land-atmosphere coupling, climate-change and extreme events +
DESCRIPTION
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. Outline. - PowerPoint PPT PresentationTRANSCRIPT
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, FranceMay 29, 2007
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
Seneviratne et al. 2006,
Nature, 443, 205-209
L-A coupling in Europe
L-A coupling in Europe
Koster et al., 2004, Science
L-A coupling in Europe
Koster et al., 2004, Science
No strong coupling in Europe? How about Mediterranean region?
NB: Results based on only one year SST conditions (1994)
L-A coupling in Europe
Koster et al., 2004, Science
No strong coupling in Europe? How about Mediterranean region?
NB: Results based on only one year SST conditions (1994)
(Koster et al. 2006, JHM)
T
Projected changes in To variability
Schär et al. 2004, Nature[%]
/
[ºC]
T
T
IPCC AR4 GCMs, JJA (2080-2100)-(1970-1990)
Seneviratne et al. 2006, Nature, suppl. inf.
PRUDENCE, CHRM, JJA (2070-2100)-(1960-1990)
Projected changes in To variability
Schär et al. 2004, Nature[%]
/
[ºC]
T
T
IPCC AR4 GCMs, JJA (2080-2100)-(1970-1990)
Seneviratne et al. 2006, Nature, suppl. inf.
PRUDENCE, CHRM, JJA (2070-2100)-(1960-1990)
Projected changes in To variability
Schär et al. 2004, Nature[%]
/
[ºC]
T
T
IPCC AR4 GCMs, JJA (2080-2100)-(1970-1990)
Seneviratne et al. 2006, Nature, suppl. inf.
PRUDENCE, CHRM, JJA (2070-2100)-(1960-1990) Large changes in To variability
What are the responsible mechanisms:Large-scale circulation patterns? Land surface processes?
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
Standard deviation of the summer (JJA) 2-m temperatureSCENCTL
CTLUNCOUPLED SCENUNCOUPLED
Summer temperature variability
(Seneviratne et al. 2006, Nature)
Most of the enhancement of summer temperature variability in SCEN disappears in the SCENUNCOUPLED simulation
Standard deviation of the summer (JJA) 2-m temperatureSCENCTL
CTLUNCOUPLED SCENUNCOUPLED
Summer temperature variability
(Seneviratne et al. 2006, Nature)
Climate change signal vs. LA couplingCLIMATE-CHANGE SIGNAL: SCEN-CTL
LA COUPLING STRENGTH IN SCEN:SCEN-SCENUNCOUPLED
CONTR. OF EXT. FACTORS TO CC SIGNALSCENUNCOUPLED-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)
CLIMATE-CHANGE SIGNAL: SCEN-CTL
LA COUPLING STRENGTH IN SCEN:SCEN-SCENUNCOUPLED
CONTR. OF EXT. FACTORS TO CC SIGNALSCENUNCOUPLED-CTLUNCOUPLED
CONTR. OF LA COUPLING TO CC SIGNAL(SCEN-SCENUNCOUPLED)-(CTL-CTLUNCOUPLED)
(Seneviratne et al. 2006, Nature)
Climate change signal vs. LA coupling
Contribution of land-atmosphere coupling to climate change signal: dominant factor in Central and Eastern Europe!
GLACE results for present climate
GLACE experiment (Koster et al. 2004; 2006): no high land-atmosphere coupling in Europe neither for temperature nor for precipitation
How is the strength of land-atmosphere coupling for present vs. future climate in our simulations?
(Koster et al. 2006, JHM)
T
(Koster et al. 2004, Science)
P
Present vs. future climate
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
€
T (COUPLED)2 − σ T
2(UNCOUPLED)
σ T (COUPLED)2
percentage of To variance explained by coupling [%]
(Seneviratne et al. 2006, Nature)
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)
Comparison IPCC AR4 GCMs: (ET,T2M) CTL time period SCEN time period Climate-change signal
(Seneviratne et al. 2006, Nature)
RCM
All GCMs
3 “best” GCMs
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
Seasonal Cycle of Soil Moisture
Month
Soil
moi
stur
e [m
m]
Mechanism for To variability increase
no limitation wet climate
below threshold (“plant wilting point”) dry climate
CTL (1961-1990)SCEN (2071-2100)
transitional climate
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)
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
Atmospheric-Terrestrial Water Balance
• Terrestrial water balance:
Atmospheric-Terrestrial Water Balance
• Terrestrial water balance:
• Atmospheric water balance:
Atmospheric-Terrestrial Water Balance
• Terrestrial water balance:
• Atmospheric water balance:
• Combined water balance:measuredstreamflow(Rs+Rg)
reanalysisdata(ERA-40)
Atmospheric-Terrestrial Water Balance
• Terrestrial water balance:
• Atmospheric water balance:
• Combined water balance:measuredstreamflow(Rs+Rg)
reanalysisdata(ERA-40)
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)
Atmospheric-Terrestrial Water Balance
Case Study: Mississippi & Illinois
Water-balance Estimates
Observations(soil moisture+groundwater+snow)
Seneviratne et al. 2004, J. Climate, 17 (11), 2039-2057
corr=0.8, r2=0.71
Dataset for Mid-latitude River Basins
• divQ & dW/dt: whole ERA-40 period (1958-2002)
• runoff data: Global Runoff Data Center (GRDC)
Hirschi et al. 2006, J. Hydrometeorology, 7(1), 39-60
Comparisons with soil moisture observations from the Global Soil Moisture Data Bank
Volga River basin (1972-85)
corr=0.8r2=0.64
“BSWB”http://iacweb.ethz.ch/data/water_balance/
• Terrestrial water balance:
• Atmospheric water balance:
• Combined water balance:
Atmospheric-Terrestrial Water Balance
Estimation of large-scale ET Atmospheric water balance:
Louie et al. 2002
Mackenzie GEWEX Study (MAGS)Peace
Estimation of large-scale ET
The water-balance estimates depend only on observed P and assimilated variables
Main limitations:
- valid only for domains > 105-106 km2
(Rasmusson 1968, Yeh et al. 1998;Seneviratne et al. 2004, J. Climate,Hirschi et al, 2006, JHM)
- Imbalances, drifts of reanalysis data
Retrospective dataset! (1958-2001, ERA-40; 2001-2007, ECMWF operational forecast analysis; e.g. Hirschi et al. 2006, GRL)
http://iacweb.ethz.ch/data/water_balance/
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
Precipitation Forcing for LSMs
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
( Koster et al, 2004: GPCP product, 1979-93)
(Fekete et al. 2004)
Fekete et al. 2004: Range between 4 state-of-the-art precipitation datasets (CRU, GPCC, GPCP, and Willmott-Matsuura)
r2 vs. ground data, yrs within 1979-93 (anomalies)
Illinois
Neva
DonDnepr
Volga
Amur
Lena
Yenisei
Ob
Effects on Catchment LSM Output
Soil moisture + snow
Precipitation
LSM results strongly dependent on quality of forcing...
Water-holding capacity
Modelling: GCMs
(Seneviratne et al. 2006, JHM)
LAND
Soil moisture memory
(Seneviratne et al. 2006, JHM)
Modelling: GCMs
Soil moisture memory
(Seneviratne et al. 2006, JHM)
Modelling: GCMs
Water-holding capacity LAND
(Seneviratne et al. 2006, JHM)
Modelling: GCMs
PSignificant range in model behaviour…
(Koster et al. 2004, Science)
Land-atmosphere coupling
Modelling: GCMs
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
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
http://www-eosdis.ornl.gov/FLUXNET/
Observations: SwissFluxnet
X Rietholzbach catchment site (Lysimeter, isotope measurements)
Will also focus on soil moisture measurements (ETH Zurich)
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
Conclusions and outlook
• Land processes important in transitional climate zones (e.g. Koster et al. 2004): seasonal forecasting, extreme eventsNB: 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
Comparison: Land datasets
Ground measurements
Atmospheric water-balance
Satellite data (SMOS, GRACE)
LSM with observed forcing
Resolution Point measurements
300-1000 km(105-106 km2)
SMOS: 40kmGRACE: ~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
Outlook
A new GEWEX study area for Europe? (hot spot of coupling)
Temporal Integration (3)
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
€
∂S∂t
+ ∂W∂t
⎧ ⎨ ⎩
⎫ ⎬ ⎭
Observations (Illinois)Integrated estimates
G97: Gutowski et al. 1997Y98: Yeh et al. 1998BR99: Berbery and Rasmuson 1999
Long-term Imbalances and Drifts (1)
EuropeWestern RussiaAsiaNorth America
Domain size (km2)
Imba
lanc
es (m
m/d
)
Rasmusson (1968)threshold for radiosonde data (2.106 km2)
Hirschi et al. 2004
Illinois (2 .105 km2)
?
Illinois (1987-96)
Soil moisture - precipitation couplingCLIMATE-CHANGE SIGNAL: SCEN-CTL
LA COUPLING STRENGTH IN SCEN:SCEN-SCENUNCOUPLED
CONTR. OF EXT. FACTORS TO CC SIGNALSCENUNCOUPLED-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)
Modelling
Vegetation - CO2 interactions
Only few models explicitly include vegetation-CO2 relationships…(enhanced water-use efficiency?, CO2 fertilization?)
(Sellers et al. 1997)
Modelling
Vegetation - CO2 interactions
(Sellers et al. 1997)
Only few models explicitly include vegetation-CO2 relationships…(enhanced water-use efficiency?, CO2 fertilization?)
(Ciais et al. 2005, Nature)
NPP, 2003
(Gedney et al. 2006, Nature)
Direct CO2 effect on runoff ?
(Fischer et al. 2006, in preparation)
Soil moisture-temperature coupling in the European summer 2003: Spring soil moisture impacted summer temperature by up to 2 oC!
Soil moisture-temperature feedbacks
Summer 2003 heatwave
(Fischer et al. 2007, J. Climate, submitted)
Summer 2003 heatwave
(Fischer et al. 2007, J. Climate, submitted)
Summer 2003 heatwave
(Fischer et al. 2007, J. Climate, submitted)
Dry or wet conditions in spring make up to 2oC difference in summer!
Summer 2003 heatwave
(Fischer et al. 2007, J. Climate, submitted)
Dry or wet conditions in spring make up to 2oC difference in summer!
Variability increases
1) DMI, HC1, HS12) DMI, HC2, HS23) HC, HadRM3H4) HC, HadAM3H, ens15) HC, HadAM3H, ens26) ETH/CHRM, HC_CTL, HC_A27) GKSS, HC_CTL, HC_A28) MPI, 3003, 30069) SMHI, HC_CTL, HC_A210) UCM, control, a211) ICTP, ref, A212) KNMI, HC1, HA213) CNRM, DA9, DE614) CNRM, DE3, DE715) CNRM, DE4, DE816) DMI, ecctrl, ecsca2‹
(Vidale al. 2006)
∆(P) vs. ∆(To), PRUDENCE models (Central Europe)
Observations: Soil moisture
Global Soil Moisture Data Bank(Robock et al. 2000, Bull. Am. Met. Soc.)
• 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)
Indirect measurements/estimates
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(Reichle et al. 2002, JHM)
GRACE twin satellites
Some new approaches
Other applications Estimation of Large-scale Evapotranspiration:
Atmospheric water balance:
Louie et al. 2002
Mackenzie GEWEX Study (MAGS)
Observations: Soil moisture
Global Soil Moisture Data Bank(Robock et al. 2000, Bull. Am. Met. Soc.)
• 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)