Rainforest-initiated wet season onset over the southern Amazon - PubMed (original) (raw)

Rainforest-initiated wet season onset over the southern Amazon

Jonathon S Wright et al. Proc Natl Acad Sci U S A. 2017.

Abstract

Although it is well established that transpiration contributes much of the water for rainfall over Amazonia, it remains unclear whether transpiration helps to drive or merely responds to the seasonal cycle of rainfall. Here, we use multiple independent satellite datasets to show that rainforest transpiration enables an increase of shallow convection that moistens and destabilizes the atmosphere during the initial stages of the dry-to-wet season transition. This shallow convection moisture pump (SCMP) preconditions the atmosphere at the regional scale for a rapid increase in rain-bearing deep convection, which in turn drives moisture convergence and wet season onset 2-3 mo before the arrival of the Intertropical Convergence Zone (ITCZ). Aerosols produced by late dry season biomass burning may alter the efficiency of the SCMP. Our results highlight the mechanisms by which interactions among land surface processes, atmospheric convection, and biomass burning may alter the timing of wet season onset and provide a mechanistic framework for understanding how deforestation extends the dry season and enhances regional vulnerability to drought.

Keywords: Amazon; evapotranspiration; monsoon onset; rainfall; rainforest.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

Fig. 1.

Distribution of land cover based on Moderate-Resolution Imaging Spectroradiometer observations from 2009. The southern Amazon (5°S to 15°S, 50°W to 70°W) is indicated by the solid white box.

Fig. 2.

Fig. 2.

Onset-relative low-pass filtered composites of area mean precipitation from Tropical Rainfall Measuring Mission (TRMM), ET and MFC from ERA-Interim, and net absorbed surface radiation from Clouds and the Earth’s Radiant Energy System (CERES) Synoptic Radiative Fluxes and Clouds (SYN1Deg) (A); vertical distributions of RH (shading) and time rates of change in equivalent potential temperature (∂θe/∂t; contour interval 0.02 K d−1) computed from Atmospheric Infrared Sounder (AIRS) observations (B); surface air temperature and column water vapor (CWV) from AIRS (C); low (<700 hPa; ∼3 km above sea level), midlow (700–500 hPa; ∼3–5.5 km), midhigh (500–300 hPa; ∼5.5–10 km), high (>300 hPa; ∼10 km), and total cloud cover from CERES SYN1deg (D); solar-induced chlorophyll fluorescence (SIF) for rainforests from the Global Ozone Monitoring Instrument 2 and fire emissions of CO2 from Version 3.1 of the Global Fire Emissions Database (E); conditional instability in the lower–middle troposphere (θe850−θe500) based on AIRS and best-fit linear slopes of δD against specific humidity (q) in the free troposphere based on TES (F). Shaded areas in A and C_–_F and error bars in A and E illustrate estimated uncertainties. Data sources, quality control criteria, and uncertainty calculations are provided in SI Text.

Fig. S1.

Fig. S1.

Onset-relative time series of precipitation from TRMM and ERA-Interim (A), net fluxes of shortwave (downward) and longwave (upward) radiation at the surface from ERA-Interim and CERES SYN1Deg (B), cloud fraction from ERA-Interim (C), and upward sensible and latent heat fluxes from ERA-Interim (D). All fluxes represent area-weighted averages over the southern Amazon region during the period 2005–2011. Uncertainties represent interannual variability across six dry-to-wet transition seasons.

Fig. S2.

Fig. S2.

Onset-relative time series of cloud radiative effect on net downward shortwave and longwave radiation at the surface (A), cloud radiative heating due to shortwave and longwave radiation between the surface and 500 hPa (assuming a mean surface pressure of 1,000 hPa and a dry atmosphere in hydrostatic balance) (B), and aerosol radiative effect on net downward shortwave and longwave radiation at the surface (C). All variables are calculated from CERES SYN1Deg data. Uncertainties represent interannual variability across six dry-to-wet transition seasons.

Fig. S3.

Fig. S3.

Onset-relative composite time series of area-weighted 5-d mean CAPE (red lines) and CINE (blue lines) calculated from daily 1∘×1∘ AIRS profiles of temperature and water vapor during ascending (daytime) (A) and descending (nighttime) (B) orbits. Larger values of CAPE indicate greater convective instability, whereas larger values of CINE indicate a larger energetic barrier to convection. Note different ranges of the CINE axis between A and B. Mean evolution of daytime values are shown in B to facilitate comparison. Uncertainties represent interannual variability across six dry-to-wet transition seasons.

Fig. S4.

Fig. S4.

Onset-relative time series of SIF from GOME-2 observations (A) and EVI calculated from MODIS observations (B). Two variations of SIF are shown, both covering the time period 2007–2014. The raw SIF data (solid line) are identical to those shown in Fig. 2_E_. SIF normalized by the cosine of the solar zenith angle (dashed line) is also shown for context and comparison with EVI. Uncertainty windows in both SIF estimates reflect interannual variability quantified as SDs across seven dry-to-wet transition seasons. The EVI data cover the time period 2005–2011, consistent with all non-SIF data shown in Fig. 2. Again, two variations are shown, one based on strict quality control standards (dashed) and one based on minimum quality control standards (solid; see SI Text, Vegetation Metrics and Fire Emissions for details). On average, late dry season increases in EVI lag late dry season increases in SIF by 10 ∼ 15 d.

Fig. S5.

Fig. S5.

Onset-relative composite time series of temperature (red shading) and moisture (blue shading) contributions to the time rate of change in θe (thick gray line) at 850 hPa based on AIRS observations. Temperature and moisture contributions are calculated following Li and Fu (19).

Fig. S6.

Fig. S6.

Annual variability of monthly mean δD in precipitation according to samples collected at Porto Velho (8.77°S, 63.92°W) and Manaus (3.12°S, 60.02°W) under the Global Network for Isotopes in Precipitation (GNIP) measurement program. Data at Porto Velho were collected sporadically between 1965 and 1981, with the sample sizes ranging from four (July and August) to nine (November). Data at Manaus were collected sporadically between 1965 and 1990, with sample sizes ranging from 10 (September) to 17 (December). Effectively zero fractionation occurs during rainforest transpiration (34). δD in transpired water vapor should therefore approximately match δD in soil water; lacking direct observations, we estimate this as the precipitation-weighted mean δD in rainfall. The precipitation-weighted mean δD based on these data are approximately −38‰ at Porto Velho and −26‰ at Manaus. Previous studies suggest that this should represent a lower bound on δD in soil water, because the isotopic content of bound water accessed by tree roots in ecosystems with clearly delineated dry and wet seasons is weighted toward that of rainfall during the dry and transition seasons, when rain rates are lower and runoff fractions are smaller (70). GNIP data can be accessed at

www.iaea.org/water

.

Fig. 3.

Fig. 3.

Joint distributions of specific humidity (q) and the deuterium content of water vapor (δD) in the lower troposphere (825–600 hPa) based on TES observations during the pretransition stage (day −90 to −60) (A), early transition (day −60 to −30) (B), late transition (day −30 to 0) (C), and the first 3 mo of the wet season (day 0 to +90) (D). The joint behaviors of q and δD under three types of idealized vertical mixing are also shown. Solid green and blue lines represent mixing (no condensation) between four dry air masses representing the dry season free troposphere and a moist air mass representing either local ET (green; q = 20 g kg−1; δD = −30‰) or ocean evaporation (blue; q = 20 g kg−1; δD = −80‰). Dashed green and blue lines represent pseudoadiabatic Rayleigh distillation from the approximate top of the ABL, in which condensation occurs in a rising air parcel and is immediately removed as precipitation. Dotted green and blue lines represent reversible moist adiabatic ascent from the approximate top of the ABL, in which condensation occurs and is assumed to remain in the parcel. These idealized models are described in detail in SI Text.

Fig. 4.

Fig. 4.

Distribution of specific humidity (Left) and δD (Right) in the free troposphere based on TES observations during the pretransition (day −90 to −60) (A), early transition (day −60 to −30) (B), late transition (day −30 to 0) (C), and early wet season (day 0 to +90) (D). Winds at 850 hPa (Left) and vertically integrated MFC (Right) based on ERA-Interim are also shown for each stage of the transition. The contour interval for MFC is 1 kg m−2 d−1.

Fig. 5.

Fig. 5.

Changes in the vertical profiles of RH (ΔRH) associated with shallow convection under clean conditions [Cloud condensation number concentrations (CCN) ≤500 cm−3], moderate aerosol pollution (500 cm−3 < CCN ≤ 1,000 cm−3), and heavy aerosol pollution (CCN >1,000 cm−3). CCN, RH profiles, and convective occurrence are based on observations collected during the Green Ocean Amazon (GOAmazon) field campaign (SI Text). ΔRH is reported in absolute differences.

Fig. S7.

Fig. S7.

Full onset-relative annual cycle of precipitation from TRMM, ET, and moisture flux convergence from ERA-Interim, and net (downward) surface radiation flux at the surface from CERES SYN1Deg (A), surface air temperature and CWV from AIRS (B), and total and vertically-resolved cloud fraction from CERES SYN1Deg (C). Data from individual years are shown without low-pass filtering to illustrate the scale of the unfiltered variability.

Fig. S8.

Fig. S8.

Onset-relative composite time series of area-mean δD of water vapor in the free troposphere (750 to 348 hPa) (A) and the ABL (surface–825 hPa) (B) based on TES satellite retrievals from ascending (∼13:30 local time) and descending (∼01:30 local time) satellite overpasses. Data in B are shown for ascending overpasses only because TES is not sufficiently sensitive to ABL δD during descending overpasses (see also Materials and Methods and Fig. S9). The evolution of ABL δD over the tropical Atlantic (0–10°N, 30–55°W) is also shown for reference. Uncertainty bounds include statistical and measurement uncertainties propagated from individual TES measurements.

Fig. S9.

Fig. S9.

Mean averaging kernels for TES observations of water vapor and HDO vapor from ascending orbits (∼13:30 local time) over the southern Amazon (Left) and the tropical Atlantic (Right) during October 2006, representative of the late dry season in the southern Amazon. Diamonds indicate pressure levels in the TES retrieval vertical grid. Averaging kernel rows for levels between the surface and 800 hPa (red lines) contribute to estimates of δD in the boundary layer (Fig. S8_B_); averaging kernel rows for levels between 800 hPa and 350 hPa contribute to estimates of δD in the free troposphere (Fig. S8_A_). The averaging kernel for descending orbits (∼01:30 local time) over the southern Amazon is similar for pressures <700 hPa, but sensitivity in the boundary layer is substantially reduced (the peak sensitivities of averaging kernel rows for pressures >800 hPa shift upward). We therefore do not consider retrievals of ABL δD (Fig. S8_B_) collected during descending passes (∼01:30 local time).

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