YunYun JIN1,2, Ming WANG1,2, PeiJun SHI1,2, SaiNi YANG1,2 1State Key Laboratory of Earth Surface Processes & Resource Ecology，Beijing 100875，China; 2Academy of Disaster Reduction and Emergency Management Ministry of Civil Affairs & Ministry of Education，Beijing Normal University，Beijing 100875，China

1. 8/29/2012 1
4th International Disaster and Risk Conference IDRC Davos 2012
"Integrative Risk Management in a Changing World - Pathways to a Resilient Society"
26-30 August 2012
Davos, Switzerland
Diagnosis of Climate-Related Risks
by Using a Bayesian Updating Method
A Case Study of Summer Temperature in China
Yunyun Jin, Ming Wang, Peijun Shi, and Saini Yang
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Beijing Normal University, Beijing, China.
E-mail: jinyun@mail.bnu.edu.cn

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1. Introduction
• Climate changes
• related risks
Changes in physical and biological systems and surface
Changes in temperature, sea level and Northern temperature 1970-2004 (IPCC AR4)
Hemisphere snow cover (IPCC AR4)

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2. Models characterizing long-term change
of climatic variables
Random
Tendencies
variations

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2.1 Models and scenarios for summer
temperature
Model 1 Model 2 Model 3
s1 s2 s3
s4
Notation Name
Model 4 Model 5 Model 6
T length of the time series
s9
t current time
s5 s7
a average temperature of the full time series
s10
a1 average temperature of the first third time series
a3 average temperature of the last third time series
s6 s11
s8 Sd stand deviation of the full time series
Sd1 stand deviation of the first third time series
s12
Sd3 stand deviation of the last third time series
K slope of the mean [ (m3 –m1) / T ]
K’ slope of the SD [ (SD3 - SD1) / T ]

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2.2 Stable and instable scenarios
Stable Instable
Case
scenarios scenarios
Criteria of Instable Scenario
s1, s2 s3, s5, s7, s9
• The ascending trend has a
slope steeper than 0.002℃
per year (ƙ>0.002℃)
s1, s2, s6, s8 s3, s10
• The random fluctuation keeps
increasing (ƙ’>0)s
s ,s
1 2, 3 s5, s7, s9
(or s4) (or s11)
s1, s2, s3 (or
s4), s6, s8, s10 none
(or s12)

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2.3 Diagnosis of climate-related risks
• System Instability Index (SII)
• Bayesian updating the weights of models (Jaeger et al, 2008)
stable instable

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2.4 Example

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3. Case study of China
• Meteorological Data
1951 ~ 2009
756 meteorological stations
daily mean air temperature
summer temperature
• Priors
uniform prior

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4. Result
farming-pastoral ecotone
coastal areas

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5. Conclusion
• The SII shows the changing process of summer
temperature abnormality.
• This finding may move one step forward to understanding the
environmental risk of China induced by global climate change.
• This method may have a broader use for diagnosing
climate related risks at various scales.
• For different spatial scales, and different meteorological
observations can finally get a unify index which can be widely used
in environmental risk analysis.

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Thank you !