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.

QUANTIFYING CLIMATE CHANGE IMPACTS IN A DATA-SCARCE ENVIRONMENT

Z. (Bob) Su (1)

With contributions fromJ. 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

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

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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).

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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.

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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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

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

(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

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(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

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A warming plateau or a cooling plateau?

Trends of LST anomalies observed over the Tibetan Plateau vs elevation

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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, HESSD; van der Velde et al., 2011, J.Cli. - in review;)

Volumetric soil moisture, ASCAT data, 1-7 July 2007

Soil moisture (m3/m3 )

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Volumetric soil moisture,AMSR-E VUA-NASA product, average 1-7 July 2007 (Pixel size 0.25°, White pixels = flag values = sea, ice, forest)

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

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 networkshowing 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

SSM/I soil moisture retrievals and measurements from Naqu,

North, East and South stations plotted over time.

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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)

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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)

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 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

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

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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