Search

Field observation networks are becoming denser, more diverse, and more mobile, while being required to provide real-time results. The ERC urbisphere program is coordinating multiple field campaigns simultaneously to collect datasets on urban atmospheric and environmental conditions and processes in cities of different sizes. The datasets are used for improving climate and weather models and services, including assessing the impact of cities on the atmosphere (e.g. aerosols, greenhouse gases) as well as the exposure of urban populations in the context of atmospheric extreme events (heat waves, heavy precipitation, air pollution). For model development and evaluation, we are using meshed networks of in-situ observations with ground-based and airborne (remote-)sensing platforms. This contribution describes the urbisphere data management infrastructure and processes required to handle a variety of data streams from primarily novel modular observation systems deployed in complex urban environments.The modular observation systems consist of short-term deployed instrumentation and are separated into three thematic modules. Module A aims at characterizing urban form and function affecting urban climates. Module B quantifies the impact of urban emissions (heat, pollutants, greenhouse gases, etc.) on the urban boundary layer over and downwind of cities. Module C provides data on human exposure at street and indoor-level. The three modules are served with consistent data management, documentation and calibration. Systems deployed in Modules A, B and C include customized automatic weather stations, (Doppler) lidars and ceilometers, scintillometers, balloon radio sounding and spectral camera imaging. Systems are street-light-mounted, located on building roof-tops or indoors as well as on mobile platforms (vehicles, drones). Data ingestion processes are automated, delivering moderate data volumes in real-time to central data infrastructure through mobile phone and IOT connectivity. A meta data system helps keep track of the location and configuration of all deployed components and forms the backbone for conversion of instrument records into location-aware, conventions-aligned and quality-assured F.A.I.R. data products. Furthermore, the data management infrastructure provides services (APIs, Apps, IDEs, etc.) for data inspection and computations by scientists and students involved in the campaigns. Select datasets are integrated in near real-time into other global or local data systems such as, e.g., AERONET, the Phenocam Network, ICOS, or PANAME, for multiple uses.Besides technical aspects and design considerations, we discuss how cooperation and attribution are safeguarded when data are being accessed for immediate academic and citizen data science.

Climate adaptation and emergency management are major challenges in cities, that benefit from the incorporation of real-time weather, air quality, differential exposure and vulnerability data. We therefore need systems that allow us to map, for example, localised thermal heat stress, heavy precipitation events or air quality spatially resolved across cities at high temporal resolution. Key to the assessment of average conditions and weather extremes in cities are systems that are capable of resolving intra-urban variabilities and microclimates at the level of people, hence in the urban canopy layer at street-level. Placing sensors at street-level, however, is challenging: Sensors need to be small, rugged, safe, and they must measure a number of quantities within limited space. Such systems may ideally require little or no external power, provide remote accessibility, sensor interoperability and real-time data transfer and must be cost-effective for mass deployment. However, these characteristics as well as a wide spectrum of observed variables are not available in current commercial sensor network solutions, hence we designed and implemented a custom partly in-house developed two-tiered sensor system for mounting and installation at 3 m height on city-owned street lights in Freiburg, Germany. Our partly in-house developed two-tiered sensor network, consisting of fifteen fully self-developed, cost-effective “Tier-I stations” and 35 commercial “Tier-II stations” (LoRAIN, Pessl Instruments GmbH), aims to fill these gaps and to provide a modular, user-friendly WSN with a high spatial density and temporal resolution for research, practical applications and the general public. The Tier-I stations were designed and optimised from the ground up, including the printed circuit board (PCB), for temporally high-resolution WSNs that support wide ranges of sensors and that is expandable. The core of the system is a low-power embedded computer (Raspberry Pi Zero) running a custom multithreaded generic logging and remote control software that locally stores the data and transmits it to a custom vapor-based TCP server via GSM. The software also features system monitoring and error detection functions, as well as remote logging. The setup can easily be expanded on the fly by adding predefined sensors to a configuration file. For better modularity, each station registers itself on the server and will be automatically integrated in all further processes and vice versa. Custom frontends as well as bidirectional communication and task distribution protocols enable remote access and across node interaction, resulting in a more easy-to-maintain system. In addition to air temperature, humidity and precipitation measured by the Tier II stations, the Tier-I station feature a ClimaVUE 50 all-in-one weather sensor and a BlackGlobe (Campbell Scientific, Inc.) that provides data on wind, radiation, pressure, lightning, solar radiation and black globe temperatures. That allows for calculation of thermal comfort indices in real-time. A webpage and the self-developed “uniWeather” (iOS-App, API) offers near-realtime data access and data interpretation for stakeholders and public outreach.

Exposure and vulnerabilities to heat stress are concentrated in cities, yet exhibit large intra-urban variability. However, existing Weather Sensor Networks (WSNs) that monitor relevant meteorological conditions are typically installed at a much coarser resolution and generally do not cover canopy-layer conditions in cities. There are few examples of fine urban-scale massive sensor networks at street-level, however, they rarely provide any data beyond air temperature and humidity needed to assess, map and calculate thermal comfort, and many street-level networks often lack the real-time data transmission and quality control procedures necessary for real-time communication.Here, we present a customizable two-tiered WSN setup, coupled with a quality and data processing chain, to quantify, map and communicate heat exposure data and resolve intra-urban variabilities in real-time. The hierarchical urban canopy-layer network developed for long-term monitoring of thermal comfort conditions (and also heavy precipitation and wind storm impacts) in the city of Freiburg, Germany, consists of two different station systems that are integrated into public street lights at a uniform height of 3 m a.g.l. Thirteen “tier I stations“ are strategically placed in representative built-up and rural areas. They are equipped with a ClimaVUE 50 all-in-one weather sensor (precipitation, wind, radiation, temperature, humidity, pressure) and a Black Globe Sensor (both from Campbell Scientific, Inc.) which enables real-time thermal comfort calculations such as the Physiologically Equivalent Temperature (PET) or the Universal Thermal Climate Index (UTCI). Tier I stations feature a custom-built multi-purpose logger which is controlled by a Raspberry Pi Zero running a custom remote control software and GSM data transmission. This allows for a highly flexible setup that can easily be expanded to include additional sensors (e.g. air quality) in the future. In addition, 35 commercial “tier II stations“ (LoRAIN, Pessl Instruments GmbH) measure air temperature, humidity and precipitation and transmit data over NB-IoT. These tier II stations significantly increase the spatial density of the WSN at a lower cost per site. In addition to urban street-light mounted locations, an additional eight sites in non-built-up locations capture areas with predominantly rural and natural land cover, with selected stations specifically measuring cold-air drainage channels into the city.With measuring and transmission intervals of one and five minutes, respectively, one major purpose of this WSN is to develop machine learning routines for data quality control and quality assessment in real-time and downscaling thermal comfort data from tier II to tier I stations and areas not covered by stations. Moreover, the WSN will provide input and validation data for numerical high-resolution modelling of urban heat exposure. Real-time visualizations inform researchers, city officials and the general public with instantaneous and historical data at neighborhood-scale.

A total of 20 urban neighbourhood-scale eddy covariance flux tower datasets are made openly available after being harmonized to create a 50 site–year collection with broad diversity in climate and urban surface characteristics. Variables needed as inputs for land surface models (incoming radiation, temperature, humidity, air pressure, wind and precipitation) are quality controlled, gap-filled and prepended with 10 years of reanalysis-derived local data, enabling an extended spin up to equilibrate models with local climate conditions. For both gap filling and spin up, ERA5 reanalysis meteorological data are bias corrected using tower-based observations, accounting for diurnal, seasonal and local urban effects not modelled in ERA5. The bias correction methods developed perform well compared to methods used in other datasets (e.g. WFDE5 or FLUXNET2015). Other variables (turbulent and upwelling radiation fluxes) are harmonized and quality controlled without gap filling. Site description metadata include local land cover fractions (buildings, roads, trees, grass etc.), building height and morphology, aerodynamic roughness estimates, population density and satellite imagery. This open collection can help extend our understanding of urban environmental processes through observational synthesis studies or in the evaluation of land surface environmental models in a wide range of urban settings. These data can be accessed from https://doi.org/10.5281/zenodo.7104984 (Lipson et al., 2022).

We present a novel method to model spatial maps of mean radiant temperature (Tmrt) in complex urban areas using a special type of fully convolutional networks - U-Net - for image to image processing. Tmrt is one of the driving factors of daytime human thermal comfort and underlies great spatial and temporal variabilities, especially in complex urban areas. Various micro scale (building-resolving) models exist to model Tmrt in urban settings. However, these models are computational expensive, albeit to varying degrees. This means, study area and time might be limited depending on spatial and temporal resolution. While this is sufficient for case studies where micro-level processes are modelled for different neighbourhoods in limited time periods, accurate calculations over a long time period are not possible (e.g. downscaling global climate projections). To overcome these computational drawbacks of physical models, we present a U-net approach for modelling Tmrt in complex urban areas.U-Nets are special types of encoder-decoder networks and allow precise image to image processing. In this study, Tmrt (at 1.1 m a.g.l.) is modelled by SOLWEIG model for 62 areas (500 x 500 m2) and on 54 days for the city of Freiburg, Germany. Training data is sampled randomly after clustering. The spatial and temporal input of SOLWEIG are in turn used as input features for the U-Net. The U-Net is trained on 56 areas and on 45 days and tested on the remaining areas and days. In addition, data from a Tmrt measurement campaign is used to validate SOLWEIG and U-Net model output.Results indicate that the proposed U-Net approach is capable to provide Tmrt in complex urban areas sufficiently. A correlation of > 0.9 and a MAE of 1.53°C between SOLWEIG and the U-Net is observed. Results show a higher MAE during day than night, which can be partly explained by the difference of absolute Tmrt values at day and night, but also by more complex prediction conditions during day: cloud cover and thus varying radiation, but also low sun angle in the morning / evening. In addition, computing times for Tmrt map predictions are significantly faster than physical models.

Thermokarst features are widespread in ice-rich regions of the circumpolar Arctic. The rate of thermokarst lake formation and drainage is anticipated to accelerate as the climate warms. However, it is uncertain how these dynamic features impact the terrestrial Arctic carbon cycle. Methane (CH4) and carbon dioxide (CO2) fluxes were measured during peak growing season using eddy covariance and chambers at Illisarvik, a 0.16 km2 thermokarst lake basin that was experimentally drained in 1978 on Richards Island, Northwest Territories, Canada. Vegetation in the basin differs markedly from the surrounding dwarf-shrub tundra and included patches of tall shrubs, grasses, and sedges with some bare ground and a small pond in the centre. During the peak growing season, temperature and wind conditions were highly variable, and soil water content decreased steadily. Basin-scaled net ecosystem CO2 exchange (NEE) measured by eddy covariance was −1.5 [CI95 %±0.2] g C−CO2 m-2d-1; NEE followed a marked diurnal pattern with no day-to-day trend during the study period. Variations in half-hourly NEE were primarily controlled by photosynthetic photon flux density and influenced by vapour pressure deficit, volumetric water content, and the presence of shrubs within the flux tower footprint, which varied with wind direction. Net methane exchange (NME) was low (8.7 [CI95 %±0.4] mgCH4m-2d-1) and had little impact on the growing season carbon balance of the basin. NME displayed high spatial variability, and sedge areas in the basin were the strongest source of CH4 while upland areas outside the basin were a net sink. Soil moisture and temperature were the main environmental factors influencing NME. Presently, Illisarvik is a carbon sink during the peak growing season. However, these results suggest that rates of growing season CO2 and CH4 exchange rates may change as the basin's vegetation community continues to evolve.

High-frequency measurements are available at five heights within and above a row-gap trellised vineyard located on a 7$$^{\circ }$$ slope in the Southern Okanagan Valley, British Columbia, Canada. During a 3-week campaign in July 2016, approximately 17% of the nocturnal conditions exhibit drainage flow along the local slope. Drainage conditions are characterized by temperature inversions beginning around $$z/h_c = 0.39$$, where z is the height above ground level (a.g.l.) and $$h_c$$ is the canopy height (2.3 m a.g.l.), and near-surface lapses. Changes in the sign of the streamwise momentum flux suggest the presence of a jet maximum around $$z/h_c = 1.65$$, while a weak inflection point is observed near the canopy top, suggesting dynamical influences from both the drainage layer and canopy layer on the turbulent flow field. The largest observed fluxes in both the streamwise momentum flux and the turbulent sensible heat flux are near the top of the canopy, consistent with the location of the inflection point. Calculated two-point length scales from along-slope distributed temperature measurements reveal that turbulent structures are smallest near the canopy top. Conditional sampling of the three-dimensional velocity components and temperature indicate that a large fraction of canopy-layer transport is driven by canopy top turbulence, with sweeps dominating over ejections, particularly at $$z/h_c = 0.65$$.

The Namib Turbulence Experiment (NamTEX) was a multinational micrometeorological campaign conducted in the central Namib Desert to investigate three-dimensional surface layer turbulence and the spatiotemporal patterns of heat transfer between the subsurface, surface, and atmosphere. The Namib provides an ideal location for fundamental research that revisits some key assumptions in micrometeorology that are implicitly included in the parameterizations describing energy exchange in weather forecasting and climate models: homogenous flat surfaces, no vegetation, little moisture, and cloud-free skies create a strong and consistent diurnal forcing, resulting in a wide range of atmospheric stabilities. A novel combination of instruments was used to simultaneously measure variables and processes relevant to heat transfer: a 3-km fiber-optic distributed temperature sensor (DTS) was suspended in a pseudo-three-dimensional array within a 300 m × 300 m domain to provide vertical cross sections of air temperature fluctuations. Aerial and ground-based thermal imagers recorded high-resolution surface temperature fluctuations within the domain and revealed the spatial thermal imprint of atmospheric structures responsible for heat exchange. High-resolution soil temperature and moisture profiles together with heat flux plates provided information on near-surface soil dynamics. Turbulent heat exchange was measured with a vertical array of five eddy-covariance point measurements on a 21-m mast, as well as by collocated small- and large-aperture scintillometers. This contribution first details the scientific goals and experimental setup of the NamTEX campaign. Then, using a typical day, we demonstrate (i) the coupling of surface layer, surface, and soil temperatures using high-frequency temperature measurements, (ii) differences in spatial and temporal standard deviations of the horizontal temperature field using spatially distributed measurements, and (iii) horizontal anisotropy of the turbulent temperature field.

International audience; The measures taken to contain the spread of COVID-19 in 2020 included restrictions of people's mobility and reductions in economic activities. These drastic changes in daily life, enforced through national lockdowns, led to abrupt reductions of anthropogenic CO2 emissions in urbanized areas all over the world. To examine the effect of social restrictions on local emissions of CO2, we analysed district level CO2 fluxes measured by the eddy-covariance technique from 13 stations in 11 European cities. The data span several years before the pandemic until October 2020 (six months after the pandemic began in Europe). All sites showed a reduction in CO2 emissions during the national lockdowns. The magnitude of these reductions varies in time and space, from city to city as well as between different areas of the same city. We found that, during the first lockdowns, urban CO2 emissions were cut with respect to the same period in previous years by 5% to 87% across the analysed districts, mainly as a result of limitations on mobility. However, as the restrictions were lifted in the following months, emissions quickly rebounded to their pre-COVID levels in the majority of sites.

In order to better understand dynamic interactions between a city and the regional atmospheric boundary layer, the 'urbisphere Berlin campaign' is being conducted during 2021-2022 in Germany within the ERC Synergy urbisphere grant. urbisphere aims to enhance understanding, forecasting, and projecting feedbacks between climate change and drivers of urban transformation. One foci is the development of the next generation of urban climate simulations with dynamic atmosphere-urban feedbacks. A key aspect of urbisphere are comprehensive measurement campaigns in different cities. These involve undertaking high-quality research observations on urban effects for observation-based studies as well as for model development and evaluation. The Berlin campaign is investigating the dynamics of the atmospheric boundary layer within and beyond the city, and how the atmosphere dynamically responds to urban surface forcings, emissions, and human activity cycles from diurnal to an annual cycle. A dense network of ground-based remote sensing instruments (e.g. automatic lidars and ceilometers, doppler-wind lidars) for mixing-layer height detection within the city and along a rural-urban-rural transect, scintillometer paths for spatially averaged information on turbulent sensible heat flux, and radiation measurements for quantification of the influence of urban emissions, aerosols and clouds on downwelling radiative fluxes is deployed. Altogether, the additional observations supplement the existing Urban Climate Observatory (UCO) in Berlin to allow for a comprehensive and spatially detailed understanding of city-atmosphere interactions, and the effect of cities on downwind regions. This contribution provides an overview of the measurement campaign and gives first insights into collected data.

Taylor’s frozen turbulence hypothesis is the most critical assumption through which time-resolving sensors may be used to derive statistics of the turbulent spatial field. Namely, it relates temporal autocorrelation to spatial correlation via the mean wind speed and is invoked in almost all boundary layer field work. Nevertheless, the conditions and scales over which Taylor’s hypothesis is valid remain poorly understood in the atmospheric boundary layer. As part of the Namib Turbulence Experiment (NamTEX) campaign in March 2020, a pseudo-3D fibre-optic distributed temperature sensing (DTS) array was installed within a 300 x 300 m area in the Namib desert. The array is X-shaped in plan view and contains 16 measurement heights from 0.45 m to 2.85 m. Fibre-optic sensing provides air temperature measurements at unprecedented spatio-temporal density (0.25 m horizontally, 0.17 m vertically, and 1 Hz) and was coupled with a vertical array of traditional sonic anemometer point measurements to investigate the relationship between spatial and temporal temperature fields. The Namib provides an ideal location for fundamental boundary layer research: homogenous flat surfaces, no vegetation, little moisture, strong solar forcing, regular and repeated clear-sky conditions, and a wide range of atmospheric stabilities. Using the NamTEX DTS array we present the first field investigation of Taylor’s hypothesis that considers boundary layer stability and is independent of wind direction. A novel method of 2d horizontal cross-correlation between all possible points of a single height of the DTS is employed to produce spatial ‘maps’ of the turbulent flow, whose velocity, direction, and size may be tracked through time.

The measures taken to contain the spread of COVID-19 in 2020 included restrictions of people's mobility and reductions in economic activities. These drastic changes in daily life, enforced through national lockdowns, led to abrupt reductions of anthropogenic CO

To quantify the ecosystem services of trees in urban environments, it is necessary to assess received direct solar radiation of each tree. While the Sky View Factor (SVF) is suitable for assessing the total incoming short- and longwave radiation fluxes, its information is limited to specific points in space. For a spatial analysis, it is necessary to sample the area for SVF. A new geometrical method, Area View Factor (AVF), for the calculation of sunlit areas is proposed. AVF is the ratio of the unhidden, projected surface of an object to the whole projected surface of an object in a complex environment. Hereby, a virtual, orthographic camera is oriented in accordance to the sun’s position in the 3D model domain. The method is implemented in the microscale model SkyHelios, utilizing efficient rendering techniques to assess AVF of all urban trees in parallel. The method was applied to Rieselfeld in Freiburg, Germany. The assessed sunlit area is compared to the SVF at the top of each tree and solar altitude angle, revealing a strong relationship between sunlit areas to solar altitude angles. This study shows that AVF is an efficient methodology to assess received direct radiation of urban trees. Based on AVF, it is possible to identify urban areas with shaded and sunlit trees, but it can also be applied to other objects in complex environments. Therefore, AVF is applicable for urban architecture or energetic research questions.

Water storage plays an important role in mitigating heat and flooding in urban areas. Assessment of the capacity of cities to store water remains challenging due to the extreme heterogeneity of the...

Tower-based measurements from within and above the urban canopy in two cities are used to evaluate several existing approaches that parametrize the vertical profiles of wind speed and temperature within the urban roughness sublayer (RSL). It is shown that current use of Monin–Obukhov similarity theory (MOST) in numerical weather prediction models can be improved upon by using RSL corrections when modelling the vertical profiles of wind speed and friction velocity in the urban RSL using MOST. Using anisotropic building morphological information improves the agreement between observed and parametrized profiles of wind speed and momentum fluxes for selected methods. The largest improvement is found when using dynamically-varying aerodynamic roughness length and displacement height. Adding a RSL correction to MOST, however, does not improve the parametrization of the vertical profiles of temperature and heat fluxes. This is expected since sources and sinks of heat are assumed uniformly distributed through a simple flux boundary condition in all RSL formulations, yet are highly patchy and anisotropic in a real urban context. Our results can be used to inform the choice of surface-layer representations for air quality, dispersion, and numerical weather prediction applications in the urban environment.

This work explores the feasibility of real-time large-eddy simulations of flow over urban canopies at the neighborhood scale. The cumulant lattice Boltzmann method is employed using a single General Purpose Graphic Processing Unit (GPGPU). In order to demonstrate the validity and efficiency of this approach we simulate wind flow in a neighborhood of Basel. Simulation results are validated against measurements from the Basel Urban Boundary Layer Experiment (BUBBLE) and are compared to previous CFD simulations. Turbulence statistics are found to be in agreement with corresponding tower measurements according to several validation metrics. While quantitative comparisons are limited to the six measurement locations of the field measurements, the available data supports the conjecture that real-time simulation of urban air flow is feasible at the neighborhood scale with the proposed numerical technique.

A short, but severe, wildfire smoke episode in July 2015, with an aerosol optical depth (AOD) approaching 9, is shown to strongly impact radiation budgets across four distinct land-use types (forest, field, urban and wetland). At three of the sites, impacts on the energy balance are also apparent, while the event also appears to elicit an ecosystem response with respect to carbon fluxes at the wetland and a forested site. Greatest impacts on radiation and energy budgets were observed at the forested site where the role of canopy architecture and the complex physiological responses to an increase in diffuse radiation were most important. At the forest site, the arrival of smoke reduced both sensible and latent heat flux substantially but also lowered sensible heat flux more than the latent heat flux. With widespread standing water, and little physiological control on evapotranspiration, the impacts on the partitioning of turbulent fluxes were modest at the wetland compared to the physiologically dominated fluxes at the forested site. Despite the short duration and singular nature of the event, there was some evidence of a diffuse radiation fertilization effect when AOD was near or below 2. With lighter smoke, both the wetland and forested site appeared to show enhanced photosynthetic activity (a greater sink for carbon dioxide). However, with dense smoke, the forested site was a strong carbon source. Given the extensive forest cover in the Pacific Northwest and the growing importance of forest fires in the region, these results suggest that wildfire aerosol during the growing season potentially plays an important role in the regional ecosystem response to smoke and ultimately the carbon budget of the region.

A snapshot of web pages describing the site location informationand observations used in the Urban-PLUMBER model evaluation projectfor urban areas. Observational data files themselves are NOT included. Access to observational data files must be separately negotiated with data owners. Webpage available at:https://urban-plumber.github.io/sites

The WMO World Weather Research Programme (WWRP) “promotes international and interdisciplinary research for more accurate and reliable forecasts from minutes to seasons, expanding the frontiers of weather science to enhance society’s resilience to high-impact weather and the value of weather information for users. In the 2016-2023 WWRP implementation plan, activities focus on 4 challenges: High-Impact Weather, Water, Urbanization, Evolving technologies. Furthermore, the WMO Global Atmosphere Watch Urban Research Meteorology and Environment (GURME) focus on the development of models and associated research activities to enhance the capabilities in providing urban-environmental forecasting and air quality services, illustrating the linkages between meteorology and air quality (https://public.wmo.int/en/programmes).This talk presents an international Research Demonstration Project (RDP), that will focus on international research on scientific urban issues addressed by both WWRP and GURME. The strategic objective of this RDP is to focus on the Olympic Games of Paris in 2024 in order to advance research on the theme of the “future Meteorological Forecasting systems at 100m (or finer) resolution for urban areas”. Such systems would prefigure the numerical weather prediction at the horizon 2030. The focus will be on themes related to extreme weather events in summer which both are influenced by and impacts urbanization: thunderstorms and strong Urban Heat Islands, and their consequences.There are 5 scientific questions that will be addressed during this Paris RDP:Nowcasting & Numerical Weather Prediction in cities at order 100m resolution High resolution thunderstorm nowcasting (probabilistic and deterministic) in the urban environment, Urban heat islands, cool areas and air quality Nowcasting and forecast in coastal cities (for the Marseilles site) How to improve and better use observational networks in urban areas, including (big) non-conventional data Conception and Communication of tailored weather, climate, environmental information at infra-urban resolution. Several High-Impact weather case studies were selected. Storm cases (starting with one the 10th July 2017) will allow to evaluate the role of the urban area on their enhancement. Extreme Heat wave aggravated by a strong Urban Heat Island are also studied (July 2019). Open urban data describing the agglomerations at very high resolution are provided. New innovative methods to produce maps of urban form characteristics (e.g. from street images) and meteorological data (from personal meteorological stations) will be explored.This talk will describe these scientific questions, as well as the common methodology approach that is being discussed within the partners. A focus will be the international experimental campaign that will take place in 2022 over the Paris agglomeration, with an Intensive Observation Period in the summer 2022. Interactions between urban surface and the atmospheric boundary layer, the interactions between air quality and aerosols between city and biogenic plumes, and the local effect of urban trees on micro-climate and chemistry are some of the axes of the campaign. It will provide additional meteorological and air quality observations, to both help to improve the nowcasting and NWP systems at urban scale, and aim to define the required additional instrumentation that should be deployed during the Olympics games themselves.

The understanding of intra-urban air temperature variations is crucial to assess strategies for cities' adaptation to impacts of present and future anthropogenic climate change. Depending on extensive measurement networks, high-resolution air temperature measurements in urban environments are challenging due to high instrumentation and maintenance costs. Here, we present a low-cost measurement device (LCD) consisting of a temperature logger and a custom-made, naturally ventilated radiation shield. Besides intercomparisons with automated weather stations (AWS) at three reference sites during record-dry summer 2018, we tested the potential of the devices using a network of 79 LCDs to assess the intra-urban variability of urban heat island (UHI) patterns in the city of Bern, Switzerland. We found positive mean measurement biases between LCDs and AWS of 0.61 to 0.93 K (RMSE: 0.78 to 1.17 K) during daytime, of which up to 82.8% of the variance could be explained statistically by solar irradiance (radiative heating) and wind speed (insufficient ventilation). During night, average measurement biases were markedly lower and eventually negative with −0.12 to 0.23 K (RMSE: 0.19 to 0.34 K). Our results highlight the importance of sensor intercomparisons being conducted at multiple locations with differing urban land-cover, structure, and metabolism given that biases varied considerably between the reference sites. Data retrieved by the city-wide measurement network showed that the LCD approach is well suited for the analysis of spatiotemporal UHI patterns during night and adds considerable value compared to the few existing AWS in detecting fine-scale air temperature variability. In conclusion, the current LCD measurement approach represents a valuable option for cost-effective analyses of urban air temperature variability across multiple scales, which may be of particular value for the development, appliance, and monitoring of adaptation strategies to climate change in cities with restricted financial resources.

This is a comment to the paper "Magnitude of urban heat islands largely explained by climate and population" by Manoli et al. (2019, Nature 573 p. 55-60; https://doi.org/10.1038/s41586-019-1512-9)

Insect outbreaks can significantly influence carbon (C) and water balances of forests. Forest tent caterpillars (FTC) (Malacosoma disstria Hubner) are one of the most prominent insects found in aspen forests in Canada and have the potential to considerably influence regional C and water fluxes. In the summer of 2016, an FTC infestation occurred in a ca. 100 -year-old trembling aspen (Populus tremuloides) stand in the southern boreal forest where the longterm research site known as Old Aspen (OA) is located. The infestation led to nearly complete defoliation of the canopy during the leafing out period when photosynthesis, and thus C uptake, is progressing towards maximum levels. We used 21 years of eddy-covariance (EC) and climate measurements covering pre-infestation and infestation periods to estimate the impact of the FTC infestation on net ecosystem production (NEP), gross ecosystem production (GEP) and evapotranspiration (E). Defoliation in 2016 reduced annual NEP to −130 g C m−2 y−1 and GEP to 798 g C m−2 y−1, respectively, which were much less than their 20-year means (NEP = 118 ± 53 g C m−2 y−1, GEP = 1057 ± 74 g C m−2 y−1), and resulted in the most negative annual NEP value of the 21 years of measurements at the OA site. NEP for 2016 was even lower than values observed during three drought years (2001–2003). However, FTC infestation caused little effect on annual E. FTC infestation reduced the near-surface remotely-measured greenness index, green chromatic coordinate (GCC), to ∼0.32 on June 10 in comparison to ∼0.40 in other years. The defoliation, observable from space as reductions in normalized difference vegetation index (NDVI) values, also showed a negligible effect on E but a large effect on the C fluxes.

The most recent mountain pine beetle (MPB) (Dendroctonus ponderosae) outbreak in British Columbia (BC), which began in the late 1990s, killed ∼54% of the mature merchantable lodgepole pine and was expected to impact gross primary productivity (GPP), ecosystem respiration (R) and thus net ecosystem productivity (NEP) of infested stands. Eddy-covariance (EC) measurements of carbon dioxide and water vapor fluxes have been made in an almost single-species not-salvage-harvested lodgepole pine stand in northern interior BC during the decade following beetle attack in 2006 to determine the effects on the carbon (C) and water balances. We modified the 3-PG (Physiological Principles in Predicting Growth) model to simulate the effects of MPB attack on the stand. Modifications included a sub-model for heterotrophic respiration, which originates from the decomposition of two substrates, soil organic matter and coarse woody debris from the falling dead trees, thus permitting the determination of R and NEP. The modified model was used to simulate C balance components using model parameter values mostly taken from the literature with some estimated from in-situ measurements. Modelled monthly and annual stand-level GPP, R and NEP agreed well with respective EC-estimated values during the decade following attack. Modelled annual GPP, R and NEP decreased by about 52%, 35% and 126%, respectively, in the first year following attack compared to pre-attack values. Modelled GPP and R, as well as EC-estimated GPP, showed a relatively steady increase over the following decade, while R derived from the EC measurements changed little in the first eight years after attack and then increased in the last two years. EC-estimated GPP in 2016 exceeded the estimated pre-attack value by 9%, while modelled and EC-estimated R in 2016 exceeded 2005 values by 6% and 15%, respectively. Modelled NEP indicated that the recovering stand became a C sink a year later than indicated by the EC measurements, but both modelled and measured annual NEP increased significantly between 2007 and 2016. Our observations and modelling results show that the remaining trees and the growing understory enabled the not-salvage-harvested stand to recover from insect attack within a decade, with the stand functioning as a weak, yet distinct, C sink in the last two years of the study period. This suggests that the management strategy of not harvesting attacked stands can be beneficial for the C balance of attacked stands with similar characteristics and in similar climates.

We propose a gap-filling model for carbon dioxide fluxes measured by eddy covariance (EC) that combines the flux variance similarity (FVS) partitioning approach with the artificial neural network (ANN) technique (FVS–ANN). 18 years of EC-measured net ecosystem production (NEP) of a Douglas-fir (Pseudotsuga menziesii) stand in British Columbia, Canada were used. FVS-partitioned fluxes were used as a training dataset for ANNs based on the environmental variables to model missing respiration and photosynthesis values. Results from the FVS–ANN model were compared with three established models using different partitioning and gap-filling approaches, namely the nighttime relationship, the daytime intercept, and an ANN-based model informed by the nighttime relationship (ANN–nighttime). In the gap-filled long-term NEP record, the 18-year mean NEP values were similar (between 225 and 239 gC m−2 year−1) and individual years showed small differences in annual NEP among the four models, demonstrating the reliability of estimated NEP when any of the models were used for gap-filling. However, the largest differences in annual gap-filled photosynthesis and respiration estimates among the four models in a given year were as large as 813 and 835 gC m−2 year−1, respectively. The FVS–ANN model consistently resulted in the smallest annual photosynthesis and respiration estimates, while the nighttime-based models resulted in the highest photosynthesis and respiration. The differences between the FVS–ANN and any of the other three models are explained by the different definitions of partitioned fluxes (i.e., values from the FVS–ANN are smaller than the other three models by leaf respiration). However, the differences may also be partly due to light inhibition of daytime respiration, which is known as the Kok effect that is believed to be adequately considered in the new FVS–ANN combination and partially in the daytime intercept model, but not in the other two models. The results suggest that previous EC-based estimates of global photosynthesis and respiration obtained by using nighttime relationships are potentially biased high.

Large-eddy simulations (LES) are used to gain insight into the effects of trees on turbulence, aerodynamic parameters, and momentum transfer rates characterizing the atmosphere within and above a real urban canopy. Several areas are considered that are part of a neighborhood in the city of Vancouver, BC, Canada where a small fraction of trees are taller than buildings. In this area, eight years of continuous wind and turbulence measurements are available from a 30 m meteorological tower. Data from airborne light detection and ranging (LiDAR) are used to represent both buildings and vegetation at the LES resolution. In the LES algorithm, buildings are accounted through an immersed boundary method, whereas vegetation is parameterized via a location-specific leaf area density. LES are performed including and excluding vegetation from the considered urban areas, varying wind direction and leaf area density. Surface roughness lengths (z0) from both LES and tower measurements are sensitive to the 0 ≤ LAI / λ f b 3 parameter, where LAI is the leaf area index and λ f b is the frontal area fraction of buildings characterizing a given canopy. For instance, tower measurements predict a 19% seasonal increase in z0, slightly lower than the 27% increase featured by LES for the most representative canopy (leaves-off LAI / λ f b = 0.74 , leaves-on LAI / λ f b = 2.24 ). Removing vegetation from such a canopy would cause a dramatic drop of approximately 50% in z0 when compared to the reference summer value. The momentum displacement height (d) from LES also consistently increases as LAI / λ f b increases, due in large part to the disproportionate amount of drag that the (few) relatively taller trees exert on the flow. LES and measurements both predict an increase in the ratio of turbulent to mean kinetic energy (TKE/MKE) at the tower sampling height going from winter to summer, and LES also show how including vegetation results in a more (positive) negatively skewed (horizontal) vertical velocity distribution – reflecting a more intermittent velocity field which favors sweep motions when compared to ejections. Within the urban canopy, the effects of trees are twofold: on one hand, they act as a direct momentum sink for the mean flow; on the other, they reduce downward turbulent transport of high-momentum fluid, significantly reducing the wind intensity at the heights where people live and buildings consume energy.

Retrogressive thaw slumps (RTS) are permafrost disturbances common on the Fosheim Peninsula, Ellesmere Island, Canada. During the 2013 growing season, three different RTS were studied to investigate the impact on vegetation composition, soil, and growing season net ecosystem exchange (NEE) of CO2 by comparing to the adjacent undisturbed tundra. Eddy covariance and static chamber measurements were used to determine NEE and ecosystem respiration (Re), respectively. Vegetation cover was significantly lower in all active disturbances, relative to the surrounding tundra, and this affected the overall impact of disturbance on CO2 fluxes. Disturbances were characterized by greater Re compared to surrounding undisturbed tundra. Over the mid-growing season (34 days), eddy covariance NEE measurements indicated that there was greater net CO2 uptake in undisturbed versus disturbed tundra. At one site, the undisturbed tundra was a weak net sink (−0.05 ± 0.02 g C m−2 day−1), while the disturbed tundra acted as a weak net source (+0.07 ± 0.04 g C m−2 day−1). At the other site, the NEE of the undisturbed tundra was −0.20 ± 0.03 g C m−2 day−1 (sink), while the disturbed tundra still sequestered CO2, but less than the undisturbed tundra (NEE = −0.05 ± 0.04 g C m−2 day−1). Two of the RTS exhibited average soil temperatures that were greater compared to the surrounding undisturbed tundra. In one case, the opposite effect was observed. All RTS exhibited elevated soil moisture (+14%) and nutrient availability (specifically nitrogen) relative to the undisturbed tundra. We conclude that RTS, although limited in space, have profound environmental impacts by reducing vegetation coverage, increasing wet soil conditions, and altering NEE during the growing season in the High Arctic.

Tree mortality due to the recent mountain pine beetle (MPB) (Dendroctonus ponderosae) outbreak in British Columbia (BC) is expected to impact evapotranspiration (E), gross primary productivity (GPP), and snow accumulation and melt, thereby influencing ecosystem hydrology. To quantify the impact on E and GPP, we have made eddy-covariance (EC) measurements of water vapor and carbon dioxide fluxes for nine years above a lodgepole pine (Pinus contorta var. latifolia) stand in northern interior BC that was not salvage harvested following beetle attack in 2006. To understand the processes and determine long-term recovery of the water and carbon balances of this stand following attack, we modified the 3-PG (Physiological Principles in Predicting Growth) model to include the effects of insect attack on the stand, and modelled monthly E, GPP and water use efficiency (WUE) of the understory and overstory. The modified model was validated using the flux and climate measurements made during the nine-year period. It generally worked well with modelled annual E and GPP agreeing reasonably well with their measured values. Modelled values of annual E and GPP decreased by about 62% and 52%, respectively, between 2005 and 2007. Annual measured E remained remarkably stable for five years after the attack, and then increased by ∼13% year−1 in the last four years; however, the model indicated a relatively steady increase of ∼13% year−1 over the 9 years. On the other hand, EC-estimated GPP showed a steady recovery (∼9% year−1) almost returning to estimated pre-attack levels within nine years. Modelled annual GPP generally agreed with EC-estimated values as well as capturing much of the interannual variability observed in the last 4 years. Projections for the following decade, depending on expected climate change, suggested that E would slightly increase, GPP would remain relatively constant, and WUE would decrease slightly. With the decrease in E, modelled drainage (Dr) increased significantly in the first year after attack compared to pre-attack estimates, but gradually decreased again in the following years. Measured and modelled Dr decreased between 2007 and 2015 by about 28% and 49%, respectively. Projections show Dr staying relatively constant for the following decade (2016–2025). Our observations that the recovery of E and GPP was faster than expected suggest that not to salvage harvest MPB-attacked stands with considerable remaining live canopy trees or understory should be a beneficial management strategy from carbon sequestration and hydrologic perspectives.

A method for directly measuring carbon dioxide (CO2) emissions using a mobile sensor network in cities at fine spatial resolution was developed and tested. First, a compact, mobile system was built using an infrared gas analyzer combined with open-source hardware to control, georeference, and log measurements of CO2 mixing ratios on vehicles (car, bicycles). Second, two measurement campaigns, one in summer and one in winter (heating season) were carried out. Five mobile sensors were deployed within a 1 × 12. 7 km transect across the city of Vancouver, BC, Canada. The sensors were operated for 3.5 h on pre-defined routes to map CO2 mixing ratios at street level, which were then averaged to 100 × 100 m grid cells. The averaged CO2 mixing ratios of all grids in the study area were 417.9 ppm in summer and 442.5 ppm in winter. In both campaigns, mixing ratios were highest in the grid cells of the downtown core and along arterial roads and lowest in parks and well vegetated residential areas. Third, an aerodynamic resistance approach to calculating emissions was used to derive CO2 emissions from the gridded CO2 mixing ratio measurements in conjunction with mixing ratios and fluxes collected from a 28 m tall eddy-covariance tower located within the study area. These measured emissions showed a range of −12 to 226 CO2 ha−1 h−1 in summer and of −14 to 163 kg CO2 ha−1 h−1 in winter, with an average of 35.1 kg CO2 ha−1 h−1 (summer) and 25.9 kg CO2 ha−1 h−1 (winter). Fourth, an independent emissions inventory was developed for the study area using buildings energy simulations from a previous study and routinely available traffic counts. The emissions inventory for the same area averaged to 22.06 kg CO2 ha−1 h−1 (summer) and 28.76 kg CO2 ha−1 h−1 (winter) and was used to compare against the measured emissions from the mobile sensor network. The comparison on a grid-by-grid basis showed linearity between CO2 mixing ratios and the emissions inventory (R2 = 0. 53 in summer and R2 = 0. 47 in winter). Also, 87 % (summer) and 94 % (winter) of measured grid cells show a difference within ±1 order of magnitude, and 49 % (summer) and 69 % (winter) show an error of less than a factor 2. Although associated with considerable errors at the individual grid cell level, the study demonstrates a promising method of using a network of mobile sensors and an aerodynamic resistance approach to rapidly map greenhouse gases at high spatial resolution across cities. The method could be improved by longer measurements and a refined calculation of the aerodynamic resistance.

Observations of carbon dioxide (CO2) mixing ratios in the urban boundary layer (UBL) are rare, even though there is potential for such measurements to be used to monitor city-scale net CO2 emissions. This work presents a unique dataset of CO2 mixing ratios observed in the UBL above Vancouver, British Columbia, Canada, by means of a tethered balloon system over a continuous 24-h summertime period. Vertical profiles of CO2 mixing ratios are found to vary according to UBL thermal structure and mechanical dynamics (development of convective and nocturnal boundary layers, vertical mixing from mechanical turbulence, horizontal advection from land–sea thermal breezes, and vertical entrainment). A box model is applied to quantify net city-scale surface emissions to the UBL volume using the measured rate of change of UBL CO2 mixing ratios and estimated CO2 advection and entrainment fluxes. The diurnal course of city-scale net emissions predicted by the model is similar to simultaneous local-scale eddy-covariance CO2 flux measurements, although there are relatively large uncertainties in hourly model calculations of horizontal advection and vertical entrainment fluxes due to inputs of regional background CO2 mixing ratios. Daily city-scale emissions totals predicted by the model (20.2 gC m−2 day−1) are 35% larger than those measured simultaneously on an urban local-scale eddy-covariance flux tower and are within 32% of a spatially scaled municipal greenhouse gas inventory. However, these methods are not expected to agree exactly because they represent different spatial source areas and include different CO2 source and sink processes.

It is critical to have long-term carbon dioxide (CO2) flux observations in forest ecosystems to understand how changing climate can affect forest carbon (C) stocks and CO2 exchange between forests and the atmosphere. In this study, fifteen years (2002–2016) of continuous eddy-covariance flux and climate measurements in an intermediate-aged Douglas-fir stand on the east coast of Vancouver Island, Canada, were analyzed. First, the eddy covariance-measured CO2 fluxes were partitioned into gross primary production and ecosystem respiration using two artificial neural networks. Second, the responses of net ecosystem production, gross primary production and ecosystem respiration to interannual climate variability, including five El Nino-Southern Oscillation events, were determined. Three hyper-parameters (number of layers, hidden units, and batch size) of each artificial neural network were set by Bayesian optimization using sequential model-based optimization while the remaining hyper-parameters were taken from the literature. The first artificial neural network was fitted using only nighttime CO2 flux data and applied to estimate nighttime and daytime ecosystem respiration values, and the second one was used to gap-fill gross primary production values. In addition, a predictor analysis was done to investigate the most influential predictors (i.e., environmental variables) within seasons and years. When applied to half-hourly data, the ecosystem respiration model had an R2 of 0.43, whereas the gross primary production model had an R2 of 0.80. The stand was a moderate C sink (average net ecosystem production of 118 ± 404 g C m−2 year−1) during the entire study period, except for the years 2002–2006 when the stand was a moderate C source. The mean annual values of gross primary production and ecosystem respiration were 1649 ± 157 g C m−2 year−1 and 1531 ± 410 g C m−2 year−1, respectively. Our analysis showed that soil temperature was the most important predictor for the ecosystem respiration model, and photosynthetically active irradiance was the most important predictor for the gross primary production model. However, during dry periods in late summer, soil moisture became the most important predictor. Interannual variability of net ecosystem production was only slightly affected by annual total precipitation, mean soil temperature and mean air temperature. Instead, it depended on spring mean air temperature (start of the growing season), summer total precipitation (indicative of water deficiency) and mean summer air temperature. El Nino and La Nina events generally resulted in lower and higher annual net ecosystem production, respectively.

Partitioning measured net ecosystem exchange of CO2 (NEE) into ecosystem respiration (Re) and gross primary production (GPP) is essential for understanding the biophysical controls on forest ecosystem carbon (C) dynamics. Obtaining Re and GPP accurately from NEE remains a challenge. In this study, we measured stable CO2 isotopologue signatures at ecosystem scale simultaneously with eddy-covariance measurements of NEE above a Douglas-fir stand on Vancouver Island, Canada. This allowed us to analyze three partitioning methods (the traditional nighttime relationship, daytime intercept, and stable C isotope methods). In all three methods, nighttime Re values were obtained from nighttime EC-measured NEE values. Over a 29-day summertime period, daily (24-h) Re and GPP obtained using the daytime intercept method were 11±4% and 6 ± 5%, respectively, lower than those estimated using the nighttime relationship method. Stable-C-isotope-partitioned daily Re and GPP were 35±13% and 19±10%, respectively, lower than when using the nighttime relationship method to obtain daytime Re. That mean daytime Re obtained using the stable C isotope method was 56% less than that estimated using the nighttime relationship method suggests that light inhibition of foliage respiration (i.e., the Kok effect) had a major effect on daytime Re in this Douglas-fir stand. The mean daytime Re obtained using the stable C isotope method was 49% lower than the value estimated using the daytime intercept method, which indicates that the daytime intercept method accounted only for a small proportion of the light inhibition effect during the most active period of the growing season.

Vegetation alters urban climates via transpirational cooling; however, unlike shorter vegetation, trees additionally provide shade and shelter. Urban canopy models (UCMs) are coupled with mesoscale models for assessment of neighbourhood-scale climate, but their representation of urban trees is limited. We present BEP-Tree, a multi-layer UCM that integrates trees instead of using the ‘tile’ approach that characterizes most urban mesoscale modelling. BEP-Tree resolves the microclimate underneath trees where outdoor human thermal exposure occurs; these conditions are largely inaccessible to current mesoscale modelling and remote sensing approaches. Moreover, BEP-Tree allows trees to protrude above buildings, enabling assessment of low-rise neighbourhoods. The new model combines existing models, including detailed radiative and hydrodynamic models that assess built-tree interactions, and includes new parameterizations for impacts of tree foliage distribution. BEP-Tree is evaluated against unique datasets from three cities enabling assessment of modelled radiation, energy exchanges and road and air temperatures across the diurnal cycle. Urban trees redirect sensible heat into latent heat and reduce pedestrian-level solar radiation, wind, and temperatures during daytime. Thermal climate and energy exchanges are more sensitive to street tree density than height. Coupled with a mesoscale model, BEP-Tree enables assessment of urban forest-induced neighbourhood-to-city scale climate impacts during fair weather periods.