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and in the Amazon

Usama Anbera,b,1, Pierre Gentinec,1, Shuguang Wangd, and Adam H. Sobela,b,d

aLamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964; bDepartment of Earth and Environmental Sciences, Columbia University, New York, NY 10027; cDepartment of Earth and Environmental Engineering and Earth Institute, Columbia University, New York, NY 10027; and dDepartment of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10027

Edited by Kerry A. Emanuel, Massachusetts Institute of Technology, Cambridge, MA, and approved August 12, 2015 (received for review March 12, 2015) The diurnal and seasonal cycles in the Amazon remain Results and Discussion poorly simulated in general circulation models, exhibiting peak We use the Research and Forecasting (WRF) model at in the wrong and rain too early in the 2-km horizontal grid spacing (see Methods for details of the day. We show that those biases are not present in -resolving runs). This resolution has been shown to be sufficient to resolve simulations with parameterized large-scale circulation. The differ- deep (no convective parameterization is used) and to ence is attributed to the representation of the morning fog layer, correctly represent the sign and magnitude of the land−atmo- and to more accurate characterization of convection and its coupling sphere interaction feedback (18), contrary to GCMs, which tend with large-scale circulation. The morning fog layer, present in the wet to exhibit soil moisture− feedbacks of opposite season but absent in the , dramatically increases cloud sign to that observed (19). We parameterize the time-dependent albedo, which reduces evapotranspiration through its modulation of large-scale vertical motion as a function of internally resolved the surface energy budget. These results highlight the importance of convection using the weak gradient (WTG) ap- the coupling between the energy and hydrological cycles and the key proximation (Methods). The WTG approach has been used in role of cloud albedo feedback for over tropical continents. many previous studies of tropical atmospheric dynamics to rep- resent the feedback between locally resolved convection and land− interactions | Amazon | hydrologic cycle | fog | larger-scale circulation (Methods) (10, 11, 20–23). Under WTG, cloud-resolving models we diagnose the horizontal average large-scale vertical motion (Fig. S1) so as to induce a vertical advective potential temper- ropical forests, and the Amazon in particular, are the biggest ature tendency, which relaxes the model’s domain-averaged po- Tterrestrial CO2 sinks on the planet, accounting for about 30% tential temperature toward that of the target profile. The same of the total net primary productivity in terrestrial ecosystems. large-scale vertical motion is then also used for vertical Hence, the of the Amazon is of particular importance for of moisture (Methods). the fate of global CO2 concentration in the atmosphere (1). The target profiles we use, representa- Besides the difficulty of estimating carbon pools (1–3), our in- tive of the wetter part of the Amazon, are taken from the 2014 capacity to correctly predict CO2 in the continental Atmospheric Radiation Measurement mobile facility located at largely results from inaccurate simulation of the tropical climate Manacapuru, near Manaus, Brazil (3°12’46.70”S, 60°35’53.0”W). (1, 2, 4, 5). More frequent and more intense droughts in par- The profiles are averaged over the month of February for the wet ticular are expected to affect the future health of the Amazon season, and September for the dry season, as shown in Fig. 1A. and its capacity to act as a major carbon sink (6–8). The land At the surface (where WTG is not directly applied; see Methods), surface is not isolated, however, but interacts with the weather the dry profile is warmer by about 5 K, reflecting the influence of and climate through a series of land−atmosphere feedback loops, higher surface heat fluxes (Fig. 1B). In the midtroposphere, the which couple the energy, carbon, and water cycles through stomata wet profile is warmer by more than 1 K. In the stratosphere, the regulation and boundary layer mediation (9). wet profile is colder by more than 5 K due to the seasonal ele- Current General Circulation Models (GCMs) fail to correctly vation of the . All these differences are consequences

represent some of the key features of the Amazon climate. In EARTH, ATMOSPHERIC, particular, they (i) underestimate the precipitation in the region Significance AND PLANETARY SCIENCES (10, 11), (ii) do not reproduce the seasonality of either pre- cipitation (10, 11) or surface fluxes such as evapotranspiration We here demonstrate that we can resolve the seasonality of (12), and (iii) produce errors in the diurnal cycle and intensity of the hydrologic cycle in the Amazon using an approach, oppo- precipitation, with a tendency to rain too little and too early in site to general circulation models, in which we resolve con- the day (13, 14). In the more humid Western part of the basin, vection and parameterize large-scale circulation as a function SCIENCES surface incoming radiation, evapotranspiration, and photosyn- of the resolved convection. The results emphasize the key role – ENVIRONMENTAL thesis all tend to peak in the dry season (15 17), whereas GCMs of cloud albedo feedback and, in particular, of the morning fog simulate peaks of those fluxes in the (10, 11). Those layer in determining the diurnal course of surface heat fluxes issues might be related to the representation of convection (1, 2, and seasonality of the surface and atmospheric heat and water – – 4, 5, 13, 14) and vegetation water stress (6 8, 15 17) in GCMs. cycles. These results indicate that our understanding of tropical We here show that we can represent the Amazonian climate climates over land can be considerably advanced by using using a strategy opposite to GCMs in which we resolve convec- coupled land−atmosphere models with explicit convection and tion and parameterize the large-scale circulation (Methods). The parameterized large-scale dynamics. simulations lack many of the biases observed in GCMs and more accurately capture the differences between the dry and wet Author contributions: U.A. and P.G. designed research; U.A. and P.G. performed research; season of the Amazon in surface heat fluxes and precipitation. U.A., P.G., and S.W. analyzed data; and U.A., P.G., S.W., and A.H.S. wrote the paper. Besides top-of-the-atmosphere insolation, the simulations require The authors declare no conflict of interest. the monthly mean temperature profile as an input. We demon- This article is a PNAS Direct Submission. strate that this profile, whose seasonal cycle itself is a product Freely available online through the PNAS open access option. − − of the coupled ocean land atmosphere dynamics, mediates the 1To whom correspondence may be addressed. Email: [email protected] or uanber@ seasonality of the Amazonian climate by modulating the vertical ldeo.columbia.edu. structure of the large-scale circulation in such a way that This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. energy is less effectively ventilated in the rainy season. 1073/pnas.1505077112/-/DCSupplemental.

www.pnas.org/cgi/doi/10.1073/pnas.1505077112 PNAS | September 15, 2015 | vol. 112 | no. 37 | 11473–11477 Downloaded by guest on September 28, 2021 θ θ −θ evening, as observed, but contrary to what GCMs predict as ABwet dry 25 mentioned above. The smaller during daytime drives dry greater surface fluxes, and, in particular, evapotranspiration and wet 50 photosynthesis, by allowing more solar radiation to reach the surface. 20 In the wet season (Fig. 3B), the diurnal cycle of cloud cover exhibits a different behavior. In addition to the increase in cloud 100 cover, there is now a distinct layer of fog above the surface that is 15 maintained by radiative cooling from the wet surface and lasts from midnight until noon, as observed in the Amazon (Figs. S3 200 and S4) and other rainforests. This morning fog layer blocks hPa Z (km) 10 radiation from reaching the surface and reduces daytime net surface shortwave radiation and evapotranspiration. Such 400 morning fog was also shown to be critical in the maintenance of 5 distinct diurnal climate equilibrium regimes by a previous mod- 600 eling study (28, 29). 800 As a result, the (Fig. 4A) and surface tem- 0 1000 perature are smaller in the wet season than in the dry season, 250 300 350 400 450 500 −6 −4 −2 0 2 because surface shortwave radiation is reduced by the fog layer K K (Fig. 4B), which more than compensates for the higher soil water Fig. 1. (A) Monthly mean observed atmospheric potential temperature availability and lower Bowen ratio in the wet season (from di- profile in the wet (February) and dry (September) season observed at urnal average of 0.4 in the dry season to 0.32 in the wet season Manacapuru, near Manaus, Brazil (3°12’46.70”S, 60°35’53.0”W) using ra- based on eddy covariance data). We note that surface observa- diosonde and used as reference profiles for the WTG method. (B) Atmo- tions are difficult to correctly measure in the presence of dew spheric potential temperature difference between the wet and dry . (eddy covariance measurements cannot correctly record mea- surements) so that most days with fog are not captured by sur- face observations; thus clear days (infrequent in the wet season; of the seasonal cycle itself; the warmer troposphere and cooler Figs. S3 and S4) with higher latent heat flux are overrepresented lower stratosphere are typical differences between states of stronger in the observations. Because the diurnal rise of the surface tur- and weaker deep convection (24). Here the ultimate cause of the bulent fluxes is delayed in the wet season by the fog layer, the − wet dry season temperature difference is presumably the stronger transition from shallow to deep convection occurs later in the insolation in the wet season, but the temperature differences are day compared with the dry season. − in part an outcome of land atmosphere interactions, as the Although the downward solar insolation at the top of the at- tropospheric temperature difference between the two seasons in mosphere in the wet season exceeds that in the dry season by − the nearby oceanic regions shows no such signal (Fig. S2). Be- more than 20 W·m 2, the net shortwave radiation at the surface cause we assume this seasonal difference in temperature profiles, is greater in the dry season (Fig. 4B). This results from a strong our simulations do not predict the seasonal changes purely as a negative cloud albedo feedback in the wet season and greater function of external boundary conditions and forcing. Rather, we reflection of shortwave radiation to space. The fog layer is an — predict part of the solution including precipitation, , and important contributor to this albedo. We further quantify the — surface fluxes given another, the free atmospheric temperature. cloud albedo feedback by computing cloud radiative forcing This allows us to understand the mediation of convection and (CRF), as the clear-sky minus all-sky upwelling flux at the top of − land atmosphere interaction by the atmospheric temperature, the atmosphere (Fig. 5). Weaker convection in the dry season similar to what has been done in studies of tropical and induces a smaller longwave CRF component compared with that the Madden−Julian oscillation (24, 25). The increase in precipitation in the wet season relative to the dry season (Fig. 2) can be attributed to the reduction in the export of moist static energy by the large-scale circulation (see Supporting Information), which is a consequence of the more stable temperature profile (25), despite the absence of an in- crease in surface fluxes or radiative heating. This increase, in turn, moistens the land surface, an important factor leading to the differences in cloud between the wet and dry seasons. In agreement with observations [here Climate Prediction Center Morphing Technique (CMORPH) (26, 27) data averaged over 10 y (2004−2014) for the months of February and September for the wet and dry seasons, respectively], our simulated precipitation maxima occur in the early to late afternoon in both seasons, with greater absolute amplitude in the wet season. In both seasons, rapid precipitation transitions from minima in the morning local hours to maxima in the afternoon are associ- ated with transitions from shallow to deep convection (Fig. 3). In the dry season (Fig. 3A), convection starts in the morning (1000 hours time zone local time) manifesting first as a sharp increase in midlevel cloud at that time coverage, and then a more gradual transition to deep convection in the afternoon associated with the timing of rainfall maxima. Midlevel cloud cover dissipates Fig. 2. Diurnal cycle of modeled (thick line) and observed (thin line) pre- overnight into the early morning. The overall cloud cover is cipitation from CMORPH along with one-third of the SD (shaded area) in the relatively small during daytime and higher in the afternoon and local time zone.

11474 | www.pnas.org/cgi/doi/10.1073/pnas.1505077112 Anber et al. Downloaded by guest on September 28, 2021 always higher in the case of the dry season profile, leading to higher evapotranspiration flux (Fig. S9). This behavior differs from convection over oceans, where stronger seasonal insolation leads to higher surface fluxes (and ), and cloud albedo is not so tightly coupled to atmospheric convection because, unlike land, the ocean can both substantially store and transport heat reducing the coupling of evaporation with short- wave incoming radiation. Methods Model Configuration. We use the WRF model version 3.3, in three spatial dimensions, with doubly periodic lateral boundary conditions. The experi- ments are conducted with Coriolis parameter f = 0. The horizontal domain size is 192 × 192 km2 with a grid spacing of 2 km. There are 50 vertical levels total, with the top level at 22 km, and 10 levels in the lowest 1 km. Gravity waves propagating vertically are absorbed in the top 5 km to prevent unphysical wave reflection off the top boundary by using the implicit damping vertical velocity scheme (32). The 2D Smagorinsky first-order tur- bulent closure scheme is used to parameterize the horizontal transports by subgrid eddies. The Yonsei University first-order closure scheme is used to parameterize nonlocal boundary layer turbulence and vertical subgrid-scale eddy diffusion (33). The surface fluxes of moisture and heat are parame- terized following Monin−Obukhov similarity theory. Microphysics is simu- lated using the Purdue−Lin bulk scheme (34), which has six species: , cloud water, cloud ice, rain, , and . Radiative fluxes are determined interactively using the National Center for Atmospheric Re- search Community Atmosphere Model version 3.0 scheme for shortwave and longwave radiation. Both surface and radiative fluxes are fully interactive. The is coupled to the Noah land surface model (LSM) (35) that has four soil layers at 10, 30, 60, and 100 cm depth. The LSM pro- vides four quantities to the atmospheric model: sensible heat flux, latent

Fig. 3. Composite of the WRF simulated 2-d cycle of fractional cloud cover for the (A) wet and (B) dry season. Note the fog layer above the surface in the wet season.

induced in the wet season. However, the shortwave CRF in the EARTH, ATMOSPHERIC,

wet season is much more negative than in the dry season due to AND PLANETARY SCIENCES the presence of the fog layer. Because the shortwave CRF dominates in the wet season, and longwave CRF dominates in the dry season, the net CRF is negative in the wet season and positive in the dry. Comparison with CRF obtained from the Clouds and the Earth’s Radiant Energy System (CERES) [the

CERES SYN1deg daily radiative fluxes (30, 31)] shows a reasonable SCIENCES

agreement with our simulations, although some biases exist. ENVIRONMENTAL Perhaps surprisingly, the seasonal difference in the top of the atmosphere seasonal mean insolation is not the dominant con- trol that determines the differences in seasonal climate, even though it is ultimately what controls the seasonal cycle in nature. In our simulations, the differences in the target potential tem- perature profiles are primarily responsible for the seasonal dif- ferences described above. We performed sensitivity experiments in which the insolation from the wet season is used with the target potential temperature profile from the dry season and vice versa (see Figs. S5−S7). We also performed sensitivity experi- Fig. 4. Diurnal cycle of (A) latent heat flux, and (B) net shortwave at the ments over Rondonia (10.7°S, 62.7°W), where the seasonal dif- surface, for WRF modeled (thick) and observed (thin) fluxes. Observed fluxes ference in insolation is greater (Figs. S8−S10). When insolation are taken from the climatology of eddy covariance fluxes observed at K34 station in Reserva Biológica do Cuieiras. We note that surface observations is varied while holding the potential temperature profile fixed, are difficult to obtain in the presence of dew (eddy covariance measure- no significant difference is found in terms of the typical pattern ments typically cannot correctly record measurements) so that the obser- of diurnal and seasonal patterns of precipitation. Again, the vations typically omit fog situations, with an overrepresentation of relatively cloud albedo adjusts so that the surface shortwave radiation is clear days compared with fog days.

Anber et al. PNAS | September 15, 2015 | vol. 112 | no. 37 | 11475 Downloaded by guest on September 28, 2021 Cloud Radiative Forcing Initial conditions are seasonally averaged quantities in the dry and wet seasons using the ERA-Interim data set. The model is run for about 40 d and 100 the analysis conducted on the equilibrium part only, which is the last 30 d. We have performed a sensitivity test on the soil moisture initial conditions, by switching the seasonal magnitudes, and they had no influence on the 50 results. The reference, target temperature profile is time-independent in each season and does not include diurnal variations (21). The modeled di- urnal cycles of surface fluxes, clouds, and precipitation are driven solely by the diurnal cycle of radiation at the top of the atmosphere. Daily mean in- 0 solation in the dry season is held fixed at 415 W·m−2 and, in the wet season,

2 − at 439 W·m 2, reflecting values at Manacapuru. W/m −50 Surface Observations. We used eddy covariance data from the K34 station located in Estação Experimental de Silvicultura Tropical 02°37’S, 60°09’W, located around 60 km from Manaus. We have used data from 2000 to 2006 available at Oak Ridge National Laboratory (36). We used quality-controlled −100 eddy covariance data, based on outliers comparison, speed (acceptable variation is two SD units from the linear regression), and unresponsive sensor checks. Eddy covariance measurements typically cannot correctly measure in the presence of dew; thus fog conditions are undersampled, which implies −150 SWCRF NETCRF LWCRF that the latent heat flux in Fig. 4 is an overestimate of the true latent heat flux (an average of fog conditions with little radiation and nonfog condi- tions with higher radiation). Fig. 5. Cloud radiative forcing, shortwave (SWCRF), longwave (LWCRF), and net (NETCRF) for the wet and dry seasons as simulated (squares) and ob- Conclusion served from CERES (circles). We have shown that, when given the top of the atmosphere in- solation and the monthly mean free-atmospheric temperature heat flux, upward longwave radiation, and upward shortwave radiation off profile, the seasonal and diurnal cycles of cloud, precipitation, the ground. The LSM prognostic land states are surface skin temperature, and surface fluxes can be simulated by a limited domain cloud- volumetric total (liquid and frozen) soil moisture at each layer, soil tem- resolving model with parameterized large-scale forcing without perature at each layer, and canopy water content. Vegetation type is ever- the biases commonly found in global climate models. green forest, surface albedo is 0.12, and the wind field is left unnudged. We can view the seasonality in the Amazon as mediated by the atmospheric temperature profile. That profile itself results from Parameterized Large-Scale Circulation and Initial Conditions. The large-scale vertical velocity is dynamically parameterized using the WTG method (20, 21). the stronger convection in the wet season, but the increased wet season precipitation in the model is still a nontrivial prediction The WTG vertical velocity WWTG is obtained by a Newtonian relaxation with a relaxation time scale τ (over which gravity waves propagate out of the of the model. It can be explained by the reduced ventilation of domain) taken here as 2 h (20, 21), moist static energy from the column, which is a consequence of the warmer tropospheric temperature profile in the wet season. θ − θ 1  0 The surface fluxes, on the other hand, are strongly controlled WWTGðzÞ = [1] τ ∂θ ∂z by the diurnal cycle of cloud albedo, and especially of wet season θ θ fog, which blocks shortwave radiation from reaching the surface where is the domain mean potential temperature, and 0 is the observed in the early morning. This fog is an essential regulator of the potential temperature profile obtained from averaged over 1 mo Amazon climate. of observations. WWTG is then linearly interpolated in the boundary layer to zero at the surface because gravity waves are not the main mode of buoy- We demonstrate that a high-resolution cloud-resolving model ancy adjustment in the boundary layer. The impact is negligible in the with parameterized large-scale circulation offers a new window daytime boundary layer as it is well-mixed so that the vertical gradients are onto the dynamics of climate in the Amazon. In the future, this null. Boundary layer height is determined interactively in the WRF model approach may allow new insights into the Amazon’s changes using the method and varies diurnally. under anthropogenic influence and the capacity of the basin to Once WWTG is obtained, it is used to define the domain-average large- act as a CO2 sink in the future. scale tendencies of potential temperature and specific over the computational domain, ACKNOWLEDGMENTS. P.G. thanks Florian Cochard for preliminary radio- sonde and profile analysis. P.G. was supported by Department of Energy ∂θ θ − θ Grants GoAmazon DE-SC0011094 and DE-FOA-0000885 on the transition = 0 , [2] between shallow and deep convection. U.A., S.W., and A.H.S. acknowledge ∂t τ Large Scale support from the National Science Foundation under Grant AGS-1008847 and the Office of Naval Research under MURI Grant (N00014-12-1-0911). ∂ ∂ q = − q Weather Research and Forecasting experiments were performed on the YETI ∂ WWTG ∂ , [3] t Large Scale z cluster at Columbia University. Data profiles at the Atmospheric Radiation Measurement site were obtained at www.arm.gov/sites/amf/mao/. Surface respectively, where q is the domain mean water vapor mixing ratio. flux observations were obtained at dx.doi.org/10.3334/ORNLDAAC/1174.

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