Local Wind Patterns Research Articles

Semi-Empirical Approach to Evaluating Model Fit for Sea Clutter Returns: Focusing on Future Measurements in the Adriatic Sea

A method for evaluating Kullback–Leibler (KL) divergence and Squared Hellinger (SH) distance between empirical data and a model distribution is proposed. This method exclusively utilises the empirical Cumulative Distribution Function (CDF) of the data and the CDF of the model, avoiding data processing such as histogram binning. The proposed method converges almost surely, with the proof based on the use of exponentially distributed waiting times. An example demonstrates convergence of the KL divergence and SH distance to their true values when utilising the Generalised Pareto (GP) distribution as empirical data and the K distribution as the model. Another example illustrates the goodness of fit of these (GP and K-distribution) models to real sea clutter data from the widely used Intelligent PIxel processing X-band (IPIX) measurements. The proposed method can be applied to assess the goodness of fit of various models (not limited to GP or K distribution) to clutter measurement data such as those from the Adriatic Sea. Distinctive features of this small and immature sea, like the presence of over 1300 islands that affect local wind and wave patterns, are likely to result in an amplitude distribution of sea clutter returns that differs from predictions of models designed for oceans or open seas. However, to the author’s knowledge, no data on this specific topic are currently available in the open literature, and such measurements have yet to be conducted.

Exploring the Influence of Urban Density and Mountainous Topography on Local Wind Patterns: An experimental study of Hongkong, China

The urban wind flow of the canopy layer has a significant impact on the urban climate, as well as the ventilation, pollutant diffusion, and thermal comfort. Therefore, the related studies are gaining attention in the last decade. The purpose of this study is to figure out the impacts of urban morphology and mountain topography on the urban wind flow, taking the pedestrian level height of Hong Kong as an example. Three wind tunnel test series were conducted, exploring scenarios with/without proposed high-rise buildings, as well as urban areas in two large city centers with/without upstream mountain terrain. Results indicate a 300m high-rise building affects wind flow up to approximately 125m upstream, yielding a 12% of velocity increase. Street wind speed peaks when the approaching wind aligns within 25° of the street's orientation, with building height and deviation on street sides significantly impacting wind speeds compared to street width. Additionally, the results reveal that mountain height primarily influences the reduction of pedestrian-level wind velocity, with less effect from distance and mountain slope in relation to the urban area.

Effects of valley topography on ozone pollution in the Lanzhou valley: A numerical case study

Valley topography is recognized for its role in constraining pollutant dispersion, which frequently results in elevated pollutant concentrations within valley regions. However, the specific mechanisms by which valley topography influences daytime ozone (O3) production, nighttime O3 depletion, and diurnal variations in O3 concentrations remain inadequately understood. This study employs the online WRF-Chem air quality model to conduct sensitivity analyses, examining the effects of valley topography on summer O3 concentrations in Lanzhou, a valley city in western China. The results indicate that valley topography generally reduces surface temperature and wind speed while also lowering the planetary boundary layer height (PBLH), which ultimately leads to increased concentrations of primary pollutants. Nevertheless, the impact of valley terrain on O3 is time-dependent, with concentrations decreasing during the day and increasing at night. In Lanzhou, valley topography contributes to a 4.4% reduction in daytime O3 concentrations. This reduction is largely due to the blocking of solar radiation by surrounding mountains, which results in a 7% decrease in shortwave radiation (SR) and a 17% decline in PBLH, subsequently limiting O3 chemical production. Although valley topography restricts pollutant dispersion, the overall effect during the day is a net decrease in O3 levels. In contrast, nighttime valley topography leads to a notable 19.8% increase in O3 concentrations. This increase is driven by mountain breezes transporting O3-rich air from the slopes into urban areas, along with a 67.4% reduction in nitrogen oxide (NO) levels. Despite intensified NO titration within the valley, the combined effect of these local wind patterns contributes to elevated O3 concentrations at night. These findings offer valuable insights into the unique factors contributing to urban O3 pollution in areas with complex valley topography.

X-Band Radar and Surface-Based Observations of Cold-Season Precipitation in Western Colorado’s Complex Terrain

Abstract Hydrologic processes associated with intermountain cold-season precipitation in the Upper Colorado River basin have important impacts on avalanche forecasting and water resource management. However, traditional weather radar networks struggle with observations in this complex terrain. Data collected during the Study of Precipitation, the Lower Atmosphere, and the Surface for Hydrometeorology (SPLASH) and its sister campaign, Surface Atmosphere Integrated Field Laboratory (SAIL) in the East River watershed of western Colorado, are used to examine a multistorm period from 23 December 2021 to 1 January 2022 that contributed 35% of the total winter precipitation in this watershed. Dual-polarization X-band radar and disdrometer measurements show ∼30-mm differences in precipitation amount at two sites in proximity over four distinct storm events within the period. Wind patterns, synoptic forcings, microphysical characteristics of precipitation, and surface meteorology are analyzed to explain the observed spatial variability of cold-season precipitation in complex mountainous terrain. Analysis shows that differences over time within this event are mainly accounted for by synoptic forcings, such as frontal passages; differences between sites are accounted for by the impact of variations in local wind patterns on precipitation microphysics. Patterns of surface precipitation intensity are compared and found to be correlated with X-band radar signatures; a relationship between a strong dendritic growth stage and intense low-density surface precipitation is reinforced by this study. This relationship demonstrates the importance of particle growth mechanisms on surface snowfall patterns in high-altitude complex terrain, underscoring the importance of realistic microphysical parameterizations. Significance Statement The amount and density of snowpack from western Colorado winter storms have significant impacts on water resources in the Upper Colorado River basin. Snowpack characteristics are affected by small-scale differences in how snow forms in the atmosphere. These differences are hard to study in the complex terrain of the Rockies, but data from the SPLASH and SAIL field campaigns allows us to investigate how snow crystal formation and mountain-driven wind patterns affect snow near the surface. Our study finds that snow crystal growth varies over small space and time scales and is likely controlled by the terrain beneath a given location and resultant local wind patterns. These results imply that predicting snowpack in the Rockies requires properly representing local wind patterns and crystal growth processes in models.

On the use of kolmogorov–arnold networks for adapting wind numerical weather forecasts with explainability and interpretability: application to madeira international airport

Abstract This study examines the application of machine learning to enhance wind nowcasting by using a Kolmogorov-Arnold Network model to improve predictions from the Global Forecast System at Madeira International Airport, a site affected by complex terrain. The research addresses the limitations of traditional numerical weather prediction models, which often fail to accurately forecast localized wind patterns. Using the Kolmogorov-Arnold Network model led to a substantial reduction in wind speed and direction forecast errors, with a performance that reached a 48.5% improvement to the Global Forecast System 3 h nowcast, considering the mean squared error. A key outcome of this study comes from the model’s ability to generate mathematical formulas that provide insights into the physical and mathematical dynamics influencing local wind patterns and improve the transparency, explainability, and interpretability of the employed machine learning models for atmosphere modeling.

Wind characteristics around a skyway bridge of high-rise buildings

To respond the expansion of urban centers, the proliferation of high-rise buildings demands a better understanding of the aerodynamic phenomena around skyway bridges connecting these structures. This analysis, utilizing the advanced computational fluid dynamics verified by wind tunnel test data, investigates the wind characteristics around such bridges, crucial for structural stability, pedestrian comfort, and aerodynamic efficiency. This study focuses on the interactions between a 2 × 2 building array with a building height-to-street width ratio of 30 and a skyway bridge, investigating those factors such as bridge influence, building structures, building height, and bridge position. Using the three-dimensional steady Reynolds-averaged Navier–Stokes equations along with the Reynolds stress model for turbulence closure, the results show that the presence of skyway bridge significantly modifies local wind patterns. Wind speed and turbulence intensity are impacted differently based on the bridge's upstream or downstream settings. Downstream bridges tend to reduce wind speeds due to the sheltering effects, while upstream placement of bridge can enhance wind flow, affecting both the structural design and pedestrian comfort. Additionally, building height variations adjacent to the bridge influence wind velocity and pressure profiles, with taller buildings intensifying wind speeds at lower levels because of the channeling effects. These insights are pivotal for optimizing the skyway bridge designs to improve airflow distribution, enhance environmental sustainability, and ease wind-caused disturbances, offering a guideline for future architectural and urban planning in high-rise districts.

Should we care about the level of detail in trees when running urban microscale simulations?

Due to lack of information and long geometry generation times, tree geometries are usually oversimplified or even ignored in Computational Fluid Dynamic (CFD) simulations that predict wind and pollutant dispersion in urban areas. Nevertheless, trees are known to impact local wind patterns and air quality levels. Thus, in this paper we explore the effects that tree models automatically reconstructed at diverse Level of Detail (LoD) (1, 2 and 3) have in numerical wind predictions. We address this by comparing the non-dimensional velocity magnitude differences between simulations with multiple tree LoDs. To further understand these differences in changing environmental contexts we use three morphologies: an isolated tree, an idealized street, canyon, and a real urban geometry from Rotterdam, The Netherlands The numerical results show that the velocity magnitude differences between the cases with LoD1 tree models and those with LoD2 tree models can be over 1.0m/s while the differences between LoD2 and LoD3 cases are rather limited, usually lower than 0.2m/s. Consequently, through this study we highlight the importance of using tree models in LoD2 or LoD3 at least for CFD simulations of wind flows in urban areas. To further support this conclusion we also analyze the impact of changing wind directions and tree Leaf Area Density (LAD) values in the impact of tree LoDs on wind. The differences found in this work linked to the level of realism in your tree models can support future studies where researchers want to make an informed choice.

Biotic response of plankton communities to Middle to Late Miocene monsoon wind and nutrient flux changes in the Oman margin upwelling zone

Abstract. Understanding past dynamics of upwelling cells is an important aspect of assessing potential upwelling changes in future climate change scenarios. Our present understanding of nutrient fluxes throughout the world's oceans emphasizes the importance of intermediate waters transporting nutrients from the Antarctic divergence into the middle and lower latitudes. These nutrient-rich waters fuel productivity within wind-driven upwelling cells in all major oceans. One such upwelling system is located along the Oman margin in the western Arabian Sea (WAS). Driven by cross-hemispheric winds, the WAS upwelling zone's intense productivity led to the formation of one of the most extensive oxygen minimum zones known today. In this study covering the Middle to Late Miocene at Ocean Drilling Program (ODP) Site 722, we investigate the inception of upwelling-derived primary productivity. This study presents new plankton assemblage data in the context of existing model- and data-based evidence constraining the tectonic and atmospheric boundary conditions for upwelling in the WAS. With this research, we build upon the original planktonic foraminifer-based research by Dick Kroon in 1991 as part of his research based on the ODP LEG 117. We show that monsoonal winds likely sustained upwelling since the emergence of the Arabian Peninsula after the Miocene Climatic Optimum (MCO) ∼ 14.7 Ma, with fully monsoonal conditions occurring since the end of the Middle Miocene Climatic Transition (MMCT) at ∼ 13 Ma. However, changing nutrient fluxes through Antarctic Intermediate and sub-Antarctic Mode Waters (AAIW/SAMW) were only established after ∼ 12 Ma. Rare occurrences of diatom frustules correspond to the maximum abundances of Reticulofenestra haqii and Reticulofenestra antarctica, indicating higher upwelling-derived nutrient levels. By 11 Ma, diatom abundance increases significantly, leading to alternating diatom blooms and high-nutrient-adapted nannoplankton taxa. These changes in primary producers are also well reflected in geochemical proxies with increasing δ15Norg. values (> 6 ‰) and high organic carbon accumulation. These proxies provide further independent evidence for high productivity and the onset of denitrification simultaneously. Our multi-proxy-based evaluation of Site 722 primary producers provides evidence for a stepwise evolution of Middle to Late Miocene productivity in the western Arabian Sea for the first time. The absence of a clear correlation with existing deep marine climate records suggests that both local wind patterns and intermediate water nutrient changes likely modulated productivity in the western Arabian Sea during the Middle to Late Miocene. Finally, we show that using a multi-proxy record provides novel insights into how plankton responded to changing nutrient conditions through time in a monsoon-wind-driven upwelling zone.

Simulating Microscale Urban Airflow and Pollutant Distributions Based on Computational Fluid Dynamics Model: A Review.

Urban surfaces exert profound influences on local wind patterns, turbulence dynamics, and the dispersion of air pollutants, underscoring the critical need for a thorough understanding of these processes in the realms of urban planning, design, construction, and air quality management. The advent of advanced computational capabilities has propelled the computational fluid dynamics model (CFD) into becoming a mature and widely adopted tool to investigate microscale meteorological phenomena in urban settings. This review provides a comprehensive overview of the current state of CFD-based microscale meteorological simulations, offering insights into their applications, influential factors, and challenges. Significant variables such as the aspect ratio of street canyons, building geometries, ambient wind directions, atmospheric boundary layer stabilities, and street tree configurations play crucial roles in influencing microscale physical processes and the dispersion of air pollutants. The integration of CFD with mesoscale meteorological models and cutting-edge machine learning techniques empowers high-resolution, precise simulations of urban meteorology, establishing a robust scientific basis for sustainable urban development, the mitigation of air pollution, and emergency response planning for hazardous substances. Nonetheless, the broader application of CFD in this domain introduces challenges in grid optimization, enhancing integration with mesoscale models, addressing data limitations, and simulating diverse weather conditions.

Investigation of Wind Power Potential in Mthatha, Eastern Cape Province, South Africa

South Africa is currently grappling with a national energy crisis and the high infrastructure costs associated with expanding the national grid to remote areas. Simultaneously, the government has made substantial efforts to harness renewable energy technologies, particularly wind energy. The average wind speed in a specific region significantly influences the energy yield from wind turbines. The vast open inland terrains, mountainous regions, and coastal areas in the Northern Cape, Eastern Cape, and Western Cape provinces of South Africa possess the most substantial wind potential. It is imperative to initiate wind energy projects in these provinces to cater to a significant portion of the local electricity demand, especially in remote areas disconnected from the national grid. Wind energy generation is inherently stochastic, subject to variations in both time and space. Consequently, it is essential to gain a comprehensive understanding of the local wind patterns to assess the feasibility of utilizing wind resources. In the Eastern Cape Province, the Mthatha area still lags in household electrification, presenting an opportunity to electrify some households using wind energy. This study aimed to evaluate the wind resource potential for Mthatha area, utilizing data spanning from 2018 to 2023, provided by the South African Weather Services. Two distribution models, the two-parameter Weibull and three-parameter Weibull, were employed to characterize the provided wind data. To determine the parameters associated with each distribution model, two estimation methods, the Maximum Likelihood Method (MLM) and the Method of Moments (MOM), were utilized. The performance of these distribution models was assessed using the Root Mean Square Error (RMSE) statistical indicator. The results showed that Mthatha area predominantly experiences low wind speeds, with an annual average wind speed of 3.30 m/s and an overall wind power density of approximately 48.48 W/m2. The prevailing winds predominantly originate from the south and east–southeast directions. Consequently, Mthatha is recommended for stand-alone applications, with the added suggestion of augmented wind turbines for the area.

Effect of built form on local wind characteristics, a case of tropical savanna climate in India

Passive techniques play a substantial role in regulating the indoor environmental quality of a building, especially in the tropics. Proper air movement is necessary for warm and humid indoors by utilising the local wind pattern. Many factors influence and alter the wind character of a region. Any built form has a significant influence on the wind and vice versa. This research extensively studies the effects of wind when it encounters different built and unbuilt spaces of an office building in India’s tropical savanna climate. Applications such as Building Information Modelling (Autodesk Revit) and Computational Fluid Dynamics simulation (Autodesk CFD) are used for analysis. The impacts on the local wind character at chosen wind path timings are studied in detail to arrive at the conclusions. Various effects like wind acceleration, corner jetting, channelling, funnelling, windbreak and cumulative effects are observed as occurring due to the influence of the building’s form on the local wind. These effects increased the wind velocity from 10kmph up to 19kmph and decreased up to 6kmph. The study concludes by arriving at several considerations while developing built forms that help to reduce negative impacts and improve positive impacts on aspects like aerodynamics, user comfort, impact on surroundings, the microclimate and structural wind loads.

Parametric Integration of CFD-based Wind Pressure Coefficients into Building Energy Models: A Novel Workflow

By integrating computational fluid dynamics (CFD)-generated wind pressure coefficients (WPCs) into building energy models, recent workflows enable the evaluation of the impacts of local wind on the energy performance of buildings. With the current workflows, however, only manual integration of WPCs into building energy models is possible. This study provides a workflow for the parametric integration of CFD-based WPCs, sampled for each window in the model, into building energy models using the Rhino-GH platform. Using this workflow, this study performed a parametric block-scale energy evaluation of 324 iterations for courtyard and high-rise layouts in various urban and building design configurations in the climate of Tel Aviv to compare the cooling load deviations between CFD-based and default WPCs. The results reveal a significant cooling load deviation between default and CFD-based WPCs, particularly in compact configurations (courtyard typology, higher FAR, and narrower street widths), indicating that default wind pressure coefficients overestimate the natural cooling potential. Further investigation of the balance between solar gains and convective heat losses reveals a surge in the impact of solar gains on cooling loads when accounting for CFD-based WPCs in the climate of Tel Aviv. This workflow can assist designers and analysts in accounting for local wind patterns in energy-driven urban design workflows.

Cyclones modulate the control of the North Atlantic Oscillation on transports into the Barents Sea

The warm Atlantic Water transported into the Barents Sea plays a crucial role in winter sea ice extent, marine ecosystems, and mid-latitude weather. The North Atlantic Oscillation is known to be an important driver for the Atlantic Water transport variability in the Barents Sea Opening. Here, we find that the dependence of the Barents Sea Opening ocean volume transport variability on the North Atlantic Oscillation is non-stationary. Our results indicate that for the period 1995 to 2005, the link between the North Atlantic Oscillation and the transport variability in the Barents Sea Opening temporarily weakened before an eventual recovery. During this period, synoptic cyclones with unusual trajectories as a consequence of pronounced atmospheric blocking in the North Atlantic sector altered the large-scale and local wind patterns. This temporarily caused a state that the Barents Sea Opening transport variability is largely locally driven instead of being driven by the North Atlantic Oscillation. Our study suggests that an adequate representation of both the North Atlantic Oscillation and cyclone activity is necessary for climate models to better predict future changes in poleward ocean heat transport and Arctic climate.

Attenuation of wind intensities exacerbates anoxic conditions leading to sulfur plume development off the coast of Peru.

The release of vast quantities of sulfide from the sediment into the water column, known as a sulfidic event, has detrimental consequences on fish catches, including downstream effects on other linked element cycles. Despite being frequent occurrences in marine upwelling regions, our understanding of the factors that moderate sulfidic event formation and termination are still rudimentary. Here, we examined the biogeochemical and hydrodynamic conditions that underpinned the formation/termination of one of the largest sulfur plumes to be reported in the Peruvian upwelling zone. Consistent with previous research, we find that the sulfur-rich plume arose during the austral summer when anoxic conditions (i.e., oxygen and nitrate depletion) prevailed in waters overlying the upper shelf. Furthermore, the shelf sediments were organically charged and characterized by low iron-bound sulfur concentrations, further enabling the diffusion of benthic-generated sulfide into the water column. While these biogeochemical conditions provided a predicate to sulfidic event formation, we highlight that attenuations in local wind intensity served as an event trigger. Namely, interruptions in local wind speed constrained upwelling intensity, causing increased stratification over the upper shelf. Moreover, disturbances in local wind patterns likely placed additional constraints on wind-driven mesoscale eddy propagation, with feedback effects on coastal elemental sulfur plume (ESP) formation. We suggest ESP development occurs as a result of a complex interaction of biogeochemistry with regional hydrodynamics.

Economical microscale predictions of wind over complex terrain from mesoscale simulations using machine learning

The ability to assess detailed wind patterns in real time is increasingly important for a variety of applications, including wind energy generation, urban comfort and environmental health, and drone maneuvering in complex environments. Machine Learning techniques are helping to develop accurate and reliable models for predicting local wind patterns. In this paper, we present a method for obtaining wind predictions with a higher resolution, similar to those from computational fluid dynamics (CFD), from coarser, and therefore less expensive, mesoscale predictions of wind in real weather conditions. This is achieved using supervised learning techniques. Four supervised learning approaches are tested: linear regression (SGD), support vector machine (SVM), k-nearest neighbors (KNn) and random forest (RFR). Among the four tested approaches, SVM slightly outperforms the others, with a mean absolute error of 1.81 m/s for wind speed and 40.6∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document} for wind direction. KNn however achieves the best results in predicting wind direction. Speedup factors of about 290 are achieved by the model with respect to using CFD.

Spatial Patterns of Major Ions and Their Relationship to Sediment Concentration in Near Surface Glacier Ice, Taylor Valley Antarctica

AbstractGlaciers form the headwaters of many watersheds and, in arid polar environments, can provide the vast majority of water to downstream systems. Headwater watersheds are critically important for setting the chemistry for downstream systems, yet we know comparatively little about the patterns and processes that generate the geochemical signature of meltwater on glacier surfaces. Here, we focus on glaciers in the McMurdo Dry Valleys of Antarctica, the largest ice‐free area on the continent, characterized by alpine glaciers flowing into broad, rocky valleys. We examine patterns from the coast inland, accumulation to ablation zones, laterally across individual glaciers, and through the zone of meltwater generation. We directly compare solutes to sediment concentrations, a major source of dissolved solutes. Our findings agree with previous work that the overall meltwater chemistry of a given glacier is a product of local sediment sources and regional wind patterns: foehn winds moving from the ice sheet to the coast and on‐shore sea breezes. Further, these patterns hold across an individual glacier. Finally, we find that the ice chemistry and sediment profiles reflect freeze‐thaw and melt processes that occur at depth. This indicates that the transport and weathering of sediment in the ice profile likely has a strong influence on supra‐ and proglacial stream chemistry. This new understanding strengthens connections between physical and geochemical processes in cold‐based polar glacier environments and helps us better understand the processes driving landscape and ecosystem connectivity.

A Wave Directionality and a Within-Year Wave Climate Variability Effects on the Long-Term Extreme Significant Wave Heights Prediction in the Adriatic Sea

The extreme significant wave height predictions often neglect within-year wave climate variability and wave directionality. Depending on a geographical region, local wind patterns and year climate variability could have an influence on the long-term prediction of waves. The Adriatic Sea having two dominant wind patterns of different characteristics, Bura and Jugo, is a great example for the case study. The 23-year hindcast wave data used in the presented study is extracted from the WorldWaves database. Based on wind and wave data, annual extreme significant wave heights generated by different wind patterns and for different months are fitted by Gumbel distribution using maximum likelihood estimation. Combined long-term extremes are then predicted by calculating system probability. It was found that considering the wave directionality, and especially the seasonality of wave climate, leads to a larger prediction of extreme significant wave heights. The extreme value prediction considering wave directionality on average yields 4% larger significant wave heights, while considering within-year climate variability leads to, on average, 8% larger extremes compared to the predictions when both effects are neglected.

Physical Characterization of Long‐Lasting Hybrid Eruptions: The 2021 Tajogaite Eruption of Cumbre Vieja (La Palma, Canary Islands)

AbstractLong‐lasting, hybrid eruptions can be of complex description and classification, especially when associated with multiple eruptive styles and multiple products. The 2021 Tajogaite eruption of La Palma, Canary Islands, was associated with a magma‐gas decoupled system that resulted in the simultaneous emission of lava flows and tephra plumes from various vents. Even though the tephra blanket (∼2 × 107 m3) represents only 7%–16% of the total erupted volume, it provides fundamental insights into the overall eruptive dynamics. Tephra was mostly dispersed NE‐SW due to a complex regional and local wind patterns and was subdivided into 3 units and 11 layers that well correlate at different distances from the vent and with both tremor data and lava emission rate. While plume height varied at the temporal scale of a few hours, the average mass eruption rate associated with the tephra blanket of the different units remained relatively constant (∼3–4 × 103 kg s−1). In contrast, the emission rate of lava largely increased after the first week and remained higher than the overall emission of tephra throughout the whole eruption (average value of ∼6 × 104 kg s−1). Based on a detailed characterization of the tephra blanket in combination with atmospheric wind, tremor, and lava emission trend, we demonstrate the need of (a) multidisciplinary strategies for the description of hybrid eruptions that account for both the duration of individual phases and the quantification of the mass of multiple products, and of (b) dedicated ash dispersal forecasting strategies that account for the frequent variations of eruptive and atmospheric conditions.

Analysis of CO<sub>2</sub>, CH<sub>4</sub>, and CO surface and column concentrations observed at Réunion Island by assessing WRF-Chem simulations

Abstract. Réunion Island is situated in the Indian Ocean and holds one of the very few atmospheric observatories in the tropical Southern Hemisphere. Moreover, it hosts experiments providing both ground-based surface and column observations of CO2, CH4, and CO atmospheric concentrations. This work presents a comprehensive study of these observations made in the capital Saint-Denis and at the high-altitude Maïdo Observatory. We used simulations of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), in its passive tracer option (WRF-GHG), to gain more insight to the factors that determine the observed concentrations. Additionally, this study provides an evaluation of the WRF-GHG performance in a region of the globe where it has not yet been applied. A comparison of the basic meteorological fields near the surface and along atmospheric profiles showed that WRF-GHG has decent skill in reproducing these meteorological measurements, especially temperature. Furthermore, a distinct diurnal CO2 cycle with values up to 450 ppm was found near the surface in Saint-Denis, driven by local anthropogenic emissions, boundary layer dynamics, and accumulation due to low wind speed at night. Due to an overestimation of local wind speed, WRF-GHG underestimates this nocturnal buildup. At Maïdo, a similar diurnal cycle is found but with much smaller amplitude. There, surface CO2 is essentially driven by the surrounding vegetation. The hourly column-averaged mole fractions of CO2 (XCO2) of WRF-GHG and the corresponding TCCON observations were highly correlated with a Pearson correlation coefficient of 0.90. These observations represent different air masses to those near the surface; they are influenced by processes from Madagascar, Africa, and further away. The model shows contributions from fires during the Southern Hemisphere biomass burning season but also biogenic enhancements associated with the dry season. Due to a seasonal bias in the boundary conditions, WRF-GHG fails to accurately reproduce the CH4 observations at Réunion Island. Furthermore, local anthropogenic fluxes are the largest source influencing the surface CH4 observations. However, these are likely overestimated. Furthermore, WRF-GHG is capable of simulating CO levels on Réunion Island with a high precision. As to the observed CO column (XCO), we confirmed that biomass burning plumes from Africa and elsewhere are important for explaining the observed variability. The in situ observations at the Maïdo Observatory can characterize both anthropogenic signals from the coastal regions and biomass burning enhancements from afar. Finally, we found that a high model resolution of 2 km is needed to accurately represent the surface observations. At Maïdo an even higher resolution might be needed because of the complex topography and local wind patterns. To simulate the column Fourier transform infrared (FTIR) observations on the other hand, a model resolution of 50 km might already be sufficient.

Impacts of Climate Change on a Coastal Wetland from Model Simulation Combining Satellite and Gauge Observations: A Case Study of Jiangsu, China

Coastal wetlands are affected by both natural processes and human activities. In the present study, the impacts of natural processes on wetland area variations along the Jiangsu coast in the East China Sea were investigated using a long-term high-resolution numerical model (55 years from 1955 to 2010) validated by satellite and gauge observations. In our 55-year simulation, a 1.33 km2 yr−1 decreasing trend of wetland area as a result of global warming was identified. It was found that the wetland area varied depending on the following temporal scales: intra-seasonal, seasonal, interannual, and decadal. Tides and steric sea level changes are responsible for the intra-seasonal and seasonal wetland variations, respectively. The long-term variations are attributable to the El Niño-Southern Oscillation and Pacific Decadal Oscillation. These basin-scale phenomena cause changes in the local wind patterns along the Jiangsu coast and impact the wetland variation on long-term scales.