Climate Impacts and Adaptation
Our research group aims to understand the intricate interactions between climate change and socioeconomic development, generally focusing on how changes in climate and weather patterns shape economic development on the global scale, as well as how global economic development shapes future changes in climate and weather patterns. Our approach spans from utilizing Earth System Models (ESMs) to developing Integrated Assessment Models (IAMs) to assess climate impacts on both global and regional economies. Our research provides valuable insights that can contribute to policy-making, adaptation, and mitigation strategies.
Global and regional climate change are significantly influenced by internal climate variability, which spontaneously occurs in the climate system and affects extreme weather events that align with the extremes of statistical distributions. However, socioeconomic climate impact studies have predominantly neglected the uncertainties stemming from this internal climate variability, challenging the attribution of economic damages to climate change. At the same time, current climate models, with their coarse spatial resolution and reliance on parameterizations, have only a limited representation of climate and weather extreme events. These limitations undermine the physical evidence base needed to support effective climate change mitigation and adaptation efforts. We use global kilometer-scale models that can capture interactions between small and large-scale climate processes. These models promise a better representation of climate variability and extreme events. Our unique approach integrates advanced climate science with economic analysis to produce information that can be directly utilized in local climate change adaptation planning.
We contribute to develop comprehensive IAMs that depict the interactions between human and natural systems. IAMs are essential for creating scenarios related to anthropogenic climate change and have been pivotal in developing Shared Socioeconomic Pathways (SSPs). A key function of IAMs is to represent how human activities affect the climate, including emissions and land use changes. However, IAMs often overlook the complex impacts of climate change on human systems and the aleatoric uncertainty due to climate variability when creating future scenarios. Many widely-used IAMs employ simple functions to translate climate indicators into economic damage. Our two-way coupling approach enhances our understanding of climate change impacts on human systems, crafts effective adaptation strategies, and hence explores plausible climate futures.
Outlined below are our current research areas aimed in addressing pressing research questions. The research areas collectively focus on finding climate change signals, assessing their economic implications, and exploring the interactions between climate and economic systems.
We have developed an “observed fingerprint” method that derives human-forced warming patterns directly from observations, bypassing model-derived human-forced warming pattern. Our method introduces a new line of evidence in assessing regional climate change and to support the crafting of effective adaptation strategies. It also aids theoretical work, as where discrepancies with model predictions emerge, it identifies gaps in understanding. With the advent of km-scale models, predictions begin to work in the same space (and set of variables) as observations making it easier to combine our “observed fingerprint” method with models to better understand the signals of climate change.
Detecting and attributing climate-induced economic damage, along with adaptation planning, is challenging due to uncertainty from climate variability. We focus on the socio-economic impacts of climate change, particularly how internal climate variability affects the uncertainty in attributing economic damage to climate change. Even minor shifts in temperature and precipitation can have significant socio-economic consequences. We analyze these changes and their impact on flooding to identify vulnerable regions. Our approach combines global kilometer-scale climate models with hydrological models to evaluate river discharge, flooded areas, and flood depth, considering both human-induced and other climate drivers.
We aim to create more plausible climate scenarios by addressing uncertainties in natural and social processes. Traditional models like ESMs and IAMs often miss key feedbacks between climate and social dynamics. Our approach bridges this gap with a fully endogenous, process-based model that integrates these dynamics. For example, we develop the Feedback-based knowledge Repository for Integrated assessments of Sea level rise Impacts and Adaptation (FRISIA), a model designed for process-based, non-equilibrium IAMs that follows a system dynamics approach. FRISIA’s new contribution is its impact and adaptation component, which is a substantially modified adaptation of the Coastal Impact and Adaptation Model (CIAM) for the use in IAMs. Our results show that the integration of additional feedbacks in FRISIA leads to emerging new behaviors, such as a potential peak and decline in sea level rise-driven storm surge damages in the early 22nd century.
Group members and publications
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