Breakout Session IV - CC&E Contributions Towards Analyzing Impacts and Consequences of Global Change Modeling Challenges and Forecasting Impacts Co-Chairs: S. Doney 1, C. Vörösmarty 2,3 1 Woods Hole Oceanographic Institution 2 University of New Hampshire 3 As of 1 September: City College of New York/CUNY
Breakout on Modeling/Forecasting Co-Chairs: S. Doney, C. Vörösmarty Original Core Questions: What research can we conduct to better address the impacts and consequences of global change? What actions would be most useful to or supportive of future assessments? What are the greatest challenges and opportunities (relevant to the breakout topic)?
New Demands on Earth System Science Global Policy on Climate Change Security Challenges –Infrastructure at Risk –Food Security –Energy Security –Water security –Economic development Public Need for Accurate Information
Agent-based models Science-driven sensor & technology development Observation networks Change detection Computationally intensive landscape models High resolution Earth System simulations Complex Information Streams
Carbon Cycle and Ecosystems Roadmap T Reduced flux uncertainties; global carbon dynamics Funded Unfunded Global Ocean Carbon / Particle Abundance N. America’s carbon budget quantified Global Atmospheric CO 2 (OCO) Reduced flux uncertainties; coastal carbon dynamics NA Carbon Global C Cycle T = Technology development Regional carbon sources/sinks quantified for planet IPCC Effects of tropical deforestation quantified; uncertainties in tropical carbon source reduced = Field Campaign Physiology & Functional Types Goals: Global productivity and land cover change at fine resolution; biomass and carbon fluxes quantified; useful ecological forecasts and improved climate change projections Vegetation 3-D Structure, Biomass, & Disturbance T Terrestrial carbon stocks & species habitat characterized Models w/improved ecosystem functions High-Resolution Atmospheric CO 2 Sub-regional sources/sinks Integrated global analyses CH 4 sources characterized and quantified Report P Vegetation (AVHRR, MODIS) Ocean Color (SeaWiFS, MODIS) Land Cover (Landsat) LDCMLand Cover (OLI) Vegetation, Fire (AVHRR, MODIS) Ocean/Land (VIIRS/NPP) Ocean/Land (VIIRS/NPOESS) Models & Computing Capacity Case Studies Process Understanding Improvements: Human-Ecosystems-Climate Interactions (Model-Data Fusion, Assimilation); Global Air-Sea Flux T Partnership N. American Carbon Program Land Use Change in Amazonia Global CH 4 ; Wetlands, Flooding & Permafrost Global C Cycle Knowledge Base 2002: Global productivity and land cover resolution coarse; Large uncertainties in biomass, fluxes, disturbance, and coastal events Systematic Observations Process controls; errors in sink reduced Coastal Carbon Southern Ocean Carbon Program, Air-Sea CO 2 Flux
0°0° 60 ° 120 ° 180 ° 240 ° 300 ° 360 ° 1950s 1960s 1970s 1980s 1990s 0°0° 60 ° 120 ° 180 ° 240 ° 300 ° 360 ° Longitude Coverage or Quality GoodPoor Year Observations Spatially/temporally patchy Quality: High to Low Challenging to explain in aggregate SYSTEMIC UNDERSTANDING Paleo 1950s 1960s 1970s 1980s 1990s Paleo INDUCTIVE PATH Specific to General DEDUCTIVE PATH General to Specific Modeled Outputs Spatially/temporally contiguous Physically-consistent but incomplete Gap-filling SYNERGY BETWEEN OBSERVATIONS AND MODELED OUTPUTS
From: Hall & O’Connell (2007), Proc. Inst. Civ.E., Original from IPCC Evolution of GCMs into ESMs Policy and Decision Support Modeling
The Day Has Arrived Where We Need to Think of Regional Carbon Inventories and Regional Ecosystem Management
Well-Intended Ooops! Decisions being made by non-scientists on an Earth system that includes biogeophysics and humans Failure of ESS knowledge to be conveyed A ‘learning moment’?? For us: forecast user needs For the rest: no silver bullets Google search: 29 April 2008
Breakout on Modeling/Forecasting Co-Chairs: S. Doney, C. Vörösmarty Are NASA research results adequately informing the assessment process? (e.g. IPCC- FAR, Millennium Ecosystem Assessment, ACIA) If so….job done! If not, what is the strategy forward: –Droning on re: “we need more data”, “we need better models”? OR….. –New ways of thinking through Different ideas on the use of data? ID high impact data? Different classes of models? Different target audiences? (e.g. private sector, media vs just other scientists) Other approaches