Transcript Quantifying climate change impacts in a data

QUANTIFYING CLIMATE CHANGE IMPACTS IN
A DATA-SCARCE ENVIRONMENT
Z. (Bob) Su (1)
With contributions from
J. Wen (2), , Y. Ma (3), P. De Rosnay (4), R. Van der Velde (1), L. Dente (1),
L. Wang (1), L. Zhong (1), S. Salama (1)
(1) Faculty Of Geo-information Science And Earth Observation (IIC), University Of Twente, Enschede, The Netherlands
(2) Cold And Arid Regions Environmental And Engineering Research Institute, Chinese Academy Of Sciences, Lanzhou, P.R. China
(3) Institute Of Tibetan Plateau Research, Chinese Academy Of Sciences, Beijing, P.R. China
(4) European Centre For Medium-range Weather Forecasts, Reading , United Kingdom
Content
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Background & Objectives
In-situ networks, satellite observations & model outputs
Quantifying hydroclimatic variables
Vegetation
Surface temperature
Soil moisture
Water levels
Climatic impacts - variations, trends, and extremes?
Suggestions and conclusion
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Background: Lack of plateau-scale measurements of
water cycle components in the Third Pole Environment
 IPCC “… Working Group II contribution
to the underlying assessment refers to
poorly substantiated estimates of rate
of recession and date for the
disappearance of Himalayan glaciers.”
(IPCC statement on the melting of Himalayan glaciers, 20 Jan. 2010).
 There is a critical lack of knowledge for this unique
environment, because, current estimates of the plateau water
balance rely at best on sparse and scarce observations
 In-situ observation data cannot provide the required accuracy,
spatial density and temporal frequency for quantification of impacts
and development of adaptation and mitigation measures.
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Objectives
 Introduce a reference observatory for in-situ soil
moisture/temperature measurement for plateau scale
monsoon system studies
 Quantification of uncertainties in satellite retrievals &
model outputs
 Identification of variations, trends, and extremes in plateau
scale hydrocliamtic variables
 Climatic impacts or monsoon pattern changes – actions for
AR5?
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ITC/CAS Soil Moisture Networks
Ngari
Maqu
Naqu
~ Network July 2008
~ Network June 2006
~ Network June 2010
ESA Dragon programme
EU FP7 CEOP-AEGIS project
ESA WACMOS project
Part I - Vegetation
 Adequacy of satellite observations of vegetation changes in relation to
hydroclimatic conditions
 (Zhong et al., 2010, Cli. Change; Zhong et al., 2011, J. Cli. In review)
MAP OF VEGETATION COVER TYPES ON THE TIBETAN PLATEAU
(1 km resolution
land cover map
from GLC2000)
Correlation coefficients of NDVI versus precipitation (P) and
NDVI versus temperature (T) of different vegetation types
(Zhong et al., 2010)
Average seasonal mean NDVI variations over the Tibetan Plateau
9-year time series of SPOT NDVI
images to infer the vegetation response
of different land cover types to climate
variability.
1. Cloud contamination from satellite
images problematic but can be
removed.
2. Vegetation density <-> general
climate pattern in the Tibetan Plateau.
The Asian monsoon had a great impact
on the seasonal variation in NDVI.
3. Vegetation density increasing in
49.87% of the total area.
4. The land cover types showed
differing correlations between NDVI
and climate variables.
Part – II Land surface temperature
 Adequacy of satellite observations for quantifying climatic impacts in LST
 (Oku and Ishikawa, 2003, JAMC; Salama et al., 2011, IEEE TGRS, in
review)
Surface Temperature Interannual Variation
Monthly mean surface temperature averaged across the
Tibetan Plateau
+ 0.2 K/yr
Surface temperature over the plateau is rising year by year.
(Oku & Ishikawa, 2003)
Daily Maximum and Minimum Surface Temperature
+ 0.13 K/yr
+ 0.39 K/yr
Maximum
Minimum
Daily minimum surface temperature rises faster
than maximum temperature.
(Oku & Ishikawa, 2003)
Decadal variations of land surface temperature observed over the
Tibetan Plateau SSM/I 1987-2008
(a) measured T2.5cm versus SSM/I TBv37GHz for both calibration (light squares)
and validation (dark circles) sets; (b) derived versus measured temperatures using the
independent validation set.
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A warming plateau or a cooling plateau?
Trends of LST anomalies derived from the 1987-2008 SSM/I data set: (a) TPE –
Tibetan Plateau and surrounding areas, (b) Tibetan Plateau
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Trends of LST anomalies observed over the Tibetan Plateau vs elevation
i) the formation of water ponds during the rainy monsoon; ii) the
growth of water reservoirs in the TP caused by snow and glacier
melting due to temperature increase ?
A cross section at 32± N of monthly LST anomalies.
(Yanai and Wu [2006] described the Tibetan Plateau as a heat source for
the atmosphere in the summer with exception of the south eastern part.)
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Part III – Soil moisture
 Adequacy of satellite observations for quantifying climatic impacts
 (Su et al., 2011, HESSD; van der Velde et al., 2011, J.Cli. - in review;)
Volumetric soil
moisture,
ASCAT data,
1-7 July 2007
Soil moisture (m3/m3 )
Volumetric soil
moisture,
AMSR-E VUA-NASA
product,
average 1-7 July 2007
(Pixel size 0.25°, White
pixels = flag values =
sea, ice, forest)
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Naqu in-situ soil moisture &
soil temperature measurements
Validation of soil moisture retrievals at Naqu site (Cold
& semi-arid), Tibetan plateau (July-October 2008)
Maqu in-situ soil moisture & soil temperature
measurements
40
km
80 km

calibrated for soil texture and derived the final soil moisture time series
Maqu site - Soil temperature (upper panel) and soil moisture (lower panel) measured at
5 cm soil depth at Maqu network
showing the average (solid green line) and standard deviation around the mean (error bars) from 1 July 2008 to 31 July
2009, using all 20 stations. The AMSR-E retrieval (+ in blue), ASCAT-2 retrieval (+ in red) and ITC-model retrieval ( ͙ in black)
for the Maqu area are also shown. The two vertical lines (in red) indicate when the measured temperature at 5 cm soil depth
was below freezing point.
Time series retrieval (SSM/I) vs in-situ observations
Soil moisture [m3 m-3]
0.5
0.4
0.3
0.2
0.1
0.0
1/1/05
1/1/06
1/1/07
1/1/08
1/1/09
Date [mm/dd/yy]
SSM/I retrievals
Naqu station
North station
East station
South station
SSM/I soil moisture retrievals and measurements from Naqu,
North, East and South stations plotted over time.
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Trends in mean and anomaly in plateau scale soil moisture (19872008, SSM/I retrievals)
Slope of the fit through absolute
soil moisture
Slope of fit the through soil
moisture anomalies
/ decade
m3 m-3 / decade
R2 of the fit through the absolute
soil moisture
R2 of the fit through the absolute
soil moisture
[%]
[%]
Trends in plateau scale soil moisture (SSM/I)
Soil moisture [m3 m-3]
0.20
0.10
Central Tibet
SE-Tibet
0.05
2.62 10-3 x -5.11
-4.63 10-3 x + 9.38
0.00
1988
Soil moisture anomaly
The center pixels of
the areas selected
within central Tibet
and SE-Tibet are
about 90.5 oE/ 33.0
oN and 103.0 oE/
25.0 oN (WSG84).
0.15
1992
1996
2000
2004
2008
2004
2008
2.0
1.0
0.0
-1.0
0.151 x -301.06
-0.146 x + 291.68
-2.0
1988
1992
1996
2000
Year
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Part IV – model outputs
 Ability of the ECMWF model in simulating and analysis of root zone
soil moisture on the Tibetan plateau
 (Su et al., 2011, JGR – in review)
Table 1. The Naqu network area - Statistics of the ECMWF operational run
(ECMWF-OI) and the ECMWF-EKF-ASCAT numerical experiment (using the EKF
soil moisture analysis with ASCAT data assimilation) compared to in-situ
measured soil moisture.
Root Mean Square Difference (RMSD), Bias (MD) and Correlation Coefficient (R).
Figure 2a. Soil moisture from the ECMWF operational run (ECMWF-OI, where the SM analysis uses
the Optimal Interpolation method) compared to in-situ measured soil moisture in the Naqu network
area.
Figure 2b. Soil moisture from the ECMWF-EKF-ASCAT run (using the EKF soil moisture analysis with
ASCAT data assimilation) compared to in-situ measured soil moisture (green) at the Naqu network
area.
Table 2. The Maqu network area - Statistics of the ECMWF operational run
(ECMWF-OI) and the ECMWF-EKF_ASCAT numerical experiment( unsing the EKF
soil moisture analysis with ASCAT data assimilation) compared to in-situ
measured soil moisture.
(Root Mean Square Difference (RMSD), Bias (MD) and Correlation Coefficient (R).
Figure 3a. Soil moisture from the ECMWF operational run (ECMWF-OI, where the
SM analysis uses the OI) compared to in-situ measured soil moisture at the Maqu
network area.
Figure 3b. Soil moisture from the ECMWF-EKF-ASCAT run (using the EKF soil
moisture analysis with ASCAT data assimilation) compared to in-situ measured
soil moisture at the Maqu network area.
(ECMWF) operational land surface analysis system and the new
soil moisture analysis scheme based on a point-wise ExtendedKalman Filter (EKF) for the global land surface
 For the cold semiarid Naqu area the ECMWF model overestimates
significantly the regional soil moisture in the monsoon seasons, which
is attributed to spurious soil texture patterns of soil texture.
 For the cold humid Maqu area the ECMWF products have comparable
accuracy to in-situ measurements. Comparison between liquid soil
moisture content from ECMWF and ground stations measurements
and satellite estimates from the ASCAT sensor shows good
performances of the ASCAT product as well as the ECMWF soil
moisture analysis.
Part IV – Water levels
ENVISAT PASS
ICESAT PASS
ENVISAT WATER LEVEL
Envisat Water Level
4728.5
Water level (m)
4728
4727.5
4727
4726.5
Water Level
4726
4725.5
4725
4724.5
4/19/2001 9/1/2002 1/14/2004 5/28/2005 10/10/2006 2/22/2008 7/6/2009 11/18/2010
Time (days)
ICESAT PLOT 2003 - 2009
ICESAT Water Level
Water Level (m)
4692.5
4692
4691.5
4691
Water Level
4690.5
4690
4689.5
9/1/2002
1/14/2004
5/28/2005
10/10/2006
Time (Days)
2/22/2008
7/6/2009
11/18/2010
Conclusions
 Global satellite products are useful but uncertain – use of
them would be critically enhanced if uncertainties can be
quantified using in-situ and high resolution data;
 Long term satellite data (e.g. soil moisture) can be used to
detect monsoon pattern changes;
 Process level understanding is critical for generation of
global products to be useful for climate change studies –
attribution of causes.
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Recommendations
 (Proposed action points or next steps)
 Satellite observations in data scarce environment are critical for quantifying
climatic impacts – space agencies should develop dedicated studies
 Uncertainties in satellite observations needed to be quantified with in-situ
reference observations data – data sharing is badly needed – a role for
GEO to coordinate
 Modeling results need to be verified before used in drawing conclusions
about climatic impacts – NWP centers & science groups
 Concerted actions needed to aggregate and analyze climatic impacts in
data scare environment – role of IPCC
 Existing studies need to be analyzed in detail – separating those based
observation data with uncertainty certification from less rigorous studies –
role of IPCC
Referances/Further Readings
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Su, Z., W. Wen, L. Dente,, R. van der Velde, 334 , L. Wang, Y. Ma, K. Yang, and Z. Hu (2011), A plateau scale soil moisture and soil
temperature observatory for the quantification of uncertainties in coarse resolution satellite products, Hydrol. Earth Sys. Sci. – Dis.
(http://www.hydrol-earth-syst-sci-discuss.net/8/243/2011/)
Su, Z., P. de Rosnay, J. Wen, L. Wang, 2011, Ability of the ECMWF 1 model in simulating and analysis of root zone soil moisture on
the Tibetan plateau, J. Geophys. R. (in review)
van Der Velde, R., Z. Su, 2009, Dynamics in land surface conditions on the Tibetan Plateau observed by ASAR, Hydrological sciences
journal , Hydrological sciences journal, 54(6), 1079-1093.
van der Velde, R., Z. Su, and Y. Ma, 2008, Impact of soil moisture dynamics on ASAR signatures and its spatial variability observed
over the Tibetan plateau. Sensors, 8(2008) 9, pp. 5479-5491.
van der Velde, R., Z. Su, M. Ek, M. Rodell, and Y. Ma, 2009, Influence of thermodynamic soil and vegetation parameterizations on
the simulation of soil temperature states and surface fluxes by the Noah LSm over a Tibetan plateau site, Hydrology and Earth
System Sciences, 13, 759-777
van der Velde, R., M. Ofwono, Z. Su, Y. ma, 2010, Long term soil moisture mapping over the Tibetan plateau using Special Sensor
Microwave Imager (SSM/I), L. Clim. (in review)
Wen, J. , Z. Su, 2003, Estimation of soil moisture from ESA Wind-scatterometer data, Physics and Chemistry of the Earth, 28(1-3),
53-61.
Wen, J. , Z. Su, 2004, An analytical algorithm for the determination of vegetation Leaf Area Index from TRMM/TMI data,
International Journal of Remote Sensing, 25(6), 1223–1234.
Wen, J. , Z. Su, 2003, A Method for Estimating Relative Soil Moisture with ESA Wind Scatterometer Data, Geophysical Research
Letters, 30 (7), 1397, doi:10.1029/ 2002GL016557.
Wen, J. , Z. Su, Y. Ma, 2003, Determination of Land Surface Temperature and Soil Moisture from TRMM/TMI Remote Sensing Data,
Journal of Geophysical Research, 108(D2), 10.1029/2002JD002176.
Zhong, L., Ma, Y., Salama, M.S., Su, Z., 2010, Assessment of vegetation dynamics and their response to variations in precipitation
and temperature in the Tibetan Plateau. Climatic change, DOI 10.1007/s10584-009-9787-8.
Zhong, L, Ma, Y., Su, Z., Salama, M.S., 2010, Estimation of Land Surface Temperature over the Tibetan Plateau using AVHRR and
MODIS Data, Adv. Atmos. Sci., doi: 10.1007/s00376-009-9133-0.
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