheric processes affecting pollution. The sources of brown carbon (BrC) at Guangzhou during 2017-2018 was characterized by positive matrix factorization with carbon isotope constraints and multiple linear regression analysis. The primary emission factors of fossil fuel combustion (FF) and biomass burning (BB) accounted for 34% and 27% of dissolved BrC absorption at λ = 365 nm, respectively. The total mean light absorption contributed by secondary sources was 39%. The absorption of FF-origin BrC was relatively stable and dominant in the summer monsoon period, whereas the absorption of BrC from BB and secondary nitrate formation increased and contributed larger fractions during the winter monsoon period. Transported BrC was estimated using an index of 7 Be/(7 Be+n 210 Pb). Higher values were generally accompanied by lower BrC absorption, whereas lower values were associated with higher BrC absorption, indicating that BrC absorption of aerosols transported from the upper-atmosphere is lower than that of aerosols transported near the surface. Based on the positive correlations between 210 Pb and BrC absorption and non-fossil dissolved organic carbon in the winter monsoon period, we estimated that the contribution of invasive BrC (include ground and upper-atmosphere level) to total absorption during the period of elevated BrC was approximately 50%, which was likely related to BB organic aerosols and secondary nitrate formation processes. This study supports radionuclides as a novel method for characterizing the sources and transport of BrC that can be applied in future atmospheric research.

Marine boundary layer clouds tend to organize into closed or open mesoscale cellular convection (MCC). Here, two-dimensional wavelet analysis is applied for the first time to passive microwave retrievals of cloud water path (CWP), water vapor path (WVP), and rain rate from AMSR-E in 2008 over the Northeast and Southeast Pacific, and the Southeast Atlantic subtropical stratocumulus to cumulus transition regions. The (co-)variability between CWP, WVP, and rain rate in 160x160 km2 analysis boxes is partitioned between four mesoscale wavelength octaves (20, 40, 80, and 160 km). The cell scale is identified as the wavelength of the peak CWP variance. Together with a machine-learning classification of cell type, this allows the statistical characteristics of open and closed MCC of various scales, and its relation to WVP, rain rate and potential environmental controlling factors to be analyzed across a very large set of cases. The results show that the cell wavelength is most commonly 40-80 km. Cell-scale CWP perturbations are good predictors of the WVP and rain rate perturbations. A universal cubic dependence of rain rate on CWP is found in closed and open cells of all scales. This suggests that aerosol control on precipitation susceptibility is not as important for open cell formation as are processes that cause increases in cloud water. For cells larger than 20 km, there is no obvious dependence of cell scale on the environmental controlling factors tested, suggesting that the cell scale may depend more on its historical evolution than the current environmental conditions.

Awu is one of the remote and little known active volcanoes of Indonesia. It is the northernmost active volcano of Sangihe arc with 18 eruptions in less than 4 centuries, causing a cumulative death toll of 11048. Two of these eruptions were classified as VEI 4. Since 2004, a lava dome occupies the center of Awu crater, channeling the fumarolic gas output along the crater wall. A combined DOAS and MultiGAS measurements highlight a relatively small SO degassing (13 t/d) into the atmosphere. In contrast the measurements spotlight an elevated and non-negligible CO emission into the atmosphere of 2600 t/d, representing 1% of the global CO emission budget from volcanoes. The cause for this high CO degassing may reside in the peculiar geodynamic context of the region, where the slowing down of arc-to-arc collision has enhanced heating of the slab, leading to greater production of fluid rich in carbon.