The above described artificial non-climatic effects influence the quality of different Essential Climate Variables (ECVs), including atmospheric (e.g., air temperature, precipitation, wind speed), oceanic (e.g., sea surface temperature), and terrestrial (e.g., albedo, snow cover) variables.
Our session calls for contributions, using data records from i) in-situ observing networks, ii) satellite observing systems, iii) reanalysis products, and/or iii) climate/earth-system model simulations based data records, on the:
• calibration, quality control, homogenization/harmonization and validation of either Fundamental Climate Data Records (FCDRs) and/or Essential Climate Variables data records (CDRs);
• development of new data records and their analysis (spatial and temporal characteristics, particularly of extremes);
• examination of observed trends and variability, as well as studies that explore the applicability of techniques/algorithms to data of different temporal resolutions (annual, seasonal, monthly, daily, and sub-daily);
• rescue and analysis of centennial meteorological observations, with focus on data prior to the 1960s, as a unique source to fill in the gap of knowledge of climate variability over century time-scales.
Producing Fundamental Data Records (FDRs) with well-defined uncertainty estimates is crucial for the development of relevant Essential Climate Variables (ECV), as prescribed by the Quality Assurance framework for Earth Observation (QA4EO) and the FIDUCEO guidelines. An FDR is a record, of sufficient duration for its application, of uncertainty-quantified sensor observations calibrated to physical units and located in time and space, together with all ancillary and lower-level instrument data used to calibrate and locate the observations and to estimate uncertainty.
However, satellite operators often do not provide contextual uncertainty products for their missions. This is particularly evident for the PROBA-V, VGT1, and VGT2 sensors, where uncertainty is typically expressed as global upper bounds rather than pixel-specific measures. Furthermore, such uncertainty information is frequently inaccessible to the broader scientific community or inconsistently formatted. This lack of clear uncertainty information constrains researchers’ ability to propagate uncertainty to higher-level ECV models accurately.
The ESA FDR4VGT project led by VITO Remote Sensing addresses this gap by producing pixel-level uncertainty estimates for two decades of harmonized satellite data. The project employs an uncertainty propagation method grounded in metrology principles. Special care has been given to the redaction of FIDUCEO compliant Digital Effect Tables (DETs) for the characterization of digital counts, calibration coefficients and ancillary information. The proposed method exposes the uncertainty estimates as an explicit analytical equation, differentiable and optimized for large scale computing. This comprehensive approach ensures adherence to FIDUCEO guidelines while balancing computational efficiency and accuracy.
Reprocessing 20+ years of Level 1A data to propagate uncertainty estimates to Level 2A projected reflectance images poses several technical challenges. Performance constraints must be considered for the propagation method, and Monte-Carlo uncertainty propagation approaches can only be done for sub-problems limited in terms of time range or scope. An uncertainty quantization using a statistical approach for the less impactful solar geometry uncertainty has been performed on the year 2021 of Proba-V Level 1 data. Level 1A uncertainties have been propagated using an analytical expression of the Law of Propagation of Uncertainty (Guide to the expression of uncertainty in measurement). DETs are prepared to characterise each identified source of uncertainty in the uncertainty diagram.
The uncertainty characterisation at Level-1 is expected to improve the retrieval of ECVs from the VGT and PROBA-V archive, as discussed in previous studies, such as the ESA SPAR@MEP project, underlying the role of improved satellite observation uncertainty characterization in enhancing image inversion performances. This enhancement will be assessed thorough the application of the CISAR algorithm to a diagnostic dataset; a comparison against a reference datases, retrieval uncertainties and ground-based measurements will be performed.
This study demonstrates the feasibility of delivering pixel-level uncertainty maps for Level-1 satellite observations using computationally efficient models. The work highlights the partial or inadequate characterization of several uncertainty contributors, which should be addressed in the preparation of future mission aiming at 1% accuracy. Additionally, this study sets the groundwork for advancing uncertainty analysis in Level 2 and Level 3 data and fulfilling key prerequisites for the delivery of FRM and higher-level CDR.
How to cite: Misk, N., Luffarelli, M., Govaerts, Y., Benhadj, I., Sterckx, S., Biasutti, R., and Niro, F.: Comprehensive pixel-level Level 1 Uncertainty Characterization for SPOT-VGT1, SPOT-VGT2, and PROBA-V following the FIDUCEO guidelines., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12496, https://doi.org/10.5194/egusphere-egu25-12496, 2025.
Estimating trends from Climate Data Records (CDRs) of Essential Climate Variables (ECVs) is necessary to detect persistent changes in Earth’s climate and geophysical processes and states. Accurately describing trend uncertainty is also essential to determining the significance of observed changes and attributing drivers. However, despite the importance of uncertainty, no established trend assessment approach properly accounts for all known sources of trend uncertainty. Most approaches either neglect part of known measurement uncertainty, such as measurement system instability, or ignore the influence of natural climate variability on trend estimation. Such neglect can result in over-confidence in trend estimates.
With the intent of providing the most realistic uncertainty intervals for climate data record time series data, this study discusses problems and limitations of current approaches. It emphasizes the need to account for the combined influence of measurement uncertainties (i.e., stability of the observational system) and natural climate variability on trend uncertainty. This study proposes a novel trend-uncertainty assessment approach unifying available measurement uncertainty information with empirical modelling of natural climate variability within the same trend-estimation framework. As a proof of concept, the proposed approach is applied to the analysis of trends in a Global Mean Sea Level (GMSL) time-series. This GMSL application demonstrates that combining available measurement uncertainty assessment with variance modelling is expected to lead to more realistic uncertainty evaluations in sea-level trends. This unified approach is potentially applicable to virtually any CDR and could enhance the reliability of climate change analysis through an improved trend uncertainty assessment in climate studies.
How to cite: Gobron, K., Hohensinn, R., Bulgin, C. E., Loizeau, X., Woolliams, E. R., Merchant, C. J., Mittaz, J., Povey, A. C., Langsdale, M., Dorigo, W., Cox, M. G., Ablain, M., Klos, A., Gruber, A., and Bogusz, J.: A Unified Framework for Trend Uncertainty Assessment in Climate Data Record: Application to the Analysis of the Global Mean Sea Level Measured by Satellite Altimetry, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11097, https://doi.org/10.5194/egusphere-egu25-11097, 2025.
We present a novel approach to homogenize daily GNSS water vapour time series using statistical methods and machine learning techniques. The procedure involves three main steps:
- Segmentation. The aim is to detect the number and position of change-points in a time series of Integrated Water Vapour (IWV) differences (GNSS minus reference), modelled as a constant (mean) value per segment superposed with a fourth order Fourier series and white noise with a monthly varying variance. The model parameters are estimated by penalized maximum likelihood algorithm, implementing Dynamic Programming search in an iterative scheme [1].
- Attribution. The aim is to predict, for each and every change-point, in which of the GNSS (G) or reference (E) series the jump in mean occurred. Information from nearby stations is introduced as additional G' and E' series, which are combined with G and E into six series of differences. A Feasible Generalized Least Squares regression is used to estimate the size of the jumps in the six series and a Random Forest classifier is used to predict which of the four base series caused the jump. The classifier is trained and validated beforehand with a large data set by using a resampling strategy [2].
- Correction. The raw G and E series are corrected for the corresponding shifts in mean that were detected and attributed to G and/or E.
The paper will present recent improvements of the attribution method, namely: i) the optimization of detection skill scores, both for the training of the classifier and application; ii) the optimization of the sample size for the resampling; iii) a refined nearby-aggregation method based on inverse distance weighting. The method is applied to a new, enhanced, data set based on more than 6000 globally-distributed GNSS stations. The impact of homogenization on IWV trends over the period 1994-2022 is presented.
[1] Quarello et al., 2022, https://doi.org/10.3390/rs14143379
[2] Nguyen et al., 2024, https://doi.org/10.1002/joc.8441
How to cite: Lebarbier, E., Nguyen, N., and Bock, O.: Homogenization of GNSS integrated water vapour time series using statistical machine learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8526, https://doi.org/10.5194/egusphere-egu25-8526, 2025.
Long-term changes in stratospheric temperatures are important for climate trend monitoring and interpreting the radiative effects of anthropogenic emissions of ozone-depleting substances and greenhouse gases. The Stratospheric Sounding Unit (SSU) onboard the historical NOAA Polar Orbiting Environmental Satellite (POES) series was a three-channel infrared radiometer designed to measure temperature profiles in the middle and upper stratospheres. Although the SSU observations were designed primarily for weather monitoring; however, due to continuity, long-term availability, and global coverage, they comprised an indispensable climate data record that had been playing a key role in estimating temperature trends in the middle and upper stratospheres for the period of 1979–2006 (Wang et al. 2012; Zou et al. 2014). On the other hand, since 2002, the hyperspectral infrared sounding measurements including the Atmospheric Infrared Sounder (AIRS), the Infrared Atmospheric Sounding Interferometer (IASI), and the Cross-track Infrared Sounder (CrIS) provides decades of infrared hyperspectral observations. Owing to their hyperspectral nature and accurate radiometric and spectral calibration, these datasets provide modern period measurements of stratospheric temperature with high data quality.
This study presents recent efforts of merging of the SSU stratospheric temperature data with AIRS. We generated the training datasets of SSU and AIRS from the UMBC 48 profiles with different scan angles using the Community Radiative Transfer Model (CRTM). A linear regression method with considering weighting function and instrument noise as constrains is developed to convert AIRS into equivalent SSU based on training datasets. By taking advantage of their overlapping period of SSU and AIRS in 2003-2006, the residual biases are further removed along the scan angels. The effects of increases in atmospheric CO2 concentration on stratosphere temperature records are also removed to make the dataset suitable for stratospheric temperature monitoring. Finnlay, the SSU-AIRS dataset is compared with the existing SSU/AMSU/ATMS dataset (Zou and Qian 2016). The differences of their variability and trend are presented. The new SSU/AIRS dataset provides another long-term observation for stratospheric temperature monitoring.
How to cite: Wang, L.: Extending Stratospheric Temperature Climate Data Records by Merging SSU with AIRS , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6603, https://doi.org/10.5194/egusphere-egu25-6603, 2025.
The Copernicus Climate Change Service (C3S) has developed the Comprehensive Upper Air Observation Network (CUON) dataset. The main geophysical variables included in CUON are temperature, humidity, and wind. The input observation data are the NOAA Integrated Global Radiosonde Archive (IGRA), the NCAR Upper-Air Database (UADB), the ERA5 observation feedback archive, and additional ascents from smaller collections, including in particular the African Monsoon Multidisciplinary Analysis (AMMA) and the World Ozone and Ultraviolet Radiation Data Centre (WOUDC). Available radiosonde, ozonesonde, and pilot-balloon (PILOT) platforms are included, even if the station record contains only a single launch. Key improvements over the aforementioned data input are the following: balloon drift estimates, observation error estimates and homogeneity adjustments for the main variables. The actual launch times were also refined as far as possible from the nominal times of reporting plus available metadata (e.g., IGRA release times). These unique features make CUON particularly suitable as an input for climate reanalysis, in particular the upcoming ERA6 reanalysis, but also other climate applications.
Comparison with ERA5 gridded data shows a sizeable reduction of representation errors and biases across all main variables, in particular in the early 2000s but also at other time periods back to the 1940s. The offline calculated observation minus background (obs-bg) departures are sometimes 30% smaller than those calculated during ERA5 assimilation. This may be explained by the offset of radiosondes during ascent as compared to their launch position, which can reach several 100km, i.e. several reanalysis gridboxes.
The obs-bg departures form the basis for comprehensive statistics-based adjustment of biases in temperature, wind direction and also humidity, using the RAOBCORE/RICH method. The corresponding software has been further improved compared to the past year, with a better treatment of data gaps.
Results from bias-adjusted temperature records indicate realistic spatial trend heterogeneity and a better fit to reprocessed satellite data products than what could be achieved in preparation to the present operational reanalysis ERA5. Temperature background departures from ERA5 increase substantially, both in terms of mean and standard deviations when going back to the early 1950s and 1940s. The present investigated whether this increase comes from poorer quality observations or from issues arising due to the less strongly observationally constrained ERA5 state during this period.
Humidity bias adjustments prove to be more delicate to implement, since it is not sufficient to shift the distributions by a mean value. Instead, it turns out to be important to adjust also the shape of the distributions. To achieved this, a quantile matching approach has been adopted, taking into account the size of the change of background departures in the time intervals before and after a potential breakpoint. The adjustment led to a reduction of obs-bg departures with respect to ERA5 but also reduced the strong spurious drying trends over continental-scale networks such as the US and China in the period 1990-2020.
The CUON dataset goes back to 1905 and will be updated at least annually. It will be made available from: https://cds.climate.copernicus.eu/datasets/insitu-comprehensive-upper-air-observation-network?tab=overview.
How to cite: Haimberger, L., Voggenberger, U., Ambrogi, F., Garcia Diez, M., and Poli, P.: The Comprehensive Upper Air Observation Network (CUON) Dataset, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10348, https://doi.org/10.5194/egusphere-egu25-10348, 2025.
The aim of this study is to contribute to the design of a reference climate station network that captures the regional variability in the climate and climatology of New Zealand (NZ).
We performed our analysis using New Zealand Re-Analysis which is a high resolution (1.5km) convection-permitting atmospheric regional reanalysis dataset over NZ spanning ~20 years. We analysed the dataset to identify climate regions that are co-varying, have similar climatology and are likely to have a similar response to climate change. To identify co-varying climate regions, we performed a Principal Component Analysis on the dataset and reconstructed it to retain 95% variance. The reconstructed data is then clustered using techniques such as k-means and self-organising maps, with the number of k-means clusters chosen based on a combination of Silhouette score and gap statistics. Secondly, we cluster on daily climatologies to isolate regions with similar climate. Finally, we cluster the differences between present daily climatology with respect to the future climatology using the Coupled Model Intercomparison Project to identify the regions which are likely to have similar climate change signals.
Using the regions derived, we present a method to optimally select a reference network representing the components of covariance, climatology and climate change.
How to cite: Srinivasan, R., Carey-Smith, T., Harper, A., Fauchereau, N., and Dean, S.: Regional analysis for the purposes of designing a reference climate station network in New Zealand, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14309, https://doi.org/10.5194/egusphere-egu25-14309, 2025.
Surface air temperature is among the most fundamental data for studying historical climate change. Two major categories of historical surface air temperature data products exist, the observational datasets produced by statistically interpolating direct thermometer measurements from weather stations, ships, and buoys (e.g. NASA, NOAA, HadCRU, and Berkeley Earth products) and the reanalysis datasets (e.g. ERA5, JMA-3Q) that use weather models to merge various data (e.g. pressure, temperature, wind) from surface, atmospheric, and satellite measurements.
Most observational datasets are relatively low resolution, but Berkeley Earth has recently introduced a new high-resolution version (0.25° x 0.25° latitude-longitude gridding, 1850-present). This presentation will compare and contrast Berkeley Earth’s high resolution temperature dataset with the similarly resolved near-surface temperature component of ERA5 reanalysis (1940-present). The Berkeley Earth high resolution dataset is an extension of Berkeley Earth’s prior work, and is derived directly from weather station and ocean temperature measurements. It maintains the substantial efforts to quality control and correct for systematic biases in surface temperature measurements, but adds machine learning and other techniques to improve the spatial resolution and accuracy of interpolated temperature fields.
Reanalysis systems, like ERA5, are a modern marvel that provide spatially complete weather estimates across many variables at relatively high spatial and temporal resolution. However, to reach their full potential, they require extensive data input streams, with generally greater accuracy in the modern satellite era than the pre-satellite era (e.g. 1940-1970). This heavy reliance on satellite data also increases the risk of systematic drift in accuracy due to changes in satellite availability or undiagnosed changes in satellite accuracy. In the specific context of surface air temperature, though ERA5 uses weather station pressure and humidity to directly refine atmospheric conditions, the weather station temperatures are only used indirectly via the estimates of surface/soil conditions. Because ERA5 is not directly constrained by weather station temperatures, systematic biases of 1-2 °C between measured surface air temperature and reanalysis estimates are common, with larger errors sometimes occurring.
Berkeley Earth and ERA5 are broadly similar, but also exhibit interesting differences. Predictably, those differences are larger in the pre-satellite era. In some regions, this gives rise not just to quasi-random variations, but also systematic differences in both seasonality and the apparent global warming trends. Large differences are more common in regions of limited data (e.g. Antarctica, Greenland, Tibet), but can also occur in other environments.
We will discuss the differences between Berkeley Earth’s new high resolution dataset and ERA5, as well as identify the contexts where we believe observational data sets are likely to be more accurate than reanalysis or vice versa.
How to cite: Rand, D. and Rohde, R.: Contrasting Berkeley Earth’s New High Resolution Temperature Dataset with ERA5, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13840, https://doi.org/10.5194/egusphere-egu25-13840, 2025.
A significant portion of the Earth's surface lacks long-term in-situ measurement of essential meteorological variables. Climate reanalysis serves as a valuable alternative to historical observations by providing homogenous and complete records of several atmospheric variables, especially in data-sparse regions. Reanalysis is produced by assimilating sparse observational data from a variety of sources into numerical weather prediction models that solve the dynamics of land, ocean, and atmospheric processes for analyzed periods. Recent generation reanalysis is now available at finer spatial and temporal resolutions, making them lucrative for hydrological and climatological studies. However, reanalysis has inherent biases that necessitate their evaluation before such application. While the assessment of reanalysis datasets is common in representing mean climatology on a daily, monthly, or seasonal scale, their ability to capture the spatial pattern of extreme temperature events and their trends remains controversial.
By comparing seven such reanalysis datasets over India (ERA5-Land, ERA5, MERRA2, CFSR, JRA55, IMDAA, and EARS) it is found that the newest generation reanalysis having a higher resolution, better captures the magnitude, frequency, and duration of hot and cold extremes. The reanalysis datasets are compared with a gauge-based gridded temperature dataset from the India Meteorological Department (IMD) to assess their suitability in representing extreme temperature events and their trends over India. For evaluation, several extreme temperature indices are calculated based on the recommendation of ETCCDI, covering the frequency, intensity, and duration of hot and cold extreme temperature events. It is also found that in response to global warming, extreme hot events are rising, and extreme cold events are decreasing in India which is also captured by most of the reanalysis. However, the reanalysis estimated trend areas and magnitudes are not similar when compared to trends with a regional station-based gridded dataset. Thus, care should be taken in selecting datasets for such applications and interpreting their trends.
How to cite: Bhattacharyya, S., Hassan, M., S Sreekesh, S. S., and Choudhary, V.: Assessing the performance of climate reanalysis datasets in capturing hot and cold extremes and their trends in India., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-800, https://doi.org/10.5194/egusphere-egu25-800, 2025.
The variability in hydro-climatological indicators severely affects environmental regulation, ecosystem sustainability and the occurrence of extreme events at a large scale. Determination of the extreme climatic indices is crucial for understanding the trend and severity of such events within a given period. Therefore, this study analyses the ETCCDI-defined temperature extreme indices using five CMIP5 models (BNU-ESM, canESM2, CNRM-CM5, MPI-ESM-LR and MPI-ESM-MR) under RCP4.5 and 8.5 scenarios for the period 2025 to 2050 in Gujarat, India. The Mann-Kendall and Sen’s slope estimator test was applied for the trend significance. The findings show that maximum and minimum temperature will increase by >1℃ under the RCP8.5 scenario by 2050. Cold spells are expected to decline significantly in both scenarios, while the multi-model mean (MMM) for warm spells exhibits an increasing trend in the RCP8.5 scenario. A significant decreasing trend is observed in cool nights and cool days in all models under both scenarios. Notably, except for the BNU-ESM and canESM2, other models project an increasing trend in warm nights however, MMM shows a significant increasing trend in the frequency of warm nights and days. Furthermore, the frequency of days with cool nights and days will decrease by >10% and ~20% respectively by the end of 2050. Spatially distribution analysis shows that the south-eastern part of Gujarat is likely to be more vulnerable to extreme events, as a higher frequency of such events has been observed over this area. It was also observed that the MPI-ESM-MR model demonstrated better predictive performance and outperformed other ensembles in the Gujarat region, as it shows a closer alignment with the MMM results.
How to cite: Kumar, S. and Pathak, B.: Assessment of Near-Future Temperature Extremes Using CMIP5 Ensembles Over Gujarat, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1853, https://doi.org/10.5194/egusphere-egu25-1853, 2025.
The United Nations' Intergovernmental Panel on Climate Change (IPCC) has reported an increase in the frequency and intensity of heavy precipitation events globally, primarily driven by human-induced climate change. The South and Southeast Asian Monsoon, particularly over India, is one of the affected regions, which has experienced significant changes in precipitation patterns. Characterized by a seasonal reversal of wind and rainfall, the Indian summer monsoon is driven by land-sea thermal contrasts and atmospheric dynamics influenced by the Himalayas, the Tibetan Plateau, and the Indian Ocean. Recent studies attribute the increased intensity of the monsoon to higher sea surface temperatures and enhanced atmospheric moisture fluxes.
Observational data and climate models indicate a general trend of increasing monsoon rainfall, although with regional variability, alongside simultaneous rises in dry spells and extreme rainfall events. Between 1951 and 2015, localized heavy rainfall events have become more frequent, while moderate rainfall events have declined, leading to more severe droughts in central India. Since the 1990s, monsoon rainfall has exhibited an upward trend, consistent with projections of enhanced land-sea thermal contrasts and warming in the Indian Ocean.
In this study, satellite-based climate datasets, such as those provided by EUMETSAT's H SAF and LSA SAF, were used to analyze monsoon dynamics. Key indicators like precipitation, soil moisture, and vegetation coverage revealed complex interactions between rainfall, surface temperature, and evapotranspiration. While the short-term period from 2008 to 2020 shows variability without clear long-term trends, notable correlations emerged, such as increased rainfall leading to cooler temperatures and enhanced soil moisture. Conversely, warmer temperatures had mixed effects on vegetation, moderated by factors such as water availability and land cover.
The observed trends align with global patterns of climate change, with both thermodynamic and dynamic processes contributing to extreme events. Future projections suggest a stronger summer monsoon and a weaker winter monsoon as global warming intensifies, driven by anthropogenic forcing and enhanced land-sea thermal contrasts. These findings highlight the intricate interplay of climatic drivers and underscore the growing need for monitoring and adaptation in response to a changing monsoon regime.
How to cite: Vreugdenhil, M., Roebling, R., Sanjeevamurthy, P., Hahn, S., and Wagner, W.: Observing changes of India's summer monsoon, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20319, https://doi.org/10.5194/egusphere-egu25-20319, 2025.
One expected impact of climate warming is an increase in sub-daily extreme rainfall. A simple thermodynamic argument suggests that extremes should increase at a rate of about 7.5%/K of warming. Convective permitting models and some in situ gauge data suggests sub-daily extreme intensity increases by more than 7.5%/K. In situ gauge data is sparse and so will miss many small-scale extreme rainfall events. Radar rainfall can sample a large region with high space and time resolution but has its own problems. Australian radar data has been homogenised through comparison with the Tropical Rainfall Measuring and Global Precipitation Missions. This gives 20+ year records for about ten sites in Eastern Australia. Radar reflectivity is converted to rainfall intensity using power-law behaviour estimates from distrometer data. Rainfall data is then averaged to 30 minute, 1hour, 2 hour and 4 hour accumulations and seasonal maxima extracted. For each radar a GEV fit with covariates on local temperature was fit to samples from the seasonal maxima. No strong evidence is found that extreme rainfall intensity increases by more than 7.5%/K.
How to cite: Tett, S., Soderholm, J., Protat, A., Bowden, A., and Alexander, L.: Towards using radar data to understand changes in sub-daily rainfall extremes: An Australian case study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2554, https://doi.org/10.5194/egusphere-egu25-2554, 2025.
The Arabian Peninsula (AP), with its harsh, arid climate, severe water scarcity, and dependence on fossil fuels, is particularly vulnerable to the impacts of climate change, amplifying risks to ecosystems, water resources, agriculture, and human health. This study explores the historical climate variability across key countries of the Arabian Peninsula, including Saudi Arabia, Oman, Kuwait, Bahrain, Qatar, the UAE, Yemen Jordan and Iraq using the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF). An in-depth analysis of long-term climatic trends and anomalies from 1960 to 2020 has been conducted on temperature and precipitation variables using high-resolution (0.5° x 0.5°) ERA5 climate data. The trend analysis was performed using the Mann-Kendall test and Sen's slope estimator to assess the statistical significance and rate of change of the variables over time. The results show a consistent and statistically significant warming trend across all countries, with minimum (Tmin), and maximum (Tmax) temperatures exhibiting an increasing trend at 95% confidence level. Among all the countries, the total change in Tmax with respect to the base year of 1960 was highest for Iraq, showing an increase of 1.49°C, followed by Saudi Arabia and Yemen with an increase of 1.39°C. In comparison, Tmin has shown more significant warming than Tmax. Notably, UAE experienced a substantial increase of 2.91°C in Tmin from the 1960 base year, where the Tmin temperature was 20.04°C. Similarly, Yemen and Saudi Arabia have also exhibited significant increases in Tmin, with Saudi Arabia showing a rise of 2.36°C and Yemen experiencing a rise of 2.23°C compared to the base year. In contrast, precipitation trends exhibit notable variability across the countries, with Iraq, Saudi Arabia, UAE, and Yemen showing a decline in precipitation, as indicated by Sen Slope values of -0.01, -0.27, -0.16, and -0.46, respectively. On the other hand, Bahrain, Kuwait, Qatar, and Jordan show an increase in precipitation. However, the changes observed in precipitation are not statistically significant, suggesting that the shifts in precipitation are less reliable and may not reflect consistent long-term trends. Across all the countries, Tmax and Tmin exhibits a significant increase on a seasonal basis at p = 0.05, except for December, January, and February (DJF) season in Bahrain, where the results are not statistically significant. On a seasonal basis the precipitation observed variability across all the countries, with some showing an increase and others a decrease, though most trends are not statistically significant. The most significant change was observed in Kuwait's June, July, August (JJA) season, where an increase in rainfall was detected at the 95% confidence level. The analysis revealed significant warming trends across the region, particularly in Tmin with statistically significant upward shifts observed in all countries. In contrast, precipitation trends exhibit high variability, with some countries experiencing slight increases and others facing decreases, though the changes remain largely non-significant. The study contributes valuable insights into the historical climatic changes in the AP, which are critical for developing future climate adaptation strategies and policy frameworks.
How to cite: Mushtaq, F., Ul Haq, A., Bharti, S., Alhems, L. M., and Farooq, M.: Regional Climate Dynamics in the Arabian Peninsula: A Study of Temperature Trends and Precipitation Uncertainty, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3014, https://doi.org/10.5194/egusphere-egu25-3014, 2025.
Hourly extreme precipitation is expected to intensify with global warming following Clausius-Clapeyron (CC) relationship. In this research, we utilized hourly precipitation and dew point temperature (DPT) data from over two thousand in-situ gauge observations spanning 1950–2018, as well as ERA5 and MERRA2 datasets, during the warm season across mainland China. Our observations clarify the spatial distribution and trend of hourly extreme precipitation in China mainland, derive the precipitation-temperature scaling relationship, and, for the first time, explore the diurnal cycle of scaling from observations, which has received limited attention in previous studies. Hourly extreme precipitation increases more significantly than at daily time scale, enhancing the probability and risk of short-term extreme precipitation events.
For hourly precipitation-temperature scaling relationship, 88.7% of stations exhibit super-CC scaling with notable regional differences. Extreme precipitation intensity increases monotonically with DPT and no ‘hook’ structure is observed in the regional scaling curve. However, ERA5 and MERRA2 predominantly show stations with sub-CC scaling, and exhibited a ‘hook’ structure at DPT about 22℃ in regional scaling curve, suggests that reanalysis datasets underestimate changes in hourly extreme precipitation in response to DPT. Noticeably, The scaling shows a pronounced diurnal cycle and exceed all-hours scaling, indicating that the mix of precipitation from different hours ultimately affects overall scaling results. Over a 39-year period, changes in extreme precipitation intensity were closely aligned with DPT throughout the diurnal cycle in inland regions. These result provides valuable insight into the shift of extreme precipitation to morning/night in some regions under climate change.
How to cite: Lei, M. and Wang, S.: Reassessing Hourly Precipitation-Temperature Scaling: The Diurnal Cycle in a Warming China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3940, https://doi.org/10.5194/egusphere-egu25-3940, 2025.
Diurnal asymmetric warming, a critical feature of climate change, significantly impacts water-carbon exchange in terrestrial ecosystems. This study analyzes the spatiotemporal characteristics and long-term trends of the global diurnal temperature range (DTR) from 1961 to 2022 using ensemble empirical mode decomposition (EEMD). Our results reveal a trend reversal in global averaged DTR around 1988, shifting from a decrease to an increase, affecting 47% of global land areas. Subsequent to the reversal, the most pronounced increases were observed in temperate regions. Seasonal analysis shows earlier reversals in spring and summer, with accelerated change rates following the reversal. Additionally, increased surface solar radiation from reduced cloud cover caused daily maximum (Tmax) temperatures to warm faster than the minimum (Tmin), leading to a reversal and intensified DTR. These complex patterns underscore the need for targeted climate policies and adaptation strategies to tackle global warming.
How to cite: Xia, H.: Increase Asymmetric Warming Rates Between Daytime and Nighttime Temperatures Over Global Land During Recent Decades, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11241, https://doi.org/10.5194/egusphere-egu25-11241, 2025.
This study analyses daily maximum and minimum temperature extremes in the Mediterranean basin using the EOBS and ERA5-Land datasets for the period 2011–2023. EOBS is a daily gridded land-only observational dataset over Europe while ERA5-Land is a global climate reanalysis dataset with hourly resolution and global coverage, both with a spatial resolution of 0.1°x0.1°.
In this study, climatic extremes were analysed. All values exceeding the 90th percentile of the data distribution or falling below the 10th percentile were used to identify extreme warm and cold temperatures, respectively. The main objective is to compare the distributions of temperature extremes for the two datasets to identify differences in the amount and magnitude.
The analysis was conducted on five climatic sub-regions of the Mediterranean Basin, including the Iberian Peninsula, Southern France–Balearic Islands, Northeastern Mediterranean, and the Southeastern Mediterranean with Turkey.
Preliminary results reveal that the number of temperature extreme detected simultaneously by the two datasets is about 80%. The best agreement between EOBS and ERA5-Land is found in the regions densely covered by near-surface measurement stations. More specifically, EOBS identifies more intense warm extremes than ERA5-Land, with most of the values ranging between 31–34 °C. For warm extremes, EOBS also captures a broader range of extreme temperature values compared to ERA5-Land in some areas, such as the Iberian Peninsula and the Southeastern Mediterranean. In both datasets, the values are characterized by temperatures ≥40°C, which represent 20% of the value above the percentile threshold. For cold extremes, the two distributions show a good agreement with approximately 25% of the values on average between -4°C and 0°C, in areas such as the Iberian Peninsula and the Western Mediterranean, while lower values between -10°C e -6°C are observed in the Eastern Mediterranean. In the Iberian Peninsula and Western Mediterranean, EOBS shows a higher cumulative probability for values lower than -10 °C, while ERA5-Land has a higher cumulative probability in the range from 0°C to -2 °C. In the Northeastern Mediterranean, the cumulative probability for temperatures < -10 °C is approximately 25% for both datasets.
Further consideration will be presented regarding the influence of orography on the differences observed between ERA5-Land and EOBS, to better understand the representation of climatic extremes in both datasets.
How to cite: Cosimato, G., Madonna, F., Rosoldi, M., Karimian Sarakhs, F., and Roberto, E.: Extreme temperature in the Mediterranean basins, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6907, https://doi.org/10.5194/egusphere-egu25-6907, 2025.
The Mediterranean warming is reshaping land-sea temperature gradients and related weather phenomena driven by differential heating, such as sea breezes (SB). Despite their importance, the response of SB to climate change remains poorly understood due to a lack of long-term studies. Changes in SB characteristics could have significant socioeconomic implications, particularly for sectors like wind power and agriculture, through shifts in the hydrological cycle and associated reductions in summer storms. This work evaluates trends in the occurrence (days) and magnitude (near-surface wind speeds) of SB across the western Mediterranean basin between 1981 and 2021. Using an objective and robust method, we identified SB events from meteorological data collected at 39 stations spanning Spain, France, Italy, Tunisia and Algeria. Daily wind speed data were homogenized to analyze annual and seasonal trends. To explore the influence of Mediterranean warming, we examined correlations between SB characteristics and anomalies in key thermal variables, including surface and low-level air temperature, land-sea air temperature contrasts, sea surface temperatures, heatwaves and solar radiation. Our results reveal basin-wide increase in SB frequency but a reduction in intensity over all timescales since 1981. Approximately 60% of regional variability in SB occurrence is linked to increased solar radiation, while Mediterranean warming accounts for a seasonal increase of up to 10% in SB days per decade. Conversely, SB intensity has weakened, particularly in Spain and the Balearic and Eastern Islands. This weakening is thought to be caused by a reinforced thermal contrast, which explains 70% of the variability in SB magnitude, with heatwaves contributing an additional 10% to the decline.
How to cite: Bedoya-Valestt, S., Azorin-Molina, C., Minola, L., Plaza Martin, N. P., Gimeno, L., Andrés-Martín, M., Vicente-Serrano, S., and Chen, D.: Sea breeze changes in the western Mediterranean mainly driven by climate warming (1981-2021), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14076, https://doi.org/10.5194/egusphere-egu25-14076, 2025.
To gain a deeper understanding of precipitation variability, it is essential to also examine the variability of the condensed water path, which is vertically integrated mass of condensed liquid (LWP) and ice water (IWP) in a column, divided by the column's area. This analysis provides valuable insight into the dynamics and physics driving temporal variations in precipitation. Additionally, since cloud formation is heavily influenced by the atmosphere's liquid and ice water content, such an evaluation will aid in addressing uncertainties related to cloud-radiation interactions in global climate models (GCM).
In this study, we analyze the spatial pattern of the condensed water path (CWP) and precipitation over Africa from 1970 to 2005, examining each season individually. We also address the performance of global climate models (CMIP5 and CMIP6) and regional climate models (CORDEX-Africa, AFR-22 and AFR-44) in simulating these patterns. Additionally, we investigate the temporal variations of these variables over the study period.
All models successfully captured seasonal variations in CWP and precipitation, though with some differences in their magnitude. For the condensed water path, results showed a bimodal pattern in West Africa (June-July-August and September-October-November), and in Central and East Africa (March-April-May and September-October-November), aligning with the seasonal migration of the intertropical convergence zone (ITCZ). A similar bimodal pattern was observed for precipitation, except in East Africa ’s minor rainfall season (September-October-November), which was not captured.
Trend analysis revealed a positive trend for all CWP datasets during the JJA season, as well as in the RCM precipitation. Conversely, GCM data showed a negative trend in the same season. In the SON season, all model outputs (except ERA5 datasets which indicated a negative trend for both CWP and precipitation) showed a positive trend for both variables.
When comparing models, CMIP5 was found to overestimate CWP over Africa, whereas CMIP6 demonstrated better performance, accurately reproducing spatial patterns with correlations ranging from 0.9 to 0.94 across seasons. Precipitation data showed a similar pattern, with CMIP6 achieving correlations between 0.87 and 0.94. Taylor skill scores further confirmed CMIP6’s improved skill, with scores exceeding 0.75 for CWP simulation and over 0.7 for precipitation in all seasons, suggesting notable progress in climate modelling.
The CORDEX-Africa models, however, demonstrated a lower correlation for spatial patterns of CWP, with AFR-44 models scoring between 0.55 and 0.68 and a slight improvement in AFR-22 models (0.61 to 0.74). For precipitation, the correlation was higher, with AFR-44 models achieving 0.74 to 0.8 and AFR-22 models reaching 0.75 to 0.85 in representing spatial patterns.
The consistent spatial variations in these variables, as shown by the ERA5, CMIP, and CORDEX models, offer valuable insights into the physics and dynamics underlying precipitation variations across Africa. However, the observed inconsistency in temporal variations warrant further investigation. A deeper understanding of these dynamics could substantially enhance our comprehension of climate change impacts in Africa.
How to cite: Yamoah, K. K., Štěpánek, P., and Farda, A.: Variability of condensed water path and precipitation over Africa., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-319, https://doi.org/10.5194/egusphere-egu25-319, 2025.
Geostationary satellites (GEOsats) provide continuous cloud and meteorological observations over fixed portions of the Earth’s surface, allowing them to monitor the development and movement of storm systems and their diurnal variation. For climate studies, geostationary observations provide added insight into cloud formation and evolution and how they influence the diurnal cycle of Earth’s radiation budget.
A long and consistent cloud record can be a valuable resource for evaluating changes in cloud systems and properties across the globe. Integrating observations from different geostationary instruments poses challenges due to their distinct characteristics, such as different spectral channels and calibrations as well as varying spatial resolutions to list a few. As a result, deriving consistent cloud properties from multiple sensors without introducing artificial discontinuities in a time series remains a complex and challenging endeavor.
A homogenized GEOsats cloud retrieval system is being developed to create cloud climate data records (CDR’s) for NASA’s CERES (Clouds and the Earth’s Radiant Energy System) mission from a long record of GEOsats that uses spectral channels common to most satellites. Thus, a 3-channel (0.6, 3.9, 11 µm) algorithm for daytime cloud detection, and a 2-channel (3.9 and 11 µm) algorithm for nighttime have been implemented and tested. Recent advances to the 3-channel processing framework include refined radiative transfer models specific to each GEOsats’ spectral bands to provide more accurate and consistent computed clear-sky TOA radiances. Machine learning approaches are also developed and implemented for estimating the a priori land surface skin temperature, and to improve cloud detection in the solar terminator and in oceanic areas with sunglint. It is anticipated that these changes will lead to more accurate and diurnally consistent derived cloud properties across satellite platforms.
This paper describes the CERES 3-channel cloud detection approach and presents results of initial cross-platform consistency and accuracy tests and evaluations with independent data from active sensors, such as CALIOP data, as well as from GEOsats analyses that utilize more spectral information. Remaining challenges and future work will be discussed.
How to cite: Trepte, Q., Smith, W., Palikonda, R., Yost, C., Scarino, B., Bedka, S., and Painemal, D.: An Advanced Cloud Detection Approach for Creating Diurnally Consistent Geostationary Satellite Cloud Climate Data Records, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2182, https://doi.org/10.5194/egusphere-egu25-2182, 2025.
NOAA interactive snow and ice cover charts of the Northern Hemisphere present a key input to operational weather prediction models and are widely used in climate change studies. Since 1997 charts are generated daily within Interactive Multisensor Snow and Ice Mapping System (IMS). When creating a snow map, analysts use the previous day map as a first guess and update it manually using satellite imagery in the optical bands. Because of clouds obscuring the view and limited time allocated for the analysis only a portion of the entire land area may be closely examined on a given day. Otherwise the snow cover is assumed unchanged since the day before. Inability to update the map over its entire domain on a daily basis results in a delayed reproduction of snow cover dynamics in the map and, hence, in degraded map accuracy. Over time various image and data analysis tools as well as several auxiliary snow cover datasets have been added to the IMS to facilitate the work of human analysts. The spatial resolution of the maps has substantially increased, and it is believed that the snow mapping time lag or the map timeliness might have also improved owing to the system enhancements. However, the extent of this latter improvement and whether it actually occurred remains uncertain.
In this work we sought to estimate the time interval between consecutive updates of the IMS snow cover map (or the revisit time interval) and to understand whether the frequency of updates and, hence the map timeliness, has improved over time. IMS snow maps at 24km spatial resolution over the 1997-2024 time period have been used. We examined daily snow extent records over several relatively small test regions and estimated the time interval between consecutive updates of the map. The focus was on the period of the most active seasonal snow melt when the rate of the snow cover retreat required daily updates of the snow map. We have found that at the inception of the IMS system in 1997, the mean frequency of updates of the IMS snow map fluctuated between once every 4 - 6 days rather than daily. By 2024 the revisit time interval dropped to about 2 days with most of the decrease occurring in the first decade of the century. With the frequency of updates improved by 2 to 4 days, the mean time lag to reproduce snow cover dynamics in the IMS snow cover map, or the map latency decreased by 1-2 days. Somewhat greater improvement in the update time interval and, hence, in the snow map latency was observed over mountainous areas as compared to predominantly flat terrains.
While decreasing snow mapping time lag and, hence, improved timeliness of IMS snow maps is certainly beneficial for operational applications, it poses challenges for the climatological use of the dataset. In the presentation we estimate and discuss the effect of improved timeliness of the product on the snow extent and snow phenology trends inferred from the IMS snow dataset.
How to cite: Romanov, P.: NOAA Interactive Snow Charts (IMS), 1997-2024: Is the snow mapping lag decreasing over time ? Implications for operational applications and snow climatology., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2938, https://doi.org/10.5194/egusphere-egu25-2938, 2025.
The study of global warming using near-surface temperature measurements requires homogenization algorithms to detect and correct inhomogeneities in time series through statistical analysis. Homogenization improves data quality and stability, enabling more reliable climate analysis and modeling. However, homogenization poses challenges, particularly the need for benchmarking datasets to ensure consistent adjustments. The recent development of reference networks, as defined by the GCOS framework, provides datasets where homogenization algorithms can be tested using metadata and measurement uncertainties that identify potential discontinuities.
This study is part of the project "Strumenti per la Mitigazione dell’Isola di Calore e il Recupero delle Aree Boschive" (SMICRAB) which integrates georeferenced data and advanced statistical modelling to develop geostatistical models and to assess vulnerability of urban and wooded areas useful for urban planning, biodiversity conservation and natural disaster risk reduction.
This study aims to optimize homogenization algorithms using data from the U.S. Climate Reference Network (USCRN). While USCRN datasets lack uncertainty estimates, these have been made available through a Copernicus project via the Climate Data Store (CDS).
The focus is on detecting breakpoints in monthly near-surface (2m) temperature series using the Standard Normal Homogeneity Test (SNHT), where breakpoints correspond to abrupt changes in time series combined with uncertainty measurements.
This work will show the results obtained from the optimization process, which examined the original series alongside trends derived using moving-average decomposition, ARIMA, and LOESS models with various spans. By comparing SNHT-detected breakpoints with those in uncertainty series, the analysis evaluates accuracy and delay in breakpoint detection. The work is supported by metadata to explain the breakpoints nature. The analysis of uncertainty measurement allows to identify a breakpoint since the uncertainty increases or decreases in correspondence with a change in the instruments or measurement setup.
The ultimate goal is to reduce false positives and enhance the reliability of adjustments, contributing to more accurate climate datasets for modeling and analysis.
How to cite: Roberto, E., Madonna, F., Karimian Sarakhs, F., and Cosimato, G.: Optimization of homogenization algorithms to detect discontinuities in near-surface (2m) temperature time series, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6408, https://doi.org/10.5194/egusphere-egu25-6408, 2025.
Meteorological, environmental, and geophysical measurements are essential for climate analysis and modelling, including weather forecasting and assessing extreme weather events such as drought and floods (WMO, 2008). Accurate meteorological data are critical, as erroneous data can significantly affect climate analyses and model validity (Llabrés-Brustenga et al., 2019). Harmonization and quality control (QC) are necessary to achieve reliable datasets.
Improved datasets can subsequently calculate drought indicators, such as the Standardized Precipitation Index (SPI) (McKee et al., 1993), and the Standardized Precipitation Evapotranspiration Index (SPEI) (Vicente-Serrano et al., 2010), which rely on meteorological data. By enhancing these indices, this work aims to improve strategies for monitoring and mitigating drought impacts.
As a case study, mean monthly temperature (Tmean °C) and cumulative precipitation (P, mm) data were collected from Ebro basin, Spain's largest catchment. Data from multiple organizations, including , were compiled for the period 1950–2023 to ensuring the highest possible accuracy and maximum station coverage. The QC process involves several steps, including test for temporal and spatial consistency, outlier detection, duplicate detection, missing data analysis, and cross-validation. Homogenization and outlier detection are the primary procedures for the monthly data series (Szentimrey, 2006; Venema et al., 2012). Proper merging of datasets from multiple providers required reprojection to align with a common spatial reference system and datum (EPSG:25830).
The workflow included the following steps: (a) Exclusion of short-length series to remove unstable or poorly accurate data; (b) Retention of stations installed before 2000 with ≥5 years of data and those installed after 2000 with ≥1 year of data, with all other stations removed; (c) Examination of temporal gaps and percentage of missing (NA) values for each station; (d) Detection of outliers, where extreme monthly temperature (>10°C) or precipitation values (>500 mm) were flagged, plotted, and compared to nearby stations. Erroneous values were removed based on expert judgment following visualisation in each subsequent step.
The QC script was developed in R and is openly accessible on GitHub: https://github.com/grumets/QCMeteoData/blob/QCMeteoData/Quality_Control.R, ensuring transparency and reproducibility.
The harmonisation process revealed challenges, including inconsistent formats across data sources and issues such as duplicated stations and measurements particularly in AEMET and XEMA datasets. Following QC, 0.35% of precipitation and 0.42% of temperature data from AEMET were removed, while only 0.13% of records from Météo France were affected, Despite assurance of dataset completeness and homogeneity by providers, the inconsistencies found showed the necessity of a more exhaustive QC procedure.
How to cite: Trypidaki, E., Batlle-Morera, A., Pesquer, L., and Domingo-Marimon, C.: Harmonizing Multi-Source Meteorological Data: A Reproducible Approach for Drought Monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8850, https://doi.org/10.5194/egusphere-egu25-8850, 2025.
Upper-air wind speed (UWS) is a crucial variable in climate change assessments and is also necessary in various socioeconomic areas such as evaluating wind energy production at the turbine height and optimizing commercial aviation routes. While reanalysis datasets have been widely utilized as essential tools for climate change analysis due to their comprehensive spatial coverage and temporal continuity, their performance in representing global UWS remains uncertain. This study evaluates four major reanalysis datasets (i.e., ERA5, ERA-Interim, MERRA-2, and JRA-55) by assessing their performance in capturing the spatio-temporal characteristics of global UWS, including climatological mean, variability, and linear trends seasonally and annually. The assessment is conducted through comparisons with radiosonde observations from two datasets: the Integrated Global Radiosounding Archive version 2 (IGRA-v2) and the homogenized Radiosounding HARMonization (RHARM) dataset distributed by the Copernicus Climate Change Service (C3S) for 1979-2023. The radiosonde observations reveal that UWS exhibits distinct vertical and zonal patterns. In the lower and middle troposphere, UWS generally remains below 10 m s⁻¹ with relatively weak non-significant trends in most stations. In contrast, the upper troposphere and lower stratosphere show pronounced zonal patterns with values up to 45 m s⁻¹, accompanied by significant increasing trends reaching 3.412 m s⁻¹ per decade. These zonal patterns demonstrate clear seasonal variations, appearing more linear in winter and wave-like in summer. While all four reanalysis datasets successfully capture the climatological patterns and seasonal variations of UWS, they show varying degrees of biases in trend estimation. These comparisons provide valuable insights for understanding UWS characteristics.
How to cite: Li, H., Azorin-Molina, C., Madonna, F., Yang, S., and Deng, K.: Assessment of global upper-air wind speed: a comparison between radiosonde observations and reanalysis, 1979-2023, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11317, https://doi.org/10.5194/egusphere-egu25-11317, 2025.
Using the changepoints identified and adjusted to produce the homogenized monthly precipitation dataset, this study developed a homogenized daily precipitation dataset for Canada, in which all data gaps are infilled for the period back to 1900 for southern stations (south of 60˚˚ N), and back to the first day of 1948 or the first day of observation before 1948 for northern stations using advanced spatial interpolation of both monthly and daily values from other stations in the region. The homogenized daily precipitation dataset was then used to assess trends in a set of precipitation extreme indices, including annual maximum one-day (RX1day) and five-day (RX5day) precipitation, as well as annual number of heavy precipitation days, R10mm (annual count of days when precipitation >=10mm). The results show that both annual maximum one-day and five-day precipitation have increased significantly at most stations across Canada over their data record periods, with most stations in the Rocky Mountains and southern Prairies showing insignificance decreases over the period of 1948-2022. Increases in the annual maximum one-day and five-day precipitation are largest and significant in central to northern Canada and in the Maritimes provinces. Annual number of heavy precipitation days has increased significantly at most stations in northern Canada.
How to cite: Wang, X. L. and Feng, Y.: Observed trends in precipitation extreme indices as inferred from a homogenized daily precipitation dataset for Canada, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7686, https://doi.org/10.5194/egusphere-egu25-7686, 2025.
This study presents the Serially Complete Precipitation dataset for South America (SC-PREC4SA), a reliable daily precipitation dataset (1960-2015) that overcomes observational gaps and maintains temporal consistency across climates. SC-PREC4SA consolidates data from 7799 weather stations, employing advanced quality control, gap-filling, and homogenization methods to improve accuracy and reliability. The quality control process addressed common and overlooked issues, resulting in considerable improvements in data integrity. Gap-filling achieved a mean prediction accuracy of 70% (60%) for wet/dry days (wet-day magnitude). These findings illustrate the reliability of the gap-filling procedure, especially in mixed climates with sparse station networks. The homogenization approach which focuses mostly on wet days, effectively reduces inhomogeneities while preserving precipitation variability and resolving any biases created during the gap-filling. The compiled dataset captures daily precipitation patterns with moderate to high accuracy, enabling the temporal coherence required for climate research. The comprehensive SC-PREC4SA framework generates multiple outputs, making it useful for a variety of applications such as climate research, hydrological modeling, and water resource management, as well as addressing long-standing issues with precipitation data availability and quality in South America. By addressing limitations in observational datasets and strengthening spatiotemporal consistency, SC-PREC4SA sets a standard for future precipitation datasets in regions with diverse climates and complex terrain. The SC-PREC4SA data collection is publicly available on figshare: https://doi.org/10.6084/m9.figshare.c.7588178.
How to cite: Huerta, A., Serrano-Notivoli, R., and Brönnimann, S.: A serially complete daily precipitation dataset for South America: quality control, gap-filling, and homogeneity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6467, https://doi.org/10.5194/egusphere-egu25-6467, 2025.
Accurate and reliable observational data are essential for understanding climate dynamics, assessing climate change impacts, and supporting informed adaptation strategies. This work highlights the pivotal role of reference measurements and climate data records accompanied by robust uncertainty quantification, in ensuring the quality and consistency of observational datasets. These datasets underpin scientific analyses and decision-making processes, particularly in the context of climate studies and applications. Within the framework of the Copernicus program, the European Centre for Medium-Range Weather Forecasts (ECMWF) operates the Copernicus Climate Change Service (C3S) with funding from the European Union. The Institute of Methodologies for Environmental Analysis (IMAA) of the National Research Council (CNR) has been engaged by ECMWF to implement the C3S2 311 Lot2 project, which builds on previous efforts in Copernicus contracts to enhance access to high-quality baseline and reference observations. This project underscores the importance of standardized measurement protocols and rigorous uncertainty assessment methodologies. The study introduces three types of datasets made available on the Copernicus Climate Data Store (CDS), which serves as a key platform for disseminating reference datasets, ensuring accessibility for researchers and stakeholders (1) upper-air reference measurements for GRUAN, (2) near-surface reference measurements from USCRN, and (3) precipitable water vapor derived from reference and reprocessed GNSS time series. The potential applications of these measurements in characterizing atmospheric conditions and investigating climate variability are discussed.
How to cite: Proto, M., Madonna, F., Rannat, K., Keernik, H., and Gardiner, T.: Enhancing Climate Studies with High-Quality Reference Observations: Insights from the Copernicus Programme, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12009, https://doi.org/10.5194/egusphere-egu25-12009, 2025.
Posters virtual: Thu, 1 May, 14:00–15:45 | vPoster spot 5
EGU25-456 | Posters virtual | VPS6
Temporal characteristics of extreme high temperatures in Wuhan since 1881Thu, 01 May, 14:00–15:45 (CEST) | vP5.13
The construction and analysis of daily temperature data series in long enough a time period is important to understand decadal to multi-decadal variability and changing trends in extreme temperature events. This paper reports a new analysis of extreme temperature indices over the last 140 yr in Wuhan, China, with an emphasis on changes in extreme high temperature changes. The daily temperature data from 9 stations from 1881 to 1950 and 1 modern station from 1951 to 2020 were used for the analysis. Based on the data, and the commonly used extreme temperature indices, the variations and long-term trends of extreme high temperature events in Wuhan since 1881 were analyzed. The results show that there was no clear warming trend in maximum temperature, but a quite large inter-annual and inter-decadal fluctuation. In contrast, there was a very significant increase in minimum temperature, with a large upward trend overall. The extreme temperature indices exhibit a periodic variability, and frequent extreme heat events have been experienced over the last 140 yr in Wuhan. Most extreme temperature indices did not exhibit remarkable changes during the first half of the period analyzed. However, the majority of the extreme temperature indices showed significant upward trends over the latter half of the 140 yr period. The possible causes of the observed changes in the extreme high temperature events in the different time periods are also discussed.
How to cite: Zheng, X., Ren, G., He, J., Zhao, Y., Ren, Y., and Yang, G.: Temporal characteristics of extreme high temperatures in Wuhan since 1881, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-456, https://doi.org/10.5194/egusphere-egu25-456, 2025.
