Evaluation of Arctic Sea Ice Drift and its Relationship with Near-surface Wind and Ocean Current in Nine CMIP6 Models from China

The simulated Arctic sea ice drift and its relationship with the near-surface wind and surface ocean current during 1979-2014 in nine models from China that participated in the sixth phase of the Coupled Model Intercomparison Project(CMIP6)are examined by comparison with observational and reanalysis datasets.Most of the models reasonably represent the Beaufort Gyre(BG)and Transpolar Drift Stream(TDS)in the spatial patterns of their long-term mean sea ice drift,while the detailed location,extent,and strength of the BG and TDS vary among the models.About two-thirds of the models agree with the observation/reanalysis in the sense that the sea ice drift pattern is consistent with the near-surface wind pattern.About the same proportion of models shows that the sea ice drift pattern is consistent with the surface ocean current pattern.In the observation/reanalysis,however,the sea ice drift pattern does not match well with the surface ocean current pattern.All nine models missed the observational widespread sea ice drift speed acceleration across the Arctic.For the Arctic basin-wide spatial average,five of the nine models overestimate the Arctic long-term(1979-2014)mean sea ice drift speed in all months.Only FGOALS-g3 captures a significant sea ice drift speed increase from 1979 to 2014 both in spring and autumn.The increases are weaker than those in the observation.This evaluation helps assess the performance of the Arctic sea ice drift simulations in these CMIP6 models from China.     

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 simulation. 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-g1.0 in CMIP3. Diagnosis of atmospheric and oceanic forcing on sea ice reveals several shortcomings and major aspects to improve upon in the future: (1) 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.     

Evaluation of pan-Arctic melt-freeze onset in CMIP5 climate models and reanalyses using surface observations

The seasonal melt-freeze transitions are fundamental features of the Arctic climate system. The representation of the pan-Arctic melt and freeze onset (north of 60°N) is assessed in two reanalyses and eleven CMIP5 global circulation models (GCMs). The seasonal melt-freeze transitions are retrieved from surface air temperature (SAT) across the land and sea-ice domains and evaluated against surface observations. While monthly averages of SAT are reasonably well represented in models, large model-observation and model–model disparities of timing of melt and freeze onset are evident. The evaluation against surface observations reveals that the ERA-Interim reanalysis performs the best, closely followed by some of the climate models. GCMs and reanalyses capture the seasonal melt-freeze transitions better in the central Arctic than in the marginal seas and across the land areas. The GCMs project that during the 21st century, the summer length—the period between melt and freeze onset—will increase over land by about 1 month at all latitudes, and over sea ice by 1 and 3 months at low and high latitudes, respectively. This larger summer-length increase over sea ice at progressively higher latitudes is related to a retreat of summer sea ice during the 21st century, since open water freezes roughly 40 days later than ice-covered ocean. As a consequence, by the year 2100, the freeze onset is projected to be initiated within roughly 10 days across the whole Arctic Ocean, whereas this transition varies by about 80 days today.     

Arctic Climate Changes Based on Historical Simulations (1900‒2013) with the CAMS-CSM

The Chinese Academy of Meteorological Sciences Climate System Model (CAMS-CSM) is a newly developed global climate model that will participate in the Coupled Model Intercomparison Project phase 6. Based on historical simulations (1900?2013), we evaluate the model performance in simulating the observed characteristics of the Arctic climate system, which includes air temperature, precipitation, the Arctic Oscillation (AO), ocean temperature/salinity, the Atlantic meridional overturning circulation (AMOC), snow cover, and sea ice. The model?data comparisons indicate that the CAMS-CSM reproduces spatial patterns of climatological mean air temperature over the Arctic (60°?90°N) and a rapid warming trend from 1979 to 2013. However, the warming trend is overestimated south of the Arctic Circle, implying a subdued Arctic amplification. The distribution of climatological precipitation in the Arctic is broadly captured in the model, whereas it shows limited skills in depicting the overall increasing trend. The AO can be reproduced by the CAMS-CSM in terms of reasonable patterns and variability. Regarding the ocean simulation, the model underestimates the AMOC and zonally averaged ocean temperatures and salinity above a depth of 500 m, and it fails to reproduce the observed increasing trend in the upper ocean heat content in the Arctic. The large-scale distribution of the snow cover extent (SCE) in the Northern Hemisphere and the overall decreasing trend in the spring SCE are captured by the CAMS-CSM, while the biased magnitudes exist. Due to the underestimation of the AMOC and the poor quantification of air–sea interaction, the CAMS-CSM overestimates regional sea ice and underestimates the observed decreasing trend in Arctic sea–ice area in September. Overall, the CAMS-CSM reproduces a climatological distribution of the Arctic climate system and general trends from 1979 to 2013 compared with the observations, but it shows limited skills in modeling local trends and interannual variability.     

The Impact of Sediment-Laden Snow and Sea Ice in the Arctic on Climate

Sea ice formed over shallow Arctic shelves often entrains sediments resuspended from the sea floor. Some of this sediment-laden ice advects offshore into the Transpolar Drift Stream and the Beaufort Gyre of the Arctic Basin. Through the processes of seasonal melting at the top surface, and the freezing of clean ice on the bottom surface, these sediments tend, over time, to concentrate at the top of the ice where they can affect the surface albedo, and thus the absorbed solar radiation, when the ice is snow free. Similarly, wind-blown dust can reduce the albedo of snow. The question that is posed by this study is what is the impact of these sediments on the seasonal variation of sea ice, and how does it then affect climate? Experiments were conducted with a coupled energy balance climate-thermodynamic sea ice model to examine the impact of including sediments in the sea ice alone and in the sea ice and overlying snow. The focus of these experiments was the impact of the radiative and not the thermal properties of the sediments. The results suggest that if sea ice contains a significant amount of sediments which are covered by clean snow, there is only a small impact on the climate system. However, if the snow also contains significant sediments the impact on sea ice thickness and surface air temperature is much more significant.     

Simulation of sea ice and ocean variability in the arctic during 1955–2002 with an intermediate complexity model

Abstract

A number of recent sea‐ice and ocean changes in the Arctic and subarctic regions are simulated using the global University of Victoria (UVic) Earth System Climate Model version 2.6. This is an intermediate complexity model which includes a three‐dimensional ocean model (MOM 2.2), an energy‐moisture balance model for the atmosphere with heat and moisture transport, and a dynamic‐thermodynamic sea‐ice model with elastic‐viscous‐plastic rheology. The model is first spun up for 1800 years with monthly wind stress forcing derived from the National Centers for Environmental Prediction (NCEP) climatology winds and a pre‐industrial atmospheric CO₂ concentration of 280 ppm. After a second spin‐up for the period 1800–1947 with daily climatology winds‐tress forcing, and a linearly increasing atmospheric CO₂ concentration, the model is run with interannually varying wind stresses for the period 1948–2002 with an average forcing interval of 2.5 days and an exponentially increasing atmospheric CO₂ concentration varying from 315 to 365 ppm. However, the analysis of the model output is only carried out for the years 1955–2002.

The simulated maximum and minimum sea‐ice areas for the Arctic are within 6% of the observed climatologies for the years 1978–2001. The model output also shows a small downward trend in sea‐ice extent, which, however, is smaller than has been observed during the past few decades. In addition, the model simulates a decrease in sea‐ice thickness in the SCICEX (SCientific ICe EXpeditions) measurement area in the central Arctic that is consistent with, but smaller than, that observed from submarine sonar profiling data.

The observed variability and magnitude of the export of sea ice through Fram Strait is quite well captured in the simulation. The change in correlation between the North Atlantic Oscillation (NAO) index and the sea‐ice export around 1977 as found in a data study by Hilmer and Jung (2000) is also reproduced. Within the Arctic basin the model simulates well the patterns and the timing of the two major regimes of wind‐forced sea‐ice drift circulation (cyclonic and anticyclonic) as found earlier by Proshutinsky and Johnson (1997). The influence of variations in the Fram Strait ice export on the strength of the North Atlantic thermohaline circulation and surface air temperature are also determined. In particular, it is shown that 3–4 years after a large ice export, the maximum meridional overturning streamfunction decreases by more than 10%.

The temperature and salinity increase at depths of 200–300 m, as observed in the eastern Arctic by Morison et al. (1998), between the USS Pargo cruise in 1993 and the Environmental Working Group (EWG) Joint USRussian Arctic Atlas climatology for the years 1948–87, are just visible in the model simulation. The increases are more noticeable, however, when the ocean model data are averaged over the pentade 1995–2000 and compared with model data averaged over the pentade 1955–60. The fact that these, and some of the other modelled changes, are smaller than the observed changes can likely be attributed to the relatively coarse resolution of the UVic Earth System Climate Model (3.6°E‐W and 1.8°N‐S). Nevertheless, the fact that the model captures qualitatively many of the recent sea‐ice and ocean changes in the Arctic suggests that it can be successfully used to investigate other Arctic‐North Atlantic Ocean climate interactions during past and future eras.     

How Well Do CMIP6 and CMIP5 Models Simulate the Climatological Seasonal Variations in Ocean Salinity?

This paper includes a comprehensive assessment of 40 models from the Coupled Model Intercomparison Project phase 5 (CMIP5) and 33 models from the CMIP phase 6 (CMIP6) to determine the climatological and seasonal variation of ocean salinity from the surface to 2000 m. The general pattern of the ocean salinity climatology can be simulated by both the CMIP5 and CMIP6 models from the surface to 2000-m depth. However, this study shows an increased fresh bias in the surface and subsurface salinity in the CMIP6 multimodel mean, with a global average of ?0.44 g kg^?1 for the sea surface salinity (SSS) and ?0.26 g kg^?1 for the 0–1000-m averaged salinity (S1000) compared with the CMIP5 multimodel mean (?0.25 g kg^?1 for the SSS and ?0.07 g kg^?1 for the S1000). In terms of the seasonal variation, both CMIP6 and CMIP5 models show positive (negative) anomalies in the first (second) half of the year in the global average SSS and S1000. The model-simulated variation in SSS is consistent with the observations, but not for S1000, suggesting a substantial uncertainty in simulating and understanding the seasonal variation in subsurface salinity. The CMIP5 and CMIP6 models overestimate the magnitude of the seasonal variation of the SSS in the tropics in the region 20°S–20°N but underestimate the magnitude of the seasonal change in S1000 in the Atlantic and Indian oceans. These assessments show new features of the model errors in simulating ocean salinity and support further studies of the global hydrological cycle.     

BCC-CSM2-MR模式对北极海冰和气候的模拟及预估

基于第六次耦合模式比较计划(CMIP6),使用新一代全球模式BCC-CSM2-MR的历史试验和未来共享社会经济路径(SSPs)数据,依据Hadley中心的海表面温度和海冰密集度数据及NCEP/NCAR I再分析资料,评估了BCC-CSM2-MR模式对北极海冰及北极气候的模拟能力,并对未来变化进行了预估.结果表明:BCC...     

近20 a驱动北极夏季迅速增暖和融冰的自然因素及过程

北极是全球气候系统平衡的重要一环,近20 a全球变暖现象中,北极迅速增温及融冰是最为引人关注的问题之一.人类影响无疑是过去几十年北极变暖背后的最主要的原因及驱动力,但气候系统的内在自然变率对北极的影响也不容忽视.本文指出,北极变暖的自然影响因子有一部分来源于热带太平洋东部海温的变化,热带太平洋通过由东部海温异常所驱动的...     

Declining summer snowfall in the Arctic: causes, impacts and feedbacks

Recent changes in the Arctic hydrological cycle are explored using in situ observations and an improved atmospheric reanalysis data set, ERA-Interim. We document a pronounced decline in summer snowfall over the Arctic Ocean and Canadian Archipelago. The snowfall decline is diagnosed as being almost entirely caused by changes in precipitation form (snow turning to rain) with very little influence of decreases in total precipitation. The proportion of precipitation falling as snow has decreased as a result of lower-atmospheric warming. Statistically, over 99% of the summer snowfall decline is linked to Arctic warming over the past two decades. Based on the reanalysis snowfall data over the ice-covered Arctic Ocean, we derive an estimate for the amount of snow-covered ice. It is estimated that the area of snow-covered ice, and the proportion of sea ice covered by snow, have decreased significantly. We perform a series of sensitivity experiments in which inter-annual changes in snow-covered ice are either unaccounted for, or are parameterized. In the parameterized case, the loss of snow-on-ice results in a substantial decrease in the surface albedo over the Arctic Ocean, that is of comparable magnitude to the decrease in albedo due to the decline in sea ice cover. Accordingly, the solar input to the Arctic Ocean is increased, causing additional surface ice melt. We conclude that the decline in summer snowfall has likely contributed to the thinning of sea ice over recent decades. The results presented provide support for the existence of a positive feedback in association with warming-induced reductions in summer snowfall.     

The effect of snow on Antarctic sea ice simulations in a coupled atmosphere-sea ice model

 The effect of a snow cover on sea ice accretion and ablation is estimated based on the ‘zero-layer’ version sea ice model of Semtner, and is examined using a coupled atmosphere-sea ice model including feedbacks and ice dynamics effects. When snow is disregarded in the coupled model the averaged Antarctic sea ice becomes thicker. When only half of the snowfall predicted by the atmospheric model is allowed to land on the ice surface sea ice gets thicker in most of the Weddell and Ross Seas but thinner in East Antarctic in winter, with the average slightly thicker. When twice as much snowfall as predicted by the atmospheric model is assumed to land on the ice surface sea ice also gets much thicker due to the large increase of snow-ice formation. These results indicate the importance of the correct simulation of the snow cover over sea ice and snow-ice formation in the Antarctic. Our results also illustrate the complex feedback effects of the snow cover in global climate models. In this study we have also tested the use of a mean value of 0.16 Wm^-1 K^-1 instead of 0.31 for the thermal conductivity of snow in the coupled model, based on the most recent observations in the eastern Antarctic and Bellingshausen and Amundsen Seas, and have found that the sea ice distribution changes greatly, with the ice becoming much thinner by about 0.2 m in the Antarctic and about 0.4 m in the Arctic on average. This implies that the magnitude of the thermal conductivity of snow is of considerable importance for the simulation of the sea ice distribution. An appropriate value of the thermal conductivity of snow is as crucial as the depth of the snow layer and the snowfall rate in a sea ice model. The coupled climate models require accurate values of the effective thermal conductivity of snow from observations for validating the simulated sea ice distribution under the present climate conditions. Received: 20 November 1997/Accepted: 27 July 1998     

一个热动力海冰模式的改进与实验

影响海冰变化的物理因素中热力和动力部分是同等重要的，但多数热动力海冰模式的热力部分考虑得较为简单。针对Hibler热动力海冰模式的不足，以1个3层热力模式为基础改进了其热力部分。比较了原模式中的零层热力模式和用于改进的3层热力模式；并应用改进前后的两种热动力模式对1983年的北极海冰进行了模拟。模拟结果表明，海冰厚度比原模式厚，季节变化减弱，海冰密集度与观测资料更为符合。     

Simulation and Improvements of Oceanic Circulation and Sea Ice by the Coupled Climate System Model FGOALS-f3-L

This study documents simulated oceanic circulations and sea ice by the coupled climate system model FGOALS-f3-L developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, under historical forcing from phase 6 of the Coupled Model Intercomparison Project (CMIP6). FGOALS-f3-L reproduces the fundamental features of global oceanic circulations, such as sea surface temperature (SST), sea surface salinity (SSS), mixed layer depth (MLD), vertical temperature and salinity, and meridional overturning circulations. There are notable improvements compared with the previous version, FGOALS-s2, such as a reduction in warm SST biases near the western and eastern boundaries of oceans and salty SSS biases in the tropical western Atlantic and eastern boundaries, and a mitigation of deep MLD biases at high latitudes. However, several obvious biases remain. The most significant biases include cold SST biases in the northwestern Pacific (over 4°C), freshwater SSS biases and deep MLD biases in the subtropics, and temperature and salinity biases in deep ocean at high latitudes. The simulated sea ice shows a reasonable distribution but stronger seasonal cycle than observed. The spatial patterns of sea ice are more realistic in FGOALS-f3-L than its previous version because the latitude–longitude grid is replaced with a tripolar grid in the ocean and sea ice model. The most significant biases are the overestimated sea ice and underestimated SSS in the Labrador Sea and Barents Sea, which are related to the shallower MLD and weaker vertical mixing.     

CMIP模式对冬季北半球大气环流与青藏高原冬春季气温相关关系的模拟评估

本文评估了36个CMIP5模式和39个CMIP6模式对近期观测中揭示的北半球冬季大气环流与高原冬春气温之间的相关关系的模拟能力。利用最大协方差(MCA)分析方法,计算并比较了观测和模式中冬季北半球200 hPa位势高度场与同后期青藏高原近地面气温的耦合关系。整体而言,大部分CMIP模式能够模拟出显著的冬季北半球大气环流与青藏高原气温之间的相关关系,且CMIP6模式模拟相关特征和作用机制的能力较CMIP5均有所提升。与观测相比,历史情景下36个CMIP5模式中有26个能够模拟出显著的大气环流与同后期高原气温之间的相关关系,其中对于相关的位势高度场空间模态的模拟明显好于对高原气温异常场空间模态的模拟。同情景下39个CMIP6模式中有37个能模拟出显著相关关系,且CMIP6模式更能模拟出观测中MCA模态的位势高度场上北极涛动(AO)和西太平洋遥相关型(WP)反相位叠加的大气环流特征。在对MCA模态时间变率的模拟上,大部分模式都能重现青藏高原整体变暖的趋势,部分模式能够模拟出观测中位势高度场时间主成分的年际变率,并且CMIP6表现要优于CMIP5。对耦合环流型的动力诊断显示,相比CMIP5模式...     

CMIP6全球气候模式对青藏高原中东部地表感热通量模拟能力评估

利用青藏高原（以下简称高原）气象台站常规观测资料、国家青藏高原科学数据中心的青藏高原地气相互作用过程高分辨率（逐小时）综合观测数据集（2005～2016）、国际耦合模式比较计划第六阶段（CMIP6）的历史模拟试验数据和卫星辐射资料,定量评估了12个全球气候模式对1979～2014年高原中东部地表感热通量的模拟能力,并对其模拟偏差进行了成因分析。结果表明,CMIP6模式可较好地重现高原地表感热通量的年循环和季节平均的空间分布型,但数值较计算感热通量偏低,主要表现为对感热通量大值区严重低估。区域平均而言,12个模式模拟的春季高原中东部感热通量的时间演变序列整体较计算感热通量偏低,其中偏差最大的模式为MIROC6,其多年均值仅为计算值的1/3左右。进一步分析发现多模式模拟的春季高原10 m高度处风速和地气温差分别偏强和偏弱,说明CMIP6模拟的春季高原感热通量偏低可主要归因于地气温差的模拟冷偏差。地气温差的模拟冷偏差在高原中东部地区普遍存在,且地表温度和空气温度均存在明显冷偏差,尤其地表温度偏差更大,这很大程度上可能与CMIP6多模式模拟的春季高原降水偏强有关。     

Regional Arctic sea ice variations as predictor for winter climate conditions

Seasonal prediction skill of winter mid and high northern latitudes climate from sea ice variations in eight different Arctic regions is analyzed using detrended ERA-interim data and satellite sea ice data for the period 1980–2013. We find significant correlations between ice areas in both September and November and winter sea level pressure, air temperature and precipitation. The prediction skill is improved when using November sea ice conditions as predictor compared to September. This is particularly true for predicting winter NAO-like patterns and blocking situations in the Euro-Atlantic area. We find that sea ice variations in Barents Sea seem to be most important for the sign of the following winter NAO—negative after low ice—but amplitude and extension of the patterns are modulated by Greenland and Labrador Seas ice areas. November ice variability in the Greenland Sea provides the best prediction skill for central and western European temperature and ice variations in the Laptev/East Siberian Seas have the largest impact on the blocking number in the Euro-Atlantic region. Over North America, prediction skill is largest using September ice areas from the Pacific Arctic sector as predictor. Composite analyses of high and low regional autumn ice conditions reveal that the atmospheric response is not entirely linear suggesting changing predictive skill dependent on sign and amplitude of the anomaly. The results confirm the importance of realistic sea ice initial conditions for seasonal forecasts. However, correlations do seldom exceed 0.6 indicating that Arctic sea ice variations can only explain a part of winter climate variations in northern mid and high latitudes.     

Interannual predictability of Arctic sea ice in a global climate model: regional contrasts and temporal evolution

The predictability of the Arctic sea ice is investigated at the interannual time scale using decadal experiments performed within the framework of the fifth phase of the Coupled Model Intercomparison Project with the CNRM-CM5.1 coupled atmosphere–ocean global climate model. The predictability of summer Arctic sea ice extent is found to be weak and not to exceed 2 years. In contrast, robust prognostic potential predictability (PPP) up to several years is found for winter sea ice extent and volume. This predictability is regionally contrasted. The marginal seas in the Atlantic sector and the central Arctic show the highest potential predictability, while the marginal seas in the Pacific sector are barely predictable. The PPP is shown to decrease drastically in the more recent period. Regarding sea ice extent, this decrease is explained by a strong reduction of its natural variability in the Greenland–Iceland–Norwegian Seas due to the quasi-disappearance of the marginal ice zone in the center of the Greenland Sea. In contrast, the decrease of predictability of sea ice volume arises from the combined effect of a reduction of its natural variability and an increase in its chaotic nature. The latter is attributed to a thinning of sea ice cover over the whole Arctic, making it more sensitive to atmospheric fluctuations. In contrast to the PPP assessment, the prediction skill as measured by the anomaly correlation coefficient is found to be mostly due to external forcing. Yet, in agreement with the PPP assessment, a weak added value of the initialization is found in the Atlantic sector. Nevertheless, the trend-independent component of this skill is not statistically significant beyond the forecast range of 3 months. These contrasted findings regarding potential predictability and prediction skill arising from the initialization suggest that substantial improvements can be made in order to enhance the prediction skill.     

On the Arctic Ocean ice thickness response to changes in the external forcing

Submarine and satellite observations show that the Arctic Ocean ice cover has undergone a large thickness reduction and a decrease in the areal extent during the last decades. Here the response of the Arctic Ocean ice cover to changes in the poleward atmospheric energy transport, F _wall, is investigated using coupled atmosphere-ice-ocean column models. Two models with highly different complexity are used in order to illustrate the importance of different internal processes and the results highlight the dramatic effects of the negative ice thickness—ice volume export feedback and the positive surface albedo feedback. The steady state ice thickness as a function of F _wall is determined for various model setups and defines what we call ice thickness response curves. When a variable surface albedo and snow precipitation is included, a complex response curve appears with two distinct regimes: a perennial ice cover regime with a fairly linear response and a less responsive seasonal ice cover regime. The two regimes are separated by a steep transition associated with surface albedo feedback. The associated hysteresis is however small, indicating that the Arctic climate system does not have an irreversible tipping point behaviour related to the surface albedo feedback. The results are discussed in the context of the recent reduction of the Arctic sea ice cover. A new mechanism related to regional and temporal variations of the ice divergence within the Arctic Ocean is presented as an explanation for the observed regional variation of the ice thickness reduction. Our results further suggest that the recent reduction in areal ice extent and loss of multiyear ice is related to the albedo dependent transition between seasonal and perennial ice i.e. large areas of the Arctic Ocean that has previously been dominated by multiyear ice might have been pushed below a critical mean ice thickness, corresponding to the above mentioned transition, and into a state dominated by seasonal ice.     

CMIP6模式大气中南北半球际涛动的季节循环

利用1958—2014年47个CMIP6模式输出资料和NCEP/NCAR再分析资料,研究了模式大气中南北涛动(InterHemispheric Oscillation,IHO)的季节变化特征,且评估了CMIP6对IHO季节特征的模拟能力。结果表明:47个CMIP6模式都能模拟出IHO的季节演变特征,但模式间存在一定差异。通过比较,筛选出模拟IHO季节循环较好的16个模式,它们能成功模拟出半球大气质量的时间演变和空间结构。进一步分析表明,水汽对IHO季节变化有抵消作用且半球内部水汽质量变化可驱动越赤道质量流的产生;地表净短波辐射夏高冬低,其加热造成的水汽蒸发在水汽质量变化中起到重要作用;地表净长波辐射在春秋变化幅度较大,与大气质量逐月变化吻合。对比再分析资料表明,CMIP6模式模拟的半球大气质量的峰谷值变化有明显的月份偏差,且CMIP6模式模拟的地表气压异常值的偏差主要出现在北太平洋、欧亚大陆、南半球中纬度地区和两极极区,模拟的南北半球的蒸发和降水量、赤道风场、地表净长波和短波辐射通量等均存在明显的偏差。     

The Arctic Amplification Debate

Rises in surface air temperature (SAT) in response to increasing concentrations of greenhouse gases (GHGs) are expected to be amplified in northern high latitudes, with warming most pronounced over the Arctic Ocean owing to the loss of sea ice. Observations document recent warming, but an enhanced Arctic Ocean signal is not readily evident. This disparity, combined with varying model projections of SAT change, and large variability in observed SAT over the 20th century, may lead one to question the concept of Arctic amplification. Disparity is greatly reduced, however, if one compares observed trajectories to near-future simulations (2010–2029), rather than to the doubled-CO₂ or late 21st century conditions that are typically cited. These near-future simulations document a preconditioning phase of Arctic amplification, characterized by the initial retreat and thinning of sea ice, with imprints of low-frequency variability. Observations show these same basic features, but with SATs over the Arctic Ocean still largely constrained by the insulating effects of the ice cover and thermal inertia of the upper ocean. Given the general consistency with model projections, we are likely near the threshold when absorption of solar radiation during summer limits ice growth the following autumn and winter, initiating a feedback leading to a substantial increase in Arctic Ocean SATs.