1. Land-atmosphere coupling, climate-change and extreme events +
Activities with regard to land flux estimations at ETH Zurich
Sonia I. Seneviratne
Institute for Atmospheric and Climate Science, ETH Zurich, Switzerland
LandFlux Meeting, Toulouse, France
May 29, 2007

2. Outline
Land-atmosphere coupling, climate change, and extreme events (Seneviratne et al. 2006)
Land-atmosphere coupling: hot spot in Europe?
Dynamics with climate change
Links with extreme events
Activities with regard to land flux estimations at ETH Zurich
Atmospheric-terrestrial water balance estimates
Some results on models’ estimates (land surface models, GCMs)
SwissFluxnet activities and Rietholzbach catchment site

3. L-A coupling in Europe
Seneviratne et al. 2006, Nature, 443,

4. L-A coupling in Europe
Koster et al., 2004, Science

5. L-A coupling in Europe
No strong coupling in Europe? How about Mediterranean region?
NB: Results based on only one year SST conditions (1994)
Koster et al., 2004, Science

6. L-A coupling in Europe T
(Koster et al. 2006, JHM) T
No strong coupling in Europe? How about Mediterranean region?
NB: Results based on only one year SST conditions (1994)
Koster et al., 2004, Science

7. Projected changes in To variability
PRUDENCE, CHRM, JJA ( )-( )
[%]
Ds/s
[ºC] DT
Schär et al. 2004, Nature
IPCC AR4 GCMs, JJA ( )-( ) DT D
Seneviratne et al. 2006, Nature, suppl. inf.

8. Projected changes in To variability
PRUDENCE, CHRM, JJA ( )-( )
[%]
Ds/s
[ºC] DT
Schär et al. 2004, Nature
IPCC AR4 GCMs, JJA ( )-( ) DT D
Seneviratne et al. 2006, Nature, suppl. inf.

9. Projected changes in To variability
PRUDENCE, CHRM, JJA ( )-( )
[%]
Ds/s
[ºC] DT
Large changes in To variability
What are the responsible mechanisms:
Large-scale circulation patterns? Land surface processes?
Schär et al. 2004, Nature
IPCC AR4 GCMs, JJA ( )-( ) DT D
Seneviratne et al. 2006, Nature, suppl. inf.

10. Seasonal Cycle of Soil Moisture
Responsible mechanisms?
Circulation patterns? (Meehl and Tebaldi 2004, Science)
Meehl and Tebaldi 2004
500 hPa: A1B-present
3-day worst heat event daily To:
A1B-present
Land surface processes? (Lenderink et al. 2007, Clim. Change; Vidale et al. 2007, Clim. Change)
Month
Soil moisture [mm]
Seasonal Cycle of Soil Moisture
Vidale et al. 2007

11. Land-atmosphere coupling experiment
Aim:
Investigate the role of land-atmosphere coupling for the predicted enhancement of summer temperature variability in Europe
Approach:
Perform regional climate simulations within the same set-up with and without land-atmosphere coupling for present and future climate conditions

12. Experimental set-up (1)
Regional Climate Model (RCM) simulations
CHRM (Lüthi et al. 1996; Vidale et al. 2003)
spatial resolution: 56 km
boundary conditions: HadCM3/ HadAM3H (PRUDENCE set-up)
two time periods: CTL ( ), SCEN ( )

13. Experimental set-up (2)
CTL,
SCEN
(“interactive”)
GCM boundary
conditions (circulation)
CTLUNCOUPLED,
SCENUNCOUPLED
(“uncoupled”)
GCM boundary
conditions (circulation)
RCM/LSM
(except soil moisture)
RCM/LSM
(except soil moisture)
soil moisture from LSM
(interannual variations)
soil moisture climatology
from CTL/SCEN
(no interannual variations)
Soil moisture [mm]
Seasonal Cycle of Soil Moisture
Soil moisture [mm]
Seasonal Cycle of Soil Moisture
(Seneviratne et al. 2006, Nature)

14. Summer temperature variability
Standard deviation of the summer (JJA) 2-m temperature
CTL
SCEN
CTLUNCOUPLED
SCENUNCOUPLED
(Seneviratne et al. 2006, Nature)

15. Summer temperature variability
Standard deviation of the summer (JJA) 2-m temperature
CTL
SCEN
Most of the enhancement of summer temperature variability in SCEN disappears in the SCENUNCOUPLED simulation
CTLUNCOUPLED
SCENUNCOUPLED
(Seneviratne et al. 2006, Nature)

16. Climate change signal vs. LA coupling
CLIMATE-CHANGE SIGNAL: SCEN-CTL
LA COUPLING STRENGTH IN SCEN:
SCEN-SCENUNCOUPLED
CONTR. OF EXT. FACTORS TO CC SIGNAL
SCENUNCOUPLED-CTLUNCOUPLED
CONTR. OF LA COUPLING TO CC SIGNAL
(SCEN-SCENUNCOUPLED)-(CTL-CTLUNCOUPLED)
Strength of land-atmosphere coupling in future climate is as large as 2/3 of the climate-change signal !
(Seneviratne et al. 2006, Nature)

17. Climate change signal vs. LA coupling
CLIMATE-CHANGE SIGNAL: SCEN-CTL
LA COUPLING STRENGTH IN SCEN:
SCEN-SCENUNCOUPLED
CONTR. OF EXT. FACTORS TO CC SIGNAL
SCENUNCOUPLED-CTLUNCOUPLED
CONTR. OF LA COUPLING TO CC SIGNAL
(SCEN-SCENUNCOUPLED)-(CTL-CTLUNCOUPLED)
Contribution of land-atmosphere coupling to climate change signal: dominant factor in Central and Eastern Europe!
(Seneviratne et al. 2006, Nature)

18. Temperature variability: Summary
Land-atmosphere coupling in future climate is extremely strong over the European continent: as large as 2/3 of the climate-change signal…
The enhancement of temperature variability in Europe is due for a large part to changes in land-atmosphere coupling characteristics: This is the dominant factor in Central and Eastern Europe!

19. GLACE results for present climate
(Koster et al. 2006, JHM) T
GLACE experiment (Koster et al. 2004; 2006): no high land-atmosphere coupling in Europe neither for temperature nor for precipitation
(Koster et al. 2004, Science) P
How is the strength of land-atmosphere coupling for present vs. future climate in our simulations?

20. Present vs. future climate
percentage of To variance explained by coupling [%]
land-atmosphere coupling strength parameter analogous to GLACE
Locally strong soil moisture-To coupling in present climate (Mediterranean; ≠GLACE)
Shift of region of strong soil moisture-To coupling from the Mediterranean to most of Central and Eastern Europe in future climate
(Seneviratne et al. 2006, Nature)

21. Comparison with IPCC AR4 GCMs
Indirect measure of coupling between soil moisture & To:
Correlation between summer evapotranspiration and temperature (ET,T2M)
Negative correlation: strong soil moisture-temperature coupling (high temperature as result of low/no evapotranspiration)
Positive correlation: low soil moisture-temperature coupling (high temperature leads to high evapotranspiration)

22. Comparison IPCC AR4 GCMs: (ET,T2M)
CTL time period
SCEN time period
Climate-change signal
RCM
All GCMs
3 “best” GCMs
(Seneviratne et al. 2006, Nature)

23. L-A coupling, Europe: present / future
Strong soil moisture-temperature coupling for the Mediterranean region in the CTL time period (≠GLACE)
Shift of region of strong soil moisture-temperature coupling to Central and Eastern Europe in future climate (transitional climate zone)
Qualitative agreement between RCM experiments and analysis of IPCC AR4 GCMs

24. Mechanism for To variability increase
no limitation wet climate
Seasonal Cycle of Soil Moisture
Soil moisture [mm]
transitional climate
CTL ( )
SCEN ( )
below threshold (“plant wilting point”) dry climate
Month

25. Summary
The projected enhancement of To variability in Central and Eastern Europe is mostly due to changes in land-atmosphere coupling
Climate change creates a new hot spot of soil moisture - To coupling in Central and Eastern Europe in the future climate (shift of climate regimes): Dynamic feature of the climate system!
LandFlux: Consider transient modifications with climate forcing (greenhouse gases, aerosols)

26. Outline
Land-atmosphere coupling, climate changes, and extreme events (Seneviratne et al. 2006a)
Land-atmosphere coupling: hot spot in Europe?
Dynamics with climate change
Links with extreme events
Activities with regard to land flux estimations at ETH Zurich
Atmospheric-terrestrial water balance estimates
Some results on models’ estimates (GSWP/GLDAS-type; GCMs)
SwissFluxnet activities and Rietholzbach catchment site

27. Atmospheric-Terrestrial Water Balance

28. Atmospheric-Terrestrial Water Balance

29. Atmospheric-Terrestrial Water Balance
Atmospheric water balance:

30. Atmospheric-Terrestrial Water Balance
reanalysis
data
(ERA-40)
Terrestrial water balance:
Atmospheric water balance:
measured
streamflow
(Rs+Rg)
Combined water balance:

31. Atmospheric-Terrestrial Water Balance
The water-balance estimates
depend only on observed or
assimilated variables (≠ P,E)
Main limitation:
valid only for domains > 105-
106 km2 (Rasmusson 1968,
Yeh et al. 1998)
reanalysis
data
(ERA-40)
Terrestrial water balance:
Atmospheric water balance:
measured
streamflow
(Rs+Rg)
Combined water balance:

32. Case Study: Mississippi & Illinois
Seneviratne et al. 2004, J. Climate, 17 (11),
Water-balance
Estimates
corr=0.8, r2=0.71
Observations
(soil moisture+
groundwater+snow)

33. Dataset for Mid-latitude River Basins
Hirschi et al. 2006, J. Hydrometeorology, 7(1), 39-60
“BSWB”
divQ & dW/dt: whole ERA-40 period ( )
runoff data: Global Runoff Data Center (GRDC)
Comparisons with soil moisture observations from the Global Soil Moisture Data Bank
Volga River basin ( )
corr=0.8
r2=0.64

34. Atmospheric-Terrestrial Water Balance
Atmospheric water balance:
Combined water balance:

35. Estimation of large-scale ET
Atmospheric water balance:
Mackenzie GEWEX Study (MAGS)
Peace
Louie et al. 2002

36. Estimation of large-scale ET
Retrospective dataset! ( ,
ERA-40; , ECMWF
operational forecast analysis;
e.g. Hirschi et al. 2006, GRL)
The water-balance estimates
depend only on observed P and
assimilated variables
Main limitations:
- valid only for domains > km2
(Rasmusson 1968, Yeh et al. 1998;
Seneviratne et al. 2004, J. Climate,
Hirschi et al, 2006, JHM)
- Imbalances, drifts of reanalysis data

37. Outline
Land-atmosphere coupling, climate change, and extreme events (Seneviratne et al. 2006)
Land-atmosphere coupling: hot spot in Europe?
Dynamics with climate change
Links with extreme events
Activities with regard to land flux estimations at ETH Zurich
Atmospheric-terrestrial water balance estimates
Some results on models’ estimates (GSWP/GLDAS-type; GCMs)
SwissFluxnet activities and Rietholzbach catchment site

38. Precipitation Forcing for LSMs
( Koster et al, 2004: GPCP product, )
Oki et al 1999: a minimum of about 30 precipitation gauges per 106 km2 or about 2 gauges per 2.5o x 2.5o GPCP grid cell are required for accurate streamflow simulations
Fekete et al. 2004: Range between 4 state-of-the-art precipitation datasets (CRU, GPCC, GPCP, and Willmott-Matsuura)
(Fekete et al. 2004)

39. Effects on Catchment LSM Output
r2 vs. ground data, yrs within (anomalies)
Illinois
Neva
Don
Dnepr
Volga
Amur
Lena
Yenisei Ob
Soil moisture + snow
Precipitation
LSM results strongly dependent on quality of forcing...

40. Modelling: GCMs Water-holding capacity LAND
Bucket model: typicially 15 cm
(Seneviratne et al. 2006, JHM)

41. Modelling: GCMs Soil moisture memory (Seneviratne et al. 2006, JHM)
Bucket model: typicially 15 cm
(Seneviratne et al. 2006, JHM)

42. Modelling: GCMs Soil moisture memory (Seneviratne et al. 2006, JHM)
Bucket model: typicially 15 cm
(Seneviratne et al. 2006, JHM)

43. Modelling: GCMs Water-holding capacity LAND
Bucket model: typicially 15 cm
(Seneviratne et al. 2006, JHM)

44. Modelling: GCMs P Land-atmosphere coupling
Significant range in model behaviour…
(Koster et al. 2004, Science)

45. Outline
Land-atmosphere coupling, climate change, and extreme events (Seneviratne et al. 2006)
Land-atmosphere coupling: hot spot in Europe?
Dynamics with climate change
Links with extreme events
Activities with regard to land flux estimations at ETH Zurich
Atmospheric-terrestrial water balance estimates
Some results on models’ estimates (GSWP/GLDAS-type; GCMs)
SwissFluxnet activities and Rietholzbach catchment site

46. Observations: FLUXNET
Worldwide CO2, water and energy flux measurements (integrating several projects such as AMERIFLUX, CARBOEUROPE, …)
At present, about 200 tower sites
however, still some serious limitations in temporal availability (in Europe, most measurements available after 1995 only)
only few sites with soil moisture measurements

47. Observations: SwissFluxnet
X Rietholzbach catchment site
(Lysimeter, isotope measurements)
Will also focus on soil moisture measurements (ETH Zurich)

48. Outline
Land-atmosphere coupling, climate changes, and extreme events (Seneviratne et al. 2006a)
Land-atmosphere coupling: hot spot in Europe?
Dynamics with climate change
Links with extreme events
Activities with regard to land flux estimations at ETH Zurich
Atmospheric-terrestrial water balance estimates
Some results on models’ estimates (GSWP/GLDAS-type; GCMs)
SwissFluxnet activities and Rietholzbach catchment site
Conclusions and outlook

49. Conclusions and outlook
Land processes important in transitional climate zones (e.g. Koster et al. 2004): seasonal forecasting, extreme events
NB: possible changes in hot spots’ location with greenhouse warming
Several methods to estimate water storage or ET, atmospheric-terrestrial water estimates are promising (retrospective datasets)
No perfect dataset: but synergies are available

50. Comparison: Land datasets
Ground measurements
Atmospheric water-balance
Satellite data (SMOS, GRACE)
LSM with observed forcing
Resolution
Point measurements km
( km2)
SMOS: 40km
GRACE: ~1000km
1km (LIS) - 50km
Main advantage
Ground truth
(...)
Retrospective dataset (1958-present); large coverage
Global coverage
Good results in regions with good forcing; higher resolution
Main limitation
Point-scale measurements; limited temporal and geographical coverage
Dependent on quality of convergence data (radiosonde vs. satellite data, drifts)
Only recent data; short timeseries; products’ limitations (top soil, low res.)
Results depen-dent on quality of forcing data; models optimized for regions with validation data

51. Outlook
A new GEWEX study area for Europe? (hot spot of coupling)

53. Temporal Integration (3)
Observations (Illinois)
Integrated estimates
Integration over longer time ranges is not straightforward due to the presence of small systematic imbalances in the monthly estimates
Comparison with imbalances from other water-balance studies
G97: Gutowski et al. 1997
Y98: Yeh et al. 1998
BR99: Berbery and
Rasmuson 1999

54. Long-term Imbalances and Drifts (1)
Rasmusson (1968)
threshold for radiosonde data (2.106 km2)
Illinois ( ) ?
Imbalances (mm/d)
Illinois ( km2)
Europe
Western Russia
Asia
North America
Domain size (km2)
Hirschi et al. 2004

55. Soil moisture - precipitation coupling
CLIMATE-CHANGE SIGNAL: SCEN-CTL
LA COUPLING STRENGTH IN SCEN:
SCEN-SCENUNCOUPLED
CONTR. OF EXT. FACTORS TO CC SIGNAL
SCENUNCOUPLED-CTLUNCOUPLED
CONTR. OF LA COUPLING TO CC SIGNAL
(SCEN-SCENUNCOUPLED)-(CTL-CTLUNCOUPLED)
appears relevant for variability enhancement in the Alpine region
this link needs to be better investigated in future studies!
(Seneviratne et al. 2006, Nature)

56. Modelling Vegetation - CO2 interactions
Only few models explicitly include vegetation-CO2 relationships…
(enhanced water-use efficiency?, CO2 fertilization?)
(Sellers et al. 1997)

57. Modelling Vegetation - CO2 interactions
Only few models explicitly include vegetation-CO2 relationships…
(enhanced water-use efficiency?, CO2 fertilization?)
(Sellers et al. 1997)
Direct CO2 effect on runoff ?
NPP, 2003
(Gedney et al. 2006, Nature)
(Ciais et al. 2005, Nature)

58. Soil moisture-temperature feedbacks
Soil moisture-temperature coupling in the European summer 2003: Spring soil moisture impacted summer temperature by up to 2 oC!
(Fischer et al. 2006, in preparation)

59. Summer 2003 heatwave
(Fischer et al. 2007, J. Climate, submitted)

60. Summer 2003 heatwave
(Fischer et al. 2007, J. Climate, submitted)

61. Summer 2003 heatwave
Dry or wet conditions in spring make up to 2oC difference in summer!
(Fischer et al. 2007, J. Climate, submitted)

62. Summer 2003 heatwave
Dry or wet conditions in spring make up to 2oC difference in summer!
(Fischer et al. 2007, J. Climate, submitted)

63. Variability increases
∆(P) vs. ∆(To), PRUDENCE models (Central Europe)
DMI, HC1, HS1
DMI, HC2, HS2
HC, HadRM3H
HC, HadAM3H, ens1
HC, HadAM3H, ens2
ETH/CHRM, HC_CTL, HC_A2
GKSS, HC_CTL, HC_A2
MPI, 3003, 3006
SMHI, HC_CTL, HC_A2
UCM, control, a2
ICTP, ref, A2
KNMI, HC1, HA2
CNRM, DA9, DE6
CNRM, DE3, DE7
CNRM, DE4, DE8
DMI, ecctrl, ecsca2‹
(Vidale al. 2006)

64. Observations: Soil moisture
Current ground observations networks of soil moisture are very limited in space and time (no data for Europe; only few observations in the former Soviet Union after 1990)
Global Soil Moisture Data Bank
(Robock et al. 2000, Bull. Am. Met. Soc.)

65. Indirect measurements/estimates
Some new approaches
GRACE twin satellites
Satellite measurements
Microwave remote sensing (e.g. SMOS)
GRACE (Gravity Recovery and Climate Experiment)
NDVI (Normalized Difference Vegetation Index)
Land surface models with observational input
Global Soil Wetness Project (GSWP)
Global Land Data Assimilation (GLDAS)
Land data assimilation with Ensemble Kalman Filter
NDVI: spectral reflectance (NIR)- spectral reflectance (VIS) / spectral reflectance (NIR) + spectral reflectance (VIS)
(Reichle et al. 2002, JHM)

66. Other applications Estimation of Large-scale Evapotranspiration:
Atmospheric water balance:
Mackenzie GEWEX Study (MAGS)
Louie et al. 2002

67. Observations: Soil moisture
Current ground observations networks of soil moisture are very limited in space and time (no data for Europe; only few observations in the former Soviet Union after 1990)
Global Soil Moisture Data Bank
(Robock et al. 2000, Bull. Am. Met. Soc.)