Interannual variability in oceanic biogeochemical processes inferred by inversion of atmospheric O 2 /N 2 and CO 2 data

Transcription

1 [Estimated interannual variability of O 2 and CO 2 fluxes] Interannual variability in oceanic biogeochemical processes inferred by inversion of atmospheric O 2 /N 2 and CO 2 data [Tellus, in review] C. Rödenbeck 1, C. Le Quéré 1,2, M. Heimann 1, and R.F. Keeling 3 1 Max Planck Institute for Biogeochemistry Jena (Germany) 2 Univ. of East Anglia and British Antarctic Survey (UK) 3 Scripps Institution of Oceanography, University of California, San Diego, California (USA) Many thanks to: Computing centers: DKRZ, GWDG

2 Atmosphere Why Oxygen? 1. Processes: CO 2 Ocean

3 Atmosphere Why Oxygen? 1. Processes: CO 2 : Competing fluxes (High latitudes) Ocean

4 Atmosphere Why Oxygen? 1. Processes: CO 2 : Competing fluxes O 2 : Reinforcing fluxes (High latitudes) Ocean

5 Atmosphere Why Oxygen? 2. APO Signal: CO 2 Ocean

6 Atmosphere Why Oxygen? 2. APO Signal: CO 2 CO 2 CO 2 Fossil fuel emissions Land biosphere Ocean

7 Atmosphere Why Oxygen? 2. APO Signal: CO 2 O 2 CO 2 O 2 CO 2 O 2 Fossil fuel emissions Land biosphere Ocean

8 Atmosphere Why Oxygen? 2. APO Signal: CO 2 O 2 = -1.4 CO 2 CO 2 O 2 = -1.1 CO 2 CO 2 O 2 Fossil fuel emissions Land biosphere Ocean

9 Atmosphere Why Oxygen? 2. APO Signal: CO 2 O 2 = -1.4 CO 2 CO 2 O 2 = -1.1 CO 2 CO 2 O 2 Carbonate buffer Upwelling Different solubility Fossil fuel emissions Land biosphere Ocean

10 Atmosphere Why Oxygen? 2. APO Signal: APO = O CO 2 CO 2 O 2 = -1.4 CO 2 CO 2 O 2 = -1.1 CO 2 [Stephens et al. (1998)] CO 2 O 2 Carbonate buffer Upwelling Different solubility Fossil fuel emissions Land biosphere Ocean

11 Atmosphere Why Oxygen? 2. APO Signal: APO = O CO 2 (APO) APO [Stephens et al. (1998)] APO Marine biological activity Mixing/stratification, upwelling Gas exchange Fossil fuel emissions Land biosphere Ocean

12 Atmosphere Atmospheric Oxygen Data Data availability: Sampling locations: fxalts fxbrwp fxcbas fxcoin fxljos dxthds fxhatn fxkums fxmlos fxsmos fxamsp fxcgos fxmqap fxpsas fxsyop fxspos SIO PU SIO NIES SIO NIES SIO SIO SIO PU SIO PU SIO PU SIO # meas./month: APO Flux (TmolO 2 /year) OCEAN TOTAL year (A.D.) Inversion runs based on: 5 sites 7 sites 16 sites (at least latitudinal coverage)

13 Method Overview [Rödenbeck, Tech.Rep. (25)] Bayesian inversion

14 Method Overview [Rödenbeck, Tech.Rep. (25)] Bayesian inversion Data: Individual flask-pair(triplett) or hourly values Data density weighting Model uncertainty by station category

15 Method Overview [Rödenbeck, Tech.Rep. (25)] Bayesian inversion Data: Individual flask-pair(triplett) or hourly values Data density weighting Model uncertainty by station category A-priori PDF Linear statistical flux model

16 Method Overview [Rödenbeck, Tech.Rep. (25)] Bayesian inversion Data: Individual flask-pair(triplett) or hourly values Data density weighting Model uncertainty by station category A-priori PDF Linear statistical flux model Solution method: Conjugate Gradients (with re-orthogonalization) A-post. covariances: exact for aggreg. quantities (by cont d iteration)

17 Method Linear statistical flux model Flux model : f(p) = }{{} f fix fixed + Fp }{{} adjustable with p pri =, pp T pri = 1 µ 1

18 Method Linear statistical flux model Flux model : f(p) = }{{} f fix fixed + Fp }{{} adjustable with p pri =, pp T pri = 1 µ 1 J = 1 2 (c obs c mod ) T Q 1 c (c obs c mod ) + µ 2 pt p

19 Method Linear statistical flux model Flux model : f(p) = }{{} f fix fixed + Fp }{{} adjustable with p pri =, pp T pri = 1 µ 1 J = 1 2 (c obs c mod ) T Q 1 c (c obs c mod ) + µ 2 pt p f pri = f fix, Q f,pri = ff T pri = 1 µ FFT implicit a-priori covar. matrix

20 Method Linear statistical flux model Flux model : f(p) = }{{} f fix fixed + Fp }{{} adjustable with p pri =, pp T pri = 1 µ 1 J = 1 2 (c obs c mod ) T Q 1 c (c obs c mod ) + µ 2 pt p f pri = f fix, Q f,pri = ff T pri = 1 µ FFT implicit a-priori covar. matrix Structure: Flux components: f(p) = i f fix,i + F i p i

21 Method Linear statistical flux model Flux model : f(p) = }{{} f fix fixed + Fp }{{} adjustable with p pri =, pp T pri = 1 µ 1 J = 1 2 (c obs c mod ) T Q 1 c (c obs c mod ) + µ 2 pt p f pri = f fix, Q f,pri = ff T pri = 1 µ FFT implicit a-priori covar. matrix Structure: Flux components: f(p) = i f fix,i + F i p i f i (x, y, t) = f fix,i (x, y, t) + f sh,i (x, y) }{{} shape weighting N t,i N s,i m t =1 m s =1 g time m t,i(t) }{{} temporal decomp. time corr. g space m s,i (x,y) } {{ } spatial decomp. space corr. p mt,m s,i

22 Method APO flux model Fixed term ( prior ): Flux components: f APO = (f O2,oc f CO2,oc XO 2 X N 2 + (f O2,ff f CO2,ff ) f N 2,oc ) Data sets: dpo 2 [Garcia & Keeling] dpco 2 [Takahashi et al.] Long-term: Ocean-interior inversions [Gloor et al., Gruber et al.] N 2 from heat flux Prior fluxes are Climatological (non-iav ) Data-driven

23 Method APO flux model Adjustable term: Flux components: f APO adj = f APO,lt adj + f APO,seas adj + f APO,var adj Spatial correlations: Temporal correlations: w(ν) ν (1/yr) Weighting: Seas.: Var.:

24 APO flux estimates 2 NH Ocean APO Flux (Tmol/year) Tropical Ocean SH Ocean (full variability, 3 selected years) Ocean

25 APO flux estimates 2 NH Ocean APO Flux (Tmol/year) Tropical Ocean SH Ocean Cmp. to dpo 2 measurements phase agreement Ocean

26 Interannual APO flux variations 2 NH Ocean APO Flux (Tmol/year) Tropical Ocean SH Ocean (deseasonalized, IAV filtered, anomalies) 5, 7, or 9 sites Ocean

27 How much detail can be resoved? 8 NH Ocean 4-4 Known truth Transport simulation Pseudo data APO Flux (Tmol/year) Tropical Ocean SH Ocean Inversion Test retrieval 5, 7, or 9 sites (Synthetic Inversion)

28 Interannual APO flux variations Test: Split sites into disjoint sets rule out local signals ALT CBA LJO MLO KUM SMO CGO PSA SPO APO Flux (Tmol/year) NH Ocean Tropical Ocean SH Ocean APO Flux (Tmol/year) NH Ocean Tropical Ocean SH Ocean 21- S5 S7 S9 SE5 SE Ocean

29 Interannual APO flux variations IAV correlation APO Tropical ENSO NH Ocean -2 MEI index APO Flux (Tmol/year) Tropical Ocean SH Ocean Ocean

30 Interannual APO flux variations IAV correlation APO Tropical ENSO 2 1 NH Ocean Significance > 97.8% both - linear correlation - Spearman rank correlation Corr.Coeff Tropical Ocean MEI index APO Flux (Tmol/year) Tropical Ocean SH Ocean Ocean

31 Interannual APO flux variations IAV correlation APO Tropical ENSO 2 1 NH Ocean reduced ventilation of the oxygen minimum Corr.Coeff Tropical Ocean MEI index APO Flux (Tmol/year) Tropical Ocean SH Ocean Ocean

32 Interannual APO flux variations IAV correlation APO Tropical ENSO 2 1 NH Ocean Sensitivity cases: - tighter prior sigma - longer spatial correlations - shorter temporal corr transport Test cases (Deterioration expected): - Const. winds (199, 1995, 1997). Corr.Coeff Tropical Ocean APO Flux (Tmol/year) Tropical Ocean SH Ocean - α = Subtr. glob. mean conc Ocean

33 Interannual APO flux variations IAV correlation APO Tropical ENSO 2 1 NH Ocean APO Conc. (per meg) Sensitivity cases: - tighter prior sigma ( ) - longer spatial correlations - shorter temporal corr. Fit to data (): (deseasonalized) Signal vs. noise... fxalts Corr.Coeff Tropical Ocean ALT APO Flux (Tmol/year) Tropical Ocean SH Ocean Ocean

34 Conclusions Why Oxygen? 1. Reinforcing processes 2. Avoid land influence ( APO)

35 Conclusions Why Oxygen? 1. Reinforcing processes 2. Avoid land influence ( APO) Method: Analog. to CO 2, [Rödenbeck, Tech.Rep. (25)] Weighted individual data values Statistical flux model (data-driven, non-iav)

36 Conclusions Why Oxygen? 1. Reinforcing processes 2. Avoid land influence ( APO) Method: Analog. to CO 2, [Rödenbeck, Tech.Rep. (25)] Weighted individual data values Statistical flux model (data-driven, non-iav) Robustness: (of target features) Cross-validation Synthetic inversion Sensitivity (prior strength, corr. lengths)

37 Conclusions Why Oxygen? 1. Reinforcing processes 2. Avoid land influence ( APO) Method: Analog. to CO 2, [Rödenbeck, Tech.Rep. (25)] Weighted individual data values Statistical flux model (data-driven, non-iav) Robustness: (of target features) Cross-validation Synthetic inversion Sensitivity (prior strength, corr. lengths) Tropical APO flux: ENSO correlation reduced ventilation of the oxygen minimum APO Flux (Tmol/year) Tropical Ocean