Assessment of climate-change impacts on alpine discharge regimes with climate model uncertainty

2006 ◽  
Vol 20 (10) ◽  
pp. 2091-2109 ◽  
Author(s):  
Pascal Horton ◽  
Bettina Schaefli ◽  
Abdelkader Mezghani ◽  
Benoît Hingray ◽  
André Musy
Keyword(s):  
Climate Change ◽  
Model Uncertainty ◽  
Climate Model ◽  
Climate Change Impacts ◽  
Climate Model Uncertainty

scholarly journals Assessing the influence of climate model uncertainty on EU-wide climate change impact indicators

2013 ◽  
Vol 120 (1-2) ◽  
pp. 211-227 ◽  
Author(s):  
Tobias Lung ◽  
Alessandro Dosio ◽  
William Becker ◽  
Carlo Lavalle ◽  
Laurens M. Bouwer
Keyword(s):  
Climate Change ◽  
Model Uncertainty ◽  
Climate Change Impact ◽  
Climate Model ◽  
Impact Indicators ◽  
Change Impact ◽  
Climate Model Uncertainty

scholarly journals “Agro-meteorological indices and climate model uncertainty over the UK”

2014 ◽  
Vol 128 (1-2) ◽  
pp. 113-126 ◽  
Author(s):  
A. E. Harding ◽  
M. Rivington ◽  
M. J. Mineter ◽  
S. F. B. Tett
Keyword(s):  
Model Uncertainty ◽  
Climate Model ◽  
The Uk ◽  
Climate Model Uncertainty

scholarly journals Producing hydrologic scenarios from raw climate model outputs using an asynchronous modelling framework

2021 ◽  
Author(s):  
Simon Ricard ◽  
Philippe Lucas-Picher ◽  
François Anctil
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Water Discharge ◽  
Climate Change Impacts ◽  
Climate Variables ◽  
Post Processing ◽  
Hydrologic Models ◽  
Simulated Climate ◽  
Meteorological Observations ◽  
The Impact

Abstract. Statistical post-processing of climate model outputs is a common hydroclimatic modelling practice aiming to produce climate scenarios that better fit in-situ observations and to produce reliable stream flows forcing calibrated hydrologic models. Such practice is however criticized for disrupting the physical consistency between simulated climate variables and affecting the trends in climate change signals imbedded within raw climate simulations. It also requires abundant good-quality meteorological observations, which are not available for many regions in the world. A simplified hydroclimatic modelling workflow is proposed to quantify the impact of climate change on water discharge without resorting to meteorological observations, nor for statistical post-processing of climate model outputs, nor for calibrating hydrologic models. By combining asynchronous hydroclimatic modelling, an alternative framework designed to construct hydrologic scenarios without resorting to meteorological observations, and quantile perturbation applied to streamflow observations, the proposed workflow produces sound and plausible hydrologic scenarios considering: (1) they preserve trends and physical consistency between simulated climate variables, (2) are implemented from a modelling cascades despite observation scarcity, and (3) support the participation of end-users in producing and interpreting climate change impacts on water resources. The proposed modelling workflow is implemented over four subcatchments of the Chaudière River, Canada, using 9 North American CORDEX simulations and a pool of lumped conceptual hydrologic models. Forced with raw climate model outputs, hydrologic models are calibrated over the reference period according to a calibration metric designed to function with temporally uncorrelated observed and simulated streamflow values. Perturbation factors are defined by relating each simulated streamflow quantiles over both reference and future periods. Hydrologic scenarios are finally produced by applying perturbation factors to available streamflow observations.

Future humidity extremes in an urban-rural context - using regional climate model projections down to convection permitting scales for Berlin

2021 ◽  
Author(s):  
Gaby S. Langendijk ◽  
Diana Rechid ◽  
Daniela Jacob
Keyword(s):  
Climate Change ◽  
Urban Areas ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Climate Data ◽  
Urban Rural

<p>Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2°C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.</p>

scholarly journals Streamflow-based evaluation of climate model sub-selection methods

2020 ◽  
Vol 163 (3) ◽  
pp. 1267-1285 ◽  
Author(s):  
Jens Kiesel ◽  
Philipp Stanzel ◽  
Harald Kling ◽  
Nicola Fohrer ◽  
Sonja C. Jähnig ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Historical Data ◽  
Model Performance ◽  
Climate Change Impacts ◽  
Climate Impact ◽  
Selection Method ◽  
Danube River ◽  
Selection Methods ◽  
Selection Of

AbstractThe assessment of climate change and its impact relies on the ensemble of models available and/or sub-selected. However, an assessment of the validity of simulated climate change impacts is not straightforward because historical data is commonly used for bias-adjustment, to select ensemble members or to define a baseline against which impacts are compared—and, naturally, there are no observations to evaluate future projections. We hypothesize that historical streamflow observations contain valuable information to investigate practices for the selection of model ensembles. The Danube River at Vienna is used as a case study, with EURO-CORDEX climate simulations driving the COSERO hydrological model. For each selection method, we compare observed to simulated streamflow shift from the reference period (1960–1989) to the evaluation period (1990–2014). Comparison against no selection shows that an informed selection of ensemble members improves the quantification of climate change impacts. However, the selection method matters, with model selection based on hindcasted climate or streamflow alone is misleading, while methods that maintain the diversity and information content of the full ensemble are favorable. Prior to carrying out climate impact assessments, we propose splitting the long-term historical data and using it to test climate model performance, sub-selection methods, and their agreement in reproducing the indicator of interest, which further provide the expectable benchmark of near- and far-future impact assessments. This test is well-suited to be applied in multi-basin experiments to obtain better understanding of uncertainty propagation and more universal recommendations regarding uncertainty reduction in hydrological impact studies.

A new approach for assessing climate change impacts in ecotron experiments

2019 ◽  
Author(s):  
Inne Vanderkelen ◽  
Jakob Zschleischler ◽  
Lukas Gudmundsson ◽  
Klaus Keuler ◽  
Francois Rineau ◽  
...  
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
New Approach ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Global Mean

Abstract. Ecotron facilities allow accurate control of many environmental variables coupled with extensive monitoring of ecosystem processes. They therefore require multivariate perturbation of climate variables, close to what is observed in the field and projections for the future, preserving the co-variances between variables and the projected changes in variability. Here we present a new experimental design for studying climate change impacts on terrestrial ecosystems and apply it to the UHasselt Ecotron Experiment. The new methodology consists of generating climate forcing along a gradient representative of increasingly high global mean temperature anomalies and uses data derived from the best available regional climate model (RCM) projection. We first identified the best performing regional climate model (RCM) simulation for the ecotron site from the Coordinated Regional Downscaling Experiment in the European Domain (EURO-CORDEX) ensemble with a 0.11° (12.5 km) resolution based on two criteria: (i) highest skill of the simulations compared to observations from a nearby weather station and (ii) representativeness of the multi-model mean in future projections. Our results reveal that no single RCM simulation has the best score for all possible combinations of the four meteorological variables and evaluation metrics considered. Out of the six best performing simulations, we selected the simulation with the lowest bias for precipitation (CCLM4-8-17/EC-EARTH), as this variable is key to ecosystem functioning and model simulations deviated the most for this variable, with values ranging up to double the observed values. The time window is subsequently selected from the RCM projection for each ecotron unit based on the global mean temperature of the driving Global Climate Model (GCM). The ecotron units are forced with 3-hourly output from the RCM projections of the five-year period spanning the year in which the global mean temperature crosses the predefined values. With the new approach, Ecotron facilities become able to assess ecosystem responses on changing climatic conditions, while accounting for the co-variation between climatic variables and their projection in variability, well representing possible compound events. The gradient approach will allow to identify possible threshold and tipping points.

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