Displays

Global data records of cloud properties are an important part for the analysis of the Earth's climate system and its variability. One of the few sources facilitating such records are the measurements of the satellite-based Advanced Very High Resolution Radiometer (AVHRR) sensor that provides spatially homogeneous and high resolved information in multiple spectral bands. This information can be used to retrieve global cloud properties covering multiple decades, as, for example, composed as part of the CM SAF Cloud, Albedo, Radiation data record based on AVHRR (CLARA) series.

In this presentation we introduce the edition 2.1 (CLARA-A2.1) of this record series, which is the temporally extended version of CLARA-A2. This extension includes three and a half more years at the end of the data record, which now covers the time period January 1982 to June 2019 (37.5 years). CLARA-A2.1 includes a comprehensive set of cloud parameters: fractional cloud cover, cloud top products, cloud thermodynamic phase and cloud physical properties, such as cloud optical thickness, particle effective radius and cloud water path. Cloud products are available as daily and monthly averages and histograms (Level 3) on a regular 0.25°×0.25° global grid and as daily, global composite products (Level 2b) with a spatial resolution of 0.05°×0.05°. Time series analyses of the CLARA-A2.1 cloud products show the homogeneity and stability of the extension.

In addition to the general characteristics of the CLARA-A2.1 record, we will summarize the results of the thorough evaluation efforts that were conducted by validation against reference observations (e.g. SYNOP, DARDAR, CALIOP) and by comparisons to similar well established data records (e.g. Patmos-X, ISCCP-H and MODIS C6.1). CLARA-A2.1 cloud products show generally a very good agreement with all the compared data sets and fulfil CM SAF's accuracy, precision and decadal stability requirements. As an additional aspect, we will touch upon the CLARA Interim Climate Data Record (ICDR) concept that will soon be used for extending CLARA-A2.1 in near-real-time mode.

How to cite: Solodovnik, I., Stein, D., Meirink, J. F., Karlsson, K.-G., and Stengel, M.: Extending the CM SAF global satellite-based climate data record of cloud properties, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20229, https://doi.org/10.5194/egusphere-egu2020-20229, 2020.

Determining the rates, mechanisms and geographic variability of sea-level change is a priority science question for the next decade of ocean research. To address these research priorities, the HOLocene SEA-level variability (HOLSEA) working group is developing the first standardized global synthesis of Holocene relative sea-level data to: (1) estimate the magnitudes and rates of global mean sea-level change during the Holocene; and (2) identify trends in spatial variability and decipher the processes responsible for geographic differences in relative sea-level change.

Here we present the efforts of the working group to compile the database, which includes over 12,000 sea-level index points and limiting data from a range of different indicators across seven continents from the Last Glacial Maximum to present. We follow a standard protocol that incorporates full consideration of vertical and temporal uncertainty for each sea-level index point, including uncertainties associated with the relationship of each indicator to past sea-level and the methods used to date each indicator. We highlight important challenges overcome to aggregate the standardized global synthesis, and discuss those that still remain. Finally. we apply a spatio-temporal empirical hierarchical statistical model to the database to estimate global sea-level variability and spatial patterns in relative sea level and its rates of change, and consider their driving mechanisms. Long-term, this effort will enhance predictions of 21st century sea-level rise, and provide a vital contribution to the assessment of natural hazards with respect to sea-level rise.

How to cite: Khan, N., Ashe, E., Kopp, R., and Horton, B. and the HOLSEA working group: Inception of a global atlas of Holocene sea levels, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12489, https://doi.org/10.5194/egusphere-egu2020-12489, 2020.

The Radio Occultation (RO) technique is based on measurements of phase shifts of GNSS radio waves by an instrument onboard a low-Earth orbiting satellite. The processing of the measurements yields the refractive index of the Earth’s atmosphere, from which the temperature, pressure, and humidity fields can be retrieved. It is a limb-sounding technique, with a high vertical resolution, and with observational information retrieved from near-surface to the upper stratosphere. Numerous studies have demonstrated the accuracy of GNSS Radio Occultation (RO) data, and their usefulness as a stable climate reference. Homogeneity of the data records are obtained by reprocessing of the data using uniform processing software and a priori data throughout the length of the climate record. We here present results from a validation of the 17-year ROM SAF RO Climate Data Record (CDR), based on a new reprocessing of Metop, CHAMP, GRACE, and COSMIC data using excess-phase and amplitude data from EUMETSAT (the Metop mission) and UCAR/CDAAC (the CHAMP, GRACE, COSMIC, and Metop missions).

A central issue for the generation of RO-based CDRs is whether data from different satellite missions can be combined to form long time series of multi-mission data. This presentation explores the consistency of gridded monthly-mean data from different RO missions through comparison with ERA-Interim reanalysis data, and through a study of mission differences during mission overlap periods. It is shown that within a core region from the upper troposphere to the middle stratosphere, roughly 8 to 35-40 kilometers (depending on latitude and geophysical variable), there is a high consistency amongst the RO missions, allowing for the construction of long-term stable data sets for use in climate studies and climate monitoring.

How to cite: Lauritsen, K. B., Gleisner, H., Nielsen, J. K., and Syndergaard, S.: The 17-year ROM SAF radio occultation climate data record, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20563, https://doi.org/10.5194/egusphere-egu2020-20563, 2020.

The time when a climate signal permanently exceeds its natural variability is called time of emergence (ToE). ToE shall serve policy makers as an indication of when to expect the climate and the environment to undergo significant changes. Identifying ToE, however, is challenging, primarily because of the lack of a standard to quantify exceedance, which, in turn, requires a definition of a natural background variability. Existing approaches often rely on a high level of arbitrary parameter values, e.g. selecting a specific number of times the standard deviation of a reference period as natural variability, selecting specific moving window widths to smooth a signal, or the arbitrary choice of a significance level for a statistical test. Such choices of course have a large influence on the final results and would in theory require exhaustive sensitivity analyses and discussion.

In order to minimize the level of parameterization for ToE estimates, we have developed a novel approach. It assesses exceedance of a climate signal by measuring distances between probability density functions (PDF) of the signal at different times (reference vs. target periods), using the Hellinger distance (HD) metric. The HD metric can be understood as the geometrical overlap of the respective PDFs and we adjusted it to describe the emergence as dissimilarity (0%-100%). In order to derive the PDFs, we use a kernel density estimator (KDE). This, however, introduces the KDE-bandwidth hyperparameter, which determines how smoothly the PDF is generated. Together with the choices for the length of the target and reference periods, and the end of the reference period, a set of less numerous but unavoidable hyperparameters are present that affect the outcome of ToE estimates. We present an extensive sensitivity analysis and highlight strengths and shortcomings of our approach with respect to the frequently used Kolmogorov–Smirnov (KS) test, and the used distance metric within it. We consider a set of synthetic datasets that show similar features as climate model temperature time series. In these datasets, we control the onset of change, variability levels, or trends in the data. Results show that our approach can more precisely identify the changes as compared to the KS-based approach. In particular when the changes in the signal are of low amplitude and sample sizes are small, our approach performs superior. The sensitivity of our approach in the considered tests to varying KDE-bandwidths is less than 5%. The approach has so far been applied on time-series of annual temperature and precipitation. Changes in the distribution of various other climate variables are potential fields of application. Associated challenges with non normally-distributed data, for example high temporal resolution precipitation data, are discussed.

How to cite: Pohl, E., Grenier, C., and Vrac, M.: Evaluation of a novel non-parametric approach to identify Time of Emergence (ToE) of climate signals , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17588, https://doi.org/10.5194/egusphere-egu2020-17588, 2020.

Despite the importance of the Yellow River to China, regional climate change over the middle reach of the Yellow River Basin (YRB) is much less assessed than other regions. This work focuses on historical and future spatiotemporal changes in mean and extreme temperature and precipitation over the upper and middle reaches of the YRB. The future mean and extreme climates for near-term (2021−2040), mid-term (2041−2060), and far-term (2081−2100) in relation to the historical (1976−2005) period are investigated based on the latest REgional MOdel (REMO). REMO driven by three CMIP5 GCMs under historical and future (RCP 2.6 and 8.5) forcings, following the Coordinated Regional Climate Downscaling Experiment (CORDEX) protocol for the East Asia domain at a spatial resolution of 0.22°, are provided by the Climate Service Center Germany (GERICS). The results show that REMO reproduces the historical mean climate state and six selected climate extreme indices reasonably well, although cold and wet biases still exist. For the far-term, mean temperature rise in winter is most remarkable, with an average of 5.9 °C under RCP8.5. As expected, future temperatures of the warmest day and the coldest night would increase and the number of frost days (FD) would decline considerably. Further, high altitude region would experience a higher mean temperature increase than low altitude region, which is likely caused by the snow-albedo feedback. The decline in FD would increase with elevation, especially under a higher emission. A substantial precipitation increase (32%) would occur in winter under RCP8.5 for the far-term period. Precipitation projections in summer and autumn vary spatially, decrease under RCP2.6 whereas increase under RCP8.5 in the whole YRB for the far-term period. Meanwhile, interannual variability of mean precipitation is expected to increase over most parts of the YRB. Future precipitation extremes, such as the daily intensity and maximum five-day precipitation are projected to increase, and the number of consecutive dry days would decline by the end of the 21st century under the RCP8.5 scenario. The results highlight that the pronounced warming in the high-altitude region together with more intense rainfall extremes could lead to increased future flood risk in the middle and lower reaches of the YRB if the high GHGs emission pathway will be followed.

How to cite: Wang, X., Chen, D., Pang, G., and Yang, M.: Historical and future climates over the Upper and Middle Reaches of the Yellow River Basin revealed by a regional climate model in CORDEX, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18557, https://doi.org/10.5194/egusphere-egu2020-18557, 2020.

Many palaeoclimate and palaeoenvironmental records have low sampling resolution, few age constraints, and are based on climate proxies that may reflect an uncertain mixture of local and regional influences. Objective spatial and temporal comparisons of multiple palaeo records and identification of regional scale trends can therefore be difficult.. Low resolution palaeo records are often excluded from regional syntheses due to low dating or sample density, however such records can contribute meaningful information to regional syntheses if their inherent uncertainties are considered. Explicitly incorporating the age uncertainties allows for a more robust interpretation of synchronous periods of change.

Here we discuss the use of a method for determining the timing of palaeoclimate events using multiple time-uncertain palaeo records. This method allows for the incorporation of a variety of records, regardless of proxy type or sampling resolution. We demonstrate the power of this method using a case study from the SHeMax project (Southern Hemisphere Last Glacial Maximum project), aiming to understanding the nature and timing of the LGM in Australia. Further expansion of our analyses will allow the incorporation of both continuous and discontinuous climate archives, interrogation of spatial and temporal synchronicity and coherency of climate changes across broad regions.

An extended LGM period, characterised by multiple distinct stages that varied regionally and in its timing and evolution, has been suggested to have occurred in Australia; however this hypothesis has yet to be tested objectively. Comparisons during this time period have been hampered by the limited number, low resolution, and age-uncertainty of terrestrial archives. In order to gain a greater understanding of the spatial and temporal patterns of climate change during MIS2, we have compiled all available proxy records of climate and envrionmental variability from across Australia for the period 35 – 15 ka (n=40). Analysing age-uncertainty in time series requires an approach that treats all data consistently. For each record, a revised age-depth model was developed using the SH13 calibration curve and Bayesian age-depth modelling techniques. Complex records (e.g. pollen records) were reduced to Principal Curves, in order to provide a one-dimensional summary of patterns of change in each data-set. Monte-Carlo change point analysis was then used to identify the timing of major changes within each record, along with the uncertainty around each change point. We assess the spatial heterogeneity of the timing of the major climatic changes during the 35 – 15 ka period and determine the probability of common timing of change across Australia. We find the onset of an extended period of relative aridity in Australia occurred synchronously (within uncertainty) at ca. 28 ka. Dry and cool conditions persisted at most sites until ca. 15 – 18 ka, with the onset of more humid conditions occurring along a latitudinal gradient. The occurrence of a millennial scale episode of increased moisture balance between ca. 25 – 21 ka is evident only in the most highly resolved records.

How to cite: Cadd, H., Petherick, L., Tyler, J., Herbert, A., Cohen, T., Sniderman, K., Schulmeister, J., Barrows, T., and Knight, J.: A continental perspective on the timing of the last glacial maximum in Australia - utilising methods for integrating multiple time-uncertain, variable resolution proxy records., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-959, https://doi.org/10.5194/egusphere-egu2020-959, 2020.

Reconstructing global patterns of past climate change requires large-scale networks of paleoclimatic archives. Generating paleoclimatic networks relies on precise synchronization of individual records with robust age control. The detailed age constrains of continuous varved lake sediments and the good preservation of isochrones from supra-regional extreme events make these records ideal for constructing large scale continental paleoclimatic networks. Yet, a global synthesis of varved lake archives is missing.

Here we present the VARved sediments DAtabase 1.0 (VARDA 1.0), the first global data compilation for varve chronologies and associated palaeoclimatic proxy records. VARDA 1.0 uses a connected data model provided by a state-of-the-art graph database, enabling custom generations of synchronized paleoclimatic networks. We report on compilation strategies for the identification of varved lakes and assimilation of high-resolution chronologies. Existing chronologies have been re-assessed and harmonized using a novel approach that infers information on sedimentation rates enclosed in varve thickness records. This information provides detailed information on the priors required for Bayesian age-depth modelling and strongly improves these results. Additionally, a synthesis of tephra layers from volcanic eruptions provides supra-regional isochrones for synchronizing even distant varved lake records. The current version (VARDA 1.0) comprises 261 datasets from 95 varved lake archives, including chronological information from ¹⁴C dating and varve thickness measurements, but also palaeoclimatological proxy data. We further explore potential applications of such networks in paleoclimatic studies, such as identifying leads and lags of regional climate change, large-scale model-data comparisons or differentiated proxy responses between archives. The VARDA graph-database and user interface can be accessed online at https://varve.gfz-potsdam.de.

How to cite: Ramisch, A., Brauser, A., Dorn, M., Blanchet, C., Brademann, B., Köppl, M., Mingram, J., Neugebauer, I., Nowaczyk, N., Ott, F., Pinkerneil, S., Plessen, B., Schwab, M. J., Tjallingii, R., and Brauer, A.: Constructing paleoclimate networks from annually laminated lake sediments – the VARDA database, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9133, https://doi.org/10.5194/egusphere-egu2020-9133, 2020.

Upper-air radiosounding observations of temperature, relative humidity and wind are a of the primary data source for climate studies. Nevertheless, historical radiosounding time series are affected by several systematic uncertainties due to change in the measurement sensors. 

In the frame of the Copernicus Climate Change Service (C3S), a novel approach, named RHARM (Radiosounding HARMonization), has been developed to homogenize temperature, humidity and wind radiosounding profile time series available from the the Integrated Global Radiosonde Archive (IGRA) and provide an estimation of the total uncertainty for each single profile. RHARM is an alternative to the few existing approaches.

RHARM is applied to daily (0000 and 1200 UTC) radiosonde data on 16 standard pressure levels (10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000 hPa) for the IGRA data from 1978 onward. Relative humidity (RH) adjustments is limited to 300 hPa owing to pervasive sensor performance issues at greater altitudes. The bias adjustments are estimated at mandatory levels only but they are also interpolated to the significant levels reported within each individual ascent profile.

This paper discusses the comparison of the monthly anomalies and trends estimated at different latitudes and pressure levels for ERA5, IGRA and RHARM. Trends are esitmated using a robust least absolute deviation method. ERA5 is the latest climate reanalysis produced by ECMWF providing hourly data on on regular latitude-longitude grids at 0.25° x 0.25° resolution, with atmospheric parameters on 137 pressure levels (available on https://cds.climate.copernicus.eu).

Differences in the comparisons among the three datasets will be discussed along with the analysis of the trends observed in the considered time series. To evaluate its homogeneity and stability, the uncertainty estimation provided in RHARM will be also compared with O-B field obtained using the to ECWMF operational forecast model as the background.

How to cite: Madonna, F., Sy, S., Amato, F., Franco, L., Gagliardi, S., Marra, F., Proto, M., Rosoldi, M., and Tramutola, E.: Comparison of anomalies and trends in IGRA, RHARM, and ERA5 temperature, humidity and wind time series, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9239, https://doi.org/10.5194/egusphere-egu2020-9239, 2020.

The homogenisation software HOMER has proven to be a reliable tool for the homogenisation of temperature and precipitation observation series. The homogenisation with HOMER requires the interaction of an operator, which makes the procedure time consuming, sensitive to arbitrary choices or error by the operator, and difficult to reproduce.

HOMER uses three methods for the detection of homogeneity breaks: A pairwise comparison method (PRODIGE) for break detection on annual, seasonal or monthly basis, a two-factor model for joint-detection (ANOVA), and an ACMANT style method for the detection of homogeneity breaks in the amplitude of the seasonal cycle. The operator reviews the results of the different methods and confirms or rejects suggested breaks. HOMER can also be run in a automatic mode, where all suggested breaks from the joint-detection and the ACMANT style detection methods are confirmed and all suggested breaks from the pairwise method are rejected. Note, that also the automatic mode of HOMER requires some interactions, such that nor this mode is suitable for batch processing. 

The homogenisation with HOMER of temperature observations at SMHI has previously been performed with a set of criteria for the confirmation of a suggested homogeneity break. These criteria has been implemented in the HOMER (interactive mode) source code by assigning the break signals from the methods different weights and applying a threshold for the sum of the weighted break signals each year for the confirmation of a break year. The user can chose to adjust these threshold and weights to fit their needs. All user interactions are removed to enable batch processing.

The new automatic mode of HOMER are applied on the synthetic benchmark data set INDECIS and to Swedish observational data from 80 coupled weather stations over the time period from 1860 to 2018. For the INDECIS data set, the positions of the breaks are known and a corresponding data set without breaks are available. Current default settings and settings optimised to minimise the deviation of the homogenised data from the INDECIS clean data are used. The results are compared with results of the interactive and standard automatic mode of HOMER, and other state-of-art homogenisation tools along with known potential homogeneity breaks from meta data.

How to cite: Joelsson, M., Slättberg, N., Carnebring, A., Sturm, C., and Engström, E.: Automation of the interactive mode of the homogenisation software HOMER for climatological applications , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1397, https://doi.org/10.5194/egusphere-egu2020-1397, 2020.

Before using climatological time series in research studies, it is necessary to perform their quality control and homogenization in order to remove possible artefacts (inhomogeneities) usually present in the raw data sets. In the vast majority of cases, the homogenization procedure allows to improve the consistency of the data, which then can be verified by means of the statistical comparison of the raw and homogenized time series. However, a new question then arises: how far are the homogenized data from the true climate signal or, in other words, what errors could still be present in homogenized data?

The main objective of our work is to estimate the uncertainty produced by the adjustment algorithm of the widely used Climatol homogenization software when homogenizing daily time series of the additive climate variables. We focused our efforts on the minimum and maximum air temperature. In order to achieve our goal we used a benchmark data set created by the INDECIS^* project. The benchmark contains clean data, extracted from an output of the Royal Netherlands Meteorological Institute Regional Atmospheric Climate Model (version 2) driven by Hadley Global Environment Model 2 - Earth System, and inhomogeneous data, created by introducing realistic breaks and errors.

The statistical evaluation of discrepancies between the homogenized (by means of Climatol with predefined break points) and clean data sets was performed using both a set of standard parameters and a metrics introduced in our work. All metrics used clearly identifies the main features of errors (systematic and random) present in the homogenized time series. We calculated the metrics for every time series (only over adjusted segments) as well as their averaged values as measures of uncertainties in the whole data set.

In order to determine how the two key parameters of the raw data collection, namely the length of time series and station density, influence the calculated measures of the adjustment error we gradually decreased the length of the period and number of stations in the area under study. The total number of cases considered was 56, including 7 time periods (1950-2005, 1954-2005, …, 1974-2005) and 8 different quantities of stations (100, 90, …, 30). Additionally, in order to find out how stable are the calculated metrics for each of the 56 cases and determine their confidence intervals we performed 100 random permutations in the introduced inhomogeneity time series and repeated our calculations With that the total number of homogenization exercises performed was 5600 for each of two climate variables.

Lastly, the calculated metrics were compared with the corresponding values, obtained for raw time series. The comparison showed some substantial improvement of the metric values after homogenization in each of the 56 cases considered (for the both variables).

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^*INDECIS is a 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). The work has been partially supported by the Ministry of Education and Science of Kazakhstan (Grant BR05236454) and Nazarbayev University (Grant 090118FD5345).

How to cite: Skrynyk, O., Aguilar, E., Guijarro, J. A., and Bubin, S.: Uncertainty of Climatol adjustment algorithm for daily time series of additive climate variables, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5365, https://doi.org/10.5194/egusphere-egu2020-5365, 2020.

Driven by the twenty-century surface air temperature rise, extreme wind events could change in their frequency and magnitude of occurrence, with drastic impacts on human and ecosystems. As a matter of fact, windstorms and extreme wind conditions contribute to more than half of the economic losses associated with natural disasters in Europe. Across Scandinavia, the occurrence of wind gust events can affect aviation security, as well as damage buildings and forests, representing severe hazards to people, properties and transport. Comprehensive extreme wind datasets and analysis can help improving our understanding of these changes and help the society to cope with these changes. Unfortunately, due to the difficulty in measuring wind gust and the lack of homogeneous and continuous datasets across Sweden, it is challenging to assess and attribute their changes. Global reanalysis products represent a potential tool for assessing changes and impact of extreme winds, only if their ability in representing observed near-surface wind statistics can be demonstrated.

In this study the new ERA5 reanalysis product has been compared with hourly near-surface wind speed and gust observations across Sweden for 2013-2017. We found that ERA5 shows better agreement with both mean wind speed and gust measurements compared to the previous ERA-Interim reanalysis dataset. Especially across coastal regions, ERA5 has a closer agreement with observed climate statistics. However, significant discrepancies are still found for inland and high-altitude regions. Therefore, the gust parametrization used in ERA5 is further analyzed to better understand if the adopted gust formulation matches the physical processes behind the gust occurrence. Finally, an improved formulation of the gust parametrization is developed across Sweden and further tested for Norway, which is characterized by more complex topography.

How to cite: Minola, L., Zhang, F., Azorin-Molina, C., Safaei Pirooz, A. A., Flay, R., Hersbach, H., and Chen, D.: Near-surface mean and gust wind speed in ERA5 across Sweden: towards an improved gust parametrization, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4691, https://doi.org/10.5194/egusphere-egu2020-4691, 2020.

The changes in near-surface (10-m height) wind speed have direct impacts on human society, such as utilization of wind energy, air pollution dispersion and dust storm frequency, which requires comprehensive assessment and improved understanding. Based on ground-based observations and multiple atmospheric reanalysis datasets, previous research revealed significant negative and positive trends in wind speed over land and oceans, respectively. In this study, we used Coupled Model Intercomparison Project Phase 6 (CMIP6) historical simulations to investigate the association between global mean wind speed changes and human-induced forcing. It is found that both unforced pre-industrial control run and historical natural forcing experiments failed in reproducing the observed trends in land and ocean wind speeds. However, the CMIP6 historical greenhouse gas forcing successfully simulated the increasing trend in ocean wind speed, while the CMIP6 historical aerosol forcing and experiments with land use changes seemed to have caused a decreasing trend in wind speeds over both land and ocean, suggesting that anthropogenic forcings are crucial drivers for the recent changes in global wind speed. Further attribution studies are needed to better understand wind speed variability under a warming climate.

How to cite: Deng, K., Azorin-Molina, C., Minola, L., and Chen, D.: Global Near-Surface Wind Speed Trends in Observation and CMIP6 Historical Simulation for 1850–2014, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4730, https://doi.org/10.5194/egusphere-egu2020-4730, 2020.

We have finished the complete digitalization of Annual Books from the Spanish meteorological service (AEMET) between 1916 to 1949. Data retrieved included monthly means of maximum and minimum temperature. In the present contribution we are going to show the new MOTEDAS_Century dataset (MOnthly TEmperature Dataset of Spain century) which has been performed matching data from the annual books and data from the national climate data bank of AEMET. The amount of stations with temperature data vary from a minimum of 228 (1938) and 2.030 (1994). This length of the time series is sometimes very short. Since we aim to analyse the information with a highest spatial density as possible we decided, instead of reconstructing series, to reconstruct monthly fields independently by using all the information available month to month between 1916 and 2015. Monthly interpolated data were converted to a high-resolution grid (10x10 km) using the Angular Distance Weighting method, resulting into a 5000 pixels grid.

The time series of annual mean temperature in Spanish mainland from 1916 to 2015 shows the well-known pattern of increase during the first decades, a slowdown in the middle of the 20^th century, and the final rise since the 1970´s, including a final stage without significant trend for the last three decades.

MOTEDAS_Century´s annual temperature average series has been compared with other analogous series from BEST (Berkelay Earth Surface Temperature) and SDAT (Spanish Daily Adjusted Temperature Series) datasets, as well as the twentieth century reanalysis for the Iberian Peninsula. The different versions resemble the global pattern, although differences exist particularly during the last three decades. The comparison of the annual mean temperature series with their counterparts in the BEST, AEMET and SDAT databases suggests that processing the newly retrieved information does not modify the behaviour patterns of mean annual temperatures in the Spanish mainland, and that the difference observed among the various sources can be attributed to a combination of effects from the different number of weather stations examined, which is very much higher in MOTEDAS_century, to the local characteristics of stations. The MOTEDAS_century grid in the anomalies format is available on request from the authors and will be in future on the website of the CLICES Project (http://clices.unizar.es).

How to cite: Peña-Angulo, D., Sandonís-Pozo, L., Brunetti, M., Beguería, S., and Gonzalez-Hidalgo, J. C.: MOTEDAS Century database, Part 1: temperature evolution in Spanish Mainland (1916-2015)., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3045, https://doi.org/10.5194/egusphere-egu2020-3045, 2020.

 Climate change assessment plays a pivotal role in impact assessment studies for better planning and management in different areas. A three-steps-integrated approach is used for climate change assessment. In the first step, homogeneous climatic zones were developed by combining two statistical approaches, cluster analysis and L-moment on the basis of Reconnaissance Drought Index (RDI).  A set of GCMs was selected for each climate zone by incorporating Bayesian Model Averaging (BMA), using the outputs of fourteen GCMs for maximum, minimum temperature and precipitation. The seven best GCMs were downscaled to higher resolution using statistical methods and considered for climate extremes assessment for each zone. The performances of GCMs are different for different climate variables, however, in some cases there is coincidence. Climate extremes were analyzed for the baseline and future periods F1 (2011-2040), F2 (2041-2070) and F3 (2071-2100) for the Representative Concentration Pathways (RCPs) 4.5 and 8.5. For precipitation under the RCP4.5, most of climate extremes have decreasing/increasing trends. Further, zone-01, zone-02, and zone-03 show increasing trends while zone-04 and zone-05 have mixed (decreasing/increasing) trends in climate extremes for all periods. For temperature, sixteen climate extreme indices were considered, some important indices are: GSL, SU25, TMAXmean, TMINmean, TN10p, TN90P, TX10p, TX90P, TNN, TNX, TXN, TXX. GSL has mixed trend (increasing/decreasing) depending on cold or hot climate zones. Similarly, TN10P and TN90P also show decreasing and increasing trends, respectively, while TX10P and TX90P have decreasing and increasing trends, respectively, in RCP4.5. TNN, TNX have mixed trends and TXN, TXX have mostly increasing trends except of few time periods in which they have decreasing and insignificant trends. The overall precipitation does not show significant changes, however, the projected intensities and frequencies are changing in future and require special consideration to save infrastructure, prevent casualties and other losses. More importantly, this study will help to address different Sustainable Development Goals of the United Nation Development Program related to climate change, hunger, environment, food security, and energy sectors.

How to cite: Khan, F., Pilz, J., Ali, S., and Muhammad, S.: Twenty First Century Climate Extremes Projection and Climate Vulnerability Risk Assessment in Homogeneous Climatic Zones using high Resolution Climate Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-206, https://doi.org/10.5194/egusphere-egu2020-206, 2020.

Surface wind speed (SWS) from meteorological observation, global atmospheric reanalysis, and geostrophic wind speed (GWS) calculated from surface pressure were used to study the stilling and recovery of SWS over China from 1960 to 2017. China experienced anemometer changes and automatic observation transitions in approximately 1969 and 2004, resulting in SWS inhomogeneity. Therefore, we divided the entire period into three sections to study the SWS trend, and found a near zero annual trend in the SWS in China from 1960 to 1969, a significant decrease of -0.24 m/s decade^-1 from 1970 to 2004, and a weak recovery from 2005 to 2017. By defining the 95^th and 5^th percentiles of monthly mean wind speeds as strong and weak winds, respectively, we found that the SWS decrease was primarily caused by a strong wind decrease of -8 % decade^-1 from 1960 to 2017, but weak wind showed an insignificant decreasing trend of -2 % decade^-1. GWS decreased with a significant trend of -3 % decade^-1 before the 1990s, during the 1990s, GWS increased with a trend of 3 % decade^-1 whereas SWS continued to decrease with a trend of 10 % decade^-1. Consistent with SWS, GWS demonstrated a weak increase after the 2000s. After detrended, both of SWS and GWS showed synchronous decadal variability, which is related to the intensity of Aleutian low pressure over the North Pacific. However, current reanalyses cannot reproduce the decadal variability, and can not capture the decreasing trend of SWS either.

How to cite: Zhang, Z. and Wang, K.: Stilling and Recovery of the Surface Wind Speed Based on Observation, Reanalysis, and Geostrophic Wind Theory over China from 1960 to 2017, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2501, https://doi.org/10.5194/egusphere-egu2020-2501, 2020.

This contribution presents the first work package (WP1) of the project “Assessing centennial wind speed variability from a historical weather data rescue project in Sweden”, funded by FORMAS – A Swedish Research Council for Sustainable Development (ref. 2019-00509); previously reported in EGU2019-17792-1. Under a warming climate, one of the major uncertainties on the causes driving the climate variability of winds over land (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).

In this study we present the first steps of 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. The aim of the WP1 is to rescue historical wind speed series available in the old weather archives at SMHI for the 1920s-1930s. 13 stations with daily wind speed data (in meters per second) during the period 1925-1938 have been selected for digitization; i.e., spanning back our records 2 decades more. To get wind observations from paper to screen we will follow the “Guidelines on Best Practices for Climate Data Rescue” of the World Meteorological Organization. Our protocol will consist 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. WP2 will ensure the quality and homogeneity of wind speed series rescued.

How to cite: Engström, E., Azorin-Molina, C., Wern, L., Hellström, S., Sturm, C., Joelsson, M., Zhang, G., Minola, L., and Chen, D.: Digitization of historical wind speed observations at the Swedish Meteorological and Hydrological Institute, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3491, https://doi.org/10.5194/egusphere-egu2020-3491, 2020.

In a context of global climate change, the scientific community has evidenced a significant decrease in wind speed, a phenomenon known as «stilling». This climate trend has mainly been observed over mid-latitude continental surfaces since the 1980s. On the contrary, other studies have detected an increase in wind speed over ocean surfaces; and there is little conclusive scientific evidence on trends in wind speed across the troposphere. Furthermore, a reversal in global terrestrial stilling has recently been documented in few regional and global studies since the 2010s. The causes associated with the climate variability of wind speed have not yet been resolved and there are many uncertainties behind the «stilling» and «recovery» phenomenon because neither the quantity nor the quality of wind speed observations is adequate. This contribution shows an overview of the IBER-STILLING project (RTI2018-095749-A-I00) funded by the Spanish Ministry of Science, Innovation and Universities.  This project aims to move forward on the assessment of wind speed and wind gusts variability and underlying causes globally, with emphasis on the Spanish territory and surrounding ocean (Atlantic) and sea (Mediterranean) surfaces. The IBER-STILLING project will collect and generate climate information of wind speed from different data sources; climate data will be subject to a comprehensive protocol for quality control and homogenization. The statistical analysis of these climate databases will allow characterizing trends and climatic cycles of wind speed, allowing a pioneering global analysis of wind speed over continental and ocean surfaces, and across the boundary layer and the entire troposphere. The project will also conduct wind-tunnel experiments to quantify biases introduced by anemometers devices. 

How to cite: Azorin-Molina, C., Brunet, M., Aguilar, E., Guijarro, J. A., Safaei Pirooz, A. A., Flay, R. G. J., Minola, L., Zhang, G., Lopez-Bustins, J.-A., McVicar, T. R., and Chen, D.: Overview of the IBER-STILLING project: Assessment and attribution of wind speed and wind gust variability, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5056, https://doi.org/10.5194/egusphere-egu2020-5056, 2020.

The climate is changing, and as a result, the Earth could experience more severe extreme weather events. Growing interest and concern about the effects of climate change on cities, infrastructures and people’s lives raises the question “how are design wind speeds influenced by different climate change scenarios?”. This study aims at (i) analysing the gust wind records of four meteorological stations across New Zealand for the period 1972-2017; (ii) investigating whether or not the long-term wind gust series have changed significantly; and (iii) how these changes can be considered in the estimation of design wind speeds to ensure the safety and reliability of the future structures.

Historical hourly and daily gust wind speed series recorded at the four selected stations were subjected to a robust quality control and homogenisation protocol to ensure all the artificial inhomogeneities resulting from factors like station relocations, anemometer height changes, instrumentation malfunctions, instrumentation changes, different sampling intervals, and observation environment changes, have been eliminated prior to any subsequent analyses. Then, annual and seasonal trends in both magnitudes and frequencies of the extreme winds were evaluated as to whether the observed trends are statistically significant or not by calculating p-values. From the derived gust trends, some recommendations are proposed for consideration in regard to revising the design wind speeds for calculating the wind loads on structures. In addition, the findings of the study are compared with gust wind speed trends in several other countries and also with IPCC 5^th assessment projections for New Zealand [1].

The main findings of this research are summarised as follows:

• The magnitude and frequency of wind gust showed negative (significant for some stations and seasons) trends.
• This result suggests that at this stage no extra multiplier is required to be applied to the New Zealand design wind speeds.
• Additional analyses of the long-term wind gust trends at more stations across New Zealand are needed.

Reference

[1] Ministry for the Environment 2018. Climate Change Projections for New Zealand: Atmosphere Projections Based on Simulations from the IPCC Fifth Assessment, 2nd Edition. Wellington: Ministry for the Environment.

How to cite: Safaei Pirooz, A. A., Flay, R. G. J., Turner, R., and Azorin-Molina, C.: Possible Effects of Climate Change on New Zealand Design Wind Speeds, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6247, https://doi.org/10.5194/egusphere-egu2020-6247, 2020.

Wind speed data recorded using different signal-processing procedures can introduce errors in the wind speed measurements. This study aims to assess the effects of a set of various moving average filter durations and turbulence intensities on the recorded maximum gust wind speeds. For this purpose, a series of wind-tunnel experiments was carried out at the University of Auckland, New Zealand, on the widely-used Vaisala WAA151 cup anemometer. The variations of gust and peak factors, and turbulence intensities measured by the cup anemometer as a function of the averaging duration and turbulence intensity are presented. The wind-tunnel results are compared with values computed from a theoretical approach, namely random process and linear system theory, and the results were also validated against values reported in the literature where possible.

To summarise, the major findings of this experimental study are:

1. The results show that increasing the effective gust duration reduces both the gust and peak factors, resulting in an underestimation of maximum gust wind speeds and an overestimation of minimum gust wind speeds.
2. The maximum difference between gust factors obtained for high (e.g. 3-s to 5-s) and low (raw, unfiltered measurements) gust durations reached values of 25% – 30% for the high turbulence conditions, and up to 5% – 10% for low turbulence intensities.
3. Gust factor ratios, an important parameter that allow the measurements from a specific gust duration to be converted to other gust durations of interest, are reported for various gust durations as a function of turbulence intensity.
4. The differences and gust factor ratios computed in this study can be applied directly to full-scale measurements, and can be used in several research areas, including analysing and homogenisation of historical wind speed time series, comparing gust climatologies of countries where different gust durations have been adopted, and so on. These factors clearly play an essential role in meteorological, climatological and wind engineering studies.

How to cite: Safaei Pirooz, A. A., Flay, R. G. J., Minola, L., Azorin-Molina, C., and Chen, D.: Effects of Sensor Response and Gust Duration on Maximum Wind Gust Measurements and Data Homogenisation , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12298, https://doi.org/10.5194/egusphere-egu2020-12298, 2020.

Typhoon and windstorm induced extreme winds (e.g., daily maximum wind speed, DMWS) cause enormous economic losses and deaths in China every year, and rapid urbanization increased surface roughness might play a key role in extreme wind speed variability. Here, observed near-surface (at 10 m height) DMWS from 115 meteorological stations and combined DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) and NPP/VIIRS (Suomi National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite) nighttime light data from 1992-2016 in Yangtze River Delta, a rapidly urbanized area of China, were used to analyze the impact of urbanization on DMWS variability. Raw wind speed observations were subject to a robust quality control and homogenization protocol using the Climatol package. The stations were firstly classified into six urbanized groups by the difference of nighttime light indices of each station between 1992 and 2016. The results show that DMWS in Yangtze River Delta has significantly (p < 0.05) declined by -0.209m s^-1 decade^-1 annually, with negative trends in most seasons, particularly in winter (-0.470 m s^-1 decade^-1, p < 0.05) and autumn (-0.300 m s^-1 decade^-1, p < 0.05), followed by spring (-0.178 m s^-1 decade^-1, p > 0.10), while a weak increase in summer DMWS was found (+0.002 m s^-1 decade^-1, p > 0.10). The stations in the highly urbanized group show a higher magnitude in the decline of annual DMWS, indicating the key role of urbanization in weakening DMWS. Further, this is confirmed by the regional climate model (RegCM4) sensitive experiments conducted with different land use and cover data, that is, DMWS in 1992 was higher in the experiment using the real land use and cover data than in the experiment using the land use and cover data in 2016.

How to cite: Zhang, G., Azorin-Molina, C., Wang, X., Shi, P., Chen, D., McVicar, T. R., and Guijarro, J. A.: Impact of rapid urbanization on the observed daily maximum wind speed variability: a case study in Yangtze River Delta (China), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13005, https://doi.org/10.5194/egusphere-egu2020-13005, 2020.

Surface wind is a fundamental meteorological variable that is relevant for a wide array of topics (e.g., crop growth, extreme events, power generation). Yet, for many regions, there is still a scarcity of good quality observational datasets and the uncertainties within data sources like reanalysis products and between those and observational databases are large, limiting the understanding of this variable and hampering the accuracy of subsequent analyses.

In order to address this need and within the frame of the NEWA's (New European Wind Atlas) project, a quality-controlled Wind Surface European Database (WiSED) is created. WiSED feeds from eight different datasets, provided by different institutions and with varying levels of quality control. This initial version is then submitted to a Quality Control (QC) process structured into six phases that deal with the detection of various issues in data quality: 1) compilation; 2) duplication errors; 3) physical consistency in the ranges of recorded values; 4) temporal consistency, regarding abnormally high/low variability in the time series; 5) detection of medium-term biases; and 6) removal of isolated records. The first three phases deal with issues often related to data storage and management, whereas the last three phases deal with measurement errors related to problems in the instruments, calibration procedures or siting.

The improved quality of the data and the high temporal and spatial resolution, as well as its spatial coverage, represents an added value over previous products available for the same region. 

This work summarises the application of the quality control, showing the results of different steps throughout it. Additionally, a preliminary analysis of the surface wind behaviour over Europe is presented.

With a maximum timespan of about 100 years, the creation of such database will allow for analyzing different aspects of both wind speed and direction variability over Europe from intra-daily to multidecadal timescales. Within the potentially relevant applications, it is worth to mention: the identification of subregions in Europe with homogeneous wind behaviour (regionalization), statistical downscaling exercises, analyses of wind extremes, wind power assessment and evaluation of climate model, both global and regional, simulations.

How to cite: Rojas-Labanda, C., González-Rouco, J. F., García-Bustamante, E., Navarro, J., Lucio-Eceiza, E. E., Van de Schrier, G., and Kaspar, F.: WiSED: A Quality-Controlled Surface Wind European Database, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19111, https://doi.org/10.5194/egusphere-egu2020-19111, 2020.

We have finished the complete digitalization of Annual Books from the Spanish meteorological service (AEMET) between 1916 to 1949. Data retrieved included monthly means of maximum and minimum temperature. In the present contribution we are going to show the new MOTEDAS_Century dataset (MOnthly TEmperature Dataset of Spain century) which has been performed matching data from the annual books and data from the national climate data bank of AEMET. The amount of stations with temperature data vary from a minimum of 228 (1938) and 2.030 (1994). This length of the time series is sometimes very short. Since we aim to analyse the information with a highest spatial density as possible we decided, instead of reconstructing series, to reconstruct monthly fields independently by using all the information available month to month between 1916 and 2015. Monthly interpolated data were converted to a high-resolution grid (10x10 km) using the Angular Distance Weighting method, resulting into a 5000 pixels grid.

The time series of annual mean temperature in Spanish mainland from 1916 to 2015 shows the well-known pattern of increase during the first decades, a slowdown in the middle of the 20^th century, and the final rise since the 1970´s, including a final stage without significant trend for the last three decades.

MOTEDAS_Century´s annual temperature average series has been compared with other analogous series from BEST (Berkelay Earth Surface Temperature) and SDAT (Spanish Daily Adjusted Temperature Series) datasets, as well as the twentieth century reanalysis for the Iberian Peninsula. The different versions resemble the global pattern, although differences exist particularly during the last three decades. The comparison of the annual mean temperature series with their counterparts in the BEST, AEMET and SDAT databases suggests that processing the newly retrieved information does not modify the behaviour patterns of mean annual temperatures in the Spanish mainland, and that the difference observed among the various sources can be attributed to a combination of effects from the different number of weather stations examined, which is very much higher in MOTEDAS_century, to the local characteristics of stations. The MOTEDAS_century grid in the anomalies format is available on request from the authors and will be in future on the website of the CLICES Project (http://clices.unizar.es).

How to cite: Sandonis, L., Peña-Angulo, D., Bruneti, M., Beguería, S., and Gonzalez-Hidalgo, J. C.: MOTEDAS Century Database, Part 2: spatial variation of temperature trends: the UP-TO-DATE effect., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3055, https://doi.org/10.5194/egusphere-egu2020-3055, 2020.

While long-term changes in measures of central tendency of climate elements, i. e. mean temperature, are well acknowledged, studies of trends in measures of their variability are much less common. This is despite the fact that trends in variability can have higher effect on climate extremes than trends in mean. Here, three measures of intra-seasonal variability are examined: 1) standard deviation of mean daily temperature 2) mean absolute value of day-to-day temperature change, 3) the range between the 90th and 10th quantile of mean daily temperature. ECA&D daily data from 180 stations and linear regression method are utilized to calculate trends of these characteristics in period from 1961 to 2012. Spatial distribution of trends in individual variability characteristics in Europe together with long-term change in mean and autocorrelation of mean temperature are demonstrated in maps. Significant trends (positive and negative) in all examined variability characteristics were found with substantial differences between seasons as well as between regions. On this basis, Europe is divided into 6 regions and trends are assessed in each reagion separately. While the most significant decrease in variability is observed in Northern Scandinavia and Iceland in winter, the most substantial increase is detected in Central and Western Europe in spring. Our results are accompanied by comparing the probability density function of daily temperature between periods 1961 – 1986 and 1987 – 2012 in each region showing how the shape of distribution of daily temperature has changed and if it could affect the changing number and value of temperature extremes.

How to cite: Krauskopf, T.: Trends in intra-seasonal temperature variability in Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9801, https://doi.org/10.5194/egusphere-egu2020-9801, 2020.

Air temperature is one of the most important meteorological element, with major impact on the earth-atmosphere energy balance. The characteristics of the surface air temperature in locations without surface meteorological measurements are usually acquired by employing spatial statistics methods. Gridded surface meteorological data are essential for evaluating the performance of climatological models, for applying statistical downscaling methods and as input data for hydrological and agrometeorological models.

In this work, we tested two categories of statistical methods (spatial and spatio-temporal) used for interpolating ground-based hourly air temperature data. The main input dataset used in this work was the quality controlled and homogenized hourly air temperatures measured between 2016 and 2017, obtained from four networks: Romanian National Meteorological Administration (ANM), National Network for Monitoring Air Quality (RNMCA), Regional Basic Synoptic Network (RBSN), and Meteorological Terminal Aviation Routine Weather Report network (METAR). 

The principal covariate used in the spatial interpolation procedures was the gap filled hourly LST data over Romania, available between 2016 to 2017, based on MSG-Seviri satellite images, which is an operational product of the Land Surface Analysis – Satellite Application Facility (LSA-SAF).  The other predictors were derived from SRTM (Shuttle Radar Topography Mission) data and from CORINE Land Cover 2018 product. The gridding was performed in a Romanian National Grid (Stereo 70), at 1000 m × 1000 m spatial resolution.

The results of the tested methods show that the mean absolute errors (MAE) and root mean square errors (RMSE) of space–time predictions are considerably lower than those of the pure spatial estimation.

This work was supported by a grant of Ministry of Research and Innovation, Romania, CNCS - UEFISCDI, project number PN-III-P1-1.1-PD-2016-1579, within PNCDI III.

How to cite: Dumitrescu, A. and Cheval, S.: Constructing gridded hourly air temperature dataset, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17976, https://doi.org/10.5194/egusphere-egu2020-17976, 2020.

Madagascar is an Island in Western Indian Ocean Region. It is mainly exposed to the easterly trade winds and has a rugged topography, which promote different local climates and biodiversity. Climate change inflicts a challenge on Madagascar socio-economic activities. However, Madagascar has low density station and sparse networks on observational weather stations to detect changes in climate. On average, one station covers more than 20 000 km² and closer neighbor stations are less correlated. Previous studies have demonstrated the changes on Madagascar climate, but this paper contributes and enhances the approach to assess the quality control and homogeneity of Madagascar daily climate data before developing climate indices over 1950 – 2018 on 28 synoptic stations. Daily climate data of minimum and maximum temperature and precipitation are exploited.

Firstly, the quality of daily climate data is controlled by INQC developed and maintained by Center for Climate Change (C3) of Rovira i Virgili University, Spain. It ascertains and improves error detections by using six flag categories. Most errors detected are due to digitalization and measurement.

Secondly, daily quality controlled data are homogenized by using CLIMATOL. It uses relative homogenization methods, chooses candidate reference series automatically and infills the missing data in the original data. It has ability to manage low density stations and low inter-station correlations and is tolerable for missing data. Monthly break points are detected by CLIMATOL and used to split daily climate data to be homogenized.

Finally, climate indices are calculated by using CLIMIND package which is developed by INDECIS^* project. Compared to previous works done, data period is updated to 10 years before and after and 15 new climate indices mostly related to extremes are computed. On temperature, significant increasing and decreasing decade trends of day-to-day and extreme temperature ranges are important in western and eastern areas respectively. On average decade trends of temperature extremes, significant increasing of daily minimum temperature is greater than daily maximum temperature. Many stations indicate significant decreasing in very cold nights than significant increasing in very warm days. Their trends are almost 1 day per decade over 1950 – 2018. Warming is mainly felt during nighttime and daytime in Oriental and Occidental parts respectively. In contrast, central uplands are warming all the time but tropical nights do not appear yet. On rainfall, no major significant findings are found but intense precipitation might be possible at central uplands due to shortening of longest wet period and occurrence of heavy precipitation. However, no influence detected on total precipitation which is still decreasing over 1950 - 2018. Future works focus on merging of relative homogenization methodologies to ameliorate the results.

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*INDECIS is a 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: Randriamarolaza, L. Y. A., Aguilar, E., and Skrynyk, O.: Indices for daily temperature and precipitation based on quality controlled and homogenized data in Madagascar, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19970, https://doi.org/10.5194/egusphere-egu2020-19970, 2020.

The reports about the climate change mostly focus about the trend of the temperature or precipitation. However, the relative humidity is also an important characteristic of the atmosphere, e.g. it impacts both the cloud and fog formation. The trends of the relative humidity in the changing climate have been found to be rather uncertain.  In this research the climatological trend of the relative humidity in the Carpathian Valley was studied. Analysis of the long-term observed database from eight meteorological stations was used to present the annual and seasonal trends of the relative humidity. The annual trend was found to be between 2-3% in every meteorological station. The results show that the relative humidity has decreased every season but in autumn, when the trend of it has not been consistent. While the most significant decrease has been occurred during spring, the decrease was negligible during autumn.

How to cite: Cséplő, A., Geresdi, I., and Horváth, Á.: Climatology of the relative humidity in the Carpathian Valley, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17816, https://doi.org/10.5194/egusphere-egu2020-17816, 2020.

The upward trend of temperatures is acknowledged and well documented, this increase in temperature is strictly connected to the rate of change in saturation vapour pressure as described by the Clausius-Clapeyron equation. According to this relationship for every rise of 1°C in the temperature, 7% more water vapour is contained in the saturated air that under the right circumstances may turn into rainfall, enhancing an increase in precipitation intensity.

This study scope is to identify any statistically significant trend in extreme rainfall and its spatial and temporal patterns and detect which morphological and climatic variables are the main drivers of the variation in the frequency and intensity of extreme rainfall events. The study focuses on the northern part of Italy, this area is of particular interest given by the diverse orography of the territory. After quality checks on the data (record length, missing  values and presence of outliers), 382 meteorological stations were selected that provided annual maximum rainfall series (AMS) for different durations, 1,3,6,12 and 24 hours over the period spanning from 1930 to 2017. Trying to maximize the reliability of the data and focusing on the period during which the global warming seems to rise markedly, we decided to focus the analysis on the period of observation going from 1960 to 2017. Also, the date of occurrence of each observation were retrieved enabling the possibility to perform a seasonality analysis on the precipitation extremes.

The presence and the significance of trends was investigated through a modified version of the non-parametric test Mann-Kendall that takes into account the effect of autocorrelation in the time series. The magnitude of the trend is instead quantified with the Theil -Sen estimator, a reliable method insensitive to outliers. The trend was also assessed through the innovative trend analysis, a graphical method able to detect also non-linear trend.

A preliminary assessment of the results returned by the Mann-Kendall test displayed an overall  larger presence of stations exhibiting increasing trend rather than decreasing (ratio 4:1). Moreover, the difference between the number of statistically significant increasing and decreasing trends seems to grow with the duration. These results are, in the vast majority of the cases, in accordance with the outcome returned by the ITA. The relationship between trend and elevation of the stations was investigated through means of scatterplots and non-linear tools, every technique adopted confirmed no correlation between the increasing trend in annual maxima and the altitude of the rain-gauge.  The seasonality was studied through boxplots and by observing the frequency of occurrences in each month.  At first glance, no clear trend or shift in the period of occurrences are observed. Instead, it is pretty clear how the dates of occurrence of shorter events (i.e. 1,3 hours) are concentrated in the summer months (convective events), while for longer duration the frequency of occurrence move towards the autumn months. Lastly, temperature data are getting gathered in order to investigate the possible link between annual maxima series of extremes precipitation and temperature as suggested by the Clausis-Clapeyron relationship.

How to cite: Cesarini, L. and Martina, M. L. V.: Are the short and intense precipitations in North of Italy affected by a significant trend? , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17807, https://doi.org/10.5194/egusphere-egu2020-17807, 2020.

The recent decades have been characterized by a noticeable warming over most of the globe. This warming has been accompanied by a global increase in precipitation, although many regions are projected to evolve towards a dryer climate. This is the case for the flanks of the subtropical dry regions, such as the Mediterranean and, more specifically, the Iberian Peninsula

In this contribution, we use climate normal extracted from the E-OBS 20.0 gridded temperature and precipitation datasets E-OBS 20.0, from the EU-FP6 project UERRA (http://www.uerra.eu) and the Copernicus Climate Change Service, and the data providers in the ECA&D project (https://www.ecad.eu), with a resolution of 0.1 deg, to assess the evolution across three 20-year periods (1951-1970; 1971-1990 and the slightly shorter 1991-2018) of the extension occupied by the Köppen-Geiger climate types. In consonance with the observed and projected climate change, we observe an increase in the Iberian Peninsula of the extension of the dry (B) types, as replacement of the colder varieties by warmer ones.

The analysis with the gridded dataset is compared to station records corresponding to the areas which swap climate-types for validation purposes.     

This work has been funded by the INDECIS project. INDECIS 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: Aguilar, E.: Changes in Köppen-Geiger Climate Types in the Iberian Peninsula using the e-OBS dataset (1950-2018). , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21292, https://doi.org/10.5194/egusphere-egu2020-21292, 2020.

Switzerland has a unique dataset of long-term manual daily snow depth time series ranging back more than 100 years for some stations. This makes the dataset predestined to be analyzed in a climatological sense. However, there are sometimes shorter (weeks, months) or longer (years) gaps in these manual snow depth series, which hinder a sound climatological analysis and reasonable conclusions. Therefore, we examine different methods for filling data gaps in daily snow depth series. We focus on longer gaps and use different methods of spatial interpolation, temperature index models and machine learning approaches to fill the data gaps. We assess the performance of the different methods by creating synthetic data gaps and set the applicability of the methods in relation to the density of the available neighboring stations, elevation and climatic setting of the target station.

How to cite: Aschauer, J., Bavay, M., Begert, M., and Marty, C.: Comparing methods for gap filling in historical snow depth time series, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17211, https://doi.org/10.5194/egusphere-egu2020-17211, 2020.

Seasonal snow is an important component of the global climate system. It is highly variable in space and time and sensitive to short term synoptic scale processes and long term climate-induced changes of temperature and precipitation. Current snow products derived from various satellite data applying different algorithms show significant discrepancies in extent and snow mass, a potential source for biases in climate monitoring and modelling. The recently launched ESA CCI+ Programme addresses seasonal snow as one of 9 Essential Climate Variables to be derived from satellite data.

In the snow_cci project, scheduled for 2018 to 2021 in its first phase, reliable fully validated processing lines are developed and implemented. These tools are used to generate homogeneous multi-sensor time series for the main parameters of global snow cover focusing on snow extent and snow water equivalent. Using GCOS guidelines, the requirements for these parameters are assessed and consolidated using the outcome of workshops and questionnaires addressing users dealing with different climate applications. Snow extent product generation applies algorithms accounting for fractional snow extent and cloud screening in order to generate consistent daily products for snow on the surface (viewable snow) and snow on the surface corrected for forest masking (snow on ground) with global coverage. Input data are medium resolution optical satellite images (AVHRR-2/3, AATSR, MODIS, VIIRS, SLSTR/OLCI) from 1981 to present. An iterative development cycle is applied including homogenisation of the snow extent products from different sensors by minimizing the bias. Independent validation of the snow products is performed for different seasons and climate zones around the globe from 1985 onwards, using as reference high resolution snow maps from Landsat and Sentinel- 2as well as in-situ snow data following standardized validation protocols.

Global time series of daily snow water equivalent (SWE) products are generated from passive microwave data from SMMR, SSM/I, and AMSR from 1978 onwards, combined with in-situ snow depth measurements. Long-term stability and quality of the product is assessed using independent snow survey data and by intercomparison with the snow information from global land process models.

The usability of the snow_cci products is ensured through the Climate Research Group, which performs case studies related to long term trends of seasonal snow, performs evaluations of CMIP-6 and other snow-focused climate model experiments, and applies the data for simulation of Arctic hydrological regimes.

In this presentation, we summarize the requirements and product specifications for the snow extent and SWE products, with a focus on climate applications. We present an overview of the algorithms and systems for generation of the time series. The 40 years (from 1980 onwards) time series of daily fractional snow extent products from AVHRR with 5 km pixel spacing, and the 20-year time series from MODIS (1 km pixel spacing) as well as the coarse resolution (25 km pixel spacing) of daily SWE products from 1978 onwards will be presented along with first results of the multi-sensor consistency checks and validation activities.

How to cite: Schwaizer, G., Keuris, L., Nagler, T., Derksen, C., Luojus, K., Marin, C., Metsämäki, S., Mudryk, L., Naegeli, K., Notarnicola, C., Salberg, A.-B., Solberg, R., Wiesmann, A., Wunderle, S., Essery, R., Gustafsson, D., Krinner, G., and Trofaier, A.-M.: Towards a long term global snow climate data record from satellite data generated within the Snow Climate Change Initiative, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19228, https://doi.org/10.5194/egusphere-egu2020-19228, 2020.

Climate data contains vital information about the global climate system. To get the desired information out of measurements, they have to be homogenous, where the variability of a time series is only caused by variations in weather and climate and not due to external influences.

Snow is an important component of this system, treated as one of the most obvious visual evidences of climate change and important for countries with mountainous environments. But most of the existing tools and algorithms that are being used for homogenization have been developed for air temperature and precipitation, whereas their application to snow depth measurements has only been rarely attempted. Until now, there have only been smaller efforts to develop methods and tools for snow series.

We are trying to break new ground by developing innovative methods that can be applied to the homogenization of longterm snow observations, as well as to demonstrate the impact of the developed adjustments on climatologies and trends. For that, we are using daily longterm snow measurements of the two most frequently measured parameters, snow depth (HS) and new snow height (HN) from the Swiss-Austrian domain.

As a first approach, we are applying the existing methods PRODIGE for the detection of multiple inhomogeneities and INTERP for the calculation of corrections with a quantile-mapping approach on a seasonal basis on selected time series.

How to cite: Resch, G., Chimani, B., Koch, R., Schöner, W., and Marty, C.: Homogenization of long-term snow observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8807, https://doi.org/10.5194/egusphere-egu2020-8807, 2020.

Since 1978, an Advanced Very-High-Resolution Radiometer (AVHRR) has flown onboard 17 polar-orbiting satellites. Together, they are the longest global record from a homogeneous set of satellite sensors. The Pathfinder Atmosphere’s Extended (PATMOS-x) dataset is a long-term cloud record derived from the AVHRR radiances, and suitable for climate analysis. It has demonstrated intersensor stability and has been rigorously compared with other cloud datasets.

However, the AVHRR alone has only limited spectral information, so cloud detection during nighttime or over ice is challenging. Therefore, performance degrades over regions with extreme diurnal patterns or low temperatures such as the poles, despite our interest.

The next production version of PATMOS-x will include numerous algorithmic changes as well as the use of High-resolution Infrared Radiation Sounder (HIRS) spectral channels to improve detection accuracy in previously difficult conditions. The low-resolution HIRS soundings are upsampled to match the AVHRR pixels through an edge-preserving process called “fusion”. The higher-resolution AVHRR imagery guides the upsampling and the resulting combination is spectrally consistent with the AVHRR and has a high spatial resolution.

For cloud detection, the difference between the AVHRR and HIRS 11μm and HIRS 6.7μm brightness temperatures has been added as a feature in the naive Bayesian cloud detector. The effect on cloud precision is seen especially in the Antarctic where false-positive cloud detections have decreased dramatically.

Other cloud properties can be improved with the new spectral channels. For example, the new cloud phase algorithm uses the HIRS 6.7μm to determine cloud phase and the AVHRR and HIRS 11μm-13.3μm beta ratio identifies overlapping clouds. Also, the 11μm, 12μm, and HIRS 13.3μm are used in the new cloud height algorithm.

We report on the development of this new version of the PATMOS-x cloud climate dataset, and the methods used to calibrate and homogenize the participating sensors. Finally, observed trends in the improved dataset will be examined and related to the old dataset. In particular, attention will be given to whether high-latitude analysis of climatic trends is finally possible on the new dataset.

How to cite: Phillips, C., Foster, M., and Heidinger, A.: PATMOS-x v6.0: Improvements to AVHRR Cloud Climate Record and Analysis of the Updated Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10692, https://doi.org/10.5194/egusphere-egu2020-10692, 2020.

Large scale analyses of climatic or ecological data are important to understand complex relationships. Often, such data are available in open repositories or national measurement programmes, others are only made available via the responsible researcher. However, merging data from various sources is often not straightforward, due to issues with the data itself or the metadata. Nevertheless, the application of such compilations offers various possibilities. In our working group, two large-scale compilations are currently constructed and applied. The Northern Hemispheric Pollen Compilation consists of data from NEOTOMA, European Pollen Database (EPD), PANGAEA and various authors. With the help of this compilation, we reconstruct climate and vegetation of large spatial and temporal scales. The circumpolar soil temperature dataset consist of data from the Global Terrestrial Network for Permafrost (GTN-P), Roshydromet, PANGAEA, Nordicana D and the National Science Foundation (NSF) Arctic Data Center. In its first version, the compilation has already been successfully applied to validate the ESA CCI Permafrost soil temperature map.

The various sources of errors and problems will be shown by the two compilations of (i) sedimentary pollen data and (ii) soil temperature data. The most general problem and error source are wrong or inaccurate coordinates. These errors arise out of coordinates provided with two decimals only, wrong conversion of DMS to decimal format, wrong coordinates etc. For most analyses, the most exact geographic position is a prerequisite, as e.g. lake size is an important parameter when reconstructing vegetation out of sedimentary pollen data. Sedimentary pollen records not located in a lake according to their given location thus need manual reposition according to the main researcher of a dataset or satellite maps. Further challenges concerning the pollen dataset pose various naming conventions or variable resolution in time. Furthermore, taxonomic resolution varies between datasets, making homogenization necessary.

But also for the soil temperature dataset, extensive checks were necessary, as even quality checked data comprise erroneous values. Furthermore, measured depths vary between datasets. For easy comparisons of soil temperature simulations against data, standardized depths were extracted. In a future step, interpolations between measured depths will help the end-users to extract the exactly needed depths and a compilation of available metadata on e.g. surrounding vegetation and borehole stratigraphy shall be provided.

All compilations will be made available on public repositories.

How to cite: Wieczorek, M., Heim, B., Böhmer, T., Gebhardt, N., Bartsch, A., and Herzschuh, U.: Challenges in creating and exemplary applications of two cross-repository data compilations on sedimentary pollen and permafrost soil temperature, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14019, https://doi.org/10.5194/egusphere-egu2020-14019, 2020.

Models that consume meteorological data have often requirements that are quite incompatible with the realities of continuously measured data: they require gapless data when datasets have gaps, they require sampling rate matching the phenomena of interest when datasets use a sampling rate dictated by energy and storage capacities, they require ‘perfect’ data when sensors have flaws.

The MeteoIO library [1] has been designed to solve this discrepancy as a meteorological data pre-processing library for numerical models (as well as other applications consuming such data), able to read measured data from a variety of sources and to standardize it into a unique representation (parameters naming and units) as well as filter, correct, resample and spatially interpolate it according to the end user’s configuration. From its very beginning, it aimed to be a toolbox that allows the user to choose from a large panel of published methods for each of the processing steps

Unfortunately, until now there has been no systematic assessment of the performance of the available methods nor recommendations on best strategies. Based on an extensive network of Automatic Weather Stations (AWS) located around Davos, Switzerland, we present our preliminary recommendations for data reconstruction and corrections. Artificially degraded data allow us to compare the reconstruction with the original data, either exclusively based on the local data or by using neighboring stations. The high quality instruments available at Davos Weissfluhjoch (2536m a.s.l.) similarly allow us to compare various correction methods applied to the simpler kind of sensors normally found on regular AWS.

[1] Bavay, M. and Egger, T., "MeteoIO 2.4.2: a preprocessing library for meteorological data", Geosci. Model Dev., 7, 3135-3151, doi:10.5194/gmd-7-3135-2014, 2014.

How to cite: Bavay, M., Fiddes, J., and Aschauer, J.: Performance assessment of data reconstruction and correction in meteorological timeseries, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19368, https://doi.org/10.5194/egusphere-egu2020-19368, 2020.