On estimating local long-term climate trends.
On estimating local long-term climate trends.
复制标题
DOI：
10.1098/rsta.2012.0287
复制
发表时间：
2013-05-28
期刊：
影响因子：
--
通讯作者：
Watkins NW
中科院分区：
文献类型：
--
作者：
Chapman SC;Stainforth DA;Watkins NW
气候敏感性通常是指大气中二氧化碳浓度加倍后全球年平均地表温度的平衡变化。评估这一变量仍然具有重大的科学意义，但其全球性质使其在很大程度上与气候科学的许多领域无关，如影响评估，也与脆弱性评估和适应规划方面的政策无关。在这里，我们专注于当地的变化和观测数据可以分析的方式，以告知我们当地的气候如何变化，自19世纪中期。从气候作为一个不断变化的分布的角度来看，我们评估了这种分布的不同分位数之间以及相同分位数的不同地理位置之间的相对变化。我们展示了观测数据如何在与特定影响或政策努力相关的特定阈值上为当地气候趋势提供指导。这也量化了气候模型作为评估气候变化影响的工具所需的详细程度。提出了从数据中提取这些局部趋势的两种方法的数学基础。这两种方法进行了比较，首先使用替代数据，以澄清的方法和它们的不确定性，然后使用观测的地表温度时间序列从四个地点在欧洲。
Climate sensitivity is commonly taken to refer to the equilibrium change in the annual mean global surface temperature following a doubling of the atmospheric carbon dioxide concentration. Evaluating this variable remains of significant scientific interest, but its global nature makes it largely irrelevant to many areas of climate science, such as impact assessments, and also to policy in terms of vulnerability assessments and adaptation planning. Here, we focus on local changes and on the way observational data can be analysed to inform us about how local climate has changed since the middle of the nineteenth century. Taking the perspective of climate as a constantly changing distribution, we evaluate the relative changes between different quantiles of such distributions and between different geographical locations for the same quantiles. We show how the observational data can provide guidance on trends in local climate at the specific thresholds relevant to particular impact or policy endeavours. This also quantifies the level of detail needed from climate models if they are to be used as tools to assess climate change impact. The mathematical basis is presented for two methods of extracting these local trends from the data. The two methods are compared first using surrogate data, to clarify the methods and their uncertainties, and then using observational surface temperature time series from four locations across Europe.
登录
查看更多内容
DOI：
10.1073/pnas.0702971104
发表时间：
2007-05-22
影响因子：
11.1
作者：
McWilliams, James C.
通讯作者：
McWilliams, James C.
DOI：
10.1098/rsta.2007.2073
发表时间：
2007-08-15
影响因子：
5
作者：
Stainforth, David A.;Downing, Thomas E.;New, Mark
通讯作者：
New, Mark
DOI：
10.1098/rsta.2007.2074
发表时间：
2007-08-15
影响因子：
5
作者：
Stainforth, D. A.;Allen, M. R.;Smith, L. A.
通讯作者：
Smith, L. A.
影响因子：
1.7
作者：
Oreskes, Naomi;Stainforth, David A.;Smith, Leonard A.
通讯作者：
Smith, Leonard A.
DOI：
10.1073/pnas.012580599
发表时间：
2002-02-19
影响因子：
11.1
作者：
Smith, LA
通讯作者：
Smith, LA