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One key source of uncertainty for weather and climate models is structural uncertainty arising from the fact that these models must simplify or approximate complex physical, chemical, and biological processes that occur in the real world. However, structural uncertainty is rarely examined in the context of simulated effects of anthropogenic heat flux in cities. Using the Weather Research and Forecasting (WRF) model coupled with a single-layer urban canopy model, it is found that the sensitivity of urban canopy air temperature to anthropogenic heat flux can differ by an order of magnitude depending on how anthropogenic heat flux is released to the urban environment. Moreover, varying model structures through changing the treatment of roof-air interaction and the parameterization of convective heat transfer between the canopy air and the atmosphere can affect the sensitivity of urban canopy air temperature by a factor of 4. Urban surface temperature and 2-m air temperature are less sensitive to the methods of anthropogenic heat flux release and the examined model structural variants than urban canopy air temperature, but their sensitivities to anthropogenic heat flux can still vary by as much as a factor of 4 for surface temperature and 2 for 2-m air temperature. Our study recommends using temperature sensitivity instead of temperature response to understand how various physical processes (and their representations in numerical models) modulate the simulated effects of anthropogenic heat flux.

We evaluate tropical cyclones (TCs) in a set of thermodynamic global warming (TGW) simulations over the continental United States (CONUS). A 12 km simulation forced by ERA5 provides a 40-year historical (1980–2019) control. Four complimentary future scenarios are generated using thermodynamic deltas applied to lateral boundary, interior, and surface forcing. We curate a data set of 4,498 6-hourly TC snapshots in the control and find a corresponding “twin” in each counterfactual, permitting a paired comparison. Warming results in an increase in mean dynamical TC intensity and moisture-related quantities, with the latter being more pronounced. TC inner cores contract slightly but outer storm size remains unchanged. The frequency with which TCs become more intense is only moderately consistent, with snapshots having increased hazards ranging from 50% to 80% depending on warming level. The fractions of TCs undergoing rapid intensification and weakening both increase across all warming simulations, suggesting elevated short-term intensity variability.

Anthropogenic heat (AH), i.e., waste heat from buildings to the ambient environment, increases urban air temperature and contributes to the urban heat island effect, which leads to more air-conditioning energy use and higher associated waste heat during summer, forming a positive feedback loop. This study used a bottom-up simulation approach to develop a dataset of the annual hourly AH profiles for 1.7 million buildings in Los Angeles (LA) County for the year 2018 aggregated at three spatial resolutions: 450 m, 12 km, and the census tract. Building AH exhibits strong seasonal and diurnal patterns, as well as large spatial variations across the urban areas. Building AH peaks in May and reaches a maximum of 878 W/m2 within one of several AH hotspots in the region. Among the three major AH components (surface convection, heat rejection from HVAC systems, and zonal air exchange), the surface convection component is the largest, accounting for 78% of the total building AH across LA County. Higher AH is attributed to large building density, a high percentage of industrial buildings, and older building stock. While AH peaks during the day, the resulting ambient temperature increases are much larger during the night. During the July 2018 heatwave in LA County, building AH (excluding the surface component) leads to a daily maximum ambient temperature increase of up to 0.6 °C and a daily minimum ambient temperature increase of up to 2.9 °C. It is recommended that reducing summer building AH should be considered by policy makers in developing mitigation measures for cities to transition to clean energy while improving heat resilience.

To demonstrate how a mega city can lead in decarbonizing beyond legal mandates, the city of Los Angeles (LA) developed science-based, feasible pathways towards utilizing 100% renewable energy for its municipally-owned electric utility. Aside from decarbonization, renewable energy adoption can lead to co-benefits such as improving urban air quality from reductions in combustion-related emissions of oxides of nitrogen (NOx), primary fine particulate matter (PM2.5) and others. Herein, we quantify changes to air pollutant concentrations and public health from scenarios of 100% renewable electricity adoption in LA in 2045, alongside aggressive electrification of end-use sectors. Our analysis suggests that while ensuring reliable electricity supply, reductions in emissions of air pollutants associated with the 100% renewable electricity scenarios can lead to 8% citywide reductions of PM2.5 concentration while increasing ozone concentration by 5% relative to a 2012 baseline year, given identical meteorology conditions. The combination of these concentration changes could result in net monetized public health benefits (driven by avoided deaths) of up to $1.4 billion in year 2045 in LA, results potentially replicable for other city-scale decarbonization scenarios.

Regional climate models can be used to examine how past weather events might unfold under different climate conditions by simulating analogue versions of those events with modified thermodynamic conditions (i.e., warming signals). Here, we apply this approach by dynamically downscaling a 40-year sequence of past weather from 1980-2019 driven by atmospheric re-analysis, and then repeating this 40-year sequence a total of 8 times using a range of time-evolving thermodynamic warming signals that follow 4 80-year future warming trajectories from 2020-2099. Warming signals follow two emission scenarios (SSP585 and SSP245) and are derived from two groups of global climate models based on whether they exhibit relatively high or low climate sensitivity. The resulting dataset, which contains 25 hourly and over 200 3-hourly variables at 12 km spatial resolution, can be used to examine a plausible range of future climate conditions in direct reference to previously observed weather and enables a systematic exploration of the ways in which thermodynamic change influences the characteristics of historical extreme events.

In the coastal regions of the western United States, atmospheric rivers (ARs) are associated with the largest precipitation generating storms and contribute up to half of annual precipitation, but the impact of ARs on the integrated hydrologic cycle, specifically on groundwater storage and hydrodynamics, is largely unknown. To better explore the hydrologic behavior of AR versus non-AR event precipitation, we present a novel combination of two water tracking methods (one in the atmosphere and one in the subsurface) to explicitly track the full lifecycle of water parcels generated by ARs. Simulations of northern California's Cosumnes River watershed during the record wet 2017 water year are performed via the coupling of a high-resolution regional climate model and a land surface-groundwater model accounting for lateral groundwater flow. Despite ARs contributing more precipitation than non-AR storms, we find less AR water is preferentially stored in aquifers by year end. Fractionally, ARs result in 300% less snow derived groundwater-recharged compared to non-AR precipitation. Rain-on-snow (RoS) plays an important role in AR-driven discharge, where over 50% of total discharge from ARs snow is from RoS events. Finally, despite record-breaking annual precipitation, simulated groundwater depletion occurs by year end due to estimates of groundwater pumping activities. The results from these simulations serve as a partial analogue of future hydrologic conditions where ARs are expected to intensify and provide a greater fraction of annual precipitation due to climate change.

Cities consume 2/3 of global energy and consequently release a large amount of anthropogenic heat into urban environments, which are already vulnerable to extreme heat risk due to the compounding effects of urban heat island and the warming climate. In this study, we use detailed process-based building energy modeling of over 1.1 million buildings in Los Angeles along with a high-resolution urban micro-climate modeling framework to assess the implications of anthropogenic heating for urban micro-climate dynamics and the feedback process between them. We uniquely distinguish between two major components of anthropogenic heating from HVAC system rejections and exhaust/relief air from buildings. We show that the less-studied anthropogenic heating from building exhaust, compared to that from HVAC systems, is mostly a nocturnal phenomenon with more significant implications for local air temperature due to the lower and more stable planetary boundary layer at night. We demonstrate that anthropogenic heating from HVAC rejection and building exhaust not only have reverse diurnal profiles, they also exhibit offsetting behaviors under increasing outdoor temperatures. Our results show that a detailed understanding of the composition of anthropogenic heating, specific to an urban environment, is required to predict its' diurnal dynamics and its’ response to a warming climate.

Decades of urbanization have created sprawling, complex, and vulnerable cities, half of which are located in water-scarce areas. With the looming effects of climate change, including increasing droughts and water shortages, there is an urgent need to better understand how urbanization impacts the water cycle at city scale. Impervious surfaces disrupt the natural flow of water, affecting groundwater recharge in water-scarce cities, such as Los Angeles, looking to harness local water resources. In the face of growing water demand, informing on opportunities to maximize potential groundwater recharge can help increase cities’ resilience. WRF–Hydro, a physics-based hydrological modeling system, capable of resolving atmospheric, land surface, and hydrological processes at city scale, is adapted to represent urban impervious surfaces. The modified model is used to assess the hydrological implications of historical urbanization. Pre- and post-urban scenarios are used to quantify the impacts of impervious surfaces on the local water budget. Our results show that urbanization in LA has vastly decreased the potential for groundwater recharge, with up to half of the water inflow being redirected from infiltration in highly urbanized watersheds, while doubling surface runoff’s share of the city’s water budget, from ~15% to 30%. This study not only sheds light on the role of imperviousness on groundwater recharge in water-scarce cities, but also offers a robust and transferable tool for the management of urban land and water resources.

Projecting the spatiotemporal changes in water resources under a no-analog future climate requires physically based integrated hydrologic models which simulate the transfer of water and energy across the earth's surface. These models show promise in the context of unprecedented climate extremes given their reliance on the underlying physics of the system as opposed to empirical relationships. However, these techniques are plagued by several sources of uncertainty, including the inaccuracy of input datasets such as meteorological forcing. These datasets, usually derived from climate models or satellite-based products, are typically only resolved on the order of tens to hundreds of kilometers, while hydrologic variables of interest (e.g., discharge and groundwater levels) require a resolution at much smaller scales. In this work, a high-resolution hydrologic model is forced with various resolutions of meteorological forcing (0.5 to 40.5 km) generated by a dynamical downscaling analysis from the regional climate model Weather Research and Forecasting (WRF). The Cosumnes watershed, which spans the Sierra Nevada and Central Valley interface of California (USA), exhibits semi-natural flow conditions due to its rare undammed river basin and is used here as a test bed to illustrate potential impacts of various resolutions of meteorological forcing on snow accumulation and snowmelt, surface runoff, infiltration, evapotranspiration, and groundwater levels. Results show that the errors in spatial distribution patterns impact land surface processes and can be delayed in time. Localized biases in groundwater levels can be as large as 5-10m and 3m in surface water. Most hydrologic variables reveal that biases are seasonally and spatially dependent, which can have serious implications for model calibration and ultimately water management decisions.

More frequent and longer duration heat waves have been observed worldwide and are recognized as a serious threat to human health and the stability of electrical grids. Past studies have identified a positive feedback between heat waves and urban heat island effects. Anthropogenic heat emissions from buildings have a crucial impact on the urban environment, and hence it is critical to understand the interactive effects of urban microclimate and building heat emissions in terms of the urban energy balance. Here we developed a coupled-simulation approach to quantify these effects, mapping urban environmental data generated by the mesoscale Weather Research and Forecasting (WRF) coupled to Urban Canopy Model (UCM) to urban building energy models (UBEM). We conducted a case study in the city of Los Angeles, California, during a five-day heat wave event in September 2009. We analyzed the surge in city-scale building heat emission and energy use during the extreme heat event. We first simulated the urban microclimate at a high resolution (500 m by 500 m) using WRF-UCM. We then generated grid-level building heat emission profiles and aggregated them using prototype building energy models informed by spatially disaggregated urban land use and urban building density data. The spatial patterns of anthropogenic heat discharge from the building sector were analyzed, and the quantitative relationship with weather conditions and urban land-use dynamics were assessed at the grid level. The simulation results indicate that the dispersion of anthropogenic heat from urban buildings to the urban environment increases by up to 20% on average and varies significantly, both in time and space, during the heat wave event. The heat dispersion from the air-conditioning heat rejection contributes most (86.5%) of the total waste heat from the buildings to the urban environment. We also found that the waste heat discharge in inland, dense urban districts is more sensitive to extreme events than it is in coastal or suburban areas. The generated anthropogenic heat profiles can be used in urban microclimate models to provide a more accurate estimation of urban air temperature rises during heat waves.

Abstract. Projecting the spatio-temporal changes to water resources under a no-analog future climate requires physically-based integrated hydrologic models, which simulate the transfer of water and energy across the earth's surface. These models show promise in the context of unprecedented climate extremes given their reliance on the underlying physics of the system as opposed to empirical relationships. However, these techniques are plagued by several sources of uncertainty, including the inaccuracy of input datasets such as meteorological forcing. These datasets, usually derived from climate models or satellite-based products, typically have a resolution of several kilometers, while hydrologic metrics of interest (e.g. discharge, groundwater levels) require a resolution at much smaller scales. In this work, a high-resolution watershed model is forced with various resolutions (0.5 to 40.5 km) of meteorological forcing generated by a dynamical downscaling analysis based on a regional climate model (WRF) to assess how the uncertainties associated with the spatial resolution of meteorological forcing affect the simulated hydrology. The Cosumnes watershed, which spans the Sierra Nevada and Central Valley interface of California (USA), exhibits semi-natural flow conditions due to its rare un-dammed river basin and is used here as a testbed to illustrate potential impacts on snow accumulation and snowmelt, surface runoff, infiltration, evapotranspiration, and groundwater levels. Results show that localized biases in groundwater levels can be as large as 5–10 m and that other metric biases (e.g. ET and snowpack dynamics) are seasonally and spatially-dependent, but can have serious implications for model calibration and ultimately water management decisions.

Many cities globally are seeking strategies to counter the consequences of both a hotter and drier climate. While urban heat mitigation strategies have been shown to have beneficial effects on health, energy consumption, and greenhouse gas emissions, their implications for water conservation have not been widely examined. Here we use a suite of satellite-supported regional climate simulations in California to show that broad implementation of cool roofs, a heat mitigation strategy, not only results in significant cooling, but can also meaningfully decrease outdoor water consumption by reducing evaporative and irrigation water demands. Irrigation water consumption across the major metropolitan areas is reduced by up to 9% and irrigation water savings per capita range from 1.8 to 15.4 gallons per day across 18 counties examined. Total water savings are found to be the highest in Los Angeles county, reaching about 83 million gallons per day. Cool roofs are a valuable solution for addressing the adaptation and mitigation challenges faced by multiple sectors in California.

The installation of roofing materials with increased solar reflectance (i.e., "cool roofs") can mitigate the urban heat island effect and reduce energy use. In addition, meteorological changes, along with the possibility of enhanced UV reflection from these surfaces, can have complex impacts on ozone and PM2.5 concentrations. We aim to evaluate the air-quality impacts of widespread cool-roof installations prescribed by California's Title 24 building energy efficiency standards within the heavily populated and polluted South Coast Air Basin (SoCAB). Development of a comprehensive rooftop area database and evaluation of spectral reflectance measurements of roofing materials allows us to project potential future changes in solar and UV reflectance for simulations using the Weather Research Forecast and Community Multiscale Air Quality (CMAQ) models. 2012 meteorological simulations indicate a decrease in daily maximum temperatures, daily maximum boundary layer heights, and ventilation coefficients throughout the SoCAB upon widespread installation of cool roofs. CMAQ simulations show significant increases in PM2.5 concentrations and policy-relevant design values. Changes in 8-h ozone concentrations depend on the potential change in UV reflectance, ranging from a decrease in population-weighted concentrations when UV reflectance remains unchanged to an increase when changes in UV reflectance are at an upper bound. However, 8-h policy-relevant ozone design values increase in all cases. Although the other benefits of cool roofs could outweigh small air-quality penalties, UV reflectance standards for cool roofing materials could mitigate these negative consequences. Results of this study motivate the careful consideration of future rooftop and pavement solar reflectance modification policies.

Cities are concentrators of complex, multi‐sectoral interactions. As keystones in the interconnected human‐Earth system, cities have an outsized impact on the Earth system. We describe a multi‐lens framework for organizing our understanding of the complexity of urban systems and scientific research on urban systems, which may be useful for natural system scientists exploring the ways their work can be made more actionable. We then describe four critical dimensions along which improvements are needed to advance the urban research that addresses urgent climate challenges: (a) solutions‐oriented research, (b) equity‐centered assessments which rely on fine‐scale human and ecological data, (c) co‐production of knowledge, and (d) better integration of human and natural systems occurring through theory, observation, and modeling. Plain Language Summary: Cities can be seen as concentrators of complex, multi‐sectoral interactions: ripples of influence across different systems travel faster, through more systems, and have greater consequences within cities than in other contexts. We describe a multi‐lens framework for organizing our understanding of the complexity of urban systems and of scientific research on urban systems. We then describe four important improvements to urban research so we can better address urgent climate challenges in cities and globally. Key Points: Cities are concentrators of complex, multi‐sectoral interactionsIn order to address climate risks, people need transformative solutions tailored to their problems and at decision‐relevant scalesA richer understanding of human and natural interactions in urban environments can support our transition to a more climate secure future [ABSTRACT FROM AUTHOR]

This study analyzes the quality of simulated historical precipitation across the contiguous United States (CONUS) in a 12-km Weather Research and Forecasting model version 4.2.1 (WRF v 4.2.1)-based dynamical downscaling of the fifth generation ECMWF atmospheric reanalysis (ERA5). This work addresses the following questions: First, how well are the 3- and 24-hr precipitation characteristics (diurnal and annual cycles, precipitation frequency, annual and seasonal mean and maximum precipitation, and distribution of seasonal maximum precipitation) represented in the downscaled simulation, compared to ERA5? And second, how does the performance of the simulated WRF precipitation vary across seasons, regions, and timescales? Performance is measured against the NCEP/EMC 4-km Stage IV and PRISM data on 3-hr and 24-hr timescales, respectively. Our analysis suggests that the 12-km WRF exhibits biases typically found in other WRF simulations, including those at convection-permitting scales. In particular, WRF simulates both the timing and magnitude of the summer diurnal precipitation peak as well as ERA5 over most of the CONUS, except for a delayed diurnal peak over the Great Plains. As compared to ERA5, both the month and the magnitude of the precipitation peak annual cycle are remarkably improved in the downscaled WRF simulation. WRF slightly overestimates 3- and 24-hr precipitation maximum over the CONUS, in contrast to ERA5 which generally underestimates these quantities mainly over eastern half of the CONUS. Notably, WRF better captures the probability density distribution (PDF) of 3- and 24-hr annual and seasonal maximum precipitation. WRF exhibits seasonally-dependent precipitation biases across the CONUS, while ERA5's biases are relatively consistent year-round over most of the CONUS. These results suggest that dynamical downscaling to a higher resolution improves upon some precipitation metrics, but is susceptible to common regional climate model biases. Consequently, if used for operational purposes, we suggest moderate bias-correction be applied to the dynamically downscaled product. [ABSTRACT FROM AUTHOR]

Projecting the spatio-temporal changes to water resources under a no-analog future climate requires physically-based integrated hydrologic models, which simulate the transfer of water and energy across the earth's surface. These models show promise in the context of unprecedented climate extremes given their reliance on the underlying physics of the system as opposed to empirical relationships. However, these techniques are plagued by several sources of uncertainty, including the inaccuracy of input datasets such as meteorological forcing. These datasets, usually derived from climate models or satellite-based products, typically have a resolution of several kilometers, while hydrologic metrics of interest (e.g. discharge, groundwater levels) require a resolution at much smaller scales. In this work, a high-resolution watershed model is forced with various resolutions (0.5 to 40.5 km) of meteorological forcing generated by a dynamical downscaling analysis based on a regional climate model (WRF) to assess how the uncertainties associated with the spatial resolution of meteorological forcing affect the simulated hydrology. The Cosumnes watershed, which spans the Sierra Nevada and Central Valley interface of California (USA), exhibits semi-natural flow conditions due to its rare un-dammed river basin and is used here as a testbed to illustrate potential impacts on snow accumulation and snowmelt, surface runoff, infiltration, evapotranspiration, and groundwater levels. Results show that localized biases in groundwater levels can be as large as 5-10 m and that other metric biases (e.g. ET and snowpack dynamics) are seasonally and spatially-dependent, but can have serious implications for model calibration and ultimately water management decisions. [ABSTRACT FROM AUTHOR]