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Aristeidis Koutroulis      
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Aristeidis Koutroulis published an article in September 2018.
Top co-authors See all
I. Tsanis

39 shared publications

School of Environmental Engineering, Technical University of Crete—TUC, Chania 73100, Greece

John Caesar

15 shared publications

Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK

Klaus Wyser

14 shared publications

Rossby Centre, SMHI, 601 76 Norrköping, Sweden

Lamprini Papadimitriou

2 shared publications

Cranfield Water Science Institute, Cranfield University, Cranfield MK43 0AL, UK

Richard A. Betts

2 shared publications

Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK

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Distribution of Articles published per year 
(2009 - 2018)
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20
 
Publications See all
Article 0 Reads 1 Citation Simulating Hydrological Impacts under Climate Change: Implications from Methodological Differences of a Pan European Ass... Aristeidis G. Koutroulis, Lamprini V. Papadimitriou, Manolis... Published: 26 September 2018
Water, doi: 10.3390/w10101331
DOI See at publisher website ABS Show/hide abstract
The simulation of hydrological impacts in a changing climate remains one of the main challenges of the earth system sciences. Impact assessments can be, in many cases, laborious processes leading to inevitable methodological compromises that drastically affect the robustness of the conclusions. In this study we examine the implications of different CMIP5-based regional and global climate model ensembles for projections of the hydrological impacts of climate change. We compare results from three different assessments of hydrological impacts under high-end climate change (RCP8.5) across Europe, and we focus on how methodological differences affect the projections. We assess, as systematically as possible, the differences in runoff projections as simulated by a land surface model driven by three different sets of climate projections over the European continent at global warming of 1.5 °C, 2 °C and 4 °C relative to pre-industrial levels, according to the RCP8.5 concentration scenario. We find that these methodological differences lead to considerably different outputs for a number of indicators used to express different aspects of runoff. We further use a number of new global climate model experiments, with an emphasis on high resolution, to test the assumption that many of the uncertainties in regional climate and hydrological changes are driven predominantly by the prescribed sea surface temperatures (SSTs) and sea-ice concentrations (SICs) and we find that results are more sensitive to the choice of the atmosphere model compared to the driving SSTs. Finally, we combine all sources of information to identify robust patterns of hydrological changes across the European continent.
Article 0 Reads 1 Citation Mapping the vulnerability of European summer tourism under 2 °C global warming Aristeidis G. Koutroulis, M. G. Grillakis, I. K. Tsanis, D. ... Published: 26 September 2018
Climatic Change, doi: 10.1007/s10584-018-2298-8
DOI See at publisher website
Article 0 Reads 5 Citations Freshwater vulnerability under high end climate change. A pan-European assessment A.G. Koutroulis, L.V. Papadimitriou, M.G. Grillakis, I.K. Ts... Published: 01 February 2018
Science of The Total Environment, doi: 10.1016/j.scitotenv.2017.09.074
DOI See at publisher website
Article 0 Reads 1 Citation A method to preserve trends in quantile mapping bias correction of climate modeled temperature Manolis G. Grillakis, Aristeidis G. Koutroulis, Ioannis N. D... Published: 28 September 2017
Earth System Dynamics, doi: 10.5194/esd-8-889-2017
DOI See at publisher website ABS Show/hide abstract
Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).
Article 0 Reads 5 Citations The effect of GCM biases on global runoff simulations of a land surface model Lamprini V. Papadimitriou, Aristeidis G. Koutroulis, Manolis... Published: 07 September 2017
Hydrology and Earth System Sciences, doi: 10.5194/hess-21-4379-2017
DOI See at publisher website ABS Show/hide abstract
Global climate model (GCM) outputs feature systematic biases that render them unsuitable for direct use by impact models, especially for hydrological studies. To deal with this issue, many bias correction techniques have been developed to adjust the modelled variables against observations, focusing mainly on precipitation and temperature. However, most state-of-the-art hydrological models require more forcing variables, in addition to precipitation and temperature, such as radiation, humidity, air pressure, and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the JULES land surface model set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four effect categories (ECs), based on the magnitude and sensitivity of runoff changes, is developed and applied. Our results show that, while globally the largest changes in modelled runoff are caused by precipitation and temperature biases, there are regions where runoff is substantially affected by and/or more sensitive to radiation and humidity. Global maps of bias ECs reveal the regions mostly affected by the bias of each variable. Based on our findings, for global-scale applications, bias correction of radiation and humidity, in addition to that of precipitation and temperature, is advised. Finer spatial-scale information is also provided, to suggest bias correction of variables beyond precipitation and temperature for regional studies.
Article 0 Reads 2 Citations A method to preserve trends in quantile mapping bias correction of climate modeled temperature Manolis G. Grillakis, Aristeidis G. Koutroulis, Ioannis N. D... Published: 07 June 2017
Earth System Dynamics Discussions, doi: 10.5194/esd-2017-53
DOI See at publisher website ABS Show/hide abstract
Bias correction of climate variables is a standard practice in Climate Change Impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long term statistics due to the time dependency that the temperature bias. Here, a method to overcome this issue without compromising the day to day correction statistics is presented. The methodology separates the model temperature signal into a normalized and a residual component relatively to the molded reference period climatology, in order to adjust the biases only for the former and preserve intact the signal of the later. The results show that the adoption of this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. The methodology is tested on daily time series obtained from five Euro CORDEX RCM models, to illustrate the improvements of this method.
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