CL5.2.6

Climate studies, in particular those related to climate change, require long, high-quality, controlled data sets that are representative both spatially and temporally. Changing the conditions in which the measurements were taken, for example relocating the station, or a change in the frequency and time of measurements, or in the instruments used may result in an fractured time series. To avoid these problems, data errors and inhomogeneities are eliminated for Hungary and data gaps are filled in by using the MASH (Multiple Analysis of Series for Homogenization, Szentimrey) homogenization procedure. Homogenization of the data series raises the problem that how to homogenize long and short data series together within the same process, since the meteorological observation network was upgraded significantly in the last decades. It is possible to solve these problems with the method MASH due to its adequate mathematical principles for such purposes. The solution includes the synchronization of the common parts’ inhomogeneities within three (or more) different MASH processing of the three (or more) datasets with different lengths. Then, the homogenized station data series are interpolated to the whole area of Hungary, to a 0.1 degree regular grid. For this purpose, the MISH (Meteorological Interpolation based on Surface Homogenized Data Basis; Szentimrey and Bihari) program system is used. The MISH procedure was developed specifically for the interpolation of various meteorological elements. Hungarian time series of daily average temperature and precipitation sum for the period 1870-2020 were used in this study, thus providing the longest homogenized, gridded daily data sets in the region with up-to-date information already included.

Supported by the ÚNKP-20-3 New National Excellence Program of the Ministry for Innovation andTechnology from the source of the National Research, Development and Innovation Fund.

How to cite: Izsák, B., Lakatos, M., Pongrácz, R., Szentimrey, T., and Szentes, O.: Creation of a representative climatological database for Hungary from 1870 to 2020, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-872, https://doi.org/10.5194/egusphere-egu21-872, 2021.

Measurements of snow depth and snowfall can vary dramatically over small distances. However, it is not clear if this applies to all derived variables and is the same for all seasons.

To analyse the impacts of local-scale variations we compiled a unique set of parallel snow measurements for the Swiss Alps consisting of 30 station pairs with up to 50 years of parallel data. Station pairs are mostly located in the same villages or within close proximity (less than 3km horizontally and 100m vertically).

We calculated a series of snow climate indicators as derived values from the daily snow depth and snowfall measurements for various seasons (DJF, MA, and November-April). Snow climate indicators include average snow depth, max. snow depth, sum of new snow as well as snow onset and disappearance dates. Further, we quantified the return levels of a 10- and 50-year event for max. snow depth and the 3-day new snow sum to investigate the impact of local-scale variations on the estimation of extreme events, which are often used for prevention measures. We computed the relative differences for all these indicators at each station pair to demonstrate the potential uncertainty.
To address the local-scale variations of the measurement sites, we calculated the potential sunshine duration for each known location using GIS and a DEM. However, information from metadata (including the exact coordinates) has to be treated with caution as it can be correct, incomplete, incorrect or simply missing at all.

We found the largest differences for all indicators in spring and the smallest in DJF and Nov-Apr. Furthermore, there is hardly any difference between DJF and Nov-Apr. Surprisingly, median differences of snow disappearance dates are rather small (three days) and similar to the ones found for snow onset dates (two days).
We tried to explain the variations of snow disappearance dates with accumulated potential sunshine duration during March and April, however, no clear relationship could be found. This suggests that the potential sunshine duration is not an appropriate proxy for local-scale variations, mainly because vegetation, buildings and the like are not available in a DEM.

How to cite: Buchmann, M., Begert, M., Brönnimann, S., and Marty, C.: Local-scale uncertainty of seasonal mean and extreme values of in-situ snow depth and snow fall measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-918, https://doi.org/10.5194/egusphere-egu21-918, 2021.

Atmospheric water plays a key role for the Earth’s energy budget and temperature distribution via radiative effects (clouds and vapour) and latent heat transport. Thus, the distribution and transport of water vapour are closely linked to atmospheric dynamics on different spatiotemporal scales. In this context, global monitoring of the water vapour distribution is essential for numerical weather prediction, climate modelling, and a better understanding of climate feedbacks.

Total column water vapour (TCWV), or integrated water vapour, can be retrieved from satellite spectra in the visible “blue” spectral range (430-450nm) using Differential Optical Absorption Spectroscopy (DOAS). The UV-vis spectral range offers several advantages for monitoring the global water vapour distribution: for instance it allows for accurate, straightforward retrievals over ocean and land even under partly-cloudy conditions.

To investigate changes in the TCWV distribution from space, the Ozone Monitoring Instrument (OMI) on board NASA’s Aura satellite is particularly promising as it provides long-term measurements (late 2004-ongoing) with daily global coverage.

Here, we present a global analysis of trends of total column water vapour retrieved from multiple years of OMI observations (2005-2020). Furthermore, we put our results in context to trends of other climate data records and validate the OMI TCWV data by comparisons to additional reference data sets.

How to cite: Borger, C., Beirle, S., and Wagner, T.: Analysis of global trends of total column water vapour from multiple years of OMI observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1170, https://doi.org/10.5194/egusphere-egu21-1170, 2021.

In a context of climate change, near-surface wind speed (SWS) has received less attention than other variables such as air temperature or precipitation, despite its undeniable environmental and socio-economic impacts. Studies suggest a generalized decrease of SWS in continental surfaces located in the middle latitudes from 1979 to 2010, the so-called stilling phenomenon, and an increase in it thereafter, which has been termed reversal or recovery phenomenon. Recent studies indicate that multidecade oscillations produced by the internal variability of the climate system are responsible for both phenomena. The aim of this work is to advance in the evaluation of the multidecadal variability and causes of the stilling and reversal in the observed SWS, covering the complete 2010s decade and focusing on the Iberian Peninsula region (IP). More specifically, the particular objectives of this study are: (i) to determine for the first time the occurrence of the reversal phenomenon in the IP over the last decade(s), identifying its onset year and its magnitude; (ii) to deepen into the relation between atmospheric teleconnection indices and observed trends in SWS; and (iii) to link atmospheric circulation changes to observed SWS variability. For that purpose, homogenized series of mean wind speed and gusts will be used, as well as data from the ERA5 reanalysis (European Centre for Medium-Range Weather Forecasting). Three SWS parameters will be analysed: monthly mean SWS anomaly; monthly mean daily peak wind gust (DPWG) anomaly; and number of days in which the value of DPWG exceeds the 90th percentile of the series considered. Trends of these parameters will be calculated, as well as the correlation between them and the modes of variability that govern in the region: North Atlantic Oscillation (NAO), Mediterranean Oscillation (MO) and Western Mediterranean Oscillation. Finally, trends of these modes of variability and of other parameters dependent on atmospheric circulation (e.g., geostrophic wind) will be calculated to try to clarify the drivers of the observed changes in the SWS.

How to cite: Utrabo-Carazo, E., Azorin-Molina, C., Serrano, E., Aguilar, E., and Brunet, M.: Recent reversal of mean wind speed and gusts across the Iberian Peninsula and its relationship with modes of climate variability, 1961-2019, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2173, https://doi.org/10.5194/egusphere-egu21-2173, 2021.

Accurate, long-term time series of near-surface air temperature (AT) are the fundamental datasets on which the magnitude of anthropogenic climate change is scientifically and societally addressed. Across the ocean, these (near-surface) climate records use Sea Surface Temperature (SST) instead of Marine Air Temperature (MAT) and blend the SST and AT over land to create datasets. MAT has often been overlooked as a data choice as daytime MAT observations from ships are known to contain warm biases due to the storage of accumulated solar energy. Two recent MAT datasets, CLASSnmat (1881 – 2019) and UAHNMAT (1900 – 2018), both use night-time MAT observations only. Daytime MAT observations in the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) account for over half of the MAT observations in ICOADS, and this proportion increases further back in time (i.e. pre-1850s). If long-term MAT records over the ocean are to be extended, the use of daytime MAT is vital.

To adjust for the daytime MAT heating bias, and apply it to ICOADS, we present the application of a physics-based model, which accounts for the accumulated energy storage throughout the day. As the ‘true’ diurnal cycle of MAT over the ocean has not been, to-date, adequately quantified, our approach also removes the diurnal cycle from ICOADS observations and generates a night-time equivalent MAT for all observations. We fit this model to MAT observations from groups of ships in ICOADS that share similar heating biases and metadata characteristics. This enables us to use the empirically derived coefficients (representing the physical energy transfer terms of the heating model) obtained from the fit for use in removal of the heating bias and diurnal cycle from ship-based MAT observations throughout ICOADS which share similar characteristics (i.e. we can remove the diurnal cycle from a ship which only reports once daily at noon). This adjustment will create an MAT record of night-time-equivalent temperatures that will enable an extension of the marine surface AT record back into the 18^th century.

How to cite: Cropper, T., Kent, E., Berry, D., Cornes, R., and Recinos-Rivas, B.: Quantifying heating biases in marine air temperature observations, ~1790 - present, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2936, https://doi.org/10.5194/egusphere-egu21-2936, 2021.

Long-term trends of local winds such as sea breezes have been less addressed in climate research, despite their impacts on broad environmental and socioeconomic spheres, such as weather and climate, agriculture and hydrology, wind-power industry, air quality or even human health, among many others. In a warming climate, sea breezes could be affected by changes on air temperature, as these onshore winds are thermally-driven by gradients between the sea-land air, but also by ocean-atmosphere oscillations or changes in large-scale atmospheric circulation. In the last few decades, advances in wind trends studies evidenced a recovery in global wind stilling during the last 10 years, and differences in the sign-magnitude of wind speed trends were found at seasonal-scale, suggesting the hypothetic effect of the reinforcement of local wind circulations in the warm seasons.

In this study, we analyze for the first time the long-term trends, multidecadal variability and possible drivers of the sea-breeze speeds and gusts in Eastern Iberian Peninsula during the last 58 years (1961-2019), using homogenized wind speed and gusts data from 16 meteorological stations. To identify potential sea breeze episodes, we developed a robust automated method based on alternative criteria. Our results suggest a decoupling between the declining sea-breeze speeds and the strengthening of the maximum gusts for much of the 1961-2019 period at annual, seasonal and monthly scales, but differences based on locations were also found. Because sea breeze changes can be driven by multiple complex factors (i.e. land use changes, land-sea air temperature gradient, complex orography, etc.), the attribution of causes is challenging. To better understand the causes behind the opposite trends between sea-breeze speeds and gusts, we investigate the effect of e.g. the changes in large-scale atmospheric circulation or physical-local factors.

How to cite: Bedoya-Valestt, S., Azorin-Molina, C., Guijarro, J. A., and Sanchez-Morcillo, V. J.: Opposite trends of sea-breeze speeds and gusts in Eastern Iberian Peninsula, 1961-2019, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3411, https://doi.org/10.5194/egusphere-egu21-3411, 2021.

Here we present the progress of the first work package (WP1) 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); previously introduced in EGU2019-17792-1 and EGU2020-3491. In a global climate change, one of the major uncertainties on the causes driving the climate variability of winds (i.e., the “stilling” phenomenon and the recent “recovery” since the 2010s) is mainly due to short availability (i.e., since the 1960s) and low quality of observed wind records as stated by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC).

The WINDGUST is a joint initiative between the Swedish Meteorological and Hydrological Institute (SMHI) and the University of Gothenburg 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.

During 2020, we worked in WP1 to rescue historical wind speed series available in the old weather archives at SMHI for the 1920s-1930s. In the process we followed the “Guidelines on Best Practices for Climate Data Rescue” of the World Meteorological Organization. Our protocol consisted on: (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. We will report the advances and current status, challenges and experiences learned during the development of WP1. Until new year 2020/2021 eight out of thirteen selected stations spanning over the years 1925 to 1948 have been scanned and digitized by three staff members of SMHI during 1,660 manhours.

How to cite: Engström, E., Azorin-Molina, C., Wern, L., Hellström, S., Sturm, C., Joelsson, M., Zhang, G., Minola, L., Deng, K., and Chen, D.: Advances in the data rescue and digitization of historical wind speed observations in Sweden: the WINDGUST project, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5848, https://doi.org/10.5194/egusphere-egu21-5848, 2021.

Homogenization techniques and missing value reconstruction have grown in importance in climatology given their relevance in establishing coherent data records over which climate signals can be correctly attributed, discarding apparent changes depending on instrument inhomogeneities, e.g., change in instrumentation, location, time of measurement.

However, it is not generally possible to assess homogenized results directly, as true data values are not known. Thus, to validate homogenization techniques, artificially inhomogeneous datasets, also called benchmark datasets, are created from known homogeneous datasets. Results from their homogenization can be assessed and used to rank, evaluate and/or validate techniques used.

Considering temperature data, the aims of this work are: i) to determine which metrics (bias, absolute error, factor of exceedance, root mean squared error, and Pearson’s correlation coefficient) can be meaningfully used to validate the best-performing homogenization technique in a region; ii) to evaluate through a Pearson correlation analysis if homogenization techniques’ performance depends on physical features of a station (i.e., latitude, altitude and distance from the sea) or on the nature of the inhomogeneities (i.e., the number of break points and missing data).

With this aims, a southern Sweden temperature database with homogeneous, maximum and minimum temperature data from 100 ground stations over the period 1950-2005 has been used. Starting from these data, inhomogeneous datasets were created introducing up to 7 artificial breaks for each ground station and an average of 107 missing data. Then, 3 homogenization techniques were applied, ACMANT (Adapted Caussinus-Mestre Algorithm for Networks of Temperature series), and two versions of HOMER (HOMogenization software in R): the standard, automated setup mode (Standard-HOMER) and a manual setup developed and performed at the Swedish Meteorological and Hydrological Institute (SMHI-HOMER).

Results showed that root mean square error, absolute bias and factor of exceedance were the most useful metrics to evaluate improvements in the homogenized datasets: for instance, RMSE for both variables was reduced from an average of 0.71-0.89K (corrupted dataset) to 0.50-0.60K (Standard-HOMER), 0.51-0.61K (SMHI-HOMER) and 0.46-0.53K (ACMANT), respectively.

Globally, HOMER performed better regarding the factor of exceedance, while ACMANT outperformed it with regard to root mean square error and absolute error. Regardless of the technique used, the homogenization quality anti-correlated meaningfully to the number of breaks. Missing data did not seem to have an impact on HOMER, while it negatively affected ACMANT, because this method does not fill-in missing data in the same drastic way.

In general, the nature of the datasets had a more important role in yielding good homogenization results than associated physical parameters: only for minimum temperature, distance from the sea and altitude showed a weak but significant correlation with the factor of exceedance and the root mean square error.

This study has been performed within the INDECIS Project, that is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462).

How to cite: Caroletti, G. N., Caloiero, T., Joelsson, M., and Coscarelli, R.: A validation scheme for homogenization techniques on a Swedish temperature network using artificial inhomogeneities (1950-2005), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6680, https://doi.org/10.5194/egusphere-egu21-6680, 2021.

Monthly averages of statistical temperature variables (i.e. monthly averages of daily maximum, minimum, and mean temperatures) are homogenised for a large part of the Swedish observational network dataset from 1850 to 2020. Data from 573–587 weather stations (depending on variable) are coupled into 299–303 time series. The coupling of time series is partly performed automatically following a set of criteria of geographical proximity, altitude, proximity to coast line, time series overlap, and correlation of the data series.

The homogenisation of the data set is performed with the recently developed homogenisation tool Bart. Bart is a fully automatic modification of the homogenisation tool HOMER. Bart uses a set of input parameters to accept or reject potential homogeneity break points suggested by the different functions of HOMER. Bart performs correction and gap filling of the data series according to the accepted homogeneity break points. A rudimentary sensitivity test is performed to examine how sensitive the homogenisation is to the selection of the input parameters assumed most important and to find a optimal set up of these parameters. Other features in Bart include a novel procedure for the selection of reference time series to account for uneven data coverage, and parallel computing to reduce the computational time.

An important application of the homogenised data set is the calculation of the climate indicator of temperature. The climate indicator of temperature is the average annual mean temperatures of thirty-nine weather stations, carefully selected to represent the climate in Sweden over the last 170 years. The use of homogenised data gives a 1.8 °C (10 a)-1 greater warming than if raw data is used from 1860 to present, the period for which data coverage is sufficient.

How to cite: Joelsson, L. M. T., Sturm, C., Södling, J., and Engtsröm, E.: Homogenisation of monthly temperatures of the Swedish observational network 1850-2020, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7642, https://doi.org/10.5194/egusphere-egu21-7642, 2021.

While time series of meteorological measurements from land-based weather stations still represent one of the basic types of data employed in climate research, it not uncommon for these records to be incomplete, interrupted by periods of missing or otherwise compromised values. Such gaps typically need to be filled before a subsequent analysis can be performed, and records from other nearby measuring sites are frequently used for this purpose. In this presentation, results of central European daily temperatures estimation from other concurrent measurements by various statistical methods are showcased, with a particular emphasis on assessing potential benefits of application of nonlinear regression techniques. Using multi-decadal daily temperature series originating from a dense network of weather stations covering the territory of the Czech Republic, we show that while nonlinear regression does not always outperform its linear counterpart, it can substantially improve accuracy of temperature estimates for some target locations. The gain is shown to be especially prominent for sites exhibiting atypical behavior compared to their local geographic neighborhood, such as isolated mountain-based stations. In addition to regression-based restoration of compromised segments in the temperature records, use of this methodology for extending the temperature records beyond their original period of measurements is also discussed, as well as its potential for homogeneity testing.

How to cite: Mikšovský, J. and Štěpánek, P.: Mending and extending observational temperature series by linear and nonlinear regression, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9708, https://doi.org/10.5194/egusphere-egu21-9708, 2021.

Long observational records of land surface air temperature are vital to our understanding of climate variability and change, as well as for testing predictions of climatic trends. However, of the relatively few long observational records which exist, many contain inhomogeneities or biases resulting from changing instrumentation, station location/surroundings and/or observing practises. One of the most significant issues is the exposure bias. Prior to the widespread adoption of louvered Stevenson-type screens in the late-19^th century, various (often insufficient) approaches were used to shield thermometers. Each approach exposed the thermometer to differing levels of solar radiation, thus introducing inhomogeneities into individual station records and biases across regions, if similar approaches were used. Poorly shielded thermometers, for example, tended to read higher during the summer half year than those in Stevenson-type screens. Despite a number of studies documenting the presence of the exposure bias in early instrumental data, relatively few corrections have been applied or incorporated into global temperature datasets. This is largely due to the pervasive nature of the bias and a lack of observational metadata impeding bias identification or estimation of the appropriate correction.

In this work we explore a range of datasets to identify the potential for exposure bias in early instrumental data. We analyse historical data, corrections applied to homogenized datasets, as well as the small number of parallel measurements from differentially-shielded thermometers, in order to better define the characteristics of the exposure bias. These characteristics are then used to identify potential instances of exposure bias in early instrumental temperature records. We consider differences in seasonal anomalies, which is a key feature of many exposure biases, as well as their geographical variation (focussing mostly, but not solely, on Europe). We analyse how these behave at stations where it is known that exposure bias has already been adjusted for (though perhaps not completely) versus those that have not been. We also make comparisons with proxy reconstructions of temperature as an independent reference that is not susceptible to the same biases as the early instrumental data.

This work forms part of the NERC-funded GloSAT project which is developing a global surface air temperature dataset starting in 1781. The ultimate aim of the work reported here is to refine the error associated with these biases, in order to improve the representation of the exposure bias in error models used for gridded instrumental temperature datasets.

How to cite: Wallis, E., Osborn, T., Taylor, M., Lister, D., and Jones, P.: Identifying exposure biases in early instrumental data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9944, https://doi.org/10.5194/egusphere-egu21-9944, 2021.

Despite the great development of more accurate and sophisticated wind-measurement instruments, cup anemometers remain today the most widely used and popular anemometer in measuring wind speeds at meteorological stations and wind farms. In addition, almost all the available long-term wind speed time series across the world have been recorded by cup anemometers. Studying the response of cup anemometers and errors associated with their measurements, and also how the cup anemometer measurements are comparable with modern sensors, is of great importance, and can affect meteorological and climatological studies of long-term wind speed trends, and also wind energy estimations. 

Although cup anemometers are known for being robust and reliable, long-term field measurements of wind speeds by these wind sensors can be associated with errors and uncertainties affecting the quality of recorded data and subsequent analyses. When analysing wind speed data, it is essential to understand these errors and compensate for them and distinguish them from the real climate signals.

A comprehensive review on various aspects of anemometry, particularly cup anemometers, is presented in this paper. This review includes the different designs and theory developed from the invention of this wind-speed measuring system to very recent works, the response characteristics of anemometers, anemometer calibration procedures, field and wind-tunnel experiments on anemometers, etc. In addition, the different sources of errors and uncertainties are introduced and methods, including statistical, mathematical and experimental approaches, proposed to quantify and remedy the effects of these errors are presented. Lastly, several comparative studies that investigated the response characteristics of different types of cup anemometers and other anemometers are reviewed.

How to cite: S. Pirooz, A. A., Flay, R. G. J., Turner, R., and Azorin-Molina, C.: State of the Art in Cup Anemometry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10399, https://doi.org/10.5194/egusphere-egu21-10399, 2021.

The study aims to outline the wind-tunnel setup and testing procedure in a currently ongoing project designed to compare the response characteristics of various anemometers widely used across the world during different historic periods. The variations of several parameters, including gust and peak factors, and turbulence intensities measured by the anemometers as a function of the averaging duration (i.e. gust duration), and turbulence intensity and wind speed of the incoming airflow will be investigated.

The outputs of the study will play an essential role in the understanding of historical wind data, and how to account for the changes in anemometers and gust duration in order to eliminate the breakpoints and shifts in wind speed time series, and to produce homogenised wind records.

The tests will be carried out in the boundary-layer wind tunnel at the University of Auckland, New Zealand. This boundary-layer wind tunnel is a closed-circuit wind tunnel with two fans, a maximum wind speed of 20 m s‒1, and a large cross-section of 3.6 m × 2.5 m (width × height), which makes the tunnel suitable for the proposed experiment. The calibration of the anemometers will be conducted in the empty wind tunnel, which has a relatively low turbulence intensity of about 1% – 1.5%. The calibration is carried out according to the recommendations of ASTM D5096-02 (2017) and using a 3D Cobra wind sensor as the reference.

Turbulence-inducing elements, such as grids and blocks, are used in the wind tunnel to replicate the random fluctuations of wind in nature, such that high turbulence intensities broadly replicate turbulence in urban areas, and low turbulence intensities are similar to those of exposed open-country and sea surface terrains.

Details of the calibration and testing procedures as well as analysing the measured data in the wind tunnel will be presented. In addition, the advantages and limitations of wind-tunnel experiments in studying anemometers compared with theoretical approaches and full-scale field measurements will be discussed. 

How to cite: S. Pirooz, A. A., Flay, R. G. J., Turner, R., and Azorin-Molina, C.: Wind-Tunnel Setup for Investigating the Response Characteristics of Anemometers, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10422, https://doi.org/10.5194/egusphere-egu21-10422, 2021.

Surface observations are usually too few and far between to properly assess multidecadal variations at the local scale and characterize historical local extreme events at the same time. A data assimilation scheme has been recently presented by Devers et al. (2020) to assimilate daily observations of temperature and precipitation into downscaled reconstructions from a global extended reanalysis through an Ensemble Kalman fitting approach and derive high-resolution fields. Recent studies also showed that assimilating observations at high temporal resolution does not guarantee correct multidecadal variations. This work thus proposes (1) to apply this scheme over France and over the 1871–2012 period based on the SCOPE Climate reconstructions background dataset (Caillouet et al., 2019) and all available daily historical surface observations of temperature and precipitation, (2) to develop an assimilation scheme at the yearly time scale and to apply it over the same period and lastly, (3) to derive the FYRE Climate reanalysis, a 25-member ensemble hybrid dataset resulting from the daily and yearly assimilation schemes, spanning the whole 1871–2012 period at a daily and 8-km resolution over France. Assimilating daily observations only allows reconstructing accurately daily characteristics, but fails in reproducing robust multidecadal variations when compared to independent datasets. Compared to reference homogenized series, FYRE Climate clearly performs better than the SCOPE Climate background in terms of bias, error, and correlation, but also better than the Safran surface reanalysis over France (Vidal et al., 2010) available from 1958 onward only. FYRE Climate also succeeds in reconstructing both local extreme events and multidecadal variability. It is made available from http://doi.org/10.5281/zenodo.4005573 (precipitation) and http://doi.org/10.5281/zenodo.4006472 (temperature). Further details on FYRE Climate can be found in Devers et al. (2021).

Caillouet, L., Vidal, J.-P., Sauquet, E., Graff, B., Soubeyroux, J.-M. (2021) SCOPE Climate: a 142-year daily high-resolution ensemble meteorological reconstruction dataset over France. Earth System Science Data, 11, 241-260. https://doi.org/10.5194/essd-11-241-2019

Devers, A., Vidal, J.-P., Lauvernet, C., Graff, B., Vannier, O. (2020) A framework for high-resolution meteorological surface reanalysis through offline data assimilation in an ensemble of downscaled reconstructions. Quarterly Journal of the Royal Meteorological Society, 2020, 146, 153-17. https://doi.org/10.1002/qj.3663

Devers, A., Vidal, J.-P., Lauvernet, C., Vannier, O. (2021) FYRE Climate: A high-resolution reanalysis of daily precipitation and temperature in France from 1871 to 2012. Climate of the Past Discussions, in review, https://doi.org/10.5194/cp-2020-156

Vidal, J.-P., Martin, E., Franchistéguy, L., Baillon, M., Soubeyroux, J.-M. (2010) A 50-year high-resolution atmospheric reanalysis over France with the Safran system. International Journal of Climatology, 30, 1627-1644. https://doi.org/10.1002/joc.2003

How to cite: Vidal, J.-P., Devers, A., Lauvernet, C., and Vannier, O.: Multidecadal assessment of the FYRE Climate daily high-resolution surface reanalysis over France (1871-2012), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10859, https://doi.org/10.5194/egusphere-egu21-10859, 2021.

We use a dataset with observations of daily precipitation from 55 homogeneity tested stations in Norway over the period 1900-2019 available from MET-Norway. These observations show that precipitation in Norway has increased monotonically by 19% since 1900. Notably, over half of the overall increase was recorded within the decade of 1980-1990. To examine possible mechanisms behind the precipitation increase, we use a diagnostic model to separate the effects of changes in vertical velocity, temperature and relative humidity. We use vertical velocity, near-surface temperature and relative humidity from two reanalysis products, ECMWF’s ERA-20C and NOAA’s 20th Century Reanalysis. The model-based precipitation estimates capture the interannual variability as well as the long-term trend, but the absolute magnitude of precipitation is underestimated. Within our model, we find that the variability in vertical velocity chiefly determines the interannual variability and long-term trends. In fact, the trend in vertical velocities contributes with more than 75% of the total modelled trend in precipitation between 1900-2019, and more than 60% of the anomalies between 1980-1990. However, over the last decades (1979 to 2019), changes in temperature and relative humidity are the main contributors to the trend. Thus, different physical processes shape the trend at different times. We hypothesize that the strong precipitation increase in the 1980’s is linked to an unusual high number of low pressure systems reaching Norway from the North-Atlantic. In recent decades, direct effects of global warming (rising temperatures and hence increased water vapour content) are thought to be the main cause of the positive trend in precipitation over Norway. 

How to cite: Konstali, K. and Sorteberg, A.: Mechanisms behind the precipitation increase in Norway, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15496, https://doi.org/10.5194/egusphere-egu21-15496, 2021.

The new HadCRUT5 data set combines meteorological station air temperature records with sea-surface temperature measurements in a data set of near-surface temperature anomalies from the year 1850 to present. Major developments in HadCRUT5 include: updates to underpinning observation data holdings; use of an updated assessment of the impacts of changing marine measurement methods; and adoption of a statistical gridding method to extend estimates into sparsely observed regions of the globe, such as the Arctic. The data are presented as a 200-member ensemble that spans the assessed uncertainty associated with adjustments for long-term observational biases, observing platform measurement errors and the interaction of observational sampling with gridding methods. The impacts of methodological changes in HadCRUT5 on diagnostics of the global climate will be discussed and compared to results derived from other state-of-the-art global data sets.

How to cite: Morice, C., Kennedy, J., Rayner, N., Winn, J., Hogan, E., Killick, R., Dunn, R., Osborn, T., Jones, P., and Simpson, I.: Advances in the HadCRUT5 record of global near-surface temperatures since 1850, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15931, https://doi.org/10.5194/egusphere-egu21-15931, 2021.

Homogenization is an important step to improve the quality of long-term observational data sets and estimate climatic trends. In this work, we use the GNSSseg/GNSSfast segmentation packages that were developed by Quarello et al., 2020, for the detection of abrupt changes in the mean of Integrated Water Vapour (IWV) data derived from GNSS measurements. The method works on the difference of the IWV time series (GNSS – reference) in order to cancel out the common climatic variations and enhance the discontinuities due to the inhomogeneities in the GNSS series. This segmentation method accounts for changes in the variance on fixed intervals (monthly) and a periodic bias (annual) due to representativeness differences between GNSS and the reference (in our case, a global atmospheric reanalysis). 
The goal of this study is to analyze the sensitivity of the segmentation method to the data properties, particularly the GNSS data processing method. Two reprocessed GNSS solutions are considered: IGS repro1, covering the period 1995-2010, and CODE REPRO2015 + OPER, covering the period 1994-2018. Next, the impact of the length of time series and missing data are investigated. Finally, the use of two different reference series is considered (ERA-Interim and ERA5 reanalyses).
The segmentation results are screened for outliers (multiple detections occurring within a distance of 80 days) and validated with respect to known equipment changes (from GNSS metadata). The impact of the data properties is analyzed by comparing the number and position of detected change-points and the fraction of validated change-points. The influence of the variance of the IWV difference series and the magnitude of the periodic bias is examined. Finally, the results are compared in terms of estimated linear trends taking the detected change-points into account.
From the multiple comparisons, we found that about 30 % of change points are similar when the GNSS processing method changed, while 60 % are similar when the CODE series is shortened to match the length of the repro1 series. These tests highlight that the segmentation results are processing-dependent and are affected by the length of the series. The impact of the data properties on the IWV trends and associated uncertainties are also quantified. Besides, it is important to note that the best segmentation result is found when the ERA5 reanalysis is used as a reference.

How to cite: Nguyen, K. N., Quarello, A., Bock, O., and Lebarbier, E.: Sensitivity of segmentation of GNSS IWV time series and trend estimates to data properties, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16099, https://doi.org/10.5194/egusphere-egu21-16099, 2021.