One of the least understood aspects in atmospheric chemistry is how urban emissions influence the formation of natural organic aerosols, which affect Earth's energy budget. The Amazon rainforest, ...during its wet season, is one of the few remaining places on Earth where atmospheric chemistry transitions between preindustrial and urban-influenced conditions. Here, we integrate insights from several laboratory measurements and simulate the formation of secondary organic aerosols (SOA) in the Amazon using a high-resolution chemical transport model. Simulations show that emissions of nitrogen-oxides from Manaus, a city of ~2 million people, greatly enhance production of biogenic SOA by 60-200% on average with peak enhancements of 400%, through the increased oxidation of gas-phase organic carbon emitted by the forests. Simulated enhancements agree with aircraft measurements, and are much larger than those reported over other locations. The implication is that increasing anthropogenic emissions in the future might substantially enhance biogenic SOA in pristine locations like the Amazon.
A 21-year climatology of Easterly Waves Disturbances (EWDs) over the tropical South Atlantic (TSA) has been examined using data from the European Centers for Medium-Range Weather Forecasting interim ...reanalysis (ERAI) and satellite data. This includes the frequency distribution of EWDs and their interannual variability. The large-scale environment associated with EWDs has been investigated for the coastal region of Northeast Brazil (NEB) for the rainy (April–August) season using a composite analysis. EWDs were first identified in ERAI, resulting in 518 observed cases. These were found to show notable interannual variability with around 16–40 episodes each year and with an average lifetime of 4–6 days. Of the identified EWDs, 97% reached the coast of NEB, of which 64% were convective in nature and 14% moved across the NEB region and reached the Amazon. The annual occurrence of EWDs seems to be lower (higher) during El Niño (La Niña). The monthly occurrence of EWDs shows higher activity in the rainy season. EWDs originate in association with four types of system: cold fronts, convective clusters from the west coast of Africa, Intertropical Convergence Zone and Tropical Upper Tropospheric Cyclonic Vortices. The composite analysis indicates strong relative vorticity and divergence anomalies at low levels, as well as in the vertical profiles of relative humidity and vertical velocity (omega). The precipitation composites show that the EWDs propagate between the TSA and NEB and contribute at least 60% of the total rainfall over the east coast of NEB throughout the rainy season.
Aerosol-cloud interactions remain the largest uncertainty in climate projections. Ultrafine aerosol particles smaller than 50 nanometers (UAP
) can be abundant in the troposphere but are ...conventionally considered too small to affect cloud formation. Observational evidence and numerical simulations of deep convective clouds (DCCs) over the Amazon show that DCCs forming in a low-aerosol environment can develop very large vapor supersaturation because fast droplet coalescence reduces integrated droplet surface area and subsequent condensation. UAP
from pollution plumes that are ingested into such clouds can be activated to form additional cloud droplets on which excess supersaturation condenses and forms additional cloud water and latent heating, thus intensifying convective strength. This mechanism suggests a strong anthropogenic invigoration of DCCs in previously pristine regions of the world.
Heat waves in Amazonia have become more frequent, longer, and more intense according to observational records. Climate change and deforestation are two significant drivers of such trends. In the ...Amazon rainforest, heat waves are still an understudied issue, in part due to limited surface observations. To date, heat waves in central Amazon have been associated with the ITCZ northward migration in austral winter, weakening moisture influx through the South American Monsoon System. This study contributes to this topic by being the first Amazon-specific analysis of heat extremes and the first in South America to jointly explore extreme heat wave events and associated synoptic atmospheric and land surface conditions. Ten of the most extreme heat waves are identified in the Southeast of Amazonia, from Era-Interim (1979 to 2018) maximum daily temperature records. Dry conditions are measured from relative humidity and evaporative fraction anomalies at surface and vertically also using the Era-Interim data. In all 10 events an extreme drying signal co-occurs with extreme heat waves. Wind patterns and anomalies revealed a consistent easterly dry advection anomalously extending to Southeast Amazonia. In addition, an intensification of the northerly South Atlantic Anticyclone wind circulation reduced the influx of moisture to Southeast Amazon, namely linked to the South American Low Level Jet. These, together, contributed to a compound effect on the extreme heat waves under near-surface drying conditions, which escalated hot temperatures to extreme heat.
Precipitation can induce a surface sensible heat flux since the raindrops are generally cooler than the surface. This precipitation‐induced sensible heat flux (QP) is typically ignored in models. ...However, during heavy rainfall, QP can be large and may not be negligible such as over India during the summer monsoon season. We provide the first results of incorporating QP in a simulation that shows ∼2% (∼5%) reduction in precipitation over India compared to the simulation without QP during a monsoonal active phase in 2017 (2018). This reduction was primarily due to a reduction in vertical advection of moisture. Additionally, QP modified the spatial distribution of precipitation with 40% of the geographical area encountering alterations of at least 20% in precipitation. This change in precipitation distribution across the region can have important implications for regional agriculture and irrigation practices. Changes in the partitioning of surface heat flux components due to QP is also discussed.
Plain Language Summary
Rainfall during the monsoon season in India has widespread influences on agriculture and water supply. Therefore, understanding and predicting monsoon rainfall is of utmost importance. Among many parameters, surface energy budget influences precipitation. One of the components of the surface energy budget is the sensible heat flux due to precipitation (QP), which arises because the temperature of raindrops is typically different (cooler) than the temperature of the surface. The QP is thought to be small and is neglected in climate models. By incorporating QP in a regional climate model, we show its influence on monsoon precipitation and surface energy budget. We found that QP reduces precipitation by ∼2% and affects the spatial distribution of precipitation, which may have implications for regional agriculture and irrigation strategies. We also show that QP leads to significant changes in the magnitude and spatial distribution of surface energy budget terms.
Key Points
Precipitation‐induced surface sensible heat flux is typically neglected in numerical weather and climate models
Model simulations show that rain‐induced sensible heat flux reduces monsoonal precipitation and changes its spatial distribution
Changes in the spatial distribution of monsoonal rain can have important implications for regional agriculture and irrigation practices
In this work, we used the MICE (Multivariate Imputation by Chained Equations) technique to impute missing daily data from six meteorological variables (precipitation, temperature, relative humidity, ...atmospheric pressure, wind speed and insolation) from 96 stations located in the northeast region of Brazil (NEB) for the period from 1961 to 2014. We then applied tests with a quality control system (QCS) developed for the detection, correction and possible replacement of suspicious data. Both the applied gap filling technique and the QCS showed that it was possible to solve two of the biggest problems found in time series of daily data measured in meteorological stations: the generation of plausible values for each variable of interest, in order to remedy the absence of observations, and how to detect and allow proper correction of suspicious values arising from observations.
Accurate regional seasonal forecasts of the rainy season are essential for the implementation of effective socioeconomic activities and policy. However, current characteristics of the period of ...occurrence of the rainy season in the Eastern Northeast Brazil (ENEB) region demonstrated that maximum precipitation varies substantially depending on the period analyzed. From 1972 to 2002, the rainy season occurred during the June–July–August (JJA) quarter, while from 1981 to 2011, it occurred in the April–May–June (AMJ) quarter. To access how these differences may be due to different patterns of sea surface temperature (SST), using observed precipitation and SST data from NOAA for the period from 1982 to 2018, this study identified the spatial patterns of inter-annual changes in Pacific and Atlantic SST related to the occurrence of the ENEB rainy seasons. We focus on the statistical method of symmetric mean absolute percentage error (sMAPE) for forecasting these periods based on SST information. Our results revealed five different quarterly periods (FMA, MAM, AMJ, MJJ, JJA) to the rainy season, in which MJJ is more prevalent. The sMAPE values of the SST patterns are inversely proportional to precipitation in the ENEB. Hence, it may be concluded that our climate analysis demonstrates that seasonal SST patterns can be used for forecasting the period of the rainy season.
Two lunar flashes are reported and fully analyzed, with one of them fulfilling every criterion preconized in the literature for the characterization of an impact, including confirmation by two ...simultaneous observations. It happened at 07:13:46 UT on 14 December 2017, at the selenographic coordinates of 9.79° (±0.06°)N and 45.42 (±0.07°)E. The peak magnitudes in the R and V bands vary from 6.3 to 7.9 and from 7.4 to 9.0, respectively, depending on the observatory, as the cameras’ exposure times were considerably different. The impactor mass is estimated to be between 1.6 and 2.0 kg, with a diameter of 10 to 11 cm, having produced a crater of 8.4 to 8.9 m in diameter. Results for the second flash are also presented and discussed, although the confirmation of an impact was not possible due to a pause in the recordings at one of the sites. The observations took place as part of an inaugural observing campaign in Brazil for lunar impact flash (LIF) detection conceived by the Brazilian Meteor Observation Network (BRAMON) and were carried out by two teams located in different states in the Northeast Region of Brazil, about 353 km apart from each other, at a time when the Moon was crossing the densest part of the Geminid meteoroid stream in 2017. The observing setups included 0.13 m and 0.2 m telescopes, both equipped with sensitive cameras. The Maceió setup probably delivered the finest definition ever reported in the literature for lunar impact monitoring, resulting in high-accuracy positioning. This will certainly aid in finding the associated crater from orbiter images, which will substantiate another work, aimed at performing a comparative analysis between the results from our photometry and the data retrieved by the LRO images. These observations were also very likely the first and the only one so far made by a normal framerate camera and a long-exposure camera simultaneously. The associated benefits are commented on. The source of the impactors is also discussed. In view of the successful results of this experience, national observing campaigns of this kind will be given continuation.
The Northeast region of Brazil (NEB) is characterized by large climate variability that causes extreme and long unseasonal wet and dry periods. Despite significant model developments to improve ...seasonal forecasting for the NEB, the achievement of a satisfactory accuracy often remains a challenge, and forecasting methods aimed at reducing uncertainties regarding future climate are needed. In this work, we implement and assess the performance of an empirical model (EmpM) based on a decomposition of historical data into dominant modes of precipitation and seasonal forecast applied to the NEB domain. We analyzed the model’s performance for the February-March-April quarter and compared its results with forecasts based on data from the North American Multi-model Ensemble (NMME) project for the same period. We found that the first three leading precipitation modes obtained by empirical orthogonal functions (EOF) explained most of the rainfall variability for the season of interest. Thereby, this study focuses on them for the forecast evaluations. A teleconnection analysis shows that most of the variability in precipitation comes from sea surface temperature (SST) anomalies in various areas of the Pacific and the tropical Atlantic. The modes exhibit different spatial patterns across the NEB, with the first being concentrated in the northern half of the region and presenting remarkable associations with the El Niño-Southern Oscillation (ENSO) and the Atlantic Meridional Mode (AMM), both linked to the latitudinal migration of the intertropical convergence zone (ITCZ). As for the second mode, the correlations with oceanic regions and its loading pattern point to the influence of the incursion of frontal systems in the southern NEB. The time series of the third mode implies the influence of a lower frequency mode of variability, probably related to the Interdecadal Pacific Oscillation (IPO). The teleconnection patterns found in the analysis allowed for a reliable forecast of the time series of each mode, which, combined, result in the final rainfall prediction outputted by the model. Overall, the EmpM outperformed the post-processed NMME for most of the NEB, except for some areas along the northern region, where the NMME showed superiority.
A climatological analysis and overlying synoptic conditions of Black Sea‐effect snowfall events were investigated for Istanbul, Turkey, during the 1971–2006 winter (DJF) periods. Using the synoptic ...climatological approach, the Lamb Weather Type (LWT) method was applied to NCEP/NCAR daily mean sea level pressure data. Basically, northwesterly (NW), northerly (N), and northeasterly (NE) circulation types (CTs), which blow from the Black Sea (BS), were thought to be important for sea‐effect snowfall events to occur. Wind speeds and flows at 850‐hPa, directional shear, and temperature difference between sea surface and 850‐hPa level (SST‐T850) thresholds were applied to these three CTs in order to find suitable snowfall cases originating from the Black‐Sea. The results showed that 4, 14, and 111 snowfall episodes occurred during NW, N, and NE circulation types over Istanbul with the 2.8, 4.1, and 3.5 cm daily mean snow cover depths (DMSCD), respectively. In particular, it was found that interaction between a surface high located over continental Europe and a low pressure located over the central Black Sea, and a relatively warm sea surface temperature (SST), and cold temperature anomaly at the low level of the atmosphere (SST‐T850 > 17°C) are a favourable environment for the development of intense Black Sea‐effect snowstorms (DMSCD > 10 cm) sourced by NE cases. A statistically significant positive relation between snow cover depths and SST‐Tmax (daily maximum temperature) under NE cases (r = 0.28, p < .05) indicated that we observe intense daily snow accumulation when land‐sea temperature contrast increases (>7°C) in the region.
A climatological analysis and overlying synoptic conditions of Black Sea‐effect snowfall events were investigated for Istanbul, Turkey, during the winter (DJF) period 1971–2006. It is found that interaction between surface high located over continental Europe and a low pressure located over central Black Sea, and relatively warm sea surface temperature (SST) and cold temperature anomaly at the low level of the atmosphere (SST‐T850 > 17°C) are favourable environment for the development of intense Black Sea‐effect snowstorms (DMSCD > 10 cm) sourced by Northeasterly flows.
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