Regional Analysis

How do winter-time extratropical cyclones change in the future over South Africa?

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.  

The heat is on: Accelerating climate action at a time of record-breaking temperatures
Machine learning based parameter sensitivity of regional climate models—a case study of the WRF model for heat extremes over Southeast Australia

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.

According to a recent study in the journal Environmental Challenges, New England natural lands provide $88 billion per year in recreation-use value to U.S. citizens who partake in wildlife-related activities. Considering that the estimated cumulative federal and state contributions to land conservation in New England amounted to less than $1 billion between 2004 and 2014, that $88 billion (the study’s estimate for the year 2016) is an impressive return on investment.

High school student co-authors MIT Joint Program journal article on recreation-use value of U.S. natural lands

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.

Atmospheric observations in China show rise in emissions of sulfur hexafluoride (SF6), a potent greenhouse gas
Evolution of system connectivity to support food production in the Indus Basin in Pakistan

Abstract: Sustainability challenges related to food production arise from multiple nature-society interactions occurring over long time periods. Traditional methods of quantitative analysis do not represent long-term changes in the networks of system components, including institutions and knowledge that affect system behavior.

Here, we develop an approach to study system structure and evolution by combining a qualitative framework that represents sustainability-relevant human, technological and environmental components (HTE), and their interactions, mediated by knowledge and institutions, with network modeling that enables quantitative metrics. We use this approach to examine the water and food system in the Punjab province of the Indus River Basin in Pakistan, exploring how food production has been sustained, despite high population growth, periodic floods, and frequent political and economic disruptions. Using network models of five periods spanning seventy-five years (1947-2022), we examine how quantitative metrics of network structure relate to observed sustainability-relevant outcomes and how potential interventions in the system affect these quantitative metrics.

We find that the persistent centrality of some and evolving centrality of other key nodes, coupled with the increasing number and length of pathways connecting them are associated with sustaining food production in the system over time. Assessment of potential interventions regulating groundwater pumping and phasing out fossil-fuels could alter network pathways and identify potential vulnerabilities for future food production.

Significance Statement: Models for informing sustainability interventions in complex adaptive systems involving nature-society interactions are challenging to construct due to lack of detailed, quantitative data on the changing structure of system interactions. Here, we develop a new approach combining qualitative descriptions of system components and interactions, with network representation for quantitative characterization of system structure. We demonstrate this approach with retrospective and prospective analyses related to food production in Pakistan’s Indus River Basin. Results identify the nodes and increasing number of pathways associated with sustained food production. Future scenarios point to production vulnerability due to conversion of arable land with implications on livelihoods for laborers and small business owners and highlight the importance of coordinating rural and urban water and land-use policies.

Pages

Subscribe to Regional Analysis