Download 2. Data and Methodology

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stxb201411142251
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Verification of the applicability of PRECIS-simulated temperature
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on the Loess Plateau of China
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Lü Zhemin, Li Zhi1, Li Jingjing, Shi Xiaoping
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College of Natural Resources and Environment, Yangling, Shaanxi, 712100, China
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Abstract: Temperature is a critical variable in plant growth and water cycle. Under the
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background of global warming, projection of potential changes in temperature can provide
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important information for related issues. Though Regional Climate Models (RCM) are popular for
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climate projection due to higher resolution compared with General Circulation Models (GCM),
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their simulation accuracy should still be assessed in details. By using ERA40 reanalysis data as the
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boundary conditions to run PRECIS, this study assessed the ability of PRECIS to simulate
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temperature on the Loess Plateau of China. The spatial distribution and temporal changes of mean,
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maximum and minimum temperature as well as extreme temperature events from PRECIS were
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compared with those observed. Results showed that the spatial distribution of the observed mean
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and minimum temperature was simulated with an absolute error of less than 2 ℃ in most regions;
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however, the spatial pattern of maximum temperature was not reproduced well since the absolute
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error was greater than 2 ℃. The temporal trends of three temperature variables were presented
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similarly as the observed, among which the mean temperature was best simulated; however, the
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performance differed at different time scales. Though the deviations of monthly temperature were
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different for each season, the change rate of annual mean temperatures was better simulated with
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deviation of 0.011℃ a-1. For extreme temperature events, most of the simulated indices had
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similar spatial distribution and temporal trend as the observed, but the deviations of change rates
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were great. Therefore, the PRECIS-simulated temperature cannot be directly used and further
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correction should be carried out on the Loess Plateau.
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Key words: Regional Climate Model; PRECIS; Applicability; The Loess Plateau of China;
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Temperature
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1. Introduction
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The Fifth Assessment Report of the Intergovernmental Panel on Climate Change concluded
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that the global average surface temperature has increased by 0.85℃ from 1880 to 2012 and has
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greatly altered natural resources and human society [1]. Furthermore, the changes in temperature
1Corresponding
author: Li Zhi(1978-), College of Natural Resources and Environment, Northwest A&F
University, Yangling 712100, Shaanxi, China. e-mail: [email protected]
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and their impacts would continue in future; therefore, it is necessary to project potential changes in
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temperature to provide useful information for development of climate change adaptation
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strategies.
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Projection of climate change mainly depends on GCMs (General Circulation Models), but
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GCMs cannot provide enough regional climate information due to low horizontal resolution
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(hundreds of km). Thus precipitation and temperature were poorly simulated at regional scale [2];
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Therefore, downscaling is essentially used to improve the performance of GCMs and provide
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detailed climate change information. Among the downscaling methods, dynamical methods by
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Regional Climate Models (RCMs) can better simulate the regional characteristics of climate since
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they can describe detailed topography, sea-land distribution, vegetation and other underlying
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surface characteristics [4]. Therefore, RCMs are useful tools to downscale GCMs.
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Some RCMs have thus been developed to downscale GCMs, such as PRECIS (Providing
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Regional Climates for Impacts Studies), RegCM4 (Regional Climate Model Version 4) [5-8]，
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CCLM (COSMO model in Climate Mode) [9], MM5V3 (Fifth-generation Penn. State/NCAR
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Meso-scale Model Version 3) [10] and RIEMS (Regional Integrated Environment Model System)
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[11]. Among the RCMs, PRECIS has been widely used in India [12], Pakistan [13], South
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America [14], Mediterranean [15] and other regions for climate projections and impact
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assessments on crop yield [16], water [17,18] and energy [17,19]. To guarantee the accuracy of
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climate prediction and impact assessment, the applicability of PRECIS has been evaluated for
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some regions. Overall, PRECIS can simulate the change trend of the climatic variables, but greater
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errors were detected for the regions with complex topography [5, 20]. Therefore, it is still
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necessary to carry out detailed assessment for a specific region before application.
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The Loess Plateau of China (CLP) is famous for its soil eroson in the world due to intensive
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rainstorms, steep slopes, low vegetation cover and erodible loessial soil [21]. On the other hand,
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the sustainable development of CLP is restricted by water scarcity since it is located in a
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semi-humid to arid transition zone. Water resource management is thus of utmost importance for
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this region. However, CLP is sensitive to global warming and tends to be warmer and drier during
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the past 50 years, which further aggravated water scarcity [22]. Therefore, its future climate
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should be projected to provide information for soil loss reduction and water resource management.
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However, the spatial distribution of climate was significantly influenced by the altitude and terrain
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due to complex topography [23]. The accuracy of RCM should thus be assessed in details for CLP.
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The objective of this study was to assess the ability of PRECIS to simulate temperature on
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CLP by comparing PRECIS-simulated and the observed temperature from 50 weather stations.
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The evaluation was carried out for the mean, maximum and minimum temperature as well as
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extreme temperature events about their spatial distribution and temporal changes. The results can
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present a guideline for model application and calibration.
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2. Data and Methodology
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2.1. Description of PRECIS and Data
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PRECIS is a regional climate modeling system developed by the UK Met Office Hadley
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Center [24] with relatively high horizontal resolution of 0.44° and 19 vertical levels in the
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atmosphere. The convection scheme is the one proposed by Gregory and Rowntree [25]. The
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lateral boundary conditions for HadRM3P are available from a range of model(s) and
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observationally based sources. In the HadRM3P, the surface physics calculations are performed
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using the four-layer soil model MOSES (Met Office Surface Exchange Scheme) [26].
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Two parts of data were needed for model evaluation, i.e. the simulated and observed
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temperature on CLP. Using ERA40 reanalysis data from ECMWF (European Centre for
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Medium-Range Weather Forecasts) as boundary conditions to run model, the temperature was
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simulated with a resolution of 50 km×50 km for the period of 1960-2000. The observed
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temperature was collected from 50 weather stations of China Meteorological Administration with
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the same period. To assess the model performance, the simulated data is extracted for the grids
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where included the weather stations (Fig. 1).
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Fig.1. Location of weather stations and distribution of PRECIS grids
2.2. Evaluation Methods
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For climate models, their ability to reproduce the spatial distribution and temporal trend in
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climatic variables was very important. This study, therefore, compared the two aspects of each
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variable between the observed and simulated on different time scales (day, month and year).
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variables included three observed indices, i.e. mean temperature (Tmean), maximum temperature
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(Tmax) and minimum temperature (Tmin), and five derived indices for extreme temperature evens
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[27] (Table 1). The spatial distribution of each variable was obtained by ordinary Kriging
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interpolation method, and the differences were computed between the two maps. For temporal
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trend, the linear trend was computed for each variable and compared between the observed and the
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simulated. T test, F test and KS (Kolmogorov-Smirnov) test (p = 0.05) was used to verify data
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from two groups whether they have similar mean values, variations and probability distribution.
The
Table 1. Definition of extreme temperature indices
Name
Define
Unit
ETR
Intra-Annual Extreme Temperature Range
（℃）
FD
Days with absolute minimum temperature < 0 ℃
(day)
GSL
Period between when Tmean > 5 ℃ for >5 d and Tmean < 5 ℃ for > 5 d
(day)
HWDI
Maximum period > 5 consecutive days with Tmax > 5 ℃ above the 1961-1990 daily Tmax
TN90
normal
Percent of time Tmin > the 90th percentile value of daily minimum temperature
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3. Results
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3.1 Spatial distribution of temperature
(day)
%
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During 1960-2000, PRECIS can simulate the spatial variations in temperature (Tmean, Tmax
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and Tmin), i.e. decrease from southeast to northwest (Fig.2a&b, d&e, g&h). However, some
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differences were detected (Fig.2c, f&i), and the simulated values were higher than the observed in
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most areas. For Tmean and Tmin, the differences between the observed and simulated were both less
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than 2 ℃ in most areas (Fig.2c&i); however, those for Tmax were greater than 2 ℃ in most areas
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and were even greater than 4 ℃ in a north-south zone (Fig.2f).
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The spatial variations can be also be interpreted by the standard deviation of temperature. The
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standard deviations of Tmean and Tmin for the observed and simulated were all close to 3.3℃, and
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the results of F test showed that 30 and 31 stations cannot reject the hypothesis that they have the
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same variance for Tmean and Tmin, respectively. The above results suggested that the simulated
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spatial variation in Tmean and Tmin were almost consistent with the observation. However, for Tmax,
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the standard deviation of the simulated values was overestimated by 0.8℃, and only 11 stations
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passed F test, which implied that PRECIS overestimated the spatial variations in Tmax.
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Fig.2. Spatial distribution of the observed and simulated annual mean temperature（a-c, T mean; d-f, T max; g-i, T min）
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3.2 Temporal trend in temperature
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For daily temperature, PRECIS can satisfactorily simulate the frequency distribution of the
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observation (Fig.3). Tmean was best simulated since the frequency distribution was almost the same
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as the observation; however, the distribution curve of the simulated Tmax / Tmin was slightly moved
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to the right/left of the observed.
a. T mean
5
Observation
Simulation
3
2
1
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121
3
2
0
20
Temperature/℃
40
Observation
Simulation
3
2
1
0
-20
C.T min
4
1
0
5
Observation
Simulation
4
Frequency/%
Frequency /%
4
b. T max
Frequency/%
5
-20
0
20
40
Temperature/℃
0
-40
-20
0
20
40
Temperature/℃
Fig.3 Frequency distributions daily temperature
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Monthly Tmean was slightly overestimated by 0.3 ℃ for both the mean values and standard
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deviation averaged across all months (Table 2), and it was overestimated in spring and summer
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while underestimated in autumn and winter (Fig.4). However, the errors for monthly mean Tmax
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and Tmin were much greater than those of Tmean. Monthly Tmax was overestimated by 3.8 ℃ while
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monthly Tmin was underestimated by 1.7 ℃ (Table 2). The errors of T max were the greatest for
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March through May with a bias of more than 5 °C, and those of Tmin were the greatest from
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October to March with a bias of more than 2°C.
Table 2 Statistics and results of statistical tests for temperature
Mean/℃
Parameter
Tmean
Monthly
temperature
Tmax
Tmin
Tmean
Annual
temperature
Tmax
Tmin
Standard
deviation/℃
OBS
8.2
10.8
SIM
8.5
11.1
OBS
14.9
10.5
SIM
18.7
11.1
OBS
2.6
10.6
SIM
0.9
11.4
OBS
8.3
3.2
SIM
8.5
3.4
OBS
14.9
3.3
SIM
18.7
4.1
OBS
2.7
3.3
SIM
1.0
3.6
T test
F test
KS test
26/50*
41/50
31/50
13/50
36/50
22/50
16/50
31/50
19/50
6/50
33/50
39/50
4/50
11/50
14/50
1/50
31/50
38/50
* The number stations passed statistical test, expressed in form of station/total station.
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On annual scale, the errors were similar as those for monthly temperature. The deviation of
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annual Tmean was the smallest (0.2 ℃), while that of Tmax and Tmin was 3.8 ℃ and -1.7 ℃,
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respectively (Table 2). The model can present the upward trend of temperature (Fig.4a-c); however,
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the change magnitude was quite different from those observed. For example, Tmean would increase
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by 0.026 ℃ a-1 and 0.016 ℃ a-1 for the observation and simulation, respectively. Similar
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phenomenon was detected for the linear trend of Tmax and Tmin.
a. Tmean
10.0
b. Tmax
Observation
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Simulation
20
y=0.016x+8.2
9.0
Temperature /℃
Temperature /℃
9.5
Observation
8.5
8.0
Simulation
y=0.017x+18.3
18
16
y=0.024x+14.5
y=0.026x-+7.7
14
7.5
7.0
1960
1970
1980
1990
12
1960
2000
1970
5
Observation
Temperature difference /℃
Temperature /℃
y=0.026x+2.1
3
2
1
y=0.015x+0.7
0
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137
138
1980
6
Simulation
4
1970
1990
2000
d. Deviation
c. Tmin
1960
1980
Year
Year
1990
2000
Year
Tmean
Tmax
Tmin
4
2
0
-2
-4
2
4
6
8
10
12
Month
Fig.4 The temporal changes of monthly and annual mean temperature
3.3 Extreme events
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The extreme temperature indices were computed to assess the ability of PRECIS to simulate
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extreme events (Table 3). Obviously, PRECIS performed best for GSL since the simulated values
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had the same statistics and change trend as those observed. TN90, ETR and FD were simulated
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satisfactorily since the relative errors of these variables were all less than 13%, while HWDI was
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not well reproduced with a relative error of 122%. However, it should be noted that the errors of
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each extreme index were quite different for stations and variables, which can be indicated by
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Figure 5.
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which implied that the spatial variation was overvalued. For GSL, TN90 and FD, more than 40
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stations/grids passed F test, which indicated that the spatial variation of these indices were well
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reproduced. However, less than 15 stations/grids passed F test for ETR and HWDI, implying the
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spatial structure was not well simulated by PRECIS.
The standard deviations of the simulated values were higher than observed (Table 3),
Table 3 Statistics and results of statistical tests for the extreme temperature indices
ETR /k
FD /d
GSL /d
HWDI /d
TN90 /%
Parameter
OBS
SIM
OBS
SIM
OBS
SIM
OBS
SIM
OBS
SIM
Mean
56.9
63.1
151.2
171.3
219.0
219.1
6.5
14.4
15.9
15.6
Standard deviation
5.8
5.9
29.6
32.1
27.3
27.5
2.6
4.5
3.3
3.9
T test
14/50
3/50
17/50
17/50
8/50
F test
13/50
42/50
46/50
11/50
45/50
KS test
20/50
43/50
45/50
8/50
45/50
150
For temporal change (Fig.5), the simulated GSL had a similar change trend and rate as the
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observation. For TN90, though the simulated change trends were different from that of the
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observation, their errors can be ignored since their absolute change rates were both 0.01% a-1. The
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change trend of the other indices was similar between the simulation and observation, but the
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change rates were obviously different and the simulated values were greater than the observed. KS
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test was carried out to further determine whether the observed and simulated data had the same
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distribution. Results indicated that 43, 45 and 45 stations/grids passed KS test for FD, GSL and
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TN90, while only 20 and 8 sites/grids passed KS test for ETR and HWDI, respectively.
a. ETR
Observation
80
240
Simulation
Time /day
Temperature/℃
200
60
y=-0.26x+62.2
50
Observation
Simulation
220
y=-0.06x+64.4
70
b.FD
y=-0.05x+171.6
180
160
140
y=-0.21x+155.1
120
100
40
80
60
0 5 10 15 20 25 30 35 40 45 50 1960
1970
1980
Year
Station Number
1990
2000
0 5 10 15 20 25 30 35 40 45 50 1960 1970 1980 1990 2000
Station Number
Year
d. HWDI
c. GSL
Observation
Simulation
260
60
Observation
Simulation
240
50
Time /day
Time /day
220
200
y=0.14x+216.6
180
160
140
40
y=0.13x+11.8
30
20
120
10
100
y=0.23x+1.8
80
0
0 5 10 15 20 25 30 35 40 45 50 1960
1970
Station Number
1980
2000
Year
e. TN90
0 5 10 15 20 25 30 35 40 45 50 1960 1970 1980 1990 2000
Station Number
Year
Observation
Simulation
26
24
y=0.01x+15.6
22
Percentage /%
1990
20
y=-0.01x+15.8
18
16
14
12
10
8
6
4
0 5 10 15 20 25 30 35 40 45 50 1960 1970 1980 1990 2000
158
Station Number
159
Year
Fig.5. Spatiotemporal variations of the observed and simulated extreme temperature indices
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4 Discussion and Conclusion
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RCMs can dynamically downscale GCMs outputs to give more detailed information of
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regional climate change; however, their precision should still be assessed to provide information
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for users about how to use the simulated data. This study thus assessed the applicability of a
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widely used RCM, i.e. PRECIS, on the Loess Plateau. The simulated spatial patterns and temporal
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changes of temperature as well as extreme indices were thoroughly compared with the
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observation.
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PRECIS can simulate the spatial pattern of temperature on the Loess Plateau, among which
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Tmean and Tmin were simulated better with deviations of less than 2 ℃ in most areas; however,
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monthly mean Tmax and its spatial variation were greatly overestimated. On different time scales,
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the temporal trend of Tmean was well simulated, while that of Tmax and Tmin was overestimated and
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underestimated, respectively. Similar results have been found in the other regions, such as
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the whole of China[5, 20], which means that the errors of PRECIS was very common.
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As extreme temperature events have great impacts on natural ecosystem and social
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development, its simulation precision is thus of utmost importance for decision-making. In general,
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except for HWDI with the greatest error of 122%, the relative errors of the other four extreme
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indices were less than 13% though spatial variations existed. As for temporal change, the change
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rates of GSL and TN90 were well reproduced. Though the change trend of the other indices was
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similar as the observation, the change rates were greatly overestimated. Overall, PRECIS
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performed well in GSL and TN90 while had poor ability to simulate ETR and HWDI.
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A short period of high or low temperature will seriously affect growth and yield of crops [28,
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29]. Under the background of global warming, the increase rate of grass coverage would be likely
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to decline and the growth of manual plantation could be suppressed, which will decrease the
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ability of vegetation to control soil erosion [20]. The performance of PRECIS will thus have great
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impacts on the impact assessment of climate change on agriculture and hydrological modeling.
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However, the relative errors of Tmean, Tmax and Tmin were +0.2, +3.8 and -1.7℃, which was much
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greater than the observed increase in global surface temperature (0.85 ℃ ). Therefore, if
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PRECIS-simulated temperature were directly applied to impact assessment, the results might be
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uncertain and even wrong. The PRECIS-simulated data should be used cautiously and calibration
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should be carried out in details.
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Acknowledgements
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This study is partially funded by the National Natural Science Foundation of China (41101022),
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Fok
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(2014YQ003&2452015105).
Ying
Tung
Foundation
(141016)
and
Chinese
Universities
Scientific
Fund
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