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Earth System Model Beyond the boundary Model • A mathematical representation of the many processes that make up our climate. • Requires: – – – – Knowledge of the physical laws that govern climate Mathematical expressions for those laws Numerical methods to solve the mathematical expressions on a computer (if needed) A computer of adequate size to carry out the calculations Why? Hypotheses Observations Numerical Simulations • Understanding of cause and effect • Predictive skill: our main tool to make predictions for the future Evolution of Climate Science Definition There is no unique definition of which processes must be represented before a climate model becomes an Earth System Model (ESM), but typically such models have at least an interactive carbon cycle component. The development of this capability was motivated by suggestions that the ability of terrestrial ecosystems and the ocean to remove carbon dioxide from the atmosphere will be limited by future climate change (e.g., Friedlingstein et al. 2006). Climate-Carbon Feedback [Friedlingstein et al. 2006] Climate-Carbon Feedback Positive feedback if the warming leads to enhanced rates of decay of organic matter in soils, or a reduction in oceanic carbon uptake, then the concentration of CO2 in the atmosphere will rise more rapidly than it would in the absence of such (positive) feedbacks, and the rate of warming will be greater as well. Negative feedback if increased CO2 in the atmosphere enhances photosynthesis and the storage of carbon in plants and soils, then CO2 levels will rise less rapidly than in the absence of this (negative) feedback, and climate change will also be slower as a result. Earth System Model (ESM) Atmospheric circulation and radiation Climate Model Sea Ice Ocean circulation Land physics and hydrology Atmospheric circulation and radiation Allows Interactive CO2 Earth System Model Sea Ice Ocean ecology and chemistry Ocean circulation Plant ecology, land use, and Biogeochemistry Land physics and hydrology Carbon cycle CO2 CO2 Diagnostic Prognostic Global Climate Model Earth System Model Multi-disciplinary Science Terrestrial ecosystems influence climate through physical, chemical, and biological processes that affect planetary energetics, the hydrologic cycle, and atmospheric composition Earth system science spans traditional disciplines Three examples Anthropogenic land cover change Photosynthesis-transpiration Leaf area index Bonan (2008) Ecological Climatology, 2nd ed (Cambridge Univ. Press) 11 History Heterogenity Dynamic Global Vegetation Model (DGVM) BIOGEOPHYSICS (CLM) CANOPY PHYSICS Radiation transfer Energy balance Temperature Aerodynamics Water balance CANOPY PHYSIOLOGY Photosynthesis (GPP) Stomatal conductance SOIL/ICE/SNOW PHYSICS Energy and water balance Temperature BIOGEOCHEMISTRY (LPJ) autotrophic respiration (RA) Net Primary Production (GPP- RA ) heterotrophic respiration X (RA) not coupled yet PHENOLOGY (IBIS) Daily Leaf Area Index DAILY STATISTICS 10-day mean temperature 10-day mean photosynthesis Growing degree-day accumulation Fire probability ECOPHYSIOLOGY Allocation Turnover Mortality COMPETITON Light Water FIRE SOIL Occurrence (moisture, fuel load) Litter Mortality (fire resistance) Soil organic matter Combustion ANNUAL STATISTICS Fire season length NPP,GPP and potential GPP Minimum monthly temperature Growing degree-days above 5℃ Precipitation Growing degree-days above heat stress Net CO2 (GPP-RA-RH) At every time step (~20minute) VEGETATION DYNAMICS (LPJ) Daily Multi-Time step Yearly ESTABLISHMENT Potential rate Canopy Gap Frost tolerance Heat stress Winter chilling Growing season warmth Low precipitation Plant Functional Type Height Plant Carbon Litter and Soil Carbon Vegetation dynamics Broadleaf Tree Shrub C3 Grass Soil Competition (10 days) Plant functional type (PFT) Deciduous, evergreen trees Shrub Grass Crop LAI (Model) Vegetation activity Phenology Winter Spring Summer Autumn Winter Time Simulated Carbon Annual cycle of LAI in ESMs Observation (GIMMS New LAI) Amplitude of LAI annual cycle climatology (1982-2005) [Jeong et al., in preparation] Poor performance Uncertainties in phenology Net ecosystem productivity 4 CTR EX1 EX2 EX3 EX4 EX5 EX6 EX8 EX4m EX 4m 2 0 [Optimal parameterization] [parameter] [structure] [hypothesis] [species] [DGVM group1] [DGVM group2] EX 5m EX5m -2 -4 -6 90 100 110 Budburst date 120 130 Carbon uptake commencement Parameter: -1.2 days -1.0 days Structure: -0.5 days - 0.0 days Hypothesis: -1.5 days -2.0 days Species: -9.7 days -11.5 days DGVMs: -9.2 days -11.1 days 140 150 Day of year [Jeong et al., 2012] Potential solution Species 25 B. papyrifera model Q. rubra model A. rubrum model A. saccharum model F. grandifolia model RMS errors [days] 20 15 10 5 0 B. papyrifera Q. rubra Early Mid A. rubrum A. saccharum F. grandifolia Late successional species [Jeong et al., 2013b; Jeong and Medvigy, in review] New paradigm “ecological realism” Managed ecosystem Crop phenology Phase 1 Phase 2 Phase 3 LAI Grain Fill Harvest Planting date 0 Leaf Emergence Time Green: climate, fertilization, and irrigation Red: human-decision Tradeoff between food benefit and climatic cost 1. Extensification (land use) Global Climate Model (one way) 2. Intensification (Irrigation, fertilization, practices) 1. Extensification (land use) 2. Intensification (Irrigation, fertilization, practices) Earth System Model (two way) 3. Interactive crop management (planting, harvesting) Current problem NCAR CESM 1.0 algorithm Sacks et al., 2010 Wheat Potential solution [Jeong et al., 2013a] Summary We need more efforts to implement ecological realism in ESMs. Human-managed phenology is the initial stage. We need systematic analysis on phenology and atmospheric CO2 by integrating satellite, ground, and Earth system model. CO2 Concentration Vegetation Activity How will changes in phenology affect the variations in annual cycle of atmospheric CO2?