Data rescue of historical wind observations in Sweden since the 1920s

Instrumental measurements of wind speed and direction from the 1920s to the 1940s from 13 stations in Sweden have been rescued and digitized, making 165 additional station years of wind data available through the Swedish Meteorological and Hydrological Institute&rsquo;s open data portal. These stations measured wind through different versions of cup-type anemometers and were mainly situated at lighthouses along the coasts and at airports. The work followed the protocol "Guidelines on Best Practices for Climate Data Rescue" of the World Meteorological Organization consisting of (i) designing a template for digitization; (ii) digitizing records in paper journals by a scanner; (iii) typing numbers of wind speed and direction data into the template and (iv) performing quality control of the raw observation data. Along with the digitization of the wind observations, meta data from the stations were collected and compiled as support to the following quality control and homogenization of the wind data. The meta data mainly consist of changes in observer and a small number of changes in instrument types and positions. The rescue of these early wind observations can help improve our understanding of long-term wind changes and multidecadal variability (e.g., the "stilling" vs. "reversal" phenomena), but also to evaluate and assess climate simulations of the past. Digitized data can be accessed through the SMHI open data portal: https://www.smhi.se/data, last access: 26 December 2022, and Zenodo repository: https://doi.org/10.5281/zenodo.5850264, last access: 26 December 2022, (Zhou et al., 2022).</p

The contribution of large‑scale atmospheric circulation to variations of observed near‑surface wind speed across Sweden since 1926

This study investigates the centennial-scale (i.e., since 1926) variability of observed nearsurface wind speed across Sweden. Results show that wind speed underwent various phases of change during 1926–2019, i.e., (a) a clear slowdown during 1926–1960; (b) a stabilization from 1960 to 1990; (c) another clear slowdown during 1990–2003; (d) a slight recovery/stabilization period for 2003–2014, which may continue with a possible new slowdown. Furthermore, the performance of three reanalysis products in representing past wind variations is evaluated. The observed low-frequency variability is properly simulated by the selected reanalyses and is linked to the variations of different large-scale atmospheric circulation patterns (e.g., the North Atlantic Oscillation). However, the evident periods of decreasing trend during 1926–1960 and 1990–2003, which drive most of the stilling in the last century, are missing in the reanalyses and cannot be realistically modeled through multiple linear regression by only using indexes of atmospheric circulation. Therefore, this study reveals that changes in large-scale atmospheric circulation mainly drive the low-frequency variability of observed near-surface wind speed, while other factors (e.g., changes in surface roughness) are crucial for explaining the periods of strong terrestrial stilling across Swede

A New Approach to Homogenize Global Subdaily Radiosonde Temperature Data from 1958 to 2018

This study develops an innovative approach to homogenize discontinuities in both mean and variance in global subdaily radiosonde temperature data from 1958 to 2018. First, temperature natural variations and changes are estimated using reanalyses and removed from the radiosonde data to construct monthly and daily difference series. A penalized maximal F test and an improved Kolmogorov–Smirnov test are then applied to the monthly and daily difference series to detect spurious shifts in the mean and variance, respectively. About 60% (40%) of the changepoints appear in the mean (variance), and ~56% of them are confirmed by available metadata. The changepoints display a country-dependent pattern likely due to changes in national radiosonde networks. Mean segment length is 7.2 (14.6) years for the mean (variance)-based detection. A mean (quantile)-matching method using up to 5 years of data from two adjacent mean (variance)-based segments is used to adjust the earlier segments relative to the latest segment. The homogenized series is obtained by adding the two homogenized difference series back to the subtracted reference series. The homogenized data exhibit more spatially coherent trends and temporally consistent variations than the raw data, and lack the spurious tropospheric cooling over North China and Mongolia seen in several reanalyses and raw datasets. The homogenized data clearly show a warming maximum around 300 hPa over 30°S–30°N, consistent with model simulations, in contrast to the raw data. The results suggest that spurious changes are numerous and significant in the radiosonde records and our method can greatly improve their homogeneity

Long-term declining trends in historical wind measurements at the Blue Hill Meteorological Observatory, 1885-2021

Trabajo presentado en AGU FALL MEETING, celebrado en Chicago (Estados Unidos) del 12 al 16 de diciembre de 2022.The Blue Hill Meteorological Observatory, located on the 635-foot summit of Great Blue Hill ten miles south of Boston, Massachusetts, has been the site of continuous monitoring of the local weather and climate since its founding in 1885. The meticulous, extensive, and high-quality climate record maintained at this location has included the measurement of wind among many other parameters since its earliest days, and this provides a unique opportunity to examine seasonal and annual wind speed trends at this site over more than 135 years. Although multiple wind sensors have been in use during this time and the height of the anemometers was raised in 1908, the wind records have been made as consistent as possible through careful analysis of these changes and the application of adjustments to ensure consistency. An analysis of wind data homogeneity is being performed to associate statistical change points in monthly mean wind speeds to the documented wind instrument metadata. The running 30-year mean wind speed at Blue Hill Observatory has decreased from 7.0 m s-1 in the middle 20th century to its present value of 5.7 m s-1 with an increase in the rate of the decline beginning around 1980, and these changes persist in all seasons. The annual wind speed time series shows a significant (p < 0.05) downward trend over the entire period of record from 1885-2021 (-0.103 m s-1 decade-1) that is steeper and is also significant for the sub-periods from 1961-2021 (-0.274 m s-1 decade-1) and 1979-2012 (-0.339 m s-1 decade-1; the lowest annual mean wind speed was recorded in 2021). In addition, daily wind data for the last 60-70 years have been digitized including wind speed, peak gust, fastest mile, and prevailing direction, and this detailed data provides further characterization of the wind changes in recent decades at this location. The declining wind speed trend at Blue Hill has significant implications for the efficiency of wind power generation in the area if it reflects a regional shift in the near-surface wind regime and for the analysis of causal changes in large-scale climate dynamics

A Revisit of Global Dimming and Brightening Based on the Sunshine Duration

Observations show that the surface incident solar radiation (Rs) decreased over land from the 1950s to the 1980s and increased thereafter, known as global dimming and brightening. This claim has been questioned due to the inhomogeneity and low spatial‐temporal coverage of Rs observations. Based on direct comparisons of ~200 observed and sunshine duration (SunDu) derived Rs station pairs, meeting data record lengths exceeding 60 months and spatial distances less than 110 km, we show that meteorological observations of SunDu can be used as a proxy for measured Rs. Our revised results from ~2,600 stations show global dimming from the 1950s to the 1980s over China (−1.90 W/m2 per decade), Europe (−1.36 W/m2 per decade), and the United States (−1.10 W/m2 per decade), brightening from 1980 to 2009 in Europe (1.66 W/m2 per decade) and a decline from 1994 to 2010 in China (−1.06 W/m2 per decade). Even if 1994–2010 is well known as a period of global brightening, the observed and SunDu‐derived Rs over China still exhibit declining trends. Trends in Rs from 1923 to 1950 are also found over Europe (1.91 W/m2 per decade) and the United States (−1.31 W/m2 per decade), but the results in Europe may not well represent the actual trend for the European continent due to poor spatial sampling.ISSN:0094-8276ISSN:1944-800

Data rescue and digitization of historical wind speed observations in Sweden

Trabajo presentado en Swedish Climate Symposium, celebrado en Norrköping (Suecia) del 16 al 18 de mayo de 2022

A century-long homogenized dataset of near-surface wind speed observations since 1925 rescued in Sweden, HomogWS-se

Trabajo presentado en EGU General Assembly, celebrado en Viena (Austria) del 23 al 27 de mayo de 2022.The main reasons for the lack of data rescue and homogenization of early near-surface wind speed (WS) observations before the 1960s are insufficient manpower and lack of funding. Funding from the Swedish Research Council for Sustainable Development (FORMAS) for a joint project (ref. 2019-00509) `Assessing centennial wind speed variability from a historical weather data rescue project in Sweden (WINDGUST)¿ among the Swedish Meteorological and Hydrological Institute, the University of Gothenburg, and the Spanish National Research Council, presents a great opportunity to rescue and homogenize the early paper-based WS data in Sweden, for creating a century-long homogenized WS dataset. 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 artificial 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 then 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 from reference series. 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. HomogWS-se will be released on an open-access data repository for community uses, including studying WS changes, assessing model simulations, and constraining future projections of WS and wind energy potential. 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: A century-long homogenized dataset of near-surface wind speed observations since 1925 rescued in Sweden for community use

Trabajo presentado en Swedish Climate Symposium, celebrado en Norrköping (Suecia) del 16 al 18 de mayo de 2022

The WINDGUST project: Results of the digitization of historical wind speed observations in Sweden

Global wind climate is one of the aspects of the ongoing climate change that until recent days has lacked robust knowledge of past and future trends. IPCC stated in AR6WG1 that the confidence in wind changes is “low” to “medium” which stress that there is still much to learn about wind changes and multidecadal variability in a warming climate (IPCC AR6WG1). One of the reasons have been a shortage of digitally available historical wind observations as input data to studies of historical variations in wind climate. Here we present the results of work package 1 of the project “Assessing centennial wind speed variability from a historical weather data rescue project in Sweden” (WINDGUST, funded by FORMAS – A Swedish Research Council for Sustainable Development (ref. 2019-00509)). The WINDGUST project is a joint initiative between the Swedish Meteorological and Hydrological Institute (SMHI), the University of Gothenburg (UGOT) and the Spanish National Research Council (CSIC) aimed at filling the key gap of short availability and low quality of wind datasets, and improve the limited knowledge on the causes driving wind speed variability in a changing climate across Sweden. In work package 1 historical wind observations from Sweden have been rescued and digitized during 2020 and 2021. Observations from 13 stations around Sweden, mostly along the coast, for the decades 1920 to 1940 were digitized, adding up to 165 stationyears of data. The digitized data from around 1920 to 2021 is freely available from the SMHI data portal: www.smhi.se/data. Meta data for the digitized stations were also collected and compiled as a support for the following quality control and homogenization in work package 2 in the WINDGUST project also presented at EGU 2022. The work followed the “Guidelines on Best Practices for Climate Data Rescue” of the World Meteorological Organization and consisted of three steps. These three steps were: (i) designing a template for digitization; (ii) digitizing papers by an imaging process based on scanning and photographs; and (iii) typing numbers of wind speed data into the template and storing the values in the observational data base at the SMHI