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.

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.

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 0.1^o×0.1^o (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.

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 0.1^ox0.1^o 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.

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.

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.

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.

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.

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.

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 0.5^o x 0.5^o latitude/longitude reanalyais temperature data since the mid-14^th 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.

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.

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.

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.

The climate in Iceland has warmed by about 1°C per century since the start of the 20^th 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 19^th 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 20^th 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.

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 1.0^oC 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.