A Review of East Asian Summer Monsoon Simulation and Projection: Achievements and Problems, Opportunities and Challenges
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摘要: 东亚夏季风对于我国东部气候具有重要影响，呈现出多种时间尺度的变化特征。在理解东亚夏季风过去和当前的变化机理、预测和预估其未来变化等方面，气候系统模式发挥着不可替代的作用。但是当前的气候模式在东亚夏季风的模拟上尚存在诸多不足，这使得其模拟结果存在不确定性，既制约了我们对过去和当前季风变化机理的准确理解，又降低了未来预测预估结果的可信度。关于造成季风模拟偏差的原因，既涉及模式本身的性能问题，又与模拟系统的构建、强迫资料的误差、乃至我们当前对季风变化规律自身的认知水平有关。本文以时间尺度为序，从气候态、日变化、年际变率、年代际变率、长期气候变化和未来预估等季风学界关注的热点问题角度，本着总结成绩、归纳问题、寻找机遇、面对挑战的目的，从七个方面系统总结了当前气候模式的水平，归纳了其主要偏差特征，讨论了影响模式性能的可能因素。内容涉及模式分辨率和地形效应、对流和云辐射效应的作用、与季风相关的热带海气相互作用关键过程、内部变率（太平洋年代际振荡）、自然变率（太阳辐照度变化和火山气溶胶强迫）和人为辐射强迫（人为温室气体和气溶胶排放）对季风变化的不同影响、热力和动力过程及气候敏感度对季风环流（副高）和降水预估不确定性的影响等。最后从优化参数、实现场地观测和过程模拟的协同、发展高分辨和对流解析模式等角度，讨论了提升东亚夏季风模拟能力的技术途径。Abstract: The East Asian Summer Monsoon (EASM), which has profound societal and economic impacts in China, has exhibited multiple time-scale variabilities. Climate models play an irreplaceable role in understanding the past changes and predicting/projecting the future changes in the EASM. However, current state of the climate models still shows evident biases in the simulation of EASM. This kind of model deficiency has limited our understanding of the mechanisms responsible for the EASM variability based on numerical model experiments and reduced the reliability of long-term climate change projection. This paper provides a synthesis review on our progresses in climate modeling study with a focus on the EASM. The achievements made in the last 5 years are summarized along with an extension to earlier studies in case of needing. The observational metrics used to gauge model performances include the climatology, diurnal cycle, interannual variability, interdecadal variability, and long-term climate change and climate projection. The following processes that are crucial to a successful modeling of monsoon mean state, variability and changes are reviewed, which include the effects of model resolution and model topography, climate effects of moist convection and cloud-radiation, monsoon-related tropical air-sea interactions, impacts of internal variability modes such as the Pacific Decadal Oscillation on the inter-decadal variability of EASM, the role of natural external forcing including solar radiation and volcanic aerosols, the contribution of anthropogenic forcing (including emissions of greenhouse gases and aerosols). In addition, the thermodynamic and dynamic processes associated with climate sensitivity that are responsible for the uncertainties in climate change projections are also discussed. Both the strengths and weakness of the current state of the art climate models are documented. Instead of simply summarizing the models' performance, the review is done along with a discussion on our understanding of the strengths and weaknesses from the perspective of dynamical/physical processes. In the final part, we provide a list of potential ways to improve the models' performance, which includes the optimization of model parameters, improvement of model physics by coordinating model studies and field campaigns, and development of high-resolution and convection-permitting models.
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图 1 （a）基于全球降水气候计划（GPCP）资料计算的1979~2010年气候态平均夏季降水[北半球6~8月（JJA），南半球12月至次年2月（DJF）]占年降水的百分比，黑线表示全球季风区；（b）基于格点化全球人口数据集（版本3）给出的2000年全球人口分布（单位：103），黑线表示全球陆地季风区，右上角给出全球季风区的总人口数及其占全球总人口的比例
Figure 1. (a) The proportion of local summer precipitation [June–July–August (JJA) in the Northern Hemisphere and December–January–February (DJF) in the Southern Hemisphere] in annual mean precipitation derived from Global Precipitation Climatology Project (GPCP) in 1979–2010, the black line indicates the regions of global monsoon; (b) the population (units: 103 persons) in 2000 AD, the black line indicates the regions of global monsoon and the number above the plot presents the percentage of population in the monsoon region to global population
图 2 （a）第三次气候耦合模式比较计划（CMIP3）和（b）第五次气候耦合模式比较计划（CMIP5）两代大气环流模式进行多模式集合所模拟的气候态夏季降水量与GPCP观测资料的差值（单位：mm d−1）。基于Song and Zhou（2014a）所用模式资料新绘制
Figure 2. Differences in climatological JJA mean precipitation between (a) phase 3 of Coupled Model Intercomparison Project (CMIP3)/ (b) phase 5 of Coupled Model Intercomparison Project (CMIP5) and GPCP (units: mm d−1). Adapted from Song and Zhou (2014a)
图 3 气候态夏季850 hPa风场（矢量，单位：m s−1）和降水量（彩色阴影，单位：mm d−1）：（a，b）NCEP2和ERA40的风场和GPCP/CMAP降水；（c，d）CMIP5和CMIP3多个大气环流模式的集合平均结果。根据Song and Zhou（2014a）重新绘制
Figure 3. Climatological distributions of JJA mean precipitation (shaded; units: mm d−1) and 850-hPa winds (vectors; units: m s−1) from (a) GPCP and NCEP2 data, (b) CMAP and ERA40 data, (c) multiple ensemble mean (MME) of CMIP5 models and (d) MME of CMIP3 models. Adapted from Song and Zhou (2014a)
图 4 高分辨率模式提高东亚夏季风雨带模拟技巧示意图。模式分辨率的提升使得青藏高原地形更加精细，在东部季风区产生正压的地形罗斯贝波，加强了该区域的相对于纬向平均的定常经向涡动平流，使得湿焓平流输送与水汽辐的加强，最终令季风雨带模拟技巧提升。基于Yao et al. (2017)绘制的机制示意图
Figure 4. Schematic of how the high-resolution climate model improves the simulation of the East Asian Summer Monsoon (EASM) rain belt. Due to the increasing model resolution, a barotropic Rossby wave response downstream of the Tibetan Plateau is generated, and meridional convergence and moisture convergence along the EASM rainbelt are further intensifies. Thus, the EASM rain belt simulation is improved in the high-resolution models. Adapted from Yao et al. (2017)
图 5 1998~2006年观测（黑色实线）和6个CMIP5大气环流模式模拟的我国副热带地区（a）西部平原（青藏高原东坡）、（b）东部平原（江淮流域）、（c）中国东海的总降水量的日变化。纵坐标为标准化后的降水日变化（气候态逐时降水值相对于日平均值的偏差再除以日平均值），横坐标为当地太阳时LST (Local Solar Time)。引自Yuan (2013)
Figure 5. Diurnal variations of total rainfall from observations (black solid lines) and simulations of 6 atmospheric models averaged over subtropical China regions: (a) western plains (eastern periphery of Tibetan Plateau), (b) eastern plains (the middle and lower reaches of Yangtze River valley) and (c) the East China Sea during 1998–2006. Cited from Yuan (2013)
图 6 海温（彩色阴影，单位：K）、降水（等值线，单位: mm d−1）和850 hPa风场（矢量，单位: m s−1）与观测的东亚夏季风指数的回归系数分布：（a）GPCP和NCEP2；（b）CMAP和ERA40；（c）CMIP3 MME；（d）CMIP5 MME；（e）高技巧模式；（f）低技巧模式。绿线（紫线）表示正（负）降水异常，等值线间隔为0.35 mm d−1。风速低于0.45 m s−1的风场部分略掉不画。黑点表示海温距平通过0.1的显著性检验
Figure 6. Horizontal distributions of SST (shaded, units: K), precipitation (contours, units: mm d−1), and 850-hPa winds (vectors, units: m s−1) regressed on the observed East Asian Summer Monsoon (EASM) index from (a) GPCP and NCEP2, (b) CMAP and ERA40, (c) MME of CMIP3, (d) MME of CMIP5, and (e) high-skill and (f) low-skill models in CMIP5. The green and purple lines show positive and negative precipitation anomalies, respectively. The black dots indicate that the regressed precipitation is significant at the 0.1 level by Student's t test
图 7 （a）1950~2009年夏季观测的SST与NCEP/NCAR东亚夏季风指数的相关系数分布；（b）CAM5全海温强迫试验（GOGA）模拟的1950~2009年东亚夏季风指数与SST相关系数分布；（c）CAM5全球海温强迫（GOGA，黑色实线）和热带海温强迫（TOGA，绿色虚线）模拟的东亚夏季风指数）、观测的太平洋年代际振荡（PDO）指数（乘以－1，柱状图）。（a，b）中黑线范围为通过5%显著性的区域。引自Li et al.（2010a）
Figure 7. Correlation coefficients between observed JJA SST at each grid box and the EASM index from (a) NCEP/NCAR reanalysis and (b) CAM3 GOGA runs. Values above the contour line in (a, b) are significant at the 5% level. (c) Time series of the observed Pacific Decadal Oscillation (PDO) index (multiplied by −1, bars) and the EASM index from the CAM3 GOGA (solid black line) and TOGA (dashed green line) simulations. Rg (Rt) on top of panel (c) is the correlation coefficient between the observed PDO index and GOGA (TOGA) simulated EASM index, the numbers in parentheses show the correlations of detrended series. Cited from Li et al. (2010a)
图 8 1958~2001年夏季海平面气压（SLP）[阴影，单位：hPa (44 a)−1]和850 hPa风场[矢量，单位：m s−1(44 a)−1]的线性趋势：（a）ERA40；（b）CMIP5 17个耦合模式全强迫试验集合平均结果；（c）同（b），但为人类强迫试验结果；（d）同（b）, 但为温室气体强迫试验结果；（e）同（b）, 但为自然强迫试验结果；（f）同（b）, 但为气溶胶强迫试验结果。（a，b）绿色方框为中国华北地区（32°N~42°N, 105°E~122°E)。打点区域表明降水通过了10%显著性水平检验。多模式集合平均（MME）基于17个CMIP5 35个成员平均得到。引自Song et al.（2014）
Figure 8. The linear trends of SLP [shaded; units: hPa (44 a)−1] and 850 hPa winds [vectors; m s−1 (44 a)−1) in JJA during 1958–2001. (a) Observations (SLP and 850 hPa winds from ERA-40), (b) all-forcing run, (c) anthropogenic-forcing run, (d) GHG-forcing run, (e) natural-forcing run, and (f) aerosol-forcing run from MME. The green box in Fig. 8a and 8b is northern China (32°N–42°N, 105°E–122°E). The dotted areas indicate that the precipitation trends are statistically significant at the 10% level. The MME is constructed by using 35 realizations from 17 CMIP5 models. Cited from Song et al. (2014)
图 9 中世纪（a）暖期和（b）小冰期夏季对流层200~500 hPa平均温度异常（相对于过去千年气候平均结果）（单位：℃），阴影表示通过5%显著性水平检验的区域。引自Man et al（2012）
Figure 9. JJA mean upper-tropospheric (500–200 hPa) temperature anomalies (units: ℃) for (a) the MWP and (b) the LIA. The anomalies are calculated relative to the millennial mean value. The black stippled regions denote areas that are statistically significant at the 5% level by using a Student's t test. Cited from Man et al (2012)
图 10 预估的850 hPa西北太平洋副热带高压（西太副高）强度变化量（横轴）与印度洋—太平洋纬向海温梯度变化量（纵轴）关系的散点图。不同的字母代表不同的模式；蓝色和红色字母分别代表RCP4.5和RCP8.5情景预估的变化量。引自He and Zhou（2015）
Figure 10. The changes of the tropical Indian Ocean (TIO) and the tropical western Pacific (TWP) SST zonal gradient as a function of the changes in the western North Pacific subtropical high intensity. The TIO-TWP zonal SST gradient is defined as the difference between the TIO SST and the TWP SST. The blue and red markers are for the projected changes under RCP4.5 and RCP8.5 scenarios, respectively. Cited from He and Zhou (2015)
图 11 （a、b）RCP4.5和（c、d）RCP8.5情景下预估850 hPa西太副高显著（a、c）增强和（b、d）减弱的模式分别合成预估的中国东部夏季850 hPa风场和降水（填色阴影，单位：mm d−1）的变化。红色打点区域表示所有参与合成的模式预估的变化量的符号一致。引自He and Zhou（2015）
Figure 11. (a) P-type models' composite of the projected changes in the precipitation (shading, units: mm d−1) and 850 hPa wind (vectors, units: m s−1) under RCP4.5 scenario. (b) Same as (a) but for the N-type models. (c, d) Same as (a, b) but under the RCP8.5 scenario. The regions with red dots indicate all of the P-type (or N-type) models agree in the sign of changes in the precipitation. Cited from He and Zhou (2015)
图 12 概率为0.5时R95T气候态相较于基准期（1986~2005）降水变化的分布。（a–c）为模式加权预估结果，（d–f）为模式未加权预估结果。第一行为2016~2035年，第二行为2046~2065年，第三行为2081~2100年。红色等值线表示零和负值，蓝色等值线表示正值。加号表示信噪比大于1的区域。引自Li et al.（2016b）
Figure 12. Geographic distribution of precipitation changes relative to 1986–2005 with probability 0.5, with two different ensemble schemes, (a–c) weighted and (d–f) unweighted, for three periods: (top) 2016–2035, (middle) 2046–2065, and (bottom) 2081–2100. Red contours indicate zero and negative values, and blue contours indicate positive values. Areas with plus symbols show that the SNR is larger than 1.0. Cited from Li et al. (2016b)
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