Atmospheric Model Intercomparison Project Experiments Research Articles

Links between winter dust over the Tibetan Plateau and preceding autumn sea ice variability in the Barents and Kara Seas

The Tibetan Plateau (TP) is characterized by heavily local dust activities, however, the mechanism of interannual variations of winter dust frequency over the TP remain poorly understood. Previous studies showed the autumn Arctic sea ice could significantly influence the winter climate over Eurasia. Whether autumn sea ice affects winter dust activity over the TP or not? Here, we used an integrated surface database to investigate possible mechanisms for interannual variability in the frequency of winter dust events above the TP. This variability, which is thought to be mainly caused by local dust emissions, shows significant correlations with sea ice concentration (SIC) in the Barents and Kara Seas during the preceding autumn. Low Barents–Kara SIC is accompanied by reduced snow depth over northern Eurasia between autumn and winter, which can enhance the Eurasian mid-latitude westerly jet stream. This strengthening increases the cyclogenesis and occurrence of strong surface wind speeds in winter, especially over the TP. In addition, a lower SIC is closely associated with reduced precipitation and snow cover in late autumn and winter over the TP, which in turn enhances warming of the land surface and reduces the area of frozen ground. These anomalies in atmospheric circulation patterns and local surface conditions promote dust events above the TP during winter. The ensemble means of Atmospheric Model Intercomparison Project experiments from Phase 6 of the Coupled Model Inter-comparison Project and the Community Atmosphere Model version 4 can generally reproduce the atmospheric circulation anomalies associated with decreased Barents–Kara SIC. This study reveals the crucial effect that SIC anomalies in the Barents and Kara Seas have on winter dust activities over the TP.

Evaluation of the interannual variability in the East Asian summer monsoon in AMIP and historical experiments of CAS FGOALS-f3-L

The evaluation of East Asian summer monsoon (EASM) simulations could improve our understanding of Asian monsoon dynamics and climate simulations. In this study, by using Phase 6 of the Coupled Model Intercomparison Project (CMIP6) experiments of the Atmospheric Model Intercomparison Project (AMIP) and historical runs of the Chinese Academy of Sciences (CAS) Flexible Global Ocean–Atmosphere–Land System (FGOALS-f3-L) model, the model simulation skill for the interannual variability in the EASM was determined. According to multivariate empirical orthogonal function (MV-EOF) analysis, the major mode of the EASM mainly emerged as a Pacific-Japan pattern in the western Pacific accompanied by a local anticyclonic anomaly with a total variance of 24.6%. The historical experiment could suitably reproduce this spatial pattern and attained a closer total variance than that attained by the AMIP experiment. The historical experiment could also better simulate the time frequency of the EASM variability than the AMIP experiment. However, the phase of principal component 1 (PC1) was not suitably reproduced in the historical experiment since no initialization procedure was applied at the beginning of the integration in the historical simulation process, whereas the sea surface temperature (SST) was preset in the AMIP experiment. Further analysis revealed that air–sea interactions in the Indian Ocean and tropical western Pacific were important for the model to provide satisfactory EASM simulations, while El Niño–Southern Oscillation (ENSO) simulation was possibly related to the climate variability in the EASM simulations, which should be further analyzed.摘要对东亚夏季季风(EASM)模拟的评估可以提高我们对亚洲季风动力和气候模拟的理解. 在这项研究中, 通过使用中国科学院(CAS)全球海洋-大气-陆地系统(FGOALS-f3-L)模式参加的第六次耦合模式相互比较计划(CMIP6)中的大气模式相互比较计划(AMIP)和历史(historical)试验, 明确了EASM的年际变率的模拟能力. 通过多变量经验正交函数(MV-EOF)分析发现, 观测的EASM的主导模态为西太平洋上的太平洋-日本模态, 并伴有局部反气旋异常. 主导模态的方差贡献率为24.6%. 历史(historical)试验可以基本再现这种空间模态, 其方差贡献率较AMIP试验更接近于观测. 与AMIP试验相比, 历史(historical)试验还能更好地模拟EASM变率的时间频率. 然而, 由于历史(historical)模拟没有在积分开始时应用初始化过程, 而AMIP试验受到海表面温度(SST)的约束, 因此主成分(PC1)的位相在历史(historical)试验中没有得到较好地再现. 进一步分析发现, 印度洋和西太平洋热带地区的海气相互作用对EASM的模拟非常重要, 而EASM气候变率的模拟可能与厄尔尼诺-南方涛动(ENSO)的模拟能力有关, 这值得进一步分析.

Variations in Summer Extreme High-Temperature Events over Northern Asia and the Possible Mechanisms

AbstractIn this study, interannual and interdecadal variations in the extreme high-temperature event (EHE) frequency over northern Asia (NA) and the associated possible mechanisms are explored. On an interannual time scale, the first two empirical orthogonal function modes of the NA EHE frequency exhibit a meridional dipole pattern (EOF1) and diagonal tripolar pattern (EOF2), respectively. The higher NA EHE frequency is related to anomalous local highs, reduced mid- to low clouds, and more solar radiation. The warmer ground further heats the overlying atmosphere through longwave radiation and sensible heat. The warm temperature advection in the lower troposphere and the drier soil conditions also favor higher EHE frequency. Further analysis reveals that the EOF1 mode is related to the Polar–Eurasian teleconnection pattern (POL), while the EOF2 mode is associated with North Atlantic Oscillation (NAO) and Pacific–Japan/East Asia–Pacific pattern (PJ/EAP). The fitted EHE frequency based on the atmospheric factors (POL, NAO, and PJ/EAP) can explain the interannual variation in the regionally averaged EHE frequency by 33.8%. Furthermore, three anomalous sea surface temperature (SST) patterns over the North Atlantic–Mediterranean Sea region and around the Maritime Continent are associated with the two EHE modes by intensifying the pronounced atmospheric teleconnections. Analysis on the simulation of five models in the Atmospheric Model Intercomparison Project experiment further confirms the impact of the pronounced SST patterns on the POL, NAO and PJ/EAP. In addition, NA EHE frequency experienced a significant interdecadal increase around the mid-1990s, which could be associated with the phase shift of the Atlantic multidecadal oscillation and long-term global warming trend.

Observed Statistical Connections Overestimate the Causal Effects of Arctic Sea Ice Changes on Midlatitude Winter Climate

AbstractDisentangling the contribution of changing Arctic sea ice to midlatitude winter climate variability remains challenging because of the large internal climate variability in midlatitudes, difficulties separating cause from effect, methodological differences, and uncertainty around whether models adequately simulate connections between Arctic sea ice and midlatitude climate. We use regression analysis to quantify the links between Arctic sea ice and midlatitude winter climate in observations and large initial-condition ensembles of multiple climate models, in both coupled configurations and so-called Atmospheric Model Intercomparison Project (AMIP) configurations, where observed sea ice and/or sea surface temperatures are prescribed. The coupled models capture the observed links in interannual variability between winter Barents–Kara sea ice and Eurasian surface temperature, and between winter Chukchi–Bering sea ice and North American surface temperature. The coupled models also capture the delayed connection between reduced November–December Barents–Kara sea ice, a weakened winter stratospheric polar vortex, and a shift toward the negative phase of the North Atlantic Oscillation in late winter, although this downward impact is weaker than observed. The strength and sign of the connections both vary considerably between individual 35-yr-long ensemble members, highlighting the need for large ensembles to separate robust connections from internal variability. All the aforementioned links are either absent or are substantially weaker in the AMIP experiments prescribed with only observed sea ice variability. We conclude that the causal effects of sea ice variability on midlatitude winter climate are much weaker than suggested by statistical associations, evident in observations and coupled models, because the statistics are inflated by the effects of atmospheric circulation variability on sea ice.

Representation of the boreal summer tropical Atlantic–western North Pacific teleconnection in AGCMs: comparison of CMIP5 and CMIP6

Previous studies have revealed that warm (cold) sea surface temperature (SST) anomalies in the northern tropical Atlantic (NTA) can enhance (weaken) the anomalous low-level anticyclone over the western North Pacific (WNP) during boreal summer. This study assesses the ability of current atmospheric general circulation models (AGCMs) to simulate such an NTA–WNP connection by using Atmospheric Model Intercomparison Project experiments from 23 Coupled Model Intercomparison Project Phase 5 (CMIP5) and 35 CMIP6 climate models. It is shown that both the CMIP5 and CMIP6 multimodel ensemble (MME) averages and the majority of the individual AGCMs can reasonably reproduce the observed pattern of the NTA-related anomalous anticyclone over the WNP during boreal summer. Overall, the performance of the CMIP6 AGCMs in representing the NTA–WNP connection is similar to that of the CMIP5 AGCMs, except that the former tends to have a smaller spread than the latter among models. Additionally, both the CMIP5 and CMIP6 MME averages as well as the individual models can reasonably represent the mechanism responsible for the boreal summer NTA–WNP connection, which involves a zonally westward-extending overturning circulation over the Pacific–Atlantic Oceans. Furthermore, the intensity of the NTA-related WNP anomalous anticyclone is positively correlated with that of the WNP local climatological convection activity for both the CMIP5 and CMIP6 AGCMs, implying that better representation of the WNP climatological convection activity may be crucial for improving the skill of AGCMs to simulate the boreal summer NTA–WNP connection. However, model bias in the simulation of climatological convection activity over the WNP remains large for the current CMIP6 AGCMs, although the bias is reduced over most of the tropical and subtropical Pacific–Atlantic regions compared to that for the CMIP5 AGCMs during boreal summer.

Evaluation of FAMIL2 in Simulating the Climatology and Seasonal‐to‐Interannual Variability of Tropical Cyclone Characteristics

AbstractWe evaluate the ability of the latest generation atmospheric general circulation model from State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences (namely, FAMIL2) in simulating some key characteristics (genesis location, track, number, and intensity) of tropical cyclones (TCs) in terms of their climatology and seasonal to interannual variability. A standard 1° × 1° atmospheric model intercomparison project experiment is carried out for the period 1979–2002, and the last 20 years of outputs are used for analysis. The same period from International Best Track Archive for Climate Stewardship (IBTrACS) is used as the observation for comparison purposes. The evaluations focus on TC activity at the global scale as well as in the three key regions of the northern Indian Ocean (NIO), western Pacific (WP) and northern Atlantic (NA). With respect to the simulated TC climatology, FAMIL2 shows notable ability in correctly reproducing the main characteristics of the genesis locations, tracks, and numbers of TC, particularly over the key regions of TC activity in the Northern Hemisphere; whereas, it underestimates the intensities of TC, as is the case with many state‐of‐the‐art climate models operating at a medium resolution. On seasonal‐to‐interannual time scales, meanwhile, FAMIL2 successfully reproduces the seasonal cycles of TC numbers over the NIO and WP regions, the former being characterized by double TC peaks (in May and October) and the latter by a maximum peak season in August. However, the model only captures these features approximately. For the simulated interannual variability of TC activity, the correlation coefficients of 20‐year TC numbers between FAMIL2 and IBTrACS are 0.22, 0.51 (95% confidence interval), and 0.49 (95% confidence interval) for the NIO, WP, and NA, respectively. We also examine the possible reasons behind the performance of FAMIL2 by investigating its subseasonal signs related to the Madden‐Julian Oscillation (MJO) and convectively coupled equatorial waves. The TC genesis potential index is employed to investigate the possible impacts of the large‐scale dynamic fields on the simulation of TC activity. Finally, the biases of simulated TC activity, as well as possible solutions for these biases, are discussed with respect to the horizontal resolution of the model. A TC forecasting case study is introduced as a first step in applying FAMIL2 to a TC forecasting system.

An Assessment of CAMS-CSM in Simulating Land–Atmosphere Heat and Water Exchanges

The Chinese Academy of Meteorological Sciences (CAMS) has been devoted to developing a climate system model (CSM) to meet demand for climate simulation and prediction for the East Asian region. In this study, we evaluated the performance of CAMS-CSM in regard to sensible heat flux (H), latent heat flux (LE), surface temperature, soil moisture, and snow depth, focusing on the Atmospheric Model Intercomparison Project experiment, with the aim of participating in the Coupled Model Intercomparison Project phase 6. We systematically assessed the simulation results achieved by CAMS-CSM for these variables against various reference products and ground observations, including the FLUXNET model tree ensembles H and LE data, Climate Prediction Center soil moisture data, snow depth climatology data, and Chinese ground observations of snow depth and winter surface temperature. We compared these results with data from the ECMWF Interim reanalysis (ERA-Interim) and Global Land Data Assimilation System (GLDAS). Our results indicated that CAMS-CSM simulations were better than or comparable to ERAInterim reanalysis for snow depth and winter surface temperature at regional scales, but slightly worse when simulating total column soil moisture. The root-mean-square differences of H in CAMS-CSM were all greater than those from the ERA-Interim reanalysis, but less than or comparable to those from GLDAS. The spatial correlations for H in CAMS-CSM were the lowest in nearly all regions, except for North America. CAMS-CSM LE produced the lowest bias in Siberia, North America, and South America, but with the lowest spatial correlation coefficients. Therefore, there are still scopes for improving H and LE simulations in CAMS-CSM, particularly for LE.

Maritime Continent seasonal climate biases in AMIP experiments of the CMIP5 multimodel ensemble

The fidelity of 28 Coupled Model Intercomparison Project phase 5 (CMIP5) models in simulating mean climate over the Maritime Continent in the Atmospheric Model Intercomparison Project (AMIP) experiment is evaluated in this study. The performance of AMIP models varies greatly in reproducing seasonal mean climate and the seasonal cycle. The multi-model mean has better skill at reproducing the observed mean climate than the individual models. The spatial pattern of 850 hPa wind is better simulated than the precipitation in all four seasons. We found that model horizontal resolution is not a good indicator of model performance. Instead, a model’s local Maritime Continent biases are somewhat related to its biases in the local Hadley circulation and global monsoon. The comparison with coupled models in CMIP5 shows that AMIP models generally performed better than coupled models in the simulation of the global monsoon and local Hadley circulation but less well at simulating the Maritime Continent annual cycle of precipitation. To characterize model systematic biases in the AMIP runs, we performed cluster analysis on Maritime Continent annual cycle precipitation. Our analysis resulted in two distinct clusters. Cluster I models are able to capture both the winter monsoon and summer monsoon shift, but they overestimate the precipitation; especially during the JJA and SON seasons. Cluster II models simulate weaker seasonal migration than observed, and the maximum rainfall position stays closer to the equator throughout the year. The tropics-wide properties of these clusters suggest a connection between the skill of simulating global properties of the monsoon circulation and the skill of simulating the regional scale of Maritime Continent precipitation.

North Pacific SST Forcing on the Central United States “Warming Hole” as Simulated in CMIP5 Coupled Historical and Uncoupled AMIP Experiments

ABSTRACTThe central United States experienced a cooling trend during the twentieth century, called the “warming hole,” most notably in the last quarter of the century when global warming accelerated. The coupled simulations of the models that participated in the Coupled Model Intercomparison Project, Phases 3 and 5 (CMIP3/5), have been unable to reproduce this abnormal cooling phenomenon satisfactorily. An unrealistic representation of the observed phasing of the Pacific Decadal Oscillation (PDO)—one of the proposed forcing mechanisms for the warming hole—in the models is considered to be one of the main causes of this effect. The CMIP5’s uncoupled Atmospheric Model Intercomparison Project (AMIP) experiment, whose duration approximately coincides with the peak warming hole cooling period, provides an opportunity, when compared with the coupled historical experiment, to examine the role of the variation in Pacific Ocean sea surface temperature (SST) in the warming hole’s formation and also to assess the skill of the models in simulating the teleconnection between Pacific SST and the continental climate in North America. Accordingly, this study compared AMIP and historical runs in the CMIP5 suite thereby isolating the role of SST forcing in the formation of the warming hole and its maintenance mechanisms. It was found that, even when SST forcing in the AMIP run was “perfectly” prescribed in the models, the skill of the models in simulating the warming hole cooling in the central United States showed little improvement over the historical run, in which SST is calculated interactively, even though the AMIP run overestimated the anti-correlation between temperature in the central United States and the PDO index. The fact that better simulation of the PDO phasing in the AMIP run did not translate into an improved summer cooling trend in the central United States suggests that the inability of the coupled CMIP5 models to reproduce the warming hole under the historical run is not mainly a result of the mismatch between simulated and observed PDO phasing, as believed.

Boreal winter rainfall anomaly over the tropical indo-pacific and its effect on northern hemisphere atmospheric circulation in CMIP5 models

Experimental outputs of 11 Atmospheric Model Intercomparison Project (AMIP) models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are analyzed to assess the atmospheric circulation anomaly over Northern Hemisphere induced by the anomalous rainfall over tropical Pacific and Indian Ocean during boreal winter. The analysis shows that the main features of the interannual variation of tropical rainfall anomalies, especially over the Central Pacific (CP) (5°S-5°N, 175°E-135°W) and Indo-western Pacific (IWP) (20°S-20°N, 110°–150°E) are well captured in all the CMIP5/AMIP models. For the IWP and western Indian Ocean (WIO) (10°S-10°N, 45°–75°E), the anomalous rainfall is weaker in the 11 CMIP5/AMIP models than in the observation. During El Nino/La Nina mature phases in boreal winter, consistent with observations, there are geopotential height anomalies known as the Pacific North American (PNA) pattern and Indo-western Pacific and East Asia (IWPEA) pattern in the upper troposphere, and the northwestern Pacific anticyclone (cyclone) (NWPA) in the lower troposphere in the models. Comparison between the models and observations shows that the ability to simulate the PNA and NWPA pattern depends on the ability to simulate the anomalous rainfall over the CP, while the ability to simulate the IWPEA pattern is related to the ability to simulate the rainfall anomaly in the IWP and WIO, as the SST anomaly is same in AMIP experiments. It is found that the tropical rainfall anomaly is important in modeling the impact of the tropical Indo-Pacific Ocean on the extratropical atmospheric circulation anomaly.

Mechanism of tropical low‐cloud response to surface warming using weather and climate simulations

To understand mechanisms of shortwave cloud‐radiative feedback to global warming in a general circulation model (GCM), we analyzed the response of tropical clouds to uniform increase of sea surface temperature in an atmospheric GCM with two different experimental designs: a single Atmospheric Model Intercomparison Project (AMIP) run for 30 years and a series of 10 day weather hindcasts following the Transpose AMIP II (TAMIP). Given the fast time scale of cloud processes, the hindcast ensemble can capture initial transient responses toward equilibrium obtained in the AMIP experiment, which shows a reduction of low clouds over tropical subsidence regions. The reduction of clouds occurs in the first 10 days in TAMIP when the marine boundary layer (MBL) is destabilized because of contrast between fast and slow warming in the MBL and aloft. Enhanced evaporation from the sea surface that should moisten the MBL through turbulent mixing is suppressed by a reduced surface wind speed associated with a slowdown of the Walker circulation. The sign of the low‐cloud change over the subsidence regime is thus determined roughly by competition between convective drying and turbulent moistening of the MBL.

Surface Air Temperature Simulations by AMIP General Circulation Models: Volcanic and ENSO Signals and Systematic Errors

Thirty surface air temperature simulations for 1979–88 by 29 atmospheric general circulation models are analyzed and compared with the observations over land. These models were run as part of the Atmospheric Model Intercomparison Project (AMIP). Several simulations showed serious systematic errors, up to 4°–5°C, in globally averaged land air temperature. The 16 best simulations gave rather realistic reproductions of the mean climate and seasonal cycle of global land air temperature, with an average error of −0.9°C for the 10-yr period. The general coldness of the model simulations is consistent with previous intercomparison studies. The regional systematic errors showed very large cold biases in areas with topography and permanent ice, which implies a common deficiency in the representation of snow-ice albedo in the diverse models. The SST and sea ice specification of climatology rather than observations at high latitudes for the first three years (1979–81) caused a noticeable drift in the neighboring land air temperature simulations, compared to the rest of the years (1982–88). Unsuccessful simulation of the extreme warm (1981) and cold (1984–85) periods implies that some variations are chaotic or unpredictable, produced by internal atmospheric dynamics and not forced by global SST patterns. Among the 16 best simulations, 8 reproduced the dominant El Niño–Southern Oscillation (ENSO) mode in the 10-yr period, which includes the 1982–83 and 1986–87 warm episodes and the 1988 cold episode. On the average, the ENSO mode explains about 30% of the total variance in surface air temperature fluctuation and has a 2-month lag from the Southern Oscillation index. In this mode, North America displays a Pacific–North American–like anomaly pattern, but Eurasia gave little response to warm SSTs in the eastern equatorial Pacific, in good agreement with results based on historical data. The special design of the AMIP experiment provides a unique opportunity to estimate the effects of the El Chichón volcanic eruption in spring 1982, which was not included in the model forcing. Comparison of the simulations with data delineated a visible global cooling in the first months following the El Chichón eruption, in addition to the cooling from the volcanic eruption of Nyamuragira in December 1981, due to the reduction of incoming solar radiation by volcanic aerosols. However, the mean climate shift in the AMIP experiment due to the forcing data discontinuity at the end of 1981 made the quantitative estimate of El Chichón global cooling influence impossible. The contrast between the simulated ENSO signal and observations shows that the major warming over the northern continents during the 1982/83 winter (DJF) is not an ENSO-like signal. Instead it is most likely a pattern resulting from the enhanced polar vortex produced by a larger pole-to-equator temperature gradient. This gradient was due to the larger absorption of radiation in low latitudes by the El Chichón volcanic sulfate aerosols in the stratosphere. These results suggest that during the Northern Hemisphere wintertime, the stratospheric polar vortex has substantial influence on surface air temperature fluctuations through its effects on vertically propagating planetary waves of the troposphere, and imply that current GCMs are deficient in simulation of stratospheric processes and their coupling with the troposphere.

Using Common Principal Components for Comparing GCM Simulations

Abstract The technique of common principal components (CPC) is applied to compare the results of a number of GCM simulations. The data used are the 120 monthly mean fields from 30 Atmospheric Model Intercomparison Project (AMIP) simulations and an ensemble of five AMIP integrations from a single GCM. The spatial grid and 120 time points allows the calculation of up to 31 covariance matrices for input into the CPC analyses. The CPC methodology is applied to a variety of model comparision problems within the context of the AMIP experiment. The aspects of the simulations used for demonstration are the seasonal cycle of precipitation over the United States, the global 200-hPa velocity potential, and the difference between the 200-hPa divergence of four closely related AMIP models and the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis and a small ensemble of simulations (five) of the European Centre for Medium-Range Weather Forecasts AMIP model. These analyses...