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Dr. Benjamin R. Lintner

Blowin’ in the wind: Atmospheric circulation, tracer transport, and CO 2 variability. Dr. Benjamin R. Lintner. Department of Atmospheric & Oceanic Sciences and Institute of Geophysics & Planetary Physics University of California Los Angeles.

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Dr. Benjamin R. Lintner

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  1. Blowin’ in the wind: Atmospheric circulation, tracer transport, and CO2 variability Dr. Benjamin R. Lintner Department of Atmospheric & Oceanic Sciences and Institute of Geophysics & Planetary Physics University of California Los Angeles I.Y. Fung, C.D. Koven (UC Berkeley); W. Buermann (UCLA) A.B. Gilliland (Air Resources Laboratory) C.J. Tucker, J.E. Pinzon (Goddard Space Flight Center) A. Angert (Weizmann Institute of Science); K.P. Bowman (Texas A&M)

  2. Concentration of CO2 Transport of CO2 0 Source of CO2 ( emission + production) Conservation of CO2

  3. Q: How can we obtain this term? Bottom-up: Field measurements (direct but costly) Top-down: Inversions (indirect but uncertain) Q: What kind of information can be extracted? Q: How much confidence do we have in G? The big picture… General inversion methodology: For a set of observations obsi,construct and minimize a cost function J assuming a set of unit source/sink basis regions ûk and a forward transport model G.

  4. Strong interhemispheric (meridional) gradient roughly co-located with the ITCZ; (generally) weak zonal gradients, reflecting rapid east-west mixing, although locally large near source regions. Lat-lon distribution of fossil fuel (FF) CO2*

  5. Lat-Height characteristics in the Tropics are broadly understood in terms of the mean meridional overturning (Hadley) circulation. Meridional gradients are relaxed aloft compared to the surface; a “reversed” vertical gradient is characteristic of low latitudes in the nonsource (Southern) hemisphere. Lat-height distribution of FF CO2

  6. 2-box model Interhemispheric exchange time (IHT): Time required forS to “catch-up” to N. Definitions:

  7. Q: How do we obtain this value? The implied “sink” partitioning is: SNSink = 2.8 PgC/yr; SSSink = 0.2 PgC/yr Anthropogenic CO2 in a 2-box model Observational Constraints: sfc= 2.5 ppmv (column2/3sfc= 1.7 ppmv) G/t = (1/2)(N +S)/t = 1.5 ppmv/yr Consider Fossil Fuel (FF): SNFF = 6 PgC/yr*;SSFF~ 0 pgC/yr ForIHT~ 1 yr  (est) = 3.0 ppmv (4.5 ppmv at surface) STotal= 3.4 PgC/yr  SNSink - SSSink= 2.6 pgC/yr (SNTotal + SSTotal) = 3.0 PgC/yr *Note: 1PgC = 0.5 ppmv (mixed over whole troposphere)

  8. Observations (see Lintner 2003) 1yr CH3CCl3 85Kr CH4 Air mass CCl3F SF6 14CO2 All Obs. Mean : 0.880.22 years TransCom Models (Denning et al., 1999) 1yr GISS-UVIC/UCB -SKYHI GFDL-GCTM All Mod. GISS MUTM NIRE TM2 CSU ANU CCC Estimates of annual-mean IHT Mean : 1.280.33 years

  9. CFC11 CH4 SF6 1.3 1.1 0.9 0.7 0.5 18 20 22 24 5 6 7 8 9 IHT versus dynamics IHT(years) Peak Hadley Strength (x 1010 kg/s) JJA Land Precip. at 18N (mm/day) Changes to model vertical (+ horizontal) resolution alter dynamics, which in turn alters IHT. Generally, stronger Hadley circulation (land region convection) favors faster IHT. From GISS simulations of Rind et al., 2007

  10. 0.73 years CFC-11 (1979-1988) IHT seasonal cycle 1.8 1.4 1.27 years GISS-UCB years 1.0 0.86 years 0.6 NCEP/MATCH 0.2 Q: What is the source of this seasonality? J F M A M J J A S O N D Seasonal cycle: ±20-30% of annual mean “fast” IHT/small IHT in winter/summer and “slow” IHT/large IHT in spring/autumn From Lintner et al., 2004

  11. Transport partitioning Defining: 

  12. DJF Streamfunction Rind et al., 2007 sensitivity From seasonal cycle 1.8 years 1.50 1.4 IHT (years) x 107 kg/month 1.25 1.00 1.0 MAM Streamfunction 8 12 16 20 Max. Hadley Strength (x 1010 kg/s) Month Meridional transport in the GISS-UCB model Mean meridional, stationary-eddy, and transient eddy: ~1/3 of annual-mean total transport (Denning et al., 1999) Seasonality dominated by mean meridional (Hadley) circulation, with fast (slow) IHT occurring when Hadley circulation is strongly asymmetric (symmetric) w.r.t. equator.

  13. January July x 107 kg/month 6 0 -16 30 January July x 105 kg/month 10 -10 -30 Regional transport characteristics

  14. Outflow associated with deep convection Small scale vertical transport: Biomass burning plumes are uplifted near the ITCZ and diverged aloft. MOPITT CO for January 20-27, 2001 at 500 mb From Edwards et al., 2003

  15. La Niña El Niño GISS-UCB NCEP/MATCH IHT interannual variability (IAV) Interannual variations: ±5-10% of annual mean “fast” IHT/small IHT during La Niña events and “slow” IHT/large IHTEl Niño events(?)

  16. 10E-40E 60E-100E 110E-150E 140W-110W 160E-160W 75W-45W 40W-10W Sources of GISS-UCB IAV Conditional- averaged STRENGTH of convection for fast IHT and slow IHT. Often, more intense convection favors faster IHT (e.g., Indian Ocean/South Asia during JJA; South America during ASO), but not always (e.g., Eastern Pacific). From Lintner et al., 2004

  17. Generally, more extreme ITCZ displacements favor faster IHT (broadly consistent with chaotic advective mixing noted by Bowman and Cohen, 1997) But there is significant region-to-region variation….. 10E-40E 60E-100E 110E-150E 140W-110W 160E-160W 75W-45W 40W-10W Sources of GISS-UCB IAV Conditional- averaged LOCATION of convection (ITCZ) for fast IHT and slow IHT. From Lintner et al., 2004

  18. Summary of IHT • 2-box model mean exchange times (IHT) of 0.8-1.2 years, with pronounced seasonality (20-30% of mean) and IAV (5-10%) • From the perspective of carbon cycle, these variations impact estimates of the meridional distribution of CO2 sources/sinks. • IHT reflects contributions from both zonal-mean and regional circulations • Results are model-dependent • Obtaining a detailed picture of 3D interhemispheric transport pathways, i.e., how (and where) transport across the “interhemispheric transport barrier” occurs, is desirable. • What are the temporal characteristics (subseasonal, seasonal, interannual) of these pathways?

  19. 155ºW, 19ºN 3397 m a.s.l. Mauna Loa Observatory (MLO), Hawai´i Hurricane Flossie

  20. Detrended Seasonal Cycle Amplitude CO2from atmos. Photosynthesis Respiration CO2to atmos. CO2 seasonality at MLO ppmv 384 380 376 372 2002 2003 2004 2005 2006 2007

  21. Keeling (1996): increasing MLO amplitude from 1960s-early 1990s consistent with high latitude land surface warming and northward “greening” 1.2 1.1 1.0 0.8 0.9 0.4 0.0 Since the early 90s, MLO amplitude has decreased but NH land surface temperatures have continued to warm. -0.4 -0.8 1960 1970 1980 1990 Relative Amp Changing MLO CO2 amplitude Mauna Loa Amplitude Temp (K) Q: What accounts for this change of behavior? Land Surface Temp (30N-80N) 2000

  22. JFM AMJ NCEP Reanalysis 700 mb wind (vectors) and streamflow (contours) JAS OND 6-10 day Lagrangian back trajectories Eurasia N. America NCEP/MATCH FFSF6HCFC22 MLO and atmospheric circulation From Lintner et al., 2006

  23. Data from simulation of Higuchi et al., 2003: Some indication of a trend over the 1990s? Role of atmospheric transport Comparison of observed MLO Amp (black) to MATCH-simulated (blue) with no source/sink variability: Decrease in MATCH over the 1990s suggests nonnegligible transport signature. Observed MLO Amp MATCH simulated Amp 1.2 1.1 1.0 Q: What is the source of the simulated trend? 2 0.9 1 0 -1 Simulated FF (AMJ) 222Rn (AMJ) -2 1960 1970 1980 1990 2000 From Buermann et al., 2007

  24. Backtrajectory cluster analysis* “Short range” cluster “Long range” cluster Yearly membership in long range cluster Yearly membership in short range cluster AMJ FF AMJ FF 2 2 1 1 0 0 -1 -1 -2 -2 1992 2000 1992 2000 1996 1996 1988 1988 *Results shown are for April-May-June (AMJ) From Lintner et al., 2006

  25. Warm-season hydrology: recent development of strong correlations for North Americaeffect of North American drought during 1998-2003 reduced carbon uptake, resulting in decreased amplitude Changing temperature and moisture influence Warm-season temperatures: persistent +ve correlations with MLO Amp from 1960s-mid 1970s but little thereafter…. From Buermann et al., 2007

  26. Respiration Temperature: 1yr-lag 1 and 2 yr-lag 2 yr-lag Cold season Eurasia Respiration Photosynthesis Warm season Temperature: 1yr-lag Cold season North America Photosynthesis Photosynthesis Warm season Temperature Drought/rain cycles Schematic of MLO amplitude controls Transport

  27. Summary of MLO • Because of the relationship of the observing site relative to large-scale circulation, MLO is most sensitive to Eurasian influence during boreal cold season and North America influence during boreal warm season. • Provides some basis for the geographic distribution of correlations • Nonstationarity/changing influence through time • Some of the 1990s downward trend may be directly attributable to changes in transport as seen at the observing site. • Less Eurasian-originating transport in boreal spring reduces amplitude (support from backtrajectories/222Rn)

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