Sea Level Rise 2006 Model Results of change in Land Water Storage and Effects on Sea-Level Katia Laval Université Pierre et Marie Curie. Paris LMD/IPSL

Global Mean Sea Level Variations from Altimetry in mm

Causes of sea level variations Steric effect: thermal expansion of the oceans water mass exchanged with other reservoirs: atmospheric water vapor and land water Sea level variations evaluated by T/P for (Black), steric effect evaluated from Ishii et al, 2003, water vapor contribution from NCEP reanalysis and residual signal.

Outline Land Surface Models Seasonal Variations of Global Sea level; Interannual variability (1997/1998) Seasonal Variations of Regional land water (GRACE) Trend of sea level height during the last 53 years related to terrestrial water storage.

Land Surface Models W PET R S Storages W: soil moisture S: Snow depth Precipitation (rain or snow): prescribed Evapotranspiration ( Rad Meteor parameters and vegetation and wetness) Runoff Snow melt

SOIL P R I BB’ D B (S B ) B’ R D Q in Q 1 out Q 2 out Q 3 out Q out = Q’ in ET Runoff Routine and ground water Soil Hydrology V1V1 V2V2 V3V3 Irrigation Flood plains Land Surface Models Orchidee; Runoff Routine scheme: Jan Polcher ;Tristan D’Orgeval fast slow

GSWP1: Evaluation of seasonal variation of land water by LSP ISLSCP-I International Satellite Land-Surface Climatology Project, produced the atmospheric forcing over the continents for 1987 and 1988  Seasonal variations of SLH evaluated by T/P and 3 LSM: LaD (GFDL), ISBA Meteo-France, Orchidee (LMD/IPSL) (Snow+soil water+ground water)  The differences could be due to :  incompatibility of the compared periods  data/model uncertainties

LMD AGCM Simulations (+Orchidee): AMIP Simulation (79-99 SST) Contribution of continental water to sea level variations Precipitations computed by the GCM Ngo-duc, T., K. Laval, J. Polcher and A. Cazenave (JGR, 2005a) Sharp contrast 1997 /1998 (Willis et al, 2004) : Observations from T/P: 13mm compared to 7mm (10mm/7mm) The variation between 1998 and 1997 is larger than internal variability

Climate-Model Biases in Seasonality revealed by Satellite Gravimetry (Swenson and Milly, 2005, WRR) Models evaluated in this study and water stores used. “X” indicates presence of term; “0” indicates absence from model.

Global map of amplitude (mm) of annual cycle of land water storage from GRACE and from five climate models. (Swenson and Milly, 2005, WRR)

. Seasonal Variations (April-May minus November 2002) of land water in mm From GRACE Orchidee without Ground Water reservoir Orchidee with Ground Water reservoir Ngo-duc, et al, 2006, submitted,WRR.

Time series of water storage variation as simulated by 2 versions of Orchidee, with and without routine scheme and ground water scheme and evaluated by Grace Mission (o). Ngo-duc, et al, 2006, submitted, WRR.

Construction of NCC data NCEP/NCAR Reanalysis 6h; 1°.875; 1948-present NCEP NPRE NCRU NCC Interpolation to the grid 1° x 1°, differences in elevation between the grids were taken into account CRU (Climate Research Unit) precipitation 0.5° x 0.5°, CRU (Climate Research Unit) temperature 0.5° x 0.5°, Radiation : SRB (Surface Radiation Budget) (NCEP/NCAR Corrected by CRU) 6-hourly, 1°x1°, dods/Dods/NCC/ (~40GB) Ngo-duc, T., J. Polcher and K. Laval (JGR, 2005b)

Effect of global land water storage on global mean sea level agreement between ORCHIDEE and LaD. (Land Dynamics LSM of GFDL) greatest variation is associated with ground water, followed by soil moisture no significant trend was detected strong decadal variability driven by precipitation, strong decrease in the beginning of 1970s Milly, P. C., D., A. Cazenave, and M. C. Gennero (Proc. Natl Acad. Sci, 2003) Ngo-duc T., K. Laval, J. Polcher, A. Lombard and A. Cazenave (GRL, 2005)

Relations between land water and thermosteric sea level fluctuations These results suggest a feedback mechanism: Ocean warmer more evaporation and continental precipitation increases continents are wetter: sea-level height decreases

Conclusions The LSMs are able to simulate the seasonal variations of global land water storage, and some interannual variability is also captured by LSMs and GCMs We need more studies to strengthen our results on regional seasonal variations -LSMs models: we must improve the reservoirs representation (lakes, dams, processes) -Grace data for several years

Conclusions Trends of terrestrial water storage have to be ascertained : -NCC data used by other LSMs -Other data (Qian et al, 2006) -Results on last years with Grace Influence of anthropogenic changes