Representativeness Evaluation of China National Climate Reference Station Network

Representativeness Evaluation of China National Climate Reference Station Network

Jianxia Guo1, Ling Chen2, Haihe Liang1, Xin Li3

1. Meteorological Observation Center of CMA, Beijing, China; 2. School of Geography, Beijing Normal University, Beijing, China;

3. Nanjing University of Information Science & Technology, Nanjing, China

气象探测中心Meteorological Observation Center

outline• Motivation • Data and Methods• Results• Conclusions• Discussions


Motivation• Climate change monitoring needs high quality and good

representative data. • Reviewing and assessing the representativeness of the

CRN timely is very important to persist the long-term observing network.

• In this paper, we just focus on the spatial representative

related to the climate zoning, underlying surface, station elevation, homogeneous grids et al. The results are expected to provide a guidance to remedy and optimize the current climate reference network.


Data and Methods• Data

– China climate zoning map (Yan Hong et al, 2002),– Land use type (1-km resolution), – Geographical elevation (1-km resolution), – Daily temperature records of CRN.

• Methods– Spatial information was integrated by the tool of geographic

information system (GIS) – The indicators of climate zoning and the statistical results of

temperature records of recent decade are compared, to judge the climate representativeness of a given station.


Results• Spatial distribution homogeneous • Distribution in different grades of climate zoning • Underlying surface representativeness• Elevation representativeness • Climate representativeness


Spatial distribution homogeneous

Fig.1 The distribution of national climate reference stations in 2.5°×2.5° grids


Climate zoning----climate belts



Climate zoning---- climate sub-zone


Climate zoning---- climate regions


Underlying surface representativeness

Crop land Forest land Grass land Water area Building area Waste land

A P N P A P N P A P N P A P N P A P N P A P N P

Frigid Temperate zone 1.5% 0 80.3% 1 14.2% 1 0.0% 0 0.0% 　 0 4.0% 　 0

Mid Temperate zone 18.0% 15 17.4% 1 40.1% 12 1.0% 0 0.2% 12 23.2% 2

Warm Temperate zone 32.4% 14 15.1% 2 15.4% 2 1.1% 0 0.6% 9 35.4% 1

Northern Subtropical zone

47.2% 13 42.9% 2 4.7% 0 4.7% 1 0.4% 2 0.0% 0

Mid subtropical zone 32.1% 11 60.5% 4 6.5% 1 0.8% 2 0.1% 4 0.0% 0

Southern subtropical zone

24.5%8

67.7%3

5.9%0

0.6%0

0.6%1

0.1%0

Marginal Tropical zone 21.2% 2 67.8% 0 9.8% 0 0.6% 0 0.2% 1 0.4% 0

Plateau Frigid zone 0.0% 0 0.0% 0 51.3% 0 5.1% 0 0.0% 0 43.6% 0

Plateau sub-frigid zone 0.5% 0 1.4% 0 83.4% 7 2.5% 0 0.0% 0 12.2% 0

Plateau sub-temperate zone

1.6%2

15.7%0

62.0%2

3.4%0

0.0%0

17.3%0

Plateau temperate zone 1.1% 0 25.6% 1 53.7% 1 2.9% 0 0.0% 0 16.6% 0

Mid tropical zone 0.0% 0 0.0% 0 0.0% 0 100% 1 0.0% 0 0.0% 0

Equatorial tropical zone 0.0% 0 0.0% 0 0.0% 0 100% 0 0.0% 0 0.0% 0

total 20.2% 65 25.6% 14 33.2% 26 1.8% 4 0.3% 29 19% 3

note ： land types of 2 stations are unknowing


Elevation representativeness


0.00000.0015

0.1

0.2

0.3

0.4

0.5

0.6

Area

Ratio

Equatorial Tropical Zone Mid Tropical Zone Marginal Tropical Zone Southern Subtropical Zone Mid Subtropical Zone Northern Subtropical Zone Plateau Temperate Zone Plateau Subtemperate Zone Plateau Subfrigid Zone Plateau Frigid Zone Warm Temperate Zone Mid Temperate Zone Frigid Temperate Zone

89% of stations distributed in area <2000m (68%), 11% of stations in 32% area (≥2000m )


Climate representativeness

19 out of 143 stations have not hold the representative feature of the climate belt they stay


Conclusions• The current climate reference network is poor in monitoring

the climate change over high elevation areas, desert areas, and marine areas.

• The proportion of the stations distributed in natural underlying surface is insufficient, but that in man made surface is overladen.

• Some climate reference stations have not kept the climatic characteristics of which climate zone they stay due to the climate change or the sitting surround changed.


Discussions• Although technical advanced enhance the capability of

sustaining the observing network, there are still more difficulties and challenges for filling the gaps of high altitude area, and other human sparse area.

• Optimizing and adjusting the existing network is a complicated and systematic project. Cost, efficiency, operation, personnel, persistence and quality of the historical records are all the factors to affect the decision.

• The potential results may be the compromise between the ideality and practice.


Thanks for your attention!

Documents

Representativeness Evaluation of China National Climate Reference Station Network