Potential impacts of anthropogenic forcing on the consecutive 2018-19 droughts in the central Europe

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.&#160;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.

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Dry Spells and Extreme Precipitation are The Main Trigger of Landslides in Central Europe

Scientific Reports ◽

10.1038/s41598-019-51148-2 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Radek Tichavský ◽

Juan Antonio Ballesteros-Cánovas ◽

Karel Šilhán ◽

Radim Tolasz ◽

Markus Stoffel

Keyword(s):

Time Series ◽

Central Europe ◽

Climate Models ◽

Regional Scale ◽

Summer Precipitation ◽

Greenhouse Climate ◽

Dry Spells ◽

Outer Western Carpathians ◽

Triggering Mechanisms

Abstract Landslides are frequently triggered by extreme meteorological events which has led to concern and debate about their activity in a future greenhouse climate. It is also hypothesized that dry spells preceding triggering rainfall may increase slope predisposition to sliding, especially in the case of clay-rich soils. Here we combined dendrogeomorphic time series of landslides and climatic records to test the possible role of dry spells and extreme downpours on process activity in the Outer Western Carpathians (Central Europe). To this end, we tested time series of past frequencies and return periods of landslide reactivations at the regional scale with a Generalized Linear Mixed (GLM) model to explore linkages between landslide occurrences and triggering climate variables. Results show that landslide reactivations are concentrated during years in which spring and summer precipitation sums were significantly higher than usual, and that triggering mechanisms vary between different types of landslides (i.e. complex, shallow or flow-like). The GLM model also points to the susceptibility of landslide bodies to the combined occurrence of long, dry spells followed by large precipitation. Such situations are likely to increase in frequency in the future as climate models predict an enhancement of heatwaves and dry spells in future summers, that would be interrupted by less frequent, yet more intense storms, especially also in mountain regions.

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Large-scale atmospheric circulation biases and changes in global climate model simulations and their importance for regional climate scenarios: a case study for West-Central Europe

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-5-7415-2005 ◽

2005 ◽

Vol 5 (4) ◽

pp. 7415-7455 ◽

Cited By ~ 6

Author(s):

A. P. van Ulden ◽

G. J. van Oldenborgh

Keyword(s):

Climate Change ◽

20Th Century ◽

Central Europe ◽

21St Century ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Climate Scenarios ◽

Model Simulations ◽

Circulation Changes

Abstract. The credibility of regional climate change predictions for the 21st century depends on the ability of climate models to simulate global and regional circulations in a realistic manner. To investigate this issue, a large set of global coupled climate model experiments prepared for the Fourth Assessment Report of the Intergovernmental Panel on Climate Change has been studied. First we compared 20th century model simulations of longterm mean monthly sea level pressure patterns with ERA-40. We found a wide range in performance. Many models performed well on a global scale. For northern midlatitudes and Europe many models showed large errors, while other models simulated realistic pressure fields. Next we focused on the monthly mean climate of West-Central Europe in the 20th century. In this region the climate depends strongly on the circulation. Westerlies bring temperate weather from the Atlantic Ocean, while easterlies bring cold spells in winter and hot weather in summer. In order to be credible for this region, a climate model has to show realistic circulation statistics in the current climate, and a response of temperature and precipitation variations to circulation variations that agrees with observations. We found that even models with a realistic mean pressure pattern over Europe still showed pronounced deviations from the observed circulation distributions. In particular, the frequency distributions of the strength of westerlies appears to be difficult to simulate well. This contributes substantially to biases in simulated temperatures and precipitation, which have to be accounted for when comparing model simulations with observations. Finally we considered changes in climate simulations between the end of the 20th century and the end of the 21st century. Here we found that changes in simulated circulation statistics play an important role in climate scenarios. For temperature, the warm extremes in summer and cold extremes in winter are most sensitive to changes in circulation, because these extremes depend strongly on the simulated frequency of eastery flow. For precipitation, we found that circulation changes have a substantial influence, both on mean changes and on changes in the probability of wet extremes and of long dry spells. Because we do not know how reliable climate models are in their predictions of circulation changes, climate change predictions for Europe are as yet uncertain in many aspects.

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Simulation of the observed climate extremes trends during 1901–2010 with INMCM5

10.5194/egusphere-egu2020-16407 ◽

2020 ◽

Author(s):

Maria Tarasevich ◽

Evgeny Volodin

Keyword(s):

Climate Models ◽

Climate Model ◽

Great Influence ◽

Coupled Model ◽

Climate Extremes ◽

Reanalysis Data ◽

Natural Systems ◽

Weather Events ◽

Intercomparison Project

Extreme climate and weather events have a great influence on society and natural systems. That&#8217;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&#8211;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.

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Snowfall in the Himalayas: an uncertain future from a little-known past

The Cryosphere ◽

10.5194/tc-9-1147-2015 ◽

2015 ◽

Vol 9 (3) ◽

pp. 1147-1167 ◽

Cited By ~ 21

Author(s):

E. Viste ◽

A. Sorteberg

Keyword(s):

Climate Models ◽

Climate Model ◽

Reanalysis Data ◽

Indus Basin ◽

Data Sets ◽

Anthropogenic Forcing ◽

Temperature And Precipitation ◽

The Mean ◽

Uncertain Future ◽

Relative Change

Abstract. Snow and ice provide large amounts of meltwater to the Indus, Ganges and Brahmaputra rivers. This study combines present-day observations and reanalysis data with climate model projections to estimate the amount of snow falling over the basins today and in the last decades of the 21st century. Estimates of present-day snowfall based on a combination of temperature and precipitation from reanalysis data and observations vary by factors of 2–4. The spread is large, not just between the reanalysis and the observations but also between the different observational data sets. With the strongest anthropogenic forcing scenario (RCP8.5), the climate models project reductions in annual snowfall by 30–50% in the Indus Basin, 50–60% in the Ganges Basin and 50–70% in the Brahmaputra Basin by 2071–2100. The reduction is due to increasing temperatures, as the mean of the models show constant or increasing precipitation throughout the year in most of the region. With the strongest anthropogenic forcing scenario, the mean elevation where rain changes to snow – the rain/snow line – creeps upward by 400–900 m, in most of the region by 700–900 meters. The largest relative change in snowfall is seen in the upper westernmost sub-basins of the Brahmaputra. With the strongest forcing scenario, most of this region will have temperatures above freezing, especially in the summer. The projected reduction in annual snowfall is 65–75%. In the upper Indus, the effect of a warmer climate on snowfall is less extreme, as most of the terrain is high enough to have temperatures sufficiently far below freezing today. A 20–40% reduction in annual snowfall is projected.

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Intraseasonal versus Interannual Measures of Land–Atmosphere Coupling Strength in a Global Climate Model: GLACE-1 versus GLACE-CMIP5 Experiments in ACCESS1.3b

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0206.1 ◽

2015 ◽

Vol 16 (5) ◽

pp. 2276-2295 ◽

Cited By ~ 18

Author(s):

Ruth Lorenz ◽

Andrew J. Pitman ◽

Annette L. Hirsch ◽

Jhan Srbinovsky

Keyword(s):

Coupling Strength ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Climate Extremes ◽

Care Needs ◽

Similar Measure ◽

Single Measure ◽

Strongly Coupled

Abstract Land–atmosphere coupling can strongly affect climate and climate extremes. Estimates of land–atmosphere coupling vary considerably between climate models, between different measures used to define coupling, and between the present and the future. The Australian Community Climate and Earth-System Simulator, version 1.3b (ACCESS1.3b), is used to derive and examine previously used measures of coupling strength. These include the GLACE-1 coupling measure derived on seasonal time scales; a similar measure defined using multiyear simulations; and four other measures of different complexity and data requirements, including measures that can be derived from standard model runs and observations. The ACCESS1.3b land–atmosphere coupling strength is comparable to other climate models. The coupling strength in the Southern Hemisphere summer is larger compared to the Northern Hemisphere summer and is dominated by a strong signal in the tropics and subtropics. The land–atmosphere coupling measures agree on the location of very strong land–atmosphere coupling but show differences in the spatial extent of these regions. However, the investigated measures show disagreement in weaker coupled regions, and some regions are only identified by a single measure as strongly coupled. In future projections the soil moisture trend is crucial in generating regions of strong land–atmosphere coupling, and the results suggest an expansion of coupling “hot spots.” It is concluded that great care needs to be taken in using different measures of coupling strength and shown that several measures that can be easily derived lead to inconsistent conclusions with more computationally expensive measures designed to measure coupling strength.

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Snowfall in the Himalayas: an uncertain future from a little-known past

The Cryosphere Discussions ◽

10.5194/tcd-9-441-2015 ◽

2015 ◽

Vol 9 (1) ◽

pp. 441-493 ◽

Cited By ~ 2

Author(s):

E. Viste ◽

A. Sorteberg

Keyword(s):

Climate Models ◽

Climate Model ◽

Reanalysis Data ◽

Indus Basin ◽

Data Sets ◽

Anthropogenic Forcing ◽

Temperature And Precipitation ◽

The Mean ◽

Uncertain Future ◽

Relative Change

Abstract. Snow and ice provide large amounts of meltwater to the Indus, Ganges and Brahmaputra rivers. This study combines present-day observations and reanalysis data with climate model projections to estimate the amount of snow falling over the basins today and in the last decades of the 21st century. Estimates of present-day snowfall based on a combination of temperature and precipitation from reanalysis data and observations, vary by factors of 2–4. The spread is large, not just between the reanalysis and the observations, but also between the different observational data sets. With the strongest anthropogenic forcing scenario (RCP 8.5), the climate models project reductions in annual snowfall by 30–50% in the Indus Basin, 50–60% in the Ganges Basin and 50–70% in the Brahmaputra Basin, by 2071–2100. The reduction is due to increasing temperatures, as the mean of the models show constant or increasing precipitation throughout the year in most of the region. With the strongest anthropogenic forcing scenario, the mean elevation where rain changes to snow – the rain/snow line – creeps upward by 400–900 m, in most of the region by 700–900 m. The largest relative change in snowfall is seen in the upper, westernmost sub-basins of the Brahmaputra. With the strongest forcing scenario, most of this region will have temperatures above freezing, especially in the summer. The projected reduction in annual snowfall is 65–75%. In the upper Indus, the effect of a warmer climate on snowfall is less extreme, as most of the terrain is high enough to have temperatures sufficiently far below freezing today. A 20–40% reduction in annual snowfall is projected.

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Climate Change Impact Assessment for Aji Basin Using Statistical Downscaling and Bias Correction of Climate Model Outputs

Current World Environment ◽

10.12944/cwe.11.2.40 ◽

2016 ◽

Vol 11 (2) ◽

pp. 670-678 ◽

Cited By ~ 1

Author(s):

N. S Vithlani ◽

H. D Rank

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Regional Climate ◽

Climate Extremes ◽

Global Climate Models ◽

Climate Indices ◽

Climate Projections

For the future projections Global climate models (GCMs) enable development of climate projections and relate greenhouse gas forcing to future potential climate states. When focusing it on smaller scales it exhibit some limitations to overcome this problem, regional climate models (RCMs) and other downscaling methods have been developed. To ensure statistics of the downscaled output matched the corresponding statistics of the observed data, bias correction was used. Quantify future changes of climate extremes were analyzed, based on these downscaled data from two RCMs grid points. Subset of indices and models, results of bias corrected model output and raw for the present day climate were compared with observation, which demonstrated that bias correction is important for RCM outputs. Bias correction directed agreements of extreme climate indices for future climate it does not correct for lag inverse autocorrelation and fraction of wet and dry days. But, it was observed that adjusting both the biases in the mean and variability, relatively simple non-linear correction, leads to better reproduction of observed extreme daily and multi-daily precipitation amounts. Due to climate change temperature and precipitation will increased day by day.

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Impacts of bias nonstationarity of climate model outputs on hydrological simulations

Hydrology Research ◽

10.2166/nh.2020.254 ◽

2020 ◽

Vol 51 (5) ◽

pp. 925-941

Author(s):

Yu Hui ◽

Yuni Xu ◽

Jie Chen ◽

Chong-Yu Xu ◽

Hua Chen

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Time Windows ◽

Natural Climate Variability ◽

Change Impact ◽

Natural Climate ◽

Precipitation And Temperature

Abstract Bias correction methods are based on the assumption of bias stationarity of climate model outputs. However, this assumption may not be valid, because of the natural climate variability. This study investigates the impacts of bias nonstationarity of climate models simulated precipitation and temperature on hydrological climate change impact studies. The bias nonstationarity is determined as the range of difference in bias over multiple historical periods with no anthropogenic climate change for four different time windows. The role of bias nonstationarity in future climate change is assessed using the signal-to-noise ratio as a criterion. The results show that biases of climate models simulated monthly and annual precipitation and temperature vary with time, especially for short time windows. The bias nonstationarity of precipitation plays a great role in future precipitation change, while the role of temperature bias is not important. The bias nonstationarity of climate model outputs is amplified when driving a hydrological model for hydrological simulations. The increase in the length of time window can mitigate the impacts of bias nonstationarity for streamflow projections. Thus, a long time period is suggested to be used to calibrate a bias correction method for hydrological climate change impact studies to reduce the influence of natural climate variability.

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Evaluation of agricultural-related extreme events in hindcast COSMO-CLM simulations over Central Europe

10.5194/egusphere-egu21-9507 ◽

2021 ◽

Author(s):

Huan Zhang ◽

Merja Tölle

Keyword(s):

Regional Climate Model ◽

Central Europe ◽

Extreme Events ◽

Climate Models ◽

Climate Model ◽

Crop Yields ◽

Regional Climate ◽

Extreme Weather ◽

Horizontal Resolution ◽

Extreme Weather Events

Convection-permitting regional climate model simulations may serve as driving data for crop and dynamic vegetation models. It is thus possible to generate physically consistent scenarios for the future-concerning effects of climate change on crop yields and pollinators. Here, we performed convection-permitting hindcast simulations with the regional climate model COSMO5.0-CLM16 (CCLM) from 1980 to 2015 with a spin-up starting at 1979. The model was driven with hourly ERA5 data, which is the latest climate reanalysis product by ECMWF and directly downscaled to 3 km horizontal resolution over central Europe. The land-use classes are described by ECOCLIMAP, and the soil type and depth by HWSD. The evaluation is carried out in terms of temperature, precipitation, and extreme weather indices, comparing CCLM output with the gridded observational dataset HYRAS from the German Weather Service. While CCLM inherits a warm/cold and dry/wet summer/winter bias found in its parent model, it reproduces the main features of the present climate of the study domain, including the distribution, the seasonal mean climate patterns, and probability density distributions. The bias for precipitation ranges between &#177;20 % and the bias for temperature between &#177;1 &#176;C compared to the observations over most of the regions. This is in the range of the bias between observational data. Furthermore, the model catches extreme weather events related to droughts, floods, heat/cold waves, and agriculture-specific events. The results highlight the possibility to directly downscale ERA5 data with regional climate models avoiding the multiple nesting approach and high computational costs. This study adds confidence to convection-permitting climate simulations of future changes in agricultural extreme events.

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Large-scale heavy precipitation and its link to atmospheric circulation in climate model outputs over central Europe

10.5194/ems2021-50 ◽

2021 ◽

Author(s):

Romana Beranova ◽

Jan Kysely

Keyword(s):

Atmospheric Circulation ◽

Central Europe ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Heavy Precipitation ◽

Human Society ◽

Circulation Types ◽

Recent Climate ◽

Precipitation Events

Heavy large-scale precipitation events are associated with large negative impacts on human society, mainly as they may trigger floods and landslides. Therefore, it is important to better understand underlying physical mechanisms leading to extremes and how they are reproduced in climate models.The present study evaluates ability of current climate models to reproduce relationships between large-scale heavy precipitation and atmospheric circulation over central Europe. We use an ensemble of 32 regional climate model (RCM) simulations with the 0.11&#176; resolution, taken from the Euro-CORDEX project. The statistics are compared for the recent climate simulations (1951-2005) against observations from the E-OBS gridded data set to identify main drawbacks of the RCMs. The large-scale heavy precipitation events are defined as days with at least 50% of all grid points over the examined area with heavy precipitation (exceeding the 75th or 90th percentile of the distribution of seasonal rainy days). The association with atmospheric circulation types is investigated through circulation types derived from sea level pressure using airflow indices (direction, strength and vorticity). The analysis is carried out separately for summer (JJA) and winter (DJF) season.The number of days with large-scale heavy precipitation per season in observations reflects the seasonal precipitation sums (the larger precipitation sum the more days). In winter, the large-scale heavy precipitation is mainly associated with the west, northwest, southwest and cyclonic circulation types while in summer with the cyclonic, north, southwest and undefined types (in the observed data). Some RCM simulations are not able to reproduce the number of days with the large-scale heavy precipitation events and their relationships with circulation, especially in summer.

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