CL5.3.5

Numerous historical sources report on hazardous past climate and weather events that had considerable impacts on society. Studying for example mechanisms of such events is however hampered by a lack of spatial weather information. Gridded high-resolution daily data sets mostly cover the past few decades. For Switzerland, Pfister et al. (2019) reconstructed daily fields of precipitation and temperature back to 1864, but the century before this year would be particularly relevant to study the transition from the Little Ice Age climate to the Anthropocene and to analyze the anomalous, volcanically-perturbed climate in the early 19^th century and late 18^th century. Here we present a daily high-resolution (1x1 km²) reconstruction of temperature and precipitation fields for Switzerland for the years 1763 to 1960 using the analog resampling method (ARM). Together with the present-day meteorological fields, this forms a 250-year data set. The ARM samples temperature and precipitation fields for the historical period from the most similar days in a reference period. These most similar days are selected based on the smallest distance calculated between the observational data in the historical period and the reference period. As observational data, we use temperature, pressure, precipitation, and precipitation occurrence. The resampled fields are then post-processed by assimilating historical temperature measurements and adjusting precipitation fields using isotonic distributional regression. Despite the much scarcer data availability in the period before 1864, evaluation results are promising for the temperature reconstruction with correlation values of on average 0.9 and root mean square errors of on average 1.8°C. For precipitation, the evaluation results are less promising with correlation values of on average 0.7 and root mean square errors of on average 5 mm. Due to its high spatial variability and the small number of records in the historical period, precipitation is more difficult to reconstruct. With the here presented data set, it is now possible to study historical weather and climate events in their spatial extent, such as the warm summer in 1807, and bring it into context leveraging other historical sources. The data set can further be used to calculate impact-relevant indices, for agricultural, phenological, and hydrological modeling.

How to cite: Imfeld, N., Pfister, L., Brugnara, Y., and Brönnimann, S.: 250 years of daily weather: a reconstruction of temperature and precipitation in Switzerland since the late 18th century, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1720, https://doi.org/10.5194/egusphere-egu22-1720, 2022.

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.

This work has partly been presented earlier in EGU2019-17792-1, EGU2020-349 and EGU21-5848.

How to cite: Engström, E., Azorin-Molina, C., Wern, L., Hellström, S., Sturm, C., Joelsson, M., Zhou, C., and Chen, D.: The WINDGUST project: Results of the digitization of historical wind speed observations in Sweden, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7366, https://doi.org/10.5194/egusphere-egu22-7366, 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.

How to cite: Zhou, C., Azorin-Molina, C., Engström, E., Minola, L., Wern, L., Hellström, S., Lönn, J., and Chen, D.: A century-long homogenized dataset of near-surface wind speed observations since 1925 rescued in Sweden, HomogWS-se, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7828, https://doi.org/10.5194/egusphere-egu22-7828, 2022.

Sudden Stratospheric Warmings (SSW) are known to have impacts on the tropospheric atmospheric circulation that can persist up to 60 days. The aim of this work is to evaluate the influence of SSW on both observed mean wind speed and daily peak wind gusts across the Northern Hemisphere for 1961-2021. A set of 26 SSW with tropospheric response, including split (12) and displaced (14) events, are chosen for this matter. Daily wind speed means and peak gust data are retrieved from the quality-controlled and sub-daily station dataset: HadISD. The ultimate goal will be to prove the ability of SSW as possible source of predictability in the medium term for surface wind speed across the Northern Hemisphere, which would have direct applications in areas such as: wind-power generation, agriculture and air quality, among many other socioeconomic and environmental issues.

How to cite: Utrabo-Carazo, E., Azorin-Molina, C., Dunn, R. J. H., Aguilar, E., and Brunet, M.: Effects of Sudden Stratospheric Warmings on the observed near-surface wind speed in the Northern Hemisphere, 1961-2021, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7497, https://doi.org/10.5194/egusphere-egu22-7497, 2022.

Near-surface wind speed has been one of the forgotten parts of the climate system due to poor quality of observational data and the challenges in its homogenization.  During the last two decades the interest in near-surface wind variability and trends has increased and two main phenomena have been found: the first one is termed “stilling”, indicating a decline of near-surface wind speed between around 1978 and 2010; the other is related to an interruption in the “stilling” since 2000s, known as a “reversal” of the wind speed trends at global and regional scales like China, Sweden or Iberian Peninsula, among others. There are uncertainties about the plausible causes of the variability of the near-surface wind speed, but last research pointed to the role played by decadal atmosphere-ocean oscillations. Under this assumption and a climate change context, a new “stilling” phase is expected for the 21^st century. In order to advance in the evaluation and attribution of the causes of the “stilling” and the “reversal” phenomena, the main objective of this study is to analyze projected changes in near-surface wind speed at regional scale, e.g. the Iberian Peninsula. The methodology consists in a comparison between observed wind speed data of the Iberian Peninsula and historical simulations from CMIP6 models, followed by a study of wind speed variability and trends of CMIP6 models under low to high greenhouse gas forcing scenarios in the future. The analyses will focus on quantifying the long-term changes in near surface wind speed and their relationship with dominant modes of variability in the Pacific and Atlantic (e.g., the Pacific Decadal Oscillation and the Atlantic Multi-decadal Oscillation).

How to cite: Andres Martin, M., Yu, Y., Shen, C., Azorin-Molina, C., Deng, K., Bedoya-Valestt, S., and Utrabo-Carazo, E.: Projected changes in near-surface wind speed over Iberian Peninsula and associated atmosphere-ocean oscillations., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8503, https://doi.org/10.5194/egusphere-egu22-8503, 2022.

Different types of climate datasets (station, gridded, reanalyses) and even individual datasets have been shown to differ in how they capture statistical properties of climate variables. Here we compare trends in precipitation totals in Europe between station data (taken from the ECA&D database), gridded data (E-OBS and CRU TS), and reanalyses (20CR, JRA-55, and NCEP/NCAR) for period 1961-2011, both annually and for individual seasons. Theil-Sen non-parametric trend estimator is used for the quantification of the trend magnitude; Mann-Kendall test is used to evaluate the significance of trends.

On the annual basis, station data indicate precipitation increases in northern Europe and decreases in southern and southeastern Europe. Whereas trends in the gridded datasets roughly agree with station data, although tend to overestimate them, reanalyses provide much more negative trends with a different geographical distribution. There is a tendency for reanalyses to overestimate precipitation in the beginning of the period at some places, whereas they underestimate precipitation near the end of the period elsewhere. Particularly notable is an excessive, and likely unrealistic, drying trend in central, southwestern, and southeastern Europe in NCEP/NCAR in most seasons. Reanalyses thus do not appear to be suitable data sources for estimation of precipitation trends.  

Reasons for the disagreement are identified by a detailed examination of local or regional time series. The reasons are varied and depend on the specific type of dataset: Station series may suffer from inhomogeneities; gridded data may be affected by different sets of stations entering the interpolation procedure at different times; while reanalyses may be affected by different kinds of data being assimilated into them in different periods.

How to cite: Huth, R. and Vít, V.: Long-term trends of precipitation in Europe: a comparison across multiple datasets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1345, https://doi.org/10.5194/egusphere-egu22-1345, 2022.

Rainfall extremes, not least short-duration (sub-daily) extremes, are associated with a range of societal hazards, notably pluvial flooding but in addition e.g. debris flow and erosion-driven nutrient transport. Fundamental for all analysis, modelling and risk assessment related to rainfall extremes is the access to and analysis of observations. In this study, rainfall observations from meteorological stations in the Nordic-Baltic region were collected, quality controlled and consistently analyzed in terms of records, return levels and trends as well as geographical, climatic and seasonal dependencies. In terms of daily extremes, long-term analyses (since 1901) were performed at 138 stations and short-term analyses (since 1969) at 724 stations. In terms of sub-daily extremes, fewer stations and shorter records are available, and long-term analyses (since 1981) were performed at 47 stations and short-term analyses (since 2000) at 370 stations. The results reflect the heterogeneous rainfall climate in the region, with longitudinal and latitudinal gradients in the return levels as well as their time of occurrence for different durations (and return periods). Trend analyses show a majority of positive trends, both at daily and sub-daily scales, with geographical differences. Observations and data from the study are provided open access and we hope that this will be useful e.g. for regional harmonization of rainfall statistics used in infrastructural design and for climate model evaluation.

How to cite: Olsson, J., Verpe Dyrrdal, A., Médus, E., Aniskeviča, S., Arnbjerg-Nielsen, K., Førland, E., Mačiulytė, V., Mäkelä, A., Post, P., Liedke Thorndahl, S., and Wern, L.: Rainfall extremes in the Nordic-Baltic region, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-866, https://doi.org/10.5194/egusphere-egu22-866, 2022.

Hail is an extreme phenomenon that can cause great material damage. Although the process of hail formation is known, there is still insufficient knowledge about this topic. For this reason, numerous researches on hail climatology around the world have been made in the last few years. In spite of the fact that Croatia is a small country, it has a relatively inhomogeneous climate. The cause of this inhomogeneity may be orography, proximity to the Adriatic Sea etc. Characteristics of hail vary in different climatic conditions, and this research will focus on characteristics of hail in the climactic conditions of costal part of the Adriatic. The results are based on hail data from 55 stations from the observation log for the period from 1973 to 2019, and ERA 5 data for instability indices. The results showed significant interannual and spatial variability, due to which it was necessary to make a division into 4 subdomains. Trend analysis showed negative trend in interannual number of hail events, and the loss of hail events was reflected on summer and autumn seasons. It was shown that on the entire coast, the highest hail activity is present in the colder part of the year, and reduced activity in the warmer part of the year. Daily patterns showed 3 daily highs – morning, noon and afternoon, and the duration was usually 5 minutes. Three stations were singled out as the ones with the most hail days a year: Senj, Drniš and Gračac. Finally, instability indices were studied (KI, CAPE, DLS and freezing level height), which could explain the atmospheric conditions in which hail occurs.

How to cite: Blašković, L., Jelić, D., Malečić, B., Omazić, B., and Telišman Prtenjak, M.: Characteristics of hail in the Croatian coastal part of the Adriatic, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2402, https://doi.org/10.5194/egusphere-egu22-2402, 2022.

Understanding the ongoing climate change is impossible without evaluating the contributions of both anthropogenic and natural variabilities. In present paper, natural variability is examined by estimating the dependence of snow cover extent on surface air temperature variations of the Northern Hemisphere for the last several decades. The relationship was evaluated with correlation analysis of the results of simulations with the ensemble of global climate models CMIP6 and the respected satellite and ERA5 reanalyzes data. The estimated snow cover extent sensitivity to the temperature changes for the last four decades (1979 2019) has been compared with that obtained for the last fifteen years (2005–2019). Seasonal features of the snow cover extent temperature relationship have been noted, particularly during the formation of snow cover in autumn. An increase in the absolute value of the sensitivity parameter of the snow cover extent to the surface air temperature changes is noted, with an overall statistically insignificant negative correlation for the last four decades.

How to cite: Parfenova, M., Arzhanov, M., and Mokhov, I. I.: Estimates of the dependence of snow cover extent on surface air temperature variations of the Northern Hemisphere for the last several decades, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12730, https://doi.org/10.5194/egusphere-egu22-12730, 2022.

Snow is an essential meteorological element and also an indicator of the fluctuating climate, resulting from change in the regime of winter precipitation and air temperature. Taking into account the recent tendencies of a warming climate, it is important to quantify these changes and their influence on snow amount and duration, which have significant consequences on several economic and environmental aspects.

Data from 20 stations with altitudes ranging from 50 up to 2925 m a. s. l. for the period 1961-2017 have been used for testing of the following winter characteristics: mean monthly air temperatures, days with snowfalls, days with rains and days with mixed precipitation and the corresponding monthly precipitation amounts. The air temperatures during snowfalls have been also examined. Except for the stations at highest altitudes (above 2000 m) no significant trends in precipitation quantities have been found. Decreasing trends of the days with snowfalls and the opposite for the days with rain and mixed precipitation have been detected. Almost all of stations show also increasing trends of the air temperatures both monthly as well as event based. Most of these trends are significant at 0.05 level. One secondary effect of this temperature rise is the enhancing of the severe wet snow events, which have been considerably intensified recently.

Another 35 stations with long data sets (1930/35 – 2019) have been used to evaluate the following snow characteristics for 3 main climatological reference periods – 1931-60, 1961-90 and 1991-2020: days with snow cover, averaged and maximal snow depth. No significant change for the maxima has been detected but the comparisons of other two variable depict significant differences.

This study has been funded by the National Science Fund in two separated projects under the contract numbers DM14/1 in the program for junior researchers and post-doc and KP-Austria-2 in the bilateral scientific program with Austria.

How to cite: Nikolov, D.: Assessment of the Recent Tendencies of the Winter Precipitations and the Snow Cover in Bulgaria, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10502, https://doi.org/10.5194/egusphere-egu22-10502, 2022.

Observations of the Earth’s atmosphere with sensors on a polar orbit employ a two-point calibration using a cold and a hot reference point. The hot reference point is an on-board blackbody target. The cold reference point is the deep-space view. In case of the moon being in the direction of the deep-space view, it provides an additional calibration target.

The infrared-relevant surface properties of the moon are well known. This allows to calculate the temperatures of the sun-illuminated parts for the different helio-centric distances (0.981 ... 1.019 au). The integration over the satellite-centric visible parts of the surface gives the total lunar flux at each HIRS observing epoch. The HIRS instruments have seen the moon under very different phase angles (typically between half moon and full moon) which makes it possible to monitor their performance over a very large range of flux values.

Analysis of calibration scans of High-resolution Infrared Radiation Sounder (HIRS) on various satellites, which are “contaminated” by the moon, helps to characterize and intercompare the performance of the sensor. We investigated both geometric and radiometric aspects. Among the former we focused on the instantaneous field of view of the various channels, which is sometimes misrepresented in documents and web pages, and compared the values from ground tests to those obtained in flight. We found that the field of view varies slightly with wavelength, and as well between short-wave and long-wave channels. Then we measured the disk-integrated flux at a variety of phase angles to provide observational constraints on a radiative model of the moon (Müller et al. 2020). Such a model is needed to compare directly the flux calibration of future HIRS-like sensors to those that were operational decades ago.

The HIRS instrument has been mounted on various satellites since 1975. By establishing the Moon as an absolute flux standard, it will become possible to clear climate data records from artificial, non-climatic effects that are common to all instruments of a certain type and that can therefore not be identified by postlaunch matchups. The long time series of HIRS is very valuable for climate research, in particular the search for trends in tropospheric humidity.

How to cite: Seibert, C. V., Burgdorf, M. J., Bühler, S. A., and Müller, T. G.: The Moon as a Tool for the Calibration of HIRS, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7192, https://doi.org/10.5194/egusphere-egu22-7192, 2022.