气候分析

介绍 Atmospheric Temperature Total Column Water Vapor

介绍

气候is the average weather in a given location, averaged over a fairly long time period, at least 10 years. When we talk about climate, we often talk about average values of meteorological or oceanographic variables, such as air temperatures, precipitation, humidity, wind speed or ocean temperature at a given location at a given time of year. If the climate changes over time, it can directly affect human activities by altering the crops that can be grown, the supply of fresh water, or the mean level of the ocean. It can also affect natural ecosystems, causing deserts to expand, wildfires to become more prevalent, or permafrost to melt.

在过去的二十年里,人们越来越关注人类产生的温室气体和其他环境污染物对地球气候的影响。这些变化是由气候模型预测的,这些模型也被用来预测未来几个世纪的变化。卫星数据记录开始足够长,足以评估几十年的变化。这些变化可以被用来研究气候变化的证据,并用来看看当气候模型被用来“预测”已经发生的变化时,是否能做得很好。

In order to produce a data record that extends long enough for climate change studies, measurements from different satellites must be intercalibrated with each other and then combined together into a single record. We have completed this process for atmospheric temperature and total column water vapor, and are about to release an intercalibrated wind speed product.

Compared to原地measurements, the main advantage of satellite data records from polar orbiting satellites is the nearly complete global coverage and homogeneous data quality. The原地数据记录在远离工业化国家的地区相当稀少,这些地区集中在陆地和北半球中纬度地区。例如,在热带太平洋东部发射的气象气球很少,尽管该地区是厄尔尼诺-南方涛动周期引起的海面温度变化最大的地区。

下面,我们将讨论利用遥感系统微波数据获得的一些基本气候结果,并讨论我们进行的一些与气候有关的研究。万博体育app网页注册万博网址是什么

Atmospheric Temperature

看到了吗高温温度Measurementpage for details about how the atmospheric temperature datasets are produced. Here we present applications of this dataset to climate change analysis. (Note: this section was updated on June 30, 2017 to include results from TLT Version 4.0)

Tropospheric Temperature

There are three tropospheric temperature datasets available from RSS,TLT.(Temperature Lower Troposphere),TMT(Temperature Middle Troposphere), andTTT.(温度总对流层,之后傅和约翰森)。使用这些数据集,我们可以调查在过去35年中对流层温度的显着变化,以及这些变化的空间模式是否与气候模型预测的那些相同。

在过去的十年中,我们一直与LLNL的Ben Santer(以及其他许多研究人员)合作,将我们的对流层结果与气候模型的预测进行比较。我们的结果总结如下:

  • Over the past 35 years, the troposphere has warmed significantly. The global average temperature has risen at an average rate of about 0.18 degrees Kelvin per decade (0.32 degrees F per decade).
  • 如果不包括在模型模拟的输入中的人为导致的温室气体增加,气候模型无法解释这种变暖。
  • 变暖的空间模式与人诱导的变暖一致。看Santer等人2008,2009,2011年和2012年有关使用MSU / AMSU数据的人类诱导大气温度变化的检测和归属。

But....

  • 对流层有不是随着大多数气候模型预测,速度变得非常迅速。请注意,此问题已被大型2015-2106 El Nino事件减少,以及RSS对流层数据集的更新版本。

To illustrate this last problem, we show several plots below. Each of these plots has a time series of TLT temperature anomalies using a reference period of 1979-2008. In each plot, the thick black line are the results from the most recent version of the RSS satellite dataset. The yellow band shows the 5% to 95% envelope for the results of 33 CMIP-5 model simulations (19 different models, many with multiple realizations) that are intended to simulate Earth's Climate over the 20th Century. For the time period before 2005, the models were forced with historical values of greenhouse gases, volcanic aerosols, and solar output. After 2005, estimated projections of these forcings were used. If the models, as a whole, were doing an acceptable job of simulating the past, then the observations would mostly lie within the yellow band.

Fig. 1. Global (70S to 80N) Mean TLT Anomaly plotted as a function of time. The black line is the time series for theRSS V4.0 MSU/AMSU大气温度数据集. 黄色波段是CMIP-5气候模拟输出的5%到95%的范围。1979年至1984年的每个时间序列平均值的平均值被设置为零,以便更容易看到随时间的变化。请注意,1998年之后,观测值可能位于模型分布的较低部分,这表明模型预测值与卫星观测值之间存在微小差异(所有时间序列均已平滑处理,以消除短于6个月的时间尺度上的变量)
图2.热带(30秒至30N)是指作为时间的函数绘制的TLT异常。The black line is the time series for theRSS V4.0 MSU/AMSU大气温度数据集黄频带是CMIP-5气候模拟的5%至95%的输出量。从1979-1984的每个时间序列平均值的平均值设置为零,因此可以更容易地看到随时间的变化。同样,在1998年之后,观察可能在模拟值的低端,表明整个模拟预测比卫星观察到的更多变暖。
为什么会存在这种差异?这意味着什么?一种可能的解释是,气候模型所使用的基本物理学中存在一个错误。除了这种可能性之外,至少还有三种其他合理的解释可以解释变暖率的差异。作为模型模拟输入的作用力存在误差(包括人为气体和气溶胶、火山气溶胶、太阳输入和臭氧变化引起的作用力)、卫星观测误差(部分通过使用不确定性集合解决),模拟中的内部气候变化序列与真实世界中发生的情况不同。我们将这四种解释称为“模型物理误差”、“模型输入误差”、“观测误差”和“不同变率序列”。它们不是相互排斥的。事实上,有确凿的科学证据表明,这四个因素都是造成这种差异的原因,而且大多数差异可以在不诉诸模型物理错误的情况下得到解释。有关所有这些原因的详细讨论,请参阅邮递on the怀疑科学blog byBen Santer和Carl Mears,以及最近in Nature Geoscience by Santer et al.

平流层温度

低平流层的温度monitored since late 1978 by the MSU and AMSU instruments. The RSS merged lower stratospheric temperature data product is called TLS, or temperature lower stratosphere. Unlike the troposphere, which warmed slowly over this period, the lower stratosphere has been cooling due to both decreases in stratospheric ozone caused by CFC’s, and increases in well-mixed greenhouse gases causes by human activity. This slow cooling trend is punctuated occasionally by temporary increases in stratospheric aerosols caused by major volcanic eruptions. In the plot below, we show the global mean temperature anomaly from the RSS TLS data, and the 5% to 95% envelope from the CMIP-5 historical simulations.
Fig. 4. Global (80S to 80N) Mean TLS Anomaly plotted as a function of time. The thick black line is the observed time series from RSS V3.3 MSU/AMSU Temperatures. The yellow band is the 5% to 95% range of output from CMIP-5 climate simulations. The mean value of each time series average from 1979-1984 is set to zero so the changes over time can be more easily seen. Note that the response to the volcanic eruptions of El Chichón (1983) and Pinatubo (1991) is too large in some of the models, and that the models tend to show less overall cooling than the observations.

这些模型捕捉到了平流层温度变化的基本特征,尽管有些模型似乎对火山喷发反应过多,也似乎显示出整体冷却太少。

Total Column Water Vapor

在海洋上,我们可以使用我们合并的水蒸气产品监测大气中水蒸气总量的分支机尺度变化,所述水蒸气产品来自SSM / I,SSMIS,AMSRE和WINDSAT的测量。万博吧manbet客户端2.0有关此数据集的描述,请参阅大气水汽测量页。随着地球对流层的温暖,它能够“保持”更多的水蒸气,而不会蒸汽冷凝成云层然后雨水。假设相对湿度保持恒定,额外的水蒸气量由克劳斯氏菌蛋白关系管辖,并且每度kelvin的水蒸气升高约7%。在图5中,水蒸气的全球增加易于看,这表明了世界上海洋的全球平均时间序列,从平均水平的百分比变化表达。
图5。全世界海洋总水汽异常时间序列的平均值,从60年代到60年代。
This increase can be formally attributed to human-induced climate change -- seeSanter等人,2007年。虽然水蒸气存在大量总体增加,但它绝不是空间均匀的。图6显示了1988 - 2017年期间水蒸气趋势的地图。
图6. 1988 - 2017年期间柱水蒸气趋势地图。
虽然世界上大部分地区都有不同程度的湿润,但赤道两侧的热带太平洋中部也有相当干燥的地区。与水汽趋势的估计误差相比,导致这种模式的水汽趋势,无论是正的还是负的,几乎都具有统计意义。
在深沉的热带地区,水蒸气的变化与大气温度的变化非常强烈相关。图7显示了来自不同卫星温度数据集的水蒸气和温度异常的时间序列。数据已经在20S到20N的纬度带中的海洋上平均。
图7.全柱蒸汽异常和温度异常的时间序列,平均在世界上海洋,从20世纪到20N。顶部面板显示时间序列。中间面板显示在1988年1月开始的运行趋势,并在X轴上结束。底板显示蒸汽趋势与TLT趋势的比率。气候模型表明该比例应为约6.2%/ k。卫星数据集的所有组合显示出更大的比例,表明测量结果显示得太多乳脂,或者变暖太少。万博吧manbet客户端2.0最新版本的RSS TLT数据集最接近期望。这是Mears和Wentz(2017)中的图13。
详细讨论了测量和CMIP-3模型输出中的这种相关性manbet客户端2.0万博吧Mears等,2007年并重新审视MEARS和WEDZ(2017)

References

米尔斯,C.A.和F.J.Wentz,(新闻界)利用日效应优化调整的卫星对流层低层大气温度数据集, J. Climate.

Santer, Benjamin D., Fyfe, John C., Pallotta, Giuliana, Flato, Gregory M., Meehl, Gerald A., England, Matthew H., Hawkins, Ed, Mann, Michael E., Painter, Jeffrey F., Bonfils, Celine, Cvijanovic, Ivana, Mears, Carl, Wentz, Frank J., Po-Chedley, Stephen, Fu, Qiang, Zou, Cheng-Zhi (2017),模型与卫星对流层变暖率的差异原因,Nature Geosci,推进在线出版物,DOI:10.1038 / NGEO2973。

Santer, B. D., J. F. Painter, C. A. Mears, C. Doutriaux, P. Caldwell, J. M. Arblaster, P. J. Cameron-Smith, N. P. Gillett, P. J. Gleckler, J. Lanzante, J. Perlwitz, S. Solomon, P. A. Stott, K. E. Taylor, L. Terray, P. W. Thorne, M. F. Wehner, F. J. Wentz, T. M. L. Wigley, L. J. Wilcox and C. Z. Zou, (2012)识别人类对大气温度的影响美国国家科学院合作伙伴nces, 110(1), 26-33, doi:10.1073/pnas.1210514109.

Santer,B.D.,C.A.Mears,C.Doutriaux,P.M.Caldwell,P.J.Gleckler,T.M.L.Wigley,S.Solomon,N.Gillett,D.P.Ivanova,T.R.Karl,J.R.Lanzante,G.A.Meehl,P.A.Stott,K.E.Taylor,P.W.Thorne,M.F.Wehner和F.J.Wentz,(2011年)Separating Signal and Noise in Atmospheric Temperature Changes: The Importance of Timescale《地球物理学杂志》。第116号决议,D22105,内政部:10.1029/2011JD016263。

Santer,B.D.,K.E.Taylor,P.J.Gleckler,C.Bonfils,T.P.Barnett,D.W.Pierce,T.M.L.Wigley,C.A.Mears,F.J.Wentz,W.Bruggemann,N.Gillett,S.A.Klein,S.Solomon,P.A.Stott和M.F.Wehner,(2009年)将模式质量信息纳入气候变化检测和归因研究,proc。Natl。阿卡。SCI。U. S. A.,106(35),14778-14783,DOI:10.1073 / PNAS.0901736106。

Santer, B. D., P. W. Thorne, L. Haimberger, K. E. Taylor, T. M. L. Wigley, J. R. Lanzante, S. Solomon, M. Free, P. J. Gleckler, P. D. Jones, T. R. Karl, S. A. Klein, C. A. Mears, D. Nychka, G. A. Schmidt, S. C. Sherwood and F. J. Wentz, (2008)对热带对流层建模和观察温度趋势的一致性,国际志科学杂志,28(13),1703-1722。

Mears,C. A.,F. J.Ventz,P. Thorne和D.Bernie(2011),Assessing uncertainty in estimates of atmospheric temperature changes from MSU and AMSU using a Monte-Carlo estimation technique, Journal of Geophysical Research, 116.

Mears,C.A.,B.D.Santer,F.J.Wentz,K.E.Taylor和M.F.Wehner,(2007年)热带海洋温度与可降水量变化的关系, Geophys. Res. Lett., 34, L24709, doi:10.1029/2007GL031936.

Santer,B.,C.,C. A. Mears,F. J.Ventz,K.E.Taylor,P. J.Lepller,T.M.L.Wigley,T.B.Barnett,J.B.Gillett,N.P.Gillett,N.P.Piers,P.A.Stott和M. F. F.F.Wehner,(2007)Identification of Human-Induced Changes in Atmospheric Moisture Content,proc。Natl。阿卡。SCI。U. S. A., 104, 15248-15253.