气候模式中一种新的云—辐射处理方法的研究及应用
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摘要
云对大气辐射传输具有重要的调节作用，但是传统气候模式对云的辐射作用的模拟却存在很大的不确定性，这主要是由于传统气候模式空间分辨率较低，因此云的次网格结构无法准确给出。随着气候模式的不断发展，云-辐射过程逐渐成为限制气候模拟准确性的一个重要因素，因此更好地表征云的次网格结构及其辐射作用无疑具有很重要的意义。本文将一种新的McICA云-辐射方案应用于国家气候中心气候模式BCC_AGCM2.0.1中，该方案一方面能够给出更接近实际的次网格云结构，另一方面在保证计算精度的同时控制了辐射计算所需的时间，因此非常适用于改进大尺度气候模式次网格云辐射传输的需要。但是该方案也会引入一定的随机扰动，可能对模拟的气候场产生影响。
本文从McICA方案的随机误差对所模拟气候场造成的随机扰动和新方案代替原方案对模拟的气候态的影响两方面，综合探讨了新方案在BCC_AGCM2.0.1中的应用效果；此外，还利用一个全球高分辨率云分辨模式的模拟结果初步研究了改进云的重叠结构、减小总云量模拟误差的方法。主要结论如下：
1. McICA随机误差引起的模拟扰动很小，随机误差对所模拟的各种气候变量的影响也很小，全球平均值与作为参考的精确独立气柱近似（ICA）结果的差别都在0.01%量级，模拟结果的纬向分布、垂直分布和典型区域内的分布等气候特征都基本上与ICA一致。因此，在BCC_AGCM2.0.1中应用McICA云-辐射方案具有较高的可信度，模拟性能的提升仍然主要取决于模式物理过程、动力框架等方面，而可以忽略随机误差的影响。
2. McICA云-辐射方案的引入使BCC_AGCM2.0.1模式对晴空和有云条件下地气系统的全球平均长、短波辐射收支的模拟都有了较大的改善，其中全天大气顶OLR模拟误差由原方案的约6.5W/m2降低到了约2.8W/m2，大气顶净短波辐射误差由约5.9W/m2降低到了约3.7W/m2。新的垂直重叠假定的使用使全球总云量的误差由约8%减小到仅有约1.4%。新方案一定程度上减小了原方案对流层温度偏低的现象，同时热带大气比湿的模拟误差比原方案减小约1/3。低纬度地区大气温、湿条件的变化也伴随着大气环流的改变，新方案一定程度上减小了热带地区垂直速度的模拟偏差。地表温度、海平面气压、降水等的分布和季节变化则与原方案相似。
3.使用气候平均的重叠参数（有效抗相关厚度）数据或者建立重叠参数与其他气象因素（如垂直风速）的参数化关系，可以体现不同地区云的重叠特征差异，有助于减小总云量模拟的误差，未来在McICA云-辐射方案下可以考虑从这两个方面来提高云量及辐射的模拟准确度。
Clouds play an important role in the atmospheric radiative transfer. However, there is stillgreat uncertainty in cloud-radiative transfer process within traditional climate models,primarily due to the low spatial resolution and lack of precise sub-grid cloud structure. As thefast development of climate models, cloud-radiative process becomes more and more abottleneck to the model development. So, it is of great importance to describe sub-grid cloudstructures and their radiative effects more realistically. In this thesis, a new cloud-radiativeframework, named McICA, is introduced into the National Climate Center‘s Global ClimateModel, called BCC_AGCM2.0.1. The McICA framework is suitable for improving therepresentation of sub-grid cloud-radiative process because it has the ability to achieve morerealistic sub-grid cloud structures, as well as keeps the radiative calculation precise withoutsignificantly increasing the CPU time. Unfortunately, one drawback of the McICA frameworkis it will introduce some random noise, which may degrade the modeled climate.
The impact of McICA noise on the modeled climate and impact of replacing the oldcloud-radiative scheme with the new one are studied in this thesis. In addition, potential waysof better representing cloud overlap structures and decrease biases in modeled total cloudfractions are studied with data from a global high-resolution cloud resolving model. The mainconclusions are as follows:
1. There is a minor perturbation of modeled climate within McICA samples, and themodeled climate fields are impacted very little by McICA noise, with global mean bias atthe order of0.01%compared to the reference ICA results. Good agreement betweenMcICA and ICA results is also illustrated from zonal mean, vertical, and domaindistributions of variables. So, it‘s highly reliable to use the McICAcloud-radiation schemein BCC_AGCM2.0.1to do climate researches. Because random noises have little impacton the modeling, the modeling ability of BCC_AGCM2.0.1still depends on its physicalparameterization and dynamic framework improvements.
2. The introduction of McICA framework in BCC_AGCM2.0.1leads to a greatimprovement in clear-sky and cloudy-sky TOA radiative budget of the Earth-atmospheresystem, with the OLR bias reduced from about6.5W/m2to2.8W/m2, and the TOA netshortwave flux bias reduced from about5.9W/m2to3.7W/m2. The use of a new cloud vertical overlap assumption results in a sharp decrease in total cloud fraction from about8%to only1.4%. The cold bias in troposphere is somewhat corrected by the new scheme,and at the same time the bias of specific humidity in the tropics is reduced by about1/3.With the improvement in temperature and specific humidity in the low-latitude area, themodeled tropical convective movements are also more realistic. The global distributionand seasonal variation of surface temperature, sea surface pressure and precipitation arecomparable between the new and old scheme.
3. A set of climatological cloud overlap parameter (decorrelation depth) data or aparameterized relationship between cloud overlap parameter and other meteorologicalvariables can be used to represent the spacialy different characters of cloud overlap andreduce biases in the modeled total cloud fraction. These provide two alternatives toimprove the modeling of cloud fractions and radiation within the McICA cloud-raditionframework.
本文从McICA方案的随机误差对所模拟气候场造成的随机扰动和新方案代替原方案对模拟的气候态的影响两方面，综合探讨了新方案在BCC_AGCM2.0.1中的应用效果；此外，还利用一个全球高分辨率云分辨模式的模拟结果初步研究了改进云的重叠结构、减小总云量模拟误差的方法。主要结论如下：
1. McICA随机误差引起的模拟扰动很小，随机误差对所模拟的各种气候变量的影响也很小，全球平均值与作为参考的精确独立气柱近似（ICA）结果的差别都在0.01%量级，模拟结果的纬向分布、垂直分布和典型区域内的分布等气候特征都基本上与ICA一致。因此，在BCC_AGCM2.0.1中应用McICA云-辐射方案具有较高的可信度，模拟性能的提升仍然主要取决于模式物理过程、动力框架等方面，而可以忽略随机误差的影响。
2. McICA云-辐射方案的引入使BCC_AGCM2.0.1模式对晴空和有云条件下地气系统的全球平均长、短波辐射收支的模拟都有了较大的改善，其中全天大气顶OLR模拟误差由原方案的约6.5W/m2降低到了约2.8W/m2，大气顶净短波辐射误差由约5.9W/m2降低到了约3.7W/m2。新的垂直重叠假定的使用使全球总云量的误差由约8%减小到仅有约1.4%。新方案一定程度上减小了原方案对流层温度偏低的现象，同时热带大气比湿的模拟误差比原方案减小约1/3。低纬度地区大气温、湿条件的变化也伴随着大气环流的改变，新方案一定程度上减小了热带地区垂直速度的模拟偏差。地表温度、海平面气压、降水等的分布和季节变化则与原方案相似。
3.使用气候平均的重叠参数（有效抗相关厚度）数据或者建立重叠参数与其他气象因素（如垂直风速）的参数化关系，可以体现不同地区云的重叠特征差异，有助于减小总云量模拟的误差，未来在McICA云-辐射方案下可以考虑从这两个方面来提高云量及辐射的模拟准确度。
Clouds play an important role in the atmospheric radiative transfer. However, there is stillgreat uncertainty in cloud-radiative transfer process within traditional climate models,primarily due to the low spatial resolution and lack of precise sub-grid cloud structure. As thefast development of climate models, cloud-radiative process becomes more and more abottleneck to the model development. So, it is of great importance to describe sub-grid cloudstructures and their radiative effects more realistically. In this thesis, a new cloud-radiativeframework, named McICA, is introduced into the National Climate Center‘s Global ClimateModel, called BCC_AGCM2.0.1. The McICA framework is suitable for improving therepresentation of sub-grid cloud-radiative process because it has the ability to achieve morerealistic sub-grid cloud structures, as well as keeps the radiative calculation precise withoutsignificantly increasing the CPU time. Unfortunately, one drawback of the McICA frameworkis it will introduce some random noise, which may degrade the modeled climate.
The impact of McICA noise on the modeled climate and impact of replacing the oldcloud-radiative scheme with the new one are studied in this thesis. In addition, potential waysof better representing cloud overlap structures and decrease biases in modeled total cloudfractions are studied with data from a global high-resolution cloud resolving model. The mainconclusions are as follows:
1. There is a minor perturbation of modeled climate within McICA samples, and themodeled climate fields are impacted very little by McICA noise, with global mean bias atthe order of0.01%compared to the reference ICA results. Good agreement betweenMcICA and ICA results is also illustrated from zonal mean, vertical, and domaindistributions of variables. So, it‘s highly reliable to use the McICAcloud-radiation schemein BCC_AGCM2.0.1to do climate researches. Because random noises have little impacton the modeling, the modeling ability of BCC_AGCM2.0.1still depends on its physicalparameterization and dynamic framework improvements.
2. The introduction of McICA framework in BCC_AGCM2.0.1leads to a greatimprovement in clear-sky and cloudy-sky TOA radiative budget of the Earth-atmospheresystem, with the OLR bias reduced from about6.5W/m2to2.8W/m2, and the TOA netshortwave flux bias reduced from about5.9W/m2to3.7W/m2. The use of a new cloud vertical overlap assumption results in a sharp decrease in total cloud fraction from about8%to only1.4%. The cold bias in troposphere is somewhat corrected by the new scheme,and at the same time the bias of specific humidity in the tropics is reduced by about1/3.With the improvement in temperature and specific humidity in the low-latitude area, themodeled tropical convective movements are also more realistic. The global distributionand seasonal variation of surface temperature, sea surface pressure and precipitation arecomparable between the new and old scheme.
3. A set of climatological cloud overlap parameter (decorrelation depth) data or aparameterized relationship between cloud overlap parameter and other meteorologicalvariables can be used to represent the spacialy different characters of cloud overlap andreduce biases in the modeled total cloud fraction. These provide two alternatives toimprove the modeling of cloud fractions and radiation within the McICA cloud-raditionframework.
引文
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