Abstract
We analyze the seasonal evolution and trends of atmospheric blocking from 1979 to 2018 using a geopotential-height-based method over two domains, one located to the west (150°–90°W, ...50°–70°S) and the other over and to the east (90°–30°W, 50°–70°S) of the Antarctic Peninsula. Spatial patterns of geopotential heights on days with blocking feature well-defined ridge axes over and west of much of South America, and days with the most extreme blocking (above the 99th percentile) showed upper-tropospheric ridge and cutoff low features that have been associated with extreme weather patterns. Blocking days were found to be more frequent in the first half of the period (1979–98) than the second (1999–2018) in all seasons in the west domain, whereas they seem to be more common over the eastern (peninsula) domain in 1999–2018 for austral winter, spring, and autumn, although these differences were not statistically significant. West of the Antarctic Peninsula, blocking days occur most frequently when the Antarctic Oscillation (AAO) is negative, whereas they are more frequent over the peninsula when the AAO is positive. We propose that our blocking index can be used to indicate atmospheric blocking affecting the Antarctic Peninsula, similar to how the Greenland blocking index has been used to diagnose blocking, its trends, and impacts over the Arctic.
ABSTRACT
This study aims to relate the intra‐seasonal rainfall variability over the Amazon basin to atmospheric circulation patterns (CPs), with particular attention to extreme rainfall events in the ...Amazon–Andes region. The CPs summarize the intra‐seasonal variability of atmospheric circulation and are defined using daily low‐level winds from the ERA‐Interim (1.5° × 1.5°) reanalysis for the 1979–2014 period. Furthermore, observational data of precipitation and high‐resolution TRMM 3B42 (∼25 km), 2A25 PR (∼5 km) and CHIRPS (∼5 km) data products are related to the CPs throughout the Amazon basin. Nine CPs are determined using a hybrid method that combines a neural network technique (self‐organizing maps, SOM) and hierarchical ascendant classification. The CPs are characterized by a specific cycle with alternative transitions and a duration of 14 days on average. This configuration initially results in northerly winds to southerly winds towards the northern or eastern Amazon basin. The related rainfall suggests that it is driven mainly by CP dynamics. In addition, we demonstrate a good agreement amongst the four rainfall data sets: observed precipitation, TRMM 3B42, TRMM 2A25 PR and CHIRPS. Furthermore, special attention is given to the Amazon–Andes transition region. Over this region, two particular CPs (CP4 and CP5) are identified as the key contributors of maximum and minimum daily rainfall, respectively. Thus, during the dry season, 40.8% (11.4%) of the CP5 (CP4) days demonstrate rainfall of less than 1 mm day−1, while during the wet season, 6.2% (14.6%) of the CP5 (CP4) days show rainfall amounts higher than the seasonal 90th percentile (10.4 mm day−1). This study provides additional information concerning the intra‐seasonal circulation variability in Amazonia and demonstrates the value of using remote sensing precipitation data in this region as a tool for forecast in areas lacking observable information.
Circulation patterns (CPs) and related rainfall anomalies (shaded) over the western Amazon basin from TRMM‐2A25 PR (a) and CHIRPS (b) data sets for the 1998–2009 time period. Winds at 850 hPa (vectors) are standardized at each grid point, and rainfall is presented in terms of percentage anomalies where blue (red) areas are associated with positive (negative) values. Grey arrows indicate the probability of CP transitions according to the legend. Limits of the South American continent and the Amazon basin up to the Tamshiyacu station (4°S, 73.16°W; black asterisk) are shown as solid lines.
The present study attempts to explore and compare the seasonal variability in chemical composition and contributions of different sources of fine and coarse fractions of aerosols (PM2.5 and PM10) in ...Delhi, India from January 2013 to December 2016. The annual average concentrations of PM2.5 and PM10 were 131 ± 79 μg m−3 (range: 17–417 μg m−3) and 238 ± 106 μg m−3 (range: 34–537 μg m−3), respectively. PM2.5 and PM10 samples were chemically characterized to assess their chemical components i.e. organic carbon (OC), elemental carbon (EC), water soluble inorganic ionic components (WSICs) and heavy and trace elements and then used for estimation of enrichment factors (EFs) and applied positive matrix factorization (PMF5) model to evaluate their prominent sources on seasonal basis in Delhi. PMF identified eight major sources i.e. Secondary nitrate (SN), secondary sulphate (SS), vehicular emissions (VE), biomass burning (BB), soil dust (SD), fossil fuel combustion (FFC), sodium and magnesium salts (SMS) and industrial emissions (IE). Total carbon contributes ∼28% to the total PM2.5 concentration and 24% to the total PM10 concentration and followed the similar seasonality pattern. SN and SS followed opposite seasonal pattern, where SN was higher during colder seasons while SS was greater during warm seasons. The seasonal differences in VE contributions were not very striking as it prevails evidently most of year. Emissions from BB is one of the major sources in Delhi with larger contribution during winter and post monsoon seasons due to stable meteorological conditions and aggrandized biomass burning (agriculture residue burning in and around the regions; mainly Punjab and Haryana) and domestic heating during the season. Conditional Bivariate Probability Function (CBPF) plots revealed that the maximum concentrations of PM2.5 and PM10 were carried by north westerly winds (north-western Indo Gangetic Plains of India).
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•Simultaneous sampling of PM2.5 and PM10 was carried out for 4 years (2013–2016).•Seasonal variations in composition and sources of PM2.5 and PM10 are studied in Delhi.•Secondary inorganic aerosol accounts for 21% of PM10 and 27% of PM2.5 mass with contrasting seasonal variations.•Traffic emission contributes greatly to PM10 while biomass burning to PM2.5, both being maximum in winters.•Maximum concentrations of PM2.5 and PM10 were coming from North West direction of Delhi (CBPF plots).
The present work explores the temporal and seasonal variabilities in composition and contributions of different sources to fine and coarse fractions of particulate matter over Delhi.
•Air-sea CO2 fluxes are synthesized based on a large dataset collected from 47 cruises during 2000–2018.•Both intra-seasonal and seasonal variations in CO2 fluxes are resolved.•Enhanced CO2 sinks on ...the NSCS shelf appeared in winter due primarily to the atmospheric CO2 rise.
This study synthesizes spatial and temporal variations in surface seawater pCO2 (partial pressure of CO2) and associated air-sea CO2 fluxes in the largest marginal sea of the North Pacific, the South China Sea (SCS), based on a large dataset collected from 47 surveys during 2000–2018. We categorized the SCS into five domains featuring different physical and biogeochemical characteristics to better understand the seasonality of SCS pCO2 dynamics and constrain the CO2 fluxes. The five domains are (A) the northern SCS shelf, (B) the northern SCS slope, (C) the SCS basin, (D) West of the Luzon Strait, and (E) the western SCS. We found a large spatial variability in sea surface pCO2 in the SCS, except during winter when values remained in a narrow range of 300 to 360 μatm. In general, seasonal variability was evident in surface water pCO2 values from the northern SCS (Domains A, B and D), with lower values during the cold seasons and higher values during the warm seasons, except in the Pearl River plume (150–650 μatm) and the area off northwest Luzon where winter upwelling occurred (370–470 μatm). In the SCS basin and the western SCS (Domains C and E), pCO2 in surface waters was generally higher than in the atmosphere (380–420 μatm). We also revealed large intra-seasonal variations in the northern SCS during monsoonal transitions in both spring and fall. In spring, pCO2 increased with temperature in the northern SCS, which was a CO2 sink in March but became a CO2 source in May with April as a transitional month. Fall is also a transitional season for the northern SCS, where it changes from a CO2 source back to a CO2 sink. The area-weighted CO2 fluxes across the entire SCS were −1.1 ± 2.2 mmol m−2 d−1 in winter, 0.9 ± 0.9 mmol m−2 d−1 in spring, 2.5 ± 1.4 mmol m−2 d−1 in summer and 1.9 ± 1.1 mmol m−2 d−1 in fall. Nevertheless, on an annual basis, the average CO2 flux from the SCS was 1.2 ± 1.7 mmol m−2 d−1. Enhanced carbon sink on the northern SCS shelf was observed in winter. The annual average CO2 flux was significantly lower than the previous estimate, which can largely be attributed to the addition of new datasets in the previously under-sampled seasons and regions.
Abstract
We investigated the relationship between the frequency of occurrence of the Orinoco low-level jet (OLLJ) and hydroclimatic variables over northern South America. We use data from the ERA5 ...atmospheric reanalysis to characterize the spatial and temporal variability of the OLLJ in light of the low-level jet (LLJ) classification criteria available in the literature. An index for the frequency of occurrence of an LLJ was used, based on the hourly maxima of wind speed. The linkages among the OLLJ, water vapor flux, and precipitation were analyzed using a composite analysis. Our results show that during December–February (DJF), the OLLJ exhibits its maximum wind speed, with values around 8–10 m s
−1
. During DJF, the analysis shows how the OLLJ transports atmospheric moisture from the tropical North Atlantic Ocean. During this season, the predominant pathway of the OLLJ is associated with an area of moisture flux divergence located over northeastern South America. During June–August (JJA), an area of moisture flux convergence associated with the northernmost location of the ITCZ inhibits the entrance of moisture from northerlies. We also show that the occurrence of the OLLJ is associated with the so-called cross-equatorial flow. During DJF, the period of strongest activity of the OLLJ is associated with the northerly cross-equatorial flow and dry season, whereas during JJA the southerly cross-equatorial flow from the Amazon River basin predominates and contributes to the rainy season over the Orinoco region.
Abstract
Numerous low-level vortices are initiated downwind of the Hoggar Mountains and progress toward the Atlantic coast on the northern path of African easterly waves (AEWs). These vortices occur ...mostly in July and August and more specifically when the northern position of the Saharan heat low (SHL) generates stronger and vertically expanded easterly winds over the Hoggar Mountains. At synoptic time scales, a composite analysis reveals that vortex initiation and westward motion are also statistically triggered by a reinforcement of these easterly winds by a wide and persistent high pressure anomaly developing around the Strait of Gibraltar and by a weak wave trough approaching from the east. The vortices are generated in the lee of the Hoggar, about 1000 km west of this approaching trough, and intensify rapidly. The evolution of the vortex perturbation is afterward comparable with the known evolution of the AEWs of the northern path and suggest a growth due to dry barotropic and baroclinic processes induced in particular by the strong cyclonic shear between the reinforced easterly winds and the monsoon flow. These results show that vortex genesis promoted by changes in orographic forcing due to the strengthening of easterly winds over the Hoggar Mountains is a source of intensification of the northern path of AEWs in July and August. These results also provide a possible mechanism to explain the role of the SHL and of particular midlatitude intraseasonal disturbances on the intensity of these waves.
Flavonoids are ubiquitous compounds commonly found in vegetables, fruits and other plant foods. Although not considered nutrients per se, consumption of various flavonoids is associated with ...established health benefits. Their biosynthesis, and therefore concentrations, are influenced by genetic, geographic and environmental conditions. Flavonoid content in foods can be seasonal, potentially influencing their total intake and biovailability. In view of the potential role of flavonoids in human health, studies published over an 11-year period (2009 to 2020) investigating links between flavonoid content and season in edible and medicinal plants, were examined. The limited studies to date focus on a small range of plant species. Within this, there is consistent evidence that flavonoid content varies according to season, particularly in relation to plant genotype and environmental conditions such as temperature, geographic location, light conditions/UV radiation and drought/water stress. Seven studies detected highest total flavonoid content at the end of winter and lowest in mid-autumn. From the included studies, rutin was the most commonly studied flavonoid, showing its highest levels in both spring and winter. These findings suggest studies on flavonoid intake should include seasonal considerations. Further studies on seasonal variations of common dietary flavonoids are warranted to enable such studies.
The simulation of the intra‐seasonal variability (ISV), especially the fidelity of monsoon intra‐seasonal oscillations (MISO) over the South Asian summer monsoon (SASM), has been evaluated in ...Atmospheric Model Intercomparison Project run of three Geophysical Fluid Dynamics Laboratory (GFDL) atmospheric general circulation models (AGCM), participating in Coupled Model Intercomparison Project phase 5. Two of the models, namely, “GFDL‐HIRAM‐C180” and “GFDL‐HIRAM‐C360” are global high‐resolution atmospheric models (HIRAMs), which are same but at different horizontal resolutions. Third model “GFDL‐CM3” is at moderate horizontal resolution, whose atmospheric component differs from HIRAMs. Results have led to the conclusion that simulation of ISV over SASM remains poor in GFDL AGCMs at higher horizontal resolution, except northwards propagation of 30–60‐day mode of MISO by HIRAMs. Our diagnostics revealed resemblance in key characteristics of ISV simulated in two HIRAMs, except spectral peak at lower frequencies (30–60 day), which is captured only by “GFDL‐HIRAM‐C180” over equatorial Indian Ocean. Based on these results, it is conjectured that although high horizontal resolution is crucial for AGCMs to reproduce observed northwards propagation of 30–60‐day mode of MISO, the simulation of nature of ISV over SASM is not influenced by the difference of horizontal resolution in two HIRAMs. Analysis implicates that a reasonable representation of meridional migration of horizontal moisture advection, the meridional gradient of low‐level specific humidity over Indian longitudes and precipitation–SST relationship over north Indian Ocean, the northwestern tropical Pacific Ocean and tropical Indian Ocean assures the northwards propagation of 30–60‐day mode of MISO in HIRAMs.
The simulation of the intra‐seasonal variability, especially the fidelity of monsoon intra‐seasonal oscillations over the South Asian summer monsoon, is evaluated in Atmospheric Model Intercomparison Project run of three Geophysical Fluid Dynamics Laboratory general circulation models from Coupled Model Intercomparison Project phase 5. Results suggests that intra‐seasonal variability simulation over South Asian summer monsoon remains poor in three Geophysical Fluid Dynamics Laboratory general circulation models, except northwards propagation of 30–60‐day mode of monsoon intra‐seasonal oscillations by high‐resolution models. In the figure, (a–d) June–September lag‐longitude plot of 10°S–10°N averaged 30–60‐day filtered precipitation anomalies (shade) and 30–60‐day filtered 850‐hPa zonal wind anomalies (contours) correlated with a reference times series of 30–60‐day filtered precipitation area averaged over equatorial Indian Ocean (5°S–5°N, 75°–100°E). (a) Observed (precipitation: GPCP and 850 hPa wind: NCEP‐NCAR reanalysis), (b) “GFDL‐HIRAM‐C180” model, (c) “GFDL‐HIRAM‐C360” model, (d) “GFDL‐CM3” model. (e–h) June–September lag‐latitude plot of 80°–100°E averaged 30–60‐day filtered precipitation anomalies (shade) and 30–60‐day filtered 850‐hPa zonal wind anomalies (contours) correlated with reference times series of 30–60‐day filtered precipitation area averaged over equatorial Indian Ocean (5°S–5°N, 75°–100°E). (e) Observed (precipitation: GPCP and 850 hPa wind: NCEP‐NCAR reanalysis), (f) “GFDL‐HIRAM‐C180” model, (g) “GFDL‐HIRAM‐C360” model, (h) “GFDL‐CM3” model.
The present study employed the latest high-resolution regional climate model (RegCM4), driven by MPI-ESM-MR boundary conditions from the CORDEX-CORE South Asia framework to investigate the possible ...projected changes in the mean and intra-seasonal variability of the Indian summer monsoon (ISM) precipitation and their associated dynamics during near future (NF; 2041–2060) and far future (FF; 2080–2099) with respect to the historical period (1995–2014) under RCP8.5 scenario. Extensive evaluation analysis indicates that the RegCM4 is fairly able to simulate the spatial–temporal distribution of the observed mean and extreme precipitation, low-level jet, and intra-seasonal variability i.e. active and break composite patterns of the precipitation anomalies over India during the historical period. A substantial decline in the projected precipitation during ISM is estimated over central and northwest India in NF (about 10–30%) as well as in FF (upto 50%), which may be attributed to the weakening and northward shift of low-level winds. The occurrences as well as the intensity of the extreme precipitation events are expected to increase over India in the future. The precipitation during the projected active spells will escalate over the monsoon core region. This is supported by the decrease in sea level pressure over land, which favors the winds to transport more moisture from the adjoining seas for the formation of convective clouds, which is partly indicated through the decline in net surface longwave radiation. On the other hand, the precipitation intensity during the projected break spells is expected to further decrease in the future.
Online measurements of volatile organic compounds (VOCs) during the entire year of 2016 were conducted at an urban site in Nanjing, Jiangsu Province of China. Seasonal variation characteristics in ...ambient VOCs levels, ratios, and sources were then analyzed to investigate which factors controlled seasonal cycles of VOCs levels during 2016. Impact of photochemistry was evaluated based on monthly average concentrations of OH radical (OH) determined by ratios of ethene versus acetylene and o-xylene versus ethylbenzene. OH exhibited higher values during summer and lower values during winter, similar with the seasonality of ozone concentrations and ultraviolet radiation, indicating the existence of photochemical influence on VOCs. Further variance analysis on VOCs levels suggested that seasonality of photochemistry played a minor role in explaining VOCs seasonal cycles. Monthly changes in VOCs sources were then analyzed using the Positive Matrix Factorization (PMF) receptor model. Seven factors were identified, including two factors related to traffic emission and other combustion processes (i.e. vehicular exhaust + combustion processes, gasoline evaporation + vehicular exhaust), two factors related to petrochemical industry (i.e. petrochemical industry#1-propane and petrochemical industry#2-propene), two factors attributed to paints and solvents use (i.e. paints and solvents use#1-toluene, paints and solvents use#2-C8 aromatics), and a factor related to biogenic emission. Gasoline evaporation + vehicular exhaust and biogenic emission both exhibited higher relative contributions during summertime. Meanwhile, relative contributions of petrochemical industry and vehicular exhaust + combustion processes showed maximum during winter and minimum during summer. Relative contributions of two factors related to paints and solvents use showed higher values in March–April and August–September. Conditional probability function (CPF) analysis identified the influence of transport on seasonal changes in VOC source contributions. Finally, the PMF results of this study were compared with recent publications. The discrepancy of VOCs sources reported by different studies implied that there is still large uncertainty in our knowledge on VOCs emissions in Nanjing, and therefore further research on evaluation of VOCs emission inventories and source apportionment is needed in future.
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•Seasonal variation patterns for different VOC species were contrasted in Nanjing.•Photochemistry played a minor role in explaining seasonal variation of VOCs levels.•Seasonal variability of VOCs levels was mainly driven by changes in VOCs sources.•Petrochemical industry and vehicular exhaust contributed more VOCs in winter.•Gasoline evaporation and biogenic emission contributed more VOCs in summer.
