Displays

As greenhouse gases continue to accumulate in Earth’s atmosphere, the nature of extreme weather events (EWEs) has been changing and is expected to change in the future. EWEs have contributions from anthropogenic climate change as well as from natural variability, which complicates attribution statements. EWERAM is a project that has been funded through the New Zealand Ministry of Business, Innovation and Employment Smart Ideas programme to develop the capability to provide, within days of an EWE having occurred over New Zealand, and while public interest is still high, scientifically defensible statements about the role of climate change in both the severity and frequency of that event. This is expected to raise public awareness and understanding of the effects of climate change on EWEs.

A team of researchers from five institutions across New Zealand are participating in EWERAM. EWE attribution is a multi-faceted problem and different approaches are required to address different research aims. Although robustly assessing the contribution of changes in the thermodynamic state to an observed event can be more tractable than including changes in the dynamics of weather systems, for New Zealand, changes in dynamics have had a large impact on the frequency and location of EWEs. As such, we have initiated several lines of research to deliver metrics on EWE attribution, tailored to meet the needs of various stakeholders, that encompass the effects of both dynamical and thermodynamical changes in the atmosphere. This presentation will give an overview of EWERAM and present the methodologies and tools used in the project.

How to cite: Tradowsky, J., Bodeker, G., Bird, L., Kremser, S., Kreft, P., Soltanzadeh, I., Rausch, J., Rana, S., Rye, G., Ziegler, A., Rosier, S., Stone, D., Dean, S., Renwick, J., Frame, D., and McDonald, A.: The Extreme Weather Event Real-time Attribution Machine (EWERAM) – An Overview, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11715, https://doi.org/10.5194/egusphere-egu2020-11715, 2020.

Constraining the non-stationarity of climate extremes is a topical area of research that is complicated by the brevity and sparsity of observational records. For regions with available data, analyses typically focus on detecting century-long changes in the annual maxima. However, these are not necessarily impact-relevant events and hence, a potentially more pressing research challenge is the detection of changes in the 1-in-100-year event. Furthermore, recent decades have seen abrupt temperature increases and therefore detecting decadal, rather than centurial, trends may be more important. An alternative approach to the traditional analysis based on observations is to pool ensemble members from seasonal prediction systems into an UNprecedented Simulated Extreme ENsemble (UNSEEN). This method creates numerous alternative pathways of reality, thus increasing the sample size. Previous studies have shown promising results that improve design value estimates by this method. Here, we use the hindcast of the ECMWF seasonal prediction system SEAS5 and pool together four lead times and 25 ensemble members, resulting in an ensemble of 100. We assess the robustness of this method in terms of the ensemble member independence, model stability and fidelity and then use the UNSEEN ensemble to detect non-stationarities in 100-year precipitation estimates over the period 1981-2016. We justify the pooling of ensemble members and lead times through a case study of autumn 3-day extreme precipitation events across Norway and Svalbard, which shows that the ensemble members are independent and that the model is stable over lead times. Despite previously reported model biases in the sea-ice extent and the sea-surface temperature in SEAS5, validation measures indicate that the model reliably reproduces ‘visible extremes’, i.e. the seasonal maxima. Using extreme value statistics, we then compare estimated return values from observations with the UNSEEN ensemble. Results indicate that the UNSEEN approach provides significantly different extreme values for return periods above 35 years. Additionally, while it is problematic to detect trends in the 100-year values from observations, the UNSEEN approach finds a significant positive trend over Svalbard. Validating UNSEEN events and trends is a complex task, but our approach reproduces ‘visible’ extremes well, building confidence in the modeled extremes. Both Norway and Svalbard have experienced severe floods from extreme precipitation events and our UNSEEN-trends approach is the first to provide an indication of the changes in these rare events. Further application of this approach can 1) help estimating design values, especially relevant for data-scarce regions 2) detect trends in rare climate extremes, including other variables than precipitation and 3) improve our physical understanding of the non-stationarity of climate extremes, through the possible attribution of detected trends.

How to cite: Kelder, T., Müller, M., Slater, L., Wilby, R., Bohlinger, P., Marjoribanks, T., Prudhomme, C., Dyrrdal, A., Nipen, T., and Ferranti, L.: UNSEEN trends: Towards detection of changes in 100-year precipitation events over the last 35 years, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-521, https://doi.org/10.5194/egusphere-egu2020-521, 2020.

For public, scientists and policy-makers, it is important to know to what extent human-induced climate change played (or did not play) a role behind changing risks of extreme weather events. Probabilistic event attribution (PEA) can provide scientific information regarding this association and reveal whether and to what extent external drivers of climate change have influenced the probability of high-impact weather events. To date, most of the PEA-based studies have focused on extreme events of mid-latitudes and predominantly events that have occurred in the developed countries. Developing countries located at the tropical monsoon regions are underrepresented in this field of research, despite that fact that these countries are highly climate vulnerable, often experience extreme weather events that cause severe damages and have the least capacity to adapt. 

Bangladesh, a South Asian country with tropical monsoon climate, is a hotspot of climate change impacts as it is vulnerable to a combination of increasing challenges from record-breaking temperatures, extreme rainfall events, more intense river floods, tropical cyclones, and rising sea levels. The unique geographical location of this country particularly exposes it to high risks of flooding and landslides caused by heavy rainfall events. Observation based studies indicate that the frequency of high-intensity rainfall events may have already increased, with significant repercussions for agriculture, health, ecosystems and economic development.

Using high resolution regional climate model (RCM) simulations from weather@home, here we quantify the risks of extreme rainfall events in Bangladesh under pre-industrial, present-day and future climate scenarios of the Paris Agreement temperature targets of 1.5°C and 2°C warming. Additionally, we assess the risks under greenhouse gas (GHG)-only climate scenario where anthropogenic aerosols are reduced to pre-industrial levels. In order to test the robustness of the RCM results, available four atmosphere only global circulation model (AGCM) simulations from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project are analysed. This enabled for the first time, a multi-model assessment of the changing risks of extreme rainfall events in Bangladesh considering anthropogenic climate change drivers.

Findings suggest that both a 1.5°C and 2.0°C warmer world is poised to experience increased seasonal mean and, to a lesser extent, increased extreme rainfall events. The risk of a 1 in 100 year rainfall event under current climate condition has already increased significantly compared with pre-industrial levels. Substantial reduction in the impacts resulting from 1.5°C compared with 2°C warming is reported in this study; however the difference is spatially and temporally variable across Bangladesh. This paper highlights that reduction in the anthropogenic aerosols play an important role in determining the overall future climate change impacts; by exacerbating the effects of GHG induced global warming and thereby increasing the rainfall intensity. The policy-makers therefore need to take stronger climate actions to avoid impacts of 2°C warmer world and consider future changes in the risks of extreme rainfall events in the face of changeable GHG and aerosol impacts.

How to cite: Rimi, R., Haustein, K., Barbour, E., Sparrow, S., Li, S., Wallom, D., and Allen, M.: A Multi-model Assessment of the Changing Risks of Extreme Rainfall Events in Bangladesh under 1.5 and 2.0 degrees’ warmer worlds , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14098, https://doi.org/10.5194/egusphere-egu2020-14098, 2020.

In 2018 & 2019 China was impacted by three extreme hydrological events. Heavy rainfall in Central China during summer 2018, heavy summer rainfall in south eastern China during 2019 and a severe drought in Yunnan in May/June of 2019. Using the Hadley Centre’s state-of-the-art attribution system the role of anthropogenic forcing in the changing risk of these events was studied. The modelling system uses two large ensembles of a 60 km resolution atmospheric model driven with sea-surface temperatures(SST), sea-ice and a package of different forcings. One ensemble  uses observed SSTs and natural and human forcings while the other uses pre-industrialised SSTs and natural forcings.  The studies were done in two week-long workshops held in China which aimed to train early career researchers to carry out event attribution studies. The methodologies used in all studies were similar. In all three cases, anthropogenic forcing reduced the risk of heavy rainfall and increased the risk of drought.  Changes in risk for the three events are surprisingly large with the probability of the  Yunnan drought increasing by a factor of 14, the probability of the summer 2019 heavy rainfall declining by a factor of four, and the probability of the summer 2018 rainfall event declining by a half.  Aerosol induced circulation changes in the model are the likely reason for these changes.

How to cite: Tett, S. and the The CSSP China Event Attribution Team: Attributing Chinese Hydrological Extreme EventsAttributing Chinese Hydrological Extreme Events, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19484, https://doi.org/10.5194/egusphere-egu2020-19484, 2020.

The damages caused by climate extremes to socio-economy and environment is unprecedented during the recent decades, and it causes even more damage when the climate extremes occur in consecutive years. Since the starting of this Century, Europe has witnessed a series of extreme droughts (2003, 2010, 2015, 2018-19) with substantial socioeconomic and ecological losses. This study, with the help of long term data inventory starting from 1766-present, evaluates the occurrence of consecutive two-year droughts using Standardized Precipitation Index (SPI) and Standard Precipitation-Evaporation Index (SPEI) during the vegetation period. Although, the 2018 drought is reported in many of the recent studies, 2019 also suffered a huge rainfall deficit together with rising atmospheric temperature. This indicates an increasing evapotranspiration rates, which may intensify the existing drought conditions that originally developed from rainfall deficits. These effects are further noticed in terms of widespread reduction in the overall vegetative development during 2018-2019.

Considering this impact, we evaluate 2018-19 droughts in terms of both SPI and SPEI and compare its extent with the extreme hot drought of 2003 to place these ongoing droughts within a climatological context. The average severity of the combined two-year drought event (2018-19) is comparable to that of the 2003 drought. However, for the 2003 event, the drought recovered during the proceeding year, which was not the case for the year 2018-19, which is evident from decline in vegetation development dynamics. Furthermore, the analysis with consecutive droughts during 2018-19 in Central Europe shows that it is a very rare event with a return period of over 200 years; and therefore can be considered as one of the most severe droughts in Europe since 1766. 

Using a suite of climate model simulations from CMIP-5 (N=12), we detected an important and potential role of human-induced climate change in increasing the risk of occurrence of the consecutive droughts over central Europe. Here, with the implementation of the fraction of attributable risk (FAR), we show the signifying role of human influence (or anthropogenic forcing) in modulating the consecutive year droughts. Furthermore, these events in the future projection of climate models suggest an increasing frequency in the latter part of 21st century.

How to cite: Hari, V., Rakovec, O., Hanel, M., Markonis, Y., and Kumar, R.: Potential impacts of anthropogenic forcing on the consecutive 2018-19 droughts in the central Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8005, https://doi.org/10.5194/egusphere-egu2020-8005, 2020.

The subtropical (south of 15deg.S) southern Africa experienced one of the most severe droughts in the record - accompanied with an exceptional heat wave – during the austral spring (October through December – the first half of the main rainy season) of 2015. The observed surface hydro-meteorological conditions led to substantial socio-economic impacts in the region - with mostly semi-arid climate and high spatial-temporal variability - where drought is the principal type of widespread natural disaster. More specifically, very low precipitation - compounded with very high surface air temperature (SAT) - caused low runoff, water shortages and restrictions, reduced electricity generation, and considerable loss of crops and livestock prompting Botswana, Namibia, Lesotho, Malawi, Swaziland and Zimbabwe to declare national drought emergencies. Every extreme event is the result of a combination of external drivers, natural (solar forcing and volcanos), and anthropogenic (carbon dioxide emissions, land use, etc.), and internal variability. The risk-based or probabilistic event attribution assesses to what extent anthropogenic forcing modifies the probability and magnitude, and hence the risk of an extreme event or a class of events to occur (i.e. to identify “the sharp edge” of climate change). This study utilises multiple long-term observations (CRU TS 4.03, GPCC v2018, NOAA PREC/L, etc.), and AGCM and CGCM historical simulations (12 models in total spread across CMIP3, CMIP5 and CMIP6 generations) to estimate risk indicators such as probability (risk) ratio (RR) and intensity change for the OND 2015 drought with respect to the beginning of the 20^th century or pre-industrial conditions. Our multi-method approach indicates significant influence of climate change in total OND precipitation, e.g. RR = 1.48 (with 95% CI: 1.20, 1.85), and precipitation-temperature (mean OND SAT) ratio fields over the subtropical southern Africa, but uncertainty of risk indicators can be substantial. The crucial elements of atmospheric circulation and teleconnections (such as Angola Low and ENSO influence) associated with this extreme event are analysed and elaborated using the latest NOAA-CIRES-DOE 20^th Century Reanalysis version 3.

How to cite: Fuckar, N., Otto, F., Lehner, F., Wolski, P., Howard, E., and Sparrow, S.: Multi-method event attribution of 2015 OND drought in subtropical southern Africa, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11903, https://doi.org/10.5194/egusphere-egu2020-11903, 2020.

Hazards associated with the combined effects of temperature and humidity can have a wide range of impacts, particularly on human health and agriculture. The human body removes metabolic heat through sweating and heat conduction, and the efficiency of these processes is reduced when ambient temperatures and humidity are high, resulting in heat stress. The effects of this range from general discomfort to increased morbidity and mortality rates, trends that have been observed during recent severe heatwaves such as those that occurred during the summer of 2019 in Europe. A number of factors may exacerbate heat stress, including intense physical activity and being located in an urban area as opposed to a rural area.

As global temperatures increase, the risk associated with heat stress hazards is expected to increase, and this signal is expected to emerge from natural variability over the coming decades, if not sooner. Here, simulations from the new CMIP6 models are analysed to investigate the timing of emergence of heat stress hazards, in order to identify regions of the globe that are particularly vulnerable to extreme heat stress and/or imminent emergence of these hazards. Event attribution techniques are also applied to estimate the impact of anthropogenic warming on the hazard risk.

How to cite: Lord, N. S. and Mitchell, D. M.: Emergence of heat stress hazards in the CMIP6 models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19302, https://doi.org/10.5194/egusphere-egu2020-19302, 2020.

Internal atmospheric variability fundamentally limits short- and medium-term climate predictability and obscures evidence of climatic changes on regional scales. We discuss the suitability of incorporating statistical learning techniques to detect global climate signals from spatial patterns.

Our detection approach uses climate model simulations and a statistical learning algorithm to encapsulate the relationship between spatial patterns of daily temperature and humidity, and key climate change metrics such as annual global mean temperature or Earth’s energy imbalance. Observations are then projected onto this relationship to detect climatic changes. We show that fingerprints of changes in climate can be assessed and detected in the observed global climate record at time steps such as months or days by comparison against a historical baseline from CMIP5 simulations or reanalyses. Detection can be achieved also when ignoring the long-term global mean warming trend.

We further discuss how these approaches could be extended by using statistical techniques that would work well under variations of specific external forcings, e.g. solar or volcanic forcing, to predict only variations in a specific external forcing. Overall, we conclude that statistical learning techniques that characterize multivariate signals from high-dimensional climate data are a useful tool for the detection of climate signals at regional and global scales.

How to cite: Sippel, S., Meinshausen, N., Fischer, E., Szekely, E., and Knutti, R.: Characterizing and detecting climate signals in observations and models using statistical learning, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13003, https://doi.org/10.5194/egusphere-egu2020-13003, 2020.

Tropospheric and stratospheric tropical temperature trends in recent decades have been notoriously hard to simulate using climate models, notably in the upper troposphere.  Aside from the warming signal itself, this has broader implications, e.g. atmospheric circulation trends depend on latitudinal temperature gradients. In this study, tropical temperature trends in the CMIP6 models are examined, from 1979 to 2014, and contrasted with trends from the RICH/RAOBCORE radiosondes, and the ERA5/5.1 reanalysis.  Confirming previous studies, we find considerable warming biases in the CMIP6 modeled trends, and show that these biases are linked to biases in surface temperature (the models warm too much).  We also uncover previously undocumented biases in the lower-middle stratosphere: the CMIP6 models appear unable to capture the time evolution of stratospheric cooling, which is non-monotonic owing to the Montreal Protocol. This troposphere-warming, stratospheric-cooling fingerprint of climate change is therefore not well captured in CMIP6 models. Finally, we quantify the relative roles of individual climate forcings in tropspheric and stratospheric temperatures, including that of internal variability.

How to cite: Mitchell, D., Lo, E., Seviour, W., and Polvani, L.: The vertical profile of tropical temperature trends: Persistent model biases in the context of forced and internal variability, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3631, https://doi.org/10.5194/egusphere-egu2020-3631, 2020.

The impacts of temperature extremes are strongly amplified with the duration by which they persist over a specific region. In the mid-latitudes, surface-weather as characterized by warm and cold temperature anomalies generally propagates eastward, following the movement of cyclones and anti-cyclones that govern the weather conditions in those regions. It has been suggested that surface weather might become more persistent in the future as a response to changes in land-atmosphere feedbacks and changes to the large-scale circulation, such as a weakening of the zonal winds or a shift in the jet due to the Hadley cell expansion.

In this study, we employ a tracking algorithm to recover the tracks of warm and cold near surface temperature anomalies in comprehensive climate simulations of current and future climates. This enables us to quantify their properties statistically. We focus on their propagation speeds, and find that the eastward movement of both warm and cold temperature anomalies is projected to significantly decrease across the Northern hemisphere mid-latitudes by the end of the century, suggesting an amplified risk of longer lasting hot- and cold extremes under future climate scenarios.

We investigate to what extent this slow-down of mid-latitude temperature anomalies can be attributed to future atmospheric circulation changes on hemispheric and regional levels, and assess how the projected changes in each model are linked to respective trends in high-latitude and land warming.

How to cite: Kornhuber, K. and Tamarin-Brodsky, T.: The projected slow-down of mid-latitude temperature anomalies, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11811, https://doi.org/10.5194/egusphere-egu2020-11811, 2020.

Anthropogenic forcing induces a Sea Surface Temperature (SST) warming and sea level rise. While these globally-averaged signals are clearly detectable, it is more difficult to detect regional deviations from these global trends, due to the strong aliasing by internal climate variability. Yet, changes in SST gradients are thought to influence the frequency of extreme IOD events and the impacts of extreme ENSO events, while regional sea level and rainfall changes have strong societal implications. Here, we investigate if such regional signals are already detectable in the tropics.

To that end, we apply the “emergence time” concept (i.e. when the climate change signal irreversibly emerges from the background climate “noise”) on historical simulations combined with RCP8.5 projections from the Coupled Model Intercomparison Project (CMIP5). By 2100, CMIP5 projections indicate a warming in relative SST (RSST), i.e. the SST change relative to its tropical mean, in the equatorial Atlantic, equatorial Pacific and Arabian Sea, and a RSST cooling (i.e. weaker warming than the tropical average) in the three subtropical gyres of the southern hemisphere. These models also project positive signals in relative Sea Level Anomalies (RSLA) in the Arabian Sea, 10°N-20°N band in the Pacific, and Benguela upwelling, and negative ones in the central Pacific, south-eastern Pacific and Indian Oceans. Rainfall increases over the equatorial Pacific and India, and decreases over Central America, the southern tropical Pacific and Atlantic. We define a regional trend as detectable when it emerges in more than 80% of the models in the CMIP5 database. With this choice, none of the RSST, RSLA and precipitation signals mentioned above are currently detectable in CMIP5. In the coming decade, the RSST warming in the Arabian Sea, cooling in the southeastern Pacific Ocean and rainfall reduction over central America become detectable, according to CMIP5. The equatorial Atlantic relative warming, Arabian Sea RSLA rise and equatorial Pacific precipitation increase would emerge before 2050. The equatorial Pacific RSST warming, southeastern Indian Ocean RSST cooling, and monsoon rainfall increase over India also become detectable before 2100. Our estimates of the emergence time could help planning a targeted observational strategy in regions, where CMIP5 indicate strong trends, and thus to verify CMIP5 projections in those regions.

How to cite: Suresh, G., Suresh, I., Izumo, T., Vialard, J., and Lengaigne, M.: Emergence of the projected trends in the tropical oceans from background climate noise in CMIP5 simulations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19900, https://doi.org/10.5194/egusphere-egu2020-19900, 2020.

Climate models predict a strengthening contrast between wet and dry regions in the tropics and subtropics (30°S-30°N), and data from the latest model intercomparison project (CMIP6) support this expectation. Rainfall in ascending regions increases, and in descending regions decreases in both climate model and reanalysis data. This strengthening contrast can be captured by tracking rainfall change each month in the wettest and driest third of the tropics and subtropics combined. Since wet and dry regions are selected individually for each model ensemble member, and the observations, and for each month, this analysis is largely unaffected by biases in location of precipitation features. Blended satellite and in situ data support the model-simulated tendency to sharpening contrasts between wet and dry regions, with rainfall in wet regions increasing substantially contrasted by a slight decrease in dry regions. These new datasets allow us to detect with more confidence the effect of external forcings on these changes, attribute them for the first time to the response to anthropogenic and natural forcings separately, and determine that the observed trends are statistically larger than the model responses. Our results show that the observed change is best explained by increasing greenhouse gases with natural forcing contributing some increase following the drop in wet region precipitation after Mount Pinatubo, while anthropogenic aerosol effects are expected to show a weak tropic-wide trend at the present time of flat global aerosol forcing. As expected from climate models, the observed signal strengthens further when focusing on the wet tail of spatial distributions in both models and data.

How to cite: Schurer, A., Hegerl, G., Ballinger, A., and Friedman, A.: Human influence strengthens the contrast between tropical wet and dry regions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16966, https://doi.org/10.5194/egusphere-egu2020-16966, 2020.

Victoria is the second-most populated and most densely populated state in Australia with a population of over 6.5 million. Over two thirds of the population live in greater Melbourne. It is also a major area for agriculture and tourism and is the second largest economy in Australia, accounting for a quarter of Australia's Gross Domestic Product. Any changes in Victoria's climate has huge impacts in these sectors. Rainfall over Victoria during the cool season (e.g. April to October) has been unusually low since the beginning of the Millennium Drought in 1997 (~12% below the 20^th century average). Cool season rainfall contributes two-third to annual rainfall and is very important for many crops and for replenishing reservoirs across the state. Here we examine the extent to which this reduction in cool season rainfall is driven by external forcing, and the prospects for future multi-decadal rainfall, taking both external forcing and internal natural climate variability into account.

We analyse simulations from 40 global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) under preindustrial and historical forcing, as well as three scenarios for the 21^st century: Representative Concentration Pathway (RCP)2.6, RCP4.5 and RCP8.5, which vary markedly in the amount of greenhouse gas emitted over the coming century. While the 1997-2018 average rainfall for cool season is below the preindustrial average in more than two-thirds of models under the three scenarios, the magnitude of the externally-forced drying is very small (median decline is around -2.5% in all three scenarios with an interquartile range around -5% to +1%). The model ensemble results suggest that external forcing contributed only 20% (interquartile range -41% to 4%) to the drying observed in 1997-2018, relative to 1900-1959. These results suggest that the observed drying was dominated by natural, internal rainfall variability. While the multi-model median is below average from 1997-2018 onwards, the externally-forced drying only becomes clear from 2010-2029, when the proportion of models exhibiting drying increases to over 90% under all three scenarios. This agreement reflects the increase in the magnitude of the externally-forced drying. We estimate that there is a 12% chance that internal rainfall variability will completely offset the externally-forced drying averaged over 2018-2037, regardless of scenario. By the late 21^st century the externally forced change under RCP8.5 is so large that drying – even after taking internally variability into account - appears inevitable. 

Confidence in the modelled projections is lowered because models have difficulty in simulating the magnitude of the observed decline in rainfall. Some of this difficulty appears to arise because most models seem to underestimate multidecadal rainfall variability. Other candidates are: the observed drying may have been primarily due to the occurrence of an extreme, internally-driven event; the models underestimate the magnitude of the externally-forced drying in recent decades; or some combination of the two. If externally-forced drying is underestimated because the response to greenhouse gases is underestimated then the magnitude of projected changes might also be underestimated.

How to cite: Rauniyar, S. and Power, S.: Relative Contribution of Anthropogenic Forcing and Natural Processes to Rainfall Variability over Victoria, Australia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21262, https://doi.org/10.5194/egusphere-egu2020-21262, 2020.

Human-induced climate change poses potential impacts on the availability of water resources. Previous assessments of warming-induced changes in dryness, however, are influenced by short observational records and show conflicting results due to uncertainties in the response of evapotranspiration. In this study we use novel observation-based water availability reconstructions from data-driven and land surface models from 1902 to 2014; a period during which the Earth has warmed approximately 1°C relative to pre-industrial conditions. These reconstructions reveal consistent changes in average water availability of the driest month of the year during the last 30 years compared to the first half of the 20^th century. We conduct a simple attribution approach based on a spatial correlation analysis between the reconstructions and different climate model simulations. Results indicate that the spatial pattern of changes is extremely likely influenced by human-induced greenhouse gas emissions as it is consistent with climate model estimates that include historical radiative forcing, whereas the pattern is not expected from natural climate variability given by climate simulations with greenhouse gas levels set to pre-industrial conditions. Changes in water availability are characterized by drier dry seasons predominantly in extratropical latitudes and including Europe, Western North America, Northern Asia, Southern South America, Australia, and Eastern Africa. Finally, we find that the intensification of the dry season is generally a consequence of increasing evapotranspiration rather than decreasing precipitation.

How to cite: Padrón, R. S., Gudmundsson, L., Ducharne, A., Lawrence, D. M., Mao, J., Peano, D., Decharme, B., Krinner, G., Kim, H., and Seneviratne, S. I.: Dry season water availability changes attributed to human-induced climate change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12658, https://doi.org/10.5194/egusphere-egu2020-12658, 2020.

It is well-known that climate change affects large scale weather patterns and local extremes all over the world as well as in Europe. These changes include the changes of precipitation occurences, amounts, and spatial patterns, which may require appropriate risk management actions. For this purpose, the first step is a thorough analysis of possible hazards associated to specific precipitation-related weather phenomena.

The primary goals of this study are (i) to examine the changes in precipitation patterns and extremes, and (ii) to explore the possible connections between changes in different lowlands across Europe. Precipitation time series are used from the E-OBS v.20 datasets on a 0.1° regular grid. Datasets are based on station measurements from Europe and are available from 1950 onward with daily temporal resolution. Altogether 14 plain regions are selected in this study to represent different parts within Europe. More specifically, five plain regions are parts of the East European Plain, two regions are located in the Scandinavian basin, five regions are located in Western Europe, and the Pannonian Plain (including mostly Hungary) is also selected. For choosing the plains and their spatial representations, objective criteria are used, namely, the elevation remains under 200 m throughout the defined area and difference between the neighbouring gridpoints within the plain region does not exceed 40 m. Daily precipitation times series are analyzed and compared for these plain regions using various statistical tools. The results represent annual and seasonal changes in average and extreme precipitation amount as well as in the frequency of precipitation occurences. Climate indices and the occurence of extreme weather conditions including wet and dry spells are also analyzed.

How to cite: Berényi, A., Bartholy, J., and Pongrácz, R.: Analysis of Precipitation Patterns and Extremes in European Lowlands, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-963, https://doi.org/10.5194/egusphere-egu2020-963, 2020.

The occurrence of extreme precipitation events is a severe concern to any nation due to its socio-economic impacts. In this study, spatiotemporal variability of precipitation extremes was analyzed over the Indian sub-continent using the quantile perturbation method (QPM). QPM is a non-parametric method that requires very few assumptions. The gridded data of precipitation with 0.5 × 0.5-degree resolution CRU (Climate Research Unit, University of East Anglia, UK) and 117 years (1901-2017) data set has been used. The result shows that the initial decade (1910s to 1940s) and the recent decade (1990s to 2000s) are the decades when significant anomalies found in most of India. The northeast part of India shows positive anomalies while the central region and northern region show negative anomalies in the 1910s. In the period of 1930-1940s central India shows positive anomalies, and the northern region shows negative anomalies. Significant positive anomalies found in the west part of southern India in the period of 1950-1960s. In the period of 1960-2000s, the northern region shows positive anomalies. Indo-Gangetic plain and central India have negative anomalies while the western part shows positive anomalies in the 2000s in most of the grid. To partially address the reason behind the perturbation correlation analysis has been applied between extreme precipitation anomaly and Indian Ocean Dipole. Results show a moderately negative correlation found in most of the eastern and north-eastern regions of India, while a positive correlation found in some northern and southern parts of India. Analysis suggests that Indian Ocean sea surface temperature might be the main driver for the decadal perturbations in precipitation extremes.

How to cite: Mandraha, S. and Ray, S.: Decadal Variability of Precipitation Extremes over India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1024, https://doi.org/10.5194/egusphere-egu2020-1024, 2020.

Worldwide studies revealed a general increase in frequency and severity of warm extreme temperature events. In this study, extreme temperature events including Heat waves (HWs) are examined. Extreme indices are calculated based on daily maximum temperature (Tx). The following definitions are employed: SU - number of days with Tx > 25 °C, umber of days with Tx > 90^th percentile, and WSDI - number of days in intervals of at least six consecutive days for which Tx is higher than the calendar day 90^th percentile. Daily values of air temperatures from 11 meteorological stations distributed across Serbia were used for the period 1949–2017.

Trends of extreme temperature events and their frequencies are examined. The period 1949–2017 are characterised by a warming of extreme temperature indices (SU, Tx90, HWs). It is found that maximum air temperatures increased at all stations, but statistically significant at 6 stations in winter, 4 stations in summer and two stations in spring. The average number of SU per station was between 63.1 in Novi Sad to 73.5 in Negotin during the summer season. Significant increase of SU is recorded in summer for 10 out of 11 stations. Positive trends of SU and Tx90 are observed for all stations and seasons, except in Novi Sad. The average number of Tx90 is about 9 for all stations in all seasons. The longest heat waves prevailed in 2012, but the most severe are recorded in 2007. Increasing of warm extreme events in Serbia are in agreement with studies for different regions of the world.

How to cite: Tosic, I., Putniković, S., and Tošić, M.: Seasonal analysis of warm extreme events in Serbia from 1949 to 2017, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1364, https://doi.org/10.5194/egusphere-egu2020-1364, 2020.

Global mean surface air temperature (SAT) has remained relative stagnant since the late 1990s, a phenomenon known as global warming hiatus. Despite widespread concern and discussion, there is still an open question about whether this hiatus exists, partly due to the biases in observations. The stable isotopic composition of precipitation in mid- and high-latitude continents closely tracks change of the air temperature, providing an alternative to evaluate global warming hiatus. Here we use the long-term precipitation δ¹⁸O records available to investigate changes in SAT over the period 1970–2016. The results reveal slight decline in δ¹⁸O anomaly from 1998 to 2012, with a slope of -0.0004‰ decade^-1 which is significantly different from that of pre-1998 interval. This downward δ¹⁸O anomaly trend suggests a slight cooling for about -0.001°C decade^-1, corroborating the recent hiatus in global warming. Our work provides new evidence for recent global warming hiatus and highlights the potential of utilizing precipitation isotope for tracking climate changes.

How to cite: Wang, R. and Liu, Z.: Stable Isotope Evidence for Recent Global Warming Hiatus, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1703, https://doi.org/10.5194/egusphere-egu2020-1703, 2020.

Long-term changes in air temperature regime have significant consequences for the atmospheric precipitation regime in Slovakia. Moreover, the combination of air temperature increase, changes in annual precipitation regime, as well as increasing proportion of liquid and mixed precipitation on its annual total, have had a profound effect on the snow cover occurrence. In majority of territory of Slovakia, with the exception of high altitudes, the stability of snow cover incidence has decreased. In the last decade of the 20th century and in the first two decades of the 21st century, there was a significant increase in mean values of the air temperature characteristics in every individual decade over the period. Very clear decline of amount of snow cover in Slovakia was recorded especially in the second decade of the 21st century however significant regional differences of measurable long-term trends have been affected by very complex natural conditions of Slovakia. The paper we analyze selected snow cover characteristics, such as the sum of snow depths as well as the number of days with snow cover in the decadal time scale for the period 1921 – 2020. The analysis is performed using the time series of daily values of snow cover at selected weather stations in different regions of Slovakia.

How to cite: Fasko, P., Markovič, L., Pecho, J., and Bochníček, O.: Decadal changes in snow cover characteristics in Slovakia over the period 1921 – 2020, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3405, https://doi.org/10.5194/egusphere-egu2020-3405, 2020.

Global mean surface air temperature (GMT) rose roughly 0.85 °C from 1880 to 2012 (IPCC 2013), attributing mainly to an increase in atmospheric greenhouse gases. For different decadal timescale periods in the past 100 years, the warming rate of different periods may significantly different. For example, IPCC AR1 (1990) point out that GMT between 1910-1940 and 1975-1990 are significantly warming, meanwhile GMT stay nearly constant between 1940 and early 1970. The phenomenon of two nearby periods showing significantly different trends is knowing as trend turning, this phenomenon is common in climate time series and crucial when climate change is investigated. However, the available detection methods for climate trend turnings are relatively few, especially for the methods which have the ability of detecting multiple trend turnings. We propose a new methodology named as the running slope difference (RSD) t-test to detect multiple trend turnings. This method employs a t-distributed statistic of slope difference to test the sub-series trends difference of the time series, thereby identify the turning-points. We compare the RSD t-test method with some other existing trend turning detection methods with an idealized time series case and several climate time series cases. And we also report the Monte Carlo simulation used to evaluate this method’s detection ability. Results show that the RSD t-test method is an effective tool for detecting trend turning in time series, and this method has three major advantages: ability to detect multiple turning-points, capacity to detect all three types of trend turning, and great performance of avoiding false alarm.

How to cite: Zuo, B., Hou, Z., Zheng, F., Sheng, L., Gao, Y., and Li, J.: Assessment of the Running Slope Difference (RSD) t-Test, a new statistical method for detecting climate trend turning, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4065, https://doi.org/10.5194/egusphere-egu2020-4065, 2020.

Aiming to provide comprehensive information for climate change at regional level, we assess temperature and heat waves and their spatiotemporal trend and time of emergence over different regions of the African continent.  We analyze observational data of Climate Research Unit Time Series version 4.03 (CRU TS) and the three state-of-the-art reanalysis datasets; European Center for Medium-Range Weather Forecasts Reanalysis 5 (ERA5), National Oceanic Atmospheric Administration’s Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) and the Japanese Meteorological Agency’s 55 years reanalysis (JRA-55). We assess changes in monthly mean temperature and the agreement between observations and reanalyses. Changes in heat waves are analyzed based on reanalysis datasets because of their high temporal resolution. Heat waves are defined using absolute and relative thresholds, the number of summer days, tropical nights, the percentage of days with maximum and mean temperature above the 90^th percentile, the warm nights and the warm spell duration index.  The results show increasing trends in monthly mean temperature in all four regions of Africa with different rate of change. A statistically significant trends in heat waves is found in all the regions.  Years of highest heat wave occurrence are identified in 2010 for Northern and Western Africa and 2016 for Eastern and Southern Africa. Minimum-temperature based indices, tropical nights and warm nights, show the highest increase in decadal trends and earliest time of emergence, respectively.

Key words: climate change; temperature; heat waves; time of emergence; reanalysis; Africa

How to cite: Engdaw, M. M., Hegerl, G. C., and Steiner, A. K.: Changes in temperature and heat waves over Africa using observational and reanalysis datasets, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6704, https://doi.org/10.5194/egusphere-egu2020-6704, 2020.

The attribution of extreme weather events, such as heavy rainfall, to anthropogenic influence typically involves the analysis of their probability in simulations of climate, such as those conducted in the C20C+ Detection and Attribution Project. The climate models used however, such as the Community Atmosphere Model (CAM), employ approximate physics that gives rise to “parameter uncertainty”—uncertainty about the most accurate or optimal values of numerical parameters within the model. Parameterisations for convective processes, for example, are well known to be influential in the simulation of precipitation extremes.

In the context of extreme event attribution, we investigate the importance of components of parameterisations—through their associated tuning parameters—relating to deep and shallow convection, and cloud and aerosol microphysics in CAM. We present results from the analysis of a large perturbed physics ensemble experiment (~12,000 years of simulation, ~1 degree horizontal resolution) designed to explore extremes in both the observed world and pre-industrial conditions. Using surrogate models based upon Gaussian processes fitted marginally to both regional and grid cell output, we have computed sensitivity measures associated with the physics parameters, for precipitation and temperature extremes and their respective “risk ratios”.

Our results reveal the high geospatial variability in averages and extremes of output variables arising from physics perturbations, and how this contrasts with low variability in estimates of risk ratios based upon the same variables. We conclude that for CAM, variability induced by perturbed physics is typically consistent across warming scenarios, and unlikely to be a significant source of uncertainty in extreme event attribution studies. However, we caution that this may not be the case in regions where relevant parameterisations are strongly active.

How to cite: Timmermans, B., Collins, W., O'Brien, T., Stone, D., and Risser, M.: Impact of parametric uncertainty on simulated climate extremes and attribution studies, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10043, https://doi.org/10.5194/egusphere-egu2020-10043, 2020.

We investigate the attribution of the flooding in Northern England that saw at least 500 homes flooded and over 1000 properties evacuated in flooded areas in 2019. This occurred during the wettest Autumn on record in some areas and also contained some very high daily rainfall totals. In the light of climate change, it is expected that intense rainfall events are to become more intense as a result of increased global average temperatures and the Clausius-Clapeyron relationship, but here we investigate quantitatively how much climate change has increased the risk of such an event to date.

We use results from the 2.2km convective permitting high resolution local UK Climate Projections (UKCP) and observations to show that more intense rainfall events may already be occurring in Autumn in the UK. This work shows using this high resolution UKCP data that a heavy rainfall event exceeding 50mm in one day in Autumn was 33-40% more likely to occur in 2019 than 1985. Further work that looks at the HadGEM3-A simulations shows that these heavy rainfall days are more likely to occur in a climate impacted by human activity than one with just natural climate forcings.

How to cite: Cotterill, D., Stott, P., and Kendon, E.: Increase in the frequency of heavy rainfall events over the UK in the light of climate change., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11025, https://doi.org/10.5194/egusphere-egu2020-11025, 2020.

The summer of 2018 was characterised by prolonged heatwaves over North-Eastern Europe, associated with persistent blocking over Scandinavia, and a jet stream that resided unusually far north on average over this sector. Whilst most event attribution studies tend to focus on the probability or intensity of extreme temperatures themselves, we instead examine whether anthropogenic climate change has affected the likelihood of the circulation pattern that lead to the 2018 hot summer. We examine trends and variability in jet latitude and blocking frequency over the Scandanavian sector in reanalyses, CMIP5 historical simulations, and in two large ensembles of HadGEM3-A simulations with and without anthropogenic forcing. Both the number of blocked days, and the average jet location for last summer were unprecedented in the observational record, and also very rare in climate model simulations. A number of the CMIP5 models examined were able to simulate realistic blocking frequency distributions. Last summer’s circulation did not appear to be part of any systematic increasing trends in blocking frequency or jet latitude in this sector. Instead, this circulation anomaly appears to be explained by a particularly large deviation of natural variability. We will then extend the analysis to examine the western European heatwaves of summer 2019 which were associated with a very different atmospheric circulation pattern –a high pressure ridge which transported warm air northwards from Northern Africa.

How to cite: Iles, C. and Vautard, R.: No evidence for climate change in the unprecedented Summer 2018 flow over Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17605, https://doi.org/10.5194/egusphere-egu2020-17605, 2020.

Wildfires constitute a major natural hazard and pose huge risk to many regions of the world. The series of large fires across both hemispheres in recent years have led to inevitable questions about how human-induced climate change may be altering the character of such events. Providing answers to these questions is a crucial step to increasing resilience to major wildfires.

Long-term projections produced by state-of-the-art climate models, even when reliable, are not always a suitable means of communicating risk. Methodologies to attribute trends in meteorological phenomena associated with high-impact events to anthropogenic influence have the potential to better communicate risk and guide adaptation strategies. While the link between a warming world and heat-related extremes (e.g. heatwaves and droughts) is reasonably well-understood, there is a lack of consensus on the most appropriate and effective methodological approach for many variables, potentially impacted by warming climate, such as wildfire attribution. The link with climate change remains poorly understood and wildfires have been largely ignored by attribution studies to date.

As a first step towards the development of a seamless, globally-applicable framework for assessing past, present and future risk in wildfire danger, we present a global attribution analysis of wildfire danger. With initial focus on observational records, we use both established and novel empirical-statistical methods to attribute historical trends in episodes of extreme weather and climate conducive to wildfire ignition and spread. Particular consideration is given to the sensitivity of attribution findings to the spatial scale upon which the analysis is conducted. We also draw attention to a series of important, often overlooked, conceptual and technical challenges in event attribution, including validation and bias-correction of climate models and discuss the value of linking attribution of recent wildfire events with future risk assessment.

How to cite: Liu, Z., Eden, J., Dieppois, B., and Blackett, M.: Towards multi-method and multi-scale attribution of global wildfire danger, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20734, https://doi.org/10.5194/egusphere-egu2020-20734, 2020.

Sea level rise increases extreme water levels and thus the flood losses from storm surge events. While it is still difficult to estimate the influence of climate change on single storms, the influence of anthropogenic climate change on sea level rise is evident. We here aim to quantify the fraction of damages caused by sea level rise for a set of flood events of the last decade. Flood-extents and the spatial distribution of damages are reconstructed from openly available data-sources. We construct counterfactual flood extents for each event by a counterfactual sea level as it would have been in a world without climate change. As we are particularly interested in losses in poorer countries that often lack high resolution data such as LiDAR based elevation maps or tide-gauge records, our methodology is transferable between regions, building on global and open data. Depending on the study site, we detect a difference between observed and counterfactual damages though uncertainties remain high. Data availability and data detail remain a major restriction.

How to cite: Treu, S., Mengel, M., and Frieler, K.: Attributing coastal flood damages to sea level rise for recent flood events, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20015, https://doi.org/10.5194/egusphere-egu2020-20015, 2020.

The planetary boundary layer height (PBL) is a critical variable in many applications such as NWP, air quality and climate models. The study of the PBL involves several process and parameters: exchange of momentum, heat, water vapour and tracers from the surface to the free atmosphere therefore,  PBL representation in numerical  models is difficult to achieve and observation are used to improve the quality of the implemented parameterizations.

This presentation will illustrate a climatology of the height of the PBL and its trend since 1978 to present at different in the Mediterranean Basin.

The height of the PBL is calculated using the maximum vertical gradient of potential temperature  (θ) obtained from radio Station belonging to the IGRA (Integrated Global Radiosonde) archive related in the Europe Region) and to GRUAN network (GCOS Reference Upper Air Network).  

The IGRA consists of quality-controlled radiosonde observations of temperature, humidity, and wind at stations across all continents. The earliest year of data is 1905, and the data are updated on a daily basis. Record length, vertical extent and resolution, and availability of variables varies among stations and over time. The GRUAN is an international reference observing network of sites measuring essential climate variables above Earth's surface, designed to fill an important gap in the current global observing system. GRUAN measurements are providing long-term, high-quality climate data records from the surface, through the troposphere, and into the stratosphere. 

An estimate of uncertainty will be also discussed and correlated with the recent climate changes at the global scale and in the Mediterranean Basin.

How to cite: Summa, D., Madonna, F., Tramutola, E., Marra, F., De Rosa, B., and Di Girolamo, P.: PBL climatology using IGRA radiosounding data in Mediterranean Basin , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19791, https://doi.org/10.5194/egusphere-egu2020-19791, 2020.

Decadal variation of spring (February–April) rainfall in Northern Taiwan and Southern China was significantly related to the Pacific Decadal Oscillation (PDO) during the twentieth century. However, this interdecadal relationship subsequently weakened, and the sea surface temperature (SST) associated with the central Pacific El Niño (CPEN) has determined the interdecadal variation of spring rainfall in Northern Taiwan and Southern China since the 1980s. In this study, the effect of CPEN-SST on the interdecadal variation of spring rainfall in Northern Taiwan and Southern China was investigated. We found that a CPEN-associated positive SST anomaly in the eastern North Pacific forced an east–west overturning circulation anomaly in the subtropical North Pacific, the descending motion of which may have generated an anticyclonic circulation anomaly in the Philippine Sea. Simultaneously, the anticyclone associated southerly winds anomaly may enhance the southwesterly in northwest of the anticyclone, which in term enhance the trough extending from Japan to Northern Taiwan. The anticyclone and trough associated with the respective southwesterly and northeasterly anomalies created a convergence environment in Northern Taiwan. In turn, this convergence environment contributed substantially to an interdecadal rainfall enhancement in Northern Taiwan and Southern China. Our results suggest that the effect of CPEN-SST on the interdecadal variation of spring rainfall in Northern Taiwan and Southern China has increased since 1980, especially during the transition period from the termination of a warm PDO phase to a cold phase in the late 1990s

How to cite: Kao, P., Hong, C.-C., and Hung, C.: Increasing Influence of Central Pacific El Niño on the Interdecadal Variation of Spring Rainfall in Northern Taiwan and Southern China Since 1980, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4565, https://doi.org/10.5194/egusphere-egu2020-4565, 2020.

Inaccurate climate trend detections may lead to incorrect conclusions about the current state and future evolution of the climate. Trend estimation based on the use of radiosonde historical time series may be significantly affected by the choice of the estimation method. In addition, the dataset subsampling both in time (due to gaps in the data records) and in space (due to need of selecting the most reliable subset of stations for each specific application) can further increase the trend uncertainty. 

Uncertainties of trend estimations have been quantified in few past investigations, considering the difference between pairs of regression methods, although limited to datasets affected by several inhomogeneities and characterized by smaller trend rates than those observed over the last two decades.

This work, carried out in the frame of the Copernicus Climate Change Service (C3S), aims to examine the sensitivity of trend estimations to linear estimation methods and to subsampling effects. The analysis is carried out using about 600 historical radiosounding time series for the period 1978-2018 available within version 2 of the Integrated Global Radiosonde Archive (IGRA).

The sensitivity of linear trends to the choice regression methods and the subsampling effects have been quantified through the comparison of four regression methods (parametric and non-parametric). The uncertainties introduced by missing data in each time series has been also quantified using a new approach, selecting different samples of stations with different amounts of missing monthly data equivalent to 0, 5, 10 and 20 years from 1978 to present. Instead, the spatial subsampling effects are quantified artificially reducing the size of the IGRA stations.

The presented work will shortly discuss results obtained for temperature and relative humidity for both night and day times  (at 0000 and 1200 UTC, respectively) at different pressure levels and latitudes.

How to cite: Sy, S., Madonna, F., Tramutola, E., Rosoldi, M., Proto, M., and Pappalardo, G.: Sensitivity of trends to estimation methods and quantification of subsampling effects in global radiosounding temperature and humidity time series, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7798, https://doi.org/10.5194/egusphere-egu2020-7798, 2020.

Extreme climate and weather events have a great influence on society and natural systems. That’s why it is important to be able to precisely simulate these events with the climate models. To asses the quality of such simulations 27 climate extremes indices were defined by ETCCDI. In the present work these indices are calculated for the 1901–2010 in order to estimate their trends.
Climate extremes trends are studied on the basis of ten historical runs with the up-to-date INM RAS climate model (INMCM5) under the scenario proposed for the Coupled Model Intercomparison Project Phase 6 (CMIP6). Developed by ECMWF ERA-20C and CERA-20C reanalyses are taken as observational data.
Trends obtained from the reanalysis data are compared with the simulation results of the INMCM5. The comparison shows that the simulated land-averaged climate extremes trends are in good agreement with the reanalysis data, but their spatial distributions differ significantly even between the reanalyses themselves.

How to cite: Tarasevich, M. and Volodin, E.: Simulation of the observed climate extremes trends during 1901–2010 with INMCM5, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16407, https://doi.org/10.5194/egusphere-egu2020-16407, 2020.

Earth System observational and model data volumes are constantly increasing and it can be challenging to discover, download, and analyze data if scientists do not have the required computing and storage resources at hand. This is especially the case for detection and attribution studies in the field of climate change research since we need to perform multi-source and cross-disciplinary comparisons for datasets of high-spatial and large temporal coverage. Researchers and end-users are therefore looking for access to cloud solutions and high performance compute facilities. The Earth System Grid Federation (ESGF, https://esgf.llnl.gov/) maintains a global system of federated data centers that allow access to the largest archive of model climate data world-wide. ESGF portals provide free access to the output of the data contributing to the next assessment report of the Intergovernmental Panel on Climate Change through the Coupled Model Intercomparison Project. In order to support users to directly access to high performance computing facilities to perform analyses such as detection and attribution of climate change and its impacts, the EU Commission funded a new service within the infrastructure of the European Network for Earth System Modelling (ENES, https://portal.enes.org/data/data-metadata-service/analysis-platforms). This new service is designed to reduce data transfer issues, speed up the computational analysis, provide storage, and ensure the resources access and maintenance. Furthermore, the service is free of charge, only requires a lightweight application. We will present a demo on how flexible it is to calculate climate indices from different ESGF datasets covering a wide range of temporal and spatial scales using cdo (Climate Data Operators, https://code.mpimet.mpg.de/projects/cdo/) and Jupyter notebooks running directly on the ENES partners: the DKRZ (Germany), JASMIN (UK), CMCC(Italy), and IPSL (France) high performance computing centers.

How to cite: Moreno de Castro, M., Kindermann, S., Fiore, S., Nassisi, P., Levavasseur, G., Juckes, M., Stephens, A., Peters, K., Morellon, S., and Joussaume, S.: Boosting climate change research with direct access to high performance computers, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19340, https://doi.org/10.5194/egusphere-egu2020-19340, 2020.