Coastal regions like New Jersey and its environs are highly susceptible not only to the direct wind and rain effects of atmospheric storms, but also to related oceanic responses like storm surges. Moreover, the area's large metropolitan areas are particularly vulnerable to summer heat waves associated with atmospheric blocking. The most extreme storm types include hurricanes, atmospheric "bombs" (storms whose central pressure falls rapidly over a 24-hour period), nor'easters and tropical storms that transition to extra-tropical storms. Very heavy storm precipitation, fed in part by the neighboring ocean can lead to inland flooding, which can combine with high sea level anomalies to produce devastating coastal inundation, as evidenced recently by both Tropical Storm Irene and Superstorm Sandy. Typical climate models with a resolution of around one degree are too coarse to capture well heat waves or the high precipitation and destructive near-surface winds generated by severe storms and hurricanes. The overarching goal of this project is to predict economic and other human system impacts of extreme weather events at regional and decadal scales, while accounting for some natural climate variability, anthropogenic influences. A multi-scale climate model in which a one-kilometer land surface model was successfully incorporated will be extended to include a multi-scale atmosphere model capable of representing cyclones and atmospheric blocking, a coastal ocean model that can produce storm surge. The environmental and climate outcomes for the target region will be integrated with existing regional economic models and current economic valuation methodologies for present and future climate conditions. This project brings together local (municipal and county) decision makers with the academic climate and socio-economic scientists to tackle some of the most urgent challenges facing society: How will our social and economic systems respond to a changing climate? The project also offers the opportunity for two graduate students to train in a highly interdisciplinary team of climate and human systems scientist and between two leading institutions preparing for challenges of the future. The expected legacy of this project is a model framework that can be used in many regions of the world, the training of next generation scientists, communication between scientists and local decision makers in vulnerable areas and outreach to the general population.
This project aims to predict some aspects of the human and economic impacts of climate change, climate variability and changing urbanized coastal environments of New Jersey and environs. It builds on a multi-scale climate model that successfully incorporated a 1 km land surface model within the framework of the Community Earth System Model (CESM) to downscale weather events to the scales of socio-economic models. The latter include models for electricity demand, land-use, decision making and macro- and micro-economic activity. The driving hypothesis is that the dominant socio-economic impacts will be responses to extreme events such as heat waves and storms. Therefore, the project will advance the downscaling by employing existing higher resolution models of the atmosphere and a regional ocean with a demonstrated capability of producing such events as a function of climate state, including related surges in sea level. Thus, with a greater degree of confidence, the project will simulate the co-evolution of the coupled climate/socio-economic systems in response to these events and will make decadal predictions of the socio-economic responses. Rather than performing ensemble simulations with the full coupled climate and socio-economic models time-slice computations for future states (2050) will be performed. Asynchronous coupling between the climate and social models will be carried out to develop uncertainty measures for the socio-economic models. The time-slice approach can be regarded as a proof of concept for the modeling framework, which can then be used in future work to study the full evolution of the system based on a given scenario.
