News Archive - Page 23 of 28

New DOE Earth System Model Released

The U.S. Department of Energy’s Energy Exascale Earth System Model (E3SM) project, supported by the Office of Science in the Biological and Environmental Research Office, released its new high-resolution earth modeling system to the broader scientific community on April 23, 2018. E3SM will have weather-scale resolution and use advanced computers to simulate aspects of Earth’s variability and anticipate decadal changes that will critically impact the U.S. energy sector in coming years. NGEE-Tropics’ next-generation dynamic vegetation model, FATES (the Functionally-Assembled Terrestrial Ecosystem Simulator), was fully integrated into E3SM and its land model in 2017, and continues to be an important part of E3SM’s model development and science campaigns.

Warm ENSO phase drives coordination between leaf and fruit phenology

Resource acquisition and reproductive strategies of tropical forest in response to the El Niño–Southern Oscillation

The Science
It has been suggested that tree phenology may be regulated by climatic oscillations. Here, we present a 30 year tropical forest dataset that suggests leaf and fruit production is coordinated with ENSO cycles, with greater leaf fall observed prior to El Niño followed by greater seed production.

Coordination between leafing and fruiting at multiple temporal scales. a Wavelet coherence between leaf fall and seed fall shows strong coordination at both seasonal and ENSO cycles (red regions). b Phase-angle histogram for leaf fall and seed fall for the seasonal cycle and c for periods of 2–7 years, corresponding to the ENSO cycle. Phase angles were calculated for areas inside the cone of influence and for coherence >0.5 in a. Negative angles (blue area) indicate that leaf fall leads seed fall

The Impact
The response of tropical forest to ENSO events and in general to drought and other environmental stress are still under exploration. Here, we show a relatively strong response of tropical phenology (fruiting and leafing) to a warming phase of ENSO. This discovery can help to understand the mechanisms of response or adaptation of plants to climate variability and pave the road to their implementation into Earth Ecosystem Models.

Summary
For the first time an interaction between phenophases of tropical plants (leafing and fruiting) is shown to be driven by large scale periodic climate variations. This interaction mirrors the dynamics between dry and wet season, suggesting adaptive strategies to optimize reproduction and resource acquisition in response to environmental stress.

Contacts (BER PM): Daniel Stover, SC-23.1, Daniel.Stover@science.doe.gov (301-903-0289)

PI Contact: Matteo Detto, Associate Researcher, Dept. of Ecology and Evolutionary Biology Princeton University and Smithsonian Tropical Research Institute, mdetto@princeton.edu

Funding
The Environmental Sciences Program of the Smithsonian Institution funded the data collection. Matteo Detto was partially supported by NGEE-Tropics. Raul Rios, Brian Harvey, and Steven Paton collected the BCI climate data.

Publications
Detto, M., Wright, S. J., Calderón, O. & Muller-Landau, H. C. Resource acquisition and reproductive strategies of tropical forest in response to the El Niño-Southern Oscillation. Nature Communications, 9, 1–8 (2018).

Rapid assessment of Hurricane Maria’s impact to Puerto Rico forests

Hurricane Maria image from NOAA

On the early morning of September 20, 2017, Hurricane Maria made landfall in southeast Puerto Rico as a strong Category 4 storm with maximum sustained winds of ~250 kph. The powerful storm traveled the island in a northwesterly direction causing widespread destruction. This study focused on a rapid assessment of Hurricane Maria’s impact to Puerto Rico’s forests. Calibrated and corrected Landsat 8 image composites for the entire island were generated using Google Earth Engine for a comparable pre-Maria and post-Maria time period that accounted for phenology. Spectral mixture analysis (SMA) using image-derived endmembers was carried out on both composites to calculate the change in the non-photosynthetic vegetation (ΔNPV) spectral response, a metric that quantifies the increased fraction of exposed wood and surface litter associated with tree mortality and crown damage from the storm. Hurricane simulations were also conducted using the Weather Research and Forecasting (WRF) regional climate model to estimate wind speeds associated with forest disturbance. Dramatic changes in forest structure across the entire island were evident from pre- and post-Maria composited Landsat 8 images. A ΔNPV map for only the forested pixels illustrated significant spatial variability in disturbance, with emergent patterns associated with factors such as slope, aspect and elevation. An initial order-of-magnitude impact estimate based on previous work indicated that Hurricane Maria may have caused mortality and severe damage to 23-31 million trees. Additional field work and image analyses are required to further detail the impact of Hurricane Maria to Puerto Rico forests.

Other resources

Plant water potential improves prediction of empirical stomatal models

The Science
We find that current leaf-level empirical models over-predict stomatal conductance during drought conditions and a recently proposed model improves predictions during drought conditions.

The Impact
Including the impairment of soil-to-leaf water transport will improve predictions of stomatal conductance during drought conditions. Many biomes contain a diversity of plant stomatal strategies during water stress.

Summary
Ecosystem models rely on empirical relationships to predict stomatal responses to changing environmental conditions, but these are not well tested during drought conditions. We compiled datasets of stomatal conductance and leaf water potential for 34 woody plant species that span global forest biomes. We tested how well three major stomatal models and a recently proposed model predicted measured stomatal conductance. We found that current models consistently over predicted stomatal conductance during dry conditions whereas the recently proposed model, which includes loss of hydraulic transport capacity, improved predictions compared to current models, particularly during droughts. Our results also show that many biomes contain a diversity of plant stomatal strategies during water stress. Such improvements in stomatal simulation will help to predict the response of ecosystems to future climate extremes.

Contacts (BER PM): Daniel Stover, SC-23.1, Daniel.Stover@science.doe.gov (301-903-0289)
Dorothy Koch, SC-23.1, Dorothy.Koch@science.doe.gov (301-903-0105)

PI Contact: William R. L. Anderegg, Univsersity of Utah, anderegg@utah.edu

Funding
Funding for this research was provided by NSF DEB EF-1340270 and the Climate Mitigation Initiative at the Princeton Environmental Institute, Princeton University. SL acknowledges financial support from the China Scholarship Council (CSC). VRD acknowledges funding from Ramón y Cajal fellowship (RYC-2012-10970). Brett T. Wolfe was supported by the Next Generation Ecosystem Experiments-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. DJC acknowledges funding from the National Science Centre, Poland (NN309 713340). WRLA was supported in part by NSF DEB 1714972.

Publications
Anderegg WRL, Wolf A, Arango-Velez A, Choat B, Chmura DJ, Jansen S, Kolb T, Li S, Meinzer F, Pita P, Resco de Dios V, Sperry JS, Wolfe BT, Pacala S (2017), Plant water potential improves prediction of empirical stomatal models, PLOS ONE. DOI:10.1371/journal.pone.0185481

Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest

Advancing the biophysical understanding of satellite-detected vegetation seasonality in the tropics

The Science
Satellite observations of Amazon forests show seasonal and inter-annual variation in canopy greenness, but the underlying biological mechanisms leading to a change in greenness have not been resolved. Here a research team from Brookhaven National Laboratory combined canopy radiative transfer models (RTMs) with field observations of Amazon forest leaf and canopy characteristics to test three hypotheses that could explain seasonality in satellite-observed canopy reflectance; (1) changes in the number of leaves per unit ground area (leaf area index), (2) changes in the fraction of the upper canopy that are leafless, and (3) changes in leaf age. They showed that canopy RTMs driven by these three factors closely matched simulated satellite-observed seasonal patterns, explaining ~70% of variability in a key reflectance-based vegetation index. Leaf area index, leafless crown fraction and leaf age accounted for 1%, 33% and 66% of modeled seasonality.

The Impact
The analysis of canopy-scale biophysics rules out satellite artifacts as significant cause of satellite-observed seasonal patterns in greenness at this site and implies that leaf phenology can explain large scale remotely-observed patterns. Thier study reconciles current controversies about satellite-detected canopy greenness, and enables more confident use of satellite observations to study climate-phenology relationships in the tropics.

Summary
The average annual cycle (2000-2014) of MODIS satellite observed canopy greenness (i.e. MAIAC EVI in red circles in panel a, which minimizes the artifacts from clouds/aerosols and sun-sensor geometry) in a Brazilian Amazon evergreen forest, the Tapajos k67 site, shows strong seasonality. This seasonality is primarily driven by canopy NIR reflectance (red circles in panel b). Here, the team combined rich, field measurements of leaf and canopy characteristics with a 3-D radiative transfer model (i.e. Forest Light Environment Simulator, FLiES) to interpret MAIAC EVI seasonality. These results show that the comprehensive FLiES model (in black rectangles; panel a and b) with all phenological input (as “P1+P2+P3” in panel c and d) did a good job at simulating MAIAC EVI and NIR reflectance seasonality. This suggests that biological processes dominate canopy-scale reflectance and greenness seasonality in this tropical forest. Further, the research teamaq did model sensitivity analysis to quantify the relative contribution of each of the three phenological factors (panel c and d), including “P1” driven by seasonal change in canopy leaf area index only, “P2” driven by seasonal change in canopy-surface leafless crown fraction alone (in red), and “P3” driven by seasonal change in canopy leaf age demography (in green). their results suggest that canopy-surface leafless crown fraction and leaf age demography control the seasonality in greenness, they did not observe any direct effect of leaf area index on greenness.

Contacts (BER PM): Daniel Stover, SC-23.1, Daniel.Stover@science.doe.gov (301-903-0289)

PI Contact
Lead author contact information: Jin Wu, Brookhaven National Laboratory, jinwu@bnl.gov
Institutional contact: Alistair Rogers, Brookhaven National Laboratory, arogers@bnl.gov

Funding
SP Serbin, A Rogers, and J Wu in part were supported by the Next-Generation Ecosystem Experiment (NGEE-Tropics) project. The NGEE-Tropics project is supported by the Office of Biological and Environmental Research in the Department of Energy, Office of Science.

Publications
Wu J, Kobayashi H, Stark SC, Meng R, Guan K, Tran NN, Gao S, Yang W, Restrepo-Coupe N, Miura T, Oliviera RC, Rogers A, Dye DG, Nelson BW, Serbin S, Huete AR, and Saleska SR. (2017) Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest. New Phytologist, doi:10.1111/nph.14939.

Look for NGEE-Tropics at the 2017 AGU Fall Meeting!

Every year, thousands of scientists, educators, students and other leaders gather at the largest worldwide conference in Earth and space sciences – the AGU Fall Meeting. This year’s Fall Meeting takes place in New Orleans, on December 11-15, 2017.

Come check out the exciting new progress being made by NGEE-Tropics and our colleagues! We’ve compiled a handy list of sessions, talks, and posters for your easy reference. If any relevant activities are missing or incorrect, please let us know.

Hope to see you in New Orleans!