Arctic Cloud Surface Response Experiment

北极云表面响应实验

• 批准号: NE/K011820/1
• 负责人: Ian Brooks
• 金额: $ 90.31万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FK011820%2F1
• 关键词: Arctic; Cloud; Surface; Response; Experiment

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.

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 年之前卫星记录中的任何一点。大面积的融化导致第一年冰的比例更大。接下来的冬天；在接下来的融化季节，这更容易破碎。气候模型未能预测最近最低值的程度，而且在很大程度上无法重现。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烈反馈过程有关。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少地表变暖；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预算的影响很小。对红外（长波）辐射预算的影响更为显着，因为云的辐射温度几乎与表面相同（比晴空温暖得多），并且大大减少了长波表面冷却。净效应是，除了盛夏的一小段时间外，北极低层云会导致地表变暖。卫星云量反演表明，春季和秋季云量随着海冰比例的减少而增加。由于融化季节开始较早，结束较晚且变暖加剧，因此春季和秋季云量的增加对变暖起到了积极的反馈作用。云是气候模型中最大的不确定性来源之一；尤其是在北极，它们是控制地表能量平衡的最大因素。云是气候模型中的亚网格尺度过程，因此必须根据解析变量进行参数化。这种参数化最终必须基于测量结果。北极地处偏远，工作环境恶劣，因此测量数据非常稀疏，云参数化很大程度上源自低纬度地区的测量数据以及测试模型，这些低纬度地区的条件与北极地区的条件有很大不同。最近对各种模型的评估表明，它们对北极云的描述在流行程度、垂直范围、含水量、微物理和辐射特性方面存在偏差。这给模拟的地表能量预算以及因此模拟的冰融化/生长带来了显着的偏差。北极云表示的改进取决于现场测量。该项目将测量云特性、边界层结构（与云紧密耦合）、表面热量和水分交换以及海冰状况。尽管很明显平均云量与冰含量相关，但尚不清楚云的特性在多大程度上取决于冰含量。我们的观测将涵盖从致密浮冰到边缘冰区到开阔水域的一系列冰况。测量计划为期 3 个月，从融化季节的高峰期一直到初秋结冰，确保我们能够在各种条件下获得大量数据，从而获得云和海冰之间的统计关系分数待确定。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、云含水量）、多普勒激光雷达（风剖面和湍流、云颗粒形状）、表面湍流通量热量、湿度和动量、下降流长波和短波辐射、冰分数和表面温度。我们将确定云的特性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有何关系。

项目成果

Direct determination of the air-sea CO 2 gas transfer velocity in Arctic sea ice regions

北极海冰区海气CO 2 气体传输速度的直接测定

• DOI: http://dx.10.1002/2017gl073593
• 发表时间: 2017
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Prytherch J

Properties of Arctic liquid and mixed phase clouds from ship-borne Cloudnet observations during ACSE 2014

2014 年 ACSE 期间船载 Cloudnet 观测的北极液体和混合相云的特性

• DOI: http://dx.10.5194/acp-2020-56
• 发表时间: 2020
• 作者: Achtert P

Shipborne eddy covariance observations of methane fluxes constrain Arctic sea emissions.

甲烷通量的船载涡流协方差观测限制了北冰洋的排放。

• DOI: http://dx.10.1126/sciadv.aay7934
• 发表时间: 2020
• 期刊: Science advances
• 影响因子: 13.6
• 作者: Thornton BF

Measurement of wind profiles by motion-stabilised ship-borne Doppler lidar

通过运动稳定船载多普勒激光雷达测量风廓线

• DOI: http://dx.10.5194/amtd-8-9339-2015
• 发表时间: 2015
• 作者: Achtert P

Warm-air advection, air mass transformation and fog causes rapid ice melt

暖空气平流、气团转变和雾导致冰快速融化

• DOI: http://dx.10.1002/2015gl064373
• 发表时间: 2015
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Tjernström M