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Multi-model climate experiments carried out as part of different phases of the Coupled Model Intercomparison Project (CMIP) are crucial for the evaluation of past and future climate change. The previous phases of CMIP have been very useful to both understand changes in the climate system and help inform climate policy. The latest phase of CMIP - CMIP6 - is developed to overcome limitations of the CMIP5, and to continue to address other ongoing scientific challenges.
From a hydrological perspective, these simulations are helpful in understanding changes to precipitation and temperature, and in turn assist with risk analysis and decision support. However, climate models are considered unreliable in simulating precipitation and temperature at fine resolution.
In this webinar, developments in the latest phase, CMIP6, are discussed along with the credibility of these projections in reproducing historical variability and trends. Biases in precipitation and temperature at global scale are presented, and limitations in assessing only basic metrics like mean and variance are discussed. The talk focuses on advanced properties (such as correlations, long-term persistence or distributional shape characteristics) that reveal valuable information on the dynamics of the models affecting their ability to create clustering of anomalies, local increasing or decreasing trends, or the potential to observe changing extremes.
Further the confidence in using the climate models for risk analysis and informed decision-making is discussed. Issues resulted in climate models significantly falling short of their potential as tools for supporting decision making, and limiting the options available for developing adaptation and mitigation measures to climate change, are detailed.
The webinar will be particularly interesting to research scholars, graduate students, young faculty members, research scientists and practitioners in the hydrology community, as well as representatives from government and non-government organizations involved in decision- and policy-making.
Dr Chandra Rupa Rajulapati is currently the PIMS-GWF Postdoctoral Fellow at the Center for Hydrology, University of Saskatchewan, Canada. Her research interests include modelling hydrologic extremes (floods and droughts), risk analysis, quantification of uncertainties using Bayesian methods, spatio-temporal modelling of hydrologic extremes, detecting trends, trend analysis, modelling spatial dependence structure, quantifying variations in dependence structure within and surrounding urban areas, and developing Intensity Duration Frequency (IDF) relationships.
She is also interested in working on inter-disciplinary water research including assessment of environmental impacts and water security. She have been working on flood risk analysis, assessing vulnerability and resilience of water infrastructure, climate change impact assessment and adaptation, modelling uncertainties in the designs of hydrologic infrastructure. During her PhD, at the Indian Institute of Science (IISc) she worked on developing methods for quantifying uncertainty in IDF relationships, variations in IDF relationships under climate change and its effect on the storm water networks. She was one of the IUKWC Junior Research Exchange candidates and undertook her exchange at the University of Exeter where she considered the ‘Quantifying resilience of water infrastructure to extreme precipitation events in urban areas’.
She is involved in a project that is developing alternative routes for storm water drains to avoid flooding using optimization techniques and developing integrated approaches incorporating uncertainties in each step of modelling. Currently, she is extensively working on the newly released climate model simulations (CMIP6), to understand their usefulness in simulating the historical climate.