Incorporation of Stable Water Isotopes in GSM and Spectrum-Nudged 28-year Simulation

Kei YOSHIMURA1,2 1: Scripps Institution of Oceanography, University of California San Diego

2: Institute of Industrial Science, The University of TokyoEmail: k1yoshimura@ucsd.edu, HP: http://meteora.ucsd.edu/~kyoshimura

HH18O

16OH

H

<HD

16O

(‘Heavy’ Water)Easy to condense

(‘Light’ Water)Easy to evaporate

0.20％

0.015％H2O

H218O

HDO

Water Isotopes

2. Results of Global Isotope Simulation with Spectral Nudging

1. Introduction

3. Comparison with in-situ observations.Summary

NOAA 32nd Annual Climate Diagnostics and Prediction Workshop, October 22-26, 2007, Tallahassee, FL, USA

References:

Majoube, J., Fractionation factor of 18O between water vapor and ice, Nature, 299, 1242, 1970.

Merlivat, L., Molecular diffusivities of H216O, HD16O, and H218O in gases, J.Chim.Phys., 69, 2864-2871, 1978.

Merlivat, L., and J. Jouzel, Global climatic interpretation of the deuterium oxygen 18 relationship for precipitation, J.Geophys.Res., 84, 5029-5033, 1979.

Jouzel, J., and L. Merlivat, Deuterium and oxygen 18 in precipitation: modeling of the isotopic effects during snow formation, J. Geophys.Res., 89, 11749-11757, 1984.

Stewart, M.K., Stable isotope fractionation due to evaporation and isotopic exchange of falling waterdrops: Applications to atmospheric processes and evaporation of lakes, J.Geophys.Res., 80, 1133-1146, 1975.

Yoshimura, K. and M. Kanamitsu, Dynamical global downscaling of global reanalysis, submitted to Mon.Wea.Rev. (In revision)

Acknowledgment: This research is funded by JSPS and CEC.

Right figure shows observation (GNIP), model mean by SWING, and IsoGSM annual climatology and seasonal departure (DFJ-JJA) of d18O and d-excess (dD-8*d18O) in precipitation. The model mean and IsoGSM agree quite well with lower d18O values in high latitudinal regions (“latitude effect”), in high elevation regions (“altitude effect”) and in inland region (“continent effect”). d-excess also shows good correspondence with observations.

Right figure: dD in column vapor and the seasonal variation, and the same of d-excess.

Bottom figure: Worden’s (2007) result showing the isotopic evidence of evaporation from raindrop. It is caused by kinetic fractionation processes. Right figure shows simulated result for same criteria.

Stable water isotopes are incorporated into NCEP/ECPC’s global spectral model in a manner similar to other isotope models. In addition, a newly developed spectral nudging technique (Yoshimura et al., 2007) is used, which allows to reproduce the actual spatial and temporal distribution of water and isotopes distributions. Divergence, vorticity, and temperature in NCEP/NCAR Reanalysis 2 data are the base fields that are nudged for more than 1000 km scales. Specific humidity remains unnudged in order to close the water budget. A T62L28 large-scale nudging simulation for 1979-2006 has now been simulated and the results show much more realistic precipitation isotope variations in comparison to other free-forecast simulations.

Not includedCloud water pred. 16 types from STATSGO Soil T62 L28Resolution Smoothed mean from USGS GTOPO30 Topography NCAR (Chen 1996) Direct evaporation 12 types from USGS Vegetation Noah, four soil layersLand model Richardson number dependent Vertical diffusion Pierrehumbert (Alpert et al. 1988) Gravity wave drag Slingo (Slingo 1987) Cloudiness M.-D. Chou (Chou and Lee 1996) Short wave rad. M.-D. Chou (Chou and Suarez 1994) Long wave rad.Monin-ObukhovSurface layer Non-local scheme (Hong and Pan 1996) Boundary layer Tiedtke scheme (Tiedtke 1983) Shallow convection Evaporation of rain included Large scale cond.

Relaxed Arakawa-Schubert (Moorthi and Suarez 1992)

Convection

Not includedCloud water pred. 16 types from STATSGO Soil T62 L28Resolution Smoothed mean from USGS GTOPO30 Topography NCAR (Chen 1996) Direct evaporation 12 types from USGS Vegetation Noah, four soil layersLand model Richardson number dependent Vertical diffusion Pierrehumbert (Alpert et al. 1988) Gravity wave drag Slingo (Slingo 1987) Cloudiness M.-D. Chou (Chou and Lee 1996) Short wave rad. M.-D. Chou (Chou and Suarez 1994) Long wave rad.Monin-ObukhovSurface layer Non-local scheme (Hong and Pan 1996) Boundary layer Tiedtke scheme (Tiedtke 1983) Shallow convection Evaporation of rain included Large scale cond.

Relaxed Arakawa-Schubert (Moorthi and Suarez 1992)

Convection

Have integrated records of phase changes during hydrologic cycles.

Iso Var

Iso. Var.

River Flow

H

H

18O

16OH

H

<

HD16O

0.20％ 0.016％

H2O

H218O HDO

(‘Heavy’ Water)Easy to condense

(‘Light’ Water)Easy to evaporate

“Fractionation” causes large heterogeneity in time and space.

a. What are Stable Water Isotopes (SWI)?

b. Why should SWI incorporated into GCMs?

c. What is merit of using NCEP/ECPC G-RSM?

d. Isotopic and other physics in Iso-GSM

a. Global Climatology of SWI in Precipitation compared with GNIP data

b. Global Climatology of SWI in Vapor compared with TES Observation.

a. Typhoon event (2006/09/14-16)

c. Seasonality of SWI in Precipitation

Top right figure: Correlation coefficient distributions of all monthly precipitation d18O compared with GNIP dataset for three SWING models and IsoGSM. Closer to one (blue) is better.Further right figure: Climatologic seasonality in selected GNIP observation sites (only 12 are shown among 660 sites). Precipitation of ECHAM4, IsoGSM, and observation are also shown.Right figure: The anomaly of d18O is shown. It is calculated by removing climatologic seasonality. All free forecast models have difficulty to simulate such second order variations.

To understand isotopic behavior in hydrologic cycles on large scales.

To help interpretation of past and current isotopic information, i.e., precipitation, ice cores, corals, stalagmite, etc.

To detect an erroneous circulation in the model and/or evaluate the model in an integrated manner. H18O

H

HD16O

All previous Iso-AGCMs (GISS, GISS-E, ECHAM, MUGCM, et al) are free forecast models. Climatology is OK, but cannot reproduce “real” variability. Therefore, incomparable with in-situ observations.

Some sort of assimilation of should be tested.

GSM is popular as NCEP Reanalysis’ model, and well tested with latest assimilations. Global Spectral Nudging (Yoshimura and Kanamitsu, 2007) is

applicable.

Common physics with the Regional Spectral Model.

Physical processesIsotopic Physical processes

Figures: from left to right, annual climatology of precipitation d18O, its seasonal differences, annual climatology of d-excess, and its seasonal differences. From top to bottom, GNIP, Model mean, and IsoGSM results are shown.

From Worden et al., 2007

In the table below, by taking simple average of correlation coefficients of monthly d18O and its anomalies over all available sites (about 200), IsoGSM with nudging has the best number. All other models are enable to simulate seasonality of isotopic climatology, but they all failed to produce accurate interannual variability, whereas IsoGSM is capable to do so.

0.350.000.03-0.03AnomCor

0.610.270.440.38Correlation

IsoGSMMUGCMGISS-EECHAM

0.350.000.03-0.03AnomCor

0.610.270.440.38Correlation

IsoGSMMUGCMGISS-EECHAM

16 925hPa

2100UTC on 15

15 945hPa12 945hPa

1004hPa 10 Sep2006

17 940hPa

18 975hPa

19

Iriomote I.Ishigaki I.

Longitude

Latit

ude

Longitude

Western Pacific

20

Taiwan0000UTC on 16

0900UTC on 16

0900UTC on 15

15 21 03 09 15 21 03 09 15 21 0314 Sep. 15 Sep. 16 Sep.

OB1 OB2 IB1 IB2

FEW

Eye

(b)

(a)REW

δ1

8O

(pe

rmil

)

0

-10

-20

-30

20

10

0d-e

xcess(p

erm

il)

0.6

0.4

0.2

0.0Sa

lin

ity (

pe

rce

nt)

(c)

Fudeyasu et al., submitted

0

-25

20

5

Model Typhoon eyeVap (clm)

Prcp

Vap (clm)

Prcp

ok

okd18O

D-excess

IsoGSM results

Cyclonic field emerges

b. Traverse Observation over Antarctic Sea (2006/01)

Uemura et al., prep.

- 180

- 170

- 160

- 150

- 140

- 130

- 120

- 110

- 100

- 90

- 802006/ 1/ 4 2006/ 1/ 10 2006/ 1/ 16 2006/ 1/ 22 2006/ 1/ 28

- 26

- 24

- 22

- 20

- 18

- 16

- 14

- 12

- 10

HDO18O

50

55

60

65

70

75

80

85

90

95

1002006/ 1/ 4 2006/ 1/ 10 2006/ 1/ 16 2006/ 1/ 22 2006/ 1/ 28

- 5

0

5

10

15

20

25

30RHd- ex

-5

0

5

10

15

20

252006/ 1/ 4 2006/ 1/ 10 2006/ 1/ 16 2006/ 1/ 22 2006/ 1/ 28

sst

Obs Model

Uemura et al., prep.

Spectral Nudging Scheme

1000 kmNudging Scale0.9Nudging Coef.U, V, TVariablesYoshimura and Kanamitsu (2007) Spectral Nudging

1000 kmNudging Scale0.9Nudging Coef.U, V, TVariablesYoshimura and Kanamitsu (2007) Spectral Nudging

Constant (18O=D=0‰)Sea waterNo fractionationLand surface

Stewart (1975)Rain drop evaporation

Merlivat and Jouzel (1979)Open water evaporation

Jouzel and Merlivat (1984)Ice crystal formation

Merlivat (1978)Diffusivity

Majoube (1970, 1971a, 1971b) Equilibrium Fractionation

Constant (18O=D=0‰)Sea waterNo fractionationLand surface

Stewart (1975)Rain drop evaporation

Merlivat and Jouzel (1979)Open water evaporation

Jouzel and Merlivat (1984)Ice crystal formation

Merlivat (1978)Diffusivity

Majoube (1970, 1971a, 1971b) Equilibrium Fractionation