scholarly journals A Bayesian Approach for Uncertainty Quantification of Extreme Precipitation Projections Including Climate Model Interdependency and Nonstationary Bias

2014 ◽  
Vol 27 (18) ◽  
pp. 7113-7132 ◽  
Author(s):  
Maria Antonia Sunyer ◽  
Henrik Madsen ◽  
Dan Rosbjerg ◽  
Karsten Arnbjerg-Nielsen
Keyword(s):  
Climate Change ◽  
Uncertainty Quantification ◽  
Extreme Precipitation ◽  
Climate Change Impact ◽  
Climate Models ◽  
Climate Model ◽  
Bayesian Framework ◽  
Change Impact ◽  
Multimodel Ensembles ◽  
Precipitation Projections

Abstract Climate change impact studies are subject to numerous uncertainties and assumptions. One of the main sources of uncertainty arises from the interpretation of climate model projections. Probabilistic procedures based on multimodel ensembles have been suggested in the literature to quantify this source of uncertainty. However, the interpretation of multimodel ensembles remains challenging. Several assumptions are often required in the uncertainty quantification of climate model projections. For example, most methods often assume that the climate models are independent and/or that changes in climate model biases are negligible. This study develops a Bayesian framework that accounts for model dependencies and changes in model biases and compares it to estimates calculated based on a frequentist approach. The Bayesian framework is used to investigate the effects of the two assumptions on the uncertainty quantification of extreme precipitation projections over Denmark. An ensemble of regional climate models from the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) project is used for this purpose. The results confirm that the climate models cannot be considered independent and show that the bias depends on the value of precipitation. This has an influence on the results of the uncertainty quantification. Both the mean and spread of the change in extreme precipitation depends on both assumptions. If the models are assumed independent and the bias constant, the results will be overconfident and may be treated as more precise than they really are. This study highlights the importance of investigating the underlying assumptions in climate change impact studies, as these may have serious consequences for the design of climate change adaptation strategies.

scholarly journals Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe

2015 ◽  
Vol 19 (4) ◽  
pp. 1827-1847 ◽  
Author(s):  
M. A. Sunyer ◽  
Y. Hundecha ◽  
D. Lawrence ◽  
H. Madsen ◽  
P. Willems ◽  
...  
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Extreme Precipitation ◽  
Climate Change Impact ◽  
Climate Models ◽  
Climate Model ◽  
Control Period ◽  
Large Variability ◽  
Impact Studies ◽  
Change Impact

Abstract. Information on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical downscaling is necessary to address climate change impacts at the catchment scale. This study compares eight statistical downscaling methods (SDMs) often used in climate change impact studies. Four methods are based on change factors (CFs), three are bias correction (BC) methods, and one is a perfect prognosis method. The eight methods are used to downscale precipitation output from 15 regional climate models (RCMs) from the ENSEMBLES project for 11 catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the downscaled time series tend to agree on the direction of the change but differ in the magnitude. Differences between the SDMs vary between the catchments and depend on the season analysed. Similarly, general conclusions cannot be drawn regarding the differences between CFs and BC methods. The performance of the BC methods during the control period also depends on the catchment, but in most cases they represent an improvement compared to RCM outputs. Analysis of the variance in the ensemble of RCMs and SDMs indicates that at least 30% and up to approximately half of the total variance is derived from the SDMs. This study illustrates the large variability in the expected changes in extreme precipitation and highlights the need for considering an ensemble of both SDMs and climate models. Recommendations are provided for the selection of the most suitable SDMs to include in the analysis.

scholarly journals Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe

2014 ◽  
Vol 11 (6) ◽  
pp. 6167-6214 ◽  
Author(s):  
M. A. Sunyer ◽  
Y. Hundecha ◽  
D. Lawrence ◽  
H. Madsen ◽  
P. Willems ◽  
...  
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Bias Correction ◽  
Extreme Precipitation ◽  
Climate Change Impact ◽  
Climate Models ◽  
Control Period ◽  
Large Variability ◽  
Impact Studies ◽  
Change Impact

Abstract. Information on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical downscaling is necessary to address climate change impacts at the catchment scale. This study compares eight statistical downscaling methods often used in climate change impact studies. Four methods are based on change factors, three are bias correction methods, and one is a perfect prognosis method. The eight methods are used to downscale precipitation output from fifteen regional climate models (RCMs) from the ENSEMBLES project for eleven catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the downscaled time series tend to agree on the direction of the change but differ in the magnitude. Differences between the statistical downscaling methods vary between the catchments and depend on the season analysed. Similarly, general conclusions cannot be drawn regarding the differences between change factor and bias correction methods. The performance of the bias correction methods during the control period also depends on the catchment, but in most cases they represent an improvement compared to RCM outputs. Analysis of the variance in the ensemble of RCMs and statistical downscaling methods indicates that up to half of the total variance is derived from the statistical downscaling methods. This study illustrates the large variability in the expected changes in extreme precipitation and highlights the need of considering an ensemble of both statistical downscaling methods and climate models.

scholarly journals What can we learn from long-term groundwater data to improve climate change impact studies?

2011 ◽  
Vol 8 (4) ◽  
pp. 7621-7655 ◽  
Author(s):  
S. Stoll ◽  
H. J. Hendricks Franssen ◽  
R. Barthel ◽  
W. Kinzelbach
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Climate Change Impact ◽  
Large Scale ◽  
Climate Models ◽  
Groundwater Resources ◽  
Groundwater Dynamics ◽  
Impact Studies ◽  
Change Impact

Abstract. Future risks for groundwater resources, due to global change are usually analyzed by driving hydrological models with the outputs of climate models. However, this model chain is subject to considerable uncertainties. Given the high uncertainties it is essential to identify the processes governing the groundwater dynamics, as these processes are likely to affect groundwater resources in the future, too. Information about the dominant mechanisms can be achieved by the analysis of long-term data, which are assumed to provide insight in the reaction of groundwater resources to changing conditions (weather, land use, water demand). Referring to this, a dataset of 30 long-term time series of precipitation dominated groundwater systems in northern Switzerland and southern Germany is collected. In order to receive additional information the analysis of the data is carried out together with hydrological model simulations. High spatio-temporal correlations, even over large distances could be detected and are assumed to be related to large-scale atmospheric circulation patterns. As a result it is suggested to prefer innovative weather-type-based downscaling methods to other stochastic downscaling approaches. In addition, with the help of a qualitative procedure to distinguish between meteorological and anthropogenic causes it was possible to identify processes which dominated the groundwater dynamics in the past. It could be shown that besides the meteorological conditions, land use changes, pumping activity and feedback mechanisms governed the groundwater dynamics. Based on these findings, recommendations to improve climate change impact studies are suggested.

Uncertainty Quantification in Multi-Model Ensembles

2018 ◽  
Author(s):  
Benjamin Mark Sanderson
Keyword(s):  
Climate Change ◽  
Uncertainty Quantification ◽  
Climate Models ◽  
Climate Model ◽  
Initial Condition ◽  
Model Errors ◽  
Model Framework ◽  
Validation Data ◽  
Model Ensembles

Long-term planning for many sectors of society—including infrastructure, human health, agriculture, food security, water supply, insurance, conflict, and migration—requires an assessment of the range of possible futures which the planet might experience. Unlike short-term forecasts for which validation data exists for comparing forecast to observation, long-term forecasts have almost no validation data. As a result, researchers must rely on supporting evidence to make their projections. A review of methods for quantifying the uncertainty of climate predictions is given. The primary tool for quantifying these uncertainties are climate models, which attempt to model all the relevant processes that are important in climate change. However, neither the construction nor calibration of climate models is perfect, and therefore the uncertainties due to model errors must also be taken into account in the uncertainty quantification.Typically, prediction uncertainty is quantified by generating ensembles of solutions from climate models to span possible futures. For instance, initial condition uncertainty is quantified by generating an ensemble of initial states that are consistent with available observations and then integrating the climate model starting from each initial condition. A climate model is itself subject to uncertain choices in modeling certain physical processes. Some of these choices can be sampled using so-called perturbed physics ensembles, whereby uncertain parameters or structural switches are perturbed within a single climate model framework. For a variety of reasons, there is a strong reliance on so-called ensembles of opportunity, which are multi-model ensembles (MMEs) formed by collecting predictions from different climate modeling centers, each using a potentially different framework to represent relevant processes for climate change. The most extensive collection of these MMEs is associated with the Coupled Model Intercomparison Project (CMIP). However, the component models have biases, simplifications, and interdependencies that must be taken into account when making formal risk assessments. Techniques and concepts for integrating model projections in MMEs are reviewed, including differing paradigms of ensembles and how they relate to observations and reality. Aspects of these conceptual issues then inform the more practical matters of how to combine and weight model projections to best represent the uncertainties associated with projected climate change.

Risk-based climate-change impact assessment for the water industry

2009 ◽  
Vol 59 (3) ◽  
pp. 443-451 ◽  
Author(s):  
O. M. Thorne ◽  
R. A. Fenner
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
Climate Change Impact ◽  
Climate Models ◽  
Assessment Tool ◽  
Global Climate Models ◽  
Future Climate ◽  
Water Industry ◽  
Climate Change Impact Assessment ◽  
Change Impact

In response to a rapidly changing and highly variable climate, engineers are being asked to perform climate-change impact assessments on existing water industry systems. There is currently no single method of best practice for engineers to interpret output from global climate models (GCMs) and calculate probabilistic distributions of future climate changes as required for risk-based impact assessments. The simplified climate change impact assessment tool (SCIAT) has been developed to address the specific needs of the water industry and provides a tool to translate climate change projections into ‘real world’ impacts or for detailed statistical analysis. Through the use of SCIAT, water system operators are provided with knowledge of potential impacts and an associated probability of occurrence, enabling them to make informed, risk-based adaptation and planning decisions. This paper demonstrates the application of SCIAT to the consideration of the impacts of climate change on reservoir water quality under future climate scenarios.

Sign in / Sign up

Export Citation Format

Share Document