Detectability of the potential climate change effect on transboundary air pollution pathways in the downwind area of China

Long-term aerosol optical depth (AOD) datasets focused on the Pacific Ocean in the downwind area of China over a 19-year period from 2003 to 2021 were derived from satellite observations, reanalysis datasets, and numerical simulations. Considering the significant year-to-year changes in the amounts of aerosols transported from China to the Pacific Ocean during this period, we proposed a metric named RAOD. This is defined as the AOD over the ocean relative to that near the eastern coast of China within the same latitude band (25-30°N). RAOD was identified as a valuable metric for quantifying the long-term changes in transboundary air pollution pathways. Our analysis revealed a clear exponential decrease in RAOD values from China toward the Pacific Ocean; this was consistent with the prevailing meteorological conditions observed over the 19-year period. However, the possible long-term changes in RAOD due to climate change were found to be insignificant and were overshadowed by much larger year-to-year variations in the meteorological field. Additionally, significant seasonal variations in the absolute slope of the linear regression between RAOD and longitude were observed, and there correlated with wind patterns in the lower troposphere. Elevated slope values in the spring and winter suggested a west-to-east aerosol transport facilitated by strong winds, whereas the lower slope values in summer and autumn indicated a northward aerosol movement under weaker winds. In recent years, aerosols have become less likely to be transported far eastward from the coast of China. Based on these findings, to enhance the detectability of the climate change impacts on meteorological field affecting transboundary air pollution pathways, the RAOD metric derived using a continued long-term satellite observation of aerosols is proposed.

Asian dust-deposition flux to the subarctic Pacific estimated using single quartz particles

Iron availability limits marine ecosystem activities in large areas of the ocean. However, the sources and seasonal supply of iron, critically important for controlling surface ocean biogeochemistry and carbon cycling, are poorly understood. The western subarctic Pacific is a high-nutrient and low-chlorophyll region, and despite high concentrations of macronutrients, iron limits phytoplankton production in summer. Here, we determine the seasonal deposition flux of Asian dust using scanning electron microscope-cathodoluminescence analysis of single quartz particles derived from the western subarctic Pacific during 2003-2022 to trace provenance. We found a high (up to 6.9 mg m-2 day-1) deposition flux of Asian dust in May, June, and early July, with an annual average of 1.0 ± 0.2 mg m-2 day-1. The supply of dissolved-iron flux calculated from Asian dust was 0.9 ± 0.3 µg m-2 day-1 during the high productivity season (April-July), which is approximately half that from the deeper part of the ocean, calculated from vertical profiles of dissolved iron. Our study provides a reliable approach for estimating iron supply from dust to the surface ocean that may be critical for sustaining biological productivity under future ocean stratification, which suppresses nutrient supply from the subsurface ocean.

Evaluation of the simulated aerosol optical properties over India: COALESCE model inter-comparison of three GCMs with ground and satellite observations

Within the framework of COALESCE project (Carbonaceous aerosol emissions, source apportionment, and climate impacts) initiative, spatio-temporal distribution of aerosol optical properties from three general circulation models are evaluated against aerosol data from satellite observations (MODIS and CALIPSO) and ground-based measurements (AERONET) for the period 2005-2014. The GCMs, NICAM-SPRINTARS (N-S), ECHAM6.3-HAM2.3 (E-H), CAM5.3 (CAM), input with identical emissions from the SMoG-India-v1 emission inventory over India nested in the CEDS global inventory, including all emission sectors except sea salt and soil dust. The annual mean total aerosol optical depth (AOD) averaged over the Indian land region is 0.38, 0.27, and 0.17 from the N-S, CAM, and E-H models respectively, while the annual mean value from the MODIS observational dataset is 0.43. Single scattering albedo predicted by E-H is lower compared to CAM and N-S while model predictions of Angstrom exponent are closer to MERRA2 dataset. However, the average total aerosol column burden over Indian landmass simulated by the models is very close and comparable to the reanalysis results. Statistical analysis of AOD between model and AERONET measurements at nine sites shows that the root mean square error varies from 0.1 to 0.4 and the index of agreement (average value) is ∼0.4. The aerosol emission and transport models, methodology for calculation of aerosol optical properties and their mixing states contributes to the diversity in the results from various models. The present study provides an analysis of limitations and uncertainties contributing to the differences between the simulations and observations, and the inter-model diversity.

Study of mid-latitude retrieval XCO2 greenhouse gas: Validation of satellite-based shortwave infrared spectroscopy with ground-based TCCON observations

Carbon dioxide (CO₂) is a major greenhouse gas. This study investigated the performance of three common algorithms, namely NIES, ACOS, and Remo Tec (SRFP). These algorithms were compared using GOSAT observation satellite data with reference data obtained from TCCON during the period 2009-2021. According to statistical evaluation, the SRFP and NIES algorithms achieved the lowest and highest correlation values of the 13 year (2009_2021) average of all sites, respectively. The average bias error values of NIES and ACOS was estimated to be less than that of SRFP approximately 0.5 ppm, while the bias within SRFP was of about 2 ppm. Comparing the RMSE and CRMS error values showed that the highest and lowest error values were related to the SRFP and NIES algorithms respectively, which were 0.37-1.67 and ppm 1.46-7.9. The researchers also compared them with monthly time changes based on ground measurements, and observed a time series of CO₂ concentration changes that well matched the trend of gas concentration values at ground stations obtained by NIES algorithm. The results showed that in most cases NIES was an effective algorithm to retrieve carbon dioxide gas concentrations, allowing the researchers to identify the main sources of greenhouse gas emissions in different areas. The clustering result in the study area showed that the continental CO₂ columnar concentration has a specific seasonal cycle, with the maximum and minimum values appearing in winter-early spring and spring-late summer, respectively. In conclusion, cluster analysis can classify the surface CO₂ column concentration values and determine the spatial distribution pattern of CO₂.

Competing and accelerating effects of anthropogenic nutrient inputs on climate-driven changes in ocean carbon and oxygen cycles

Nutrient inputs from the atmosphere and rivers to the ocean are increased substantially by human activities. However, the effects of increased nutrient inputs are not included in the widely used CMIP5 Earth system models, which introduce bias into model simulations of ocean biogeochemistry. Here, using historical simulations by an Earth system model with perturbed atmospheric and riverine nutrient inputs, we show that the contribution of anthropogenic nutrient inputs to past global changes in ocean biogeochemistry is of similar magnitude to the effect of climate change. Anthropogenic nutrient inputs increase oceanic productivity and carbon uptake, offsetting climate-induced decrease and accelerating climate-driven deoxygenation in the upper ocean. Moreover, accounting for anthropogenic nutrient inputs improves the known carbon budget imbalance and model underestimation of the observed decrease in the global oxygen inventory. Considering the effects of both nutrient inputs and climate change is crucial in assessing anthropogenic impacts on ocean biogeochemistry.

Progress in Dust Modelling, Global Dust Budgets, and Soil Organic Carbon Dynamics

Dust emission is an important corollary of the soil degradation process in arid and semi-arid areas worldwide. Soil organic carbon (SOC) is the main terrestrial pool in the carbon cycle, and dust emission redistributes SOC within terrestrial ecosystems and to the atmosphere and oceans. This redistribution plays an important role in the global carbon cycle. Herein, we present a systematic review of dust modelling, global dust budgets, and the effects of dust emission on SOC dynamics. Focusing on selected dust models developed in the past five decades at different spatio-temporal scales, we discuss the global dust sources, sinks, and budgets identified by these models and the effect of dust emissions on SOC dynamics. We obtain the following conclusions: (1) dust models have made considerable progress, but there are still some uncertainties; (2) a set of parameters should be developed for the use of dust models in different regions, and direct anthropogenic dust should be considered in dust emission estimations; and (3) the involvement of dust emission in the carbon cycle models is crucial for improving the accuracy of carbon assessment.

An Adjustment Approach for Aerosol Optical Depth Inferred from CALIPSO

The verification and correction of CALIPSO aerosol products is key to understanding the atmospheric environment and climate change. However, CALIPSO often cannot detect the full profile of aerosol for the low instrument sensitivity near the surface. Thus, a correction scheme for the aerosol extinction coefficient (AECs) in the planetary boundary layer (PBL) is proposed to improve the quality of the CALIPSO-based aerosol optical depth (AOD) at 532 nm. This scheme assumed that the aerosol is vertically and uniformly distributed below the PBL, and that the AECs in the whole PBL are equal to those at the top of the PBL; then, the CALIPSO AOD was obtained by vertically integrating AECs throughout the whole atmosphere. Additionally, the CALIPSO AOD and corrected CALIPSO AOD were validated against seven ground-based sites across eastern China during 2007–2015. Our results show that the initial CALIPSO AOD obtained by cloud filtering was generally lower than that of the ground-based observations. After accounting for the AECs in the PBL, the adjustment method tended to improve the CALIPSO AOD data quality. The average R (slope) value from all sites was improved by 7% (46%). Further, the relative distance between the ground track of CALIPSO and the ground station exhibited an influence on the validation result of CALIPSO AOD. The retrieval precision of CALIPSO AOD worsened with the increase in water vapor in the atmosphere. Our findings indicate that our scheme significantly improves the accuracy of CALIPSO AOD, which will help to provide alternative AOD products in the presence of severe atmospheric pollution.

Enhanced Simulation of an Asian Dust Storm by Assimilating GCOM-C Observations

Dust aerosols have great effects on global and regional climate systems. The Global Change Observation Mission-Climate (GCOM-C), also known as SHIKISAI, which was launched on 23 December 2017 by the Japan Aerospace Exploration Agency (JAXA), is a next-generation Earth observation satellite that is used for climate studies. The Second-Generation Global Imager (SGLI) aboard GCOM-C enables the retrieval of more precious global aerosols. Here, the first assimilation study of the aerosol optical thicknesses (AOTs) at 500 nm observed by this new satellite is performed to investigate a severe dust storm in spring over East Asia during 28–31 March 2018. The aerosol observation assimilation system is an integration of the four-dimensional local ensemble transform Kalman filter (4D-LETKF) and the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) coupled with the Non-Hydrostatic Icosahedral Atmospheric Model (NICAM). Through verification with the independent observations from the Aerosol Robotic Network (AERONET) and the Asian Dust and Aerosol Lidar Observation Network (AD-Net), the results demonstrate that the assimilation of the GCOM-C aerosol observations can significantly enhance Asian dust storm simulations. The dust characteristics over the regions without GCOM-C observations are better revealed from assimilating the adjacent observations within the localization length, suggesting the importance of the technical advances in observation and assimilation, which are helpful in clarifying the temporal–spatial structure of Asian dust and which could also improve the forecasting of dust storms, climate prediction models, and aerosol reanalysis.

First X-ray Spectroscopic Observations of Atmospheric Titanium Species: Size Dependence and the Emission Source

Titanium dioxide (TiO2) in mineral dust is considered as one of the driving forces of photocatalytic reaction at the aerosol surface in the atmosphere. As a precursor of mineral dust, soil contains ilmenite (FeTiO3) and titanite (CaSiTiO5), which have lower photochemical reactivities than TiO2. However, Ti species other than TiO2 in aerosol particles are not well recognized due to the lack of observation in ambient samples. In this study, Ti species in size-fractionated aerosol samples collected in the Noto Peninsula, Japan, were determined by macroscopic and semi-microscopic X-ray absorption fine structure spectroscopy. Regardless of aerosol particle size, Ti species were primarily composed of rutile, anatase, ilmenite, and titanite. Semi-microscopic Ti speciation showed that Ti-poor spots associated with mineral dust were composed of a mixture of rutile, anatase, ilmenite, and titanite, and Ti-rich spots were primarily composed of TiO2 (rutile or anatase) derived from authigenic minerals or anthropogenic materials. Thus, the Ti species in aerosol particles, especially mineral dust, were not composed solely of TiO2 polymorphs. Therefore, the photochemical reactivities of Ti in aerosol particles may be overestimated when laboratory experiments or model studies employ TiO2 as the representative Ti species.

Comparison and evaluation of the simulated annual aerosol characteristics over China with two global aerosol models

In this study, simulations of the annual mean aerosol budget, aerosol optical properties, and surface mass concentration in 2006 in China are performed with two aerosol interactive global atmosphere models, namely, the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) coupled with the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) and the Beijing Climate Center Atmospheric General Circulation Model (BCC_AGCM) coupled with the Canadian Aerosol Module (CAM) online. The observed and simulated aerosol optical depths (AODs) exhibit similar horizontal distributions with large values over eastern and central China, and sulfate aerosols contribute the main differences between the AODs simulated by NICAM and BCC_AGCM. The simulated sulfate and dust surface concentrations are more consistent with observations compared with the simulated carbonaceous surface concentrations, and both models can reproduce the decreasing tendency of the sulfate surface concentration from urban sites to rural sites. However, the dust emission and deposition levels in China simulated by BCC_AGCM are three times as high as those simulated by NICAM, and the major sink processes of the anthropogenic sulfate, black carbon (BC), and organic carbon (OC) aerosols over China are very different between the two models. The emission and deposition results, which are closely related to the model-assumed aerosol particle size distribution, indicate that the current aerosol size distribution used in the two models should be further improved. The differences in dust emission parameterizations also lead significant discrepancies in aerosol cycles and the dust emission scheme is an important factor determining the magnitudes of global and regional dust emission fluxes.

Aerosol Effective Radiative Forcing in the Online Aerosol Coupled CAS-FGOALS-f3-L Climate Model

The effective radiative forcing (ERF) of anthropogenic aerosol can be more representative of the eventual climate response than other radiative forcing. We incorporate aerosol–cloud interaction into the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System (CAS-FGOALS-f3-L) by coupling an existing aerosol module named the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) and quantified the ERF and its primary components (i.e., effective radiative forcing of aerosol-radiation interactions (ERFari) and aerosol-cloud interactions (ERFaci)) based on the protocol of current Coupled Model Intercomparison Project phase 6 (CMIP6). The spatial distribution of the shortwave ERFari and ERFaci in CAS-FGOALS-f3-L are comparable with that of most available CMIP6 models. The global mean 2014–1850 shortwave ERFari in CAS-FGOALS-f3-L (−0.27 W m−2) is close to the multi-model means in 4 available models (−0.29 W m−2), whereas the assessing shortwave ERFaci (−1.04 W m−2) and shortwave ERF (−1.36 W m−2) are slightly stronger than the multi-model means, illustrating that the CAS-FGOALS-f3-L can reproduce the aerosol radiation effect reasonably well. However, significant diversity exists in the ERF, especially in the dominated component ERFaci, implying that the uncertainty is still large.

Relationships Between Supermicrometer Sea Salt Aerosol and Marine Boundary Layer Conditions: Insights From Repeated Identical Flight Patterns

The MONterey Aerosol Research Campaign (MONARC) in May-June 2019 featured 14 repeated identical flights off the California coast over the open ocean at the same time each flight day. The objective of this study is to use MONARC data along with machine learning analysis to evaluate relationships between both supermicrometer sea salt aerosol number (N>1) and volume (V>1) concentrations and wind speed, wind direction, sea surface temperature (SST), ambient temperature (Tamb), turbulent kinetic energy (TKE), relative humidity (RH), marine boundary layer (MBL) depth, and drizzle rate. Selected findings from this study include the following: (i) Near surface (<60 m) N>1 and V>1 concentration ranges were 0.1-4.6 cm-3 and 0.3-28.2 μm3 cm-3, respectively; (ii) four meteorological regimes were identified during MONARC with each resulting in different N>1 and V>1 concentrations and also varying horizontal and vertical profiles; (iii) the relative predictive strength of the MBL properties varies depending on predicting N>1 or V>1, with MBL depth being more highly ranked for predicting N>1 and with TKE being higher for predicting V>1; (iv) MBL depths >400 m (<200 m) often correspond to lower (higher) N>1 and V>1 concentrations; (v) enhanced drizzle rates coincide with reduced N>1 and V>1 concentrations; (vi) N>1 and V>1 concentrations exhibit an overall negative relationship with SST and RH and an overall positive relationship with Tamb; and (vii) wind speed and direction were relatively weak predictors of N>1 and V>1.

The great Atlantic Sargassum belt

Pelagic Sargassum is abundant in the Sargasso Sea, but a recurrent great Atlantic Sargassum belt (GASB) has been observed in satellite imagery since 2011, often extending from West Africa to the Gulf of Mexico. In June 2018, the 8850-kilometer GASB contained >20 million metric tons of Sargassum biomass. The spatial distribution of the GASB is mostly driven by ocean circulation. The bloom of 2011 might be a result of Amazon River discharge in previous years, but recent increases and interannual variability after 2011 appear to be driven by upwelling off West Africa during boreal winter and by Amazon River discharge during spring and summer, indicating a possible regime shift and raising the possibility that recurrent blooms in the tropical Atlantic and Caribbean Sea may become the new norm.

Effect of global atmospheric aerosol emission change on PM2.5-related health impacts

Background: Previous research has highlighted the importance of major atmospheric aerosols such as sulfate, through its precursor sulfur dioxide (SO2), black carbon (BC), and organic carbon (OC), and their effect on global climate regimes, specifically on their impact on particulate matter measuring ≤ 2.5 μm (PM2.5). Policy regulations have attempted to address the change in these major active aerosols and their impact on PM2.5, which would presumably have a cascading effect toward the change of health risks. Objective: This study aimed to determine how the change in the global emissions of anthropogenic aerosols affects health, particularly through the change in attributable mortality (AN) and years of life lost (YLL). This study also aimed to explore the importance of using AM/YLL in conveying air pollution health impact message. Methods: The Model for Interdisciplinary Research on Climate was used to estimate the gridded atmospheric PM2.5 by changing the emission of SO2, BC, and OC. Next, the emissions were utilized to estimate the associated cause-specific risks via an integrated exposure-response function, and its consequent health indicators, AM and YLL, per country. Results: OC change yielded the greatest benefit for all country income groups, particularly among low-middle-income countries. Utilizing either AM or YLL did not alter the order of benefits among upper-middle and high-income countries (UMIC/HIC); however, using either health indicator to express the order of benefit varied among low- and low-middle-income countries (LIC/LMIC). Conclusions: Global and country-specific mitigation efforts focusing on OC-related activities would yield substantial health benefits. Substantial aerosol emission reduction would greatly benefit high-emitting countries (i.e. China and India). Although no difference is found in the order of health outcome benefits in UMIC/HIC, caution is warranted in using either AM or YLL for health impact assessment in LIC/LMIC.

Improving the Non-Hydrostatic Numerical Dust Model by Integrating Soil Moisture and Greenness Vegetation Fraction Data with Different Spatiotemporal Resolutions

Dust storms are devastating natural disasters that cost billions of dollars and many human lives every year. Using the Non-Hydrostatic Mesoscale Dust Model (NMM-dust), this research studies how different spatiotemporal resolutions of two input parameters (soil moisture and greenness vegetation fraction) impact the sensitivity and accuracy of a dust model. Experiments are conducted by simulating dust concentration during July 1–7, 2014, for the target area covering part of Arizona and California (31, 37, -118, -112), with a resolution of ~ 3 km. Using ground-based and satellite observations, this research validates the temporal evolution and spatial distribution of dust storm output from the NMM-dust, and quantifies model error using measurements of four evaluation metrics (mean bias error, root mean square error, correlation coefficient and fractional gross error). Results showed that the default configuration of NMM-dust (with a low spatiotemporal resolution of both input parameters) generates an overestimation of Aerosol Optical Depth (AOD). Although it is able to qualitatively reproduce the temporal trend of the dust event, the default configuration of NMM-dust cannot fully capture its actual spatial distribution. Adjusting the spatiotemporal resolution of soil moisture and vegetation cover datasets showed that the model is sensitive to both parameters. Increasing the spatiotemporal resolution of soil moisture effectively reduces model’s overestimation of AOD, while increasing the spatiotemporal resolution of vegetation cover changes the spatial distribution of reproduced dust storm. The adjustment of both parameters enables NMM-dust to capture the spatial distribution of dust storms, as well as reproducing more accurate dust concentration.

Evaluation of the aerosol vertical distribution in global aerosol models through comparison against CALIOP measurements: AeroCom phase II results

The ability of 11 models in simulating the aerosol vertical distribution from regional to global scales, as part of the second phase of the AeroCom model intercomparison initiative (AeroCom II), is assessed and compared to results of the first phase. The evaluation is performed using a global monthly gridded data set of aerosol extinction profiles built for this purpose from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) Layer Product 3.01. Results over 12 subcontinental regions show that five models improved, whereas three degraded in reproducing the interregional variability in Z _{α0-6 km}, the mean extinction height diagnostic, as computed from the CALIOP aerosol profiles over the 0-6 km altitude range for each studied region and season. While the models' performance remains highly variable, the simulation of the timing of the Z _{α0-6 km} peak season has also improved for all but two models from AeroCom Phase I to Phase II. The biases in Z _{α0-6 km} are smaller in all regions except Central Atlantic, East Asia, and North and South Africa. Most of the models now underestimate Z _{α0-6 km} over land, notably in the dust and biomass burning regions in Asia and Africa. At global scale, the AeroCom II models better reproduce the Z _{α0-6 km} latitudinal variability over ocean than over land. Hypotheses for the performance and evolution of the individual models and for the intermodel diversity are discussed. We also provide an analysis of the CALIOP limitations and uncertainties contributing to the differences between the simulations and observations.

Challenges in constraining anthropogenic aerosol effects on cloud radiative forcing using present-day spatiotemporal variability

A large number of processes are involved in the chain from emissions of aerosol precursor gases and primary particles to impacts on cloud radiative forcing. Those processes are manifest in a number of relationships that can be expressed as factors dlnX/dlnY driving aerosol effects on cloud radiative forcing. These factors include the relationships between cloud condensation nuclei (CCN) concentration and emissions, droplet number and CCN concentration, cloud fraction and droplet number, cloud optical depth and droplet number, and cloud radiative forcing and cloud optical depth. The relationship between cloud optical depth and droplet number can be further decomposed into the sum of two terms involving the relationship of droplet effective radius and cloud liquid water path with droplet number. These relationships can be constrained using observations of recent spatial and temporal variability of these quantities. However, we are most interested in the radiative forcing since the preindustrial era. Because few relevant measurements are available from that era, relationships from recent variability have been assumed to be applicable to the preindustrial to present-day change. Our analysis of Aerosol Comparisons between Observations and Models (AeroCom) model simulations suggests that estimates of relationships from recent variability are poor constraints on relationships from anthropogenic change for some terms, with even the sign of some relationships differing in many regions. Proxies connecting recent spatial/temporal variability to anthropogenic change, or sustained measurements in regions where emissions have changed, are needed to constrain estimates of anthropogenic aerosol impacts on cloud radiative forcing.

Spatial correlativity of atmospheric particulate components simultaneously collected in Japan

The simultaneous sampling of total suspended particles was performed at 14 sites in Japan during July 2008-June 2009. The spatial correlativity of each particulate composition toward Osaka was obtained for nine selected sites to overview the chemical composition and geographical distribution of particulate components across a wide range of areas nationwide. The spatial correlatives of atmospheric particulate components were extended to an even wider range of areas up to 950 km distance (meso-alpha scale region, >200 km) for a far-reaching distance analysis unique in the literature. Overall, the spatial correlations of ionic species and both organic and elemental carbons were significant, suggesting their shared advections, including their long-range transport from East Asia. Although sulfate ions are widely dispersed across Japan, such is not necessarily correlated with organic and elemental carbon, possibly indicating that the sulfate emission source, including long-range transport, differs from that of carbonaceous particulates. By contrast, the characteristics of spatial correlatives of metallic constituents vary; for example, particulate Pb and Cd show a significantly wide range of spatial correlatives to Osaka, while Mn-though limited to cities neighboring Osaka-shows significant spatial correlations. Other metallic constituents showed no significant spatial correlatives, indicating the effects of local pollutants. Moreover, the extent of the spatial dispersion of the particulate components and the relationships among chemical components were analyzed via factor analysis to highlight the effects of long-range inflow and local original emissions. In this treatment, 13 particulate components among the 19 measured were implicated in long-range transport.

New Statistical Model for Variability of Aerosol Optical Thickness: Theory and Application to MODIS Data over Ocean

A novel model for the variability in aerosol optical thickness (AOT) is presented. This model is based on the consideration of AOT fields as realizations of a stochastic process, that is the exponent of an underlying Gaussian process with a specific autocorrelation function. In this approach AOT fields have lognormal PDFs and structure functions having the correct asymptotic behavior at large scales. The latter is an advantage compared with fractal (scale-invariant) approaches. The simple analytical form of the structure function in the proposed model facilitates its use for the parameterization of AOT statistics derived from remote sensing data. The new approach is illustrated using a year-long global MODIS AOT dataset (over ocean) with 10 km resolution. It was used to compute AOT statistics for sample cells forming a grid with 5° spacing. The observed shapes of the structure functions indicated that in a large number of cases the AOT variability is split into two regimes that exhibit different patterns of behavior: small-scale stationary processes and trends reflecting variations at larger scales. The small-scale patterns are suggested to be generated by local aerosols within the marine boundary layer, while the large-scale trends are indicative of elevated aerosols transported from remote continental sources. This assumption is evaluated by comparison of the geographical distributions of these patterns derived from MODIS data with those obtained from the GISS GCM. This study shows considerable potential to enhance comparisons between remote sensing datasets and climate models beyond regional mean AOTs.

Influence of contemporary carbon originating from the 2003 Siberian forest fire on organic carbon in PM2.5 in Nagoya, Japan

In May 2003, high concentrations of organic carbon (OC) in PM2.5 were measured in Nagoya, a representative metropolitan area in Japan. To investigate the influence of possible forest fires on PM2.5 in Japan via long-range aerosol transport, the radiocarbon ((14)C) concentrations of PM2.5 samples from April 2003 to March 2004 were measured. (14)C concentrations in total carbon (TC) from May to early June showed higher values than those in other periods. The OC/elemental carbon (EC) ratios from May to early June were also significantly higher than the ones in other periods. In addition, OC concentrations from May to early June were typically high. These results indicate that the abundant OC fraction from May to early June in Nagoya consisted predominantly of contemporary carbon. Furthermore, simulations of diffusion and transport of organic matter (OM) in East Asia showed that abundant OM originating from East Siberia spread over East Asia and Japan in May and early June. Backward air mass trajectories from this time frame indicate that the air mass in Nagoya likely first passed through East Siberia where fire events were prevalent. However, the backward trajectories showed that the air mass after early June did not originate mainly from Siberia, and correspondingly, the (14)C and OC concentrations showed lower values than those from May to early June. Therefore, the authors conclude that contemporary carbon originating from the forest fire in East Siberia was transported to Nagoya, where it significantly contributed to the high observed concentrations of both OC and (14)C.

Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5

Fine particulate matter (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {PM}_{2.5}$$\end{document}PM2.5) has a considerable impact on human health, the environment and climate change. It is estimated that with better predictions, US$9 billion can be saved over a 10-year period in the USA (State of the science fact sheet air quality. http://www.noaa.gov/factsheets/new, 2012). Therefore, it is crucial to keep developing models and systems that can accurately predict the concentration of major air pollutants. In this paper, our target is to predict \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {PM}_{2.5}$$\end{document}PM2.5 concentration in Japan using environmental monitoring data obtained from physical sensors with improved accuracy over the currently employed prediction models. To do so, we propose a deep recurrent neural network (DRNN) that is enhanced with a novel pre-training method using auto-encoder especially designed for time series prediction. Additionally, sensors selection is performed within DRNN without harming the accuracy of the predictions by taking advantage of the sparsity found in the network. The numerical experiments show that DRNN with our proposed pre-training method is superior than when using a canonical and a state-of-the-art auto-encoder training method when applied to time series prediction. The experiments confirm that when compared against the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {PM}_{2.5}$$\end{document}PM2.5 prediction system VENUS (National Institute for Environmental Studies. Visual Atmospheric Environment Utility System. http://envgis5.nies.go.jp/osenyosoku/, 2014), our technique improves the accuracy of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {PM}_{2.5}$$\end{document}PM2.5 concentration level predictions that are being reported in Japan.

Improvement of aerosol optical properties modeling over Eastern Asia with MODIS AOD assimilation in a global non-hydrostatic icosahedral aerosol transport model

A new global aerosol assimilation system adopting a more complex icosahedral grid configuration is developed. Sensitivity tests for the assimilation system are performed utilizing satellite retrieved aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the results over Eastern Asia are analyzed. The assimilated results are validated through independent Aerosol Robotic Network (AERONET) observations. Our results reveal that the ensemble and local patch sizes have little effect on the assimilation performance, whereas the ensemble perturbation method has the largest effect. Assimilation leads to significantly positive effect on the simulated AOD field, improving agreement with all of the 12 AERONET sites over the Eastern Asia based on both the correlation coefficient and the root mean square difference (assimilation efficiency). Meanwhile, better agreement of the Ångström Exponent (AE) field is achieved for 8 of the 12 sites due to the assimilation of AOD only.

Modeling of black carbon in Asia using a global-to-regional seamless aerosol-transport model

In Asia, the evaluation of black carbon (BC) using global aerosol-transport models has been incomplete due to a lack of available measurements. Recently, new measurements and datasets at various Asian sites have become available for use in model validation. In this study, we compared the BC mass concentrations obtained by an aerosol-coupled global nonhydrostatic model adopting a uniform-grid system with in-situ measurements and other models over Asia. The results revealed that our model, as well as other global models, was unable to reproduce the observed BC values at most sites in China and India, most likely due to strong local hotspots. To overcome the inconsistency between the models and measurements, we developed a new aerosol-transport model using a stretched-grid system for high-resolution simulations with up to approximately 10 km grids. Our model can be used as a global-to-regional seamless aerosol-transport model for low to high horizontal resolution simulations.