Land–atmosphere interactions in the tropics – a review

Gentine, Pierre; Massmann, Adam; Lintner, Benjamin R.; Alemohammad, Seyed Hamed; Fu, Rong; Green, Julia K.; Kennedy, Daniel; Arellano, Jordi Vilà‐Guerau de

doi:10.5194/hess-23-4171-2019

Cited by 52 publications

(41 citation statements)

References 259 publications

(290 reference statements)

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“…In this context, large epistemic model uncertainties can have considerable impacts on our ability to forecast the growth rate of atmospheric CO 2 . Additionally, vegetation responses to water stress can influence land-atmosphere coupling (Gentine et al, 2019;Koster, 2004;Lemordant, Gentine, Stéfanon, Drobinski, & Fatichi, 2016;Seneviratne et al, 2013), since vegetation cover and canopy conductance affect land surface energy balance. This will have a large impact on our skill to model the coupled hydrological, plant physiological and meteorological processes and thus robustly projecting climate change (Miralles, Gentine, Seneviratne, & Teuling, 2018).…”

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confidence: 99%

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Paschalis

Fatichi

Zscheischler

et al. 2020

Global Change Biology

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Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model-data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Intermodel variation is generally large and model agreement varies with timescales. In severely water-limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily-monthly) timescales and reduces on longer (seasonal-annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter-model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models. K E Y W O R D S drought, irrigation, rainfall manipulation experiment, terrestrial biosphere models

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confidence: 99%

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Paschalis

Fatichi

Zscheischler

et al. 2020

Global Change Biology

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“…We show that all precipitation datasets exhibit similar regional patterns of P with the highest values of annual precipitation occurring in the Colombian Amazon (northwestern region), while the lowest precipita-tion rates are depicted in Peru (western region), Bolivia (southwestern region), and some parts of Brazil (southeastern region). These observations coincide with macroclimatic factors, such as the migration of the Intertropical Convergence Zone (ITZC) and the South Atlantic Convergence Zone (SACZ), the activity of aerial rivers, and large-scale circulation patterns across South America, [70][71][72][73][74] including the Eastern Andean jet, also known as the Orinoco jet (northernmost leg of the SALJET), 75,76 and the two phases of the ENSO, 20,23,24 land surface-atmosphere interactions, 21,[77][78][79] vegetation activity, and precipitation recycling. [80][81][82] Regarding E p , we show that both E p products produce quite different spatial results.…”

Section: Discussionmentioning

confidence: 71%

Uncertainty of runoff sensitivity to climate change in the Amazon River basin

Carmona

Renner

Kleidon

et al. 2020

Annals of the New York Academy of Sciences

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We employ the approach of Roderick and Farquhar (2011) to assess the sensitivity of runoff (R) given changes in precipitation (P), potential evapotranspiration (E p), and other properties that change the partitioning of P (n) by estimating coefficients that predict the weight of each variable in the relative change of R. We use this framework using different data sources and products for P, actual evapotranspiration (E), and E p within the Amazon River basin to quantify the uncertainty of the hydrologic response at the subcatchment scale. We show that when estimating results from the different combinations of datasets for the entire river basin (at Óbidos), a 10% increase in P would increase R on average 16%, while a 10% increase in E p would decrease R about 6%. In addition, a 10% change in the parameter n would affect the hydrological response of the entire basin around 5%. However, results change from catchment to catchment and are dependent on the combination of datasets. Finally, results suggest that enhanced estimates of E and E p are needed to improve our understanding of the future scenarios of hydrological sensitivity with implications for the quantification of climate change impacts at the regional (subcatchment and subbasin) scale in Amazonia.

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“…Particularly, it provides a desirable condition for isolating the influence of the surface, as its proximity to the surrounding forest imply similar synoptic conditions. In addition, the deep roots of the rainforest lead to only moderate water stress in the dry season (Lee et al., 2005; Nepstad et al., 1994), and the surface latent heat flux and humidity are fairly high (Gentine et al., 2019; Wu & Lee, 2019). Thus, the differences between the river and the forest will predominantly be LST, which induces a local river breeze circulation (Silva Dias et al., 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Studies (e.g., Taylor et al, 2012) have shown that thermally driven convergence may be more prominent in semi-arid regions and determines a negative soil moisture feedback for afternoon precipitation. In Amazonia, by contrast, researchers (Gentine et al, 2019;J. F. Wang et al, 2009) claim that the temperature effect is essential only for shallow convection instead of deep precipitating convection when comparing unperturbed rainforest against neighboring deforested pastures.…”

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confidence: 99%

Suppressed Daytime Convection Over the Amazon River

Lee

Wang

et al. 2021

JGR Atmospheres

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We investigated the interaction between surface conditions and precipitating convection by comparing the Amazon River against the surrounding forest. Despite similar synoptic conditions within a few tens of kilometers, the river surface is substantially cooler than the surrounding forest during the day and warmer at night. We analyzed 20 years of high‐resolution satellite precipitation data and confirmed previous findings of daytime rainfall reduction over the river for the whole Amazon Basin. The percentage reduction is strongest during the dry‐to‐wet transition season. In addition, the percentage reduction of individual tributary is significantly correlated with the Laplacian of surface temperature, which causes thermally driven surface divergence and suppresses local convection. Additionally, nighttime rainfall is enhanced over tributaries near the Atlantic coast during the wet season. A regional climate model then simulates the local rainfall anomalies associated with the river. Above the river, moisture diverges near the surface and converges above the surface before the daytime rainfall, partially driven by the horizontal gradient of humidity. Unlike the river, moisture convergence within the boundary layer is more critical for the rainfall above the forest region. Our studies suggest that strong thermal contrast can be important in deriving heterogeneous convection in moist tropical regions.

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Land–atmosphere interactions in the tropics – a review

Cited by 52 publications

References 259 publications

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Uncertainty of runoff sensitivity to climate change in the Amazon River basin

Suppressed Daytime Convection Over the Amazon River

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