Model-observation handshake Research increasingly in the context of improving representation of coupled Earth system models – Improved process representation.

Slides:



Advertisements
Similar presentations
2010 update of GCOS IP in support of UNFCCC Paul Mason and Stephan Bojinski GCOS Steering Committee September 2010.
Advertisements

Land Surface Evaporation 1. Key research issues 2. What we learnt from OASIS 3. Land surface evaporation using remote sensing 4. Data requirements Helen.
1 Funded by NSF Program: Water and Carbon in Earth System Funded by NSF Program: Water and Carbon in Earth System Interactions between Water, Energy and.
CMIP5: Overview of the Coupled Model Intercomparison Project Phase 5
DataModel When data and model are in isolation We are getting …
Carbon Cycle and Ecosystems Important Concerns: Potential greenhouse warming (CO 2, CH 4 ) and ecosystem interactions with climate Carbon management (e.g.,
National Soil Carbon Network Breakout Thanks to our rapporteur: Mark Waldrop participants.
Estimating forest structure in wetlands using multitemporal SAR by Philip A. Townsend Neal Simpson ES 5053 Final Project.
MODIS Science Team Meeting - 18 – 20 May Routine Mapping of Land-surface Carbon, Water and Energy Fluxes at Field to Regional Scales by Fusing Multi-scale.
Office of Science Office of Biological and Environmental Research J Michael Kuperberg, Ph.D. Dan Stover, Ph.D. Terrestrial Ecosystem Science AmeriFlux.
CSIRO LAND and WATER Estimation of Spatial Actual Evapotranspiration to Close Water Balance in Irrigation Systems 1- Key Research Issues 2- Evapotranspiration.
Office of Science Office of Biological and Environmental Research G. L. Geernaert Climate and Environmental Sciences Division Workshop on Community Modeling.
O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY 1 Carbon Cycle Modeling Terrestrial Ecosystem Models W.M. Post, ORNL Atmospheric Measurements.
Forrest G. Hall 1 Thomas Hilker 1 Compton J. Tucker 1 Nicholas C. Coops 2 T. Andrew Black 2 Caroline J. Nichol 3 Piers J. Sellers 1 1 NASA Goddard Space.
Is It True? At What Scale? What Is The Mechanism? Can It Be Managed? 150 Is The New 80: Continuing Carbon Storage In Aging Great Lakes Forests UMBS Forest.
Hydrologic model benchmarks: Synthetic test cases, CZO data, and continental-scale diagnostics CUAHSI Community Modeling Working Group, San Francisco,
Data assimilation in land surface schemes Mathew Williams University of Edinburgh.
Science themes: 1.Improved understanding of the carbon cycle. 2.Constraints and feedbacks imposed by water. 3.Nutrient cycling and coupling with carbon.
Getting Ready for the Future Woody Turner Earth Science Division NASA Headquarters May 7, 2014 Biodiversity and Ecological Forecasting Team Meeting Sheraton.
The role of the Chequamegon Ecosystem-Atmosphere Study in the U.S. Carbon Cycle Science Plan Ken Davis The Pennsylvania State University The 13 th ChEAS.
BIOME-BGC estimates fluxes and storage of energy, water, carbon, and nitrogen for the vegetation and soil components of terrestrial ecosystems. Model algorithms.
Ocean Synthesis and Air-Sea flux evaluation Workshop Global Synthesis and Observations Panel (GSOP) Organized by Lisan Yu, Keith Haines, Tony Lee WHOI,
The Merton Report an AIMES/IGBP-ESA partnership As Earth System science advances and matures, it must be supported by robust and integrated observation.
Translation to the New TCO Panel Beverly Law Prof. Global Change Forest Science Science Chair, AmeriFlux Network Oregon State University.
Lola Fatoyinbo Agueh – NASA GSFC Collaboration – Interest in Large field campaigns/ ecosystem-scale experiments to validate models. – International collaboration.
Office of Science Office of Biological and Environmental Research DOE Workshop on Community Modeling and Long-term Predictions of the Integrated Water.
Puget Sound Information Challenge Experiences and Lessons Learned.
Integration of biosphere and atmosphere observations Yingping Wang 1, Gabriel Abramowitz 1, Rachel Law 1, Bernard Pak 1, Cathy Trudinger 1, Ian Enting.
Some challenges of model-data- integration a collection of issues and ideas based on model evaluation excercises Martin Jung, Miguel Mahecha, Markus Reichstein,
Spatial Model-Data Comparison Project Conclusions Forward models are very different and do not agree on timing or spatial distribution of C sources/sinks.
Soil and Water Conservation Modeling: MODELING SUMMIT SUMMARY COMMENTS Dennis Ojima Natural Resource Ecology Laboratory COLORADO STATE UNIVERSITY 31 MARCH.
A parametric and process- oriented view of the carbon system.
Flux observation: Integrating fluxes derived from ground station and satellite remote sensing 王鹤松 Hesong Wang Institute of atmospheric physics, Chinese.
Free-Air Carbon Dioxide Enrichment – FACE BERAC review of DOE-funded experiments (’07) –ORNL FACE –Duke Forest FACE (FACTS I) –Rhinelander Aspen FACE (FACTS.
Terrestrial Carbon Observations TCO Previous Strategy 1- better identify the potential end users, and their requirements 2- organize and coordinate reliable.
Key information from FDOS Global distribution of plant communities as described by quantitative traits [and their association with phylogenetic composition??]
Site-Level Model-Data Comparison A Proposed NACP Interim Synthesis Project Ken Davis, Peter Thornton, Kevin Schaefer, Dan Riciutto Coordinators.
1. Synthesis Activities on Hydrosphere and Biosphere Interactions Praveen Kumar Department of Civil and Environmental Engineering University of Illinois.
International Collaboration on Data Assimilation in Terrestrial Carbon Cycle Science CARBON FUSION
MOSES calibration for coniferous forest Chris Huntingford, Peter Cox, Richard Harding …..
WP3 WP6 USE CASE DATA MODEL FUSION USING PHENOLOGICAL DATA TO INFORM PRODUCTIVITY MODEL Andy Fox, David Moore, Jesus Marco de Lucas, Jeff Taylor, and many.
Flux Measurements and Systematic Terrestrial Measurements 1.discuss gaps and opportunities What are gaps? 2. brainstorm ideas about collaborative projects.
Systematic Terrestrial Observations: a Case for Carbon René Gommes with C. He, J. Hielkema, P. Reichert and J. Tschirley FAO/SDRN.
Daniel Metcalfe and numerous others Oxford University Centre for the Environment Drought impacts on leaf morphology and respiration.
Arctic Biosphere-Atmosphere Coupling across multiple Scales (ABACUS) Why is this SO important for understanding global change?
Goal: to understand carbon dynamics in montane forest regions by developing new methods for estimating carbon exchange at local to regional scales. Activities:
CarboEurope: The Big Research Lines Annette Freibauer Ivan Janssens.
Fluxnet 2009 Progress Dennis Baldocchi, Rodrigo Vargas, Youngryel Ryu, Markus Reichstein, Dario Papale, Deb Agarwal, Catharine Van Ingen AmeriFlux 2009.
CFusion and NCEO. NCEO Components Ciais et al IGOS-P Integrated Global Carbon Observing Strategy Global Carbon Data Assimilation System.
Citation: Richardson, J. J, L.M. Moskal, S. Kim, Estimating Urban Forest Leaf Area Index (LAI) from aerial LiDAR. Factsheet # 5. Remote Sensing and.
Wanda R. Ferrell, Ph.D. Acting Director Climate and Environmental Sciences Division February 24, 2010 BERAC Meeting Atmospheric System Research Science.
Figure 1. (A) Evapotranspiration (ET) in the equatorial Santarém forest: observed (mean ± SD across years of eddy fluxes, K67 site, blue shaded.
Success and Failure of Implementing Data-driven Upscaling Using Flux Networks and Remote Sensing Jingfeng Xiao Complex Systems Research Center, University.
Using Modelling to Address Problems Scientific Enquiry in Biology and the Environmental Sciences Modelling Session 2.
DIAS INFORMATION DAY GLOBAL WATER RESOURCES AND ENVIRONMENTAL CHANGE Date: 09/07/2004 Research ideas by The Danish Institute of Agricultural Sciences (DIAS)
Where/how could we change the overall process of field project implementation to improve in our mission of answering key science questions? Are we open.
Model-data intercomparison for NACP Yiqi Luo and James Randerson.
Results from the Reflex experiment Mathew Williams, Andrew Fox and the Reflex team.
Forest Research, 20 February 2009 Understanding the carbon cycle of forest ecosystems: a model-data fusion approach Mathew Williams School of GeoSciences,
References: 1)Ganguly, S., Samanta, A., Schull, M. A., Shabanov, N. V., Milesi, C., Nemani, R. R., Knyazikhin, Y., and Myneni, R. B., Generating vegetation.
The International Ocean Colour Coordinating Group International Network for Sensor Inter- comparison and Uncertainty assessment for Ocean Color Radiometry.
Old and Not-So-Old Research Progress/Plans for Ankur Desai Penn State University, Department of Meteorology Cheas 2003 Meeting, Woodruff, WI, 29 June –
Terrestrial-atmosphere (1)
Ecosystem Demography model version 2 (ED2)
Constructive Cost Model
PI: Steven Pawson (GMAO) Atmosphere:
Comparing Simulated and Observed Gross Primary Productivity
Systems Engineering for Mission-Driven Modeling
Carbon Model-Data Fusion
Forest growth models and new methods
Presentation transcript:

Model-observation handshake Research increasingly in the context of improving representation of coupled Earth system models – Improved process representation – Reduced uncertainty Supporting model-data fusion – Benefits of multiple data constraints – INFUSION model-data fusion activity Large number of participating sites/models Treatment of data for data assimilation (e.g. energy balance correction) Dealing with “dark data” (formats, units, accessibility) Data formats for BADM data (biological, ancillar, disturbance, metadata) List of priority variables needed for models, remote sensing calibration, and synthesis studies

Model-data fusion: More attention needed Needs to be more integral part of model development – Process complexity ≠ accuracy Many models have serious problems representing key carbon cycle variables Task is daunting, modelers need to start with simple methods – Component model calibration – Limit pre-processing – Easy for model to ingest Efforts needed (working group?) on refining assimilation-ready data products for global models – Bringing in “dark” data – Multi-scale, multi-variable – Integrated uncertainty estimates Courtesy Sasha Hararuk, Yiqi Luo

Variables of interest to modelers Vcmax – slope of light use efficiency curve Soil moisture profile Soil turn-over Soil heterogeneity Leaf to canopy scaling factor Characterization of light penetration inside canopy Soil carbon, biomass

Challenge: Translating Between Model Need Observation Capabilities Vcmax GPP LAI and Leaf nitrogen ??? Canopy Leaf Scaling Model-data fusion

Ideas Uncertainties should be provided where possible Add requirement that data be submitted in standard formats to funding calls Identify ways to make BADM easier for sites to provide AmeriFlux information request – Send top two data variables most needed from sites – send to along with an explanation of why they are