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Ambient temperatures have an impact on human health, with unfavourably warm and cold conditions both associated with elevated mortality risk. By modulating the temperature in urban environments, urban heat islands (UHIs) can therefore both amplify the impact of heat and offer protection against cold weather. In this study, we quantify the impact of UHI on human mortality at 500m resolution for 85 European cities using air temperature simulations and age-dependent epidemiological temperature-mortality relationships for each city. On an annual basis, UHIs have weak net protective effects for most cities examined. This is due to the prevalence of cold to mild days in these cities when an increase in temperature is associated with slight reductions in mortality risk. On a daily basis, however, UHIs induce the greatest impact during heat extreme days, with a median of 39% increase in risk compared to a 7% reduction during cold extreme days. A valuation of such mortality risk reveals that the annual cost of UHI-related heat mortality is comparable to air pollution-related mortality costs as well as transit costs. Cities with Arid climates and Temperate Dry Summer climates in Southern Europe tend to experience the greatest protective UHI effects during cold extreme weather and the least adverse effect during heat extremes, while cities with Cold climates in Eastern and Northern Europe tend to benefit the least during cold extremes. Annually, the net impact of UHI is most strongly correlated with each city’s vulnerability to heat and cold and the ratio of warm vs. cold days in a year.

Precision irrigation management requires operational monitoring of crop water status. However, there is still some controversy on how to account for crop water stress. To address this question, several physiological, several physiological metrics have been proposed, such as the leaf/stem water potentials, stomatal conductance, or sap flow. On the other hand, thermal remote sensing has been shown to be a promising tool for efficiently evaluating crop stress at adequate spatial and temporal scales, via the Crop Water Stress Index (CWSI), one of the most common indices used for assessing plant stress. CWSI relates the actual crop evapotranspiration ET (related to the canopy radiometric temperature) to the potential ET (or minimum crop temperature). However, remotely sensed surface temperature from satellite sensors includes a mixture of plant canopy and soil/substrate temperatures, while what is required for accurate crop stress detection is more related to canopy metrics, such as transpiration, as the latter one avoids the influence of soil/substrate in determining crop water status or stress. The Two-Source Energy Balance (TSEB) model is one of the most widely used and robust evapotranspiration model for remote sensing. It has the capability of partitioning ET into the crop transpiration and soil evaporation components, which is required for accurate crop water stress estimates. This study aims at evaluating different TSEB metrics related to its retrievals of actual ET, transpiration and stomatal conductance, to track crop water stress in a vineyard in California, part of the GRAPEX experiment. Four eddy covariance towers were deployed in a Variable Rate Irrigation system in a Merlot vineyard that was subject to different stress periods. In addition, root-zone soil moisture, stomatal conductance and leaf/stem water potential were collected as proxy for in situ crop water stress. Results showed that the most robust variable for tracking water stress was the TSEB derived leaf stomatal conductance, with the strongest correlation with both the measured root-zone soil moisture and stomatal conductance gas exchange measurements. In addition, these metrics showed a better ability in tracking stress when the observations are taken early after noon., Funding and logistical support for the GRAPEX project were provided by E. & J. Gallo Winery and from the NASA Applied Sciences-Water Resources Program (Grant no. NNH17AE39I). This research was also supported in part by the U.S. Department of Agriculture, Agricultural Research Service. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.

The impact of climate change on the environment has become increasingly visible today, and foreseeing future climate events, which involves long-term prediction of climate variables (e.g., temperature, wind speed, precipitation, etc.) at a local small scale in a local region, is crucial for disaster risk management. General Circulation Models (GCMs) allow for the simulation of multiple climate variables, decades into the future (often till the year 2100). GCM simulations, however, are at a global large scale (from 100 km to 600 km) and are too coarse to monitor climate change at the local small scale. Statistical downscaling approaches are often applied to the GCM simulations to allow the evaluation of the GCM outputs at the local scale. Machine learning-based techniques are popular approaches for statistical downscaling. In this paper, we provide an overview of GCM downscaling with machine learning and present a case study that leverages deep learning to downscale weekly averages of the daily minimum and maximum temperatures in the Hackensack–Passaic watershed in New Jersey.

The resilience of communities has emerged as a major goal in policy and practice. Cities, states, and counties within the United States and around the world are passing laws requiring the incorporation of climate-related hazard vulnerability assessments within their master plan updates for resilience planning and design. The resilience of communities under present and future scenarios is thus becoming a cornerstone of decision making and actions. Decisions that would enhance resilience, however, span multiple sectors and involve various stakeholders. Quantifying community resilience is a key step in order to describe the preparedness level of communities, and subsequently locating non-resilient areas to further enhance their capacity to endure disasters. Two main approaches are currently being pursued to evaluate resilience. The first approach is the "community resilience" developed mainly by social scientists and planners, and it captures social resilience using numerous pre-disaster attributes to describe the functioning of a community. This approach subsumes that pre-disaster attributes can predict the community resilience to a disaster. The second approach is adopted for infrastructure resilience, mostly used by engineers, and it focuses on robustness, redundancy, resourcefulness, and rapidity. This approach is appropriate for systems that are operated by highly skilled personnel and where the actions are of engineering type. In this paper, we provide an overview of the two approaches, and we leverage their limitations to propose a hybrid approach that combines community and infrastructure capitals into an Area Resilience metric, called ARez. ARez captures the role/impact of both infrastructure and community and combines five sectors: energy, public health, natural ecosystem, socio-economic, and transportation. We present a proof-of-concept for the ARez metric, showing its practicality and applicability as a direct measure for resilience, over various time scales.

Water conservation efforts for California’s agricultural industry are critical to its sustainability through severe droughts like the current one and others experienced over the last two decades. This is most critical for perennial crops, such as vineyards and orchards, which are costly to plant and maintain and constitute a significant fraction of the regional water use. It is no longer feasible to access groundwater for irrigation to replace deficit surface water resources during drought due to a significant overdraft of aquifers and new regulation limiting its use. To achieve significant water savings, the actual crop water use or evapotranspiration (ET) needs to be mapped from field to regional scales on a daily basis. This can only be achieved using remote sensing-based models, particularly thermal-based energy balance models that are sensitive to deficit irrigation conditions. The two-source energy balance (TSEB) model has been successfully applied over vineyards in California, but challenges still remain. In particular, much of the irrigated cropland in the California Central Valley is affected by advection of hot dry air masses from surrounding non-irrigated areas and the TSEB model appears to need modifications to adequately estimate ET under such conditions, as well as the partitioning between evaporation and transpiration. This study investigates the application of the TSEB model, using local observations in a vineyard having significant advection. Four versions of the transpiration algorithm in TSEB are applied and evaluated with tower eddy covariance measurements spanning 4 growing seasons. The results suggest the performance of the original transpiration algorithm based on Priestley–Taylor used in TSEB is satisfactory in all but the most extreme advective conditions, while a transpiration algorithm based on Shuttleworth–Wallace with a canopy resistance formula, which relates maximum stomata conductance to vapor pressure deficit (VPD), performs well in all cases. These modifications have potential for improving regional applications of the TSEB model in support of water management in the Central Valley., Funding and logistical support for the GRAPEX project were provided by E. & J. Gallo Winery and from the NASA Applied Sciences-Water Resources Program (Grant No. NNH17AE39I). This research was also supported in part by the U.S. Department of Agriculture, Agricultural Research Service.

Effective urban planning for climate-driven risks relies on robust climate projections specific to built landscapes. Such projections are absent because of a near-universal lack of urban representation in global-scale Earth system models. Here, we combine climate modelling and data-driven approaches to provide global multi-model projections of urban climates over the twenty-first century. The results demonstrate the inter-model robustness of specific levels of urban warming over certain regions under climate change. Under a high-emissions scenario, cities in the United States, Middle East, northern Central Asia, northeastern China and inland South America and Africa are estimated to experience substantial warming of more than 4 K—larger than regional warming—by the end of the century, with high inter-model confidence. Our findings highlight the critical need for multi-model global projections of local urban climates for climate-sensitive development and support green infrastructure intervention as an effective means of reducing urban heat stress on large scales. An urban climate model emulator has been used with a multi-model archive to estimate that in a high-emissions scenario, many cities will warm by over 4 K during local summers. Near-global relative humidity decreases highlight the potential for green infrastructure and more efficient urban cooling mechanisms.

Offshore wind and wave power are renewable energy sources that share the marine environment. Collocating these systems can reduce power variability; however, the extent of this differs between sites. Regional differences in combined system variability are investigated. Potential power output of wind and wave systems is simulated for sites throughout the US East and West Coasts and UK North Sea using field observations of met-ocean conditions. The power production potential is approximated using an idealized power curve of the Siemens Gamesa SWT-6.0-154 6 MW wind turbine and the Pelamis 750 kW wave energy converter power matrix. The regional comparative approach provides novel insight into collocated system power variability on the US East Coast, and how it contrasts with the previously studied US West Coast and UK North Sea. Hourly and diurnal variability are found to decrease on the US West Coast and UK North Sea, with minimal effect on the US East Coast. The benefits for the West Coast extend to seasonal time scales (but as expected not to annual scales), but benefits for all regions depend on the specific wind-wave capacity mix. Reduced variability can benefit collocated wind and wave power, but these effects differ regionally and must be assessed locally.

The partitioning of ecosystem evapotranspiration and carbon dioxide fluxes into their plant and ground components is a critical research priority to better understand the water cycle and ecosystem function. Despite advances in different measurement techniques and partitioning models in the last decades, much is still unknown regarding the importance of different components of H2O and CO2 fluxes in ecosystems. In this work, we compare three partitioning methods that are based on analysis of conventional high frequency eddy-covariance (EC) data: the flux variance similarity method, the modified relaxed eddy accumulation methods, and the conditional eddy covariance method. First, we test these methods using fields experimental data, comparing them to other reference measurements for the components fluxes (gas chambers and leaf levels measurements). Subsequently, we develop a novel approach for simulating these fluxes in large eddy simulations and apply it to further probe the performance, assumptions, and relative skill of the three methods. The findings allow us to recommend partitioning best practices for their implementation, and to develop methods for the joint analyses of the various approaches.

High air pollution concentration levels and increased urban heat island intensity, are amongst the most critical contemporary urban health concerns. This is the reason why various municipalities are starting to invest in extensive direct air quality and microclimate sensing networks. Through the study of these datasets it has become evident that the understanding of inter-urban environmental gradients is imperative to effectively introduce urban land-use strategies to improve the environmental conditions in the neighborhoods that suffer the most, and develop city-scale urban planning solutions for a better urban health. However, given economic limitations or divergent political views, extensive direct sensing environmental networks have yet not been implemented in most cities. While the validity of citizen science environmental datasets is often questioned given that they rely on low-cost sensing technologies and fail to incorporate sensor calibration protocols, they can offer an alternative to municipal sensing networks if the necessary Quality Assurance / Quality Control (QA/QC) protocols are put in place.This research has focused on the development of a QA/QC protocol for the study of urban environmental data collected by the citizen science PurpleAir initiative implemented in the Bay Area and the city of Los Angeles where over 700 purple air stations have been implemented in the last years. Following the QA/QC process the PurpleAir data was studied in combination with remote sensing datasets on land surface temperature and normalized difference vegetation index, and geospatial datasets on socio-demographic and urban fabric parameters. Through a footprint-based study, and for all PurpleAir station locations, the featured variables and the buffer sizes with higher correlations have been identified to compute the inter-urban environmental gradient predictions making use of 3 supervised machine learning models: - Regression Tree Ensemble, Support Vector Machine, and a Gaussian Process Regression.

Large Eddy Simulations (LES) of neutral flow over regular. arrays of cuboids are conducted to explore connections between momentum (z0m) and scalar (z0s) roughness lengths in urban environments, and how they are influenced by surface geometry. As LES resolves the obstacles but not the micro-scale boundary layers attached to them, the aforementioned roughness lengths are analyzed at two distinct spatial scales. At the micro-scale (roughness of individual facets, e.g. roofs), it is assumed that both momentum and scalar transfer are governed by accepted arguments for smooth walls that form the basis for the LES wall model. At the macro-scale, the roughness lengths are representative of the aggregate effects of momentum and scalar transfer over the resolved roughness elements of the whole surface, and hence they are directly computed from the LES. The results indicate that morphologically-based parameterizations for macro-scale z0m are adequate overall. The relation between the momentum and scalar macro-roughness values, as conventionally represented by log(z0m/z0s) and assumed to scale with Re n (where Re is a roughness Reynolds number), is then interpreted using surface renewal theory (SRT). SRT predicts n = 1/4 when only Kolmogorov-scale eddies dominate the scalar exchange, whereas n =1/2 is predicted when large eddies limit the renewal dynamics. The latter is found to better capture the LES results. However, both scaling relations indicate that z0s decreases when z0m increases for typical urban geometries and scales. This is opposite to how their relation is usually modeled for urban canopies (i.e. z0s/z0m is a fixed value smaller than unity).

Atmospheric boundary-layer dynamics over heterogeneous surfaces is significant to a wide array of geophysical and engineering applications. Yet, despite over five decades of intense efforts by the research community, numerous open research questions remain. This underlines the complexity of the physical processes that are excited by heterogeneity, the multitude of patterns and manifestations that it can display, and the importance of the implications to research in the atmospheric sciences and beyond. Here, existing knowledge is reviewed and a path forward for research is proposed, starting with the smaller scales near a surface transition and proceeding to the influence on large-scale dynamics and their forecasting.

The literature includes a wide variety of definitions or perceptions of non-stationarity. Non-stationarity can be expressed in terms of turbulence statistics or variability of the forcing of the turbulence such as time changes of the wind vector, the horizontal pressure gradient, or the surface heat flux. Our survey emphasizes the development of non-equilibrium turbulence caused by non-stationary forcing. The degree of non-equilibrium of the turbulence is most reliably evaluated following the local flow rather than using the more available fixed-point measurements. We survey methods to eliminate non-stationary records or partially filter out the non-stationarity. We also summarize issues with parametrization of the non-stationarity. Non-stationarity over the sea can be complex due to time-dependent wave state and surface roughness. The impact of non-stationarity is generally less understood in the stable boundary layer compared to the unstable boundary layer.

While the decrease in air pollutant concentration during the COVID-19 lockdown is well documented, neighborhood-scale and multi-city data have not yet been explored systematically to derive a generalizable quantitative link to the drop in vehicular traffic. To bridge this gap, high spatial resolution air quality and georeferenced traffic datasets were compiled for the city of London during three weeks with significant differences in traffic. The London analysis was then augmented with a meta-analysis of lower-resolution studies from 12 other cities. The results confirm that the improvement in air quality can be partially attributed to the drop of traffic density, and more importantly quantifies the elasticity (0.71 for NO

Highlights • Daily moments of COVID19 cases reveal consistent scaling from 10 km to 2600 km. • COVID19 cases are found to have multi-scaling with a multifractality kernel. • The spatial correlation of infections approach that of the underlying population. • Re-opening urban centers affects spread and evolution of the disease for months., The spatial template over which COVID-19 infections operate is a result of nested societal decisions involving complex political and epidemiological processes at a broad range of spatial scales. It is characterized by ‘hotspots’ of high infections interspersed within regions where infections are sporadic to absent. In this work, the sparseness of COVID-19 infections and their time variations were analyzed across the US at scales ranging from 10 km (county scale) to 2600 km (continental scale). It was found that COVID-19 cases are multi-scaling with a multifractality kernel that monotonically approached that of the underlying population. The spatial correlation of infections between counties increased rapidly in March 2020; that rise continued but at a slower pace subsequently, trending towards the spatial correlation of the population agglomeration. This shows that the disease had already spread across the USA in early March such that travel restriction thereafter (starting on March 15th 2020) had minor impact on the subsequent spatial propagation of COVID-19. The ramifications of targeted interventions on spatial patterns of new infections were explored using the epidemiological susceptible-infectious-recovered (SIR) model mapped onto the population agglomeration template. These revealed that re-opening rural areas would have a smaller impact on the spread and evolution of the disease than re-opening urban (dense) centers which would disturb the system for months. This study provided a novel way for interpreting the spatial spread of COVID-19, along with a practical approach (multifractals/SIR/spectral slope) that could be employed to capture the variability and intermittency at all scales while maintaining the spatial structure.

We investigate the significance of turbulent kinetic energy (TKE, $k$) and its variability in the atmospheric surface layer (ASL), needed in many applications. The statistics of $k$ around its mean state is studied using the probability density function $p(k)$ for ASL flows with significant buoyancy forcing. A nonlinear Langevin equation that preserves $p(k)$ but allows linear relaxation of $k$ to its mean state is suggested and tested using multiple ASL data sets that span various stabilities and surface roughness conditions. Model parameters for the proposed Langevin equation are derived from similarity theory, reproducing measured $p(k)$ with minimal Kullback-Leibler divergence.

Large-eddy simulations are conducted to contrast momentum and passive scalar transport over large, three-dimensional roughness elements in a turbulent channel flow. Special attention is given to the dispersive fluxes, which are shown to be a significant fraction of the total flux within the roughness sublayer. Based on pointwise quadrant analysis, the turbulent components of the transport of momentum and scalars are found to be similar in general, albeit with increasing dissimilarity for roughnesses with low frontal blockage. However, strong dissimilarity is noted between the dispersive momentum and scalar fluxes, especially below the top of the roughness elements. In general, turbulence is found to transport momentum more efficiently than scalars, while the reverse applies to the dispersive contributions. The effects of varying surface geometries, measured by the frontal density, are pronounced on turbulent fluxes and even more so on dispersive fluxes. Increasing frontal density induces a general transition in the flow from a wall bounded type to a mixing layer type. This transition results in an increase in the efficiency of turbulent momentum transport, but the reverse occurs for scalars due to reduced contributions from large-scale motions in the roughness sublayer. This study highlights the need for distinct parameterizations of the turbulent and dispersive fluxes, as well as the importance of considering the contrasts between momentum and scalar transport for flows over very rough surfaces.

Cool and green roofs are widely adopted measures for curtailing summertime urban heat islands. Existing numerical studies to assess their effectiveness and cooling benefits usually assume an unrealistic 100% coverage across the entire metropolis. This study investigates the scale dependence of the absolute cooling benefits and efficiency (cooling per adapted roof area) of cool and green roofs in a typical summer when they are installed over 25% of building rooftops at local, city, or regional scales. Six major U.S. cities with active climate action plans in different geoclimatic zones are compared through high-resolution simulations using the Weather Research and Forecasting model. The results reveal that reductions in 2-m air temperature over the urban core increase non-linearly with the intervention area, and the benefits of both roof types scale similarly. This scale-dependence of urban core cooling is not universal, but is rather controlled by the shape of metropolitan areas and wind pattern. The siting of mitigation measures hence plays an important role especially under windy conditions, and some urban cores are not able to achieve a noticeable and consistent cooling by retrofitting their own rooftops. Regional-scale deployments of mitigation strategies, on the other hand, yield a more substantial temperature reduction but with a lower efficiency. The scale-dependence of regional cooling efficiency showed remarkable similarity across studied cities, yielding a potentially generalizable power law. The successful resiliency plans for cities should account for the scale dependence and geoclimatic determinants of the achievable cooling, and identify the target neighborhoods of most interest.

Using a detailed model, sensitivity analyses are conducted to identify the leading physical determinants and heat fluxes that control energy exchange between surface runoff and urban pavements during rainfall. These analyses confirm that pavement characteristics, such as albedo and thermal effusivity, strongly influence the initial temperature of the pavement before rain starts. Moreover, this sensitivity propagates to the runoff and pavement temperatures as well as to sensible heat and evaporation fluxes during and after rainfall. Heat transfer inside the runoff and pavement during rainfall is also very sensitive to the rain temperature and is the leading process in surface cooling (the classically important sensible and latent heat fluxes to the atmosphere are minor contributors). Finally, based on the findings from the sensitivity analyses, using a bulk energy approach, a reduced version of the full model is proposed. This simple model uses the spatially averaged temperatures of the runoff and pavement and can predict their temperatures and the associated energy fluxes almost as accurately as the full model. The reduced model has the added advantages of computational efficiency and simplicity of implementation in coarse earth system models.

A large-scale study of methane emissions from well pads was conducted in the Marcellus shale (Pennsylvania), the largest producing natural gas shale play in the United States, to better identify the prevalence and characteristics of superemitters. Roughly 2100 measurements were taken from 673 unique unconventional well pads corresponding to ∼18% of the total population of active sites and ∼32% of the total statewide unconventional natural gas production. A log-normal distribution with a geometric mean of 2.0 kg h-1 and arithmetic mean of 5.5 kg h-1 was observed, which agrees with other independent observations in this region. The geometric standard deviation (4.4 kg h-1) compared well to other studies in the region, but the top 10% of emitters observed in this study contributed 77% of the total emissions, indicating an extremely skewed distribution. The integrated proportional loss of this representative sample was equal to 0.53% with a 95% confidence interval of 0.45-0.64% of the total production of the sites, which is greater than the U.S. Environmental Protection Agency inventory estimate (0.29%), but in the lower range of other mobile observations (0.09-3.3%). These results emphasize the need for a sufficiently large sample size when characterizing emissions distributions that contain superemitters.

A method for converting a soil water reflectometer to a water level sensor is presented. The sensor essentially records the time of travel of an electromagnetic pulse along two parallel rods, which is then used to infer the relative permittivity (dielectric constant) of the medium around these two rods. While the sensor is designed to capture the relative permittivity variation of soil with its moisture content, the immersion depth of the rods in water (or potentially other dielectric liquids) also influences the average dielectric constant of the air-water medium between the rods (consequently affecting its permittivity). Here we show how this change in permittivity can be calibrated to measure small liquid depths. An empirical calibration equation is developed; it accounts for the impact of water salinity, whereas the temperature effect on the measurements is shown to be negligible.

Significance Spatial analysis of daily Covid-19 cases at the US county scale revealed a dynamic multifractal scaling of infections, spanning from 10 to 2,600 km and consistently trending toward that of the susceptible population. A susceptible–infected–recovered model was expanded to include spatial spread across counties using a spatial kernel. The reproduction number Rb (average number of persons infected by an infected person) decreased because of interventions (masks, social distancing). The model shows that reducing Rb in isolation is not sufficient to stem the spread of the disease and concomitant measures such as curfews and lockdowns may be needed. The Rb of 2.0 estimated here in July to October 2020 is large, hinting at super-spreaders and super-spreader events., The tempo-spatial patterns of Covid-19 infections are a result of nested personal, societal, and political decisions that involve complicated epidemiological dynamics across overlapping spatial scales. High infection “hotspots” interspersed within regions where infections remained sporadic were ubiquitous early in the outbreak, but the spatial signature of the infection evolved to affect most regions equally, albeit with distinct temporal patterns. The sparseness of Covid-19 infections in the United States was analyzed at scales spanning from 10 to 2,600 km (county to continental scale). Spatial evolution of Covid-19 cases in the United States followed multifractal scaling. A rapid increase in the spatial correlation was identified early in the outbreak (March to April). Then, the increase continued at a slower rate and approached the spatial correlation of human population. Instead of adopting agent-based models that require tracking of individuals, a kernel-modulated approach is developed to characterize the dynamic spreading of disease in a multifractal distributed susceptible population. Multiphase Covid-19 epidemics were reasonably reproduced by the proposed kernel-modulated susceptible–infectious–recovered (SIR) model. The work explained the fact that while the reproduction number was reduced due to nonpharmaceutical interventions (e.g., masks, social distancing, etc.), subsequent multiple epidemic waves still occurred; this was due to an increase in susceptible population flow following a relaxation of travel restrictions and corollary stay-at-home orders. This study provides an original interpretation of Covid-19 spread together with a pragmatic approach that can be imminently used to capture the spatial intermittency at all epidemiologically relevant scales while preserving the “disordered” spatial pattern of infectious cases.

Urban areas have distinct features (e.g. impervious surfaces) which modify the energy-water balance at the upper subsurface, lower atmosphere, and over the land surface. Moreover, the atmosphere and groundwater are strongly coupled in places with shallow groundwater. To improve the understanding of urban atmospheric-hydrological processes, their interconnections, and their impacts on other environmental processes, a new fully-coupled urban atmosphere-surface-subsurface hydrometeorological model was developed. The new model brings together WRF-PUCM (Princeton Urban Canopy Model) with ParFlow (a 3D variably saturated groundwater model with an integrated 2D overland flow component) to build WRF-PUCM-PF. The new model and the original non-coupled WRF-PUCM were both applied to a small watershed (10.64 km2) in a heavily urbanized area in the Baltimore metropolitan region as a demonstration test case. To capture atmospheric-hydrological processes at scales closer to urban heterogeneous land cover, models were run at a 90-m horizontal resolution using the LES mode in WRF. The analysis period after the two models were spun up to an identical initial condition spanned 96 hours from July 19 to July 23, 2008. The period was selected as it started with a drydown period for 40 hours followed by several intense rain events. This period allowed evaluation of both models' responses to dry-down and rain events. First the models were run with homogeneous similar hydrogelogic input to isolate the effect of terrestrial hydrology implementations in each model. In response to rain events, the homogeneous WRF-PUCM model output gained and retained a 40% greater amount of soil moisture (area-averaged) compared to the homogeneous WRF-PUCM-PF case. WRF-PUCM performed poorly in lateral distribution of water due to its 1D implementation of subsurface hydrology and lack of overland flow parameterization. The spatial distribution of soil moisture at the end of the simulation in a homogeneous WRF-PUCM model looked similar to the cumulative spatial distribution rain at the end of the simulation with no indication of surface topography impact on soil moisture distribution. On the other hand, lateral movement of water in WRF-PUCM-PF resulted in a more realistic distribution of soil moisture following topography. To further analyze the impact of urban areas, results of WRF-PUCM-PF simulations incorporating heterogeneous subsurface hydrogeology were compared with WRF-PUCM with its 2D implementation of hydrogeology units for the region. The heterogeneous WRF-PUCM model generated a 10-fold greater area-averaged soil moisture increase compared to the heterogeneous WRF-PUCM-PF case. Influenced by lateral hydrology and impervious surfaces, the heterogeneous WRF-PUCM-PF model output, generated lower latent heat flux, resulting in half of the domain having higher land surface temperatures (2-10 ◦C), compared to the heterogeneous WRF-PUCM model. Overall, the new model provides a tool that can enhance simulation of urban areas by combining ParFlow’s representation of terrestrial hydrology, PUCM’s improved representation of the urban heterogeneous energy and water balance, and incorporation of higher-resolution urban heterogeneous microclimatic variations.

Given the benefits of fine mapping of large urban areas affordably, mobile environmental sensing technologies are becoming increasingly popular to complement the traditional stationary weather and air quality sensing stations. However the reliability and accuracy of low-cost mobile urban technologies is often questioned. This paper presents the design of a fast-response, autonomous and affordable Mobile Urban Sensing Technology (MUST) for the acquisition of high spatial resolution environmental data. Only when accurate neighborhood scale environmental data is affordable and accessible for architects, urban planners and policy makers, can design strategies to enhance urban health be effectively implemented. The results of an experimental air quality sensing campaign developed within Princeton University Campus is presented.

This is a reply to Martilli et al. (2020), Summer average urban-rural surface temperature differences do not indicate the need for urban heat reduction (https://doi.org/10.31219/osf.io/8gnbf).

Large-scale circulations around a city are co-modulated by the urban heat islandand by regional wind patterns. Depending on these variables, the circulations fall into different regimes ranging from advection-dominated (plume regime) to convection-driven (bubble regime). Using dimensional analysis and large-eddy simulations, this study investigates how these different circulations scale with urban and rural heat fluxes, as well as up stream wind speed. Two dimensionless parameters are shown to control the dynamics of the flow: (1) the ratio of rural to urban thermal convective velocities that contrasts their respective buoyancy fluxes and (2) the ratio of bulk inflow velocity to the convection velocity in the rural area. Finally, the vertical flow velocities transecting the rural to urban transitions are used to developa criterion for categorizing different large-scale circulations into plume, bubble or transitional regimes. The findings have implications for city ventilation since bubbleregimes are expected to trap pollutants, as well as for scaling analysis in canonical mixed-convection flows.

Temporal dynamics of urban warming have been extensively studied at the diurnal scale, but the impact of background climate on the observed seasonality of surface urban heat islands (SUHIs) remains largely unexplored. On seasonal time scales, the intensity of urban–rural surface temperature differences ( Δ T s ) exhibits distinctive hysteretic cycles whose shape and looping direction vary across climatic zones. These observations highlight possible delays underlying the dynamics of the coupled urban–biosphere system. However, a general argument explaining the observed hysteretic patterns remains elusive. A coarse-grained model of SUHI coupled with a stochastic soil water balance is developed to demonstrate that the time lags between radiation forcing, air temperature, and rainfall generate a rate-dependent hysteresis, explaining the observed seasonal variations of Δ T s . If solar radiation is in phase with water availability, summer conditions cause strong SUHI intensities due to high rural evaporative cooling. Conversely, cities in seasonally dry regions where evapotranspiration is out of phase with radiation show a summertime oasis effect controlled by background climate and vegetation properties. These seasonal patterns of warming and cooling have significant implications for heat mitigation strategies as urban green spaces can reduce Δ T s during summertime, while potentially negative effects of albedo management during winter are mitigated by the seasonality of solar radiation.

Increasing urbanization is likely to intensify the urban heat island effect, decrease outdoor thermal comfort and enhance runoff generation in cities. Urban green spaces are often proposed as a mitigation strategy to counteract these adverse effects and many recent developments of urban climate models focus on the inclusion of green and blue infrastructure to inform urban planning. However, many models still lack the ability to account for different plant types and oversimplify the interactions between the built environment, vegetation, and hydrology. In this study, we present an urban ecohydrological model, Urban Tethys-Chloris (UT&C), that combines principles of ecosystem modelling with an urban canopy scheme accounting for the biophysical and ecophysiological characteristics of roof vegetation, ground vegetation and urban trees. UT&C is a fully coupled energy and water balance model that calculates 2 m air temperature, 2 m humidity, and surface temperatures based on the infinite urban canyon approach. It further calculates all urban hydrological fluxes, including transpiration as a function of plant photosynthesis. Hence, UT&C accounts for the effects of different plant types on the urban climate and hydrology, as well as the effects of the urban environment on plant well-being and performance. UT&C performs well when compared against energy flux measurements of eddy covariance towers located in three cities in different climates (Singapore, Melbourne, Phoenix). A sensitivity analysis, performed as a proof of concept for the city of Singapore, shows a mean decrease in 2 m air temperature of 1.1 °C for fully grass covered ground, 0.2 °C for high values of leaf area index (LAI), and 0.3 °C for high values of Vc,max (an expression of photosynthetic activity). These reductions in temperature were combined with a simultaneous increase in relative humidity by 6.5 %, 2.1 %, and 1.6 %, for fully grass covered ground, high values of LAI, and high values of Vc,max, respectively. Furthermore, the increase of pervious vegetated ground is able to significantly reduce surface runoff. These results show that urban greening can lead to a decrease in urban air temperature and surface runoff, but this effect is limited in cities characterized by a hot, humid climate., Geoscientific Model Development Discussions, ISSN:1991-962X, ISSN:1991-9611

In polar regions, where the boundary layer is often stably stratified, atmospheric models produce large biases depending on the boundary-layer parametrizations and the parametrization of the exchange of energy at the surface. This model intercomparison focuses on the very stable stratification encountered over the Antarctic Plateau in 2009. Here, we analyze results from 10 large-eddy-simulation (LES) codes for different spatial resolutions over 24 consecutive hours, and compare them with observations acquired at the Concordia Research Station during summer. This is a challenging exercise for such simulations since they need to reproduce both the 300-m-deep convective boundary layer and the very thin stable boundary layer characterized by a strong vertical temperature gradient (10 K difference over the lowest 20 m) when the sun is low over the horizon. A large variability in surface fluxes among the different models is highlighted. The LES models correctly reproduce the convective boundary layer in terms of mean profiles and turbulent characteristics but display more spread during stable conditions, which is largely reduced by increasing the horizontal and vertical resolutions in additional simulations focusing only on the stable period. This highlights the fact that very fine resolution is needed to represent such conditions. Complementary sensitivity studies are conducted regarding the roughness length, the subgrid-scale turbulence closure as well as the resolution and domain size. While we find little dependence on the surface-flux parametrization, the results indicate a pronounced sensitivity to both the roughness length and the turbulence closure.

We define the new concept of an environmental neighborhood as the surrounding area influencing the environmental quality at a given point in a city, and propose a novel methodology to measure its spatial extent. We compute the spatial correlation of air quality and urban parameters from high spatial resolution datasets for New York City, where the urban characteristics are averaged over variable urban footprint sizes, ranging from 25 m × 5 m to 5000 m × 5000 m. The scale at which these correlations peak indicates the extent of the neighboring area that influences pollutant concentrations deviations from the city-wide average. The results indicate that the scale of these environmental neighborhoods ranges from ∼1000 m (for attributes such as road area or building footmark) down to ∼200 m (for building use or green area). Selecting this optimal neighborhood scale is thus critical for identifying the urban fabric and activity attributes that have the largest influence on air quality; smaller footprints do not contain all the pertinent urban surface information while larger footprints contain irrelevant, potentially misleading information. The quantification of this scale of influence therefore enables more effective and localized policies and interventions to improve urban environmental quality and reduce urban health disparities. More broadly, the findings indicate that, in a wide range of environmental and ecological applications where surface heterogeneity is a primary driver, the scale of analysis is not an external parameter to be chosen, but rather an internal parameter dictated by the problem physics.

Many urban areas are vulnerable to heat-induced hazards. In the so-called Urban Heat Island (UHI), trapped heat flux within the building canopy increases the surface temperature of cities, and it is revealed that UHI has a non-linear synergy with extreme heatwaves. Therefore, fast/accurate temperature prediction is essential to mitigate the risk, improving the community's resilience. In this paper, we introduce a probabilistic model to forecast the meso-scale surface temperature, at a relatively low computational cost. The proposed model is developed to reduce the computational cost of the Numerical Weather Prediction (NWP) models. We calibrate the proposed model by processing the outcomes of an NWP model (i.e., the Princeton Urban Canopy Model coupled to the Weather Research and Forecast; WRF-PUCM) that reanalyzes historical temperature. The calibrated model is integrated into a Kalman-Filter scheme to update the predictions with the collected data.

Mobile laboratory measurements provide information on the distribution of CH4 emissions from point sources such as oil and gas wells, but uncertainties are poorly constrained or justified. Sources of uncertainty and bias in ground-based Gaussian-derived emissions estimates from a mobile platform were analyzed in a combined field and modeling study. In a field campaign where 1009 natural gas sites in Pennsylvania were sampled, a hierarchical measurement strategy was implemented with increasing complexity. Of these sites, ∼ 93 % were sampled with an average of 2 transects in 4 concentrations in a turbulent environment. The LES output and LES-derived emission estimates were used to compare with the results of a standard Gaussian approach. The LES and Gaussian-derived emission rates agreed within a factor of 2 in all except one case; the average difference was 25 %. A controlled release was also used to investigate sources of bias in either technique. The Gaussian method agreed with the release rate more closely than the LES, underlining the importance of inputs as sources of uncertainty for the LES. The LES was also used as a virtual experiment to determine an optimum number of repeat transects and spacing needed to produce representative statistics. Approximately 10 repeat transects spaced at least 1 min apart are required to produce statistics similar to the observed variability over the entire LES simulation period of 30 min. Sources of uncertainty from source location, wind speed, background concentration and atmospheric stability were also analyzed. The largest contribution to the total uncertainty was from atmospheric variability; this is caused by insufficient averaging of turbulent variables in the atmosphere (also known as random errors). Atmospheric variability was quantified by repeat measurements at individual sites under relatively constant conditions. Accurate quantification of atmospheric variability provides a reasonable estimate of the lower bound for emission uncertainty. The uncertainty bounds calculated for this work for sites with > 50 ppb enhancements were 0.05–6.5q (where q is the emission rate) for single-transect sites and 0.5–2.7q for sites with 10+ transects. More transects allow a mean emission rate to be calculated with better precision. It is recommended that future mobile monitoring schemes quantify atmospheric variability, and attempt to minimize it, under representative conditions to accurately estimate emission uncertainty. These recommendations are general to mobile-laboratory-derived emissions from other sources that can be treated as point sources.

This paper investigates the effects of baroclinic pressure gradients on mean flow and turbulence in the diabatic atmospheric boundary layer (ABL). Large-eddy simulations are conducted where the direction of the baroclinicity, its strength, and the surface buoyancy flux are systematically varied to examine their interacting effects. The thermal wind vector, which represents the vertical change in the geostrophic wind vector resulting from horizontal temperature gradients, significantly influences the velocity profiles, the Ekman turning, and the strength and location of the low-level jet (LLJ). For instance, cold advection and positive (negative) geostrophic shear increased (decreased) friction velocity and changed the LLJ elevation. Given the baroclinicity strength and direction under neutral conditions, a simple reduced model is proposed and validated here to predict the general trends of baroclinic mean winds. The baroclinic effects on turbulence intensity and structure are even more intricate, with turbulent kinetic energy (TKE) profiles displaying an increase of TKE magnitude with height for some cases. When a fixed destabilizing surface heat flux is added, a positive geostrophic shear favors streamwise aligned roll-type structures, which are typical of neutral ABLs. Conversely, a negative geostrophic shear promotes cell-type structures, which are typical of strongly unstable ABLs. Furthermore, baroclinicity increases shear in the outer ABL and tends to make the outer flow more neutral by decreasing the Richardson flux number. These findings are consequential for meteorological measurements and the wind-energy industry, among others: baroclinicity alters the mean wind profiles, the TKE, coherent structures, and the stability of the ABL, and its effects need to be considered.

High Reynolds number wall-bounded turbulent flows subject to buoyancy forces are fraught with complex dynamics originating from the interplay between shear generation of turbulence ($S$) and its production or destruction by density gradients ($B$). For horizontal walls, $S$ augments the energy budget of the streamwise fluctuations, while $B$ influences the energy contained in the vertical fluctuations. Yet, return to isotropy remains a tendency of such flows where pressure–strain interaction redistributes turbulent energy among all three velocity components and thus limits, but cannot fully eliminate, the anisotropy of the velocity fluctuations. A reduced model of this energy redistribution in the inertial (logarithmic) sublayer, with no tuneable constants, is introduced and tested against large eddy and direct numerical simulations under both stable ($B) and unstable ($B>0$) conditions. The model links key transitions in turbulence statistics with flux Richardson number (at $Ri_{f}=-B/S\approx$$-2$, $-1$ and $-0.5$) to shifts in the direction of energy redistribution. Furthermore, when coupled to a linear Rotta-type closure, an extended version of the model can predict individual variance components, as well as the degree of turbulence anisotropy. The extended model indicates a regime transition under stable conditions when $Ri_{f}$ approaches $Ri_{f,max}\approx +0.21$. Buoyant destruction $B$ increases with increasing stabilizing density gradients when $Ri_{f}, while at $Ri_{f}\geqslant Ri_{f,max}$ limitations on the redistribution into the vertical component throttle the highest attainable rate of buoyant destruction, explaining the ‘self-preservation’ of turbulence at large positive gradient Richardson numbers. Despite adopting a ‘framework of maximum simplicity’, the model results in novel and insightful findings on how the interacting roles of energy redistribution and buoyancy modulate the variance budgets and the energy exchange among the components.