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The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) Experiment was carried out in the environs of Manaus, Brazil, in the central region of the Amazon basin for 2 years from 1 January 2014 through 31 December 2015. The experiment focused on the complex interactions among vegetation, atmospheric chemistry, and aerosol production on the one hand and their connections to aerosols, clouds, and precipitation on the other. The objective was to understand and quantify these linked processes, first under natural conditions to obtain a baseline and second when altered by the effects of human activities. To this end, the pollution plume from the Manaus metropolis, superimposed on the background conditions of the central Amazon basin, served as a natural laboratory. The present paper, as the introduction to the special issue of GoAmazon2014/5, presents the context and motivation of the GoAmazon2014/5 Experiment. The nine research sites, including the characteristics and instrumentation of each site, are presented. The sites range from time point zero (T0) upwind of the pollution, to T1 in the midst of the pollution, to T2 just downwind of the pollution, to T3 furthest downwind of the pollution (70 km). In addition to the ground sites, a low-altitude G-159 Gulfstream I (G-1) observed the atmospheric boundary layer and low clouds, and a high-altitude Gulfstream G550 (HALO) operated in the free troposphere. During the 2-year experiment, two Intensive Operating Periods (IOP1 and IOP2) also took place that included additional specialized research instrumentation at the ground sites as well as flights of the two aircraft. GoAmazon2014/5 IOP1 was carried out from 1 February to 31 March 2014 in the wet season. GoAmazon2014/5 IOP2 was conducted from 15 August to 15 October 2014 in the dry season. The G-1 aircraft flew during both IOP1 and IOP2, and the HALO aircraft flew during IOP2. In the context of the Amazon basin, the two IOPs also correspond to the clean and biomass burning seasons, respectively. The Manaus plume is present year-round, and it is transported by prevailing northeasterly and easterly winds in the wet and dry seasons, respectively. This introduction also organizes information relevant to many papers in the special issue. Information is provided on the vehicle fleet, power plants, and industrial activities of Manaus. The mesoscale and synoptic meteorologies relevant to the two IOPs are presented. Regional and long-range transport of emissions during the two IOPs is discussed based on satellite observations across South America and Africa. Fire locations throughout the airshed are detailed. In conjunction with the context and motivation of GoAmazon2014/5 as presented in this introduction, research articles including thematic overview articles are anticipated in this special issue to describe the detailed results and findings of the GoAmazon2014/5 Experiment.

Comparative measurements of radiation flux components and turbulent fluxes of energy and CO2 are made at two sites in South West Amazonia: one in a tropical forest reserve and one in a pasture. The data were collected from February 1999 to September 2002, as part of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA). During the dry seasons, although precipitation and specific humidity are greatly reduced, the soil moisture storage profiles down to 3.4 m indicate that the forest vegetation continues to withdraw water from deep layers in the soil. For this reason, seasonal changes observed in the energy partition and CO2 fluxes in the forest are small, compared to the large reductions in evaporation and photosynthesis observed in the pasture. For the radiation balance, the reflected short wave radiation increases by about 55% when changing from forest to pasture. Combined with an increase of 4.7% in long wave radiation loss, this causes an average reduction of 13.3% in net radiation in the pasture, compared to the forest. In the wet season, the evaporative fraction (lambdaE/Rn) at the pasture is 17% lower than at the forest. This difference increases to 24% during the dry season. Daytime CO2 fluxes are 20-28% lower (in absolute values) in the pasture compared to the forest. The night-time respiration in the pasture is also reduced compared to the forest, with averages 44% and 57% lower in the wet and dry seasons, respectively. As the reduction in the nocturnal respiration is larger than the reduction in the daytime uptake, the combined effect is a 19-67% higher daily uptake of CO2 in the pasture, compared to the forest. This high uptake of CO2 in the pasture site is not surprising, since the growth of the vegetation is constantly renewed, as the cattle remove the biomass. [PUBLICATION ABSTRACT]

We analyzed errors and uncertainties in time-integrated eddy correlation data for sites in the Amazon. A well-known source of potential error in eddy correlation is through possible advective losses of CO2emissions during calm nights. There are also questions related to the treatment of low frequencies, non-horizontal flow, and uncertainties in, e.g., corrections for tube delay and frequency loss, as well as the effect of missing data. In this study, we systematically explore these issues for the specific situation of flux measurements at two Amazon forest sites. Results indicate that, for this specific environment with tall forest and tall towers, errors and uncertainties caused by data spikes, delay corrections, and high-frequency loss are small (<3% on an annual basis). However, sensitivities to the treatment of low frequencies and non-horizontal flow can be large, especially if the landscape is not homogeneous. Given that there is no consensus on methodology here, this represents an uncertainty of 10-25% on annual total carbon uptake. The other large uncertainty is clearly in the nighttime fluxes. Two different ways to evaluate the validity of these fluxes resulted in at least a 100% difference of annual totals. Finally, we show that uncertainty (standard errors) associated with data gaps can be reduced to $<0.5 Mg\\cdot ha^{-1}\\cdot yr^{-1}$ if data are covering at least half of the time, with random spread. Overall uncertainty, on annual CO2fluxes, excluding the nighttime dilemma, is estimated at ± 12% (central Amazon site) to$\\pm 32\\%$(southwest Amazon site). Additionally, the nighttime uncertainty is of similar magnitude as the time-integrated fluxes themselves.

Measurements of CH4 mixing ratio, vertical gradients and turbulent fluxes were carried out in a tropical forest (Reserva Biológica Cuieiras), about 60 km north of Manaus, Brazil. The methane mixing ratio and flux measurements were performed at a height of 53 m (canopy height 35 m). In addition, vertical CH4 gradients were measured within the canopy using custom made air samplers at levels of 2, 16 and 36 m above ground. The methane gradients within the canopy reveal that there is a continuous methane source at the surface. No clear evidence for aerobic methane emission from the canopy was found. The methane fluxes above the canopy are small but consistently upwards with a maximum early in the morning. The measured fluxes are in agreement with the observed CH4 gradient in the canopy. In the morning hours, a strong canopy venting peak is observed for both CH4 and CO2, but for CO2 this peak is then superimposed by photosynthetic uptake, whereas the peak lasts longer for CH4. Monthly averaged diurnal cycles of the CH4 mixing ratio show a decrease during daytime and increase during nighttime. The magnitude of the difference in CH4 mixing ratio between day and night gradually increases throughout the wet season. The fluxes required to explain the nighttime increase are in agreement with the nighttime fluxes measured above the canopy, which implies that the CH4 increase in the nighttime boundary layer originates from local sources.

Summary The numerical regional model (Eta) coupled with the Simplified Simple Biosphere Model (SSiB) was used to investigate the impact of land cover changes on the regional climate in Amazonia. Four 13-month integrations were performed for the following scenarios: (a) no deforestation, (b) current conditions, (c) deforestation predicted for 2033, and (d) large scale deforestation. All initial and prescribed boundary conditions were kept identical for all integrations, except the land cover changes. The results show that during the dry season the post-deforestation decrease in root depth plays an important role in the energy budget, since there is less soil moisture available for evapotranspiration. In all scenarios there was a significant increase in the surface temperature, from 2.0 °C in the first scenario, up to 2.8 °C in the last one. In both the scenarios (b) and (c), the downward component of the surface solar radiation decreased due to an increase in the cloud cover over the deforested areas, which contributed to a further reduction of the net radiation absorbed at the surface. The cloud mechanism, where an increase in albedo is balanced by an increase in downward solar radiation, was not detected in any of these scenarios. In scenarios (a), (b) and (c), a negative feedback mechanism was observed in the hydrological cycle, with greater amounts of moisture being carried to the deforested areas. The increase in moisture convergence was greater than the reduction in evapotranspiration for both scenarios (b) and (c). This result, and the meso-scale thermodynamic processes caused an increase in precipitation. A different situation was observed in the large-scale deforestation scenario (d): a local increase of moisture convergence was observed, but not sufficiently intense to generate an increase in precipitation; the local evapotranspiration decrease was dominant in this scenario. Therefore, the partial deforestation in Amazonia can actually lead to an increase in precipitation locally. However, if the deforestation increases, this condition becomes unsustainable, leading to drier conditions and, consequently, to reduced precipitation in the region.

This is a study of the annual and interannual variability of regional rainfall produced by the Center for Weather Forecasts and Climate Studies/Center for Ocean, Land and Atmospheric Studies (CPTEC/COLA) atmospheric global climate model. An evaluation is made of a 9-member ensemble run of the model forced by observed global sea surface temperature (SST) anomalies for the 10-year period 1982-1991. The Brier skill score and, Relative Operating Characteristics (ROC) are used to assess the predictability of rainfall and to validate rainfall simulations, in several regions world wide. In general, the annual cycle of precipitation is well simulated by the model for several continental and oceanic regions in the tropics and mid latitudes. Interannual variability of rainfall during the peak rainy season is realistically simulated in Northeast Brazil, Amazonia, central Chile, and southern Argentina-Uruguay, Eastern Africa, and tropical Pacific regions, where the model shows good skill. Some regions, such as northwest Peru-Ecuador, and southern Brazil exhibit a realistic simulation of rainfall anomalies associated with extreme El Niño warming conditions, while in years with neutral or La Niña conditions, the agreement between observed and simulated rainfall anomalies is not always present. In the monsoon regions of the world and in southern Africa, even though the model reproduces the annual cycle of rainfall, the skill of the model is low for the simulation of the interannual variability. This is indicative of mechanisms other than the external SST forcing, such as the effect of land-surface moisture and snow feedbacks or the representation of sub-grid scale processes, indicating the important role of factors other than external boundary forcing. The model captures the well-known signatures of rainfall anomalies of El Niño in 1982-83 and 1986-87, indicating its sensitivity to strong external forcing. In normal years, internal climate variability can affect the predictability of climate in some regions, especially in monsoon areas of the world.[PUBLICATION ABSTRACT]

The carbon and water cycles for a southwestern Amazonian forest site were investigated using the longest time series of fluxes of CO2 and water vapor ever reported for this site. The period from 2004 to 2010 included two severe droughts (2005 and 2010) and a flooding year (2009). The effects of such climate extremes were detected in annual sums of fluxes as well as in other components of the carbon and water cycles, such as gross primary production and water use efficiency. Gap-filling and flux-partitioning were applied in order to fill gaps due to missing data, and errors analysis made it possible to infer the uncertainty on the carbon balance. Overall, the site was found to have a net carbon uptake of ≈5 t C ha(-1) year(-1), but the effects of the drought of 2005 were still noticed in 2006, when the climate disturbance caused the site to become a net source of carbon to the atmosphere. Different regions of the Amazon forest might respond differently to climate extremes due to differences in dry season length, annual precipitation, species compositions, albedo and soil type. Longer time series of fluxes measured over several locations are required to better characterize the effects of climate anomalies on the carbon and water balances for the whole Amazon region. Such valuable datasets can also be used to calibrate biogeochemical models and infer on future scenarios of the Amazon forest carbon balance under the influence of climate change.

This paper describes recent developments in climate modeling at Météo-France related to land surface processes. The implementation of a simple land surface parameterization, Interactions between Soil Biosphere Atmosphere (ISBA), has gained from previous validations and calibrations at local scale against field datasets and from aggregation procedures devised to define effective land surface properties. Specific improvements for climate purposes are introduced: spatial variability of convective rainfall in canopy drainage estimation and subsurface gravitational percolation. The methodology used to derive climatological maps of land surface parameters at the grid-scale resolution of the model from existing databases for soil and vegetation types at global scale is described. A 3-yr integration for the present day climate with a T42L30 version of the climate model has been performed. Results obtained compare favorably with available observed climatologies related to the various components of the continental surface energy and water budgets. Differences are due mostly to a poor simulation of the precipitation field. However, some differences suggest specific improvements in the surface scheme concerning representation of the bare soil albedo, the surface runoff, and the soil moisture initialization. As a first step prior to tropical deforestation experiments presented in Part II, regional analyses over the Amazon forest indicate that the modeled evaporation and net radiation are in good agreement with data collected during the Amazon Region Micrometeorological Experiment campaign.

Nocturnal turbulent kinetic energy (TKE) and fluxes of energy, CO2 and O3 between the Amazon forest and the atmosphere are evaluated for a 20-day campaign at the Amazon Tall Tower Observatory (ATTO) site. The distinction of these quantities between fully turbulent (weakly stable) and intermittent (very stable) nights is discussed. Spectral analysis indicates that low-frequency, nonturbulent fluctuations are responsible for a large portion of the variability observed on intermittent nights. In these conditions, the low-frequency exchange may dominate over the turbulent transfer. In particular, we show that within the canopy most of the exchange of CO2 and H2O happens on temporal scales longer than 100 s. At 80 m, on the other hand, the turbulent fluxes are almost absent in such very stable conditions, suggesting a boundary layer shallower than 80 m. The relationship between TKE and mean winds shows that the stable boundary layer switches from the very stable to the weakly stable regime during intermittent bursts of turbulence. In general, fluxes estimated with long temporal windows that account for low-frequency effects are more dependent on the stability over a deeper layer above the forest than they are on the stability between the top of the canopy and its interior, suggesting that low-frequency processes are controlled over a deeper layer above the forest.

The Amazon Basin plays key role in atmospheric chemistry, biodiversity and climate change. In this study we applied nanoelectrospray (nanoESI) ultra-high-resolution mass spectrometry (UHRMS) for the analysis of the organic fraction of PM2.5 aerosol samples collected during dry and wet seasons at a site in central Amazonia receiving background air masses, biomass burning and urban pollution. Comprehensive mass spectral data evaluation methods (e.g. Kendrick mass defect, Van Krevelen diagrams, carbon oxidation state and aromaticity equivalent) were used to identify compound classes and mass distributions of the detected species. Nitrogen- and/or sulfur-containing organic species contributed up to 60 % of the total identified number of formulae. A large number of molecular formulae in organic aerosol (OA) were attributed to later-generation nitrogen- and sulfur-containing oxidation products, suggesting that OA composition is affected by biomass burning and other, potentially anthropogenic, sources. Isoprene-derived organosulfate (IEPOX-OS) was found to be the most dominant ion in most of the analysed samples and strongly followed the concentration trends of the gas-phase anthropogenic tracers confirming its mixed anthropogenic–biogenic origin. The presence of oxidised aromatic and nitro-aromatic compounds in the samples suggested a strong influence from biomass burning especially during the dry period. Aerosol samples from the dry period and under enhanced biomass burning conditions contained a large number of molecules with high carbon oxidation state and an increased number of aromatic compounds compared to that from the wet period. The results of this work demonstrate that the studied site is influenced not only by biogenic emissions from the forest but also by biomass burning and potentially other anthropogenic emissions from the neighbouring urban environments.