The carbon response during the 2015 El Niño: harbinger of things to come?

Transcription

1 The carbon response during the 2015 El Niño: harbinger of things to come? Junjie Liu 1, Kevin W. Bowman 1, David Schimel 1, Nicolas C. Parazoo 1, Zhe Jiang 2, Meemong Lee 1, A. Anthony Bloom 1, Debra Wunch 3, Christian Frankenberg 1, 4, Ying Sun 1+, Christopher W. O Dell 5, Kevin R. Gurney 6, Dimitris Menemenlis 1, Michelle Girerach 1, David Crisp 1, and Annmarie Eldering 1 1 Jet Propulsion Laboratory California Institute of Technology 2National Center for Atmospheric Research. 3 University of Toronto. 4. California Institute of Technology 5. Colorado State University 6. Arizona State University California Institute of Technology. Government sponsorship acknowledged 2012 California Institute of Technology. Government sponsorship acknowledged

2 Largest CO2 Growth Rate in 50 years 3.05 ppm yr -1 (2015) 2.93 ppm yr -1 (1998) 2015 had the highest atmospheric growth record in the Mauna Loa record, beating out the 1998 growth rate. Growth rate was 50% higher than the previous year but anthropogenic emissions were roughly the same. What were the spatial drivers of this growth rate? How are they related to climate forcing?

3 NASA CMS-Flux Carbon Monitoring System-Flux Framework Surface Observations Carbon Cycle Models Inversion System Atmospheric Observations Anthropogenic emissions GEOS-Chem Terrestrial exchange Ocean exchange 4D-var/LETKF GOSAT/OCO-2 SIF, Jason SST, nightlights, etc. Posterior Carbon Fluxes (CO 2, CH 4, CO) OCO-2 CO2, GOSAT CO2 and CH4, MOPITT CO Attribution The NASA Carbon Monitoring System Flux (CMS-Flux) attributes atmospheric carbon variability to spatially resolved fluxes driven by data-constrained process models across the global carbon cycle.

4 T precip Forcing the situation Tropical South America Tropical Africa Tropical Asia 2015 was an extreme year: Driest year over tropical South America Hottest year over tropical Africa Warm and dry over tropical Asia

5 Study in contrast Estimate and contrast fluxes during an extreme year (2015) (OCO-2) against a nominal year (2011) (GOSAT). The total flux inferred from CMS-Flux can be decomposed into a sum of terms representing key processes within the carbon cycle. Net flux into the atmosphere is positive Fossil Fuel Ocean Biomass burning NEP Chemical Source Source: OCO-2 GOSAT FFDAS ECCO2 Darwin MOPITT GOSAT GEOS-Chem

6 Tropical drivers of the atmospheric growth rate in 2015 relative to ΔNBE, GtC Trop. S. America ΔNBE, ΔGPP, GtC GtC ΔT, K Trop. Africa Trop. S. America ΔGPP, ΔFire, GtC GtC ΔT, Δprecip, K mm/day Tropical Asia Trop. Africa ΔFire, Δ(respiration), GtC GtC Δprecip, mm/day Tropical Asia Δ(respiration), GtC The tropics released 2.4 ± 0.34 Gt more carbon into the atmosphere in 2015 than in 2011 The tropics accounted for 78.7% of the global total 3.0 GtC NBE difference, 88% the atmospheric CO 2 growth rate differences -1-1 Liu et al, in Rev

7 Contrasting responses to climate forcing ΔNBE, GtC Trop. S. America ΔNBE, ΔGPP, GtC GtC ΔT, K Trop. Africa Trop. S. America ΔGPP, ΔFire, GtC GtC ΔT, Δprecip, K mm/day Tropical Asia Trop. Africa ΔFire, Δ(respiration), GtC GtC Δprecip, mm/day Tropical Asia Δ(respiration), GtC The three tropical continents have approximately equal contributions but are associated with different drivers. Asian flux anomaly is dominated by increased fire and reduced productivity S. American flux anomaly is dominated by reduced productivity. African flux anomaly is dominated by increased respiration Liu et al, in Rev

8 Pattern of Extreme forcing Contour: GPP-weighted precipitation diff ; shaded: difference larger than 2σ 2 Contour: GPP-weighted T diff ; shaded: difference larger than 2σ 2 The number of dry month difference between 2015 and 2011 The monthly mean precipitation over tropical S. America and tropical Asia was lower by 2.9 σ and 2.2 σ. In both regions, the dry season (monthly precipitation less than 100 mm) lengthened by about 1-3 months from 2011 to Tropical Africa T were higher by 1.6 σ. Liu et al, in Rev

9 Pattern of response to extreme forcing Masked regions with precip difference larger than 2σ 2 a. Masked regions with PRECIP difference larger than σ In tropical S. America where precipitation was 3.5 σ lower than average accounted for virtually all of the ± 0.24 GtC increase Masked regions with T difference larger than 2σ 2 b. Masked regions ΔNBE, with GtC T difference larger than σ Trop. S. America ΔGPP, GtC ΔT, K Trop. Africa ΔFire, GtC Δprecip, mm/day Tropical Asia Δ(respiration), GtC In tropical Africa, about half of the NBE increase ( ± 0.18 GtC) occurred in regions where temperature differences exceeded 4 σ, covering less than 30% of the land area Trop. S. America Trop. Africa Tropical Asia ΔNBE, GtC ΔGPP, GtC ΔFire, GtC Δ(respiration), GtC ΔT, K Δprecip, mm/day -0.1 In tropical Asia, which was both excessively dry and hot, biomass burning dominated flux. In contrast to 97-98, BB only account for 17% of total tropical NBE Liu et al, in Rev

10 Conclusions CO2 growth rate mitigation requires attribution of forcing and feedbacks at the spatial scales on which they occur. The tropics released 2.4 ± 0.34 Gt more carbon into the atmosphere in 2015 than in 2011 accounting for 78.7% of the global total 3.0 GtC NBE difference, and 88% of the atmospheric CO2 growth rate differences. While tropical continental contributions were roughly the same, the dominant carbon processes were different: S. America (GPP), Africa (Resp), and Asia (Fire) Fluxes associated with climate extremes were the dominant drivers of the tropical fluxes.

11 Backup

12 Orbital Carbon Observatory (OCO-2) Collect spectra of CO 2 & O 2 absorption in reflected sunlight over the globe 16 day repeat cycle 10km 1.29x2.25-km footprint; eight cross-track footprints create a swath width of 10.3 km Launched in June, 2014 into an afternoon, polar sunsynchronous orbit as part of the NASA A-Train constellation, OCO-2 provides dry-column mole fraction CO2 (XCO2). Compared to TCCON, median differences are less than 0.5 ppm and RMS differences typically below 1.5 ppm (Wunch et al, 2016 AMTD)

13 Frankenberg et al, 2011 GPP inferred from solar induced fluorescence Frankenberg et al, 2011 Optimal estimation provides a framework to determine GPP that accounts for uncertainty in the fluorescence, prior uncertainty in GPP, satellite coverage and timing. x a =mean Trendy GPP y: GOSAT SIF at time {t i } F(x): Observation operator: GPP to GOSAT overpass S n : Error in GOSAT SIF, S a :Ensemble Trendy spread Parazoo et al, 2013

14 Respiration: combustion Measurements of Pollution in the Atmosphere Carbon monoxide is a byproduct of incomplete combustion. MOPITT provides CO verticals with near surface sensitivity. CMS-Flux estimates CO from MOPITT and converts to CO 2 CO 2 from biomass burning is calculated from CO/CO2 ratios (Andreae and Merlet, GBC, 2001) Emission factors are a function of dry mass (given) and burning efficiency, which is a function of plant function type. Emission factors

15 OCO-2 XCO2 (2015) GOSAT XCO2 (2011) Do 2015 OCO-2 and 2011 GOSAT have relative bias? TCCON XCO2 TCCON XCO2 The relative differences between OCO-2 X CO2 and GOSAT X CO2 were negligible when both were compared to X CO2 from Total Carbon Column Observing Network

16 Validating against independent aircraft Globe, 2011, rms(prior)=1.1ppm, rms(post)=0.3ppm NA, 2011, rms(prior)=1.2ppm, rms(post)=0.3ppm observations trop, 2011, rms(prior)=0.2ppm, rms(post)=0.2ppm Globe, 2015, rms(prior)=1.8ppm, rms(post)=ppm NA, 2015, rms(prior)=1.8ppm, rms(post)=ppm trop, 2015, rms(prior)=0.6ppm, rms(post)=0.5ppm The posterior CO2 concentrations have been improved after assimilating satellite XCO2 observations