Author's Summary: Many modeling studies depend on direct air capture (DAC) in their 1.5°C stabilization scenarios. These studies rely on assumptions that are overly optimistic regarding the cost and scaling-up of DAC systems. This can lead to highly misleading results that can ultimately impact the ability to reach climate stabilization goals.

Abstract: Despite the commitments to the Paris Agreement’s goal of pursuing efforts to limit the global temperature increase to 1.5°C, the world exceeded this target for most if not all of 2023, raising questions about its longer-term feasibility. Most modeling studies rely on carbon dioxide removal (CDR) or negative emission technologies, such as direct air capture (DAC), bioenergy with carbon capture and storage (BECCS) and afforestation/reforestation, to keep long-term temperature targets in reach.1 DAC, in particular, has drawn substantial interest in recent years because it can generate high-quality carbon removal credits. Specifically, (1) the removal is immediate as opposed to over time as in, for example, afforestation/reforestation projects, (2) it is straightforward to measure and verify the “net” amount of carbon removed, and (3) when coupled with geologic storage, the CO2 will remain out of the atmosphere for millennia or more. 

While these advantages are compelling, there are also many practical challenges associated with real-world deployment of DAC that affect its cost and potential deployment, including challenges related to scaling-up, energy usage and siting. However, many modeling studies diminish or neglect these challenges, assuming costs of DAC deployment that do not align with the engineering realities of the technology.

Overly simplified or optimistic consideration of these challenges can lead to highly misleading results related to mitigation and adaptation strategies and their associated costs, and ultimately impact the ability to reach climate stabilization goals.

Key Points

• Our study optimises WRF model parameters for Southeast Australia heat extremes, enhancing the accuracy of the model simulation. 

• G-BO method finds optimal parameter ranges, substantially improving the simulation of temperature and humidity. 

• Results suggest updating WRF model's default settings for better extreme heat event simulations.

Abstract 

In Numerical Weather Prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, parameter uncertainty in physics parameterization schemes significantly impacts model output. Our study adopts a Bayesian probabilistic approach, building on prior research that identified temperature (T) and relative humidity (Rh) as sensitive to three key WRF parameters during southeast Australia’s extreme heat events. Using Gaussian process regression-based Bayesian Optimisation (G-BO), we accurately estimated the optimal distributions for these parameters. Results show that the default values are outside their corresponding optimal distribution bounds for two of the three parameters, suggesting the need to reconsider these default values. Additionally, the robustness of the optimal parameter distributions is validated by their application to an independent extreme heat event, not included in the optimisation process. In this test, the optimised parameters substantially improved the simulation of T and Rh, highlighting their effectiveness in enhancing simulation accuracy during extreme heat conditions. 

Plain Language Summary 

This study aims to enhance the accuracy of a numerical weather model called the Weather Research and Forecasting (WRF) model, especially for simulating extreme heat events in Southeast Australia. Typically, the accuracy of such models depends on specific settings, which are often set to default values. Our research used a method known as Gaussian process regression-based Bayesian Optimisation (G-BO) to determine the best range of values for these settings. We found that the default settings were not optimal. By applying G-BO, we identified more effective values that substantially improved the model’s simulations of temperature and humidity during heat extremes. This improvement was consistent even when tested on an independent extreme heat event. These advancements are vital for more accurate weather forecasting, which is essential for emergency services, electricity management, and agriculture planning during extreme heat conditions.

Authors' Summary: The desire of policymakers and public finance institutions to understand the contribution of water infrastructure to the wider economy, rather than the value of project-level outputs in isolation, has spawned a multidisciplinary branch of water resource planning that integrates traditional biophysical modeling of water resource systems with economy-wide models, including computable general equilibrium models. Economy-wide models include several distinct approaches, including input–output models, macro-econometric models, hybrid input–output macro-econometric models, and general equilibrium models—the term “economy-wide” usually refers to a national level analysis, but could also apply to a sub-national region, multi-nation regions, or the world. A key common characteristic of these models is that they disaggregate the overall economy of a country or region into a number of smaller units, or optimizing agents, who in turn interact with other agents in the economy in determining the use of inputs for production, and the outcomes of markets for goods. These economic agents include industries, service providers, households, governments, and many more. Such a holistic general equilibrium modeling approach is particularly useful for understanding and measuring social costs, a key aim in most cost–benefit analyses (CBAs) of water infrastructure investments when the project or program will have non-marginal impacts and current market prices will be impacted and an appropriately detailed social accounting matrix is available.

This article draws on examples from recent work on low- and middle-income countries (LMICs) and provides an outline of available resources that are necessary to conduct an economy-wide modeling analysis. LMICs are the focus of larger water resource investment potential in the 21st century, including large-scale hydropower, irrigation, and drinking water supply. A step-by-step approach is illustrated and supports the conclusion that conditions exist to apply these models much more broadly in LMICs to enhance CBAs.