Can we predict the variability of ecosystem and carbon cycle
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1 Can we predict the variability of ecosystem and carbon cycle Ning Zeng Dept. Atmospheric and Oceanic Science and Earth System Science Interdisciplinary Center University of Maryland Contributors: M. Heimann, A. Mariotti, C. Roedenbeck, P Wetzel, E. Maier-Reimer H. Qian, R. Iacono, E. Munoz E.Kalnay, G.J.Collatz, A. Vintzileos, S.Lord August 9, IGSNRR, China
2 The Disturbed Carbon Cycle Pools PgC (10 15 g) Fluxes PgC/y Atmosphere (3.3/y) 0.4 Land use 1.6 Vege 600 Fossil Fuel Soil 1500 Surface Ocean 1000 Marine biota 3 Deep Ocean 38, missing 2.3 Sediment and rocks 66,000,000 Fossil fuel 5000
3 The missing carbon sink and the future of carbon sources and sinks Fossil + Land-use = Atmosphere + Ocean + Land 1990s: Pg/y 2050s: 16???? (IPCC A1B scenario) The missing or residual land carbon sink
4 CO2 ΔCO2 from climate feedback Land uptake Δ Land uptake Enhanced global warming from carbon-climate interaction: the C4MIP results --- UMD Earth System Model (CABO) UMD model Ocean uptake Δ Ocean uptake Major differences in land response: interannual variability as a testbed Friedlingstein et al., J. Climate in press
5 Difference: 2071/ /79 Net Primary Production Total land carbon
6 Atmospheric CO2 Variability Lagged Correlations Corr = months lag dco2/soi --- dco2/dt Emission -SOI 3-6 months lag Hydrology/SOI
7 20 th Century Observed Physical Climate Forcing Coupled Carbon Model Atmosphere Transport Reanalysis Atmospheric CO2 Precipitation Temperature Radiation wind,vapor Land Precip, Temp (CRU, CMAP, GISS) Soil Moisture/temperature Vegetation+ Soil Carbon Carbon Exchange Ocean Physics Circulation Temperature Salinity Reanalysis Ocean Chemistry/Biology Goal: To understand the changing carbon cycle in the 20 th Century
8 The VEgetation-Global Atmosphere-Soil Model (VEGAS) arbon allocation Photosynthesis 120 Turnover Atmospheric CO2 Autotrophic respiration 60 NPP 60 4 Plant Functional Types: Broadleaf tree Needleleaf tree C3 Grass (cold) C4 Grass (warm) 3 Vegetation carbon pools: Leaf Heterotrophic Root respiration (Rh) Wood 60 3 Soil carbon pools: Fast Intermediate Slow
9 Photosynthesis: VEGAS II Light (PAR, LAI, Height), soil moisture, temperature, CO2 Respiration: Temperature, soil moisture, lower soil pools slower decay Competition: Fire: Net growth, shading => fractional cover moisture, fuel load, PFT dependent resistance Wetland/CH4: moisture, topography gradient Carbon 13: C3/C4 competition: temperature, CO2
10 Leaf Area Index Vegetation Height
11 Vegetation Dynamics over the Amazon after disturbance Total LAI C4 grass Broadleaf tree Needleaf tree Leaf Root Wood Fast Intermediate Slow
12 Mechanisms of Interannual Variability I Modeled land-atmo flux vs. MLO CO2 growth rate Terrestrial carbon model forced by observed climate variability
13 Land vs. ocean fluxes Land contributes to most of the interannual variability, with significant contribution from ocean Modeling results supported by in-situ data and inversion Land: VEGAS Ocean: HAMOCC
14 El Nino 97/98 Inversion Roedenbeck 2003 VEGAS
15 PC1 PC2 Spatial patterns from multi-variate EOF analysis C flux Tropics during El Nino 1) Drier and warmer conditions coexist at tropical locations recip 1+) Drier and warmer across much of tropical land during El Nino Temp 2) Less precip => Less growth (lower NPP) and more fire => Less C uptake Higher T => more respiration (higher Rh) => more C release
16 Land-atmo Carbon Flux Jun1996-Apr2000
17 Recent Anomalous growth in CO Proposed explanations: 1. Fire in Siberia, North America, and other places 2. Accelerated carbon emission from China, India 3. Mid-latitude drought
18 Mid-latitude Drought:
19 Recent Anomalous growth in CO2
20 Conclusions: variability The Recent Understanding Such high correlation between CO2 and ENSO is mainly due to a conspiracy between climate anomalies and plant/soil physiology anomalously large CO2 growth can be explained by a (so far) unusual midlatitude drought, a possible glimpse into a warmer world the mechanisms and processes underlying such interactions provides crucial insight into the fate of anthropogenic CO2 and the degree of future climate change variability may be predictable!
21 Seasonal-interannual Prediction of Ecosystem and Carbon Cycle Two strands of recent research made this a real possibility Significantly improved skill in atmosphere-ocean prediction system, such as CFS at NCEP Development of dynamic ecosystem and carbon cycle models that are capable of capturing major interannual variabilities, when forced by realistic climate anomalies A pilot study at U Maryland: 1. Feasibility study using a prototype eco-carbon prediction system 2. Dynamic vegetation as an interactive component?
22 The NCEP Climate Forecast System (CFS, Saha et al. 2005) CFS captures major ENSO and other seasonal-interannual variability no warming trend, why?
23 Forecasting Procedure I Climate Predition CFS (9mon, 15 members) CFS (9mon, 15 members) Spinup Precip Temp Precip Temp Ecosystem+ Carbon Model VEGAS Initialization I VEGAS Predicted Eco-carbon Output 9mon, 15 members 1 mo forecast ensemble mean Output 9mon, 15 members Month 1 Month 2
24 Forecasting procedure II L=3 L=2 L=1 Ensemble mean L=0 t-1 t t+1
25 NEE( validation validation') and MLO CO2 NEE (land-atmo C flux): VEGAS forced by observed climate (Precip, T) This will be called observed as there is no true observation available Ocean contribution smaller, so NEE can be compared with MLO CO2
26 Plumes: NPP, P, T etc.
27 Predicted global cabon flux 'Observed' Lead time from 0 to 8 months 1. CFS/VEGAS captures most of the interannual variability, but 2. Amplitude is underestimated
28 Anomaly Correlation NEE
29 Summary of skill for anomaly correlation
30 Predicted NPP vs. Validation' Regression Lead = 0 month Lead = 4 months
31 NPP and R h (NEE=R h -NPP)
32 Improvement of the prediction system: The NPP Problem
33 El Nino Jun97-May98 NEE/LAI Combining statistical with dynamical method to improve the prediction
34 Beyond ENSO: Midlatitude Drought Other variability not related to ENSO or other known climate modes, can also be captured in a dynamical prediction system
35 Conclusions: prediction Encouraging results (better than expected) Issues Overestimation at midlatitude screws up global NPP, and too small NEE compared to MLO CO2 Other analysis methods? Terminology: forecast, hindcast, retrospective Implications of prediction Applications to ecosystem and carbon cycle A new framework for study eco-carbon response and feedback to climate Identifying ways of incorporating eco-carbon dynamics in the next generation of climate prediction models
36 Extratropics: forward model comparison with atmo inversion VEGAS Atmo. Inversion (Bousquet et al., 2000)
37
38 Carbon flux from various regions
39 Inversion Roedenbeck (2003) Post-Pinatubo Jun1991-May1993 VEGAS
40
41 NPP and carbon pools
42 Why CO2 correlates so well with ENSO: A conspiracy theory Tropics during El Nino Precipitation Temperature decrease increase Out of phase NPP decrease Rh increase Additive Land-atmo flux (Rh-NPP) increase + Spatially coherent climate anomalies Large land-atmo C flux
43
44 Fire carbon flux during El Nino CASA (satellite fire, climate) VEGAS (climate only) Mean El Nino Anomalies Input: satellite fire counts, climate Input: climate only
45 Fire in the US: Natural vs anthropogenic factors Model Observation
46 El Nino-like climate under global warming? Carbon consequencies. Similar processes may operate on interannual time scales and under global warming scenarios
47 An Earth System Model at UMD Team Members N. Zeng, R. Murtugudde, A. Busalacchi, R. DeFries (U of Maryland) Collaborators J. Christian (CCCMA) G. J. Collatz (NASA/GSFC) M. Heimann, C. Roedenbeck (Max-Planck Inst.) A. Mariotti, R. Iacono (ENEA) R. Feely (PMEL)
48 Drying or Warming?
49 NPP/NEE original A Drift Problem 1. Model drift with lead time Fix: Removed climatology for each lead time (L) 2. Trend in Modeled NPP Detrended 3. No warming trend in CFS Detrended Lead time from 0 to 8 months
50 Predicted global cabon flux 'Observed' Lead time from 0 to 8 months 1. CFS/VEGAS captures most of the interannual variability, but Amplitude is underestimated
51 Prec and Temp
52 Anom Correlation NPP
53 Oceanic CO2 flux based on pco2 measurements R. Feely (PMEL)
54 Anomaly correlation Precip
55 Anomaly Correlation Temperature
56 Skill P/T/NEE/NPP...
57 (Tropical) Predicted NPP vs. Validation' Lead = 0 month Lead = 4 months
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