Transcript ILEAPS presentation

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
• 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
L-A coupling in Europe
Seneviratne et al. 2006,
Nature, 443, 205-209
L-A coupling in Europe
Koster et al., 2004, Science
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
L-A coupling in Europe
T
(Koster et al. 2006, JHM)
No strong
coupling in
Europe? How
about
Mediterranean
region?
NB: Results
based on only
one year SST
conditions
(1994)
Koster et al., 2004, Science
Projected changes in To variability
PRUDENCE, CHRM, JJA (2070-2100)-(1960-1990)
DT
Ds/s
Schär et al. 2004, Nature
[%]
[ºC]
IPCC AR4 GCMs, JJA (2080-2100)-(1970-1990)
DT
Ds
Seneviratne et al. 2006, Nature, suppl. inf.
Projected changes in To variability
PRUDENCE, CHRM, JJA (2070-2100)-(1960-1990)
DT
Ds/s
Schär et al. 2004, Nature
[%]
[ºC]
IPCC AR4 GCMs, JJA (2080-2100)-(1970-1990)
DT
Ds
Seneviratne et al. 2006, Nature, suppl. inf.
Projected changes in To variability
PRUDENCE, CHRM, JJA (2070-2100)-(1960-1990)
DT
Large changes
in To variability
Ds/s
Schär et al. 2004, Nature
[%]
[ºC]
IPCC AR4 GCMs, JJA (2080-2100)-(1970-1990)
DT
Ds
What are the
responsible
mechanisms:
Large-scale
circulation
patterns? Land
surface
processes?
Seneviratne et al. 2006, Nature, suppl. inf.
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
Summer temperature variability
Standard deviation of the summer (JJA) 2-m temperature
CTL
CTLUNCOUPLED
SCEN
SCENUNCOUPLED
(Seneviratne et al. 2006, Nature)
Summer temperature variability
Standard deviation of the summer (JJA) 2-m temperature
CTL
CTLUNCOUPLED
SCEN
SCENUNCOUPLED
Most of the
enhancement of
summer temperature
variability in SCEN
disappears in the
SCENUNCOUPLED
simulation
(Seneviratne et al. 2006, Nature)
Climate change signal vs. LA coupling
CLIMATE-CHANGE SIGNAL:
SCEN-CTL
CONTR. OF EXT. FACTORS TO CC SIGNAL
SCENUNCOUPLED-CTLUNCOUPLED
LA COUPLING STRENGTH IN SCEN:
SCEN-SCENUNCOUPLED
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 vs. LA coupling
CLIMATE-CHANGE SIGNAL:
SCEN-CTL
CONTR. OF EXT. FACTORS TO CC SIGNAL
SCENUNCOUPLED-CTLUNCOUPLED
Contribution of landatmosphere coupling
to climate change
signal: dominant factor
in Central and Eastern
Europe!
LA COUPLING STRENGTH IN SCEN:
SCEN-SCENUNCOUPLED
CONTR. OF LA COUPLING TO CC SIGNAL
(SCEN-SCENUNCOUPLED)-(CTL-CTLUNCOUPLED)
(Seneviratne et al. 2006, Nature)
GLACE results for present climate
T
GLACE experiment (Koster et al.
2004; 2006): no high land-atmosphere
coupling in Europe neither for
temperature nor for precipitation
(Koster et al. 2006, JHM)
P
How is the strength of landatmosphere coupling for present vs.
future climate in our simulations?
(Koster et al. 2004, Science)
Present vs. future climate
percentage of To variance
explained by coupling [%]
s T2 (COUPLED)  s T2 (UNCOUPLED)
s T2 (COUPLED)

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)
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 moisturetemperature coupling (high temperature as
result of low/no evapotranspiration)
Positive correlation: low soil moisturetemperature coupling (high temperature
leads to high evapotranspiration)
Comparison IPCC AR4 GCMs: (ET,T2M)
CTL time period
SCEN time period
Climate-change signal
RCM
3 “best”
GCMs
All
GCMs
(Seneviratne et al. 2006, Nature)
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
Soil moisture [mm]
Mechanism for To variability increase
Seasonal Cycle of Soil Moisture
CTL (1961-1990)
SCEN (2071-2100)
Month
no limitation
wet climate
transitional
climate
below threshold
(“plant wilting point”)
dry 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
Atmospheric-Terrestrial Water Balance
• Terrestrial water balance:
Atmospheric-Terrestrial Water Balance
• Terrestrial water balance:
• Atmospheric water balance:
Atmospheric-Terrestrial Water Balance
• Terrestrial water balance:
reanalysis
data
(ERA-40)
• Atmospheric water balance:
• Combined water balance:
measured
streamflow
(Rs+Rg)
Atmospheric-Terrestrial Water Balance
The water-balance estimates
depend only on observed or
assimilated variables (≠ P,E)
Main limitation:
valid only for domains > 105106 km2 (Rasmusson 1968,
Yeh et al. 1998)
• Terrestrial water balance:
reanalysis
data
(ERA-40)
• Atmospheric water balance:
• Combined water balance:
measured
streamflow
(Rs+Rg)
Case Study: Mississippi & Illinois
Seneviratne et al. 2004, J. Climate, 17 (11), 2039-2057
corr=0.8, r2=0.71
Water-balance
Estimates
Observations
(soil moisture+
groundwater+snow)
Dataset for Mid-latitude River Basins
“BSWB”
Hirschi et al. 2006, J. Hydrometeorology, 7(1), 39-60
http://iacweb.ethz.ch/data/water_balance/
•
divQ & dW/dt: whole ERA-40
period (1958-2002)
•
runoff data: Global Runoff Data
Center (GRDC)
Comparisons with soil moisture
observations from the Global Soil
Moisture Data Bank
Volga River basin (1972-85)
corr=0.8
r2=0.64
Atmospheric-Terrestrial Water Balance
• Terrestrial water balance:
• Atmospheric water balance:
• Combined water balance:
Estimation of large-scale ET
Atmospheric water balance:
Mackenzie GEWEX Study (MAGS)
Peace
Louie et al. 2002
Estimation of large-scale ET
http://iacweb.ethz.ch/data/water_balance/
Retrospective dataset! (1958-2001,
ERA-40; 2001-2007, ECMWF
operational forecast analysis;
e.g. Hirschi et al. 2006, GRL)
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
The water-balance estimates
depend only on observed P and
assimilated variables
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
( Koster et al, 2004: GPCP product, 1979-93)
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 WillmottMatsuura)
(Fekete et al. 2004)
Effects on Catchment LSM Output
r2 vs. ground data, yrs within 1979-93 (anomalies)
Illinois
Soil moisture
+ snow
Volga
Don
Neva
Ob
Lena
Dnepr
Amur
Precipitation
Yenisei
LSM results strongly dependent on quality of forcing...
Modelling: GCMs
Water-holding capacity
LAND
(Seneviratne et al. 2006, JHM)
Modelling: GCMs
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
Land-atmosphere coupling
P
Significant range in
model behaviour…
(Koster et al. 2004, Science)
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 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
Comparison: Land datasets
Ground
measurements
Atmospheric waterbalance
Satellite data
(SMOS,
GRACE)
LSM with
observed forcing
Resolution
Point
measurements
300-1000 km
(105-106 km2)
SMOS: 40km
GRACE:
~1000km
1km (LIS) - 50km
Main
advantage
Ground truth
(...)
Retrospective
dataset (1958present); large
coverage
Global coverage
Good results in
regions with good
forcing; higher
resolution
Dependent on quality
of convergence data
(radiosonde vs.
satellite data, drifts)
Only recent
data; short
timeseries;
products’
limitations (top
soil, low res.)
Results dependent on quality of
forcing data;
models optimized
for regions with
validation data
Main limitation Point-scale
measurements;
limited temporal
and geographical
coverage
Outlook
A new GEWEX
study area for
Europe? (hot
spot of
coupling)
Temporal Integration (3)
Observations (Illinois)
Integrated estimates
S W 
 


t

t



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
Long-term Imbalances and Drifts (1)
Hirschi et al. 2004
Illinois (1987-96)
S
 
t
S W 
 

t 
t
Imbalances (mm/d)
?
Rasmusson (1968)
threshold for radiosonde
data (2.106 km2)


Illinois (2 .105 km2)
Europe
Western Russia
Asia
North America
Domain size (km2)
Soil moisture - precipitation coupling
CLIMATE-CHANGE SIGNAL:
SCEN-CTL
CONTR. OF EXT. FACTORS TO CC SIGNAL
SCENUNCOUPLED-CTLUNCOUPLED
• appears relevant for
variability enhancement in
the Alpine region
LA COUPLING STRENGTH IN SCEN:
SCEN-SCENUNCOUPLED
CONTR. OF LA COUPLING TO CC SIGNAL
(SCEN-SCENUNCOUPLED)-(CTL-CTLUNCOUPLED)
• 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
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)
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)
Summer 2003 heatwave
(Fischer et al. 2007,
J. Climate, submitted)
Summer 2003 heatwave
(Fischer et al. 2007,
J. Climate, submitted)
Summer 2003 heatwave
Dry or wet
conditions in
spring make
up to 2oC
difference in
summer!
(Fischer et al. 2007,
J. Climate, submitted)
Summer 2003 heatwave
Dry or wet
conditions in
spring make
up to 2oC
difference in
summer!
(Fischer et al. 2007,
J. Climate, submitted)
Variability increases
∆s(P) vs. ∆s(To),
PRUDENCE models
(Central Europe)
1)
2)
3)
4)
5)
6)
7)
8)
9)
10)
11)
12)
13)
14)
15)
16)
(Vidale al. 2006)
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‹
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.)
Indirect measurements/estimates
Some new approaches
Satellite measurements
• Microwave remote sensing (e.g. SMOS)
• GRACE (Gravity Recovery and Climate
Experiment)
• NDVI (Normalized Difference Vegetation Index)
GRACE twin satellites
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)
Other applications
Estimation of Large-scale Evapotranspiration:
Atmospheric water balance:
Mackenzie GEWEX Study (MAGS)
Louie et al. 2002
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.)