ORCID Profile
0000-0002-1772-5361
Current Organisation
Oak Ridge National Laboratory
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Publisher: Springer Science and Business Media LLC
Date: 13-08-2018
DOI: 10.1038/S41559-018-0626-Z
Abstract: Survival rates of large trees determine forest biomass dynamics. Survival rates of small trees have been linked to mechanisms that maintain bio ersity across tropical forests. How species survival rates change with size offers insight into the links between bio ersity and ecosystem function across tropical forests. We tested patterns of size-dependent tree survival across the tropics using data from 1,781 species and over 2 million in iduals to assess whether tropical forests can be characterized by size-dependent life-history survival strategies. We found that species were classifiable into four 'survival modes' that explain life-history variation that shapes carbon cycling and the relative abundance within forests. Frequently collected functional traits, such as wood density, leaf mass per area and seed mass, were not generally predictive of the survival modes of species. Mean annual temperature and cumulative water deficit predicted the proportion of biomass of survival modes, indicating important links between evolutionary strategies, climate and carbon cycling. The application of survival modes in demographic simulations predicted biomass change across forest sites. Our results reveal globally identifiable size-dependent survival strategies that differ across erse systems in a consistent way. The abundance of survival modes and interaction with climate ultimately determine forest structure, carbon storage in biomass and future forest trajectories.
Publisher: American Meteorological Society
Date: 07-2019
Abstract: Climate model evaluation is complicated by the presence of observational uncertainty. In this study we analyze daily precipitation indices and compare multiple gridded observational and reanalysis products with regional climate models (RCMs) from the North American component of the Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) multimodel ensemble. In the context of model evaluation, observational product differences across the contiguous United States (CONUS) are also deemed nontrivial for some indices, especially for annual counts of consecutive wet days and for heavy precipitation indices. Multidimensional scaling (MDS) is used to directly include this observational spread into the model evaluation procedure, enabling visualization and interpretation of model differences relative to a “cloud” of observational uncertainty. Applying MDS to the evaluation of NA-CORDEX RCMs reveals situations of added value from dynamical downscaling, situations of degraded performance from dynamical downscaling, and the sensitivity of model performance to model resolution. On precipitation days, higher-resolution RCMs typically simulate higher mean and extreme precipitation rates than their lower-resolution pairs, sometimes improving model fidelity with observations. These results document the model spread and biases in daily precipitation extremes across the full NA-CORDEX model ensemble. The often-large ergence between in situ observations, satellite data, and reanalysis, shown here for CONUS, is especially relevant for data-sparse regions of the globe where satellite and reanalysis products are extensively relied upon. This highlights the need to carefully consider multiple observational products when evaluating climate models.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-10918
Abstract: Predicting the fate of the terrestrial ecosystems and their role in the Earth system requires a quantitative and mechanistic understanding of carbon, water, and energy exchanges between the land surface and the atmosphere. While the current generation of land surface models show skill in representing many ecosystem processes, they largely disagree in the integrated response of the terrestrial biosphere to climatic change. These disagreements may be reconciled by confronting models with the erse and expanding suite of Earth system observations in order to better constrain the underlying processes. In light of this goal, we have implemented substantial developments to the CARbon DAta-MOdel FraMework (CARDAMOM)& #8212 a data assimilation system that optimally estimates parameters of a parsimonious ecosystem model& #8212 which expand its original scope as a diagnostic tool for estimating carbon states and fluxes into a system that can infer and predict the response of carbon, water and energy cycles to climate and CO2 concentrations at seasonal-to-decadal timescales. CARDAMOM 3.0 retains all functionality and model structures of previous versions, but now features a flagship model which includes coupled carbon, water, and energy cycles, along with semi-mechanistic representations of photosynthetic assimilation, allocation, phenology, autotrophic and heterotrophic respiration, snow and cold-weather processes, and soil hydrology. Additionally, the underlying framework was substantially updated in order to facilitate community use of CARDAMOM by simplifying the interface and increasing the ease with which users can integrate new observations and develop new model structures. With these new developments, CARDAMOM 3.0 provides a versatile tool for applying information from a broad array of Earth observation data to investigate carbon, water, and energy cycles and their responses to climate and atmospheric CO2 across the full range of terrestrial ecosystems, from leaf level to continental scales.
Publisher: American Meteorological Society
Date: 10-2020
Abstract: This study utilizes Bayesian model averaging (BMA) as a framework to constrain the spread of uncertainty in climate projections of precipitation over the contiguous United States (CONUS). We use a subset of historical model simulations and future model projections (RCP8.5) from the Coupled Model Intercomparison Project phase 5 (CMIP5). We evaluate the representation of five precipitation summary metrics in the historical simulations using observations from the NASA Tropical Rainfall Measuring Mission (TRMM) satellites. The summary metrics include mean, annual and interannual variability, and maximum and minimum extremes of precipitation. The estimated model average produced with BMA is shown to have higher accuracy in simulating mean rainfall than the ensemble mean (RMSE of 0.49 for BMA versus 0.65 for ensemble mean), and a more constrained spread of uncertainty with roughly a third of the total uncertainty than is produced with the multimodel ensemble. The results show that, by the end of the century, the mean daily rainfall is projected to increase for most of the East Coast and the Northwest, may decrease in the southern United States, and with little change expected for the Southwest. For extremes, the wettest year on record is projected to become wetter for the majority of CONUS and the driest year to become drier. We show that BMA offers a framework to more accurately estimate and to constrain the spread of uncertainties of future climate, such as precipitation changes over CONUS.
Publisher: Copernicus GmbH
Date: 12-02-2016
Abstract: Abstract. Although plant photosynthetic capacity as determined by the maximum carboxylation rate (i.e., Vc, max25) and the maximum electron transport rate (i.e., Jmax25) at a reference temperature (generally 25 °C) is known to vary considerably in space and time in response to environmental conditions, it is typically parameterized in Earth system models (ESMs) with tabulated values associated with plant functional types. In this study, we have developed a mechanistic model of leaf utilization of nitrogen for assimilation (LUNA) to predict photosynthetic capacity at the global scale under different environmental conditions. We adopt an optimality hypothesis to nitrogen allocation among light capture, electron transport, carboxylation and respiration. The LUNA model is able to reasonably capture the measured spatial and temporal patterns of photosynthetic capacity as it explains ∼ 55 % of the global variation in observed values of Vc, max25 and ∼ 65 % of the variation in the observed values of Jmax25. Model simulations with LUNA under current and future climate conditions demonstrate that modeled values of Vc, max25 are most affected in high-latitude regions under future climates. ESMs that relate the values of Vc, max25 or Jmax25 to plant functional types only are likely to substantially overestimate future global photosynthesis.
Location: United States of America
Location: United States of America
Location: United States of America
No related grants have been discovered for Elias Massoud.