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1 stxb201411142251 2 Verification of the applicability of PRECIS-simulated temperature 3 on the Loess Plateau of China 4 Lü Zhemin, Li Zhi1, Li Jingjing, Shi Xiaoping 5 College of Natural Resources and Environment, Yangling, Shaanxi, 712100, China 6 7 Abstract: Temperature is a critical variable in plant growth and water cycle. Under the 8 background of global warming, projection of potential changes in temperature can provide 9 important information for related issues. Though Regional Climate Models (RCM) are popular for 10 climate projection due to higher resolution compared with General Circulation Models (GCM), 11 their simulation accuracy should still be assessed in details. By using ERA40 reanalysis data as the 12 boundary conditions to run PRECIS, this study assessed the ability of PRECIS to simulate 13 temperature on the Loess Plateau of China. The spatial distribution and temporal changes of mean, 14 maximum and minimum temperature as well as extreme temperature events from PRECIS were 15 compared with those observed. Results showed that the spatial distribution of the observed mean 16 and minimum temperature was simulated with an absolute error of less than 2 ℃ in most regions; 17 however, the spatial pattern of maximum temperature was not reproduced well since the absolute 18 error was greater than 2 ℃. The temporal trends of three temperature variables were presented 19 similarly as the observed, among which the mean temperature was best simulated; however, the 20 performance differed at different time scales. Though the deviations of monthly temperature were 21 different for each season, the change rate of annual mean temperatures was better simulated with 22 deviation of 0.011℃ a-1. For extreme temperature events, most of the simulated indices had 23 similar spatial distribution and temporal trend as the observed, but the deviations of change rates 24 were great. Therefore, the PRECIS-simulated temperature cannot be directly used and further 25 correction should be carried out on the Loess Plateau. 26 Key words: Regional Climate Model; PRECIS; Applicability; The Loess Plateau of China; 27 Temperature 28 29 1. Introduction 30 The Fifth Assessment Report of the Intergovernmental Panel on Climate Change concluded 31 that the global average surface temperature has increased by 0.85℃ from 1880 to 2012 and has 32 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] 33 and their impacts would continue in future; therefore, it is necessary to project potential changes in 34 temperature to provide useful information for development of climate change adaptation 35 strategies. 36 Projection of climate change mainly depends on GCMs (General Circulation Models), but 37 GCMs cannot provide enough regional climate information due to low horizontal resolution 38 (hundreds of km). Thus precipitation and temperature were poorly simulated at regional scale [2]; 39 Therefore, downscaling is essentially used to improve the performance of GCMs and provide 40 detailed climate change information. Among the downscaling methods, dynamical methods by 41 Regional Climate Models (RCMs) can better simulate the regional characteristics of climate since 42 they can describe detailed topography, sea-land distribution, vegetation and other underlying 43 surface characteristics [4]. Therefore, RCMs are useful tools to downscale GCMs. 44 Some RCMs have thus been developed to downscale GCMs, such as PRECIS (Providing 45 Regional Climates for Impacts Studies), RegCM4 (Regional Climate Model Version 4) [5-8], 46 CCLM (COSMO model in Climate Mode) [9], MM5V3 (Fifth-generation Penn. State/NCAR 47 Meso-scale Model Version 3) [10] and RIEMS (Regional Integrated Environment Model System) 48 [11]. Among the RCMs, PRECIS has been widely used in India [12], Pakistan [13], South 49 America [14], Mediterranean [15] and other regions for climate projections and impact 50 assessments on crop yield [16], water [17,18] and energy [17,19]. To guarantee the accuracy of 51 climate prediction and impact assessment, the applicability of PRECIS has been evaluated for 52 some regions. Overall, PRECIS can simulate the change trend of the climatic variables, but greater 53 errors were detected for the regions with complex topography [5, 20]. Therefore, it is still 54 necessary to carry out detailed assessment for a specific region before application. 55 The Loess Plateau of China (CLP) is famous for its soil eroson in the world due to intensive 56 rainstorms, steep slopes, low vegetation cover and erodible loessial soil [21]. On the other hand, 57 the sustainable development of CLP is restricted by water scarcity since it is located in a 58 semi-humid to arid transition zone. Water resource management is thus of utmost importance for 59 this region. However, CLP is sensitive to global warming and tends to be warmer and drier during 60 the past 50 years, which further aggravated water scarcity [22]. Therefore, its future climate 61 should be projected to provide information for soil loss reduction and water resource management. 62 However, the spatial distribution of climate was significantly influenced by the altitude and terrain 63 due to complex topography [23]. The accuracy of RCM should thus be assessed in details for CLP. 64 The objective of this study was to assess the ability of PRECIS to simulate temperature on 65 CLP by comparing PRECIS-simulated and the observed temperature from 50 weather stations. 66 The evaluation was carried out for the mean, maximum and minimum temperature as well as 67 extreme temperature events about their spatial distribution and temporal changes. The results can 68 present a guideline for model application and calibration. 69 2. Data and Methodology 70 2.1. Description of PRECIS and Data 71 PRECIS is a regional climate modeling system developed by the UK Met Office Hadley 72 Center [24] with relatively high horizontal resolution of 0.44° and 19 vertical levels in the 73 atmosphere. The convection scheme is the one proposed by Gregory and Rowntree [25]. The 74 lateral boundary conditions for HadRM3P are available from a range of model(s) and 75 observationally based sources. In the HadRM3P, the surface physics calculations are performed 76 using the four-layer soil model MOSES (Met Office Surface Exchange Scheme) [26]. 77 Two parts of data were needed for model evaluation, i.e. the simulated and observed 78 temperature on CLP. Using ERA40 reanalysis data from ECMWF (European Centre for 79 Medium-Range Weather Forecasts) as boundary conditions to run model, the temperature was 80 simulated with a resolution of 50 km×50 km for the period of 1960-2000. The observed 81 temperature was collected from 50 weather stations of China Meteorological Administration with 82 the same period. To assess the model performance, the simulated data is extracted for the grids 83 where included the weather stations (Fig. 1). 84 85 86 Fig.1. Location of weather stations and distribution of PRECIS grids 2.2. Evaluation Methods 87 For climate models, their ability to reproduce the spatial distribution and temporal trend in 88 climatic variables was very important. This study, therefore, compared the two aspects of each 89 variable between the observed and simulated on different time scales (day, month and year). 90 variables included three observed indices, i.e. mean temperature (Tmean), maximum temperature 91 (Tmax) and minimum temperature (Tmin), and five derived indices for extreme temperature evens 92 [27] (Table 1). The spatial distribution of each variable was obtained by ordinary Kriging 93 interpolation method, and the differences were computed between the two maps. For temporal 94 trend, the linear trend was computed for each variable and compared between the observed and the 95 simulated. T test, F test and KS (Kolmogorov-Smirnov) test (p = 0.05) was used to verify data 96 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 97 3. Results 98 3.1 Spatial distribution of temperature (day) % 99 During 1960-2000, PRECIS can simulate the spatial variations in temperature (Tmean, Tmax 100 and Tmin), i.e. decrease from southeast to northwest (Fig.2a&b, d&e, g&h). However, some 101 differences were detected (Fig.2c, f&i), and the simulated values were higher than the observed in 102 most areas. For Tmean and Tmin, the differences between the observed and simulated were both less 103 than 2 ℃ in most areas (Fig.2c&i); however, those for Tmax were greater than 2 ℃ in most areas 104 and were even greater than 4 ℃ in a north-south zone (Fig.2f). 105 The spatial variations can be also be interpreted by the standard deviation of temperature. The 106 standard deviations of Tmean and Tmin for the observed and simulated were all close to 3.3℃, and 107 the results of F test showed that 30 and 31 stations cannot reject the hypothesis that they have the 108 same variance for Tmean and Tmin, respectively. The above results suggested that the simulated 109 spatial variation in Tmean and Tmin were almost consistent with the observation. However, for Tmax, 110 the standard deviation of the simulated values was overestimated by 0.8℃, and only 11 stations 111 passed F test, which implied that PRECIS overestimated the spatial variations in Tmax. 112 113 114 Fig.2. Spatial distribution of the observed and simulated annual mean temperature(a-c, T mean; d-f, T max; g-i, T min) 115 3.2 Temporal trend in temperature 116 For daily temperature, PRECIS can satisfactorily simulate the frequency distribution of the 117 observation (Fig.3). Tmean was best simulated since the frequency distribution was almost the same 118 as the observation; however, the distribution curve of the simulated Tmax / Tmin was slightly moved 119 to the right/left of the observed. a. T mean 5 Observation Simulation 3 2 1 120 121 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 122 Monthly Tmean was slightly overestimated by 0.3 ℃ for both the mean values and standard 123 deviation averaged across all months (Table 2), and it was overestimated in spring and summer 124 while underestimated in autumn and winter (Fig.4). However, the errors for monthly mean Tmax 125 and Tmin were much greater than those of Tmean. Monthly Tmax was overestimated by 3.8 ℃ while 126 monthly Tmin was underestimated by 1.7 ℃ (Table 2). The errors of T max were the greatest for 127 March through May with a bias of more than 5 °C, and those of Tmin were the greatest from 128 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. 129 130 On annual scale, the errors were similar as those for monthly temperature. The deviation of 131 annual Tmean was the smallest (0.2 ℃), while that of Tmax and Tmin was 3.8 ℃ and -1.7 ℃, 132 respectively (Table 2). The model can present the upward trend of temperature (Fig.4a-c); however, 133 the change magnitude was quite different from those observed. For example, Tmean would increase 134 by 0.026 ℃ a-1 and 0.016 ℃ a-1 for the observation and simulation, respectively. Similar 135 phenomenon was detected for the linear trend of Tmax and Tmin. a. Tmean 10.0 b. Tmax Observation 22 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 0 136 137 138 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 139 The extreme temperature indices were computed to assess the ability of PRECIS to simulate 140 extreme events (Table 3). Obviously, PRECIS performed best for GSL since the simulated values 141 had the same statistics and change trend as those observed. TN90, ETR and FD were simulated 142 satisfactorily since the relative errors of these variables were all less than 13%, while HWDI was 143 not well reproduced with a relative error of 122%. However, it should be noted that the errors of 144 each extreme index were quite different for stations and variables, which can be indicated by 145 Figure 5. 146 which implied that the spatial variation was overvalued. For GSL, TN90 and FD, more than 40 147 stations/grids passed F test, which indicated that the spatial variation of these indices were well 148 reproduced. However, less than 15 stations/grids passed F test for ETR and HWDI, implying the 149 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 151 observation. For TN90, though the simulated change trends were different from that of the 152 observation, their errors can be ignored since their absolute change rates were both 0.01% a-1. The 153 change trend of the other indices was similar between the simulation and observation, but the 154 change rates were obviously different and the simulated values were greater than the observed. KS 155 test was carried out to further determine whether the observed and simulated data had the same 156 distribution. Results indicated that 43, 45 and 45 stations/grids passed KS test for FD, GSL and 157 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 160 161 4 Discussion and Conclusion 162 RCMs can dynamically downscale GCMs outputs to give more detailed information of 163 regional climate change; however, their precision should still be assessed to provide information 164 for users about how to use the simulated data. This study thus assessed the applicability of a 165 widely used RCM, i.e. PRECIS, on the Loess Plateau. The simulated spatial patterns and temporal 166 changes of temperature as well as extreme indices were thoroughly compared with the 167 observation. 168 PRECIS can simulate the spatial pattern of temperature on the Loess Plateau, among which 169 Tmean and Tmin were simulated better with deviations of less than 2 ℃ in most areas; however, 170 monthly mean Tmax and its spatial variation were greatly overestimated. On different time scales, 171 the temporal trend of Tmean was well simulated, while that of Tmax and Tmin was overestimated and 172 underestimated, respectively. Similar results have been found in the other regions, such as 173 the whole of China[5, 20], which means that the errors of PRECIS was very common. 174 As extreme temperature events have great impacts on natural ecosystem and social 175 development, its simulation precision is thus of utmost importance for decision-making. In general, 176 except for HWDI with the greatest error of 122%, the relative errors of the other four extreme 177 indices were less than 13% though spatial variations existed. As for temporal change, the change 178 rates of GSL and TN90 were well reproduced. Though the change trend of the other indices was 179 similar as the observation, the change rates were greatly overestimated. Overall, PRECIS 180 performed well in GSL and TN90 while had poor ability to simulate ETR and HWDI. 181 A short period of high or low temperature will seriously affect growth and yield of crops [28, 182 29]. Under the background of global warming, the increase rate of grass coverage would be likely 183 to decline and the growth of manual plantation could be suppressed, which will decrease the 184 ability of vegetation to control soil erosion [20]. The performance of PRECIS will thus have great 185 impacts on the impact assessment of climate change on agriculture and hydrological modeling. 186 However, the relative errors of Tmean, Tmax and Tmin were +0.2, +3.8 and -1.7℃, which was much 187 greater than the observed increase in global surface temperature (0.85 ℃ ). Therefore, if 188 PRECIS-simulated temperature were directly applied to impact assessment, the results might be 189 uncertain and even wrong. The PRECIS-simulated data should be used cautiously and calibration 190 should be carried out in details. 191 192 Acknowledgements 193 This study is partially funded by the National Natural Science Foundation of China (41101022), 194 Fok 195 (2014YQ003&2452015105). Ying Tung Foundation (141016) and Chinese Universities Scientific Fund 196 197 198 199 200 201 202 203 204 205 References [1] IPCC. Climate change 2013: The physical science basis. 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