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Amazon, French Guiana, Suriname, Guyana, and Venezuela. This region is hereafter referred as the EA and is outlined in Fig. 1a . Given the EA’s close proximity to the equator, the motivation for this paper is to understand if local processes, such as the diurnal variation, amplify the remote ENSO forcing. 2. Model description and data a. Model description The Center for Ocean–Land–Atmosphere Studies (COLA) coupled climate model ( Misra et al. 2007 ; Misra and Marx 2007 ) is used in this study. Its
Amazon, French Guiana, Suriname, Guyana, and Venezuela. This region is hereafter referred as the EA and is outlined in Fig. 1a . Given the EA’s close proximity to the equator, the motivation for this paper is to understand if local processes, such as the diurnal variation, amplify the remote ENSO forcing. 2. Model description and data a. Model description The Center for Ocean–Land–Atmosphere Studies (COLA) coupled climate model ( Misra et al. 2007 ; Misra and Marx 2007 ) is used in this study. Its
same region, Ffield (2005) describes the SST signatures of anticyclonic NBC rings as they pass northwestward through the Amazon–Orinoco River plume with relatively cool SSTs during July through December in comparison to the warm SSTs associated with the plume. The Amazon–Orinoco River plume and NBC rings are assessed in this paper specifically during the 1 June through 30 November Atlantic hurricane season, to identify their impact on the upper-ocean temperatures in the region, as well as to
same region, Ffield (2005) describes the SST signatures of anticyclonic NBC rings as they pass northwestward through the Amazon–Orinoco River plume with relatively cool SSTs during July through December in comparison to the warm SSTs associated with the plume. The Amazon–Orinoco River plume and NBC rings are assessed in this paper specifically during the 1 June through 30 November Atlantic hurricane season, to identify their impact on the upper-ocean temperatures in the region, as well as to
-induced disturbances ( Herold and Skutsch 2011 ). Forest degradation is therefore difficult to characterize because accurate estimates require lengthy observation periods to track the gradual forest changes caused by fire and/or unsustainable logging ( Lambin 1999 ). Remote sensing offers many opportunities for monitoring forest degradation, including the extensive coverage of inaccessible areas, such as the Amazon region, and information about historical trajectories in land-cover changes ( Herold and Skutsch
-induced disturbances ( Herold and Skutsch 2011 ). Forest degradation is therefore difficult to characterize because accurate estimates require lengthy observation periods to track the gradual forest changes caused by fire and/or unsustainable logging ( Lambin 1999 ). Remote sensing offers many opportunities for monitoring forest degradation, including the extensive coverage of inaccessible areas, such as the Amazon region, and information about historical trajectories in land-cover changes ( Herold and Skutsch
transpiration: Scaling up from leaf to region. Adv. Ecol. Res. , 15 , 1 – 49 . 10.1016/S0065-2504(08)60119-1 Keller, M. , and Coauthors , 2004 : Ecological research in the Large-Scale Biosphere-Atmosphere Experiment in Amazonia: Early results. Ecol. Appl. , 14 , S3 – S16 . 10.1890/03-6003 Kruijt, B. , and Coauthors , 2004 : The robustness of eddy correlation fluxes for Amazon rain forest conditions. Ecol. Appl. , 14 , S101 – S113 . 10.1890/02-6004 Lee, X. H. , 1998 : On
transpiration: Scaling up from leaf to region. Adv. Ecol. Res. , 15 , 1 – 49 . 10.1016/S0065-2504(08)60119-1 Keller, M. , and Coauthors , 2004 : Ecological research in the Large-Scale Biosphere-Atmosphere Experiment in Amazonia: Early results. Ecol. Appl. , 14 , S3 – S16 . 10.1890/03-6003 Kruijt, B. , and Coauthors , 2004 : The robustness of eddy correlation fluxes for Amazon rain forest conditions. Ecol. Appl. , 14 , S101 – S113 . 10.1890/02-6004 Lee, X. H. , 1998 : On
suffer greatly as a result of global warming. For example, Scholze et al. (2006) use model projections of the twenty-first century from 16 coupled atmosphere–ocean GCMs to drive a dynamic vegetation model, with the goal of identifying ecosystems around the world that may be vulnerable to climate change. The Amazon basin is identified as a region at high risk for extensive forest loss. Increasing our confidence in predictions of a possible die back in the Amazon rain forest vegetation would
suffer greatly as a result of global warming. For example, Scholze et al. (2006) use model projections of the twenty-first century from 16 coupled atmosphere–ocean GCMs to drive a dynamic vegetation model, with the goal of identifying ecosystems around the world that may be vulnerable to climate change. The Amazon basin is identified as a region at high risk for extensive forest loss. Increasing our confidence in predictions of a possible die back in the Amazon rain forest vegetation would
(10°S–12.5°N, 80°–35°W; marked rectangle in Fig. 5b ). Comparison indicates that between half and two-thirds of the westerly bias attributed to the tropics ( Fig. 5a ) originates in the South American region. The sector’s influence is far from local, extending up to the African shores. The maximum westerly response, ∼1.6 m s −1 , is located ∼30°W, that is, close to the location of the CAM3 peak westerly bias. Structure of the surface response, with westerlies to the east of the eastern Amazon
(10°S–12.5°N, 80°–35°W; marked rectangle in Fig. 5b ). Comparison indicates that between half and two-thirds of the westerly bias attributed to the tropics ( Fig. 5a ) originates in the South American region. The sector’s influence is far from local, extending up to the African shores. The maximum westerly response, ∼1.6 m s −1 , is located ∼30°W, that is, close to the location of the CAM3 peak westerly bias. Structure of the surface response, with westerlies to the east of the eastern Amazon
21%. The underestimation found in the central Amazon and Negro River basins are compensated by an overestimation in other areas such as the upper Solimões and tributary river basins located in the lower Amazon basin, such as Xingu. In the upper Solimões River basin, seasonal variation is well represented ( r = 0.84), although HyMAP overestimates flood extent in both wet and dry seasons (RE = 59%). Peaks are significantly overestimated in this region during the years 1993/94 and 1999
21%. The underestimation found in the central Amazon and Negro River basins are compensated by an overestimation in other areas such as the upper Solimões and tributary river basins located in the lower Amazon basin, such as Xingu. In the upper Solimões River basin, seasonal variation is well represented ( r = 0.84), although HyMAP overestimates flood extent in both wet and dry seasons (RE = 59%). Peaks are significantly overestimated in this region during the years 1993/94 and 1999
attributed to an El Niño event through a reduced water vapor transport in April–August caused by an anomalous divergence over the western Amazon (i.e., 1998). In 2010, both oceanic episodes caused the strongest drought observed in this region ( Espinoza et al. 2011 ; Marengo et al. 2011 ). Fig . 1. JFM 2012 rainfall anomalies (mm day −1 ) from TRMM 3B42 v7 for (a) the Amazon basin and (b) the Peruvian Amazonas basin at Tamshiyacu. (c) Mean (1970–2011) hydrological year at Tamshiyacu station (black line
attributed to an El Niño event through a reduced water vapor transport in April–August caused by an anomalous divergence over the western Amazon (i.e., 1998). In 2010, both oceanic episodes caused the strongest drought observed in this region ( Espinoza et al. 2011 ; Marengo et al. 2011 ). Fig . 1. JFM 2012 rainfall anomalies (mm day −1 ) from TRMM 3B42 v7 for (a) the Amazon basin and (b) the Peruvian Amazonas basin at Tamshiyacu. (c) Mean (1970–2011) hydrological year at Tamshiyacu station (black line
mesoscale convective systems there. A northerly LLJ regime causes low level moisture divergence over the Amazon and convergence and anomalous precipitation in the La Plata Basin ( Wang and Fu 2004 ). As in two previous global warming simulation studies, in the 2-K experiment the LLJ strengthens in spring, as measured by the South American LLJ index: the mean 850-hPa meridional winds averaged over the region 15°–20°S, 65°–55°W ( Wang and Fu 2004 ). In the 2-K experiment, the LLJ index northerlies
mesoscale convective systems there. A northerly LLJ regime causes low level moisture divergence over the Amazon and convergence and anomalous precipitation in the La Plata Basin ( Wang and Fu 2004 ). As in two previous global warming simulation studies, in the 2-K experiment the LLJ strengthens in spring, as measured by the South American LLJ index: the mean 850-hPa meridional winds averaged over the region 15°–20°S, 65°–55°W ( Wang and Fu 2004 ). In the 2-K experiment, the LLJ index northerlies
. 2006 ). When the low-level northwesterly flow intensifies within a narrow region along the east side of the Andes it forms what is known as the South American low-level jet (SALLJ) ( Vera et al. 2006 ). This jet is responsible for a significant portion of the mass and water transport from the Amazon to subtropical latitudes throughout the year with peak transports observed during the monsoon ( Vera et al. 2006 ; Marengo et al. 2012 ; Martinez and Dominguez 2014 ). Variability in this jet occurs
. 2006 ). When the low-level northwesterly flow intensifies within a narrow region along the east side of the Andes it forms what is known as the South American low-level jet (SALLJ) ( Vera et al. 2006 ). This jet is responsible for a significant portion of the mass and water transport from the Amazon to subtropical latitudes throughout the year with peak transports observed during the monsoon ( Vera et al. 2006 ; Marengo et al. 2012 ; Martinez and Dominguez 2014 ). Variability in this jet occurs
