Understanding Measurements of Climate Sensitivity

地球的平均温度是由大气层顶部的入射和出射辐射能量的平衡决定的，出射能量通常随平均温度而增加和减少。 输出能量的温度依赖性稳定了地球的气候，因为温度的升高会导致输出能量的增加，从而使地球冷却，而温度的降低则会产生相反的效果。 温度对输出能量的恢复作用可以用一个气候反馈参数来量化，该参数总结了共同决定全球效应的各种机制的净效应。 对于内部产生的气候变率，如厄尔尼诺现象，它会导致全球温度暂时上升，反馈参数决定了温度恢复到其长期气候值的速度。 对于外部强迫的气候变化，如二氧化碳（CO2）增加造成的变暖，反馈参数决定了由于CO2增加的辐射效应而最终导致的变暖量。通常认为，同样的反馈参数也适用于由内部气候变率引起的全球温度变化（如厄尔尼诺事件期间的温度上升）和二氧化碳（CO2）增加和其他外部因素造成的永久性气候变化。 但是，PI和其他人的初步工作表明，气候模式中从内部变率和强迫变化模拟计算的参数值之间存在系统性差异，这可能是由于与内部变率和强迫变化相关的温度异常的空间模式的差异，以及不同地区不同反馈机制的突出。 强迫反应和内部变率之间反馈强度的差异将使从观测中估计反馈参数的工作复杂化，因为必要的观测包括对大气层顶部辐射通量的卫星测量，而这些测量只能持续15年。 在如此短的时间内，辐射通量的变化主要受内部变率的影响，因此，从中得出的反馈参数值只能代表内部变率，不应用于评估气候对CO2增加的敏感性。 PI确定的另一个问题是，反馈参数可能有很强的十年到十年的变化，至少在目前条件下的长期气候模式模拟。 这种变异性可能还与温度异常的空间模式及其对特定反馈机制的影响有关（例如，与通常发生在高纬度的冰雪有关的冰和雪反馈），在试图根据观测估计气候敏感性时也必须加以考虑。以及反馈参数的年代际变化的可能性，使用观测和模式模拟相结合。 大部分的工作集中在一个替代的反馈参数的基础上的平均温度在对流层中层（500毫巴），在这一水平上计算的参数值显示更大的协议之间的观测和模拟比他们的同行基于表面温度。这种一致性促使进一步研究模型模拟中导致反馈参数差异的过程，其优点是记录和输出的时间很长，包括难以确定的详细细目，观测云的长波和短波辐射效应等数量。由于希望限制大气CO2增加会导致多少变暖，这项工作具有更广泛的影响特别是考虑到模型模拟的估计值存在很大的不确定性。该项目还支持两名研究生，从而为这一研究领域的未来劳动力提供支持。

The mean temperature of the earth is determined by the balance of incoming and outgoing radiant energy at the top of the atmosphere, and the outgoing energy generally increases and decreases with mean temperature. The temperature dependence of outgoing energy stabilizes earth's climate, as an increase in temperature produces an increase in outgoing energy which cools the planet, and a decrease in temperature has the opposite effect. The restoring effect of temperature on outgoing energy can be quantified by a climate feedback parameter which summarizes the net effect of a variety of mechanisms which together determine the global effect. For internally generated climate variability such as El Nino, which produces a temporary increase in global temperature, the feedback parameter determines how quickly the temperature will return to its long-term climatological value. For externally forced climate change, such as the warming produced by an increase in carbon dioxide (CO2), the feedback parameter determines the amount of warming that will ultimately occur as a result of the radiative effect of the CO2 increase.It is commonly assumed that the same feedback parameter applies very generally to global temperature changes produced by internal climate variability (like the temperature increase during El Nino events) and permanent climate change forced by increases in carbon dioxide (CO2) and other external factors. But preliminary work by the PI and others suggests a systematic difference between parameter values calculated from internal variability and forced change simulations in climate models, possibly due to differences in the spatial patterns of temperature anomalies associated with internal variability and forced change, and the fact that different feedback mechanisms are prominent in different regions. A difference in feedback strength between forced response and internal variability would complicate efforts to estimate the feedback parameter from observations, as the necessary observations include satellite measurements of the incoming and outgoing top-of-atmosphere radiative fluxes that are only available for a 15-year period. Changes in radiative fluxes over such a short period are dominated by internal variability, thus the feedback parameter value derived from them would only be representative of internal variability and should not be used to assess the sensitivity of climate to CO2 increases. A further issue identified by the PI is that the feedback parameter may have strong decade-to-decade variability, at least in long model simulations of climate under present-day conditions. Such variability, likely also related to the spatial patterns of temperature anomalies and their consequences for specific feedback mechanisms (for instance the albedo feedback associated with ice and snow which generally occur at higher latitudes), would also have to be taken into account when attempting to estimate climate sensitivity from observations.This project considers possible differences in feedback parameter between internal variability and forced change, as well as the possibility of decadal variability in the feedback parameter, using a combination of observations and model simulations. Much of the work focuses on an alternative feedback parameter based on the mean temperature at a mid-tropospheric level (500mb), as parameter values calculated at this level show greater agreement between observations and simulations than their counterparts based on surface temperature. This agreement motivates further examination of the processes contributing to differences in feedback parameter in model simulations, which have the advantage of very long periods of record and outputs which include detailed breakdowns of hard-to-observe quantities such as the longwave and shortwave radiative effects of clouds.The work has broader impacts due to the desirability of constraints on how much warming can result from increases in atmospheric CO2 and other greenhouse gases, particularly given the large uncertainty in estimates from model simulations. The project also supports two graduate students, thereby providing for the future workforce in this research area.