Developing a monthly radiative kernel for surface albedo change from satellite climatologies of Earth's shortwave radiation budget: CACK v1.0
Developing a monthly radiative kernel for surface albedo change from satellite climatologies of Earth's shortwave radiation budget: CACK v1.0
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DOI：
10.5194/gmd-12-3975-2019
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发表时间：
2019-09-09
影响因子：
5.1
通讯作者：
O'Halloran, Thomas L.
中科院分区：
文献类型：
--
作者：
Bright, Ryan M.;O'Halloran, Thomas L.
由于土地利用-土地覆被变化（LULCC）有可能改变地表辐射强迫，因此LULCC科学界需要简单和透明的工具，用于根据地表辐射强迫变化（Delta alpha（s））预测辐射强迫（Delta F）。为此，辐射核技术--由气候模拟界开发，用于诊断大气环流模式（GCM）内的内部反馈--已被LULCC科学界采用，作为对Delta alpha进行离线Delta F计算的工具。然而，代码和数据背后的GCM内核是不容易透明的，和用于获得它们的大气状态变量的气候变化在时间周期和持续时间都有很大的不同。基于观测的内核提供了一个有吸引力的替代基于大气环流模型的内核，可以每年更新，成本相对较低。在这里，我们提出了一个辐射内核的表面辐射变化的基础上，一个新的，简化的参数化的短波辐射传输驱动的云和地球的辐射能量系统（CERES）的能量平衡和填充（EBAF）产品的输入。当构建在16年的气候学（2001-2016）上时，我们发现基于CERES的CACK -与四个GCM的平均内核（rRMSE = 14%）非常一致。当新的参数化基础CACK应用于模拟两个GCM内核使用自己的边界通量作为输入，我们发现更大的协议（平均均方根误差= 7.4%），这表明这种简单而透明的参数化代表了一个可信的候选人基于卫星的替代GCM内核。我们记录和计算CACK的不确定性的各种来源，并将它们作为更广泛的数据集（CACK v1.0）的一部分，同时提供展示其应用的示例。
Due to the potential for land-use-land-cover change (LULCC) to alter surface albedo, there is need within the LULCC science community for simple and transparent tools for predicting radiative forcings (Delta F) from surface albedo changes (Delta alpha(s)). To that end, the radiative kernel technique - developed by the climate modeling community to diagnose internal feedbacks within general circulation models (GCMs) - has been adopted by the LULCC science community as a tool to perform offline Delta F calculations for Delta alpha(s). However, the codes and data behind the GCM kernels are not readily transparent, and the climatologies of the atmospheric state variables used to derive them vary widely both in time period and duration. Observation-based kernels offer an attractive alternative to GCM-based kernels and could be updated annually at relatively low costs. Here, we present a radiative kernel for surface albedo change founded on a novel, simplified parameterization of shortwave radiative transfer driven with inputs from the Clouds and the Earth's Radiant Energy System (CERES) Energy Balance and Filled (EBAF) products. When constructed on a 16-year climatology (2001-2016), we find that the CERES-based albedo change kernel or CACK - agrees remarkably well with the mean kernel of four GCMs (rRMSE = 14 %). When the novel parameterization underlying CACK is applied to emulate two of the GCM kernels using their own boundary fluxes as input, we find even greater agreement (mean rRMSE = 7.4 %), suggesting that this simple and transparent parameterization represents a credible candidate for a satellite-based alternative to GCM kernels. We document and compute the various sources of uncertainty underlying CACK and include them as part of a more extensive dataset (CACK v1.0) while providing examples showcasing its application.