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The response of marine low cloud systems to changes in aerosol concentration represents one of the largest uncertainties in climate simulations. Major contributions to this uncertainty are derived from poor understanding of aerosol under natural conditions and the perturbation by anthropogenic emissions. The eastern North Atlantic (ENA) is a region of persistent but diverse marine boundary layer (MBL) clouds, whose albedo and precipitation are highly susceptible to perturbations in aerosol properties. In this study, we examine MBL aerosol properties, trace gas mixing ratios, and meteorological parameters measured at the Atmospheric Radiation Measurement Climate Research Facility's ENA site on Graciosa Island, Azores, Portugal, during a 3-year period from 2015 to 2017. Measurements impacted by local pollution on Graciosa Island and during occasional intense biomass burning and dust events are excluded from this study. Submicron aerosol size distribution typically consists of three modes: Aitken (At, diameter Dp<∼100 nm), accumulation (Ac, Dp within ∼100 to ∼300 nm), and larger accumulation (LA, Dp>∼300 nm) modes, with average number concentrations (denoted as NAt, NAc, and NLA below) of 330, 114, and 14 cm−3, respectively. NAt, NAc, and NLA show contrasting seasonal variations, suggesting different sources and removal processes. NLA is dominated by sea spray aerosol (SSA) and is higher in winter and lower in summer. This is due to the seasonal variations of SSA production, in-cloud coalescence scavenging, and dilution by entrained free troposphere (FT) air. In comparison, SSA typically contributes a relatively minor fraction to NAt (10 %) and NAc (21 %) on an annual basis. In addition to SSA, sources of Ac-mode particles include entrainment of FT aerosols and condensation growth of Aitken-mode particles inside the MBL, while in-cloud coalescence scavenging is the major sink of NAc. The observed seasonal variation of NAc, being higher in summer and lower in winter, generally agrees with the steady-state concentration estimated from major sources and sinks. NAt is mainly controlled by entrainment of FT aerosol, coagulation loss, and growth of Aitken-mode particles into the Ac-mode size range. Our calculation suggests that besides the direct contribution from entrained FT Ac-mode particles, growth of entrained FT Aitken-mode particles in the MBL also represent a substantial source of cloud condensation nuclei (CCN), with the highest contribution potentially reaching 60 % during summer. The growth of Aitken-mode particles to CCN size is an expected result of the condensation of sulfuric acid, a product from dimethyl sulfide oxidation, suggesting that ocean ecosystems may have a substantial influence on MBL CCN populations in the ENA.

Seasonal cycle of China summer precipitation has significant impacts on its subseasonal predictability; yet current understanding of seasonal evolution of the intraseasonal variability (ISV) remains limited. Here, we show that the ISV of China summer precipitation features a distinct three-stage evolution during early summer, Meiyu season, and late summer. There are two common leading ISV modes: a uniform mode (UM) over southeastern China and a dipole mode (DM) between coastal southeastern China and mid-lower reaches of the Yangtze River Basin, which occur during all three stages with a dominant period of 8–15 days in the early and late summers and 8–25 days during Meiyu season. These two modes show southward propagation in early summer, but they are independent from each other in the other two stages. In early summer, both UM and DM are only related to mid-latitude wave train, and no preceding signal is found in the tropics due to the weak western North Pacific (WNP) monsoon trough. In contrast, during the Meiyu season and late summer, preceding tropical signals are observed when the WNP monsoon trough becomes strong. In the late summer, the effect of mid-latitude wave train is weak as the westerly jet-induced wave guide is far away from southeastern China. An improved subseasonal prediction system is expected to be benefited from consideration of the seasonal evolution of China summer precipitation ISV.

Social distancing to combat the COVID-19 pandemic has led to widespread reductions in air pollutant emissions. Quantifying these changes requires a business-as-usual counterfactual that accounts for the synoptic and seasonal variability of air pollutants. We use a machine learning algorithm driven by information from the NASA GEOS-CF model to assess changes in nitrogen dioxide (NO.sub.2) and ozone (O.sub.3) at 5756 observation sites in 46 countries from January through June 2020. Reductions in NO.sub.2 coincide with the timing and intensity of COVID-19 restrictions, ranging from 60 % in severely affected cities (e.g., Wuhan, Milan) to little change (e.g., Rio de Janeiro, Taipei). On average, NO.sub.2 concentrations were 18 (13-23) % lower than business as usual from February 2020 onward. China experienced the earliest and steepest decline, but concentrations since April have mostly recovered and remained within 5 % of the business-as-usual estimate. NO.sub.2 reductions in Europe and the US have been more gradual, with a halting recovery starting in late March. We estimate that the global NO.sub.x (NO + NO.sub.2) emission reduction during the first 6 months of 2020 amounted to 3.1 (2.6-3.6) TgN, equivalent to 5.5 (4.7-6.4) % of the annual anthropogenic total. The response of surface O.sub.3 is complicated by competing influences of nonlinear atmospheric chemistry. While surface O.sub.3 increased by up to 50 % in some locations, we find the overall net impact on daily average O.sub.3 between February-June 2020 to be small. However, our analysis indicates a flattening of the O.sub.3 diurnal cycle with an increase in nighttime ozone due to reduced titration and a decrease in daytime ozone, reflecting a reduction in photochemical production.

FLUXNET comprises globally distributed eddy-covariance-based estimates of carbon fluxes between the biosphere and the atmosphere. Since eddy covariance flux towers have a relatively small footprint and are distributed unevenly across the world, upscaling the observations is necessary to obtain global-scale estimates of biosphere–atmosphere exchange. Based on cross-consistency checks with atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global vegetation models (DGVMs), here we provide a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods, forcing data sets and sets of predictor variables were employed. Spatial patterns of mean GPP are consistent across FLUXCOM and DGVM ensembles (R2>0.94 at 1∘ spatial resolution) while the majority of DGVMs show, for 70 % of the land surface, values outside the FLUXCOM range. Global mean GPP magnitudes for 2008–2010 from FLUXCOM members vary within 106 and 130 PgC yr−1 with the largest uncertainty in the tropics. Seasonal variations in independent SIF estimates agree better with FLUXCOM GPP (mean global pixel-wise R2∼0.75) than with GPP from DGVMs (mean global pixel-wise R2∼0.6). Seasonal variations in FLUXCOM NEE show good consistency with atmospheric inversion-based net land carbon fluxes, particularly for temperate and boreal regions (R2>0.92). Interannual variability of global NEE in FLUXCOM is underestimated compared to inversions and DGVMs. The FLUXCOM version which also uses meteorological inputs shows a strong co-variation in interannual patterns with inversions (R2=0.87 for 2001–2010). Mean regional NEE from FLUXCOM shows larger uptake than inversion and DGVM-based estimates, particularly in the tropics with discrepancies of up to several hundred grammes of carbon per square metre per year. These discrepancies can only partly be reconciled by carbon loss pathways that are implicit in inversions but not captured by the flux tower measurements such as carbon emissions from fires and water bodies. We hypothesize that a combination of systematic biases in the underlying eddy covariance data, in particular in tall tropical forests, and a lack of site history effects on NEE in FLUXCOM are likely responsible for the too strong tropical carbon sink estimated by FLUXCOM. Furthermore, as FLUXCOM does not account for CO2 fertilization effects, carbon flux trends are not realistic. Overall, current FLUXCOM estimates of mean annual and seasonal cycles of GPP as well as seasonal NEE variations provide useful constraints of global carbon cycling, while interannual variability patterns from FLUXCOM are valuable but require cautious interpretation. Exploring the diversity of Earth observation data and of machine learning concepts along with improved quality and quantity of flux tower measurements will facilitate further improvements of the FLUXCOM approach overall.

Anthropogenic mercury emissions are transported through the atmosphere as gaseous elemental mercury (Hg(0)) before they are deposited to Earth’s surface. Strong seasonality in atmospheric Hg(0) concentrations in the Northern Hemisphere has been explained by two factors: anthropogenic Hg(0) emissions are thought to peak in winter due to higher energy consumption, and atmospheric oxidation rates of Hg(0) are faster in summer. Oxidation-driven Hg(0) seasonality should be equally pronounced in the Southern Hemisphere, which is inconsistent with observations of constant year-round Hg(0) levels. Here, we assess the role of Hg(0) uptake by vegetation as an alternative mechanism for driving Hg(0) seasonality. We find that at terrestrial sites in the Northern Hemisphere, Hg(0) co-varies with CO2, which is known to exhibit a minimum in summer when CO2 is assimilated by vegetation. The amplitude of seasonal oscillations in the atmospheric Hg(0) concentration increases with latitude and is larger at inland terrestrial sites than coastal sites. Using satellite data, we find that the photosynthetic activity of vegetation correlates with Hg(0) levels at individual sites and across continents. We suggest that terrestrial vegetation acts as a global Hg(0) pump, which can contribute to seasonal variations of atmospheric Hg(0), and that decreasing Hg(0) levels in the Northern Hemisphere over the past 20 years can be partly attributed to increased terrestrial net primary production.

Abstract The transition scale L t from balanced geostrophic motions to unbalanced wave motions, including near-inertial flows, internal tides, and inertia–gravity wave continuum, is explored using the output from a global 1/48° horizontal resolution Massachusetts Institute of Technology general circulation model (MITgcm) simulation. Defined as the wavelength with equal balanced and unbalanced motion kinetic energy (KE) spectral density, L t is detected to be geographically highly inhomogeneous: it falls below 40 km in the western boundary current and Antarctic Circumpolar Current regions, increases to 40–100 km in the interior subtropical and subpolar gyres, and exceeds, in general, 200 km in the tropical oceans. With the exception of the Pacific and Indian sectors of the Southern Ocean, the seasonal KE fluctuations of the surface balanced and unbalanced motions are out of phase because of the occurrence of mixed layer instability in winter and trapping of unbalanced motion KE in shallow mixed layer in summer. The combined effect of these seasonal changes renders L t to be 20 km during winter in 80% of the Northern Hemisphere oceans between 25° and 45°N and all of the Southern Hemisphere oceans south of 25°S. The transition scale’s geographical and seasonal changes are highly relevant to the forthcoming Surface Water and Ocean Topography (SWOT) mission. To improve the detection of balanced submesoscale signals from SWOT, especially in the tropical oceans, efforts to remove stationary internal tidal signals are called for.

Current estimates of agricultural ammonia (NH3) emissions in China differ by more than a factor of 2, hindering our understanding of their environmental consequences. Here we apply both bottom-up statistical and top-down inversion methods to quantify NH3 emissions from agriculture in China for the year 2008. We first assimilate satellite observations of NH3 column concentration from the Tropospheric Emission Spectrometer (TES) using the GEOS-Chem adjoint model to optimize Chinese anthropogenic NH3 emissions at the 1∕2°  ×  2∕3° horizontal resolution for March–October 2008. Optimized emissions show a strong summer peak, with emissions about 50 % higher in summer than spring and fall, which is underestimated in current bottom-up NH3 emission estimates. To reconcile the latter with the top-down results, we revisit the processes of agricultural NH3 emissions and develop an improved bottom-up inventory of Chinese NH3 emissions from fertilizer application and livestock waste at the 1∕2°  ×  2∕3° resolution. Our bottom-up emission inventory includes more detailed information on crop-specific fertilizer application practices and better accounts for meteorological modulation of NH3 emission factors in China. We find that annual anthropogenic NH3 emissions are 11.7 Tg for 2008, with 5.05 Tg from fertilizer application and 5.31 Tg from livestock waste. The two sources together account for 88 % of total anthropogenic NH3 emissions in China. Our bottom-up emission estimates also show a distinct seasonality peaking in summer, consistent with top-down results from the satellite-based inversion. Further evaluations using surface network measurements show that the model driven by our bottom-up emissions reproduces the observed spatial and seasonal variations of NH3 gas concentrations and ammonium (NH4+) wet deposition fluxes over China well, providing additional credibility to the improvements we have made to our agricultural NH3 emission inventory.

To investigate the characteristics of PM2.5 and its major chemical components, formation mechanisms, and geographical origins in the two megacities, Chengdu (CD) and Chongqing (CQ), in Sichuan Basin of southwest China, daily PM2.5 samples were collected simultaneously at one urban site in each city for four consecutive seasons from autumn 2014 to summer 2015. Annual mean concentrations of PM2.5 were 67.0 ± 43.4 and 70.9 ± 41.4 µg m-3 at CD and CQ, respectively. Secondary inorganic aerosol (SNA) and organic matter (OM) accounted for 41.1 and 26.1 % of PM2.5 mass at CD, and 37.4 and 29.6 % at CQ, respectively. Seasonal variations of PM2.5 and major chemical components were significant, usually with the highest mass concentration in winter and the lowest in summer. Daily PM2.5 concentration exceeded the national air quality standard on 30 % of the sampling days at both sites, and most of the pollution events were at the regional scale within the basin formed under stagnant meteorological conditions. The concentrations of carbonaceous components were higher at CQ than CD, likely partially caused by emissions from the large number of motorcycles and the spraying processes used during automobile production in CQ. Heterogeneous reactions probably played an important role in the formation of SO42-, while both homogeneous and heterogeneous reactions contributed to the formation of NO3-. Geographical origins of emissions sources contributing to high PM2.5 masses at both sites were identified to be mainly distributed within the basin based on potential source contribution function (PSCF) analysis.

Recent work on atmospheric rivers (ARs) has led to a characterization of these impactful features as primarily cold-season phenomena. Here, an all-season analysis of AR incidence in the North Pacific basin is performed for the period spanning 1979–2014 using the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis dataset. An occurrence-based detection algorithm is developed and employed to identify and characterize ARs in instantaneous fields of anomalous vertically integrated water vapor transport. The all-season climatology and variability of AR frequencies due to the seasonal cycle, the El Niño–Southern Oscillation (ENSO), the Madden–Julian oscillation (MJO), and their interactions are presented based on composites of the detected features. The results highlight that ARs exist throughout the year over the North Pacific, although their preferred locations shift substantially throughout the year. This seasonal cycle manifests itself as northward and westward displacement of ARs during the Northern Hemisphere warm seasons, rather than an absolute change in the number of ARs within the domain. It is also shown that changes to the North Pacific mean-state due to ENSO and the MJO may enhance or completely offset the seasonal cycle of AR activity, but that such influences on AR frequencies vary greatly based on location.

Satellite-based models have been widely used to simulate vegetation gross primary production (GPP) at the site, regional, or global scales in recent years. However, accurately reproducing the interannual variations in GPP remains a major challenge, and the long-term changes in GPP remain highly uncertain. In this study, we generated a long-term global GPP dataset at 0.05∘ latitude by 0.05∘ longitude and 8 d interval by revising a light use efficiency model (i.e., EC-LUE model). In the revised EC-LUE model, we integrated the regulations of several major environmental variables: atmospheric CO2 concentration, radiation components, and atmospheric vapor pressure deficit (VPD). These environmental variables showed substantial long-term changes, which could greatly impact the global vegetation productivity. Eddy covariance (EC) measurements at 95 towers from the FLUXNET2015 dataset, covering nine major ecosystem types around the globe, were used to calibrate and validate the model. In general, the revised EC-LUE model could effectively reproduce the spatial, seasonal, and annual variations in the tower-estimated GPP at most sites. The revised EC-LUE model could explain 71 % of the spatial variations in annual GPP over 95 sites. At more than 95 % of the sites, the correlation coefficients (R2) of seasonal changes between tower-estimated and model-simulated GPP are larger than 0.5. Particularly, the revised EC-LUE model improved the model performance in reproducing the interannual variations in GPP, and the averaged R2 between annual mean tower-estimated and model-simulated GPP is 0.44 over all 55 sites with observations longer than 5 years, which is significantly higher than those of the original EC-LUE model (R2=0.36) and other LUE models (R2 ranged from 0.06 to 0.30 with an average value of 0.16). At the global scale, GPP derived from light use efficiency models, machine learning models, and process-based biophysical models shows substantial differences in magnitude and interannual variations. The revised EC-LUE model quantified the mean global GPP from 1982 to 2017 as 106.2±2.9 Pg C yr−1 with the trend 0.15 Pg C yr−1. Sensitivity analysis indicated that GPP simulated by the revised EC-LUE model was sensitive to atmospheric CO2 concentration, VPD, and radiation. Over the period of 1982–2017, the CO2 fertilization effect on the global GPP (0.22±0.07 Pg C yr−1) could be partly offset by increased VPD (-0.17±0.06 Pg C yr−1). The long-term changes in the environmental variables could be well reflected in global GPP. Overall, the revised EC-LUE model is able to provide a reliable long-term estimate of global GPP. The GPP dataset is available at https://doi.org/10.6084/m9.figshare.8942336.v3 (Zheng et al., 2019).