scispace - formally typeset
Search or ask a question
Author

Ian M. Gillespie

Bio: Ian M. Gillespie is an academic researcher from Maynooth University. The author has contributed to research in topics: Homogeneity (statistics) & Surface air temperature. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Papers
More filters
Journal ArticleDOI
TL;DR: In this article, the potential of spatially-interpolated sparse-input reanalysis products 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-plus-neighbour series was compared.
Abstract: 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.

6 citations

Journal ArticleDOI
TL;DR: In this paper , the applicability of sparse input reanalysis to identify breakpoints in available basic station data is explored, and adjustments are then applied using a variety of reanalysis and neighbour-based approaches to produce four distinct estimates.
Abstract: State‐of‐the‐art homogenisation approaches for any test site rely upon the availability of a sufficient number of neighbouring sites with similar climatic conditions and a sufficient quantity of overlapping measurements. These conditions are not always met, particularly in poorly sampled regions and epochs. Modern sparse‐input reanalysis products which are constrained by observed sea surface temperatures, sea‐ice and surface pressure observations, continue to improve, offering independently produced surface temperature estimates back to the early 19th century. This study undertakes an exploratory analysis on the applicability of sparse‐input reanalysis to identify breakpoints in available basic station data. Adjustments are then applied using a variety of reanalysis and neighbour‐based approaches to produce four distinct estimates. The methodological independence of the approach may offer valuable insights into historical data quality issues. The resulting estimates are compared to Global Historical Climatology Network version 4 (GHCNMv4) at various aggregations. Comparisons are also made with five existing global land surface monthly time series. We find a lower rate of long‐term warming which principally arises in differences in estimated behaviour prior to the early 20th century. Differences depend upon the exact pair of estimates, varying between 15 and 40% for changes from 1850–1900 to 2005–2014. Differences are much smaller for metrics starting after 1900 and negligible after 1950. Initial efforts at quantifying parametric uncertainty suggest this would be substantial and may lead to overlap between these new estimates and existing estimates. Further work would be required to use these data products in an operational context. This would include better understanding the reasons for apparent early period divergence including the impact of spatial infilling choices, quantification of parametric uncertainty, and a means to update the product post‐2015 when the NOAA‐CIRES‐DOE 20CRv3 sparse input reanalysis product, upon which they are based, presently ceases.

Cited by
More filters
Journal ArticleDOI
TL;DR: In this paper, the authors analyzed the variability of the Choco jet and Caribbean low-level jet with consideration of the simultaneous Pacific interdecadal oscillation (PDO) and Atlantic multidecadal oscillations (AMO) low-frequency mean states and their effects on the atmospheric circulation and rainfall in northwestern South America and Central America for the 1900-2015 period, during the seasons with the highest intensities of the CJ (September-November (SON)) and the CLLJ (June-August).
Abstract: This study analyzes the variability of the Choco jet (CJ) and Caribbean low-level jet (CLLJ) with consideration of the simultaneous Pacific interdecadal oscillation (PDO) and Atlantic multidecadal oscillation (AMO) low-frequency mean states and their effects on the atmospheric circulation and rainfall in northwestern South America and Central America for the 1900–2015 period, during the seasons with the highest intensities of the CJ (September–November (SON)) and the CLLJ (June–August). Variations in the sea surface temperature (SST) anomaly positioning in the eastern Pacific, tropical North Atlantic (TNA)/Caribbean Sea during different mean states restrict the anomalous circulation, and, consequently, the intensity of the CJ and CLLJ. During the warm AMO (WAMO)/cold PDO (CPDO), the SST gradient from the tropical Pacific into the TNA, accompanied by a cyclonic circulation near the east coast of the Americas, intensifies the west–east circulation in the region, strengthening the CJ and weakening the CLLJ during SON such that rainfall increases over Colombia, Central America and in adjacent oceans. During the cold AMO (CAMO)/warm PDO (WPDO) phase, a relative east/west SST gradient occurs in TNA, consistent with a cyclonic circulation in western TNA, establishing an anomalous southwest–northwestward circulation from the eastern Pacific into the Caribbean basin, forming a well-configured CJ, increasing precipitation over Central America and its adjacent oceans. For the CLLJ, during CAMO phases, the anticyclonic circulations extended over most of the TNA favor its intensification from 30° W to the Caribbean Sea. In contrast, during WAMO, the cyclonic circulation near the east coast of the United States restricts its intensification to the Caribbean Sea region. To the best of our knowledge, the results presented here are new and might be useful in atmospheric modeling and extreme event studies.

9 citations

Posted ContentDOI
TL;DR: Zhou et al. as discussed by the authors created a century-long homogenized near-surface wind speed (WS) observation dataset to improve our knowledge about the uncertainty and causes of current WS stilling and recovery.
Abstract: Abstract. Creating a century-long homogenized near-surface wind speed (WS) observation dataset is essential to improve our knowledge about the uncertainty and causes of current WS stilling and recovery. Here, we rescued paper-based WS records dating back to the 1920s at 13 stations in Sweden and established a four-step homogenization procedure to generate the first 10-member centennial homogenized WS dataset (HomogWS-se) for community use. First, background climate variation in the rescued WS series was removed, using a verified reanalysis series as a reference series to construct a difference series. A penalized maximal F test at a significance level of 0.05 was then applied to detect spurious change-points. About 38 % of the detected change-points were confirmed by the known events recorded in metadata, and the average segment length split by the change-points is ~11.3 years. A mean-matching method using up to five years of data from two adjacent segments was used to adjust the earlier segments relative to the latest segment. The homogenized WS series was finally obtained by adding the homogenized difference series back onto the subtracted reference series. Compared with the raw WS data, the homogenized WS data is more continuous and lacks significant non-climatic jumps. The homogenized WS series presents an initial WS stilling and subsequent recovery until the 1990s, whereas the raw WS fluctuates with no clear trend before the 1970s. The homogenized WS shows a 25 % reduction in the WS stilling during 1990–2005 than the raw WS, and this reduction is significant when considering the homogenization uncertainty. The homogenized WS exhibits a significantly stronger correlation with the North Atlantic Oscillation (NAO) than that of the raw WS (0.54 vs 0.29). These results highlight the importance of the century-long homogenized WS series in increasing our ability to detect and attribute multidecadal variability and changes in WS. The proposed homogenization procedure enables other countries or regions to rescue their early climate data and jointly build global long-term high-quality datasets. HomogWS-se is publicly available from the Zenodo repository at http://doi.org/10.5281/zenodo.5850264 (Zhou et al., 2022).

2 citations

Journal ArticleDOI
TL;DR: The homogenized wind speed series exhibits a significantly stronger correlation with the North Atlantic oscillation index than that of the raw series (0.54 vs. 0.29) as mentioned in this paper .
Abstract: Abstract. Creating a century-long homogenized near-surface wind speed observation dataset is essential to improve our current knowledge about the uncertainty and causes of wind speed stilling and recovery. Here, we rescued paper-based records of wind speed measurements dating back to the 1920s at 13 stations in Sweden and established a four-step homogenization procedure to generate the first 10-member centennial homogenized wind speed dataset (HomogWS-se) for community use. Results show that about 38 % of the detected change points were confirmed by the known metadata events, and the average segment length split by the change points is ∼11.3 years. Compared with the raw wind speed series, the homogenized series is more continuous and lacks significant non-climatic jumps. The homogenized series presents an initial wind speed stilling and subsequent recovery until the 1990s, whereas the raw series fluctuates with no clear trend before the 1970s. The homogenized series shows a 25 % reduction in the wind speed stilling during 1990–2005 than the raw series, and this reduction is significant when considering the homogenization uncertainty. The homogenized wind speed series exhibits a significantly stronger correlation with the North Atlantic oscillation index than that of the raw series (0.54 vs. 0.29). These results highlight the importance of the century-long homogenized series in increasing our ability to detect and attribute multidecadal variability and changes in wind speed. The proposed homogenization procedure enables other countries or regions to rescue their early climate data and jointly build global long-term high-quality datasets. HomogWS-se is publicly available from the Zenodo repository at https://doi.org/10.5281/zenodo.5850264 (Zhou et al., 2022).

1 citations

Journal ArticleDOI
TL;DR: Ascension Island has had a long intermittent record of instrumental weather recording as mentioned in this paper, and the authors developed monthly records of mean temperature and precipitation totals (for 1924-2020 when an almost complete record is available) from the three principal recording sites: the capital Georgetown and two gauges at Wideawake Airfield.
Abstract: Ascension Island has had a long intermittent record of instrumental weather recording. Here, we develop monthly records of mean temperature and precipitation totals (for 1924-2020 when an almost complete record is available) from the three principal recording sites: the capital Georgetown and two gauges at Wideawake Airfield. Although some of the data are in global climate databases, we have sourced as much data as possible from the primary sources in the United Kingdom, the United States and from the island itself. Air temperature shows statistically significant warming since 1950 of 0.54°C and since 1979 of 0.40°C, and agrees closely with sea surface temperatures, taken from the seas around the island, back to the start of the island series in 1924. Although the island is too small to be in Reanalyses, the warming trends of air temperatures from these products also agree, but the absolute air temperature values are about 1°C cooler than measured on the island. Annual precipitation on the island indicates it is very arid, with a long-term average of only 165mm. Occasionally, heavy monthly precipitation totals occur (always between February and June) which bring severe damage to the island’s infrastructure and ecosystems. The heaviest monthly total was 334mm for April 1985.
Book ChapterDOI
01 Jan 2023