• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    CMIP6 Evaluation and Projection of Temperature and Precipitation over China

    2021-04-20 00:42:12XiaolingYANGBotaoZHOUYingXUandZhenyuHAN
    Advances in Atmospheric Sciences 2021年5期

    Xiaoling YANG, Botao ZHOU*, Ying XU, and Zhenyu HAN

    1Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing 210044, China

    2School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China

    3National Climate Center, China Meteorological Administration, Beijing 100081, China

    ABSTRACT This article evaluates the performance of 20 Coupled Model Intercomparison Project phase 6 (CMIP6) models in simulating temperature and precipitation over China through comparisons with gridded observation data for the period of 1995–2014, with a focus on spatial patterns and interannual variability. The evaluations show that the CMIP6 models perform well in reproducing the climatological spatial distribution of temperature and precipitation, with better performance for temperature than for precipitation. Their interannual variability can also be reasonably captured by most models,however, poor performance is noted regarding the interannual variability of winter precipitation. Based on the comprehensive performance for the above two factors, the “highest-ranked” models are selected as an ensemble (BMME).The BMME outperforms the ensemble of all models (AMME) in simulating annual and winter temperature and precipitation, particularly for those subregions with complex terrain but it shows little improvement for summer temperature and precipitation. The AMME and BMME projections indicate annual increases for both temperature and precipitation across China by the end of the 21st century, with larger increases under the scenario of the Shared Socioeconomic Pathway 5/Representative Concentration Pathway 8.5 (SSP585) than under scenario of the Shared Socioeconomic Pathway 2/Representative Concentration Pathway 4.5 (SSP245). The greatest increases of annual temperature are projected for higher latitudes and higher elevations and the largest percentage-based increases in annual precipitation are projected to occur in northern and western China, especially under SSP585. However, the BMME, which generally performs better in these regions, projects lower changes in annual temperature and larger variations in annual precipitation when compared to the AMME projections.

    Key words: CMIP6 evaluation and projection, temperature, precipitation, ensemble

    1. Introduction

    The Coupled Model Intercomparison Project (CMIP)has been coordinating simulations conducted by international modeling groups since the 1990s, with the aim of improving the performance of climate models and enhancing the scientific understanding of the climate system. This project has become a major tool for climate science, and remarkable progress has been achieved for global climate models (GCMs) (Eyring et al., 2016; Stouffer et al., 2017).At present, the CMIP phase 6 (CMIP6) is being carried out(Simpkins, 2017). Compared with previous phases, the number of experiments that have been designed in the CMIP6 is the largest. The physical processes of the CMIP6 models are more complicated, and their resolutions are enhanced(Eyring et al., 2016; Zhou et al., 2019). These CMIP6 simulations will support climate change research in the upcoming several years (Zhou et al., 2019).

    Temperature and precipitation are essential indicators for climate change. In the past few years, many research efforts have been devoted to model evaluations and projections of temperature and precipitation over China within the CMIP phase 5 (CMIP5) framework. The evaluations indicated that the CMIP5 models, in general, show reasonable performance in capturing the geographical distributions of surface temperature and precipitation (e.g., Xu and Xu, 2012b;Guo et al., 2013; Huang et al., 2013; Kumar et al., 2014;Sun et al., 2015; Jiang et al., 2016). Compared with CMIP phase 3 (CMIP3), the CMIP5 performances were improved for temperature while there was little performance change for precipitation (Kumar et al., 2014; Sun et al., 2015; Jiang et al., 2016). Some common biases, such as topographyrelated cold biases, underestimations of southeast-northwest precipitation gradients, and overestimations of the magnitudes of the interannual variability of temperature and precipitation, are also present in the CMIP5 simulations (Su et al., 2013; Chen et al., 2014; Bao and Feng, 2016; Jiang et al., 2016). For the CMIP5 projections, an overall warming of temperature and a general increase in precipitation are projected over China by the end of the 21st century under the Representative Concentration Pathways (RCPs), further noting the expectation of larger changes to occur for higher RCPs (e.g., Xu and Xu, 2012a; Su et al., 2013; Chen and Frauenfeld, 2014; Hu et al., 2015; Wu et al., 2015; Tan et al., 2016; Zhang et al., 2017).

    In general, these findings improve our knowledge of the simulation abilities of CMIP5 models and future climate changes under the RCP scenarios. Questions naturally arise regarding the performance of CMIP6 models for the climate in China and how China’s climate would change in the context of the new CMIP6 scenarios (i.e., Shared Socioeconomic Pathways, SSPs), which represents the motivation of this study. Some recent studies have begun to evaluate and project the East Asian monsoon climate using CMIP6 data(Chen et al., 2020; Ha et al., 2020; Jiang et al., 2020; Nie et al., 2020; Zhu et al., 2020; Xin et al., 2020) and have indicated a general improvement of CMIP6 models compared with CMIP5 models regarding simulations of both mean temperature and precipitation as well as extreme temperature and precipitation events. For example, a smaller spread is observed among CMIP6 models as well as a weaker cold bias of temperature and a weaker underestimation of the southeast–northwest precipitation gradient (Chen et al.,2020; Jiang et al., 2020). However, more detailed regional analysis is still needed. In addition, some previous CMIP studies have used the multimodel ensemble for projecting the climate of China (e.g., Xu and Xu, 2012a; Chen and Frauenfeld, 2014; Zhou et al., 2014; Tian et al., 2015; Wu et al.,2015; Wang et al., 2017) and some have used the optimal model ensemble (e.g., Chen and Sun, 2009, 2013; Chen et al., 2011; Hu et al., 2015; Zhou et al., 2018a; Rao et al.,2019). Determining the nature of the differences between the optimal model ensemble and the multimodel ensemble for CMIP6 projections of climate over China is also an area of concern in this study. Addressing this topic will aid in the understanding of uncertainties in the projections.

    2. Data and methods

    Simulation data from 20 CMIP6 models (Table 1) are used in this study. For each model, the near-surface air temperature and precipitation results from the historical simulation and the SSP245 and SSP585 experiments are employed. The SSP245 and SSP585 reflect a set of alternative futures of social development and greenhouse gas emission. The SSP245 represents the combined scenario of a moderate socio-economic development path (i.e., SSP2) with the medium-low radiation forcing which peaks at 4.5 W mby 2100. The SSP585 represents the combined scenario of a high energy-intensive, socio-economic developmental path(i.e., SSP5) with strong radiative forcing which peaks at 8.5 W mby 2100 (O'Neill et al., 2016; Riahi et al., 2017).

    The observed temperature and precipitation data of CN05.1 with a resolution of 0.25°×0.25° (Wu and Gao,2013) are used to validate the performance of the CMIP6 models. For convenience, all data are converted to the same 1°×1° grid using a bilinear interpolation scheme before analysis. As recommended by the CMIP6, the period 1995–2014 is used as the reference period for the evaluation and projection. The ensemble in this study is calculated with the same weight. The statistical significance is examined by the Student’s t-test.

    A Taylor diagram (Taylor, 2001) is used to evaluate spatial distributions of temperature and precipitation over China. This diagram provides a concise statistical summary of how well a simulated pattern matches an observed pattern in terms of the spatial correlation coefficient (SCC), the root-mean-square error (RMSE), and the ratio of variances.The interannual variability of the simulations relative to the observations is assessed by the interannual variability skill score (IVS) (Gleckler et al., 2008; Scherrer, 2011), which is calculated as

    where STDand STDare the standard deviations of the simulation and observation, respectively. IVS is a symmetric variability statistic that is used to measure the similarity of interannual variation between the simulation and observation. A smaller IVS value indicates a better simulation of interannual variability.

    To quantitatively examine regional differences, following Zhou et al. (2014), we divide China into eight subregions: Northeast China (NEC; 39°–54°N, 119°–134°E),North China (NC; 36°–46°N, 111°–119°E), East China(EC; 27°–36°N, 116°–122°E), Central China (CC;27°–36°N, 106°–116°E), Northwest China (NWC;36°–46°N, 75°–111°E), Tibetan Plateau (SWC1; 27°–36°N,77°–106°E), Southwest China (SWC2; 22°–27°N,98°–106°E), and South China (SC; 20°–27°N, 106°–120°E)(see Fig. 1), all of which are based on administrative boundaries and societal and geographical conditions (National Report Committee, 2007).

    3. Evaluations

    3.1. Climatology and interannual variability

    Figures 2a–f show the climatological spatial distributions of annual, winter (December to February, DJF), and summer (June to August, JJA) temperatures from observations and the ensemble simulation of all models (AMME),respectively. In general, the AMME simulated spatial patterns approximate those of the observations. However, relat-ive to the observations, a general underestimation of annual temperature is noted over most of China in the AMME simulation. The most pronounced cold bias is located in the Tibetan Plateau (Fig. 2g). This phenomenon was also present in the CMIP3 and CMIP5 simulations as revealed by previous studies (Jiang et al., 2005; Xu and Xu, 2012a;Jiang et al., 2016). For winter (Fig. 2h) and summer (Fig. 2i)temperatures, there are notable warm biases in parts of northern China, in addition to the cold bias in the Tibetan Plateau.

    Table 1. Basic information for the CMIP6 models used in this study.

    Fig. 1. Domains and topography (shading, units: m) of eight sub-regions in China. NEC: Northeast China; NC: North China; EC: East China; CC:Central China; NWC: Northwest China; SWC1: Tibetan Plateau; SWC2:Southwest China; SC: South China.

    For observed precipitation (Figs. 3a–c), the annual,winter, and summer precipitation amounts decrease from the southeast coast to the northwest areas. These spatial patterns are captured by the AMME simulation (Figs. 3d–f) but with overall wet biases (Figs. 3g–i). The wet bias for annual precipitation appears in most parts of northern and western China, particularly on the northern and southern flanks of the Tibetan Plateau (Fig. 3g), which was also reported for the CMIP3 and CMIP5 simulations (Jiang et al., 2005; Xu and Xu, 2012a; Jiang et al., 2016). Compared with the CMIP5, the wet bias in the CMIP6 models was observed to be smaller (Jiang et al., 2020; Zhu et al., 2020). The spatial distributions of wet biases for winter precipitation resemble that for annual precipitation, but with larger bias magnitudes (Fig. 3h). Besides the wet bias, dry biases are also notable for summer precipitation in parts of Northwest China and East China (Fig. 3i).

    Figure 4 shows the Taylor diagrams for annual, winter,and summer temperature and precipitation over China as simulated by the 20 CMIP6 models and AMME against the observations. The azimuthal position of the model point indicates the SCC between the simulated and observed patterns.The distance from the reference point (REF) to the model point indicates the normalized RMSE of the simulation relative to the observation. The radial distance from the origin to the model point indicates the ratio of standard deviations between the simulation and observation. The overall model biases are excluded in this diagram. Clearly, the CMIP6 models show better performance for temperature than for precipitation. For temperature, regardless of whether winter, summer, or annual mean values are used, the SCCs between the simulations and observations are all greater than 0.9, the RMSEs of the simulations relative to the observations are generally below 0.5, and the ratios of variances to the observations are close to 1 for most models. These results indicate that the CMIP6 models effectively capture the climatological distributions in terms of annual, summer, and winter temperatures.

    Fig. 2. Spatial distributions of (a?c) observed temperature (units: °C), (d?f) AMME simulated temperature (units: °C), and(g?i) AMME simulation biases from the observation (simulation minus observation, units: °C) for the period 1995–2014.The panels from the left to right side are for annual (ANN), winter (DJF), and summer (JJA), respectively. The black lines in(g)?(i) show the boundary of subregions. Note that the scales of color bars are different.

    Fig. 3. Spatial distributions of (a?c) observed precipitation (units: mm), (d?f) AMME simulated precipitation (units: mm),and (g?i) AMME simulation biases from the observation ((simulation minus observation)/observation, units: %) for the period 1995–2014. The panels from the left to right side are for annual (ANN), winter (DJF), and summer (JJA),respectively. The black lines in (g)?(i) show the boundary of subregions. Note that the scales of color bars are different.

    Fig. 4. Taylor diagrams of (a) annual (ANN), (b) winter (DJF), and (c) summer (JJA) temperature (red dots; units: °C) and precipitation (blue dots; units: mm) over China for the period 1995–2014. The black dot in each panel represents AMME.

    Compared with temperature, the SCCs for precipitation over China are relatively lower and the RMSEs are relatively higher. Specifically, the SCCs and RMSEs are mainly in the range of 0.6–0.9 (still statistically significant) and 0.5–1, respectively. In addition, the ratios of variances mostly lie between 1 and 1.5. Overall, the simulations of most models are reliable for the spatial patterns of annual,summer, and winter precipitation, although the variances are overestimated.

    Figure 5 presents the IVS values of the simulations for the interannual variability of annual, winter, and summer temperature and precipitation over China. In this study, the IVS values were first calculated in each grid of China and then averaged. For temperature (Fig. 5a), the IVS values are below 1.5 for all models except for CanESM5 which shows a value of 4.0 in summer. This suggests that the CMIP6 models can reasonably reproduce the observed interannual variability of annual, winter, and summer temperature. In comparison, the model performances for the interannual variability of annual and winter temperatures are better than their performances in summer. For precipitation (Fig. 5b), though the IVS values are larger than those for temperature, the relatively low IVS values in annual mean and summer imply a reasonable reproduction of the observed interannual variability by the CMIP6 models. It also reflects the dominant contribution of summer precipitation to annual precipitation (Sui et al., 2013). There is a large range for the winter IVS values, which vary from 7.1 to 62.9 and are much larger than those of annual mean and summer. This result indicates large inconsistencies among the models and poor simulations for the interannual variability of winter precipitation.

    According to Gleckler et al. (2008), the rankings for all models that considered the three factors of the Taylor diagram and the interannual variability skill score are summarized in Fig. 6. This figure depicts the overall performance of individual models. A smaller ranking value indicates a better performing model. The rankings for the Taylor diagram are the average of the rankings of SCC, RMSE, and ratio of variance. On the whole, the AMME outperforms its ensemble members in a comprehensive manner. For a given individual model, the performance ranks are somewhat different for different metrics. Considering the comprehensive performance for both spatial patterns and IVS, the relatively“highest-ranked” and “l(fā)owest-ranked” models are selected based on Fig. 6 and listed in Table 2. For these “highestranked” and “l(fā)owest-ranked” models, their comprehensive performances (arithmetic average of the rankings for Taylor Diagram and IVS) rank in the top three and bottom three among all models, respectively. Note that ACCESS-ESM1-5 and CESM2-WACCM (ACCESS-ESM1-5 and CESM2)show the same ranking for annual (summer) precipitation.

    Some studies have shown that increasing the model resolution is an effective way to improve the performance of model simulations (Yao et al., 2017; Zhou et al., 2018b;Bador et al., 2020), thus we examine the relationships between the model performances and resolutions. The analyses show that the comprehensive performances of the models and their resolutions are significantly correlated. The correlation coefficients are 0.50 and 0.81 for annual and summer temperatures, respectively. The comprehensive performance of the models for winter precipitation also show a significant correlation of 0.65 with their resolutions, which is consistent with the previous finding that model resolution influences the simulation of winter precipitation in China (Gao et al., 2006; Jiang et al., 2016, 2020).

    Fig. 5. Interannual variability skill score (IVS) of the CMIP6 models for annual (ANN), winter (DJF), and summer(JJA) (a) temperature and (b) precipitation over China. Note that the IVS for winter precipitation is divided by 10.

    Fig. 6. Portrait diagram of the rankings of model performance for annual (ANN), winter (DJF), and summer (JJA)(a) temperature (units: °C) and (b) precipitation (units: mm). The colors in the label bar indicate the rankings. A smaller ranking number indicates a better model performance. Columns from the left to the right side in each group show the rankings of the SCC, ratio of variances, and RMSE, mean rankings of the three factors in the Taylor diagram, and IVS rankings, respectively.

    Table 2. Highest and lowest ranking models selected for the ensembles for annual (ANN), winter (DJF), and summer (JJA) temperature and precipitation.

    3.2. Comparison of different ensemble simulations

    Figure 7 shows the spatial distributions of the biases from the “highest-ranked ” model ensemble (hereafter BMME) and the “l(fā)owest-ranked” model ensemble (hereafter WMME) for annual temperature and precipitation. Compared with the AMME simulation (Fig. 2g), the cold bias over the Tibetan Plateau is reduced in the BMME simulation (Fig. 7a) and augmented in the WMME simulation(Fig. 7b). The regionally averaged BMME, AMME, and WMME biases in SWC1 are ?1.3°C, ?2.0°C, and ?4.3°C,respectively (Fig. 8a). From a seasonal perspective, the performance of the BMME for winter temperature is better than that of the AMME and WMME simulations over SWC1, CC, EC, SC, and SWC2 (Fig. 8c). However, due to an overall warm bias, the BMME does not perform better than the AMME in simulating summer temperature but does indicate a smaller spread (Fig. 8e).

    Fig. 7. Spatial distributions of (a, c) BMME and (b, d) WMME simulation biases for annual (a, b)temperature (simulation minus observation, units: °C) and (c, d) precipitation [(simulation minus observation)/observation, units: %]. The black lines show the boundary of subregions.

    For annual precipitation, the wet biases in the AMME simulation (Fig. 3g) decrease in the BMME simulation (Fig.7c) and increase in the WMME simulation (Fig. 7d). When regionally averaged, the percentage-based wet biases over NWC, SWC1, NC, and NEC are 199%, 191%, 45%, and 28% respectively for the WMME simulation. These decrease to 136%, 147%, 40%, and 32% for the AMME simulation; the wet biases further reduce to 39%, 96%, 4%, and 23% in the BMME simulation, respectively (Fig. 8b). Similar results are obtained for the simulation of winter precipitation (Fig. 8d). Nevertheless, there is no improvement in the BMME simulation for summer precipitation over subregions except for EC, NWC, and NEC when compared to the AMME and WMME simulations, although the model spread is reduced.

    In short, the BMME generally shows better performance than the AMME and WMME in reproducing the spatial patterns of annual and winter temperature and precipitation, particularly in subregions with complex terrain. Similarly, regardless of whether for annual, winter, or summer temperature (precipitation), the BMME presents the smallest IVS values, followed by the AMME and then by the WMME. The IVS values for annual, winter, and summer temperature (precipitation) over China are 0.1 (0.9), 0.2 (8.3),and 0.3 (1.0) from the BMME simulation, 0.2 (1.4), 0.3(22.2), and 0.5 (1.0) from the AMME simulation, and 0.6(2.4), 0.7 (43.4), and 1.1 (1.4) from the WMME simulation,respectively.

    4. Projected Changes

    Figure 9 displays the temporal evolution of the projected changes in annual temperature and precipitation over China under SSP245 and SSP585 from the AMME,BMME, and WMME. Similar to the CMIP5 projection(e.g., Xu and Xu, 2012a; Chen and Sun, 2013; Tian et al.,2015; Tan et al., 2016), an increasing trend toward the end of the 21st century is projected for annual temperature and precipitation, with larger increases under SSP585 than under SSP245. Relative to the reference period of 1995–2014, the increases in annual temperature (precipitation) by the end of the 21st century that are projected by the AMME are 2.7°C (17%) under SSP245 and 5.4°C (30%) under SSP585. Compared with the AMME projection, the BMME(WMME) projects a smaller (larger) increase in temperature and a larger (smaller) increase in precipitation under each scenario. At the end of the 21st century under SSP245 and SSP585, the BMME projected increases in annual temperature (precipitation) are 2.4°C and 4.8°C (24% and 45%),respectively, and those projected by the WMME are 3.3°C and 7.0°C (13% and 25%), respectively.

    Fig. 8. Biases of the BMME, AMME, and WMME simulations for annual (ANN), winter (DJF), and summer (JJA)temperature (left panel, units: °C) and precipitation (right panel, units: %) in eight subregions of China. Boxes indicate the range of biases from the ensemble models and the black lines show the ensemble mean values. Note that the vertical scales are different.

    Fig. 9. Time series of annual (a) temperature (units: °C) and (b) precipitation (units: %) anomalies (relative to 1995–2014) over China for the observation (green), historical simulation (black), SSP245 (red), and SSP585 (blue).Solid, dashed, and dotted lines indicate the BMME, AMME, and WMME simulations, respectively. The shadings show the AMME ensemble spread. The time series are smoothed with a 20-yr running mean filter.

    Fig. 10. Projected changes in annual temperature (units: °C) under (a–c) SSP245 and (d–f) SSP585 over the period 2081–2100 relative to the reference period 1995–2014 from (a, d) BMME, (b, e) AMME, and (c, f) WMME. Black solid dots indicate those grids with statistically significant changes at the 95% level. The values in the lower-left corners represent the changes averaged over China.

    Fig. 11. Projected changes in (a) annual (ANN), (b) winter (DJF), and (c) summer (JJA) temperature (units: °C) under SSP245 and SSP585 over the period 2081–2100 relative to the reference period 1995–2014. Boxes indicate the interquartile model spread (i.e., 25th and 75th quantiles), horizontal lines indicate the AMME values, and whiskers show the AMME ensemble ranges. Black rectangles and blue solid dots represent the BMME and WMME values, respectively.

    Figure 10 further illustrates the spatial distributions of the projected changes in annual temperature under SSP245 and SSP585 by the end of the 21st century. The annual temperature is projected to increase across China, with much stronger warming under SSP585. The greatest warming under SSP585 occurs at higher latitudes and higher elevations of China such as in NEC, NWC, and SWC1. In contrast, the warming is comparable across subregions under SSP245. Of particular interest, the warming magnitudes under both scenarios gradually increase from the BMME projection to the AMME projection and then to the WMME projection, which may be associated with different climate sensitivities in the models and/or regional land-atmosphere feedbacks (Zhou and Chen, 2015; Tokarska et al., 2020;Zelinka, et al., 2020). When regionally averaged over subregions (Fig. 11a), the BMME projects an increase of 2.1°C(SWC2) to 2.6°C (NC) under SSP245. The increases projected by the AMME range from 2.3°C (SWC2) to 2.9°C(NEC), which are slightly higher than the BMME projection in each subregion. Those projected by the WMME are in a range of 2.5°C (SC) to 3.7°C (SWC1), which are the highest among the three ensemble projections. Under SSP585, the greatest changes that exceed 5°C as projected by the AMME, occur in NEC (5.8°C), NWC (5.7°C), NC(5.4°C), and SWC1 (5.4°C). The magnitudes of increase over these subregions decrease to 5.2°C, 5.0°C, 4.9°C, and 4.8°C, respectively, in the BMME projection, while they increase to 7.6°C, 7.6°C, 7.1°C, and 7.4°C in the WMME projection, respectively. From a seasonal perspective, the AMME and WMME generally project larger increases in winter temperature than in summer temperature by the end of the 21st century under SSP585. The BMME projected increases in winter temperature are also larger than those of summer temperature in NEC, SWC1, SWC2, and SC, but it is reversed in NWC, NC, CC, and EC (Figs. 11b, c). In addition, the BMME projected increases in winter temperature are generally higher than the AMME projection and lower than the WMME projection (Fig. 11b); the increases in summer temperature from the BMME projection are larger than the AMME and WMME projections (Fig. 11c). Moreover,the spreads of the projections for annual, winter, and summer temperature from the BMME ensemble members are narrowed, compared with the projections from the AMME and WMME ensemble members (Figure not shown).

    The spatial distributions of the projected percentage changes for annual precipitation under SSP245 and SSP585 are shown in Fig. 12. By the end of the 21st century, the annual precipitation is projected to increase uniformly across the country, and the magnitude of the increase is greater under SSP585 than under SSP245. Moreover, the projected percentage increases are larger in northern China than in southern China. The largest increase is anticipated over western China due to the drier climate toward the north and northwest areas, which is consistent with the CMIP5 projection (Zhou et al., 2014). Although the three ensembles show resemblances in their spatial distributions for the projected changes, salient differences exist in the magnitudes among the projections. For instance, compared with the AMME projection under SSP585 (Fig. 12e), the percentage increases of annual precipitation over northern and western China are enhanced in the BMME projection, whereas they are reduced in the WMME projection. There are no obvious differences among the three ensembles for the precipitation projections over eastern China.

    Figure 13a summarizes the projected percentage changes in annual precipitation over subregions. Under SSP585, annual precipitation amounts, as projected by the BMME in NWC, NC, NEC, and SWC1 (i.e., northern and western regions of China) are expected to increase by 81%,43%, 35%, and 32% at the end of the 21st century, respectively, which are larger than those of the AMME projection(45%, 30%, 25%, 29%, respectively) and the WMME projection (33%, 21%, 20%, 29%, respectively). Note that the historical simulation of the BMME shows the smallest biases from the observation in these subregions (Fig. 8b). The case for the SSP245 scenario is generally similar but with smaller magnitudes of percentage increase. Seasonal changes in precipitation in winter (Fig. 13b) and summer (Fig. 13c), as projected from the three ensembles under SSP585, generally approximate those of annual precipitation in the above subregions. We also notice that the magnitudes of percentage increases are much more pronounced in winter than in summer. For example, in the NWC, NC, and NEC subregions, the increases of winter precipitation from the BMME projection are 262%, 145%, and 106%, compared with 28%, 30%, and 29% increases for summer, respectively.This result suggests a relatively larger contribution to annual precipitation change from the increased winter precipitation. However, because of the different climatology of seasonal precipitation, it may not necessarily reflect the absolute changes in precipitation values. A similar seasonal change but with smaller magnitudes of percentage increase is noted in CC. The opposite seasonal pattern is projected in SC, where the projected percentage increases in summer are larger than those in winter. The cases for other subregions(e.g., SWC2 and EC) are diverse for the three ensemble projections.

    Fig. 12. Same as in Fig. 10, but for annual precipitation (units: %).

    Fig. 13. Same as in Fig. 11, but for precipitation (units: %). Note that the vertical scales are different.

    5. Conclusion

    In this study, we evaluated the performance of 20 CMIP6 models in simulating temperature and precipitation over China from the perspective of spatial pattern and interannual variability. Generally, the CMIP6 models show a good ability to capture the climatological distributions of temperature and precipitation, with better performance for temperature than for precipitation. The interannual variability of temperature and precipitation can be reasonably reproduced by most models, although poor performance is shown for that of winter precipitation. Comparative analysis conducted by Jiang et al. (2020) also indicated that the performances have improved from CMIP5 to CMIP6 for climatological temperature and precipitation, but show little improvement for their interannual variability. Based upon the comprehensive model performance regarding both spatial patterns and interannual variability, the ensembles of the “highest-ranked” models (BMME), the “l(fā)owest-ranked” models (WMME), and all models (AMME) are determined. It is worth noting that the relevant model rankings could differ for other applications(e.g., climate extremes). The differences among the aforementioned three ensembles for simulations and projections of temperature and precipitation over China are examined. The main findings are summarized below:

    (1) The BMME outperforms the AMME and WMME for the simulation of annual and winter temperature and precipitation, particularly in those subregions with complex terrain. Nevertheless, there is no salient improvement for summer temperature or precipitation over most of the subregions.

    (2) The three ensembles all project increased temperature and precipitation over China by the end of the 21st century, accompanied by larger increases under SSP585 than under SSP245. For the SSP585 scenario, the greatest warming is projected to occur at higher latitudes and at higher elevations of China (such as in NEC, NWC, and SWC1). The largest percentage-based increase in annual precipitation is anticipated in northern and western China.

    (3) The three ensembles project different magnitudes of increase in temperature and precipitation. By the end of the 21st century under SSP585, the warming of annual temperature is the lowest in the BMME projection, which increases successively in the AMME and WMME projections. The BMME projected percentage increases in annual, summer,and winter precipitation over northern and western China are larger than those projected from the AMME and WMME.

    We acknowledge the World Climate Research Program’s Working Group on Coupled Modeling and thank the climate modeling groups for producing and sharing their model outputs. This research was jointly supported by the National Key Research and Development Program of China(2018YFA0606301) and the National Natural Science Foundation of China (42025502, 41991285, 42088101).

    高清视频免费观看一区二区| 国产精品熟女久久久久浪| 热99国产精品久久久久久7| 内地一区二区视频在线| 免费看不卡的av| 精品国产一区二区三区久久久樱花| 国产精品国产三级专区第一集| 国产国语露脸激情在线看| 精品第一国产精品| a 毛片基地| 欧美成人午夜免费资源| 18禁国产床啪视频网站| 亚洲欧美精品自产自拍| 日韩精品有码人妻一区| 黄片播放在线免费| 中文字幕av电影在线播放| 国产成人av激情在线播放| 日韩 亚洲 欧美在线| 久久久久久久亚洲中文字幕| 亚洲经典国产精华液单| 国产精品偷伦视频观看了| 国产激情久久老熟女| 高清欧美精品videossex| 久久精品国产亚洲av天美| 欧美激情 高清一区二区三区| av网站免费在线观看视频| 日韩大片免费观看网站| av国产久精品久网站免费入址| 美女视频免费永久观看网站| 亚洲国产精品999| 男女啪啪激烈高潮av片| 午夜久久久在线观看| 欧美 亚洲 国产 日韩一| 中文字幕制服av| 欧美老熟妇乱子伦牲交| 91午夜精品亚洲一区二区三区| 精品久久蜜臀av无| 婷婷色综合www| 十八禁高潮呻吟视频| 日韩熟女老妇一区二区性免费视频| 中文字幕人妻熟女乱码| 久久99热这里只频精品6学生| 少妇熟女欧美另类| 国产成人午夜福利电影在线观看| 男女免费视频国产| 大码成人一级视频| a级毛片黄视频| 91午夜精品亚洲一区二区三区| 男的添女的下面高潮视频| 精品午夜福利在线看| 一级毛片我不卡| 99久久人妻综合| 精品酒店卫生间| 波多野结衣一区麻豆| 美女xxoo啪啪120秒动态图| 亚洲欧美中文字幕日韩二区| 国产色爽女视频免费观看| 成年女人在线观看亚洲视频| 国产午夜精品一二区理论片| 一区二区日韩欧美中文字幕 | 9热在线视频观看99| 少妇熟女欧美另类| 99久久人妻综合| 18+在线观看网站| 成人亚洲精品一区在线观看| 满18在线观看网站| 免费人妻精品一区二区三区视频| 一区二区av电影网| 欧美另类一区| 免费av中文字幕在线| 国产亚洲欧美精品永久| 午夜免费鲁丝| 丝袜在线中文字幕| 日韩一区二区视频免费看| 久久久久国产网址| 国产极品天堂在线| 日韩制服骚丝袜av| 一区二区三区乱码不卡18| 精品亚洲乱码少妇综合久久| av.在线天堂| 日韩大片免费观看网站| 老司机影院成人| 国产xxxxx性猛交| 日本黄大片高清| 大片电影免费在线观看免费| 欧美日韩av久久| 少妇的逼水好多| 亚洲激情五月婷婷啪啪| av天堂久久9| 国产日韩欧美视频二区| 久久av网站| 成人手机av| 两个人看的免费小视频| 蜜桃国产av成人99| 成年人免费黄色播放视频| 五月伊人婷婷丁香| 91在线精品国自产拍蜜月| 精品99又大又爽又粗少妇毛片| 看免费成人av毛片| 国产麻豆69| 亚洲国产最新在线播放| 久久久久久久亚洲中文字幕| 日本黄大片高清| 国产亚洲午夜精品一区二区久久| 免费黄色在线免费观看| 十八禁网站网址无遮挡| 国产极品粉嫩免费观看在线| 男男h啪啪无遮挡| 黑人巨大精品欧美一区二区蜜桃 | 亚洲精品国产色婷婷电影| 一二三四在线观看免费中文在 | 久久这里只有精品19| 精品卡一卡二卡四卡免费| 日日啪夜夜爽| 制服丝袜香蕉在线| 97在线视频观看| 欧美成人午夜精品| 不卡视频在线观看欧美| 免费播放大片免费观看视频在线观看| 9191精品国产免费久久| 久久久久久久亚洲中文字幕| 视频在线观看一区二区三区| 国国产精品蜜臀av免费| 人妻系列 视频| 欧美+日韩+精品| 婷婷色综合大香蕉| 久久精品国产综合久久久 | 九色成人免费人妻av| 18禁在线无遮挡免费观看视频| 美女xxoo啪啪120秒动态图| 亚洲欧美日韩卡通动漫| 国产在视频线精品| 哪个播放器可以免费观看大片| 波野结衣二区三区在线| 赤兔流量卡办理| 在线精品无人区一区二区三| 国产男女超爽视频在线观看| 母亲3免费完整高清在线观看 | 国产男女超爽视频在线观看| 国产精品偷伦视频观看了| 日韩三级伦理在线观看| 免费观看性生交大片5| 51国产日韩欧美| 国产成人精品一,二区| 90打野战视频偷拍视频| 午夜日本视频在线| 在线观看三级黄色| 18禁在线无遮挡免费观看视频| 如何舔出高潮| h视频一区二区三区| 汤姆久久久久久久影院中文字幕| 美女福利国产在线| 免费人成在线观看视频色| 亚洲av国产av综合av卡| 日日爽夜夜爽网站| 亚洲国产毛片av蜜桃av| 亚洲精品日韩在线中文字幕| 黄色怎么调成土黄色| 国产深夜福利视频在线观看| 欧美精品人与动牲交sv欧美| 最近中文字幕高清免费大全6| 黑人巨大精品欧美一区二区蜜桃 | 丰满饥渴人妻一区二区三| www.av在线官网国产| 一本色道久久久久久精品综合| 国产亚洲精品久久久com| 两个人免费观看高清视频| 黄片无遮挡物在线观看| 国产免费视频播放在线视频| 免费黄频网站在线观看国产| 国产黄色免费在线视频| 久久韩国三级中文字幕| 久久国产精品男人的天堂亚洲 | 久热久热在线精品观看| 国产精品久久久久久久电影| 97在线视频观看| 欧美 日韩 精品 国产| 岛国毛片在线播放| 国产精品一国产av| 人人澡人人妻人| 成年av动漫网址| 久久人妻熟女aⅴ| 丰满迷人的少妇在线观看| 亚洲国产精品国产精品| 国产极品天堂在线| 少妇猛男粗大的猛烈进出视频| 午夜影院在线不卡| 男人操女人黄网站| av国产久精品久网站免费入址| 午夜免费观看性视频| 男人操女人黄网站| 亚洲国产精品成人久久小说| 欧美日本中文国产一区发布| 爱豆传媒免费全集在线观看| 少妇熟女欧美另类| 精品国产露脸久久av麻豆| 亚洲欧洲日产国产| 久热久热在线精品观看| 欧美精品一区二区大全| 国产成人免费无遮挡视频| 免费黄频网站在线观看国产| 高清黄色对白视频在线免费看| 久久热在线av| av黄色大香蕉| 亚洲精品日韩在线中文字幕| 欧美日韩国产mv在线观看视频| 日韩一区二区视频免费看| 婷婷色综合大香蕉| 丝袜脚勾引网站| 在线精品无人区一区二区三| 国产精品人妻久久久影院| 一级黄片播放器| 亚洲精品美女久久av网站| 午夜激情久久久久久久| 男人舔女人的私密视频| 国产精品 国内视频| 亚洲 欧美一区二区三区| 高清在线视频一区二区三区| 亚洲精品乱码久久久久久按摩| 毛片一级片免费看久久久久| 久久亚洲国产成人精品v| 日韩制服骚丝袜av| 热re99久久精品国产66热6| 欧美日韩综合久久久久久| 男女高潮啪啪啪动态图| 国产在线一区二区三区精| 男女边摸边吃奶| 国产成人a∨麻豆精品| 亚洲精品久久成人aⅴ小说| 观看av在线不卡| www日本在线高清视频| 在线精品无人区一区二区三| 日本猛色少妇xxxxx猛交久久| 熟妇人妻不卡中文字幕| 大香蕉久久成人网| av不卡在线播放| 91精品三级在线观看| 亚洲精品色激情综合| 女人被躁到高潮嗷嗷叫费观| 天天躁夜夜躁狠狠久久av| 卡戴珊不雅视频在线播放| 亚洲精品久久成人aⅴ小说| 国产一区二区三区av在线| 欧美人与性动交α欧美软件 | 毛片一级片免费看久久久久| 尾随美女入室| 大片电影免费在线观看免费| 高清在线视频一区二区三区| 观看av在线不卡| 欧美xxxx性猛交bbbb| 国产成人精品在线电影| 国产精品女同一区二区软件| 男女无遮挡免费网站观看| 哪个播放器可以免费观看大片| 曰老女人黄片| 香蕉丝袜av| 观看美女的网站| 亚洲三级黄色毛片| 人人妻人人澡人人看| 日韩一区二区三区影片| 男女免费视频国产| 性色avwww在线观看| 免费在线观看完整版高清| 欧美精品国产亚洲| 精品亚洲乱码少妇综合久久| 两性夫妻黄色片 | 欧美日韩综合久久久久久| 综合色丁香网| 80岁老熟妇乱子伦牲交| 777米奇影视久久| 国产欧美日韩一区二区三区在线| 纵有疾风起免费观看全集完整版| 久久精品国产综合久久久 | 毛片一级片免费看久久久久| 久久久久久久久久成人| 欧美国产精品一级二级三级| 国产日韩欧美亚洲二区| 久久精品国产自在天天线| 久久久久视频综合| 51国产日韩欧美| 亚洲第一区二区三区不卡| 日本午夜av视频| 亚洲欧美一区二区三区国产| 亚洲一码二码三码区别大吗| 日韩免费高清中文字幕av| 国产一区二区三区综合在线观看 | 91精品三级在线观看| 夫妻午夜视频| 老女人水多毛片| 欧美人与性动交α欧美精品济南到 | 大香蕉久久网| 免费大片18禁| 亚洲人成网站在线观看播放| 日韩中文字幕视频在线看片| 丁香六月天网| 国产极品粉嫩免费观看在线| 国产精品久久久久久久久免| 欧美亚洲 丝袜 人妻 在线| 国产精品熟女久久久久浪| 亚洲人成77777在线视频| 在线观看美女被高潮喷水网站| 精品一品国产午夜福利视频| 亚洲精品色激情综合| 99久久人妻综合| 五月伊人婷婷丁香| 国产成人欧美| 日本与韩国留学比较| a 毛片基地| 国产精品久久久久久久电影| 亚洲高清免费不卡视频| 欧美激情极品国产一区二区三区 | videosex国产| 久久青草综合色| 国产精品人妻久久久久久| 国产精品人妻久久久影院| 日日啪夜夜爽| 亚洲国产精品专区欧美| 久久人人97超碰香蕉20202| 91国产中文字幕| 亚洲精品成人av观看孕妇| 亚洲一码二码三码区别大吗| www日本在线高清视频| 国产熟女欧美一区二区| 王馨瑶露胸无遮挡在线观看| av有码第一页| 两个人看的免费小视频| 天天影视国产精品| 亚洲国产精品专区欧美| 国产乱人偷精品视频| 日本午夜av视频| 另类精品久久| 国产黄频视频在线观看| 国产乱来视频区| 久久99一区二区三区| 日韩欧美精品免费久久| 插逼视频在线观看| 午夜91福利影院| videosex国产| 精品卡一卡二卡四卡免费| 五月开心婷婷网| 成人漫画全彩无遮挡| 精品一区二区三卡| 777米奇影视久久| 欧美精品高潮呻吟av久久| 精品一区二区免费观看| 精品亚洲成a人片在线观看| 99热国产这里只有精品6| 国产精品蜜桃在线观看| 日本色播在线视频| 亚洲精品国产av蜜桃| 如日韩欧美国产精品一区二区三区| a级毛色黄片| 91午夜精品亚洲一区二区三区| a 毛片基地| 欧美精品一区二区大全| 一二三四在线观看免费中文在 | 我要看黄色一级片免费的| 五月开心婷婷网| 一二三四中文在线观看免费高清| 亚洲国产日韩一区二区| 亚洲欧美日韩卡通动漫| 亚洲经典国产精华液单| av在线播放精品| 看免费成人av毛片| 丝袜美足系列| 国产高清不卡午夜福利| 性色avwww在线观看| 美女脱内裤让男人舔精品视频| 丰满少妇做爰视频| 国产男女超爽视频在线观看| 高清欧美精品videossex| 全区人妻精品视频| 亚洲精品日本国产第一区| 成年美女黄网站色视频大全免费| 亚洲精品国产av成人精品| 99精国产麻豆久久婷婷| 99久国产av精品国产电影| 中文字幕另类日韩欧美亚洲嫩草| 亚洲精品日本国产第一区| 天天操日日干夜夜撸| 9热在线视频观看99| 永久网站在线| 美女内射精品一级片tv| 久久毛片免费看一区二区三区| 亚洲美女黄色视频免费看| 22中文网久久字幕| 午夜福利影视在线免费观看| 不卡视频在线观看欧美| av一本久久久久| 中文乱码字字幕精品一区二区三区| 久久女婷五月综合色啪小说| 伦理电影免费视频| 免费黄网站久久成人精品| 亚洲av国产av综合av卡| 曰老女人黄片| 久久99一区二区三区| 2021少妇久久久久久久久久久| 久久这里有精品视频免费| 国产欧美日韩综合在线一区二区| 久久国产亚洲av麻豆专区| 午夜免费男女啪啪视频观看| 少妇人妻久久综合中文| 麻豆乱淫一区二区| 国产成人一区二区在线| 99热网站在线观看| 欧美3d第一页| 亚洲精品aⅴ在线观看| 99久久综合免费| 亚洲,欧美精品.| 777米奇影视久久| 久久精品国产亚洲av天美| 91国产中文字幕| 日韩 亚洲 欧美在线| 欧美日韩综合久久久久久| 看免费av毛片| 久久这里有精品视频免费| 女人精品久久久久毛片| 亚洲国产欧美日韩在线播放| 久久久亚洲精品成人影院| 99热6这里只有精品| 欧美精品一区二区免费开放| 午夜精品国产一区二区电影| 色婷婷av一区二区三区视频| 巨乳人妻的诱惑在线观看| 亚洲欧洲日产国产| 亚洲在久久综合| 中文字幕av电影在线播放| 成人亚洲欧美一区二区av| 最近手机中文字幕大全| 欧美精品一区二区大全| 激情视频va一区二区三区| 一区在线观看完整版| 黑人巨大精品欧美一区二区蜜桃 | 三上悠亚av全集在线观看| 色婷婷av一区二区三区视频| 久久婷婷青草| 在线观看美女被高潮喷水网站| av有码第一页| 欧美国产精品va在线观看不卡| 日韩一本色道免费dvd| 啦啦啦啦在线视频资源| 人人妻人人澡人人看| av在线老鸭窝| 亚洲精品国产色婷婷电影| 精品人妻熟女毛片av久久网站| 啦啦啦视频在线资源免费观看| 美女脱内裤让男人舔精品视频| 美女国产高潮福利片在线看| 超色免费av| 三上悠亚av全集在线观看| 成人国产av品久久久| 精品一区二区三区视频在线| 婷婷色麻豆天堂久久| 亚洲国产欧美日韩在线播放| 十分钟在线观看高清视频www| 亚洲丝袜综合中文字幕| 亚洲情色 制服丝袜| 少妇的丰满在线观看| av视频免费观看在线观看| 久久久久久久精品精品| 在线天堂最新版资源| 曰老女人黄片| av免费观看日本| 午夜免费鲁丝| 日韩欧美一区视频在线观看| 久久精品久久精品一区二区三区| 90打野战视频偷拍视频| 下体分泌物呈黄色| 日本午夜av视频| av黄色大香蕉| 国产片内射在线| 欧美激情极品国产一区二区三区 | 大片电影免费在线观看免费| 一本一本久久a久久精品综合妖精 国产伦在线观看视频一区 | 久久97久久精品| 你懂的网址亚洲精品在线观看| 亚洲四区av| 欧美亚洲 丝袜 人妻 在线| 精品99又大又爽又粗少妇毛片| 精品久久蜜臀av无| 国产成人一区二区在线| 咕卡用的链子| 最新的欧美精品一区二区| 香蕉精品网在线| 国产欧美日韩综合在线一区二区| 国产色婷婷99| 黑人欧美特级aaaaaa片| 国产精品一区二区在线观看99| 中文字幕精品免费在线观看视频 | 亚洲精品国产av蜜桃| 精品久久蜜臀av无| 伦精品一区二区三区| 日韩欧美精品免费久久| 亚洲精品日韩在线中文字幕| 欧美成人午夜免费资源| 色哟哟·www| 在线精品无人区一区二区三| 又黄又粗又硬又大视频| 最新的欧美精品一区二区| 九九爱精品视频在线观看| 2018国产大陆天天弄谢| 黄色配什么色好看| 久久精品国产综合久久久 | 亚洲精品国产av蜜桃| 777米奇影视久久| 亚洲性久久影院| 一区二区三区乱码不卡18| 亚洲精品日韩在线中文字幕| 久久精品国产a三级三级三级| 一级a做视频免费观看| 欧美日本中文国产一区发布| 女性被躁到高潮视频| 国产精品成人在线| 男女午夜视频在线观看 | 亚洲经典国产精华液单| 狂野欧美激情性bbbbbb| 国产欧美日韩综合在线一区二区| 成年动漫av网址| 视频中文字幕在线观看| 在线天堂最新版资源| 麻豆精品久久久久久蜜桃| 搡女人真爽免费视频火全软件| 午夜福利乱码中文字幕| 久久97久久精品| 男女下面插进去视频免费观看 | 久久久久视频综合| 亚洲成av片中文字幕在线观看 | 97精品久久久久久久久久精品| 日韩精品有码人妻一区| 久久久精品免费免费高清| 国产av国产精品国产| 国产女主播在线喷水免费视频网站| 亚洲国产欧美日韩在线播放| 国产亚洲欧美精品永久| 国产一区二区激情短视频 | 一区二区日韩欧美中文字幕 | 深夜精品福利| 精品久久久精品久久久| 街头女战士在线观看网站| 国产日韩欧美亚洲二区| 黑人欧美特级aaaaaa片| 欧美xxⅹ黑人| 18禁动态无遮挡网站| 欧美精品国产亚洲| 少妇高潮的动态图| a 毛片基地| 亚洲美女视频黄频| 亚洲精华国产精华液的使用体验| 热re99久久国产66热| 边亲边吃奶的免费视频| 内地一区二区视频在线| 国产亚洲最大av| 精品国产一区二区三区久久久樱花| 人妻系列 视频| 一本色道久久久久久精品综合| 亚洲久久久国产精品| 国产精品久久久久成人av| 哪个播放器可以免费观看大片| 久久狼人影院| 中文字幕av电影在线播放| 日韩一本色道免费dvd| 国产乱人偷精品视频| 黑人欧美特级aaaaaa片| 亚洲国产精品一区二区三区在线| 午夜精品国产一区二区电影| 久久久精品94久久精品| 欧美最新免费一区二区三区| 天堂俺去俺来也www色官网| 七月丁香在线播放| 国产探花极品一区二区| 中文乱码字字幕精品一区二区三区| 国产极品粉嫩免费观看在线| 亚洲人成网站在线观看播放| 美国免费a级毛片| 91国产中文字幕| 国产色婷婷99| 亚洲成人手机| 日本与韩国留学比较| 成人综合一区亚洲| 热re99久久国产66热| av在线观看视频网站免费| 欧美精品高潮呻吟av久久| 国产av国产精品国产| 亚洲精品乱码久久久久久按摩| 涩涩av久久男人的天堂| 青青草视频在线视频观看| 天堂中文最新版在线下载| 在线亚洲精品国产二区图片欧美| www.熟女人妻精品国产 | 国产精品99久久99久久久不卡 | 中文字幕人妻熟女乱码| 少妇的逼水好多| 春色校园在线视频观看| 看非洲黑人一级黄片| 80岁老熟妇乱子伦牲交| 最近手机中文字幕大全| 欧美日韩精品成人综合77777| 少妇的逼水好多| 99久久精品国产国产毛片| 国产欧美日韩综合在线一区二区| 国产不卡av网站在线观看| 亚洲精品国产av成人精品| 观看av在线不卡| 国产成人午夜福利电影在线观看| 大香蕉97超碰在线| 日韩不卡一区二区三区视频在线| tube8黄色片| 在线观看国产h片| 乱人伦中国视频| 日韩一本色道免费dvd| 欧美丝袜亚洲另类| 日本wwww免费看| 久久久久久久亚洲中文字幕| 两个人看的免费小视频| 女人久久www免费人成看片|