汽车全生命周期碳排放研究与分析

为研究供应链对汽车全生命周期碳排放的影响,通过中国工业碳排放信息系统收集供应链碳排放数据,并对某款车型全生命周期碳排放进行研究与核算分析,发现原材料生产、零件生产及整车使用阶段等上下游供应链碳排放是整车生命周期碳排放的主要来源,并提出供应链碳排放管理的建议。

黑碳对北冰洋积雪和海冰影响的模拟研究

当黑碳沉降到冰雪表面时,可使冰雪表面反照率降低,对短波辐射的吸收增加,黑碳的变化对海冰融化过程的影响值得研究。本文利用CICE海冰模式进行数值模拟,并定量分析北冰洋冰雪中黑碳造成的影响。研究表明,在不同黑碳数据源的强迫下,1980-2014年间,模拟结果给出的夏季北冰洋反照率平均下降为0.82%~1.71%,最终造成海冰面积下降了0.97%~1.93%,而在巴伦支海、喀拉海以及拉普捷夫海,夏季黑碳造成的海冰面积下降约为北冰洋整体的2-3倍。不同黑碳沉降强迫下的模拟结果均显示,1980-1995年,北冰洋区域黑碳对反照率的影响呈现减小趋势,但在1996-2014年,黑碳影响转为增加趋势。在低纬度海区,由于海冰的消退,黑碳的辐射效应呈现减小趋势,而在高纬度海区,由于多年冰内黑碳的累积效应,黑碳的辐射影响呈现增强效应。

北极海冰多参数同化对海冰密集度模拟的改进

文章基于CICE海冰模式和PDAF并行数据同化框架,使用局地误差子空间变换卡尔曼滤波方法(LESTKF),将海冰密集度、海冰厚度和海冰干舷资料同化到模式中,设计实验研究了多参数同化对北极海冰密集度模拟的改进。结果显示,数据同化对北极海冰密集度模拟具有良好的改善作用,同化实验的平均偏差、均方根误差和平均绝对误差相对于控制实验均有明显减小,同化实验在夏季对海冰密集度和范围的模拟改善最为明显,多参数同化可以提高海冰密集度和范围模拟的精度和可靠性。

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。

CICE海冰模式中反照率相关参数对北极海冰模拟的影响

国家气候中心气候系统模式BCC_CSM2.0最新耦合了美国Los Alamos国家实验室发展的海冰模式CICE5.0,为试验模式中与反照率相关参数的敏感性及其对模拟结果的影响,提高模式对北极海冰的模拟能力,选取海冰模式中3个主要参数进行了敏感性试验。利用以BCC_CSM2.0耦合框架为基础建立的海冰-海洋耦合模式,选取CORE资料为大气强迫场开展试验,试验的3个参数分别为冰/雪表面反射率、雪粒半径和雪粒半径参考温度。结果表明,参数取值的不同对北极海冰的模拟有显著的影响,优化后的取值组合极大提高了模式的模拟能力,主要表现在:(1)改善了对北极冬季海冰厚度的模拟,海冰厚度增大,与观测资料更为吻合;(2)显著提高了对北极夏季海冰密集度的模拟能力,从而模拟的北极海冰范围年际循环与观测更为一致。参数取值的优化改进了模式对海冰反照率的模拟,进而影响了冰面短波辐射的吸收和海冰表层的融化,最终提高了模式对海冰密集度和厚度的模拟效果。

软件测试中的窗口测试

当前图形界面用户窗口(GUI)在软件开发中大规模地使用,对软件测试提出了挑战。如何能够对软件的窗口进行正确的功能测试,是软件测试面临的一个重要问题。本文提出了窗口控件交互执行因果图(CICE)法。使窗口测试能够按照一定的规则进行。并且给出测试用例的生成算法,自动生成测试用例。

北极偶极子型大气环流对北极海冰影响的数值模拟研究

利用美国冰雪数据中心提供的海冰范围和由ERA-Interim提供的月均海平面气压提取的北极偶极子型大气环流(AD)指数分析得出,12~2月的AD平均指数与2月北极海冰范围正相关,5~8月的平均AD指数与9月北极海冰范围负相关。为了区分冬季/夏季AD型大气环流影响下热力、动力强迫对海冰密集度的影响,本文利用CICE海冰模式进行了一系列数值模拟试验。研究表明,模式能够再现正/负位相的AD型大气环流下海冰密集度分布的主要特点,对北极海冰的模拟能力满足研究需求。冬季,北极海冰密集度对AD型风场的变化不敏感,负位相的AD型大气环流通过热力作用或风驱动以外的强迫作用造成大西洋扇区边缘海海冰密集度减小;夏季,正位相的AD型大气环流影响下,东格陵兰海之外的海区海冰密集度减小,这种影响主要来自于风场强迫,风场以外的强迫作用对大西洋扇区边缘海和巴伦支海海冰密集度偏小也有重要贡献。

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。

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。

Impacts of SIS and CICE as Sea Ice Components in BCC_CSM on the Simulation of the Arctic Climate

Sea ice is a sensitive indicator of climate change and an important component of climate system models. The Los Alamos Sea Ice Model 5.0(CICE5.0) was introduced to the Beijing Climate Center Climate System Model(BCC_CSM) as a new alternative to the Sea Ice Simulator(SIS). The principal purpose of this paper is to analyze the impacts of these two sea ice components on simulations of basic Arctic sea ice, atmosphere, and ocean states. Two sets of experiments were conducted with the same configurations except for the sea ice component used, i.e., SIS and CICE. The distributions of sea ice concentration and thickness reproduced by the CICE simulations in both March and September were closer to actual observations than those reproduced by SIS simulations, which presented a very thin sea ice cover in September. Changes in sea ice conditions also brought about corresponding modifications to the atmosphere and ocean circulation. CICE simulations showed higher agreement with the reference datasets than did SIS simulations for surface air temperature, sea level pressure, and sea surface temperature in most parts of the Arctic Ocean. More importantly, compared with simulations with SIS, BCC_CSM with CICE revealed stronger Atlantic meridional overturning circulation(AMOC), which is more consistent with actual observations. Thus, CICE shows better performance than SIS in BCC_ CSM. However, both components demonstrate a number of common weaknesses, such as overestimation of the sea ice cover in winter, especially in the Nordic Sea and the Sea of Okhotsk. Additional studies and improvements are necessary to develop these components further.

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.

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.

海冰模式中融池参数化方案的伴随模式参数估计

融池是影响北极海冰变化的重要因子。但在目前广泛使用的海冰模式CICE6.0中,融池模拟与观测存在较大差异。CICE6.0中的CESM融池参数化方案可以计算融池覆盖率与深度,但其中的部分重要参数存在一定的经验性,影响了融池模拟。本研究为CICE6.0海冰模式的CESM融池参数化方案开发了伴随模式,利用CICE6.0海冰模式、融池伴随模式和L-BFGS极小化算法,使用一年冰及多年冰区域的MODIS卫星融池覆盖率观测数据,对CESM参数化方案中的融池纵横比参数进行了分时段分区的参数估计。结果表明:使用伴随模式可以有效地对融池参数进行估计;得到的融池参数随时空变化,符合融池过程时空变化强烈的特征;估计的参数值用于模拟,减小了融池覆盖率的模拟误差,与MODIS观测更为一致。

Advantages of the latest Los Alamos Sea-Ice Model(CICE):evaluation of the simulated spatiotemporal variation of Arctic sea ice

The Los Alamos Sea-Ice Model(CICE)is one of the most popular sea-ice models.All versions of it have been the main sea-ice module coupled to climate system models.Therefore,evaluating their simulation capability is an important step in developing climate system models.Compared with observations and previous versions(CICE4.0 and CICE5.0),the advantages of CICE6.0(the latest version)are analyzed in this paper.It is found that CICE6.0 has the minimum interannual errors,and the seasonal cycle it simulates is the most consistent with observations.CICE4.0 overestimates winter sea-ice and underestimates summer sea-ice severely.Meanwhile,the errors of CICE5.0 in winter are larger than for the other versions.The main attention is paid to the perennial ice and the seasonal ice.The spatial distribution of root-mean-square errors indicates that the simulated errors are distributed in the Atlantic sector and the outer Arctic.Both CICE4.0 and CICE5.0 underestimate the concentration of the perennial ice and overestimate that of the seasonal ice in these areas.Meanwhile,CICE6.0 solves this problem commendably.Moreover,the decadal trends it simulates are comparatively the best,especially in the central Arctic sea.The other versions underestimate the decadal trend of the perennial ice and overestimate that of the seasonal ice.In addition,an index used to objectively describe the difference in the spatial distribution between the simulation and observation shows that CICE6.0 produces the best simulated spatial distribution.

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.

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℃ and 1.0℃, respectively.

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.

Intensity of Level Ice Simulated with the CICE Model for Oil-Gas Exploitation in the Southern Kara Sea, Arctic

Sea ice is the predominant natural threat to marine structures and oil-gas exploitation in the Arctic.However,for ice-resistant structural design,long-term successive level ice thickness measurements are still lacking.To fill this gap in the southern Kara Sea,the Los Alamos Sea Ice Model(CICE)is applied to achieve better simulation at the local and regional scales.Based on the validation against ice thickness observations in March and April in 1980-1986,the statistical root-mean-square error is determined to be less than 0.2 m.Then,based on the hindcast data,the spatiotemporal distributions of level ice thickness are analyzed annually,seasonally,and monthly,with thicker level ice of 1.2-1.5 m in spring and large ice-free zones in September and October.For floating platforms,a novel ice grade criterion with five classifications,namely,excellent,good,moderate,severe,and catastrophic,is pro-posed.The first two grades are most suitable for offshore activities,particularly from August to October,and the moderate grade is acceptable if with ice-resistant protections.Furthermore,hostile ice conditions are discussed in terms of the generalized extreme value distribution.The statistics reveal that at a return period of 100 yr,extreme level ice is primarily between 0.6 m and 1.0 m in December.The present investigation could be a useful reference for a feasibility study of the potential risk analysis and ice-resistant operation of oil-gas exploitation in the Arctic.