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Future climate change depends on properties of the climate system and the external forcing factors that drive the global energy budget. Among those properties are climate sensitivity, the rate at which heat is mixed into the deep ocean, and the aerosol forcing on the planet. In this dissertation, we use the newly updated Massachusetts Institute of Technology Earth System Model (MESM) to derive the joint probability distribution function (PDF) for model parameters that represent the aforementioned climate system properties. Climate sensitivity (ECS) in the model is set through an adjustment to the cloud feedback parameter. The vertical diffusion coefficient, Kv, represents the mixing of heat into the deep ocean by all mixing processes. The net anthropogenic aerosol forcing parameter, Faer, estimates the contribution of aerosol cooling to the global energy budget. Using an 1800-member ensemble of MESM runs where the model parameters have been systematically varied, we derive PDFs for the model parameters by comparing the model output against historical observations of surface temperature and global mean ocean heat content. In particular, we answer four main research questions: (1) How are the parameter PDFs derived using the MESM ensemble different from those using a previous version of the model?, (2) How do the estimates change when recent surface temperature and ocean heat content observations are included in the model diagnostics used to evaluate model performance?, (3) How does internal climate variability lead to uncertainty in the parameter estimates?, and (4) What impact do the changes in PDFs have on estimates of future warming, namely estimates of transient climate response (TCR)? We show that estimates of climate sensitivity increase and the aerosol forcing is less negative when using MESM. These shifts are the result of a new forcing suite used to drive the model. By extending the length of the model diagnostics one decade at a time, we show that recent temperature patterns impact our estimates of the climate system properties. The continued rise in surface temperature leads to higher values of ECS, while the increased rate of heat storage in the ocean leads to lower estimates of ECS and higher estimates of Kv. We show that the parameter distributions are sensitive to the internal variability in the climate system and that using a single variability estimate can lead to PDFs that are too narrow. Throughout the dissertation, we show that estimates of transient climate response are correlated with ECS and Kv. Namely, higher ECS and weaker Kv lead to higher values of TCR. When considering all of these factors, we arrive at our best estimate for the climate system properties. We estimate the 90-percent confidence interval for climate sensitivity to be 2.7 to 5.4 degrees C with a mode of 3.5 degrees C. Our estimate for Kv is 1.9 to 23.0 cm2s−1 with a mode of 4.41 cm2s−1. Faer is estimated to be between -0.4 and -0.04 Wm−2 with a mode of –0.25 Wm−2. Lastly, we estimate TCR to be between 1.4 and 2.1 degrees C with a mode of 1.8 degrees C.

 

MIT Joint Program Co-Director John Reilly, former U.S. Vice President Albert Gore and other experts explore extreme implications of climate change in Meltdown Earth, a video in the NowThis: Apocalypse online series. The video description reads: "Rising ocean waters, scorching temperatures, food scarcity, and disease – here's how humans could ultimately be responsible for the end of the world."  

The electricity system is transitioning from a system comprised primarily of dispatchable generators to a system increasingly reliant on wind and solar power|intermittent sources of electricity with output dependent on meteorological conditions, adding both variability and uncertainty to the system. Dispatchable generators with a high ratio of fixed to variable costs have historically relied on operating at maximum output as often as possible to spread these fixed costs over as much electricity generation as possible. Higher penetrations of intermittent capacity create market conditions that lead to lower capacity factors for these generators, presenting an economic challenge. Increasing penetrations of intermittent capacity, however, also leads to more volatile electricity prices, with highest prices in hours that renewable sources are unavailable. The ability of dispatchable generators to provide energy during these high priced hours may counteract the loss of revenue from reduced operating hours. Given the disparate revenues received in this volatile market, the relative competitiveness of generation technologies cannot be informed by their cost alone; the value of generators based on their production profiles must also be considered. Consequently, comparisons of generator competitiveness based on traditional metrics such as the levelized cost of electricity are misleading, and power system models able to convey the relative value of generators should instead be used to compare generator competitiveness.

The purpose of this thesis is to assess the relative competitiveness of generation technologies in an efficient market under various penetrations of intermittent power. This work is specifically concerned with the relative competitiveness of power plants equipped with carbon capture and storage (CCS) technology, nuclear power plants, and renewable generation capacity. In order to assess relative competitiveness, this work presents an extensive literature review of the costs and technical flexibility of generators, with particular attention to CCS-equipped and nuclear capacity. These costs and flexibility parameters are integrated into a unit commitment model. The unit commitment model for co-optimized reserves and energy (UCCORE), developed as part of this thesis, is a mixed integer linear programming model with a focus on representing hourly price volatility and the intertemporal operational constraints of thermal generators. The model is parameterized to represent the ERCOT power system and is used to solve for generator dispatch and marginal prices at hourly intervals over characteristic weeks. Data from modeled characteristic weeks is interpolated to estimate generator profits over a year to allow for a comparison of generator competitiveness informed by both costs and revenues.

Scenario analysis conducted using the UCCORE model shows that the difference in energy prices captured by generators becomes an important driver of relative competitiveness at modest penetrations of intermittent power. Increasing the ratio of intermittent to dispatchable capacity causes intermittent generators to depress market prices during the hours they are available due to their coordinated output. Prices, however, rise in hours when intermittent capacity is unavailable because of scarcity of available capacity. This work develops the weighted value factor to compare the revenues of intermittent and dispatchable generation capacity. The weighted value factor is the market value of a generators production profile relative to an ideal generator dispatched at full capacity for all hours. The results show that as the proportion of intermittent capacity increases, the relative value of dispatchable generators also increases and at an increasing rate. At high penetrations of intermittent capacity, the power system experiences increasing risk of generation shortages leading to exceptionally high prices. In these systems, dispatchable generators able to capture peak pricing become most profitable. At lower penetrations of intermittent capacity, peak pricing remains influential, but is less extreme and the relative importance of low capital and fixed costs increases. The sensitivity of generator profitability to assumed value of lost load, oil and gas price, and carbon price is also assessed.

The key implication of these results is that efficient price signals may lead to opportunities for investment in dispatchable generators as the proportion of intermittent capacity on a power system increases. Markets and models that do not capture the full hourly volatility of efficient energy prices, however, are missing critical signals. The importance of these signals on relative competitiveness increases with the penetration of intermittent power. Without accounting for price volatility, markets and models will undervalue dispatchable capacity and overvalue intermittent capacity.

Extreme precipitation events pose a significant threat to public safety, natural and managed resources, and the functioning of society. Changes in such high-impact, low-probability events have profound implications for decision-making, preparation and costs of mitigation and adaptation efforts. Understanding how extreme precipitation events will change in the future and enabling consistent and robust projections is therefore important for the public and policymakers as we prepare for consequences of climate change.

Projection of extreme precipitation events, however, particularly at the local scale, presents a critical challenge: the climate model-based simulations of precipitation that we currently rely on for such projections—general circulation models (GCMs)—are not very realistic, mainly due to the models’ coarse spatial resolution. This coarse resolution precludes adequate representation of highly influential, small-scale features such as moisture convection and topography. Regional circulation models (RCMs) provide much higher resolution and better representation of such features, and are thus often perceived as an optimum approach to producing more accurate heavy precipitation statistics than GCMs. However, they are much more computationally intensive, time-consuming and expensive to run.

In a previous paper, the researchers developed an algorithm that detects the occurrence of heavy precipitation events based on climate models’ well-resolved, large-scale atmospheric circulation conditions associated with those events—rather than relying on these models’ simulated precipitation. The algorithm’s results corresponded with observations with much greater precision than the model-simulated precipitation.

In this paper, the researchers show that using output from RCMs rather than GCMs for the new algorithm does not improve the precision of simulated extreme precipitation frequency. The algorithm thus presents a robust and economic way to assess extreme precipitation frequency across a broad range of GCMs and multiple climate change scenarios with minimal computational requirements.   

 

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