Time of emergence of climate signals over China under the RCP4.5 scenario

Transcription

1 Climatic Change (2014) 125: DOI /s y Time of emergence of climate signals over China under the RCP4.5 scenario Yue Sui & Xianmei Lang & Dabang Jiang Received: 6 June 2013 /Accepted: 16 May 2014 /Published online: 30 May 2014 # Springer Science+Business Media Dordrecht 2014 Abstract The signal of climate change is emerging against a background of natural internal variability. The time of emergence (ToE) is an indicator of the magnitude of the climate change signal relative to this background variability and may be useful for climate impact assessments. In this work, we examined the ToE of surface air temperature and precipitation over China under a medium mitigation scenario Representative Concentration Pathway 4.5 based on 30 satisfactory global climate models that are chosen from the Coupled Model Intercomparison Project Phase 5. Major conclusions are: the earliest ToE of annual and seasonal temperature occurs in the eastern Qinghai-Tibetan Plateau between 2006 and 2012 for S/N>1.0 and between and 2030 for S/N>2.0, which is years sooner than in Northeast China where the latest ToE appears in the country. Consistent with previous studies at the global scale, the median ToE for most of China occurs sooner in summer (2008 for S/N>1.0 and 2045 for S/N>2.0), while for Northeast and North China the median ToE occurs sooner in autumn ( for S/N>1.0 and 2040 for S/N>2.0). For the ToE of temperature, the inter-model uncertainty is at least 24 years in all five regions of concern and more than 85 years in some seasons, and the inter-model uncertainty in one season for which the earliest median ToE occurs is the smallest among the seasons. For precipitation, the early ToE occurs in the northeastern Qinghai-Tibetan Plateau for the annual mean, and seasonally it occurs first in winter in northern Northeast China and southwestern Northwest China and in winter and spring in the northeastern Qinghai-Tibetan Plateau. For southern China, the median ToE will not occur until Y. Sui: X. Lang : D. Jiang (*) Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing , China jiangdb@mail.iap.ac.cn X. Lang Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing , China Y. Sui University of Chinese Academy of Sciences, Beijing , China

2 266 Climatic Change (2014) 125: Introduction The Earth is warming, and the observed increase in global average temperature since the mid- 20th century can very likely be attributed to the human influence (IPCC 2007). However, climate is more unpredictable in the regions with large natural variability. A key issue is to detect and project statistically significant anthropogenic climate changes. One approach to doing this utilizes the time when the signal of climate change exceeds its natural variability, and the time is termed time of emergence (ToE) (Hawkins and Sutton 2012). The ToE describes the magnitude of the climate change signal relative to its natural variability and may be useful for climate impact assessments. The signal of climate change is emerging against a background of natural internal variability. The Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) projected that the ToE of seasonal temperature is generally later than that of annual temperature, and that the ToE for most regions is generally sooner in the summer than in other seasons (Christensen et al. 2007). Based on Coupled Model Intercomparison Project Phase 3 (CMIP3) simulations, Mahlstein et al. (2011) and Hawkins and Sutton (2012) addressed the ToE of global temperature, showing that the signal of anthropogenic climate changes is emerging against the background of natural climate variability. The former study showed that the earliest emergence of significant warming occurs in the summer season in low latitude countries, and the latter study also showed that the median ToE occurs several decades sooner in low latitudes, particularly in boreal summer, than in mid-latitudes. Both studies are consistent with the results of the IPCC AR4, although different approaches were utilized. For precipitation, the IPCC AR4 projected that the ToE is the soonest (20 25 years) for the polar areas, the longest (more than 100 years) for the low latitude areas and intermediate (40 65 years) for the mid-latitude areas (Christensen et al. 2007). Others have given projections generally similar to those in the IPCC AR4. Giorgi and Bi (2009) used CMIP3 simulations to investigate the ToE for 14 greenhouse gas-forced precipitation change hotspots and showed that high latitude hotspots have a ToE in the early decades of the 21st century, low and midlatitude hotspots in the Northern Hemisphere have a ToE in the middle century, and low latitude hotspots in the Southern Hemisphere have a ToE in the late century or beyond. Note that the above studies of ToE for temperature and precipitation are viewed from a continental or global perspective. Climate change projections at the regional scale are more meaningful, because regional climate change greatly affects the social and natural systems, particularly for highly populated regions such as China. In recent years, considerable effort has been made by Chinese scientists to project changes in climate across the globe as well as locally in East Asia (e.g., Ding et al. 2007; LuandFu 2010; Jiang et al. 2011; Gao et al. 2012; Jiang et al. 2012; Wangetal.2012; Jiang and Tian 2013; Lang and Sui 2013). However, to the best of our knowledge, no studies have focused specifically on the ToE for China. Therefore, the purpose of this study is to address whether the ToE of climate signals over China will occur under a medium mitigation scenario Representative Concentration Pathway (RCP) 4.5 (Thomson et al. 2011) and if yes where the ToE will occur first. 2 Data and methods Data from 36 Global Climate Models (GCMs) are available from the CMIP5 of the World Climate Research Programme at this stage. All 36 GCMs have performed the pre-industrial control run, the 20th century experiment with all forcing (or historical run) and the projection

3 Climatic Change (2014) 125: simulation forced with the RCP4.5 scenario in the set of experiments described by Taylor et al. (2012). These climate model data are freely accessible at the Program for Climate Model Diagnosis and Intercomparison website We use only the first run for each model to treat all of the models equally. Basic information on the 36 GCMs and their experiments is given in Table 1. Two datasets are used to assess GCMs ability to simulate the present climate: the daily temperature dataset over China (Xu et al. 2009, hereafter referred to as CN05) and the daily precipitation dataset over East Asia (Xie et al. 2007, hereafter referred to as EA_V0409) during the period That time window is considered the reference period. Based on the range of the horizontal resolutions of 36 GCMs, all simulation and observation data are regridded to a relatively mid-range horizontal grid resolution of 2 2. Note that ToE should be somewhat affected by the different spatial scale at which the analysis is undertaken (McSweeney and Jones 2013). Seasonal analyses are performed according to common procedure for winter (December January February, DJF), spring (March April May, MAM), summer (June July August, JJA) and autumn (September October November, SON). To identify the relatively satisfactory GCMs in simulating the climatological temperature and precipitation over China, two preconditions are set. First, the spatial correlation coefficients between simulations and observations must be positive and statistically significant at the 99 % confidence level, and second, the normalized centered root mean square differences must be smaller than 0.50 for temperature and 1.50 for precipitation. The former is used to guarantee the reliability of the models for observed spatial patterns, and the latter to constrain internal model errors. As such, based on 240 grid points across China, the spatial correlation coefficients are calculated model by model, as are the standard deviation and centered root mean square difference of each simulation with respect to observation for the 20-year average of the reference period For surface air temperature, Fig. 1a shows that spatial correlation coefficients range from 0.87 to 0.99, all of which are statistically significant at the 99 % confidence level. Normalized centered root mean square differences range from 0.19 to 0.65, with the values of BCC-CSM1.1, BNU-ESM, FGOALS-g2, FIO-ESM, MIROC-ESM and MIROC-ESM-CHEM being greater than 0.50 and those of the remaining GCMs being smaller than Based on the above two preconditions and the model results, 30 GCMs pass this examination. For precipitation, Fig. 1b shows that spatial correlation coefficients range from to Except for BNU-ESM, FIO-ESM and MIROC-ESM, the spatial correlation coefficients of all the other 33 GCMs are statistically significant at the 99 % confidence level. Normalized centered root mean square differences range from 0.52 to 1.79, with the values of BNU-ESM and FIO-ESM being greater than 1.50 and those of the remaining GCMs being smaller than Considering the above two preconditions, 33 GCMs pass this second examination. The GCMs which pass the criteria of both temperature and precipitation are deemed to relatively satisfactory models. In this way, a total of 30 GCMs are used in the subsequent analysis. There is no single metric of ToE. It depends on user-driven choices of variables, space and time scales, the baseline relative to which changes are measured, and the threshold at which emergence is defined (Kirtman et al. 2013). The ToE in our work is defined as follows. Estimating the ToE of any climate signals requires estimating the climate change signal (S)and the climate noise (N). Consistent with the definition from Hawkins and Sutton (2012), the internal variability of a climate system is considered climate noise. The natural variability of the climate system occurs in the absence of external forcing and includes processes intrinsic to the atmosphere, the ocean and the coupled ocean atmosphere system (Madden 1976; Feldstein 2000; Deser et al. 2012). The ToE is defined as the time at which the signal-to-noise ratio, S/N,

4 268 Climatic Change (2014) 125: Table 1 Basic information on the 36 CMIP5 models and their experiments Model ID Model name Country Atmospheric horizontal resolution Length of run analyzed Pre-industrial control (year) Historical RCP ACCESS1.0 Australia ACCESS1.3 Australia * BCC-CSM1.1 China BCC-CSM1.1 (m) China * BNU-ESM China CanESM2 Canada CCSM4 USA CESM1(BGC) USA CESM1(CAM5) USA CMCC-CM Italy CMCC-CMS Italy CNRM-CM5 France CSIRO-Mk3.6.0 Australia * FGOALS-g2 China * FIO-ESM China GFDL-CM3 USA GFDL-ESM2G USA GFDL-ESM2M USA GISS-E2-H USA GISS-E2-H-CC USA GISS-E2-R USA GISS-E2-R-CC USA HadGEM2-CC UK HadGEM2-ES UK INM-CM4 Russia IPSL-CM5A-LR France IPSL-CM5A-MR France IPSL-CM5B-LR France MIROC5 Japan * MIROC-ESM Japan * MIROC-ESM-CHEM Japan MPI-ESM-LR Germany MPI-ESM-MR Germany MRI-CGCM3 Japan NorESM1-M Norway NorESM1-ME Norway The length of the historical run and RCP4.5 experiments covers the periods 1986 and , respectively, and asterisks represent the models not chosen for analysis crosses particular threshold values (such as 0.5, 1.0 and 2.0) and remains above that threshold value thereafter. The signal and the noise of both the temperature and precipitation are

5 Climatic Change (2014) 125: Fig. 1 Taylor diagram (Taylor 2001) displaying normalized pattern statistics of climatological annual and seasonal mean (a) surface air temperature and (b) precipitation in China between 36 GCMs and observation for the reference period Each number represents a model ID (see Table 1). The reference (REF) indicates observation from (a) CN05 and(b) EA_V0409. Black, blue, green, red and orange numbers show simulations of annual, DJF, MAM, JJA and SON means, respectively. The correlation coefficient between a model and the reference is given by the azimuthal position of the model, with oblique dotted lines showing the 99 % confidence level. Normalized standard deviation of a model is the radial distance from the origin, with cambered dotted lines showing the value of Normalized centered root mean square difference between a model and the reference is their distance apart, with cambered solid lines showing values of (a)0.50 and(b) In short, the nearer the distance between a number and REF, the better the performance of the corresponding model calculated separately for each GCM at each grid cell. Relative to the reference period 1986, the time series of the signal of each GCM from 2006 to 2099 is indicated by a change in 19-year average, a time period that is sufficient to filter out inter-annual variability (Giorgi and Bi 2009). For example, the signal at year is the difference between the periods and The pre-industrial control simulation from each GCM is used to estimate the noise, which is defined as the inter-annual standard deviation of the seasonal or annual mean. The observational estimate of noise is derived from the standard deviation of the linear detrended seasonal or annual mean. 3Results 3.1 Surface air temperature Surface air temperature noise To estimate the ToE, we first need to investigate temperature noise over China. Figure 2a b shows the observational and the median model estimates of noise for annual temperature. Both of their geographical distributions are similar, with a spatial correlation coefficient of The median model noise of annual temperature is K and is large at high latitudes and on the Qinghai-Tibetan Plateau. The spatial distribution and magnitude of noise agree overall with Hawkins and Sutton (2012), who used the previous CMIP3 dataset. Compared to the observation, GCMs tend to have large noise in North China, the Qinghai-Tibetan Plateau and

6 270 Climatic Change (2014) 125: Fig. 2 Observational estimates of noise in annual (a) surface air temperature from CN05 for and (c) precipitation from EA_V0409 for 1962, and the median model estimates of noise in annual (b)temperature and (d) precipitation from 30 GCMs based on the pre-industrial control run. The (a) and (b) plots have units of [K], and (c) and(d) have units of [mm d 1 ] western Southwest China and little noise in portions of northwestern China. It should be mentioned that, like the previous results for the global scale (Hawkins and Sutton 2012), the ensemble range of noise for the individual models is larger than the median estimate over China, for example, with the 10 % estimate of noise being K and the 90 % being K. The difference of noise among the GCMs should affect the ToE to some extent. In general, the GCMs that overestimate the noise have the later ToE, and the GCMs that underestimate the noise have the earlier ToE. Seasonally, the geographical distribution of the median temperature noise is similar to the annual pattern, but with a larger range of K Median ToE of surface air temperature Based on the median estimate of the signal and noise from the 30 GCMs, Fig. 3 shows the median ToE for two S/N thresholds, 1.0 and 2.0, of annual and seasonal temperature under RCP4.5. The earliest emergence for annual temperature occurs approximately around the Qinghai-Tibetan Plateau and in southern Xinjiang province, in which the median ToE for S/ N>1.0 is between 2006 and 2009 and for S/N>2.0 between and For Northeast China, the median ToE for S/N>1.0 is delayed by 6 10 years, and the ToE for S/N>2.0 is between and For the other part of East China, the median ToE for S/N>1.0 is delayed by 2 8 years, and the ToE for S/N>2.0 is between 2025 and. Seasonally, the earliest median ToE occurs in summer in the eastern Qinghai-Tibetan Plateau and northeastern South China, with the median ToE for S/N>1.0 being between 2008 and 2012 and for S/N>2.0 between and For Northeast and North China, the median ToE occurs first in autumn, with being for S/N>1.0 and for

7 Climatic Change (2014) 125: Fig. 3 Median ToE of annual and seasonal surface air temperature for S/N>1.0 and S/N>2.0, respectively. The regions indicated by black polygons in (a) are used in Figs. 4 and 6. Regions 1 to 5 correspond to northwestern China, the eastern Qinghai-Tibetan Plateau, North China, northeastern China and southeastern China, respectively. The numbers 1, 2, 3 and 4 in (c) and(d) indicate DJF, MAM, JJA and SON, respectively S/N>2.0. For the rest of China, the median ToE occurs first in summer, with being between 2012 and for S/N>1.0 and 2030 and 2040 for S/N>2.0. Previous works showed that the ToE occurs sooner in boreal summer for the globe, except in the central Arctic. The present national scale analysis differs somewhat from the global pattern. For most of China, the median ToE also occurs sooner in summer, while for Northeast China and North China, the median ToE occurs sooner in autumn ToE of surface air temperature in five regions The median ToE and each GCM s own ToE for S/N>1.0 of annual and seasonal temperature in five roughly equal-area regions (as indicated in Fig. 3a) are given in Fig. 4. Regions1to5 correspond to northwestern China, the eastern Qinghai-Tibetan Plateau, North China, northeastern China and southeastern China, respectively. The median ToE of annual temperature in regions 1 to 5 occurs in 2009, 2006, 2012, 2016 and 2011, respectively. This result is similar to Fig. 3a, but somewhat earlier than that. The noise variance decreases with averaging, and hence the ToE for an area mean is generally earlier than the mean ToE for that area. The scatters in Fig. 4 demonstrate the ensemble range in the estimates of ToE from the individual GCMs. The ensemble range in ToE of annual temperature ranges from 27 (region 2) to 42 years (region 4) in all five regions. Another measure for the ensemble range is the interquartile range, which explicitly specifies the range of the central 50 % of the data. The interquartile range of ToE is only 3 years for region 2 and around 10 years for the other regions, indicative of a smaller inter-model uncertainty of ToE for region 2.

8 272 Climatic Change (2014) 125: (a) Region 1 (b) Region 2 (c) Region 3 Annual(0) DJF(2) MAM(0) JJA(0) SON(0) Annual(0) DJF(0) MAM(0) JJA(0) SON(0) Annual(0) DJF(1) MAM(0) JJA(0) SON(0) (d) Region 4 Annual(0) DJF(1) MAM(1) JJA(0) SON(0) (e) Region 5 Annual(0) DJF(3) MAM(0) JJA(0) SON(1) Median ACCESS1.3 CanESM2 CESM1(BGC) CMCC-CM CNRM-CM5 GFDL-CM3 GFDL-ESM2M GISS-E2-H-CC GISS-E2-R-CC HadGEM2-ES IPSL-CM5A-LR IPSL-CM5B-LR MPI-ESM-LR MRI-CGCM3 NorESM1-ME ACCESS1.0 BCC-CSM1.1(m) CCSM4 CESM1(CAM5) CMCC-CMS CSIRO-Mk3.6.0 GFDL-ESM2G GISS-E2-H GISS-E2-R HadGEM2-CC INM-CM4 IPSL-CM5A-MR MIROC5 MPI-ESM-MR NorESM1-M Fig. 4 ToE for S/N>1.0 of surface air temperature derived from each GCM s own signal and noise for annual, DJF, MAM, JJA and SON in regions (a) 1,(b) 2,(c) 3,(d) 4and(e) 5 that are illustrated in Fig. 3a. Thegrey histogram indicates the median ToE for S/N>1.0 which is derived from the median signal and median noise of 30 GCMs. The error bars are the upper and lower quartiles of those 30 GCMs ToE.Thenumbers in the brackets of x-axis indicate the number of GCMs that do not cross the S/N>1.0 threshold until 2090 Also seen in Fig. 4 is the seasonal variation, showing a shift to earlier median ToE in summer for regions 1, 2 and 5 and in autumn for regions 3 and 4. The earliest median ToE for regions 1 to 5 occurs in 2012, 2008, 2015, 2017 and 2009, respectively, consistent with Fig. 3c. The ensemble range of ToE ranges from 24 years in summer for region 5 to more than 85 years in autumn for region 5. Except region 2 where the ensemble range of ToE in spring and summer is smaller than in winter and autumn, for the other regions the ensemble range of ToE in summer and autumn is smaller than in winter and spring. The inter-quartile range of ToE forregions1to5 is 14 (summer) 28 (winter) years, 6 (summer) 12 (winter) years, 8 (autumn) 17 (winter) years, 9 (autumn) 36 (winter) years and 8 (summer) 15 (winter and spring) years, respectively. In all, the earlier the median ToE for S/N>1.0 in one region occurs, the smaller the inter-model uncertainty of ToE for S/N>1.0 in this region is; and for one region, the earlier the median ToE for S/N>1.0 in one season occurs, the smaller the inter-model uncertainty of ToE for S/N>1.0 in this season is. As far as ToE for S/N>2.0 is concerned, the similar results can be obtained. 3.2 Precipitation Precipitation noise The observational and the median model estimates of noise for annual precipitation are presented in Fig. 2c d. Their geographical distributions agree well with each other, with a

9 Climatic Change (2014) 125: spatial correlation coefficient of The noise is mm d 1 and decreases from southeastern to northwestern China. Compared to the observation, GCMs tend to have large noise around the Qinghai-Tibetan Plateau and little noise in portions of southeastern and northwestern China. For individual models, the 10 % (90 %) estimate of noise is ( ) mm d 1, indicating a larger range than the median estimate. On the seasonal scale, the geographical distribution of precipitation noise is similar to the annual pattern, with the values of mm d 1. For most of China, the noise is the smallest in winter and is the largest in summer Median ToE of precipitation The median ToE of annual and seasonal precipitation under RCP4.5 is shown in Fig. 5 using two S/N thresholds, 0.5 and 1.0. For the annual mean, the early emergence appears in the northeastern Qinghai-Tibetan Plateau, with the median ToE being between and 2030 for S/N>0.5 and later than 2060 for S/N>1.0. In Northeast and Northwest China, the median ToE for S/N>0.5 is between 2030 and In North China, the median ToE for S/N>0.5 is between 2060 and 2090, and the emergence for S/N>1.0 does not occur until For southern China, the emergence does not occur until 2090 for both thresholds. Seasonally, the median ToE for S/N>1.0 in most of China does not occur until The earliest median ToE for S/N>0.5 is between and in winter in northern Northeast China and parts of western and southern Xinjiang province and in winter and spring in a small part of the northeastern Qinghai-Tibetan Plateau. For the middle reach of the Yangtze River valley and southern North China, the early ToE appears in spring after. For southeastern China, northern North China, southern Northeast China and the southwestern Qinghai-Tibetan Fig. 5 Median ToE of annual and seasonal precipitation for S/N>0.5 and S/N>1.0, respectively. No occurrence of ToE is left blank. The numbers 1, 2, 3 and 4 in (c) and(d) indicate DJF, MAM, JJA and SON, respectively

10 274 Climatic Change (2014) 125: Plateau, the median ToE does not occur until For the rest of northern China, the earlier ToE occurs between and 2090 in winter ToE of precipitation in five regions The median ToE and each GCM s own ToE for S/N>0.5 of annual and seasonal precipitation in five roughly equal-area regions (as indicated in Fig. 3a) are shown in Fig. 6. Thefigure caption has the same meaning as Fig. 4, but for precipitation for S/N>0.5. At the regional scale, the early median ToE of annual precipitation occurs in 2030 for region 2. The median ToE of annual precipitation for region 5 does not occur until These results are similar to Fig. 5a. The inter-model uncertainty of ToE for region 2 is smaller than for the other regions, with the fourth-spread being On the seasonal scale, the earlier median ToE for regions 1 and 4 occurs in winter in 2022 and 2051, and for regions 2 and 3 in spring in 2040 and For region 5, the median ToE for all seasons does not occur until The ensemble range of ToE for seasonal precipitation is large, ranging from 2006 for region 1 in winter and spring to later than 2090 for all seasons in the five regions. The inter-quartile range of ToE in spring for region 2 is , and the range in winter for region 1 ranges is The upper quartile of ToE for the other seasons in all five regions does not occur until In short, though the inter-model (a) Region 1 (b) Region 2 (c) Region 3 Annual(7) DJF(7) MAM(8) JJA(24) SON(14) Annual(3) DJF(15) MAM(5) JJA(9) SON(10) Annual(8) DJF(15) MAM(11) JJA(13) SON(19) (d) Region 4 Annual(8) DJF(9) MAM(16) JJA(16) SON(14) (e) Region 5 Annual(14) DJF(23) MAM(16) JJA(19) SON(24) Median ACCESS1.3 CanESM2 CESM1(BGC) CMCC-CM CNRM-CM5 GFDL-CM3 GFDL-ESM2M GISS-E2-H-CC GISS-E2-R-CC HadGEM2-ES IPSL-CM5A-LR IPSL-CM5B-LR MPI-ESM-LR MRI-CGCM3 NorESM1-ME ACCESS1.0 BCC-CSM1.1(m) CCSM4 CESM1(CAM5) CMCC-CMS CSIRO-Mk3.6.0 GFDL-ESM2G GISS-E2-H GISS-E2-R HadGEM2-CC INM-CM4 IPSL-CM5A-MR MIROC5 MPI-ESM-MR NorESM1-M Fig. 6 ToE for S/N>0.5 of precipitation derived from each GCM s own signal and noise for annual, DJF, MAM, JJA and SON in regions (a) 1,(b) 2,(c) 3,(d) 4and(e) 5 that are illustrated in Fig. 3a. Thegrey histogram indicates the median ToE for S/N>0.5 which is derived from median signal and median noise of 30 GCMs. The error bars are the upper and lower quartiles of those 30 GCMs ToE.Thenumbers in the brackets of x-axis indicate the number of GCMs that do not cross the SN>0.5 threshold until 2090

11 Climatic Change (2014) 125: uncertainty of ToE for seasonal precipitation is large, it is certain, based on the whole of the 30 GCMs, that the ToE for S/N>0.5 will occur first in winter for region 1 and in spring for region 2 in the 21st century, and that the ToE for S/N>0.5 for region 5 will not occur until As far as ToE for S/N>1.0 is concerned, the ToE will also occur first in winter for region 1 and in spring for region 2. 4 Concluding remarks Based on the 30 satisfactory GCMs chosen from CMIP5, we examine the ToE for temperature and precipitation over China under the RCP4.5 scenario. Our major conclusions are as follows. For the natural variability, the median model estimates of annual and seasonal temperature noise are large at high latitudes and on the Qinghai-Tibetan Plateau, with the values K. The annual and seasonal precipitation noise is mm d 1 and decreases from southeastern to northwestern China, with the smallest in winter and the largest in summer overall. Comparatively, GCMs generally agree in pattern with the observational noise, but with a somewhat different magnitude, implying that there is room for improving the models ability in simulating natural climate variability for better estimate of ToE. The earliest ToE of annual temperature occurs approximately around the Qinghai- Tibetan Plateau and in southern Xinjiang province, and it is about a decade sooner than in Northeast China where the ToE is later than the other parts of China. Seasonally, the earliest median ToE occurs in summer in the eastern Qinghai-Tibetan Plateau and northeastern South China. For Northeast and North China the median ToE occurs sooner in autumn, and for the rest of China the median ToE occurs sooner in summer. The inter-model uncertainty is at least 24 years in all five regions of concern and more than 85 years in some seasons, and the inter-model uncertainty in autumn for Northeast China and North China or in summer for the other regions is the smallest among the seasons. On the other hand, the early ToE of annual precipitation occurs in the northeastern Qinghai-Tibetan Plateau. The earliest median ToE of seasonal precipitation occurs in winter in northern Northeast China and southwestern Xinjiang province and in winter and spring in the northeastern Qinghai-Tibetan Plateau. The inter-model uncertainty of ToE for precipitation is large. Finally, it should be noted again that there is no single metric of emergence. ToE is affected by the choice of variables, space and time scales, the baseline relative to which changes are measured, and the threshold at which emergence is defined. A limitation of our work is that it is based on the current CMIP5 models. This ensemble is unlikely to span the full range of ToE, but more importantly there are a number of sources of uncertainty that have not been addressed, such as emissions scenarios and the impact of natural multi-decadal variability (particularly relevant to precipitation). In addition, the present study focuses only on temperature and precipitation. In future work, special attention will be given to the assessment of ToE for other climate variables and climate events to identify regions where might experience climate outside of natural variability soonest. Acknowledgments We sincerely thank the two anonymous reviewers for their helpful comments and suggestions on the manuscript. This research was supported by the National Basic Research Program of China (2012CB955401), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB ), and the National Natural Science Foundation of China ( and ).

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