FGOALS-g2模式模拟和预估的全球季风区极端降水及其变化

利用LASG/IAP（中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室）全球耦合模式FGOALS-g2,评估了其对全球季风区极端气候指标的模拟能力,并讨论了RCP8.5排放情景下21世纪季风区极端气候指标的变化特征。总体而言,模式对季风区总降水和极端气候指标1997-2014年气候态和年际变率的空间分布均具有一定的模拟能力。偏差主要表现在模式低估了亚洲季风强降水中心,低估了中雨（10-20mm d-1）和大雨（20-50 mm d-1）的频率而高估了暴雨（〉50 mm d-1）频率。在RCP8.5排放情景下,由于可降水量的增加,模式预估的全球季风区极端降水、降水总量和降水强度将持续增加。到2076-2095年,极端降水和降水强度在北美季风区增加最显著（约22%和17%）,降水总量在澳大利亚增加最显著（约37%）。然而,FGOALS-g2对全球季风区平均的日降水量低于1 mm的连续最大天数（CDD）的预估变化不显著,这是由于预估的CDD在陆地季风区将增加,而在海洋季风区将减少。对各子季风区的分析显示,CDD在南美季风区变长最显著,达到30%,在澳洲季风区变短最显著,达到40%,这与两季风区日降水量低于1 mm的降水事件发生频率变化不同有关。

耦合模式FGOALS-g2中大西洋经向翻转流对3个典型浓度路径的响应

利用2个关于大西洋经向翻转流(Atlantic Meridional Overturning Circulation,AMOC)的指数:AMOC指数(15oN^65oN、深度为500 m以下的AMOC的最大值)和AMOC扩展指数(15oN^65oN、深度为2000~2500m的AMOC的最大值),研究了耦合模式FGOALS-g2(Grid-point Version 2 of Flexible Global Ocean-AtmosphereLand System Model)中的AMOC在CMIP5(Coupled Model Intercomparison Project Phase 5)的3个典型浓度路径(Representation Concentration Pathways,RCP)(RCP2.6、RCP4.5和RCP8.5分别对应于2100年时490、650和1370 ppm的CO2浓度水平)下的响应问题,发现:在RCP2.6和RCP4.5浓度路径下,2006~2040年时间段内AMOC指数和AMOC扩展指数都呈现快速下降的趋势,2041~2100年时间段内AMOC指数逐渐恢复,AMOC扩展指数基本维持不变;在RCP8.5浓度路径下,2006~2100年时间段内AMOC指数和AMOC扩展指数都表现出快速下降的趋势。通过分析FGOALS-g2中北大西洋深水的成因发现:3个典型浓度路径下AMOC的长期变化趋势主要受到GIN(Greenland–Iceland–Norwegian)海域的深水形成率的调控,而AMOC的年代际尺度的变化则主要受到Labrador海域深水形成率的控制。同时揭示了:由于北大西洋2000 m深度附近的层结稳定性在RCP2.6和RCP4.5下(相比于1980~2005年)提高了30%~40%,使得由AMOC指数恢复产生的深水无法继续下沉,从而导致AMOC扩展指数没有出现恢复的现象。

The Flexible Global Ocean-Atmosphere-Land System Model, Grid-point Version 2: FGOALS-g2

This study mainly introduces the development of the Flexible Global Ocean-Atmosphere-Land System Model： Grid-point Version 2 （FGOALS-g2） and the preliminary evaluations of its performances based on re- sults from the pre-industrial control run and four members of historical runs according to the fifth phase of the Coupled Model Intercomparison Project （CMIP5） experiment design. The results suggest that many obvi- ous improvements have been achieved by the FGOALS-g2 compared with the previous version, FGOALS-gl, including its climatological mean states, climate variability, and 20th century surface temperature evolution. For example, FGOALS-g2 better simulates the frequency of tropical land precipitation, East Asian Monsoon precipitation and its seasonal cycle, MJO and ENSO, which are closely related to the updated cumulus parameterization scheme, as well as the alleviation of uncertainties in some key parameters in shallow and deep convection schemes, cloud fraction, cloud macro/microphysical processes and the boundary layer scheme in its atmospheric model. The annual cycle of sea surface temperature along the equator in the Pacific is significantly improved in the new version. The sea ice salinity simulation is one of the unique characteristics of FGOALS-g2, although it is somehow inconsistent with empirical observations in the Antarctic.

Oceanic Climatology in the Coupled Model FGOALS-g2: Improvements and Biases

The present study examines simulated oceanic climatology in the Flexible Global Ocean-Atmosphere- Land System model, Grid-point Version 2 （FGOALS-g2） forced by historical external forcing data. The oceanic temperatures and circulations in FGOALS-g2 were found to be comparable to those observed, and substantially improved compared to those simulated by the previous version, FGOALS-gl.0. Compared with simulations by FGOALS-gl.0, the shallow mixed layer depths were better captured in the eastern Atlantic and Pacific Ocean in FGOALS-g2. In the high latitudes of the Northern Hemisphere, the cold biases of SST were about 1℃-5℃ smaller in FGOALS-g2. The associated sea ice distributions and their seasonal cycles were more realistic in FGOALS-g2. The pattern of Atlantic Meridional Overturning Circulation （AMOC） was better simulated in FGOALS-g2, although its magnitude was larger than that found in observed data. The simulated Antarctic Circumpolar Current （ACC） transport was about 140 Sv through the Drake Passage, which is close to that observed. Moreover, Antarctic Intermediate Water （AAIW） was better captured in FGOALS-g2. However, large SST cold biases （〉3℃） were still found to exist around major western boundary currents and in the Barents Sea, which can be explained by excessively strong oceanic cold advection and unresolved processes owing to the coarse resolution. In the Indo-Pacific warm pool, the cold biases were partly related to the excessive loss of heat from the ocean. Along the eastern coast in the Atlantic and Pacific Oceans, the warm biases were due to overestimation of shortwave radiation. In the Indian Ocean and Southern Ocean, the surface fresh biases were mainly due to the biases of precipitation. In the tropical Pacific Ocean, the surface fresh biases （〉2 psu） were mainly caused by excessive precipitation and oceanic advection. In the Indo-Pacific Ocean, fresh biases were also found to dominate in the upper 1000 m, except in the northeastern Indian Ocean. There were warm and salty biases （3℃-4℃ and 1-2 psu） from the surface to the bottom in the Labrador Sea, which might be due to large amounts of heat transport and excessive evaporation, respectively. For vertical structures, the maximal biases of temperature and salinity were found to be located at depths of 〉600 m in the Arctic Ocean, and their values exceeded 4℃ and 2 psu, respectively.

Simulation of Sea Ice in FGOALS-g2: Climatology and Late 20th Century Changes

Sea ice is an important component in the Earth＇s climate system. Coupled climate system models are indispensable tools for the study of sea ice, its internal processes, interaction with other components, and projection of future changes. This paper evaluates the simulation of sea ice by the Flexible Global Ocean- Atmosphere-Land System model Grid-point Version 2 （FGOALS-g2）, in the fifth phase of the Coupled Model Inter-comparison Project （CMIP5）, with a focus on historical experiments and late 20th century simu：ation. Through analysis, we find that FGOALS-g2 produces reasonable Arctic and Antarctic sea ice climatology and variability. Sea ice spatial distribution and seasonal change characteristics are well captured. The decrease of Arctic sea ice extent in the late 20th century is reproduced in simulations, although the decrease trend is lower compared with observations. Simulated Antarctic sea ice shows a reasonable distribution and seasonal cycle with high accordance to the amplitude of winter-summer changes. Large improvement is achieved as compared with FGOALS-gl.0 in CMIP3. Diagnosis of atmospheric and oceanic forcing on sea ice reveals several shortcomings and major aspects to improve upon in the future： （I） ocean model improvements to remove the artificial island at the North Pole; （2） higher resolution of the atmosphere model for better simulation of important features such as, among others, the Icelandic Low and westerly wind over the Southern Ocean; and （3） ocean model improvements to accurately receive freshwater input from land, and higher resolution for resolving major water channels in the Canadian Arctic Archipelago.

An Assessment of Indo-Pacific Oceanic Channel Dynamics in the FGOALS-g2 Coupled Climate System Model

Lag correlations of sea surface temperature anomalies （SSTAs）, sea surface height anomalies （SSHAs）, subsurface temperature anomalies, and surface zonal wind anomalies （SZWAs） produced by the Flexible Global Ocean-Atmosphere-Land System modeh Grid-point Version 2 （FGOALS-g2） are analyzed and com- pared with observations. The insignificant, albeit positive, lag correlations between the SSTAs in the south- eastern tropical Indian Ocean （STIO） in fall and the SSTAs in the central-eastern Pacific cold tongue in the following summer through fall are found to be not in agreement with the observational analysis. The model, however, does reproduce the significant lag correlations between tile SSHAs in the STIO in fall and those in the cold tongue at the one-year time lag in the observations. These, along with the significant lag correlations between the SSTAs in the STIO in fall and the subsurface temperature anomalies in the equatorial Pacific vertical section in the following year, suggest that the Indonesian Throughflow plays an important role in propagating the Indian Ocean anomalies into the equatorial Pacific Ocean. Analyses of the interannual anomalies of the Indonesian Throughflow transport suggest that the FGOALS-g2 climate system simulates, but underestimates, the oceanic channel dynamics between the Indian and Pacific Oceans. FGOALS-g2 is shown to produce lag correlations between the SZWAs over the western equatorial Pacific in fall and the cold tongue SSTAs at the one-year time lag that are too strong to be realistic in comparison with observations. The analyses suggest that the atmospheric bridge over the Indo-Pacific Ocean is overestimated in the FGOALS-g2 coupled climate model.

Variability of Atlantic Meridional Overturning Circulation in FGOALS-g2

The variability of Atlantic Meridional Overturning Circulation （AMOC） in the pre-industrial control experiment of the Flexible Global Ocean-Atmosphere-Land System model, Grid-point Version 2 （FGOALS-g2） was investigated using the model outputs with the most stable state in a 512-yr time window from the total 1500-yr period of the experiment. The period of AMOC in FGOALS-g2 is double peaked at 20 and 32 years according to the power spectrum, and 22 years according to an auto-correlation analysis, which shows very obvious decadal variability. Like many other coupled climate models, the decadal variability of AMOC in FGOALS-g2 is closely related to the convection that occurs in the Labrador Sea region. Deep convection in the Labrador Sea in FGOALS-g2 leads the AMOC maximum by 3-4 years. The contributions of thermal and haline effects to the variability of the convection in three different regions [the Labrador, Irminger and Greenland-Iceland- Norwegian （GIN） Seas] were analyzed for FGOALS-g2. The variability of convection in the Labrador and Irminger Seas is thermally dominant, while that in the colder GIN Seas can be mainly attributed to salinity changes due to the lower thermal expansion. By comparing the simulation results from FGOALS-g2 and 11 other models, it was found that AMOC variability can be attributed to salinity changes for longer periods （longer than 35 years） and to temperature changes for shorter periods.

气候系统模式FGOALS-g3模拟的全球季风:版本比较和海气耦合过程影响分析

本文基于观测和再分析资料,采用水汽收支诊断和合成分析方法,对新一代气候系统模式FGOALS-g3模拟的全球季风进行了系统评估,给出其较之前版本FGOALS-g2的优缺点,并通过与其大气分量模式GAMIL结果的比较,讨论了海气耦合过程的影响。结果表明,FGOALS-g3能合理再现全球季风气候态的基本特征,包括年平均、年循环模态、季风降水强度和季风区范围等,但模式低估陆地季风区年平均降水,高估海洋平均降水,模拟的热带地区春秋非对称模态偏强。研究指出FGOALS-g3模拟的陆地季风区范围偏小,这与模式模拟的夏季水汽垂直平流(尤其是热力项)偏小有关。年际变率上,FGOALS-g3能再现El Niño年全球季风降水偏少的整体特征,其不足之处在于部分季风区的降水异常存在一定偏差,例如其模拟的El Niño年西非季风区降水偏多和西南印度洋的偶极子型降水异常,均与观测分布不一致,且模式中西北太平洋季风区降水较观测偏多。这是由于El Niño年,模式中西非高层无弱辐合中心,且海洋性大陆较观测偏暖,对流中心西移。相较于FGOALS-g2,FGOALS-g3对环流、季风降水的年际变率和季风–ENSO关系的模拟有改善。比较耦合和非耦合模拟结果,耦合模式的偏差大多源自大气模式本身,海气耦合过程部分提高了对亚澳季风区和热带印度洋的降水和环流的模拟,但耦合过程引起的海温偏差增强了气候态上印度半岛的干偏差和热带印度洋的湿偏差。

Precipitation Changes in Wet and Dry Seasons over the 20th Century Simulated by Two Versions of the FGOALS Model

Seasonal precipitation changes over the globe during the 20th century simulated by two versions of the Flexible Global Ocean-Atmosphere-Land System （FGOALS） model are assessed. The two model versions differ in terms of their AGCM component, but the remaining parts of the system are almost identical. Both models reasonably reproduce the mean-state features of the timings of the wet and dry seasons and related precipitation amounts, with pattern correlation coefficients of 0.65-0.84 with observations. Globally averaged seasonal precipitation changes are analyzed. The results show that wet sea- sons get wetter and the annual range （precipitation difference between wet and dry seasons） increases during the 20th century in the two models, with positive trends covering most parts of the globe, which is consistent with observations. However, both models show a moistening dry season, which is opposite to observations. Analysis of the globally averaged moisture budget in the historical climate simulations of the two models shows little change in the horizontal moisture advection in both the wet and dry seasons. The globally averaged seasonal precipitation changes are mainly dominated by the changes in evaporation and vertical moisture advection. Evaporation and vertical moisture advection combine to make wet seasons wetter and enhance the annual range. In the dry season, the opposite change of evaporation and vertical moisture advection leads to an insignificant change in precipitation. Vertical moisture advection is the most important term that determines the changes in precipitation, wherein the thermodynamic component is dominant and the dynamic component tends to offset the effect of the thermodynamic component.

Evaluation of Snow Depth and Snow Cover Fraction Simulated by Two Versions of the Flexible Global Ocean–Atmosphere–Land System Model

Based on historical runs,one of the core experiments of the fifth phase of the Coupled Model Intercomparison Project （CMIP5）,the snow depth （SD） and snow cover fraction （SCF） simulated by two versions of the Flexible Global OceanAtmosphere-Land System （FGOALS） model,Grid-point Version 2 （g2） and Spectral Version 2 （s2）,were validated against observational data.The results revealed that the spatial pattern of SD and SCF over the Northern Hemisphere （NH） are simulated well by both models,except over the Tibetan Plateau,with the average spatial correlation coefficient over all months being around 0.7 and 0.8 for SD and SCF,respectively.Although the onset of snow accumulation is captured wellby the two models in terms of the annual cycle of SD and SCF,g2 overestimates SD/SCF over most mid-and high-latitude areas of the NH.Analysis showed that g2 produces lower temperatures than s2 because it considers the indirect effects of aerosols in its atmospheric component,which is the primary driver for the SD/SCF difference between the two models.In addition,both models simulate the significant decreasing trend of SCF well over （30°-70°N） in winter during the period 1971-94.However,as g2 has a weak response to an increase in the concentration of CO2 and lower climate sensitivity,it presents weaker interannual variation compared to s2.