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We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe. We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE. We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 % of the simulated CO2 mole fraction differs by >2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures (“plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well. We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at https://doi.org/10.18160/20Z1-AYJ2 (van der Woude, 2022a).

Modelling rainfall extremes and dry periods over the Southeast Asia (SEA) region is challenging due to the characteristics of the region, which consists of the Maritime Continent and a mountainous region; it also experiences monsoonal conditions, as it is located between the Asian summer monsoon and the Australian summer monsoon. Representing rainfall extremes is important for flood and drought assessments in the region. This paper evaluates extreme rainfall climatic indices from regional climate models from CORDEX Southeast Asia and compares them with the results of high-resolution global climate models with a comparable spatial resolution from the HighResMIP experiment. Observations indicate a high intensity of rainfall over areas affected by tropical cyclones and long consecutive dry day periods over some areas in Indochina and the southern end of Indonesia. In the model simulations, we find that both coupled and sea surface temperature-forced HighResMIP model experiments are more similar to the observations than CORDEX model results. However, the models produce a poorer simulation of precipitation intensity-related indices due to model biases in the rainfall intensity. This bias is higher in CORDEX than in HighResMIP and is evident in both the low- and high-resolution HighResMIP model versions. The comparable performances of HighResSST (atmosphere-only runs) and Hist-1950 (coupled ocean–atmosphere runs) demonstrate the accuracy of the ocean model. Comparable performances were also found for the two different resolutions of HighResMIP, suggesting that there is no improvement in the performance of the high-resolution HighResMIP model compared to the low-resolution HighResMIP model. © 2022 The Authors. International Journal of Climatology published by John Wiley Sons Ltd on behalf of Royal Meteorological Society.

Ongoing urbanization highlights the need for a better understanding and high resolution modelling of the urban climate. In this study, we combine rural observations by WMO surface stations, weather radar data and urban crowd-sourced observations with very fine-scale modelling efforts for Amsterdam, The Netherlands. As a model, we use the Weather Research and Forecasting (WRF) mesoscale model with 3D variational data assimilation at a 100-m resolution in the innermost model domain. In order to enable the assimilation of observations within the urban canopy, we develop a scheme to reduce urban temperature biases by adjusting urban fabric temperatures. The scheme is tested against independent urban observations for the summer month of July 2014 and specifically for a hot period and an extreme precipitation event. We find data assimilation reduces biases in temperature and wind speed. Within the city, the most significant improvement is the reduction of negative temperature biases during clear nights, which implies a better prediction of the Urban Heat Island (UHI). Concerning precipitation, the fractional skill score improves incrementally when additional observations are assimilated, and the largest impact is seen from the assimilation of weather radar observations.

Winds in coastal areas are particularly complex due to the drastic change of roughness and thermal properties between the sea and the land areas. Besides, the coastal topography, the shoreline irregularities, the surface state (land cover/use, soil moisture, sea surface temperature (SST), wind waves), and the interactions with the upper parts of the atmospheric boundary layer (ABL) add more complexity to the final characteristics of the surface winds of these regions.Among coastal winds, coastal breezes are especially common thermally driven flows formed in mid-latitude regions under fair-weather synoptic conditions. Under these situations, the thermal gradient between the sea and the ocean becomes more important, generating pressure gradient forces that lead to onshore winds during the daytime and offshore during the night. The impacts of these winds are broad and varied: they transport humidity, pollutants, and other physical properties in these regions; they can initiate convection (and even trigger the formation of storms), and they also drive the surface coastal currents, among others. From a societal point of view, the coastal breezes are crucial for the wind power industry, air-quality forecasts, maritime sports, and simply for the refreshing impact they cause, an aspect especially important in some areas commonly affected by extreme maximum temperatures and heat waves. Therefore, a correct understanding of the physical characteristics of the coastal breezes is a needed step to correctly forecast them and to be able to investigate their future trends.In this work, we present an observational analysis of the coastal breezes observed in the Gulf of Cádiz. We highlight some differences found between the breezes formed at the sea and at the land areas from observational measurements. Besides, we use the mesoscale Weather Research and Forecasting (WRF) model to simulate key case studies, showing how the interaction with the background synoptic wind is very important for the final characteristics that the breezes have. Related to this, we have observed how the effect of the changes in the surface (SST, soil moisture) on the breeze’s characteristics depend on the wind vertical profile (background wind). That is, surface changes impact the ABL mixing and the momentum transfer from higher levels, which seems to be the main mechanism that impact the breezes at lower levels, even more than the surface thermal gradient effect.

Climate change phenomena represent the most serious threats to human well-being and sustainable development. This affects several dimensions of human life from individual health issues to economic growth, passing through civil protection, with strong impacts on European territories and in several other world regions. The I-CHANGE (Individual Change of HAbits Needed for Green European transition, 2021-2025) project faces the challenge of engaging and promoting the active participation of citizens (students, cultural associations, cyclists, boat men etc) for addressing climate change, sustainable development and environmental protection in the framework of the European Green Deal , the European Climate Pact and the European Biodiversity Strategy for 2030.The overall driving concept is that citizens and civil society have a central role in the definition of environmental protection and climate action and their direct involvement is essential to drive a true shift and promotion of changes of behaviors towards more sustainable patterns. I-CHANGE represents a change of paradigm achievable through a multidisciplinary and participatory approach: improvement of data usability - citizen understanding raises through the observation of the environmental impacts of human activities; active participation of citizens - the active involvement of citizens through a set of Living Labs (LLs) located in different socio-economic contexts (Europe, Middle-East and Africa); climate change awareness – the development of clear understanding for citizens of the scientific processes underlying climate change and environmental protections; behaviour change – addressing the climate crisis requires a focus on high-impact behaviours and high-emitting groups. First year and half of I-CHANGE activities will be presented and discussed.

This article provides high-resolution information on the projected changes in annual extreme rainfall and high and low streamflow events over Southeast Asia under extreme climate change. The analysis was performed using the bias-corrected result of the High-resolution Model Intercomparison Project (HighResMIP) multi-model experiment for the period 1971–2050. Eleven rainfall indices were calculated along with streamflow simulation using the PCR-GLOBWB hydrological model. The historical period 1981–2010 and the near-future period 2021–2050 were considered for this analysis. Results indicate that over Indochina, Myanmar faces more challenges in the near future. The east coast of Myanmar will experience more extreme high rainfall conditions, while northern Myanmar will have longer dry spells. Over the Indonesian maritime continent, Sumatra and Java will suffer from the increase in dry spell length of up to 40 %, while the increase of extreme high rainfall will occur over Borneo and mountainous areas in Papua. Based on the streamflow analysis, the impact of climate change is more prominent in a low flow event than in a high flow event. The majority of rivers in the central Mekong catchment, Sumatra, the Malaysian peninsula, Borneo, and Java will experience more extreme low flow events. More extreme dry conditions in the near future are also seen from the increasing probability of future low flow occurrences, which reaches 101 % and 122 % on average over Sumatra and Java, respectively. Finally, the changes in extreme high and low streamflow events are more pronounced in rivers with steep hydrographs, while rivers with shallow hydrographs have a higher risk in the probability of low flow change. Our study highlights the importance of catchment properties in aggregating and/or buffering the impact of extreme climate change.

Dry deposition to the surface is one of the main removal pathways of tropospheric ozone (O3). We quantified for the first time the impact of O3 deposition to the Arctic sea ice on the planetary boundary layer (PBL) O3 concentration and budget using year-round flux and concentration observations from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign and simulations with a single-column atmospheric chemistry and meteorological model (SCM). Based on eddy-covariance O3 surface flux observations, we find a median surface resistance on the order of 20,000 s m−1, resulting in a dry deposition velocity of approximately 0.005 cm s−1. This surface resistance is up to an order of magnitude larger than traditionally used values in many atmospheric chemistry and transport models. The SCM is able to accurately represent the yearly cycle, with maxima above 40 ppb in the winter and minima around 15 ppb at the end of summer. However, the observed springtime ozone depletion events are not captured by the SCM. In winter, the modelled PBL O3 budget is governed by dry deposition at the surface mostly compensated by downward turbulent transport of O3 towards the surface. Advection, which is accounted for implicitly by nudging to reanalysis data, poses a substantial, mostly negative, contribution to the simulated PBL O3 budget in summer. During episodes with low wind speed (

Urban climate-responsive design interventions assessed quantitatively.Green infrastructure interventions perform the best regarding climate adaptation.Solar technology interventions perform the best regarding climate mitigation.Street scale interventions combine adaptation and mitigation to a larger extent.Building scale interventions present less synergies than street scale interventions.

Dry deposition is an important removal mechanism for tropospheric ozone (O3). Currently, O3 deposition to oceans in atmospheric chemistry and transport models (ACTMs) is generally represented using constant surface uptake resistances. This occurs despite the role of solubility, waterside turbulence and O3 reacting with ocean water reactants such as iodide resulting in substantial spatiotemporal variability in O3 deposition and concentrations in marine boundary layers. We hypothesize that O3 deposition to the Arctic Ocean, having a relatively low reactivity, is overestimated in current models with consequences for the tropospheric concentrations, lifetime and long-range transport of O3. We investigate the impact of the representation of oceanic O3 deposition to the simulated magnitude and spatiotemporal variability in Arctic surface O3. We have integrated the Coupled Ocean-Atmosphere Response Experiment Gas transfer algorithm (COAREG) into the mesoscale meteorology and atmospheric chemistry model Polar-WRF-Chem (WRF) which introduces a dependence of O3 deposition on physical and biogeochemical drivers of oceanic O3 deposition. Also, we reduced the O3 deposition to sea ice and snow. Here, we evaluate WRF and CAMS reanalysis data against hourly averaged surface O3 observations at 25 sites (latitudes > 60∘ N). This is the first time such a coupled modeling system has been evaluated against hourly observations at pan-Arctic sites to study the sensitivity of the magnitude and temporal variability in Arctic surface O3 on the deposition scheme. We find that it is important to nudge WRF to the ECMWF ERA5 reanalysis data to ensure adequate meteorological conditions to evaluate surface O3. We show that the mechanistic representation of O3 deposition over oceans and reduced snow/ice deposition improves simulated Arctic O3 mixing ratios both in magnitude and temporal variability compared to the constant resistance approach. Using COAREG, O3 deposition velocities are in the order of 0.01 cm s−1 compared to ∼ 0.05 cm s−1 in the constant resistance approach. The simulated monthly mean spatial variability in the mechanistic approach (0.01 to 0.018 cm s−1) expresses the sensitivity to chemical enhancement with dissolved iodide, whereas the temporal variability (up to ±20 % around the mean) expresses mainly differences in waterside turbulent transport. The mean bias for six sites above 70∘ N reduced from −3.8 to 0.3 ppb with the revision to ocean and snow/ice deposition. Our study confirms that O3 deposition to high-latitude oceans and snow/ice is generally overestimated in ACTMs. We recommend that a mechanistic representation of oceanic O3 deposition is preferred in ACTMs to improve the modeled Arctic surface O3 concentrations in terms of magnitude and temporal variability.

Climate change phenomena represent the most serious threats to human well-being and sustainable development. This affects several dimensions of human life from individual health issues to economic growth, passing through civil protection, with strong impacts on European territories and in several other world regions. The I-CHANGE (Individual Change of HAbits Needed for Green European transition, 2021-2025) project faces the challenge of engaging and promoting the active participation of citizens for addressing climate change, sustainable development and environmental protection in the framework of the European Green Deal , the European Climate Pact and the European Biodiversity Strategy for 2030. The overall driving concept is that citizens and civil society have a central role in the definition of environmental protection and climate action and their direct involvement is essential to drive a true shift and promotion of changes of behaviors towards more sustainable patterns. I-CHANGE represents a change of paradigm achievable through a multi-disciplinary and participatory approach: improvement of data usability - citizen awareness raises through the observation of the environmental impacts of human activities; active participation of citizens - the active involvement of citizens through a set of Living Labs (LLs) located in different socio-economic contexts (Europe, Middle-East and Africa); climate change awareness - the development of clear understanding for citizens of the scientific processes underlying climate change and environmental protections. First year of I-CHANGE activities will be presented and discussed.

Urbanization affects the initiation and intensification of convective activities by changing local meteorological variables, which alters the atmosphere's convective processes. Therefore, proper urban surface information is required to model the energy partitioning pattern and its contrast with neighboring grid cells. In this study, the mesoscale weather research and forecasting (WRF) model is configured with satellite-derived urban fraction for optimal rainfall simulation and to evaluate its impact on the simulated rainfall over Kampala, Uganda. The WRF urban parameter values associated with the considered urban fraction are adjusted based on the literature reviews. The satellite-derived urban fraction represents the more realistic extent and intensity of the urban class with a more representative urban fraction. Three different simulations are performed to distil the impact of changing urban fractions as well as of adjusting urban parameters: (1) DUF_DUP, which uses the default urban fraction and default urban parameter values, (2) DUF_AUP, which uses the default urban fraction with adjusted urban parameter values, and (3) SUF_AUP, which uses the satellite-derived urban fraction and adjusted urban parameter values. A single extreme rainfall event, which caused a flood hazard in Kampala on 25 June 2012, is used for all three simulations. The simulated peak rainfall and its spatial distribution over the Kampala catchment are evaluated using observed rainfall data from gauging stations and satellite data from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Results indicate that the simulated rainfall is overestimated compared to CHIRPS and underestimated when comparing gridcell values with gauging station records. However, the SUF_AUP simulation shows relatively better results with a lower absolute relative error score compared to the other two simulations. Compared with the default urban fraction, the satellite-derived urban fraction represents the more realistic urban extent and intensity. As a result, SUF_AUP results in a more realistic rainfall simulation compared to when using the default urban fraction. Rainfall analysis for both 24-hour and 2-hour indicates that the presence of an urban landscape alters both the structure and propagation of high-intensity rainfall over the city, mainly due to the impact of the urban landscape on the different meteorological variables leading to modifying mechanisms associated with rainfall.Keywords: rainfall, Default urban fraction, Kampala, urban parameter, Updated urban fraction, and WRF model

Dry deposition is an important removal mechanism for tropospheric ozone (O3). Its deposition to oceans is generally represented in atmospheric chemistry transport models using constant surface uptake resistances. However, observations show quite large spatiotemporal variability expressing differences in solubility, waterside turbulence and O3 reacting with iodide and dissolved organic matter. We hypothesize that for the Arctic O3 deposition is overestimated with consequences for background concentrations and lifetime of O3 also due to changes in long-range transport of O3 and its precursors. These are focal points of a project on observing and modelling of Arctic climate-active trace gas exchange as a contribution to the MOSAiC observational campaign with the icebreaker Polarstern being trapped in the Arctic sea-ice for ~1 year.We have coupled the Coupled Ocean-Atmosphere Response Experiment Gas transfer algorithm (COAREG) to the mesoscale meteorology and atmospheric chemistry model Polar-WRF-Chem (PWRF-C). This includes a further development including a two-layer scheme for O3 deposition to oceans and coupling to recently updated ocean water composition databases. We have also reduced the deposition of O3 to sea ice based on a previous study of snow-ice O3 deposition.In this study, we evaluate the performance of PWRF-C with hourly-averaged surface O3 measurements above 60 ºN and with O3 sondes. We show that the more mechanistic representation of O3 deposition over oceans and strongly reduced deposition over snow and ice results in improved simulated Arctic O3 mixing ratios. We found that it is important to nudge PWRF-C to the ECMWF ERA-Interim wind fields which secures a fair comparison of the model with measurements regarding their footprint. Our study indicates that representation of ocean and sea-ice O3 deposition in atmospheric chemistry models must be revised to improve the representation of Arctic O3 concentrations and chemistry.

Urban Land Surface Models (ULSM) are developed to simulate the urban climate and vary in their complexity. The need for this complexity was assessed by two successive systematic intercomparison projects. Both projects focused on the energy balance and found the latent heat flux to be the most challenging flux to model. However, these projects did not address the closure of the water balance, although the energy balance is directly linked to the water balance. This study aims to assess the representation and dynamics of the water balance in 14 ULSMs from the Urban-PLUMBER project each ran for 20 sites. The water balance could not be evaluated by straightforwardly comparing the model results against measurements since most water balance fluxes are not measured. Therefore, the water storage dynamics were derived from the modelled water balance fluxes. We examined the inter-model variation in both the storage dynamics and the separate fluxes and developed seven indicators of a well-captured water balance. The variation in both the fluxes and the storage dynamics is in the same order of magnitude as the size of the fluxes themselves. The indicators show that no ULSM in this study can consistently reproduce a physically realistic water balance regardless of the model’s complexity. As the water balance is linked to the energy balance, the poor water balance representation may explain the poor performance for the latent heat flux. The linked balances illustrate model evaluations and comparisons should extend beyond the target variables of the model to all processes that directly influence these variables.

Understanding the physical processes that affect the turbulent structure of the nocturnal urban boundary layer is essential for improving forecasts of air quality and the air temperature in urban areas. Low-level jets have been shown to affect turbulence in the nocturnal urban boundary layer. We investigate the interaction of a mesoscale low-level jets with the urban boundary layer during a 60-h case study. We use observations from two Doppler lidars and results from two high-resolution numerical-weather-prediction models (Weather Research and Forecasting model, and the Met Office Unified Model for limited-area forecasts for the U.K.) to study differences in the occurrence frequency, height, wind speed, and fall-off of low-level jets between an urban (London, U.K.) and a rural (Chilbolton, U.K.) site. The low-level jets are elevated (≈ 70 m) over London, due to the deeper urban boundary layer, while the wind speed and fall-off are slightly reduced with respect to the rural low-level jets. Utilizing two idealized experiments in the WRF model, we find that topography strongly affects low-level jets characteristics, but there is still a substantial urban influence. Finally, we find that the increase in wind shear under the low-level jets enhances the shear production of turbulent kinetic energy and helps to maintain the vertical mixing in the nocturnal urban boundary layer.Reference:Tsiringakis, A., Theeuwes, N.E., Barlow, J.F., GJ. Steeneveld, 2022: Interactions Between the Nocturnal Low-Level Jets and the Urban Boundary Layer: A Case Study over London. Boundary-Layer Meteorol 183, 249–272. https://doi.org/10.1007/s10546-021-00681-7

We present the CarbonTracker Europe High-Resolution system that estimates carbon dioxide (CO2) exchange over Europe at high-resolution (0.1 x 0.2°) and in near real-time (about 2 months latency). It includes a dynamic fossil fuel emission model, which uses easily available statistics on economic activity, energy-use, and weather to generate fossil fuel emissions with dynamic time profiles at high spatial and temporal resolution (0.1 x 0.2°, hourly). Hourly net biosphere exchange (NEE) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEE is downscaled to 0.1 x 0.2° using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map, and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. An ocean flux extrapolation and downscaling based on wind speed and temperature for Jena CarboScope ocean CO2 fluxes is included in our product. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe. We assess the ability of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 drought, (b) to capture the reduction of fossil fuel emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ERA5, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city centre of Amsterdam (The Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and fossil fuel emissions (COVID-19 pandemic in 2020). After transport with TM5, the CTE-HR fluxes have lower root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor-person inversion). RSMEs are close to those of the reanalysis with the data assimilation system CarbonTracker Europe (CTE). This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE. We furthermore compare CO2 observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different sectors in summer and winter. Interestingly, in periods where synoptic scale transport variability dominates CO2 variations, the CTE-HR fluxes perform similar to low-resolution fluxes (5–10x coarsened). The remaining 10 % of simulated CO2 mole fraction differ by > 2ppm between the low-resolution and high-resolution flux representation, and are clearly associated with coherent structures ("plumes") originating from emission hotspots, such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely-populated region like the Amsterdam city centre, our fluxes underestimate the magnitude of measured eddy-covariance fluxes, but capture their substantial diurnal variations in summer- and wintertime well. We conclude that our product is a promising tool to model the European carbon budget at a high-resolution in near real-time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near real-time monitoring and modeling, for example as a-priori flux product in a CO2 data-assimilation system. The data is available at https://doi.org/10.18160/20Z1-AYJ2.

The Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer Program (ISOBAR) is a research project investigating stable atmospheric boundary layer (SBL) processes, whose representation still poses significant challenges in state-of-the-art numerical weather prediction (NWP) models. In ISOBAR ground-based flux and profile observations are combined with boundary layer remote sensing methods and the extensive usage of different unmanned aircraft systems (UAS). During February 2017 and 2018 we carried out two major field campaigns over the sea ice of the northern Baltic Sea, close to the Finnish island of Hailuoto at 65°N. In total 14 intensive observational periods (IOPs) resulted in extensive SBL datasets with unprecedented spatiotemporal resolution, which will form the basis for various numerical modeling experiments. First results from the campaigns indicate numerous very stable boundary layer (VSBL) cases, characterized by strong stratification, weak winds, and clear skies, and give detailed insight in the temporal evolution and vertical structure of the entire SBL. The SBL is subject to rapid changes in its vertical structure, responding to a variety of different processes. In particular, we study cases involving a shear instability associated with a low-level jet, a rapid strong cooling event observed a few meters above ground, and a strong wave-breaking event that triggers intensive near-surface turbulence. Furthermore, we use observations from one IOP to validate three different atmospheric models. The unique finescale observations resulting from the ISOBAR observational approach will aid future research activities, focusing on a better understanding of the SBL and its implementation in numerical models.

To enhance the understanding of the impact of small-scale processes in the polar climate, this study focuses on the relative role of snow-surface coupling, radiation and turbulent mixing in stable boundary layer over Tibetan Plateau. This is the first attempt to reveal physical processes under stable stratification with WRF-3D model. All cases are characterized by three different stable boundary layer archetypes, namely, a radiative night, an intermittently turbulent night, and a fully turbulent night (all at clear-sky conditions). First a set of WRF configurations that vary in parametrization schemes for the planetary boundary layer, and land surface are evaluated. we find a wide variety in the state of the atmosphere and the surface variables for the selected parameterization schemes after intercomparisons. To understand this variety, we implement the sensitivity runs to examine which physical process is most crucial, using a unique analysis method so-called ‘process diagrams’. The variation between the sensitivity runs display a relative orientation of model sensitivities to variations in each of the governing processes and these can explain the variety of model results obtained in the intercomparison of different parameterization schemes.

The development of urban areas impacts the local climate and hydrology. Cities have been modelled with an array of models with different complexities. These models are called urban land surface models (ULSM) and focus on radiation, and turbulent sensible and latent heat fluxes. Grimmond et al. (2010) evaluated these models finding that the latent heat flux is the most challenging to simulate. This flux is part of both the energy balance and water balance, as the latent heat flux is the energy equivalent of the mass evapotranspiration. Thus, the hydrological circumstances may be crucial to correctly model the turbulent heat fluxes. However, the representation of the water balance in these models has not been the focus of a multi-model evaluation. As a part of the follow-up project to the work by Grimmond et al. and Urban-PLUMBER we evaluated the representation of the water balance in ULSMs with varying complexity and representation of the water balance. It is difficult to evaluate the water balance fluxes against observations, as not all terms are observed. For example, changes in water storage require knowledge of the state of all the individual stores (e.g. soil moisture, detention ponds). Analysis of 14 models shows a large spread in the magnitude of the individual water balance fluxes. The rate of reduction of the latent heat flux/evapotranspiration during periods without rainfall varies widely between models, consistent with literature (e.g. Jongen et al., 2022). Initial analysis suggests that models that simulate the water balance and conserve mass are more likely to accurately simulate turbulent heat fluxes. It is thus crucial that both the water and energy balance are accounted for in future urban model improvements.

The use of crowdsourcing – obtaining large quantities of data through the Internet – has been of great value in urban meteorology. Crowdsourcing has been used to obtain urban air temperature, air pressure, and precipitation data from sources such as mobile phones or personal weather stations (PWSs), but so far wind data have not been researched. Urban wind behaviour is highly variable and challenging to measure, since observations strongly depend on the location and instrumental set-up. Crowdsourcing can provide a dense network of wind observations and may give insight into the spatial pattern of urban wind. In this study, we evaluate the skill of the popular “Netatmo” PWS anemometer against a reference for a rural and an urban site. Subsequently, we use crowdsourced wind speed observations from 60 PWSs in Amsterdam, the Netherlands, to analyse wind speed distributions of different Local Climate Zones (LCZs). The Netatmo PWS anemometer appears to systematically underestimate the wind speed, and episodes with rain or high relative humidity degrade the measurement quality. Therefore, we developed a quality assurance (QA) protocol to correct PWS measurements for these errors. The applied QA protocol strongly improves PWS data to a point where they can be used to infer the probability density distribution of wind speed of a city or neighbourhood. This density distribution consists of a combination of two Weibull distributions, rather than the typical single Weibull distribution used for rural wind speed observations. The limited capability of the Netatmo PWS anemometer to measure near-zero wind speed causes the QA protocol to perform poorly for periods with very low wind speeds. However, results for a year-long wind speed climatology of the wind speed are satisfactory, as well as for a shorter period with higher wind speeds.

An intercomparison between 10 single-column (SCM) and 5 large-eddy simulation (LES) models is presented for a radiation fog case study inspired by the Local and Non-local Fog Experiment (LANFEX) field campaign. Seven of the SCMs represent single-column equivalents of operational numerical weather prediction (NWP) models, whilst three are research-grade SCMs designed for fog simulation, and the LESs are designed to reproduce in the best manner currently possible the underlying physical processes governing fog formation. The LES model results are of variable quality and do not provide a consistent baseline against which to compare the NWP models, particularly under high aerosol or cloud droplet number concentration (CDNC) conditions. The main SCM bias appears to be toward the overdevelopment of fog, i.e. fog which is too thick, although the inter-model variability is large. In reality there is a subtle balance between water lost to the surface and water condensed into fog, and the ability of a model to accurately simulate this process strongly determines the quality of its forecast. Some NWP SCMs do not represent fundamental components of this process (e.g. cloud droplet sedimentation) and therefore are naturally hampered in their ability to deliver accurate simulations. Finally, we show that modelled fog development is as sensitive to the shape of the cloud droplet size distribution, a rarely studied or modified part of the microphysical parameterisation, as it is to the underlying aerosol or CDNC.

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.

We report on redesigning the undergraduate course in synoptic meteorology and weather forecasting at Wageningen University (the Netherlands) to meet the current-day requirements for operational forecasters. Weather strongly affects human activities through its impact on transportation, energy demand planning, and personal safety, especially in the case of weather extremes. Numerical weather prediction (NWP) models have developed rapidly in recent decades, with reasonably high scores, even on the regional scale. The amount of available NWP model output has sharply increased. Hence, the role and value of the operational weather forecaster has evolved into the role of information selector, data quality manager, storyteller, and product developer for specific customers. To support this evolution, we need new academic training methods and tools at the bachelor’s level. Here, we present a renewed education strategy for our weather forecasting class, called Atmospheric Practical, including redefined learning outcomes, student activities, and assessments. In addition to teaching the interpretation of weather maps, we underline the need for twenty-first-century skills like dealing with open data, data handling, and data analysis. These skills are taught using Jupyter Python Notebooks as the leading analysis tool. Moreover, we introduce assignments about communication skills and forecast product development as we aim to benefit from the internationalization of the classroom. Finally, we share the teaching material presented in this paper for the benefit of the community.

An intercomparison between 10 single-column (SCM) and 5 large-eddy simulation (LES) models is presented for a radiation fog case study inspired by the LANFEX field campaign. 7 of the SCMs represent single-column equivalents of operational numerical weather prediction (NWP) models, whilst 3 are research-grade SCMs designed for fog simulation, and the LES are designed to reproduce in the best manner currently possible the underlying physical processes governing fog formation. The LES model results are of variable quality, and do not provide a consistent baseline against which to compare the NWP models, particularly under high aerosol or cloud droplet number (CDNC) conditions. The main SCM bias appears to be toward over-development of fog, i.e. fog which is too thick, although the inter-model variability is large. In reality there is a subtle balance between water lost to the surface and water condensed into fog, and the ability of a model to accurately simulate this process strongly determines the quality of its forecast. Some NWP-SCMs do not represent fundamental components of this process (e.g. cloud droplet sedimentation) and therefore are naturally hampered in their ability to deliver accurate simulations. Finally, we show that modelled fog development is as sensitive to the shape of the cloud droplet size distribution, a rarely studied or modified part of the microphysical parametrization, as it is to the underlying aerosol or CDNC.

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

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...

With the purpose of revising World Meteorological Organization’s Commission for Instruments and Methods of Observation (WMO/CIMO) Guide #8 on weather stations siting, and in the framework of EMPIR project ENV58 MeteoMet 2, an experiment to evaluate metrologically the maximum influence of a paved road on 2-m air temperature measurements (“road siting effect”) has been designed, installed and run in Italy. It consists of a 100-m long array of seven measurement stations, at increasing distances from a local road, equipped with shielded Pt100 thermometers and ancillary sensors (hygrometers, anemometers, solar radiation meters). Data coming from 1 year of observations, has been analysed for daily climatological metrics, finding that the road mostly effects minimum temperatures, with average values of ~ 0.30±0.18 °C at a distance of 1 m; then, in order to quantify the instrumental effect on the measurement, data was filtered by applying a Generalized Additive Model, selecting only times when the effect is more intense (during nights, in presence of low winds coming from the road), and the road siting effect has been calculated by modelling the maximum temperature differences by using Extreme Values Analysis. The 1-year return value on 10-min measurements is 1.22±0.30 °C at 1 m from the road, with a gradual decline (~ 0.1 °C/m), while an extrapolation to 100-year return level gives a value of 1.71±0.79 °C. Analysis also show the possibility of calculating an asymptotic upper limit to these values, providing there are enough data to lower the associated uncertainties. These results, published in the International Journal of Climatology (Coppa et al 2021, https://doi.org/10.1002/joc.7044) is a first step towards a redefinition of the weather station classification scheme of WMO/CIMO Guide #8, together with building and tree effects experiments which have been run in parallel with the road siting experiment here presented and which will be presented elsewhere. Raw data is also available at Zenodo.org (Coppa et al 2020, https://doi.org/10.5281/ZENODO.4300299)

With increasing urbanization and ongoing climate change there is a need to develop and evaluate modelling infrastructure for urban weather and climate. In this study we evaluate three urban canopy models for a heat-wave case study in Amsterdam (The Netherlands), notably the single-layer urban canopy model (SLUCM) and the building environment parameterization (BEP) and the BEP+BEM (BEP+Building Energy model) urban canopy models within the WRF infrastructure. Model results are evaluated against a network of near surface observations of air temperature, turbulent surface fluxes, SODAR wind profiles, and radio soundings of temperature and humidity taken in the city center of Amsterdam.We find that the BEP+BEM model outperforms the other schemes for the near surface air temperature, with a bias of -0.66 K for BEP+BEM, -1.51 K for BEP and-1.56 K for SLUCM. However, WRF produces an elevated inversion level that, at the same time, is substantially (~ 2-8 K) weaker than observed in the radiosoundings.To estimate the future increase in energy demand by air conditioning, we project this heatwave case study to the future. To do so, we force the WRF model with increased temperatures in initial and boundary conditions following the four KNMI climate scenarios. With the climate scenario with the largest warming (WH-scenario) we find a 2-m temperature increase of ~3 K during daytime compared to the current climate. Finally we find that for this scenario the energy consumption by air-conditioning increases between 25% and 40% in the city center compared to the current climate (with constant number of airco’s installed).

This study focuses on the assessment of the impact of downscaling seasonal forecasts from the Climate Forecast System version 2 (CFSv2) using the Weather Research and Forecasting (WRF) mesoscale model over Zimbabwe on a spatial resolution of 21km and 7km for Southern Africa and Zimbabwe respectively. We used a 7-day re-initialization simulation strategy for 212 days per season and was repeated for eights seasons between 2010 and 2018. The impact of downscaling global seasonal forecasts was further evaluated in crop forecasting using the WOrld FOod STudies (WOFOST) model. Statistical analysis of the forecasted seasonal rainfall revealed a reduction of the bias from about -2 mm/day from CFSv2 forecasts to about 0.5mm/day from WRF forecasts in most parts of the country. We also found that an improvement in seasonal tercile rainfall prediction from 25%, 50%, and 75% by CFSv2 in three different regions to about 62.5% by WRF in all regions. Substantial improvement was achieved in Standard Precipitation Index-driven seasonal forecasts with two regions with a percent correct of 75% and region 2 with 100% by WRF compared to 62.5% by CFSv2 in all regions. Hence, the characterization of seasonal rainfall in terms of drought forecasts is better than the tercile rainfall prediction system and will be more beneficial to farmers in Zimbabwe. WRF seasonal rain forecasts improved both in magnitude and in forecasting the onset of the growing season. This was indicated by the accumulated absolute maize yield error which factored in a miss of onset of the growing season by each model. WRF outperformed CFSv2 for maize and sorghum yield forecasts in 6, 6, and 8 (out of 8) seasons in Karoi, Masvingo, and Gweru sites respectively. WRF forced crop simulations reduced mean absolute percent error of maize yield by 12.2% and sorghum yield by 9.3 % from CFSv2 forced simulations. Our results also show that maize will be more productive and less risky at Karoi and Masvingo and sorghum at the Gweru site. In our view, there should be no farming of both maize and sorghum at Beitbridge due to the high risk of crop failure unless a proper irrigation system is in place.

To reduce the vulnerability of urban areas facing high temperatures, it is useful to develop methods to obtain the urban heat island (UHI) intensity. However, it is hard to equip all cities with extensive measurement networks and alternative UHI diagnostic methods are needed. Accordingly, in this paper we evaluate and revise the diagnostic equation designed by Theeuwes et al. (2017) and analyze its application for Xi'an (China), based on long-term summer meteorology data. The evaluation of the default diagnostic equation shows that limited accuracy for Xi'an is caused by the sum of morphological parameters being used outside the original range of calibration. Subsequently, we propose an extended equation, which adds the building fraction to express the morphology of additional spatial categories. This new equation is calibrated against a 3-year dataset and independently validated with data from another year. In addition, a class prediction with three spatial categories is proposed, and verified by independent data of 20 stations in Xi'an in 2018, which enables this formula to be applied in more cities. Altogether, the extended diagnostic equation is an effective method to evaluate the daily maximum UHI intensity (UHImax) on neighborhood-scale, which can be generalized for a whole city area and presents internal intensity differences with multi-points.

In this study, the sensitivity of the Weather Research and Forecasting (WRF) model to simulate the life cycle of a dense fog event that occurred on 23–24 January 2016 is evaluated using different model configurations. For the first time, intensive observational periods (IOPs) were made during the unique winter fog experiment (WIFEX) that took place over Delhi, India, where air quality is serious during the winter months. The multiple sensitivity experiments to evaluate the WRF model performance included parameters such as initial model and boundary conditions, vertical resolution in the lower boundary layer (BL), and the planetary BL (PBL) physical parameterizations. In addition, the model sensitivity was tested using various configurations that included domain size and grid resolution. Results showed that simulations with a high number of vertical levels within the lower PBL height (i.e., 10 levels below 300 m) simulated the accurate timing of fog formation, development, and dissipation. On the other hand, simulations with less vertical levels in the PBL captured only the mature physical characteristics of the fog cycle. A comparison of six local PBL schemes showed little variation in the onset of fog life cycle in comparison to observations of visibility. However, comparisons of observations with thermodynamical values such as 2-m temperature and longwave radiation showed poor relationships. Overall, quasi-normal scale elimination (QNSE) and MYNN 2.5 PBL schemes simulated the complete fog life cycle correctly with high liquid water content (LWC; 0.5/0.35 g m −3 ), while other schemes only responded during the mature phase.

Fog is a critical weather phenomenon for safety and operations in aviation. Unfortunately, the forecasting of radiation fog remains challenging due to the numerous physical processes that play a role and their complex interactions, in addition to the vertical and horizontal resolution of the numerical models. In this study we evaluate the performance of the Weather Research and Forecasting (WRF) model for a radiation fog event at Schiphol Amsterdam Airport (The Netherlands) and further develop the model towards a 100 m grid spacing. Hence we introduce high resolution land use and land elevation data. In addition we study the role of gravitational droplet settling, advection of TKE, top-down diffusion caused by strong radiative cooling at the fog top. Finally the impact of heat released by the terminal areas on the fog formation is studied. The model outcomes are evaluated against 1-min weather observations near multiple runways at the airport.Overall we find the WRF model shows an reasonable timing of the fog onset and is well able to reproduce the visibility and meteorological conditions as observed during the case study. The model appears to be relatively insensitive to the activation of the individual physical processes. An increased spatial resolution to 100 m generally results in a better timing of the fog onset differences up to three hours, though not for all runways. The effect of the refined landuse dominates over the effect of refined elevation data. The modelled fog dissipation systematically occurs 3-4 h hours too early, regardless of physical processes or spatial resolution. Finally, the introduction of heat from terminal buildings delays the fog onset with a maximum of two hours, an overestimated visibility of 100-200 m and a decrease of the LWC with 0.10-0.15 g/kg compared to the reference.

Simulating high-intensity rainfall events that trigger local floods using a Numerical Weather Prediction model is challenging as rain-bearing systems are highly complex and localized. In this study, we analyze the performance of the Weather Research and Forecasting (WRF) model’s capability in simulating a high-intensity rainfall event using a variety of parameterization combinations over the Kampala catchment, Uganda. The study uses the high-intensity rainfall event that caused the local flood hazard on 25 June 2012 as a case study. The model capability to simulate the high-intensity rainfall event is performed for 24 simulations with a different combination of eight microphysics (MP), four cumulus (CP), and three planetary boundary layer (PBL) schemes. The model results are evaluated in terms of the total 24-h rainfall amount and its temporal and spatial distributions over the Kampala catchment using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. Rainfall observations from two gauging stations and the CHIRPS satellite product served as benchmark. Based on the TOPSIS analysis, we find that the most successful combination consists of complex microphysics such as the Morrison 2-moment scheme combined with Grell-Freitas (GF) and ACM2 PBL with a good TOPSIS score. However, the WRF performance to simulate a high-intensity rainfall event that has triggered the local flood in parts of the catchment seems weak (i.e., 0.5, where the ideal score is 1). Although there is high spatial variability of the event with the high-intensity rainfall event triggering the localized floods simulated only in a few pockets of the catchment, it is remarkable to see that WRF is capable of producing this kind of event in the neighborhood of Kampala. This study confirms that the capability of the WRF model in producing high-intensity tropical rain events depends on the proper choice of parametrization combinations.

This study improves surface wind predictions in an unresolved valley using an artificial neural network (ANN). Forecasting winds in complex terrain with a mesoscale model is challenging. This study assesses the quality of 3-km wind forecasts by the Weather Research and Forecasting (WRF) model and the potential of post-processing by an ANN within the 1-2 km wide Cadarache Valley in southeast France. Operational wind forecasts for 110m above ground level and the near-surface vertical potential temperature gradient with a lead time of 24-48h were used as ANN input. Observed horizontal wind components at 10m within the valley were used as targets during ANN training. We use the Directional ACCuracy (DACC45, wind direction error ≤ 45°) and mean absolute error to evaluate the WRF direct model output and the ANN results. By post-processing, the score for DACC45 improves from 56% in the WRF direct model output to 79% after applying the ANN. Furthermore, the ANN performed well during the day and night, but poorly during the morning and afternoon transitions. The ANN improves the DACC45 at 10m even for poor WRF forecasts (direction bias ≥ 45°) from 42% to 72%. A shorter lead time and finer grid spacing (1 km) showed negligible impact which suggests that a 3 km grid spacing and a 24-48h lead time is effective and relatively cheap to apply. We find that WRF performs well in near-neutral conditions and poorly in other atmospheric stability conditions. The ANN post-treatment consistently improves the wind forecast for all stability classes to a DACC45 of about 80%. The study demonstrates the ability to improve Cadarache valley wind forecasts using an ANN as post-processing for WRF daily forecasts.

With the purpose of revising World Meteorological Organization's Commission for Instruments and Methods of Observation (WMO/CIMO) Guide #8 on weather stations siting, an experiment to evaluate metrologically the maximum influence of a paved road on 2-m air temperature measurements (“road siting effect”) has been designed, installed and run in Italy. It consists of a 100-m long array of seven measurement stations, at increasing distances from a local road, equipped with shielded Pt100 thermometers and ancillary sensors. Data coming from 1 year of observations have been analysed for daily climatological metrics, finding that the road mostly effects minimum temperatures, with average values of ~0.30 ± 0.18 °C at a distance of 1 m; then, in order to quantify the instrumental effect on the measurement, data were filtered by applying a generalized additive model, selecting only times when the effect is more intense (during nights, in presence of low winds coming from the road), and the road siting effect has been calculated by modelling the maximum temperature differences by using extreme values analysis. The 1-year return value on 10-min measurements is 1.22 ± 0.30 °C at 1 m from the road, with a gradual decline (~0.1 °C·m−1), while an extrapolation to 100-year return level gives a value of 1.71 ± 0.79 °C with a decline rate of about 0.17 °C·m−1. This is a first step towards a redefinition of the weather station classification scheme of WMO/CIMO Guide #8, together with building and tree effects experiments which have been run in parallel with the road siting experiment here presented and which will be presented elsewhere.