Infrastructure & Investment

Abstract: The Modeling Dynamic Systems for Sustainable Development Special Feature showcases recent advances in modeling that, if more widely adopted, could significantly improve the capacity of sustainability science researchers to test theory, mobilize data, and evaluate interventions. The contributors show how new methods and approaches for analyzing complex interactions in nature–society systems can help to link knowledge with action in society’s efforts to address the core challenges of sustainable development.

Abstract: Shared community scenarios of societal and environmental system changes have underpinned a broad range of research and assessment studies over the past several decades. These scenarios have largely aimed to address specific questions within broad issue areas like climate change or biodiversity and generally provided information on the drivers of change. The consequences of those drivers, such as impacts on society and policy responses, have tended to be left to the research community to investigate, using scenarios of drivers as inputs to their studies, producing projections of a disparate set of relevant output metrics. While this approach has had many benefits, it has fallen short of producing a robust, comparable literature describing outcomes across studies in common metrics.

We argue that new scenarios are needed that extend current approaches to be organized around common outcome metrics for the well‐being and resilience of society and ecosystems. We propose an approach that would focus on agreed-upon outcomes for well‐being and resilience as well as critical drivers of change, cut across issues and scales in multiple sectors, and draw on new systematic methods of scenario generation and discovery to highlight scenarios that are most critical in understanding societal risks and responding to them.

Research derived from this outcome‐based scenario development approach would facilitate improved assessment of risks of and responses to a range of stressors and the multi‐sector interactions they generate.

Plain Language Summary: Scenarios are visions of how the world might unfold. They can consist of stories, numerical projections, or both. Typically scenarios describe trends in drivers of change—factors like population and economic growth, how fast technological progress occurs, and changes to the climate system. Historically, small sets of common scenarios have been widely used by the global change research community. Researchers use the scenarios as inputs to project the consequences of the drivers, for example, for agricultural production, water availability, or the costs of decarbonization.

We propose that new scenarios are needed that include outcomes (consequences) not just for physical or managed systems, but also for human well‐being and resilience, including health, poverty, and household food, water, and energy security. Further, the scenarios should not only include well‐being outcomes, but be organized around them. That is, scenarios should be designed not necessarily to span a wide range of drivers, but rather to span a wide range of well‐being and resilience outcomes.

Designing scenarios around the ultimate outcomes of interest will improve the assessment of risks and responses related to well‐being and resilience. New quantitative methods for generating and identifying scenarios can facilitate this process.

Abstract: Extratropical cyclones (ETCs) in South Africa usually occur during the winter (June to August), specifically influencing the Western Cape, causing extreme rain and strong winds.

We investigate future changes in these winter-time ETCs using the simulations from three CORDEX-CORE Africa models. Each of these models was driven by three Coupled Model Intercomparison Project phase 5 (CMIP5) General Circulation Models (GCMs), resulting in nine sets of simulations. The simulations are from 1970-2100, with scenarios starting from 2006. We identified the cyclone tracks using the Hodges tracking algorithm, which used 6-hourly relative vorticity data at 850 hPa level. We chose a 20-year historical period from 1986 to 2005 for comparison with a future period of the same length from 2080 to 2099, focusing on the Representative Concentration Pathway (RCP) 8.5 scenario for the future projections.

We observed a projected decrease in the number of ETCs in the future. The average track distance and duration are also projected to reduce. These reductions are statistically significant. We explored the future changes in the ETC-associated rainfall, which is also projected to be reduced in the future. We are currently looking at extending our analysis with the high-resolution 4 km gridded Climate Predictions for Africa (CP4A) data and see how our earlier results compare with the high-resolution data.

At the XLVI (46th) MIT Global Change Forum on March 28-29, 2024, more than 100 attendees from industry, academia, government and NGOs gathered at the Samberg Conference Center on the MIT campus to explore climate change trends, physical and economic climate impacts, and policy and communications strategies to accelerate climate action as global temperatures continue to soar.  

Abstract: Heatwaves and bushfires cause substantial impacts on society and ecosystems across the globe. Accurate information of heat extremes is needed to support the development of actionable mitigation and adaptation strategies. Regional climate models are commonly used to better understand the dynamics of these events. These models have very large input parameter sets, and the parameters within the physics schemes substantially influence the model's performance. However, parameter sensitivity analysis (SA) of regional models for heat extremes is largely unexplored.

Here, we focus on the southeast Australian region, one of the global hotspots of heat extremes. In southeast Australia Weather Research and Forecasting (WRF) model is the widely used regional model to simulate extreme weather events across the region. Hence in this study, we focus on the sensitivity of WRF model parameters to surface meteorological variables such as temperature, relative humidity, and wind speed during two extreme heat events over southeast Australia. Due to the presence of multiple parameters and their complex relationship with output variables, a machine learning (ML) surrogate-based global SA method is considered for the SA. The ML surrogate-based Sobol SA is used to identify the sensitivity of 24 adjustable parameters in seven different physics schemes of the WRF model.

Results show that out of these 24, only three parameters, namely the scattering tuning parameter, multiplier of saturated soil water content, and profile shape exponent in the momentum diffusivity coefficient, are important for the considered meteorological variables. These SA results are consistent for the two different extreme heat events. Further, we investigated the physical significance of sensitive parameters. This study's results will help in further optimising WRF parameters to improve model simulation.

To achieve the aspirational goal of the Paris Agreement on climate change—limiting the increase in global average surface temperature at 1.5 degrees Celsius above pre-industrial levels—will require its 196 signatories to dramatically reduce their greenhouse gas (GHG) emissions.

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