Arctic Cloud Surface Response Experiment

北极海冰状况正在迅速变化。2012年夏天，2007年夏末的最小范围记录被打破，自2007年以来，每年的最小范围都低于2007年之前卫星记录的任何一点。大面积的融化导致第二年冬天第一年的冰的比例更大；在接下来的融化季节，这更容易破碎。气候模型无法预测，或在很大程度上无法重现最近极小期的程度。北极海冰迅速减少的原因尚不清楚，但据信与北极的几个强反馈过程有关。这里有趣的是云反馈。在低纬度和中纬度地区，低层云通过反射太阳辐射来减缓地表的变暖；然而，低太阳辐射和高反射表面（海冰和雪）的结合意味着低云对太阳辐射收支的影响很小。对红外（长波）辐射收支的影响更为显著，因为云的辐射温度几乎与地面相同（比晴朗的天空温暖得多），并大大减少了长波表面的冷却。净效应是，除了盛夏的一小段时间外，北极低空云层的作用是使地表变暖。卫星反演的云量显示，在春季和秋季，云量随着海冰分数的减少而增加。随着变暖的加剧，融化季节开始得更早，结束得更晚，随之而来的春季和秋季云量的增加对变暖起到了积极的反馈作用。云是气候模式中最大的不确定性来源之一；特别是在北极，它们是控制地表能量平衡的最大因素。在气候模式中，云是亚网格尺度的过程，因此必须根据已分解的变量进行参数化。这种参数化最终必须以测量为基础。北极地处偏远，工作环境恶劣，因此测量数据非常稀少，云的参数化很大程度上来自低纬度地区的测量数据，而这些地区的条件与北极地区有很大的不同。最近对各种模式的评价表明，它们对北极云的代表在流行度、垂直范围、含水量、微物理和辐射特性方面存在偏差。这在模拟的地表能量收支中引入了显著的偏差，因此模拟的冰融化/增长。北极云图的改进依赖于实地测量。该项目将测量云的性质、边界层结构（云与边界层紧密耦合）、表面热量和水分的交换以及海冰状况。虽然很明显，平均云量与冰含量相关，但尚不清楚云的性质在多大程度上取决于冰含量。我们的观测将跨越一系列的冰况，从密集的浮冰，到边缘冰带，再到开阔的水域。测量计划为期3个月，从融化季节的高峰期一直持续到初秋的冻结期，确保我们将在广泛的条件下获得大量数据，从而确定云和海冰比例之间的统计关系。测量将包括多普勒云雷达（云特性、动力学和湍流）、扫描和垂直指向微波辐射计（温度和湿度剖面、云水含量）、多普勒激光雷达（风廓线和湍流、云粒子形状）、地表热、水分和动量的湍流通量、下流长波和短波辐射、冰部分和地表温度。我们将确定云的性质如何随表面条件而变化，以及它们与将它们连接到表面的边界层湍流结构的关系。

Arctic sea ice conditions are changing rapidly. The summer of 2012 saw the late summer minimum extent record of 2007 shattered, and every year since 2007 has seen a minimum below any point in the satellite record prior to 2007. The large area of melt results in a greater fraction of first year ice the following winter; this is more easily fragmented during the following melt season. Climate models have failed to predict, of for the most part reproduce, the extent of recent minima. The reasons for the rapid decline in Arctic sea ice are not well understood, but are believed to relate to several strong feedback processes in the Arctic. Of interest here is a cloud feedback.At low and mid-latitudes low level clouds act to reduce warming at the surface by reflecting solar radiation; however, the combination of low solar radiation and a highly reflective surface (sea ice and snow) mean that the impact of low cloud on the solar radiation budget is small. The impact on the infra red (longwave) radiative budget is more significant since the clouds radiate at almost the same temperature as the surface (much warmer than a clear sky) and greatly reduce longwave surface cooling. The net effect is that, except for a short period at the height of summer, low level Arctic clouds act to warm the surface. Satellite retrievals of cloud cover show that in the spring and autumn cloud cover increases as sea ice fraction decreases. As the melt season starts earlier and ends later with increased warming, the accompanying increase in spring and autumn cloud cover acts as a positive feedback on warming.Clouds are one of the largest sources of uncertainty within climate models; particularly so in the Arctic where they are the single largest factor controlling the surface energy balance. Clouds are sub-gridscale processes in climate models, so must be parameterised in terms of resolved variables. Such parameterizations must ultimately be based upon measurements. The Arctic is remote and a harsh environment in which to work, consequently measurements are very sparse and cloud parameterisations are largely derived from - and models tested against - measurements at lower latitudes where conditions differ substantially from those in the Arctic. Recent evaluations of a variety of models show that their representation of Arctic clouds suffer biases in prevalence, vertical extent, water content, microphysical and radiative properties. This introduces significant biases into the modelled surface energy budget, and hence modelled ice melt/growth.Improvements in cloud representation for the Arctic depend upon in situ measurements. This project will make measurements of cloud properties, boundary layer structure (to which the clouds are closely coupled), surface exchanges of heat and moisture, and the sea ice conditions. Although it is clear that mean cloud cover is correlated with ice fraction, it is not known to what extent the properties of the cloud depend upon ice fraction. Our observations will span a range of ice conditions from dense pack ice, through the marginal ice zone into open water. The 3-month duration of the measurement programme, through the peak of the melt season and into the early autumn freeze up, ensures that we will obtain a large volume of data over a wide range of conditions, allowing statistical relationships between cloud and sea ice fraction to be determined. Measurements will include Doppler cloud radar (cloud properties, dynamics, and turbulence), scanning and vertically pointing microwave radiometers (temperature and humidity profiles, cloud water content), Doppler lidar (wind profiles and turbulence, cloud particle shape), surface turbulent fluxes of heat, moisture and momentum, downwelling long and shortwave radiation, ice fraction, and surface temperature. We will determine how cloud properties vary with surface conditions, and how they relate to the boundary layer turbulent structure which links them to the surface.