CICE5.0与BCC_CSM2.0模式的耦合及对北极海冰的模拟评估

本文将美国Los Alamos国家实验室发展的最新海冰模式CICE5.0引入国家气候中心气候系统模式BCC_CSM2.0,替代原有的海冰模式SIS,形成一个新的耦合模式。在此基础上,评估新耦合模式对1985-2009年北极海冰的模拟性能,检验引入CICE5.0后对耦合模式中北极海冰、海洋和大气模拟结果的改进。结果表明,引入CICE5.0后,模式能较好地模拟出北极海冰的空间分布、季节以及年际变化特征。相比于旧版本耦合模式,新耦合模式模拟的北极多年冰增多、一年冰减少,同时,海冰增厚、海冰流速减慢,模拟效果得到显著改进,对波弗特涡流模拟的改善尤为明显。进一步分析发现,相比于SIS,CICE5.0对北极海冰特别是海冰厚度模拟性能的提升,在耦合进入BCC_CSM2.0后,会触发冰-温的正反馈机制,改进了模式对海平面气压场、表层气温和海表温度的模拟,由此进一步提高了模式对北极海冰的模拟能力。     

A Sensitivity Study of Arctic Ice-Ocean Heat Exchange to the Three-Equation Boundary Condition Parametrization in CICE6

In this study, we perform a stand-alone sensitivity study using the Los Alamos Sea ice model version 6 (CICE6) to investigate the model sensitivity to two Ice-Ocean (IO) boundary condition approaches. One is the two-equation approach that treats the freezing temperature as a function of the ocean mixed layer (ML) salinity, using two equations to parametrize the IO heat exchanges. Another approach uses the salinity of the IO interface to define the actual freezing temperature, so an equation describing the salt flux at the IO interface is added to the two-equation approach, forming the so-called three-equation approach. We focus on the impact of the three-equation boundary condition on the IO heat exchange and associated basal melt/growth of the sea ice in the Arctic Ocean. Compared with the two-equation simulation, our three-equation simulation shows a reduced oceanic turbulent heat flux, weakened basal melt, increased ice thickness, and reduced sea surface temperature (SST) in the Arctic. These impacts occur mainly at the ice edge regions and manifest themselves in summer. Furthermore, in August, we observed a downward turbulent heat flux from the ice to the ocean ML in two of our three-equation sensitivity runs with a constant heat transfer coefficient (0.006), which caused heat divergence and congelation at the ice bottom. Additionally, the influence of different combinations of heat/salt transfer coefficients and thermal conductivity in the three-equation approach on the model simulated results is assessed. The results presented in this study can provide insight into sea ice model sensitivity to the three-equation IO boundary condition for coupling the CICE6 to climate models.     

Analysis and comparison of heat flux of landfast ice during 2016 in the Prydz Bay,Antarctica

Long term in situ atmospheric observation of the landfast ice nearby Zhongshan Station in the Prydz Bay was performed from April to November 2016. The in situ observation, including the conventional meteorological elements and turbulent flux, enabled this study to evaluate the sea ice surface energy budget process. Using in situ observations, three different reanalysis datasets from the European Centre for Medium-Range Weather Forecasts Interim Re-analysis(ERA-Interim), National Centers for Environmental Prediction Reanalysis2(NCEP R2), and Japanese 55-year Reanalysis(JRA55), and the Los Alamos sea ice model, CICE, output for surface fluxes were evaluated. The observed sensible heat flux(SH) and net longwave radiation showed seasonal variation with increasing temperature. Air temperature rose from the middle of October as the solar elevation angle increased.The ice surface lost more energy by outgoing longwave radiation as temperature increased, while the shortwave radiation showed obvious increases from the middle of October. The oceanic heat flux demonstrated seasonal variation and decreased with time, where the average values were 21 W/m~2 and 11 W/m~2, before and after August,respectively. The comparisons with in situ observations show that, SH and LE(latent heat flux) of JRA55 dataset had the smallest bias and mean absolute error(MAE), and those of NCEP R2 data show the largest differences.The ERA-Interim dataset had the highest spatial resolution, but performance was modest with bias and MAE between JRA55 and NCEP R2 compare with in situ observation. The CICE results(SH and LE) were consistent with the observed data but did not demonstrate the amplitude of inner seasonal variation. The comparison revealed better shortwave and longwave radiation stimulation based on the ERA-Interim forcing in CICE than the radiation of ERA-Interim. The average sea ice temperature decreased in June and July and increased after September,which was similar to the temperature measured by buoys, with a bias and MAE of 0.9°C and 1.0°C, respectively.     

Modeling of Arctic Sea Ice Variability During 1948–2009: Validation of Two Versions of the Los Alamos Sea Ice Model(CICE)

The Los Alamos sea ice model(CICE) is used to simulate the Arctic sea ice variability from 1948 to 2009. Two versions of CICE are validated through comparison with Hadley Centre Global Sea Ice and Sea Surface Temperature(Had ISST) observations. Version 5.0 of CICE with elastic-viscous-plastic(EVP) dynamics simulates a September Arctic sea ice concentration(SASIC) trend of –0.619 × 1012 m2 per decade from 1969 to 2009, which is very close to the observed trend(-0.585 × 1012 m2 per decade). Version 4.0 of CICE with EVP dynamics underestimates the SASIC trend(-0.470 × 1012 m2 per decade). Version 5.0 has a higher correlation(0.742) with observation than version 4.0(0.653). Both versions of CICE simulate the seasonal cycle of the Arctic sea ice, but version 5.0 outperforms version 4.0 in both phase and amplitude. The timing of the minimum and maximum sea ice coverage occurs a little earlier(phase advancing) in both versions. Simulations also show that the September Arctic sea ice volume(SASIV) has a faster decreasing trend than SASIC.     

白令海冰间湖的数值模拟及影响模拟准确度的关键因素

冬季在北白令海陆架区域频繁地出现潜热冰间湖,对当地的生态系统和北极盐跃层贡献很大.将CICE海冰模式应用到该区域,采用高分辨率(6.37 km)网格,模拟2002年11月至2003年4月的海冰变化过程,模拟的海冰总面积和海冰密集度与AMSR-E/Aqua卫星遥感结果吻合很好,其中两者日平均海冰总面积在模拟期间的相关系数达到0.97.模拟结果表明,东北风将海冰向南输运在东西走向的海岸南部形成冰间湖,反映了潜热冰间湖形成和演化的动力过程.对卫星观测数据,将海冰密集度<75%作为冰间湖的判据;而对数值模拟结果,确定海冰密集度<70%为冰间湖的判据.据此讨论白令海4个区域的冰间湖形成过程,与卫星数据进行比较,大部分冰间湖得到很好的模拟.深入讨论了影响冰间湖模拟准确度的主要因素,认为选用恰当的阈值、提高气象强迫场的空间和时间分辨率有助于提高模拟效果.对部分海域的冰间湖模拟效果不佳,需要发展冰海耦合模式才能最终解决.     

CICE海冰模式中融池参数化方案的比较研究

冰面融池的反照率介于海水和海冰之间,获得较准确的融池覆盖率对认识极区气冰海耦合系统的热量收支有重要意义。在数值模式中,融池覆盖率的模拟结果直接影响到冰面反照率计算的准确性,本文对CICE5.0中的3种融池参数化方案进行了较系统的比较分析,结果显示3种方案各有优缺点,模拟结果都存在一些问题。cesm方案中判断融池冻结的条件更为合理。比较而言,融池冻结条件更改后的topo方案模拟的北冰洋区域平均融池覆盖率的年际变化幅度、融池覆盖范围、融池发展盛期持续时间与MODIS数据最接近。通过修改CICE5.0中的代码漏洞,研究了融池水的垂向渗透效应,这一效应会带来一些负面影响,如lvl方案中多年冰上几乎没有融池,说明目前的CICE模式中对于海冰渗透性演化或其他物理机制的处理仍有待改进。最后,着重讨论了topo方案的改进思路。     

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.     

最新版海冰模式CICE的优势:基于北极海冰时空特征的评估

【研究目的】海冰模式CICE(Los Alamos sea ice model)作为当前国际上的主流海冰模式之一,已被耦合进了大部分地球系统模式,对该模式模拟能力的评估工作是发展地球系统模式的重要参考依据。【创新点】通过观测数据与不同版本CICE模式对北极海冰数值模拟结果进行对比分析,研究了最新版本CICE6.0模拟能力及优势。【重要结论】CICE6.0模拟结果的年际误差最小,且季节变化与观测值最为吻合。相较而言,CICE4.0严重高估了冬季海冰总面积及低估了夏季海冰总面积,而CICE5.0在冬季的误差明显大于其他版本。此外,我们也关注了三个模式对多年冰和季节冰的模拟效果,从其均方根误差空间分布看出:模拟误差主要出现在中央海区及其周边海域。CICE4.0和CICE5.0在这些区域模拟的多年冰偏少、季节冰偏多,均低估了多年冰的变化趋势,且高估了季节冰的变化趋势;CICE6.0很好地解决了这些问题,其模拟的多年冰和季节冰的趋势最接近观测值,特别在北冰洋中部。总的来说,CICE6.0模拟的北极海冰在各方面都优于其他版本。     

Melt Pond Scheme Parameter Estimation Using an Adjoint Model

Melt ponds significantly affect Arctic sea ice thermodynamic processes. The melt pond parameterization scheme in the Los Alamos sea ice model(CICE6.0) can predict the volume, area fraction(the ratio between melt pond area to sea ice area in a model grid), and depth of melt ponds. However, this scheme has some uncertain parameters that affect melt pond simulations. These parameters could be determined through a conventional parameter estimation method, which requires a large number of sensitivity simulations. The adjoint model can calculate the parameter sensitivity efficiently. In the present research, an adjoint model was developed for the CESM(Community Earth System Model) melt pond scheme. A melt pond parameter estimation algorithm was then developed based on the CICE6.0 sea ice model, melt pond adjoint model,and L-BFGS(Limited-memory Broyden-Fletcher-Goldfard-Shanno) minimization algorithm. The parameter estimation algorithm was verified under idealized conditions. By using MODIS(Moderate Resolution Imaging Spectroradiometer)melt pond fraction observation as a constraint and the developed parameter estimation algorithm, the melt pond aspect ratio parameter in CESM scheme, which is defined as the ratio between pond depth and pond area fraction, was estimated every eight days during summertime for two different regions in the Arctic. One region was covered by multi-year ice(MYI) and the other by first-year ice(FYI). The estimated parameter was then used in simulations and the results show that:(1) the estimated parameter varies over time and is quite different for MYI and FYI;(2) the estimated parameter improved the simulation of the melt pond fraction.