Upload
jaafar
View
63
Download
0
Tags:
Embed Size (px)
DESCRIPTION
Quantifying Climate Change Impacts In Data-scarce Environments. Z. (Bob) Su (1) Email: [email protected] or [email protected] www.itc.nl/wrs With contributions from Y. Ma (3), J. Wen (2), P. De Rosnay (4), R. Van der Velde (1), L. Dente (1), - PowerPoint PPT Presentation
Citation preview
Quantifying Climate Change Impacts
In Data-scarce Environments
Z. (Bob) Su (1)Email: [email protected] or [email protected]
www.itc.nl/wrs
With contributions fromY. Ma (3), J. Wen (2), P. De Rosnay (4), R. Van der Velde (1), L. Dente (1),
L. Wang (1), L. Zhong (1), S. Salama (1), J. Timmermans (1)
(1) Faculty Of Geo-information Science And Earth Observation (IIC), University Of Twente, Enschede, The Netherlands
(2)Institute Of Tibetan Plateau Research, Chinese Academy Of Sciences, Beijing, P.R. China
(3) Cold And Arid Regions Environmental And Engineering Research Institute, Chinese Academy Of Sciences, Lanzhou, P.R. China
(4) European Centre For Medium-range Weather Forecasts, Reading , United Kingdom
What is the difference?
Content
1. Background & Objectives2. In-situ networks, satellite observations & model outputs3. Quantifying hydroclimatic variables
Vegetation Surface temperature Soil moisture Water levels
4. Climatic impacts - variations, trends, and extremes? 5. Suggestions and conclusion
3
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).
4
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
Current 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.
Objectives
A framework for quantification of climate change impacts and attribution of causes
Quantification of uncertainties in satellite retrievals & model outputs using in-situ observations
Identification of variations, trends, and extremes in plateau scale hydroclimatic variables
Climatic impacts or monsoon pattern changes – actions for stakeholders?
5
Earth Observation and Water Resources
Precipitation (100%)
Water Storage in Ice and Snow
Evaporation (100%)
Groundwater Storage & Flow
River Discharge (35%)
Soil Moisture
RadiationRadiation
Vapour Transport (35%)
Evaporation/Transpiration
(65%)
Condensation (65%)
Water Resources Management
Maqu
Naqu
Ngari
~ Network July 2008
~ Network June 2006
~ Network June 2010
ITC/CAS Soil Moisture Networks
ESA Dragon programmeEU FP7 CEOP-AEGIS projectESA WACMOS project
Tibetan Soil Moisture Experiments at location of CAMP/Tibet sites
(in collaboration with Prof. Yaoming Ma, ITP/CAS)
GEWEX Asian Monsoon Experiment (GAME) in the Tibet Plateau (GAME/Tibet,1996-2000)GEWEX Asian Monsoon Experiment (GAME) in the Tibet Plateau (GAME/Tibet,1996-2000)
CEOP (Coordinated Enhanced Observing Period) Asia-Australia Monsoon Project in the Tibetan CEOP (Coordinated Enhanced Observing Period) Asia-Australia Monsoon Project in the Tibetan Plateau (CAMP/Tibet, 2001-2005)Plateau (CAMP/Tibet, 2001-2005)
Coordinators: Y.M.Ma & T.D.Yao, K.Ueno & T.Koike
ESA Dragon Programme,EU FP7 CEOP -AEGIS
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 press)
MAP OF VEGETATION COVER TYPES ON THE TIBETAN PLATEAU
(1 km resolution land cover map from GLC2000)
Average seasonal mean NDVI variations over the Tibetan Plateau
Vegetation response to climate variability
1. Vegetation density <-> general climate
pattern in the Tibetan Plateau. The Asian
monsoon had a great impact on the seasonal
variation in NDVI.
2. Correlations between NDVI and climate
variables differ for different land uses.
3. Vegetation density increasing in 49.87%
of the total area.
What are the causes?
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
(Oku & Ishikawa, 2003)
Monthly mean surface temperature averaged across the Tibetan Plateau
+ 0.2 K/yr
Surface temperature over the plateau is rising year by year.
Daily Maximum and Minimum Surface Temperature
Daily minimum surface temperature rises faster than maximum temperature.
Maximum Minimum
+ 0.13 K/yr + 0.39 K/yr
(Oku & Ishikawa, 2003)
Decadal variations of land surface temperature observed over the Tibetan Plateau SSM/I 1987-2008
15
(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.
Trends of LST anomalies derived from the 1987-2008 SSM/I data set: (a) TPE – Tibetan Plateau and surrounding areas, (b) Tibetan Plateau
16
A warming plateau or a cooling plateau?
Trends of LST anomalies observed over the Tibetan Plateau vs elevation
17
A cross section at 32± N of monthly LST anomalies.
(Yanai and Wu [2006] described the Tibetan Plateau as a heat source forthe atmosphere in the summer with exception of the south eastern part.)
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 ?
Part III – Soil moisture
Adequacy of satellite observations for quantifying climatic impacts
(Su et al., 2011, HESS; van der Velde et al., 2011, J.Cli. - in review)
Volumetric soil moisture, ASCAT data, 1-7 July 2007
Soil moisture (m3/m3 )
19
Volumetric soil moisture,AMSR-E VUA-NASA product, average 1-7 July 2007 (Pixel size 0.25°, White pixels = flag values = sea, ice, forest)
Validation of soil moisture retrievals at Naqu site (Cold & semi-arid), Tibetan plateau (July-Oct. 2008)
Maqu in-situ soil moisture & soil temperature measurements
calibrated for soil texture and derived the final soil moisture time series
80 km
40 km
Maqu site - Soil temperature (upper panel) and soil moisture (lower panel) measured at 5 cm soil depth at Maqu network
Time series retrieval (SSM/I) vs in-situ observations
SSM/I soil moisture retrievals and measurements from Naqu,
North, East and South stations plotted over time.
23
1/1/05 1/1/06 1/1/07 1/1/08 1/1/09
Date [mm/ dd/ yy]
0.0
0.1
0.2
0.3
0.4
0.5
Soil
moi
stur
e [m
3 m
-3]
SSM/ I ret rievalsNaqu stat ionNorth stat ionEast stat ionSouth stat ion
Trends in mean and anomaly in plateau scale soil moisture (1987-2008, SSM/I retrievals)
Slope of fit the through soil moisture anomalies
Slope of the fit through absolute soil moisture
R2 of the fit through the absolute soil moisture
R2 of the fit through the absolute soil moisture
m3m
-3/ decade
/ decade[%
]
[%]
Trends in plateau scale soil moisture (SSM/I)
25
1988 1992 1996 2000 2004 20080.00
0.05
0.10
0.15
0.20
Soil
moi
stur
e [m
3 m
-3]
Central T ibetSE-T ibet 2. 62 10-3 x -5. 11-4. 63 10 -3 x + 9. 38
1988 1992 1996 2000 2004 2008
Year
-2.0
-1.0
0.0
1.0
2.0
Soil
moi
stur
e an
omal
y
0. 151 x -301. 06-0. 146 x + 291. 68
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).
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)
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.
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 Extended-Kalman 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
ICESAT PLOT 2003 - 2009
Part IV –Terrestrial Water Storage
• An example in the Upper Reach of Yellow River
• (in prep. Su et al., 2011)
Space Gravimetry (The Gravity Recovery and Climate Experiment (GRACE). )
Upper Reach of Yellow River basin in northeast Qinghai-Tibetan plateau
Time series of TWS of Upper Reach of Yellow River(Top GRACE obs, bottom GLDAS output)
Monthly Yellow River discharge at Lanzhou hydrological station (Top).Annual discharge at Lanzhou hydrological station from 2003 to 2009 (Bottom)
Cumulative discharge anomalies and TWS (right axis) estimated from GRACE observations and GLDAS state variables
(circles - the measured discharge at Lanzhou station, solid line - estimates by GRACE/GLDAS)
Describe• Trends (change)
• Variability (natural cycle)
• Outliers
Understand• Attribution (variability vs. error)
• Consistency Process (e.g. Volcanic eruption, fire/aerosol)
• Feedback links (e.g. ENSO teleconnection)
A Roadmap From Process Understanding To AdaptationClimate Change Adaptation In Water Resources
Detect• Hot Spot
• Quality issue
• Outside Envelope
Predict• Impacts
Adapt• Consequences
ESA STSE programme:
Water Cycle Multimission Observation Strategy (WACMOS)
Clouds (Precipitation, 100%)
Water Storage in Ice and Snow
Groundwater Storage & Flow
Discrharge to ocean (35%)
Soil Moisture
RadiationRadiation
Water vapour (transport to land 35%, condensation 65%)
Evaporation (land 65%, ocean 100%)
Water Resources Management
42
Continental scale simulations 1 Jan – 9 Dec 2009, grid resolution 25 KM
Skin temperature Precipitation(convective + non-convective)
Latent heat flux (Evaporation/transpiration)
Soil moisture of top layer Soil moisture of second layer
Water budget of the Naqu Basin on the Tibetan Plateau
dW/dt
44
Water budget of the Naqu Basin on the Tibetan Plateau
dS/dt
45
wacmos.org 46
WACMOS.org
An outlook – a niche for actionClimate change adaptation may determine most likely the future of the mankind.
ITC Water resources and Environmental Managementhttp://www.itc.nl/Pub/Study/Courses/C10-WREM-MSC-01http://www.itc.nl/wrs/
47
ITC MSc/PhD programme ITC MSc/PhD programme Water Resource and Environmental Management Water Resource and Environmental Management
Recommendations
(Proposed action points)
Satellite observations in data scarce environment are critical for quantifying climatic impacts – application agencies should develop dedicated capacities
Uncertainties in satellite observations needed to be quantified with in-situ reference observations data – data sharing is badly needed – a role for stakeholders?
Modeling results need to be verified before used in drawing conclusions about climatic change impacts – NWP centers & science groups
Concerted actions needed to aggregate and analyze climatic impacts in data scare environment
Existing studies need to be analyzed in detail – separating those based observation data with uncertainty certification from less rigorous studies
Referances/Further Readings
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.
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.
49
Can we adapt?