CL2.6 | Global and regional climate observation and monitoring
EDI PICO
Global and regional climate observation and monitoring
Convener: Colin Morice | Co-conveners: Gerard van der Schrier, Samantha Burgess, Uwe Pfeifroth, Agnieszka Faulkner
PICO
| Mon, 15 Apr, 08:30–12:30 (CEST)
 
PICO spot 5
Mon, 08:30
Global and regional climate monitoring is essential for tracking and recording the state of the Earth’s climate. Sustained monitoring provides an observational basis for our understanding of climate change and variability and allow current events to be placed into the context of the past. Climate monitoring includes assessment of all aspects of the climate system across the atmosphere, land, oceans, and cryosphere. It draws upon information from in situ and satellite observational data products as well as dynamical reanalyses. It includes assessment of spatial and temporal variability and change, climate extremes, and derived global and regional climate indices and diagnostic series.

This session welcomes contributions including:
- Advances in observational climate and reanalysis products that underpin global and regional monitoring in the context of past changes.
- Applications of observations and reanalyses in global and regional assessments of climate change and variability.
- Studies that provide a robust view of current state of the climate and its uncertainty, for example using a range of data sources and/or climate variables.
- Communication of climate monitoring programmes and of activities to communicate information on the state of the climate.

PICO: Mon, 15 Apr | PICO spot 5

Chairpersons: Colin Morice, Agnieszka Faulkner
08:30–08:35
Climate Extremes Monitoring and Earth Observation
08:35–08:37
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EGU24-6442
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ECS
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Virtual presentation
Evaluating heat extremes in the Sahel using ESA-CCI Land Surface Temperature data
(withdrawn)
Amina Maroini and Clement Albergel
08:37–08:39
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PICO5.2
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EGU24-19560
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ECS
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On-site presentation
Pia Nielsen-Englyst, Jacob Høyer, Wiebke Kolbe, and Ioanna Karagali

The Arctic is warming faster than any other region. Despite much attention there has actually been limited consensus on the magnitude of Arctic amplification over time. Within the framework of the Copernicus Marine Monitoring Service Sea Ice Thematic Assembly Center, the first gap-free (L4) of combined sea surface temperature (SST) and sea ice surface temperature (IST) climate data record of the Arctic (>58°N) has been developed for the period 1982-2021. The data set has been generated using optimal interpolation to combine multiple infrared satellite observations to daily, gap-free fields with a spatial resolution of 0.05 degrees. The combination of SST and IST provides a consistent climate indicator which can be used to monitor day-to-day variations as well as climate trends in the Arctic Ocean. Validation against in situ measurements from drifting buoys, moored buoys and Argo floats shows mean differences of 0.01 °C, 0.04 °C and 0.04 °C and standard deviations of 0.54 °C, 0.56 °C and 0.51 °C, respectively for the open ocean. Over sea ice, validation shows a mean difference of 1.52 °C and standard deviation of 3.12 °C, for skin surface temperatures. For air temperatures from the North Pole (NP) ice drifting stations as well as ECMWF distributed buoys and CRREL buoys, validation shows mean differences of −2.35 °C, −3.21 °C and –2.87 °C and standard deviations of 3.12 °C, 3.34 °C and 3.36 °C, respectively. Analysis of the CDR show sea and sea-ice surface temperature of the Arctic has risen with about 4.5 °C over the period 1982–2021, with a peak warming of around 10 °C in the northeastern Barents Sea. The L4 ISTs have been converted to near surface air temperatures (T2m) and initial results indicate that it is possible to derive reliable T2m over sea ice based on the satellite-observed L4 ISTs. The satellite-derived L4 T2m product provides an important supplement to the sparse in situ air temperature network in the Arctic and to the existing model-based air temperatures. It has a large potential to be used for assimilation, global surface temperature reconstructions or for evaluation of global reanalyses and climate models. 

How to cite: Nielsen-Englyst, P., Høyer, J., Kolbe, W., and Karagali, I.: Satellite-based surface temperatures of the Arctic ocean and sea ice, 1982–2021. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19560, https://doi.org/10.5194/egusphere-egu24-19560, 2024.

08:39–08:41
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PICO5.3
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EGU24-18474
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ECS
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On-site presentation
Magnus Suhr

Understanding and monitoring the melting of the Greenland Ice Sheet (GrIS) is crucial for contemporary climate assessment and discussions, given its potential to contribute up to 7 metres to the global sea level rise [1]. In 2021, the National Snow and Ice Data Center (NSIDC) estimated an annual contribution of 400 billion tons of meltwater from the Greenland and Antarctic ice sheets to the world’s oceans [2], impacting ocean salinity and temperature, thereby influencing ocean circulation. This study focuses on surface melt, exploring observational challenges in polar regions and proposing a method using remotely sensed data from infrared (IR) and passive microwave (PMW) satellites to create a composite surface melt product for the GrIS.

Field campaigns and in-situ measurements are limited in polar regions due to harsh conditions, making remote sensing from satellites an essential tool. Infrared data provides high spatial resolution but is sensitive to clouds, while passive microwave data, with lower spatial resolution, penetrates clouds effectively. The study combines these types of data to derive surface melt using, in part, the Cross-Polarized-Gradient-Ratio (XPGR) method, which accounts for their complementary strengths and weaknesses [3]. Additionally, an extended version (ExtXPGR) is employed, considering additional factors [4]. The study discusses the challenges and advantages of using both infrared and microwave data and highlights the importance of threshold definitions for detecting melt in each type of data.

Validation involves comparing the derived surface melt product with in-situ measurements from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) [5] and the Danish Meteorological Institute (DMI) [6]. The study also uses data from the NSIDC’s Ice Sheets Today project for further validation [7]. The results show promising agreement between the derived surface melt product and the various validation sources.

The study addresses challenges in data processing, including regridding/interpolation, and combining different data sources to achieve a temporally and spatially stable composite surface melt product covering the GrIS from 2002 to 2018.

Overall, this study contributes to the comprehensive understanding of GrIS surface melt by proposing a methodology that integrates infrared and passive microwave data, providing a valuable tool for climate researchers.

 

[1] NASA, Greenland Sea Level Rise. https://climate.nasa.gov/faq/30/if-all-of-earths-ice-melts-and-flows-into-the-ocean-what-would-happen-to-the-planets-rotation/ 

[2] NSIDC, Ice sheets and why they matter, https://nsidc.org/learn/parts-cryosphere/ice-sheets/why-ice-sheets-matter 

[3] W. Abdalati and K. Steffen, Passive microwave-derived snow melt regions on the Greenland ice sheet, doi: 10.1029/95GL00433,Journal: Geophysical Research Letters, 22,7 ,787-790, 1995, https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/95GL00433

[4] X. Fettweis and M. Tedesco and M. van den Broeke and J. Ettema, Melting trends over the Greenland ice sheet (1958–2009) from spaceborne microwave data and regional climate models, doi: 10.5194/tc-5-359-2011, journal: The Cryosphere, 5, 359–375, 2011, https://tc.copernicus.org/articles/5/359/2011/tc-5-359-2011.pdf 

[5] PROMICE and GEUS, Programme for Monitoring of the Greenland Ice Sheet & Greenland Climate Network, https://promice.org/ 

[6] DMI, GEUS, and DTU, PolarPortal, Monitoring Ice and Climate in the Arctic, http://polarportal.dk/en/home/ 

[7] NSIDC, Ice Sheets Today, https://nsidc.org/ice-sheets-today 

How to cite: Suhr, M.: Greenland surface melt product from remotely sensed multi-sensor surface temperatures., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18474, https://doi.org/10.5194/egusphere-egu24-18474, 2024.

08:41–08:43
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PICO5.4
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EGU24-15129
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On-site presentation
Volodymyr Osadchyi, Oleg Skrynyk, Vladyslav Sidenko, Enric Aguilar, Jose Guijarro, Tamás Szentimrey, Olesya Skrynyk, Liudmyla Palamarchuk, Dmytro Oshurok, Igor Kravchenko, Zoryna Kyreyeva, and Dmytro Pinchuk

In this contribution, we present the results of the development of long gridded climate time series, which cover the territory of Ukraine for the period of 1946-2020 (75 years). The spatial resolution of the developed data is 0.1o×0.1o (approximately 10 km in both longitude and latitude directions), while their time discreteness is 1 day. Four essential climate variables are included in the dataset, namely daily sums of atmospheric precipitation and daily minimum, mean and maximum air temperature. The created gridded product is based on the complete collection of weather measurements, performed at 178 meteorological stations of Ukraine, which constitute the modern national observation network. Quality assurance check, homogenization and gridding of the station time series were performed by means of widely used and well approved climatological software, i.e. INQC, Climatol and MISH, respectively. The produced gridded time series were statistically compared on the monthly and daily time scales with several existing data sets, which have the same spatial resolution (i.e., previously developed gridded monthly data of Ukraine, ERA5-Land, E-OBS). The comparison showed good accordance with UA monthly data (partly obtained from other paper sources than the daily data) and acceptable agreement with ERA5-Land and E-OBS data. The developed long gridded time series are of great importance as they were built with the involvement of as many real weather measurements as possible. Therefore, they can be used as a reference for a wide variety of climatological applications for the territory of Ukraine.

 

Oleg Skrynyk acknowledges the support from the MSCA4Ukraine fellowship program, which is funded by the European Union.

How to cite: Osadchyi, V., Skrynyk, O., Sidenko, V., Aguilar, E., Guijarro, J., Szentimrey, T., Skrynyk, O., Palamarchuk, L., Oshurok, D., Kravchenko, I., Kyreyeva, Z., and Pinchuk, D.: Gridded data of daily atmospheric precipitation and minimum, mean and maximum air temperature for Ukraine, 1946-2020, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15129, https://doi.org/10.5194/egusphere-egu24-15129, 2024.

08:43–08:45
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PICO5.5
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EGU24-16497
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On-site presentation
Oleg Skrynyk, Enric Aguilar, and Caterina Cimolai

Climate/weather extremes such as heat waves (HWs) are of the great interest to study as they have the significant harmful effect on the environment and society. There are many researches dealing with the calculation of HW metrics and their long-term trends on both the global and regional/national spatial scale. In our work based on a case study of Ukraine, we aimed to quantify the uncertainty of HW metric calculations, which might originate from climate input data. To this end, we used a mini statistical ensemble of several gridded data sets of maximum daily air temperature (TX), covering the territory of Ukraine for the period of 1950-2020 (70 years) with the same spatial resolution. The ensemble included ERA5 reanalysis data (remapped by means of the CDO software to the finer grid of 0.1ox0.1o with different interpolation algorithms), ERA5-Land, E-OBS (the ensemble mean) and Ukrainian gridded observation data previously developed for the period of 1946-2020. We defined a HW as an event when conditions (TX in our case) above criteria (90-th percentile calculated based on the WMO standard 1961-1990 reference period) persist at least three consecutive days, with permission of a 1-day time gap. Four HW metrics were considered, namely heat wave number (HWN), duration (HWD), frequency (HWF) and amplitude (HWA). The calculation of yearly time series of the HW metrics was performed by means of the R package heatwaveR for each grid point of the domain and each member of the constructed statistical ensemble. The uncertainty of the HW metrics was defined as a difference between min and max metric’s values calculated for different members of the ensemble. We also calculated the range of the possible variations in long term trends of obtained yearly time series of the HW metrics. Our results showed that depending on climate data used for HW climatology analysis, the calculation results might differ significantly for a particular grid point and year. However, on average (over the whole domain and the period under study), variation of the HW metrics is not so pronounced. Moderate variations are also observed in long-term trends of the metric time series.

 

This work has received funding through the MSCA4Ukraine project, which is funded by the European Union

How to cite: Skrynyk, O., Aguilar, E., and Cimolai, C.: Uncertainty of heat wave metric calculations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16497, https://doi.org/10.5194/egusphere-egu24-16497, 2024.

08:45–08:47
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PICO5.6
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EGU24-13311
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ECS
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On-site presentation
Caterina Cimolai, Enric Aguilar, and Oleg Skrynyk

In the context of climate change, the escalation of temperature extremes and the persistence of heatwaves have become subjects of growing concern. This study examines Argentina's heatwave patterns from 1950 to 2022, using the ERA5-LAND dataset. The use of this dataset allows for a detailed exploration with high spatial resolution (0.1° x 0.1°) and temporal precision, facilitating regional analyses in countries with vast territories, such as Argentina. The investigation focuses on analyzing temporal and spatial variations through four key metrics: heatwave duration, frequency, mean, and maximum temperature. It also explores seasonal disparities, distinguishing between the Warm Season (WS) and Cold Season (CS), and delves into the influence of the ENSO cold and warm phases (La Niña,El Niño). We use 2-meter temperature data on an hourly basis to calculate daily maximum (Tx) and minimum (Tn) temperatures and detect heatwaves. Our findings reveal an overall escalation in heatwaves across the majority of Argentine regions. Southern Patagonia and the Northwest emerge as hotspots with the most significant upward trends in heatwave metrics, while the Litoral region (Northeast) experiences noteworthy increases, particularly in its northern areas. Conversely, the South of Buenos Aires province (Central region) exhibits decreasing trends in specific areas. Specifically, for Tn, the Warm Season (WS) highlights more significant positive trends in heatwave metrics in most regions, with South Patagonia and the Northwest consistently displaying increases in all metrics. However, for Tx, positive significant trends are observed in both Warm and Cold Seasons, with the North West registering increases in all metrics except Cold Season intensity. Furthermore, the study identifies variations in heatwave occurrences during El Niño and La Niña phases. La Niña events contribute to an increased heatwave frequency across all regions, as evidenced by Tn and Tx metrics. In Patagonia, La Niña amplifies all studied heatwave metrics for both temperature variables. Conversely, during El Niño months, heatwave intensities increase nationwide, excluding Patagonia. This comprehensive research contributes to existing knowledge by providing a detailed, high spatial resolution understanding of heatwave behavior, offering invaluable insights for adapting to extreme temperature events like heatwaves. 

Key words: extreme events, ERA5-LAND, reanalysis 

How to cite: Cimolai, C., Aguilar, E., and Skrynyk, O.: Assessing Argentina's Heatwave Dynamics (1950-2022): A Comprehensive Analysis of Temporal and Spatial variability using ERA5-LAND , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13311, https://doi.org/10.5194/egusphere-egu24-13311, 2024.

08:47–08:49
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PICO5.7
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EGU24-12207
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ECS
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On-site presentation
Giulio Settanta, Piero Fraschetti, Francesca Lena, Walter Perconti, and Emanuela Piervitali

Recent records about extreme events linked to high temperatures have enhanced the attention over the heat wave phenomenon. 2022 summer has seen a worrisome heat-related excess mortality in Europe and 2023 is the hottest year of record at the global scale, according to the Copernicus Climate Change Services.

Heat waves are a major natural hazard for the most fragile parts of the population. In order to understand their impact, how communities can adapt, and to provide reliable scenarios over the close future, the key point lies in the quantitative addressing of all the aspects that compose heat waves. These include the monitoring of their change in frequency and intensity over time.

This study is focused on the analysis of several temperature-extremes indices, including those specifically related to heat waves. Each of them regards a specific aspect, such as number, frequency, duration and severity. The dataset is based upon a set of quality-controlled and homogenized daily maximum and minimum temperature data from more than 250 ground-based weather stations in Italy. To catch the tendencies of the recent Italian climate, the period analysed covers the latest 33 year time span: 1991 – 2023. Data have been extracted for the most part from SCIA, the Italian national system for climate data collection, processing, and dissemination (www.scia.isprambiente.it) and have therefore undergone a series of automatic procedures which ensure the data quality.

Two analyses have been used in this study: the first one is based on single stations time series and reports an extrapolated trend of the indices over the Italian territory; the second analysis is targeted to look for a global signal about temperature extremes behaviour and therefore considers stations’ data in an aggregated form. The latter analysis splits the 33 year time frame into three decades plus the last 3 years, to get a statistically robust assessment of climate tendencies over time.

The two analyses are found in agreement in depicting a heat wave presence in Italy which is increasing. In particular, half of the stations report an increase of 4 days or more of heat waves per decade, with a national average value of almost 2.5 days per decade. Such behaviour is observed in terms of persistency and intensity too, composing an alarming picture for both the close and the far future. The quantitative findings of the present work can support the development of adaptation measures in the country.

This work is supported by the Italian PNC - Investimento E.1- SALUTE AMBIENTE - BIODIVERSITA' - CLIMA, Missione 6 - Linea di Investimento 1.4, progetto: "Cobenefici di salute ed equità a supporto dei piani di risposta ai cambiamenti climatici". Area A-6. Codice PREV-A-2022-12376994 - Financed by the European Union - NextGenerationEU

How to cite: Settanta, G., Fraschetti, P., Lena, F., Perconti, W., and Piervitali, E.: Recent tendencies of extreme heat events in Italy: from 1991 to present days, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12207, https://doi.org/10.5194/egusphere-egu24-12207, 2024.

08:49–08:51
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PICO5.8
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EGU24-2906
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ECS
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On-site presentation
Long-term trend and variability in surface temperatures over Emilia-Romagna from 1962 to 2022
(withdrawn)
Davide Sabatani, Valentina Pavan, Gabriele Antolini, and Federico Grazzini
08:51–08:53
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PICO5.9
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EGU24-11518
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ECS
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On-site presentation
Raul R. Wood, Joren Janzing, Amber van Hamel, Jonas Götte, Dominik Schumacher, and Manuela I. Brunner

To study and model hydro-climatic extremes (e.g., droughts and floods), high quality, multivariate and spatially consistent meteorological datasets are necessary. In-situ measurements, however, often don`t cover these multivariate data needs, and are variable in space and time. New generations of high-resolution reanalysis products, with continental to global scales, are available that offer a wide range of internally consistent land surface variables. However, it is yet unclear which of these datasets are most suitable for hydro-climatic impact studies, e.g., to assess the spatiotemporal connectedness of floods and droughts, or to quantify the multivariate meteorological drivers of these extremes. Here, we present a comparison of multiple high-resolution reanalysis datasets (i.e., ERA5(-land), CERRA(-land) and CHELSA-v2) with a gridded observational product from MeteoSwiss. We compare various climatological statistics of precipitation and temperature, such as differences in the mean and the tails of the distribution, as well as the consistency of temporal trends and interannual variability. We further analyze differences in selected univariate and multivariate climate indicators, such as the annual maximum 1–5-day precipitation, the number of dry/wet days, or the fraction of solid/liquid precipitation. Lastly, we present the spatiotemporal consistency of several observed hydro-climatological extreme events, including the drought in 2018 and the floods in 2005 over Switzerland.

For most of the reanalysis products the analysis shows a clear elevational dependence in the biases, i.e., increasing with elevation, compared to the gridded observational dataset. The regional reanalysis product CERRA(-land) can overall reduce the biases in the general climatological statistics (e.g., means, tails of the distribution), but shows inconsistencies when moving to the event scale. It for example shows inconsistencies in the temporal evolution and severity of the 2018 drought in Switzerland, whereas the other reanalysis products are more consistent. This presentation gives a comprehensive overview of the differences in the current state-of-the-art reanalysis datasets over the complex terrain of Switzerland.

How to cite: Wood, R. R., Janzing, J., van Hamel, A., Götte, J., Schumacher, D., and Brunner, M. I.: High-resolution climate reanalyses datasets for hydro-climatic impact studies over Switzerland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11518, https://doi.org/10.5194/egusphere-egu24-11518, 2024.

08:53–08:55
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PICO5.10
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EGU24-15349
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ECS
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On-site presentation
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Victoria Bauer and Simon Scherrer

Heavy precipitation events and their changes due to climate change affect many aspects of daily life in the Alpine region. In this study, we revisit the long-term (1901-2022) evolution of daily and multi-day heavy precipitation intensity and frequency, discuss trends for sub-daily to multi-day events in the recent period 1981-2022, and investigate possible elevation dependencies in the complex topography of Switzerland. Station measurements from the dense operational network of MeteoSwiss from all parts of the country and elevation levels are analyzed. We find that daily maximum precipitation and the frequency of precipitation events exceeding the 99th daily percentile have increased since 1901, with a peak in the 1980s and some decline thereafter. For the more recent period 1981-2022, positive trends in summer heavy precipitation intensity are found for short (10 minutes to 3 hours) events, but no changes are found in the frequency of these events. For longer events (one to five days), however, decreases in intensity and frequency are found, especially for the winter half-year. We hypothesize that the opposing trends on long and short time scales are caused by the superposition of thermodynamics (i.e. the main forcing of anthropogenic climate change) and internal variability of atmospheric dynamics. We also observe a small negative elevation dependence of the long-term trends up to 2300 m. For the 1981-2022 trends, no strong elevation dependence is found for sub-daily events. For daily events we find small opposing negative summer and positive winter elevation dependencies. The reason for these trends remains unclear. Our results underline the need to better investigate the interplay between climate change, internal variability of large-scale dynamics and elevation for heavy precipitation in the complex Alpine terrain. Longer observational records with high spatial and temporal resolution will help to answer this open question.

How to cite: Bauer, V. and Scherrer, S.: The observed evolution of sub-daily to multi-day heavy precipitation in Switzerland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15349, https://doi.org/10.5194/egusphere-egu24-15349, 2024.

08:55–08:57
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PICO5.11
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EGU24-13380
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ECS
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On-site presentation
Benjamin Goffin, Prakrut Kansara, and Venkataraman Lakshmi

Characterizing climates (and climatic changes) is particularly important to meet Sustainable Development Goal (SDG) and inform adaptation strategies. To that end, one can capture climate heterogeneinity by focusing on extreme precipitation amounts. Our work went a step further by considering how extreme precipitation contribute to annual precipitation. Across Europe, we calculated the fraction of annual precipitation contributed by very wet days (R95pTOT). Looking at this problem from the opposite angle, we also calculated how many of the Wettest Days in a year constitute 50% of the annual precipitation (WD50). We applied these indices to gridded datasets of daily observations (e.g. E-OBS, CPC, and GPCC) at various spatial resolutions. WD50 showed that 22 to 34 days make up half of the annual precipitation throughout many parts of Europe. Aside from spatial variability, these values also fluctuated from year to year. Additionally, we found substantial differences across data products and spatial resolutions. Overall, our study highlighted how different precipitation indices and gridded datasets can capture (or fail to capture) climate heterogeneinity across Europe.

How to cite: Goffin, B., Kansara, P., and Lakshmi, V.: Assessing Climate Heterogeneity across Europe with the Wettest Days Contributing to 50% of Annual Precipitation (WD50), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13380, https://doi.org/10.5194/egusphere-egu24-13380, 2024.

08:57–08:59
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PICO5.12
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EGU24-16501
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ECS
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On-site presentation
Anna-Maria Tilg, Maral Habibi, Barbara Chimani, and Marion Greilinger

Gridded data is highly appreciated for many climate related applications due to their spatial information compared to single station locations. The quality and additional value of a gridded dataset is depending on the method itself and on the amount of additional information, e.g. topographical data, used for the interpolation. Furthermore, there is an ongoing discussion on the implications of using gridded datasets of primary climate parameters like temperature or precipitation to derive gridded datasets of climate indices. Within the project SDGHUB (https://www.sdghub.at/), we are exploring the influence of different approaches on the final gridded climate index dataset.  

The focus is on the frequently used climate index of hot days (days with a maximum air temperature above or equal to 30 °C). In the first approach the hot days were computed from the gridded climate dataset SPARTACUS (Hiebl and Frei, 2016), while in the second approach the hot days were directly interpolated considering station values. SPARTACUS is a national gridded dataset of Austria, available on a daily basis with a 1 km-spatial resolution via the DataHub of GeoSphere Austria (https://data.hub.geosphere.at/). It covers the period from 1961 onwards and includes the parameters of maximum, minimum and mean air temperature and rain amount.  

For the second approach the efficacy of several geostatistical interpolation methods, including Kriging with External Drift (KED), Ordinary Kriging (OK), and Regression Kriging (RK), with a particular emphasis on their ability to represent the spatial variability of hot days was explored. The comparison showed that KED provides the best results, as the complex terrain in Austria can be considered best. Therefore, this method was used for further analysis. To further improve the interpolation results of KED, different data transformations were tested, with the square root transformation emerging as the most effective one. As for SPARTACUS, the direct interpolation was done on a 1 km-scale for Austria.

To evaluate the performance of the two respective approaches a cross-validation is applied.

The presentation will include information on the interpolation methods and provide insights into the evaluation and differences of the two datasets as well as the effect of station density. The final dataset of hot days will be available via the DataHub of GeoSphere Austria with a monthly, seasonal and annual resolution.

 

References

Hiebl J, Frei C (2016) Daily temperature grids for Austria since 1961 – concept, creation and applicability. Theor Appl Climatol 124:161–178. https://doi.org/10.1007/s00704-015-1411-4

 

Acknowledgement

The project SDGHUB is funded by the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility and Technology (BMK) via the ICT of the Future Program - FFG No 892212.

How to cite: Tilg, A.-M., Habibi, M., Chimani, B., and Greilinger, M.: Gridded datasets of climate indices: comparison of two approaches, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16501, https://doi.org/10.5194/egusphere-egu24-16501, 2024.

08:59–09:01
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PICO5.13
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EGU24-15289
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ECS
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On-site presentation
Eric Sun, Hsin-Cheng Huang, Kuan-Hui Elaine Lin, and Wan-Ling Tseng

To understand and reconstruct paleoclimate, historical records are commonly used. In this study, we utilize the REACHES (Reconstructed East Asian Climate Historical Encoded Series) data, derived from Chinese historical documents, to derive new 0.5o x 0.5o latitude/longitude reanalyais temperature data since the mid-14th centuries. The REACHES reconstructed temperature index (four-point ordinal scale from -2 extreme cold to 1 warm) data covers more than 1,400 sites in the east China. But it has a significant flaw in a large number of missing values most likely reflecting normal weather (index value 0) and so produced a very biased and skewed distribution. To enhance the prediction for the missing data and improve REACHES data quality, we apply simple kriging to impute the missing data and set the mean of the underlying spatial process to zero (normal weather) to adjust for the missing patterns. To improve climate reconstruction accuracy in China, we propose a data assimilation approach by combining the REACHES reconstructed temperature index data with Last Millennium Ensemble (LME) reanalysis data. We propose a nonstationary time series model for the LME data and apply regularized maximum likelihood with a fused lasso penalty for parameter estimation. We treat the resulting distribution as the prior for historical temperatures, which are then updated to acquire refined temperatures based on the REACHES data using the Kalman filter and smoother. Overall, our approaches, which combine historical climate records, climate models, and statistical techniques, provide insights into past climate variations and enhance the accuracy of historic temperature estimation in east Asia.

 

How to cite: Sun, E., Huang, H.-C., Lin, K.-H. E., and Tseng, W.-L.: A new 0.5° x 0.5° REACHES-LME reanalysis temperature dataset for East Asia since the 14th century, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15289, https://doi.org/10.5194/egusphere-egu24-15289, 2024.

09:01–09:03
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PICO5.14
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EGU24-5945
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ECS
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On-site presentation
André Claro, André Fonseca, Helder Fraga, and João Santos

This study consisted of the assessment of the Iberian Peninsula’s (IP) susceptibility to precipitation extreme events (PEE) and aridity across a long historical period of 1950–2022 and a shorter period of 1981–2022, based on the calculation of eight extreme precipitation and two aridity indices. Furthermore, two recently developed extreme precipitation susceptibility indices were also applied, namely a composite index and a principal component analysis-based index. ERA5-Land reanalysis data were used for those calculations, previously bias-corrected with the Iberia01 observational dataset as a baseline in their overlapping period of 1971–2015, following a quantile-mapping approach. A trend analysis performed for the two periods reveals an annual and seasonal drying trend over southwestern, central, and northeastern regions, as well as a wetting trend over the southeast annually. Regarding the PEE contribution to total precipitation (which is higher over eastern IP, and around 24% to 28%), it is increasing in several coastal regions during winter, and in north-central regions during summer and annually. High to very high susceptibility areas, which correspond to approximately 50% of the IP, are located on the mountains’ Atlantic-facing (western IP mountains) or Mediterranean-facing (eastern IP mountains) side, while the inner IP plateaus reveal low to moderate susceptibility. Our results agree with previous studies and show with high detail the susceptibility to PEEs and the recent past trends of all IP regions, which is a novelty comparatively to those studies. This highly detailed information can be used, e.g., to improve the assessment and mitigation of urban flood risks, mitigate water scarcity in the agro-food industry, or prevent crop destruction during extreme precipitation events.

How to cite: Claro, A., Fonseca, A., Fraga, H., and Santos, J.: Susceptibility of the Iberian Peninsula to extreme precipitation and aridity: a high-resolution analysis for 1950–2022, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5945, https://doi.org/10.5194/egusphere-egu24-5945, 2024.

09:03–10:15
Chairpersons: Colin Morice, Agnieszka Faulkner
Monitoring the State of Global and Regional Climate
10:45–10:47
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PICO5.1
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EGU24-4732
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On-site presentation
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Richard Betts, Stephen Belcher, Leon Hermanson, Albert Klein Tank, Jason Lowe, Chris Jones, Colin Morice, Nick Rayner, Adam Scaife, and Peter Stott

It will be important to know when global warming has reached 1.5°C, as this will be a key marker in global policy given the ambition to pursue efforts to limit warming to this level. But how should the temperature increase be defined in this context? The Global Stocktake agreed at COP28 in Dubai noted “global warming of about 1.1 °C” based on the IPCC 6th Assessment Report, but this number applies to the average of 2011-2020 and hence is already out of date. We propose that the metric for current global warming should allow immediate of identification of passing particular levels of global warming, such as 1.5°C, to avoid unnecessary delays in responding to the exceedance. We also propose that the metric should be consistent with the definition of future Global Warming Levels in the IPCC 6th Assessment Report, which uses 20-year means of projected temperature anomalies with an exceedance year defined as the mid-point of the 20-year period. Without this consistency, the apparent time of reaching 1.5°C could differ from the time previously projected by the IPCC merely because of differences in the definition, which could be misinterpreted as indicting that global warming had reached 1.5°C either earlier or later than projected. This could either undermine confidence in projections or misinform discussions on action to address climate change.

While various indicators are already in use that provide a more instantaneous measure of global warming, none are consistent with the IPCC definition of future GWLs nor are suitable for use as a baseline for impacts assessments. To address this, we propose a new metric, the Current Global Warming Level (CGWL), which uses a 20-year average over the previous 10 years from observations and the next 10 years from a forecast or projections. Here we compare the CGWL with the various indicators currently in use for quantifying the current level of global mean temperature change, and compare their indications of global temperature change over recent decades and of the current level of global warming. We also compare the year of exceeding past global warming levels of 0.5°C, 1.0°C and 1.2°C for each indicator. We use a combined observational dataset following IPCC methods and process the indicators from this. For each indicator, we explain potential difficulties that could arise from its use to assess when global warming reaches 1.5°C relative to pre-industrial, and explain the rationale for our proposed indicator, the Current Global Warming Level.

How to cite: Betts, R., Belcher, S., Hermanson, L., Klein Tank, A., Lowe, J., Jones, C., Morice, C., Rayner, N., Scaife, A., and Stott, P.: Approaching 1.5°C: What is the Current Global Warming Level?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4732, https://doi.org/10.5194/egusphere-egu24-4732, 2024.

10:47–10:49
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PICO5.2
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EGU24-11519
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ECS
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On-site presentation
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Octave Tessiot and Aurélien Ribes

Climate change monitoring and adaptation policies require reliable and updated indicators about global warming. Reports of the Intergovernmental Panel on Climate Change (IPCC) provide updated indicators every 5 to 7 years, based in particular on the latest available data and evidence. In the last report, projections were derived from climate model simulations constrained by observations. Current warming was derived from observations averaged over the last 10 years, or again a combination of model and observations in attribution statements.

Here we explore the possibility of updating present and future warming estimates on a yearly basis, by incorporating new observations to observational constraints. First, we show that adding the latest temperature observations each year leads to a continuous improvement in estimating warming projections. In particular, warming estimates are not affected by year-to-year internal variability. Second, regarding current warming, we show that observational constraints can be used to derive an estimate of the forced warming for the current year, without having to average over the last 10 years - as the IPCC does in its latest report. This provides an unbiased estimate of current warming, without adding further variance. Third, we show that updating model data can lead to a gap in the estimate of current and future warming, but this remains within the uncertainties we estimate. Finally, we argue that annual updates of current and future warming estimates provide accurate, robust, and reliable information about climate change, while remaining consistent with previous years' estimates.

How to cite: Tessiot, O. and Ribes, A.: Towards monitoring climate change on a yearly basis using observational constraints., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11519, https://doi.org/10.5194/egusphere-egu24-11519, 2024.

10:49–10:51
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PICO5.3
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EGU24-5499
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On-site presentation
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Jessica Blunden, Robert Dunn, and John Kennedy

One of the goals of climate monitoring is to ensure that the ongoing changes in the Earth's climate system are placed in context against longer-term changes and are clearly and widely communicated.  There are a number of reports produced on an annual basis which collate and synthesise the outputs from climate monitoring products for a range of essential climate variables (ECVs) and extreme climate events.  Among the most highly regarded peer-reviewed global-scale publications, a provisional version of the World Meteorological Organisation (WMO) State of the Global Climate report feeds into the United Nations Framework Convention on Climate Change Conference of the Parties (UNFCCC COP) process each year, directly informing policy makers and stakeholders about the current state of several key climate metrics.  The more comprehensive BAMS State of the Climate report is an almanac of the major events of each year and an assessment of more than three-dozen ECVs that encompass Earth's land, oceans, cryosphere, and atmosphere, which provides a useful reference document for a wide range of stakeholders, from the general public to educators to private and public decision makers.  In this presentation we outline the process behind each report, the wide range of information they contain, and give pointers on how to get more involved.

How to cite: Blunden, J., Dunn, R., and Kennedy, J.: Destinations of Climate Monitoring information: State of the Climate reports, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5499, https://doi.org/10.5194/egusphere-egu24-5499, 2024.

10:51–10:53
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PICO5.4
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EGU24-14715
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ECS
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On-site presentation
 Promoting the Peaceful Use of IMS Data for Climate Research and Climate Change Monitoring             
(withdrawn)
Makena Riungu, Vera Miljanovic, and Martin Kalinowski
10:53–10:55
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PICO5.5
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EGU24-5536
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On-site presentation
Romana Beranova and Radan Huth

Understanding long-term changes in precipitation is essential for climate research. However, it is a well-established fact that various types of data (station, gridded, reanalysis) can yield different results in extreme and trend statistical analyses. In this contribution, we investigate the long-term changes in precipitation characteristics across different data sources, focusing on the European land area. Our study incorporates station data from the ECA&D project, station data interpolated onto a regular grid (Eobs, Regen) and reanalyses (NCEP/NCAR, 20CR, ERA5). The objective is to explore the seasonal trends of precipitation total amounts, intensity, and probability. The analysis concentrates on the long-term trends of these precipitation characteristics from 1961 to 2010, with emphasis on winter and summer. Trends are estimated using the nonparametric Sen’s slope estimator, and the Mann-Kendall test is applied to assess the statistical significance of these trends. In general, a similar trend pattern is evident within the ECA&D, Eobs and ERA5 datasets. Conversely, the NCEP/NCAR and 20CR reanalyses exhibit the most significant disparities in trends for all precipitation characteristics. Our aim is not only to identify differences between the data sources but also to pinpoint the causes behind these differences.

How to cite: Beranova, R. and Huth, R.: A multi-dataset trend analysis of precipitation characteristics over Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5536, https://doi.org/10.5194/egusphere-egu24-5536, 2024.

10:55–10:57
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PICO5.6
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EGU24-18622
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On-site presentation
Analyzing Climate Trends in Southern Africa: A Comparative Study of Observed and Modeled Data on Regional Warming
(withdrawn)
Izidine Pinto and Kwesi Quagraine
10:57–10:59
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EGU24-14413
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ECS
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Virtual presentation
Marcus Silveira, Luiz Aragão, and Patrick Keys

In the Brazilian Amazon, large-scale and spatially explicit evidence of observed climate change in the recent decades is still scarce but important for monitoring areas that require climate action if changes persist over a long-term. Many areas in this region have faced high levels of deforestation during the last decades, leading to changes in energy fluxes that may amplify the effects on climate caused by globally increasing greenhouse gas (GHG) emissions.

In this study we assessed how land surface temperature and rainfall changed across space and seasonal intervals during the last two decades. We also aimed to understand if highly deforested areas were more likely to experience positive or negative changes at a particular seasonal interval.

We focused on the Amazon rainforest domain over Brazil, dividing the region into 0.5° grid cells. We used forest cover mapping from MapBiomas to compute total deforestation from 2003 to 2021 relative to the cell’s area. For land surface temperature (LST), we used data from the MODIS MYD11A1 product, selecting daily daytime observations. For rainfall volume we used daily estimates from CHIRPS. For each year from 2003-2021, we performed aggregations for the annual, driest quarter (three consecutive driest months based on the climatology) and wettest quarter for each cell, averaging LST and summing rainfall volume. We then subtracted the 2013-2021 to 2003-2012 averages in each cell, excluding extreme drought years to reduce changes from internal climate variability such as extreme El Niño. 

We found that, over the last two decades, annual LST and rainfall increased on average in the Brazilian Amazon by 0.31°C and 4% respectively. At least 80% of cells experienced increases in LST at any of the time intervals, with warming greater than 1.5°C in some locations. Areas with substantial warming were predominant in southern Amazon at the annual scale and driest quarter, but widely dispersed in the wettest quarter. In terms of rainfall, increases were found for 68% of cells at the annual scale and ~56.5% in the other time intervals. The driest quarter had the largest frequency of rainfall reductions and increases >20%. In all time intervals, substantial rainfall decreases were found in the states of Roraima and southwestern Amazonas, whereas substantial rainfall increases were found in eastern Acre.

For all cells with deforestation >10%, more than 70% had LST warming >0.5°C annually and during the driest quarter, in contrast to only 40% in the wettest quarter; in cells with no long-term deforestation (<=0.5%), the proportion was less than 8%. In all time intervals, areas with >10% deforestation had a frequency of both rainfall increases and decreases about 10-15 percentage points greater, suggesting no clear pattern for a greater likelihood in rainfall changes.

We find that regional climate change in the Amazon is very dynamic in space and season, especially in terms of rainfall changes, requiring careful examination. We also demonstrate how deforestation has been leading to a greater intensity of LST warming in the region, representing a regional climate forcing that may be as important as increased GHG emissions.

How to cite: Silveira, M., Aragão, L., and Keys, P.: Land surface temperature and rainfall changes in the Brazilian Amazon during the last two decades: variability in space, season, and by deforestation intensity , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14413, https://doi.org/10.5194/egusphere-egu24-14413, 2024.

10:59–11:01
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EGU24-12091
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ECS
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Virtual presentation
Husayn El Sharif, Gustavo Cárdenas-Castillero, Shivani Chougule, Aris Georgakakos, and Juan Pimento

Panama is vulnerable to the effects of climate change, particularly the increase in ocean and atmospheric temperatures, changes in precipitation patterns, and rises in sea level. During the last decades, regional and local studies have reported a warming trend in the Central American region, including Panama. Temperature data from Central Panama indicate an increase of approximately 1°C since the 1970's. A 142-year precipitation record from the Panama Canal Authority shows a clear trend towards an increased frequency of severe storms and droughts during the last 25 years. The purpose of these historical climate assessments is twofold: first, to assess whether statistically significant climatic trends exist in the historical record (indicating that climatic change is occurring), and second, to establish climatic baselines against which to evaluate the consistency and relative change of future climate projections vis-à-vis the historical record. Historical climatic data includes precipitation, maximum and minimum air temperature, and surface downwelling solar radiation (MJ/m2) from all existing ground stations in Panama.

The significance of trends and other metrics of climatic change will be assessed through non-parametric statistical tests. Numerical trends will be developed and assessed for each and all climatic variables of interest for the 30 most recent years at different temporal and spatial scales. This analysis will be conducted on annual, monthly, and daily time scales and at the national, hydroclimatic region, province, and district spatial scales. In addition to the four climatic variables referenced above, trends will also be assessed for the following climatic indices at the same spatial scales referenced above: heat waves, annual drought duration, average and maximum rainfall deficit during annual droughts, biennial drought duration, and average and maximum rainfall deficit during biennial droughts. This historical weather data analysis is crucial for water management, water availability, Panama Canal operation, agriculture, and energy, helping Panamanian society quantify and manage climate risks.

How to cite: El Sharif, H., Cárdenas-Castillero, G., Chougule, S., Georgakakos, A., and Pimento, J.: Historical Climate Assessments in Panama, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12091, https://doi.org/10.5194/egusphere-egu24-12091, 2024.

11:01–11:03
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PICO5.7
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EGU24-17206
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On-site presentation
Halldór Björnsson, Kristín Ólafsdóttir, and Guðfinna Aðalgeirsdóttir

The climate in Iceland has warmed by about 1°C per century since the start of the 20th century. The warming has been intermittent with strong decadal variablility but the last two decades stand out as the warmest since continuous measurements began in the 19th century. Changes in precipitation, snow fraction and extreme precipitation have also occurred. The warming has impacted glaciers which have lost 16% of their mass and 19% of their area since the early 20th century. Several small glaciers have vanished, new pro-glacial lakes have formed and significant changes have occurred in the drainage system from some glaciers. The iso-static rebound resulting from the ice mass loss is widespread and exceeds 1 cm per year on the southeastern shore where it is highest. The warming has generally increased the productivity of plants leading to observed greening.

The recently published Climate Change Impact Assessment for Iceland, the fourth since the start of this century concluded that Climate Change is having a strong impact on physical and biological systems, with increasing societal impacts, especially with increasing risks associated with natural hazards such as flash floods, landslides, subglacial eruptions, and coastal flooding.

How to cite: Björnsson, H., Ólafsdóttir, K., and Aðalgeirsdóttir, G.: Recent climate change in Iceland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17206, https://doi.org/10.5194/egusphere-egu24-17206, 2024.

11:03–11:05
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PICO5.8
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EGU24-13394
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On-site presentation
Akiko Tanaka and Shusaku Goto

Past ground surface temperature (GST) changes have been reconstructed from borehole temperature-depth profiles in many areas over the world, but there are still significant uncertainties in understanding regional responses. We reconstruct the past GST changes in southwestern Japan over the past century from borehole temperature-depth profiles using the Bayesian least-squares method to invert borehole temperatures to produce the histories. This analysis reveals that the average reconstructed GST shows temperature increases of about 1.0oC during the past century. This is consistent with nearby meteorological annual surface air temperature (SAT) and area-averaged annual mean sea surface temperature (SST) reported by the Japan Meteorological Agency. The consistency suggests that GST from borehole temperature-depth profiles shares information with the mean SAT and SST records over long time scales. Reconstructed GST changes using other temperature profiles at different parts of in and around Japan showed that the amplitude of the temperature increase varies by site. This suggests that further research with better spatial resolution is required.

How to cite: Tanaka, A. and Goto, S.: Japan warming inferred by combining borehole temperatures with surface air and mean sea surface temperatures, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13394, https://doi.org/10.5194/egusphere-egu24-13394, 2024.

11:05–12:30