Large Differences in Diffuse Solar Radiation among Current-Generation Reanalysis and Satellite-Derived Products
Large Differences in Diffuse Solar Radiation among Current-Generation Reanalysis and Satellite-Derived Products
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DOI：
10.1175/jcli-d-20-0979.1
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发表时间：
2021-08-01
影响因子：
4.9
通讯作者：
Lee, X.
中科院分区：
文献类型：
--
作者：
Chakraborty, T.;Lee, X.
尽管地表短波辐射k -向下箭头分为其漫射（k -向下箭头，K-d）和直接波束（k -向下箭头，K-b）分量与陆地能量和碳预算等因素相关，但缺乏对再分析和卫星衍生产品之间这种划分的大规模比较。在这里，我们利用大约1400年的观测数据，评估了四个当代再分析数据集（NOAA-CIRES-DOE, NCEP-NCAR， MERRA-2和ERA5）和一个卫星衍生产品（CERES）中的k -向下箭头，k -向下箭头，k -d和k -向下箭头，k -b以及太阳辐射的扩散分数k(d)。虽然K-down箭头中的系统正偏差与先前的研究一致，但网格化K-down箭头，K-d和K-down箭头，K-b的偏差在方向和大小上都有变化，无论是每年还是跨季节。云量的模式间变率有力地解释了向下k箭头K-d和向下k箭头K-b的偏倚。在欧洲和中国，网格产品中k向下箭头、K-d的长期（10年及以上）趋势与相应的观测值有显著差异，网格平均35年趋势显示出一个数量级的变化。在MERRA-2再分析（包括云和同化的气溶胶）中，云和气溶胶的减少相互加强，形成了欧洲上空变亮的趋势，而气溶胶增加的影响压倒了中国上空云量减少的影响。这里看到的模式间变率k(d)（从CERES到MERRA-2从0.27到0.50）表明，短波参数化方案及其在气候模式中的输入存在实质性差异，并可能导致耦合模拟中的模式间变率。根据这些结果，我们呼吁对CMIP6模型中的K-down箭头，K-d和K-down箭头，K-b进行系统评价。意义声明阳光的方向会被空气中的微粒和云层所改变。这被称为漫射光，它会影响太阳能的产生和植物的生长。在这里，我们解决了以往研究中的一个空白，并比较了全球数据集中的漫射光。我们发现数据集之间存在很大差异，这主要是由不同的云量造成的。与地面测量结果相比，我们发现这些差异存在于大多数地点和不同季节。过去35年漫射光的变化在不同的数据集之间也有很大的差异。我们的结果要求在所有当前的全球模型中对漫射光进行更大规模的比较。这样做可以帮助我们更好地控制未来的气候变化。
Although the partitioning of shortwave radiation K-down arrow at the surface into its diffuse (K-down arrow,K-d) and direct beam (K-down arrow,K-b) components is relevant for, among other things, the terrestrial energy and carbon budgets, there is a dearth of large-scale comparisons of this partitioning across reanalysis and satellite-derived products. Here we evaluate K-down arrow, K-down arrow,K-d, and K-down arrow,K-b, as well as the diffuse fraction k(d) of solar radiation in four current-generation reanalysis datasets (NOAA-CIRES-DOE, NCEP-NCAR, MERRA-2, and ERA5) and one satellite-derived product (CERES) using approximate to 1400 site-years of observations. Although the systematic positive biases in K-down arrow are consistent with previous studies, the biases in gridded K-down arrow,K-d and K-down arrow,K-b vary in direction and magnitude, both annually and across seasons. The intermodel variability in cloud cover strongly explains the biases in both K-down arrow,K-d and K-down arrow,K-b. Over Europe and China, the long-term (10 yr and longer) trends in K-down arrow,K-d in the gridded products differ noticeably from corresponding observations and the grid-averaged 35-yr trends show an order of magnitude variability. In the MERRA-2 reanalysis, which includes both clouds and assimilated aerosols, the reductions in both clouds and aerosols reinforce each other to establish brightening trends over Europe, whereas the effect of increasing aerosols overwhelms the effect of decreasing cloud cover over China. The intermodel variability in k(d) seen here (from 0.27 to 0.50 from CERES to MERRA-2) suggests substantial differences in shortwave parameterization schemes and their inputs in climate models and can contribute to intermodel variability in coupled simulations. From these results, we call for systematic evaluations of K-down arrow,K-d and K-down arrow,K-b in CMIP6 models.SIGNIFICANCE STATEMENT The direction of sunlight can be changed by particles and clouds in the air. This is known as diffuse light, and it affects solar energy generation and plant growth. Here, we address a gap in previous studies and compare the diffuse light in global datasets. We find large differences between datasets, explained mostly by differing cloud amounts. When compared with measurements from the ground, we find that these differences exist for most sites and across seasons. The change in diffuse light over the last 35 years also varies widely among datasets. Our results call for larger-scale comparisons of diffuse light in all current-generation global models. Doing so can help us to better constrain future climate change.