Assessing potential of sparse‐input reanalyses for centennial‐scale land surface air temperature homogenisation,International Journal of Climatology

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Assessing potential of sparse‐input reanalyses for centennial‐scale land surface air temperature homogenisation
International Journal of Climatology ( IF 3.5 ) Pub Date : 2020-10-19 , DOI: 10.1002/joc.6898
Ian M. Gillespie ₁ , Leo Haimberger ₂ , Gilbert P. Compo _{3,

4} , Peter W. Thorne ₁

Affiliation

Observations from the historical meteorological observing network contain many artefacts of non‐climatic origin which must be accounted for prior to using these data in climate applications. State‐of‐the‐art homogenisation approaches use various flavours of pairwise comparison between a target station and candidate neighbour station series. Such approaches require an adequate number of neighbours of sufficient quality and comparability – a condition that is met for most station series since the mid‐20th Century. However, pairwise approaches have challenges where suitable neighbouring stations are sparse, as remains the case in vast regions of the globe and is common almost everywhere prior to the early 20th Century. Modern sparse‐input centennial reanalysis products continue to improve and offer a potential alternative to pairwise comparison, particularly where and when observations are sparse. They do not directly ingest or use land‐based surface temperature observations, so they are a formally independent estimate. This may be particularly helpful in cases where structurally similar changes exist across broad networks, which challenges current techniques in the absence of metadata. They also potentially offer a valuable methodologically distinct method, which would help explore structural uncertainty in homogenisation techniques. The present study compares the potential of spatially‐interpolated sparse‐input reanalysis products to neighbour‐based approaches to perform homogenisation of global monthly land surface air temperature records back to 1850 based upon the statistical properties of station‐minus‐reanalysis and station‐minus‐neighbour series. This shows that neighbour‐based approaches likely remain preferable in data dense regions and epochs. However, the most recent reanalysis product, NOAA‐CIRES‐DOE 20CRv3, is potentially preferable in cases where insufficient neighbours are available. This may in particular affect long‐term global average estimates where a small number of long‐term stations in data sparse regions will make substantial contributions to global estimates and may contain missed data artefacts in present homogenisation approaches.

中文翻译：

评估稀疏输入再分析对百年尺度土地表面空气均质化的潜力

来自历史气象观测网络的观测结果包含许多非气候来源的文物，在将这些数据用于气候应用之前必须加以考虑。最新的同质化方法在目标电台和候选相邻电台系列之间使用了各种成对比较的方式。这样的方法需要足够数量的邻居，它们必须具有足够的质量和可比性，这是自20世纪中叶以来大多数台站系列都满足的条件。但是，成对方式在稀疏合适的邻近站的情况下会遇到挑战，这在全球广大地区仍然如此，并且在20世纪初之前几乎在所有地方都很普遍。稀疏输入的百年历史再分析产品不断改进，为成对比较提供了潜在的替代方法，特别是在稀疏的地方和时间。他们不直接摄取或使用陆基地表温度观测值，因此它们是形式上独立的估计。这在跨大型网络存在结构上相似的更改的情况下特别有用，这在缺少元数据的情况下挑战了当前的技术。他们还可能提供一种有价值的方法学上独特的方法，这将有助于探索均质化技术中的结构不确定性。本研究将空间插值的稀疏输入再分析产品与基于邻域的方法进行比较的潜力，该方法基于站点负再分析和站点负负的统计特性，可以对1850年之前的全球每月地面气温记录进行均质化。邻居系列。这表明基于邻居的方法可能在数据密集的区域和时代仍然是可取的。但是，在邻居数量不足的情况下，最好使用最新的重新分析产品NOAA‐CIRES‐DOE 20CRv3。这可能会特别影响长期的全球平均估计，其中稀疏数据区域中的少量长期站将对全球估计做出重大贡献，并且可能包含当前均质化方法中遗漏的数据伪像。

更新日期：2020-10-19

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