IsoMATSIRO development results Kei YOSHIMURA IIS Univ of

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Iso-MATSIRO development & results Kei YOSHIMURA IIS, Univ. of Tokyo JAPAN H H H

Iso-MATSIRO development & results Kei YOSHIMURA IIS, Univ. of Tokyo JAPAN H H H 18 O H 16 O D 16 O H

Outlines l l Descriptions of Iso-MATSIRO EQY 1 results l l Suggestions of experiments

Outlines l l Descriptions of Iso-MATSIRO EQY 1 results l l Suggestions of experiments at new sites l l Issues on “Plausibility” Yakutsk/Russia (Siberia) Tak/Thailand (Sub-tropics & Paddy field) (GSWP-like) Global run Previous Studies l l “Reanalyses-forced” Atmos. Isotope Circulation Model Colored Moisture Analysis (CMA)

MATSIRO (Takata et al. , 2003, GPC) (Minimal Advanced Treatments of Surface Interaction and

MATSIRO (Takata et al. , 2003, GPC) (Minimal Advanced Treatments of Surface Interaction and Run. Off) l l Si. B-type LSM 5 soil layers (default): l l l TOP model for base flow l l l Richards equation for vertical water movement C&H for hydraulic conductivity Ground water table depth is considered Snow cover and Glacier formation 12 soil types (Cosby et al. , 1984) 13 veg. types No Mosaic (currently)

Iso-MATSIRO l l l Each of water-related variables has its isotopic concentration. Water Mass

Iso-MATSIRO l l l Each of water-related variables has its isotopic concentration. Water Mass and Isotopic Mass are always balanced. Kinetic fractionations of l l l Soil evap. /subl. Intercepted water evap. /subl. Transpiration Snow sublimation are taken into account. No soil diffusion δEc δEt δSs δEb Rs δR Rb

Iso. MAT Calculation Flow 1. Albedo (Canopy / Snow / Ice ) l 2.

Iso. MAT Calculation Flow 1. Albedo (Canopy / Snow / Ice ) l 2. Turbulence Parameters (Bulk coefficients for bare soil / Canopy / Stomata) l Soil / Canopy / Stomatal resistance for with/without snow) Roughness l Snow Albedo l 3. 4. 5. 6. 7. 8. 9. Upward Radiations Sensible / Latent heat fluxes with/without snow Soil / Canopy / Stomatal Isotopic fluxes for with/without snow Canopy water balance Snow Area / Snow water balance (max. 3 layers) Runoff (Saturated / Infiltration excess / Overflow / Baseflow ) Soil water (Ice formation/melting) (5 layers) Vegetation water (1 layer)

Kinetic Fractionation -Jouzel’s modification of C&G l Assume that C&G is applicable to any

Kinetic Fractionation -Jouzel’s modification of C&G l Assume that C&G is applicable to any surface conditions (Canopy/Stomata/Soil, etc). Evaporation flux Rair, qair h=0~1 Isotopic flux V Equilibrium Ro Diffusion Req, qeq h=1 Isotopic ratio of evaporation flux

EQY 1 Simulations l l Iterate 1 year until equilibrium. Manaus, Munich, & Tumbarumba

EQY 1 Simulations l l Iterate 1 year until equilibrium. Manaus, Munich, & Tumbarumba Forcing: REMOiso, 15 min. Parameter: l l l Soil type (given) Veg. type l Manaus: Broadleaf evergreen forest l Munich: High latitude deciduous forest & Woodland l Tumb: Broadleaf deciduous forest & Woodland LAI (given)

Times for Equilibrium l Compare 00: 00, 1 Jan and 24: 00, 30, Dec.

Times for Equilibrium l Compare 00: 00, 1 Jan and 24: 00, 30, Dec. l l l Manaus l l 2 y for H 2 O, 4 y for H 218 O, 5 y for HDO Munich l l Water Threshold: 10 -5 mm in all soil water Isotopic Threshold: 10 -5 mm*SMOW in all soil water 4 y for H 2 O, 8 y for H 218 O, 10 y for HDO Tumbarumba l 3 y for H 2 O, 3 y for H 218 O, 5 y for HDO

Some Results (pls see Matt’s HP ) l Seasonal changes… 18 O in Canopy

Some Results (pls see Matt’s HP ) l Seasonal changes… 18 O in Canopy Evap at Munich 18 O in Soil Evap. at Manaus

Plausible? –Vertical profile of Soil SWI

Plausible? –Vertical profile of Soil SWI

Water Flux (mm/year) Manaus δ 18 O in water flux (‰) Tumbarumba δ 18

Water Flux (mm/year) Manaus δ 18 O in water flux (‰) Tumbarumba δ 18 O in water flux (‰) Water Flux (mm/year) Plausible? 2 -Annual budget and seasonal variability Tumbarumba Manaus

Plausible? 3 -Diurnal Change of SWI in Veg. Manaus Tumbarumba Munich

Plausible? 3 -Diurnal Change of SWI in Veg. Manaus Tumbarumba Munich

Plausible? 4 - Delta-Plot for Monthly Scale

Plausible? 4 - Delta-Plot for Monthly Scale

The Questions for each ILSS from Kendal l l l l Why the variation

The Questions for each ILSS from Kendal l l l l Why the variation in amplitude of diurnal cycles in deltas? Reservoirs: (if not reservoir size changes, ) Seem to depend on degree of corresponded water fluxes. Fluxes: ? ? What mechanisms are causing isotope variations? 1. Isotopic Forcings (of course) 2. Humidity variation (diurnal/seasonal) 3. Reservoirs sizes (soil/canopy/vegetation) 4. Latent heat partitioning (in case w/o tree? )

Suggestions for New Sites? ? l l Sub-Tropics, Thailand Permafrost, Siberia

Suggestions for New Sites? ? l l Sub-Tropics, Thailand Permafrost, Siberia

1. Tak, Thailand Tak Tower Made by Dr. Shin Miyazaki

1. Tak, Thailand Tak Tower Made by Dr. Shin Miyazaki

100 m Instruments Automatic monitoring from 2002 Rd. S Rd. L An a. Rd.

100 m Instruments Automatic monitoring from 2002 Rd. S Rd. L An a. Rd. S l. E H es. T 4 AP 30 m U Q Ta 0 m P G TG WG Made by Dr. Shin Miyazaki

View from tower (dry and rainy seasons) Made by Dr. Shin Miyazaki

View from tower (dry and rainy seasons) Made by Dr. Shin Miyazaki

MATSIRO Performance at tropical monsoon climate in Tak, Thailand By Shin Miyazaki (IIS, U-Tokyo),

MATSIRO Performance at tropical monsoon climate in Tak, Thailand By Shin Miyazaki (IIS, U-Tokyo), Wonsik Kim (NIAES), and Kei Yoshimura (IIS, U-Tokyo)

Soil moisture up-most (IGBP) Red:observation, black:simulation Rainy season Dry season-1 Dry season 2 •

Soil moisture up-most (IGBP) Red:observation, black:simulation Rainy season Dry season-1 Dry season 2 • Dry: Obs≒ sim, Rainy: Obs>>sim   Made by Dr. Shin Miyazaki

Latent heat flux (IGBP) Red:observation, black:simulation • Dry: sim≒obs, Rainy: sim≒obs Made by Dr.

Latent heat flux (IGBP) Red:observation, black:simulation • Dry: sim≒obs, Rainy: sim≒obs Made by Dr. Shin Miyazaki

2. Yakutsk, Russia Spasskaya Pad Experimental forest of IBPC: GAME/Siberia field observation site Yakutsk

2. Yakutsk, Russia Spasskaya Pad Experimental forest of IBPC: GAME/Siberia field observation site Yakutsk • Great forest with little precip. (=200 mm/y) • Lena river basin • Permafrost Made by Dr. Atsuko Sugimoto

Precip 18 O in Yakutsk Summer   18 O - high value - low

Precip 18 O in Yakutsk Summer   18 O - high value - low l Winter 18 O - low value - high l 18 O(‰) d d excess (‰) d Made by Dr. Atsuko Sugimoto

Different precip. amt. From year to year JJA prec (mm) 46 1998 DRY!! 177

Different precip. amt. From year to year JJA prec (mm) 46 1998 DRY!! 177 1999 WET!! 81 2000 Made by Dr. Atsuko Sugimoto

Soil water storage function for coming years Dry Summer Upward water flux l Melted

Soil water storage function for coming years Dry Summer Upward water flux l Melted ice was used for transpiration. Wet Summer Downward water flux l Water remained after transpiration Pass winter as ice. Stabilize transpiration Wet Dry Soil water+Ice (g/cm 3) frozen 0. 4 Soil water 18 O Ice lenz 0. 8 Made by Dr. Atsuko Sugimoto

Soil water storage 1998 46 1999 177 2000 Water equivalent (mm) JJA prec (mm)

Soil water storage 1998 46 1999 177 2000 Water equivalent (mm) JJA prec (mm) 1998 2001 1999 81 2000 Large Interannual variation P=E+R+ Q Innegligible!!! (Sugimoto et al. , 2003) Made by Dr. Atsuko Sugimoto

Outlines l l Descriptions of Iso-MATSIRO EQY 1 results l l Suggestions of experiments

Outlines l l Descriptions of Iso-MATSIRO EQY 1 results l l Suggestions of experiments at new sites l l Issues on “Plausibility” Yakutsk/Russia (Siberia) Tak/Thailand (Sub-tropics & Paddy field) (GSWP-like) Global run Previous Studies l l “Reanalyses-forced” Atmos. Isotope Circulation Model Colored Moisture Analysis (CMA)

Isotopically A-L Coupled Global Simulation (still offline) Calculation flow for Each Time step GAMERean.

Isotopically A-L Coupled Global Simulation (still offline) Calculation flow for Each Time step GAMERean. Upper Meteor. Qu, Qv, W, P, E ICM Isotopes in vapor/precip. Surface Meteor. U, V, q, T, p, P Iso. MAT Isotopes in Evap.

δ 18 O Distribution, Apr-Oct, 98 Surface Soil Water Total Runoff Precipitation Total Evaporation

δ 18 O Distribution, Apr-Oct, 98 Surface Soil Water Total Runoff Precipitation Total Evaporation

Prcp. δ 18 O (‰) Validation in Chiangmai, 99 E: 18 N (Precip. δ

Prcp. δ 18 O (‰) Validation in Chiangmai, 99 E: 18 N (Precip. δ 18 O) Bias Cor. RMSE -3. 2‰ 0. 74 4. 2‰ 0. 3‰ 0. 76 2. 7‰

Global Validation (Prcp. δ 18 O) Simulated δ 18 O (‰) GNIP δ 18

Global Validation (Prcp. δ 18 O) Simulated δ 18 O (‰) GNIP δ 18 O (‰) Honestly, this is NOT evidence of land impact on atm is large. It tells reasonable range of vapor isotopes are supplied. Bias Cor. RMSE -3. 4‰ 0. 69 4. 6‰ 1. 0‰ 0. 70 3. 1‰

River discharge isotope estimates with iso-TRIP δsr 1 O 1 δsr 2 O 2

River discharge isotope estimates with iso-TRIP δsr 1 O 1 δsr 2 O 2 δsr 3 O 3 SR δsr R δr v O Original TRIP: Oki and Sud (1998) Observations

δ 18 O (‰) Isotopic variation at the estuary of Chaophraya l Obs. range

δ 18 O (‰) Isotopic variation at the estuary of Chaophraya l Obs. range Runoff from Iso-Bucket is always through soil buffer, whereas iso-MAT runoff is mainly precip. -direct. Too large fluctuation

Outlines l l Descriptions of Iso-MATSIRO EQY 1 results l l Suggestions of experiments

Outlines l l Descriptions of Iso-MATSIRO EQY 1 results l l Suggestions of experiments at new sites l l Issues on “Plausibility” Yakutsk/Russia (Siberia) Tak/Thailand (Sub-tropics & Paddy field) (GSWP-like) Global run Previous Studies l l “Reanalyses-forced” Atmos. Isotope Circulation Model Colored Moisture Analysis (CMA)

(Atmospheric) Isotope (18 O) Circulation Model Yoshimura et al. 2003, 2004, JGR “Reanalyses-forced” offline

(Atmospheric) Isotope (18 O) Circulation Model Yoshimura et al. 2003, 2004, JGR “Reanalyses-forced” offline atmospheric model.

Reproduced Daily δ 18 O Variations well GAME only Chi Suk Ban Cor. RMSE

Reproduced Daily δ 18 O Variations well GAME only Chi Suk Ban Cor. RMSE 0. 76 0. 74 0. 56 4. 2 ‰ 4. 1 ‰ 3. 5 ‰ Chi Suk Ban Cor. RMSE 0. 80 0. 77 0. 60 2. 9 ‰ 2. 8 ‰ GAME + GPCP Yoshimura et al. , 2003, JGR

Global Distribution of δ 18 O is reproduced, too. Corr. coef. b/w monthly obs’d&est’d

Global Distribution of δ 18 O is reproduced, too. Corr. coef. b/w monthly obs’d&est’d prcp iso. for 1979 -93. (blue is good) Int-ann. variations of prcp iso. Yoshimura et al. , 2004, JGR

Colored Moisture Analysis 1. 25˚x 1. 25˚ W QUin QVin P E QVout Indian

Colored Moisture Analysis 1. 25˚x 1. 25˚ W QUin QVin P E QVout Indian Ocean QUout Pacific Ocean Indochina Pen. Bengal Gulf “Tag” spatial attribution onto evaporated water Sea: 60,Land: 20 2 D grid-plume model (vertical one layer) Fully mix in a timestep (10 min. ) Variables (Q, W, P, E) are externally given. Contents of water on today are analyzed

How Indian Ocean water moves?

How Indian Ocean water moves?

On a global scale

On a global scale

Continental cycling Ratio Yoshimura et al. , 2004, JMSJ

Continental cycling Ratio Yoshimura et al. , 2004, JMSJ

Chiangmai, Apr. -Oct. 1998 Contents of each origin in water vapor (precipitable water)

Chiangmai, Apr. -Oct. 1998 Contents of each origin in water vapor (precipitable water)

Bangkok, Apr. -Oct. 1998 Contents of each origin in water vapor (precipitable water)

Bangkok, Apr. -Oct. 1998 Contents of each origin in water vapor (precipitable water)

Thanks for your attention! l l l Yoshimura, K. , T. Oki, and K.

Thanks for your attention! l l l Yoshimura, K. , T. Oki, and K. Ichiyanagi, Evaluation of twodimensional atmospheric water circulation fields in reanalyses by using precipitation isotopes databases, J. Geophys. Res. , 109, doi: 10. 1029/2004 JD 004764, 2004. Yoshimura, K. , T. Oki, N. Ohte, and S. Kanae, Colored moisture analysis estimates of variations in 1998 Asian monsoon water sources, J. Meteor. Soc. Japan, 82, 1315 -1329, 2004. Yoshimura, K. , T. Oki, N. Ohte, and S. Kanae, A quantitative analysis of short-term 18 O variability with a Rayleigh-type isotope circulation model. J. Geophys. Res. , 108(D 20), 4647, doi: 10. 1029/2003 JD 003477, 2003. E-mail: kei@iis. u-tokyo. ac. jp Happy to have good cooperation with you

Sensitivity test - # of layers δ 18 O in soil water (‰) Depth

Sensitivity test - # of layers δ 18 O in soil water (‰) Depth (cm) 5 8 layers 2 m 4 m depth

δ 18 O in water (‰) mm/year Water/Isotopes Partitioning at 100 E 17 N

δ 18 O in water (‰) mm/year Water/Isotopes Partitioning at 100 E 17 N

l However, Systematic underestimation. l Possibly due to land originated water? ? Bar: Obs.

l However, Systematic underestimation. l Possibly due to land originated water? ? Bar: Obs. 1998 CMA results Line: Sim. Underestimation becomes larger Land originated water becomes more Yoshimura 2004, JMSJ

Global d-excess (δD-8*δ 18 O) estimation l Comparison with GNIP Systematic bias

Global d-excess (δD-8*δ 18 O) estimation l Comparison with GNIP Systematic bias

δ 18 O, δD, d-excess in Chiangmai δ 18 O d-excess δD

δ 18 O, δD, d-excess in Chiangmai δ 18 O d-excess δD