ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2012, VOL. 5, NO. 6, 483−488

Changes in Extreme Events as Simulated by a High-Resolution Regional Climate Model for the Next 20–30 Years over China XU Ji-Yun1, SHI Ying2, and GAO Xue-Jie2 1 Zhejiang Climate Center, Hangzhou 310017, China 2 The Laboratory of Climate Study, National Climate Center, China Meteorological Administration, Beijing 100081, China

Received 16 May 2012; revised 19 June 2012; accepted 19 June 2012; published 16 November 2012

Abstract In this paper, the changes in temperature and precipitation extremes over the next 20–30 years (2021– 2050) in relative to the present day (1986–2005) under the Intergovernmental Panel on Climate Change (IPCC) Spe-cial Report on Emissions Scenarios (SRES) A1B scenario are analyzed based on a high-resolution climate change simulation performed by a regional climate model (the Abdus Salam International Center for Theoretical Physics (ICTP) RegCM3). The extreme indices of summer days (SU), frost days (FD), and growing season length (GSL) for temperature and simple daily intensity index (SDII), number of days with precipitation ≥10 mm d–1 (R10), and consecutive dry days (CDD) for precipitation are used as the indicators of the extremes. The results show that the indices simulated by RegCM3 in the present day show good agreement with the observed. A general increase in SU, a decrease in FD, and an increase in GSL are found to occur in the next 20–30 years over China. A general in-crease in SDII, an increase in R10 over western China, and a decrease in R10 in north, northeast, and central China are simulated by the model. Changes in CDD are characterized by a decrease in the north and an increase in the south and the Tibetan Plateau.

Keywords: climate change, regional climate model, ex-treme events, China Citation: Xu, J.-Y., Y. Shi, and X.-J. Gao, 2012: Changes in extreme events as simulated by a high-resolution re-gional climate model for the next 20–30 years over China, Atmos. Oceanic Sci. Lett., 5, 483–488.

1 Introduction Increasing attention has been given to the changes in

climatic extremes in the climate change studies because of their critical impacts on human society, economic devel-opment, natural ecosystems, and the environment (e.g., Mearns et al., 2001; Emori et al., 2005; Gao et al., 2006; Tebaldi et al., 2006; Diffenbaugh et al., 2007; Torma et al., 2011).

Previous analyses have addressed the observed and projected trends in the frequency and intensity of various individual extreme climate events. For example, Frich et al. (2002) found a global increase in warm summer nights, a decrease in the number of frost days, and a decrease in intra-annual extreme temperature ranges while extreme precipitation showed more mixed patterns of change, but

Corresponding author: SHI Ying, shiying@cma.gov.cn

significant increases were seen in the amount derived from wet spells and the number of heavy rainfall events. Increasing research efforts have also been devoted to the changes of extreme events over China, either by analyz-ing observation data or making projections using global or regional climate models (GCMs and RCMs) (e.g., Gao et al., 2002; Gao, 2007; Zhai et al., 2005; Zhang et al., 2006; Xu et al., 2009; Shi et al., 2010; Sun et al., 2010; Feng et al., 2011; Ren et al., 2011). While much has been learned from previous works, further research is still needed to better address this topic.

Recently, Gao et al. (2012a, b) has performed a 150- year continuous climate change simulation (1951–2100) using an RCM at a 25-km grid spacing. In this paper, we report the future changes of temperature and precipitation extremes based on this simulation, with a focus on the next 20–30 years (2021–2050 in relative to the present day of 1986–2005).

The paper is organized as follows. A short description of the simulation and the extreme indices is presented in Section 2. In Section 3, the simulated indices are com-pared against the observation to validate the model. The projected changes are then discussed in Section 4, and conclusions are presented in Section 5.

2 The simulations and extreme indices The RCM simulation employed in the present study is

conducted by the regional climate model of the Abdus Salam International Centre for Theoretical Physics (ICTP) RegCM version 3 (RegCM3) (Pal et al., 2007). The model is one-way nested within the global model of Center for Climate System Research (CCSR)/National Institute for Environment Studies (NIES)/Frontier Research Center for Global Change (FRCGC) MIROC3.2_hires (the Model for Interdisciplinary Research on Climate) (Hasumi and Emori, 2004) with the emission scenario of SRES A1B (Nakicenovic et al., 2000).

The horizontal grid spacing of RegCM3 is 25 km, and the model domain covers continental China and surround-ing areas. The model physics include surface processes performed with the biosphere-atmosphere transfer scheme (BATS, Dickinson et al., 1993), planetary boundary layer computations employed the non-local formulation of Holtslag et al. (1990), resolvable scale precipitation rep-resented via the scheme of Pal et al. (2000), convective precipitation represented using the mass flux scheme of Grell (1993), and the atmospheric radiative transfer com-

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puted using the radiation package from the National Cen-ter for Atmospheric Research (NCAR) community cli-mate model CCM3 (Kiehl et al., 1996).

As reported by Gao et al. (2012a), both the spatial dis-tribution and magnitude of the observed temperature and precipitation are well reproduced by the model. In De-cember-January-February (DJF), a warm bias can be found in the high latitude areas, while a cold bias exists in the Tibetan Plateau and southern China. In June-July- August (JJA), the cold bias is still in the Tibetan Plateau, and a warm bias is found in northern China as well as in the deserts in the northwest. The model tends to under-estimate the precipitation in southern China, and over-estimate it elsewhere, both in DJF and JJA. The chan-ges of temperature and precipitation in the next 20–30 years show similar patterns compared with the middle and end of the 21st century, as reported by Gao et al. (2012a), but to a lesser extent. A general warming is simulated with greater values in the north compared with the south. For precipitation, a decrease in the south and increase in the north are simulated in DJF. In JJA, the decrease ex-tends from northeast to southwest China, and an increase over southern China and most places in northwest China can be found.

The gridded daily temperature and precipitation obser-vation dataset developed by Wu and Gao (2012) at a 0.25°× 0.25° (latitude by longitude) resolution based on observa-tions from more than 2400 meteorological stations are used to validate the model performance. The model out-puts are interpolated bi-linearly to this 0.25°×0.25° grid size to facilitate the inter-comparison. The density of the observation stations is much higher in eastern China com-pared with the west, especially over the Tibetan Plateau (Fig. 1 in Wu and Gao, 2012). This discrepancy may lead to a less reliable gridded observation dataset, making it di-fficult to evaluate the model’s performance over these areas.

Six indices are employed in the study, as shown in Ta-ble 1, with three for temperature and three for precipita-tion from Frich et al. (2002) and Expert Team on Climate Change Detection and Indices (ETCCDI, http://cccma. seos.uvic.ca/ETCCDMI/). According to Frich et al. (2002) and ETCCDI, summer days (SU) is used to calculate the warm days with a maximum temperature higher than 25°C. Frost days (FD) measures air frost, sampling the winter half-year in all extra-tropical regions, particularly the beginning and end of the cold season in many conti-nental and high latitude climates. The length of the ther-mal growing season (GSL) samples spring and autumn anomalies in the higher latitudes. The simple daily inten-sity index (SDII) and the number of days with precipita-tion ≥10 mm (R10) similarly summarize the wet part of the year, while the maximum number of consecutive dry days (CDD) could potentially become a valuable drought indicator for the dry part of the year.

3 Validation of the present day simulations The observed and simulated temperature indices of SU,

FD, and GSL are first presented and compared in Fig. 1. As can be seen in the figure, the model generally captures

the observed spatial pattern of the three indices well, al-though with some discrepancies concerning the amounts. For SU, greater values are simulated in the area extending from south of the Yangtze River to northern China and the basins in the northwest due to the warm bias of the model in the warm seasons over these areas (Gao et al., 2012a). In the meantime, the cold bias in the cold months over the Tibetan Plateau, Sichuan Basin, and Hua’nan leads to the overestimation of FD in these places. A shorter GSL than the observed is found over the Tibetan Plateau, the Si-chuan Basin, southernmost China, and the northeast, cor-responding to the model errors in simulating the spring and autumn temperatures.

For the precipitation indices, as shown in Figs. 2a and 2b, while the spatial pattern is generally reproduced, an underestimation of SDII in southern China and its overes-timation in western China are found in the simulation. The underestimation of SDII in southern China, which is in the humid climate zone, indicates a reduced capability of the model in simulating heavy precipitation events over the region. Similar performances of the model were found when driven by a different GCM (Shi et al., 2010).

RegCM3 reproduced well the observed R10, with some overestimation in northern and northeastern China, western China, and along the middle reaches of the Yang-tze River (Figs. 2c and 2d). The distribution of CDD in the observation follows that of the mean precipitation (Gao et al., 2012a), with smaller values in the south, lar-ger values in the north, and maxima in northwestern China, where an arid and semi-arid climate dominates. The model captures the features of CDD with smaller values in the south and maxima in Northwest China. However, a lower CDD is simulated over the northeast and the Tibetan Plateau, which is primarily associated with the wet bias over these areas in the dry seasons (Gao et al., 2012a).

4 Changes of the extremes in the next 20–30 years

The changes in the temperature indices of SU, FD, and GSL in the next 20–30 years are shown in Figs. 3a–c, respectively. A significant increase in SU is found over China, except over the Tibetan Plateau, where cold tem-peratures prevail over its high altitudes. This increase is greater in eastern China, with the values usually larger than 20 d a–1, while the maxima of 30 d a–1 are found over southwest China and Hua’nan. The increase is also large along the mountain edges in the northwest.

Similarly, a widespread decrease in FD is observed in Fig. 3b due to the warming. In eastern China, the largest decrease is found in the area between the Yangtze and Yellow River. A lesser decrease is found in the northern-most part of the northeast and the southern coast. The former is a cold region, while the latter is already warm enough in the present-day climate (Figs. 1c and 1d). A significant decrease in FD exceeding 30 d a–1 is found in the eastern and southern parts of the Tibetan Plateau and along the edges of the mountains in the northwest.

The change pattern of GSL is similar to FD, as shown

NO. 6 XU ET AL.: CHANGES IN EXTREME EVENTS FOR THE NEXT 20–30 YEARS OVER CHINA 485

Figure 1 Distribution of SU, FD, and GSL for the present day (1986–2005) (Units: d a–1): (a) SU from observation; (b) SU from RegCM3 simula-tion; (c) FD from observation; (d) FD from RegCM3 simulation; (e) GSL from observation; (f) GSL from RegCM3 simulation.

Table 1 Indices of climate extremes.

Indices Definition Units SU Number of summer days, days with maximum

temperature >25°C d

FD Number of frost days, days with minimum temperature <0°C

d

GSL Growing season length: period between when T >5°C for >5 d and T <5°C for >5 d

d

SDII Simple daily intensity index: annual total/ number of R ≥1 mm d–1

mm d–1

R10 Number of days with precipitation ≥10 mm d–1

d

CDD Maximum number of consecutive dry days (R <1 mm d–1)

d

in Fig. 3c, characterized by the largest increase found in the eastern and southern part of the Tibetan Plateau, fol-lowed by the area from the lower reaches of the Yellow River down to the Yangtze River Basin.

Moving to the precipitation indices, a general increase in SDII is simulated over many areas, with the rest show-ing fewer changes over China (Fig. 4a). An increase of as much as 10%–25% can be found in the areas distributed in southern China, Inner-Mongolia, the Tibetan Plateau, and northwestern China.

The change in R10 shows a distinct west-east contrast over China (Fig. 4b). A significant percentage increase in R10 is found over western China, except in the central

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Figure 2 Distribution of SDII, R10, and CDD for the present day (1986–2005) (units: mm d–1 and d a–1): (a) SDII from observation; (b) SDII from RegCM3 simulation; (c) R10 from observation; (d) R10 from RegCM3 simulation; (e) CDD from observation; (f) CDD from RegCM3 simulation.

Tibetan Plateau and other portions of the region. The in-crease is greater than 50% over many areas. A dominant decrease in R10 is found in northeastern and central China which is largely associated with the simulated de-crease in precipitation over these regions (Gao et al., 2012a). Little change and a slight increase in R10 are found in the south of the Yangtze River.

Different from the pattern of R10, a north-south ori-ented pattern of negative-positive (decrease-increase) change is found for CDD, which is in line with the change in precipitation in the cold (and dry in the meantime) season (Gao et al., 2012a). The decrease in CDD is larger

over northeast China, with the values in excess of 10%– 25%. The increase in CDD in the south and the Tibetan Plateau up to 25%–50% will lead to more drought events there. Note that regions with a longer CDD but a greater R10 can be found when comparing Figs. 4b and 4c (e.g., parts of southern China, the Tibetan Plateau), indicating a greater occurrence of flood- and drought-producing events may occur in the same place under the warmer climate conditions in the future. This phenomenon has been reported in many climate change simulations, such as a simulation over the Mediterranean by the same model (RegCM3) (Gao et al., 2006).

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Figure 3 Change of SU, FD, and GSL in the next 20–30 years (2021– 2050 in relative to 1986–2005) (units: d a–1): (a) SU; (b) FD; (c) GSL.

Figure 4 Change of SDII, R10, and CDD in the next 20–30 years (2021– 2050 in relative to 1986–2005) (units: %): (a) SDII; (b) R10; (c) CDD.

5 Conclusions and discussions

Changes in the extremes in the next 20–30 years over China as measured by six indices are investigated in the paper based on a high-resolution regional climate model simulation. We first evaluate the performance of the model by comparing its simulation against observations. The results show that RegCM3 can reasonably reproduce both the temperature and precipitation indices. Discrep-ancies in the temperature indices can be found due to the warm and cold biases of the model in different areas,

while the model’s capability in simulating heavy precipi-tation still needs to be improved.

A broad increase in SU, decrease in FD, and increase in GSL are simulated in the model, as expected in a warmer climate. An increase in SDII is found in many places, indicating an increased precipitation intensity in the warmer conditions following an enhanced hydrologi-cal cycle. The change of heavy precipitation as measured by R10 shows distinct geographic differences. A substan-tial increase is found in the west, while a decrease is found in northern and northeastern China. As a drought

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indicator, a pronounced increase in CDD is found in southern China, including the southwest, which suffered from successive drought disasters in recent years (e.g., Huang et al., 2012). The decrease in CDD in northern China may be favorable to the local society and ecosys-tems. However, further investigations are needed to un-derstand whether this decrease may compensate for the greater evapotranspiration in the warming climate.

Finally, we present the results of only one RCM driven by one GCM in this paper. A comparison of our results with previous RCM simulations (e.g., Gao et al., 2011) and more simulations of different RCMs driven by dif-ferent GCMs, as well as the employment of the near-term simulations of GCMs as the driving field (Hibbard et al., 2007), are needed to increase our confidence in the pro-jection of the changes of climate extremes in the next 20–30 years over China. Acknowledgements. This research was jointly supported by the National Basic Research Program of China (Grant No. 2009CB 421407) and the China-UK-Swiss Adapting to Climate Change in China Project (ACCC)-Climate Science.

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