ALBERT

Description: In Amazon forests, the relative contributions of climate, phenology, and disturbance to net ecosystem exchange of carbon (NEE) are not well understood. To partition influences across various timescales, we use a statistical model to represent eddy-covariance-derived NEE in an evergreen eastern Amazon forest as a constant response to changing meteorology and phenology throughout a decade. Our best fit model represented hourly NEE variations as changes due to sunlight, while seasonal variations arose from phenology influencing photosynthesis and from rainfall influencing ecosystem respiration, where phenology was asynchronous with dry-season onset. We compared annual model residuals with biometric forest surveys to estimate impacts of drought disturbance. We found that our simple model represented hourly and monthly variations in NEE well (R2=0.81 and 0.59, respectively). Modeled phenology explained 1 % of hourly and 26 % of monthly variations in observed NEE, whereas the remaining modeled variability was due to changes in meteorology. We did not find evidence to support the common assumption that the forest phenology was seasonally light- or water-triggered. Our model simulated annual NEE well, with the exception of 2002, the first year of our data record, which contained 1.2 MgC ha−1 of residual net emissions, because photosynthesis was anomalously low. Because a severe drought occurred in 1998, we hypothesized that this drought caused a persistent, multi-year depression of photosynthesis. Our results suggest drought can have lasting impacts on photosynthesis, possibly via partial damage to still-living trees.

Description: Canopy and aerodynamic conductances (gC and gA) are two of the key land surface biophysical variables that control the land surface response of land surface schemes in climate models. Their representation is crucial for predicting transpiration (λET) and evaporation (λEE) flux components of the terrestrial latent heat flux (λE), which has important implications for global climate change and water resource management. By physical integration of radiometric surface temperature (TR) into an integrated framework of the Penman–Monteith and Shuttleworth–Wallace models, we present a novel approach to directly quantify the canopy-scale biophysical controls on λET and λEE over multiple plant functional types (PFTs) in the Amazon Basin. Combining data from six LBA (Large-scale Biosphere-Atmosphere Experiment in Amazonia) eddy covariance tower sites and a TR-driven physically based modeling approach, we identified the canopy-scale feedback-response mechanism between gC, λET, and atmospheric vapor pressure deficit (DA), without using any leaf-scale empirical parameterizations for the modeling. The TR-based model shows minor biophysical control on λET during the wet (rainy) seasons where λET becomes predominantly radiation driven and net radiation (RN) determines 75 to 80 % of the variances of λET. However, biophysical control on λET is dramatically increased during the dry seasons, and particularly the 2005 drought year, explaining 50 to 65 % of the variances of λET, and indicates λET to be substantially soil moisture driven during the rainfall deficit phase. Despite substantial differences in gA between forests and pastures, very similar canopy–atmosphere "coupling" was found in these two biomes due to soil moisture-induced decrease in gC in the pasture. This revealed the pragmatic aspect of the TR-driven model behavior that exhibits a high sensitivity of gC to per unit change in wetness as opposed to gA that is marginally sensitive to surface wetness variability. Our results reveal the occurrence of a significant hysteresis between λET and gC during the dry season for the pasture sites, which is attributed to relatively low soil water availability as compared to the rainforests, likely due to differences in rooting depth between the two systems. Evaporation was significantly influenced by gA for all the PFTs and across all wetness conditions. Our analytical framework logically captures the responses of gC and gA to changes in atmospheric radiation, DA, and surface radiometric temperature, and thus appears to be promising for the improvement of existing land–surface–atmosphere exchange parameterizations across a range of spatial scales.

Description: The cooling trend of the Neogene resulted in the diachronous development of glacial conditions, southern high latitudes glaciating before northern. This cooling culminated in the early Pleistocene (2.54 Ma) onset of major glaciation, during which large areas of high latitude land and sea in both hemispheres were periodically blanketed with ice. However, this onset and its impact at low latitude abyssal depths are elusive. This paper examines the abyssal benthic foraminiferal commun ity in the Atlantic Ocean near the Northern Equatorial Countercurrent, in ODP Hole 926A (Ceara Rise), off the River Amazon. Using an assemblage turnover index (ATI), a related conditioned-on-boundary index (CoBI) and SHE analysis, we show how a change in mean assemblage turnover coincides with the early Pleistocene onset of northern hemisphere major glaciation. The community comprises primarily phytodetritivores (Alabaminenella weddellensis, Epistominella exigua and Globocassidulina subglobosa), the proportional abundances of which differ before and after 2.54 Ma. Coupled with changes in the abundances of Nuttallides umbonifera, Bulimina spp. and Uvigerina spp., alterations in the abundances of the phytodetritivores after 2.54 Ma show that the adjustment in biotic turnover at depth resulted from enhanced organic carbon flux from sea surface water. This was probably related to forced southward repositioning of the inter-tropical convergence zone (ITCZ) by the growth of the northern hemisphere ice caps. This was coupled with an increase in bottom current velocity, as shown by a change in the abundance of Cibicidoides wuellerstorfi.

Description: The Ecosystem Demography model version 2.2 (ED-2.2) is a terrestrial biosphere model that simulates the biophysical, ecological, and biogeochemical dynamics of vertically and horizontally heterogeneous terrestrial ecosystems. In a companion paper (Longo et al., 2019a), we described how the model solves the energy, water, and carbon cycles, and verified the high degree of conservation of these properties in long-term simulations that include long-term (multi-decadal) vegetation dynamics. Here, we present a detailed assessment of the model's ability to represent multiple processes associated with the biophysical and biogeochemical cycles in Amazon forests. We use multiple measurements from eddy covariance towers, forest inventory plots, and regional remote-sensing products to assess the model's ability to represent biophysical, physiological, and ecological processes at multiple timescales, ranging from subdaily to century long. The ED-2.2 model accurately describes the vertical distribution of light, water fluxes, and the storage of water, energy, and carbon in the canopy air space, the regional distribution of biomass in tropical South America, and the variability of biomass as a function of environmental drivers. In addition, ED-2.2 qualitatively captures several emergent properties of the ecosystem found in observations, specifically observed relationships between aboveground biomass, mortality rates, and wood density; however, the slopes of these relationships were not accurately captured. We also identified several limitations, including the model's tendency to overestimate the magnitude and seasonality of heterotrophic respiration and to overestimate growth rates in a nutrient-poor tropical site. The evaluation presented here highlights the potential of incorporating structural and functional heterogeneity within biomes in Earth system models (ESMs) and to realistically represent their impacts on energy, water, and carbon cycles. We also identify several priorities for further model development.

Description: Estimates of CO2 fluxes that are based on atmospheric data rely upon a meteorological model to simulate atmospheric CO2 transport. These models provide a quantitative link between surface fluxes of CO2 and atmospheric measurements taken downwind. Therefore, any errors in the meteorological model can propagate into atmospheric CO2 transport and ultimately bias the estimated CO2 fluxes. These errors, however, have traditionally been difficult to characterize. To examine the effects of CO2 transport errors on estimated CO2 fluxes, we use a global meteorological model-data assimilation system known as "CAM–LETKF" to quantify two aspects of the transport errors: error variances (standard deviations) and temporal error correlations. Furthermore, we develop two case studies. In the first case study, we examine the extent to which CO2 transport uncertainties can bias CO2 flux estimates. In particular, we use a common flux estimate known as CarbonTracker to discover the minimum hypothetical bias that can be detected above the CO2 transport uncertainties. In the second case study, we then investigate which meteorological conditions may contribute to month-long biases in modeled atmospheric transport. We estimate 6 hourly CO2 transport uncertainties in the model surface layer that range from 0.15 to 9.6 ppm (standard deviation), depending on location, and we estimate an average error decorrelation time of ∼2.3 days at existing CO2 observation sites. As a consequence of these uncertainties, we find that CarbonTracker CO2 fluxes would need to be biased by at least 29%, on average, before that bias were detectable at existing non-marine atmospheric CO2 observation sites. Furthermore, we find that persistent, bias-type errors in atmospheric transport are associated with consistent low net radiation, low energy boundary layer conditions. The meteorological model is not necessarily more uncertain in these conditions. Rather, the extent to which meteorological uncertainties manifest as persistent atmospheric transport biases appears to depend, at least in part, on the energy and stability of the boundary layer. Existing CO2 flux studies may be more likely to estimate inaccurate regional fluxes under those conditions.

Description: In Amazon forests, the relative contributions of climate, phenology, and disturbance to net ecosystem exchange of carbon (NEE) are not well understood. To partition influences across various timescales, we use a statistical model to represent eddy covariance-derived NEE in an evergreen Eastern Amazon forest as a constant response to changing meteorology and phenology throughout a decade. Our best fit model represented hourly NEE variations as changes due to sunlight, while seasonal variations arose from phenology influencing photosynthesis and from rainfall influencing ecosystem respiration, where phenology was asynchronous with dry season onset. We compared annual model residuals with biometric forest surveys to estimate impacts of drought-disturbance. We found that our simple model represented hourly and monthly variations in NEE well (R2=0.81, 0.59 respectively). Our model also simulated annual NEE well, with exception to 2002, the first year of our data record, which contained 1.2MgCha−1 of residual net emissions, because photosynthesis was anomalously low. Because a severe drought occurred in 1998, we hypothesized that this drought caused a persistent, multi-year depression of photosynthesis. We did not find evidence to support the common assumption that droughts or disturbances affected this region during 2005 or 2010, nor that the forest phenology was seasonally light- or water-triggered. Our results suggest drought can have lasting impacts on photosynthesis, possibly via partial damage to still-living trees.

Description: The Ecosystem Demography Model version 2.2 (ED-2.2) is a terrestrial biosphere model that simulates the biophysical and biogeochemical cycles of dynamic ecosystems while considering the role of vertical structure of plant communities and the heterogeneity of such structures across the landscape. In a companion paper, we described in detail how the model solves the energy, water, and carbon cycles, and verified the excellent conservation of such properties in long-term simulation. Here, we present a thorough assessment of the model's ability to represent multiple processes associated with the biophysical and biogeochemical cycles, with focus on the Amazon forest. We used multiple measurements from eddy covariance towers, forest inventory plots and regional remote-sensing products to assess the model's ability to represent biophysical, physiological, and ecological processes at multiple time scales ranging from sub-daily to century-long. The ED-2.2 model accurately describes the vertical distribution of light, water fluxes and the storage of water, energy and carbon in the canopy air space, the regional distribution of biomass in tropical South America, and the variability of biomass as a function of environmental drivers. In addition, ED-2.2 also simulates emerging properties of the ecosystem found in observations, such as the relationship between biomass and mortality rates and wood density, although the relationships predicted by the model were biased. We also identified some of the model limitations, such as the model's tendency to overestimate the magnitude and seasonality of heterotrophic respiration, and to overestimate growth rates in a nutrient-poor tropical site. The evaluation presented here highlights the potential of incorporating structural and functional heterogeneity within biomes in ESMs, to realistically represent the role of forest structure and composition on energy, water, and carbon cycles, as well as the priority areas for further model development.

Description: Estimates of CO2 fluxes that are based on atmospheric measurements rely upon a meteorology model to simulate atmospheric transport. These models provide a quantitative link between the surface fluxes and CO2 measurements taken downwind. Errors in the meteorology can therefore cause errors in the estimated CO2 fluxes. Meteorology errors that correlate or covary across time and/or space are particularly worrisome; they can cause biases in modeled atmospheric CO2 that are easily confused with the CO2 signal from surface fluxes, and they are difficult to characterize. In this paper, we leverage an ensemble of global meteorology model outputs combined with a data assimilation system to estimate these biases in modeled atmospheric CO2. In one case study, we estimate the magnitude of month-long CO2 biases relative to CO2 boundary layer enhancements and quantify how that answer changes if we either include or remove error correlations or covariances. In a second case study, we investigate which meteorological conditions are associated with these CO2 biases. In the first case study, we estimate uncertainties of 0.5–7 ppm in monthly-averaged CO2 concentrations, depending upon location (95% confidence interval). These uncertainties correspond to 13–150% of the mean afternoon CO2 boundary layer enhancement at individual observation sites. When we remove error covariances, however, this range drops to 2–22%. Top-down studies that ignore these covariances could therefore underestimate the uncertainties and/or propagate transport errors into the flux estimate. In the second case study, we find that these month-long errors in atmospheric transport are anti-correlated with temperature and planetary boundary layer (PBL) height over terrestrial regions. In marine environments, by contrast, these errors are more strongly associated with weak zonal winds. Many errors, however, are not correlated with a single meteorological parameter, suggesting that a single meteorological proxy is not sufficient to characterize uncertainties in atmospheric CO2. Together, these two case studies provide information to improve the setup of future top-down inverse modeling studies, preventing unforeseen biases in estimated CO2 fluxes.