High-resolution Atmospheric Model Research Articles

Impacts of Local and Remote SST Warming on Summer Circulation Changes in the Western North Pacific

Abstract This study explores how future SST warming in remote ocean basins may affect the western North Pacific (WNP) wet season climate by applying a high-resolution atmospheric general circulation model to conduct a series of numerical experiments. A marked precipitation and tropical cyclone (TC) activity reduction, as well as enhanced anticyclonic circulation, in the WNP is projected in AMIP experiments forced by SST change in a future warming scenario. The sensitivity experiments reveal that various SST warming phenomena (e.g., in the global SST warming pattern, the tropical ocean belt, the Indian Ocean, the tropical Atlantic, and the subtropical northeast Pacific) and the increase in greenhouse gas concentration could weaken the precipitation, TC activity, and circulation. By contrast, the SST warming in the WNP and eastern equatorial Pacific has opposite and mixed effects, respectively, and tends to weakly offset the dominant influences of remote ocean warming. These results indicate that the WNP, being the epicenter of the global teleconnection of divergent and rotational flow, is susceptible to the influence of the SST warming in remote ocean basins. The remote forcing as projected in future scenarios would overwhelm the enhancing effect of local SST warming and weaken the circulation, convection, and TC activity in the WNP. These findings further the understanding of how the decreased precipitation and enhanced subtropical high in the WNP may be easily triggered by remote SST warming as revealed in the AMIP-type simulations. How this effect would be affected by air–sea coupling needs further investigation.

An extreme precipitation event over Dronning Maud Land, East Antarctica - A case study of an atmospheric river event using the Polar WRF Model

Extreme precipitation events (EPEs) are crucial in Antarctica, impacting the Antarctic ice sheet's surface mass balance and stability. Comprehensive case studies are essential for better understanding these events and the underlying processes driving them. Here, we investigate an extreme snowfall event in Dronning Maud Land (DML), East Antarctica on November 8 and 9, 2015. This event contributed approximately 22 % of the annual accumulation in less than two days and exhibited high spatial variability in precipitation distribution. We employed a high-resolution atmospheric model specifically optimized for the polar regions (Polar WRF) and ERA5 reanalysis data to analyze the event in detail. Our findings highlight the importance of a blocking high-pressure ridge of record strength that effectively blocked and diverted a strong extratropical cyclone into DML, ultimately leading to the heavy snowfall event. The sudden deepening of the cyclone was initiated by a ‘jet streak’ in the upper atmosphere that steered the system southeastwards towards the Antarctic coast. Notably, we observed an anomalously high poleward moisture transport in the form of a strong atmospheric river on November 7, 2015. This atmospheric river originated in the South Atlantic Ocean and tracked poleward from the 30°S-40°S latitude band. Vertical cross-sections of the model outputs indicate that most of the precipitation was concentrated in regions with steep orography along the path of the atmospheric river. This interaction between the atmospheric river and the steep terrain led to the uplift of maritime air, resulting in heavy snowfall. This study highlights the significance of extreme upper and lower atmospheric conditions in driving intense moisture transport towards coastal DML. The interaction between the atmospheric river and the steep orography contributed to heavy snowfall, underscoring the importance of considering orographic influences in understanding EPEs in Antarctica.

The role of diabatic heating/cooling in outer rainbands in the secondary eyewall formation and evolution in a numerically simulated tropical cyclone

In this study, the role of diabatic heating/cooling in outer rainbands (ORBs) in the formation and evolution of the secondary eyewall of a numerically simulated tropical cyclone (TC) is investigated. This is done through a series of sensitivity experiments under idealized conditions using a high-resolution cloud-resolving atmospheric model. The results show that artificially increasing diabatic heating in rainbands enhances convective activities in ORBs and leads to an earlier secondary eyewall formation (SEF), and later the faster weakening and earlier dissipation of the primary eyewall. Reducing diabatic heating in ORBs weakens the rainbands and delays the SEF but prolongs the duration of the double eyewall structure if the SEF occurs. Reducing diabatic cooling in ORBs enhances convective activity in rainbands but has little effect on convection in the primary eyewall prior to the SEF. However, it results in a widened eyewall structure and a stronger TC after the eyewall replacement. Increasing diabatic cooling in ORBs largely suppresses convection in rainbands and prohibits the SEF. These results demonstrate that diabatic heating/cooling in ORBs plays important roles in the SEF and evolution. Since diabatic heating/cooling in rainbands is sensitive to the near-core environmental relative humidity, our results demonstrate the critical importance of large-scale environmental moist condition to the formation and evolution of secondary eyewall in TCs. In addition, it is also found that when the area-averaged diabatic heating rate in ORBs becomes similar in magnitude to that in the primary eyewall, the secondary eyewall forms. Plain language summaryPrevious studies have demonstrated the importance of diabatic heating/cooling in outer rainbands to the structure and intensity changes of tropical cyclones (TCs) with a single eyewall. It is unclear whether and how diabatic heating/cooling in outer rainbands may affect the formation and evolution of the secondary eyewall in TCs. These issues have been addressed based on a series of sensitivity experiments under idealized conditions using a high-resolution atmospheric model. Results show that diabatic heating in outer rainbands is favorable for the secondary eyewall formation (SEF). Increasing diabatic heating in outer eyewall can lead to faster weakening and thus earlier dissipation of the primary eyewall. Diabatic cooling in outer rainbands suppresses convection in outer rainbands and prohibits the SEF. Since diabatic heating/cooling in outer rainbands is sensitive to the near-core environmental relative humidity, our results demonstrate the importance of the large-scale environmental moist condition to the SEF of TCs. We also found that when the area-averaged diabatic heating rate in outer rainbands becomes similar in magnitude to that in the primary eyewall, the secondary eyewall would form, which can be considered as a measure of the SEF in TCs. Key points1.Increasing diabatic heating in outer rainbands leads to earlier SEF, and faster weakening and earlier dissipation of the primary eyewall.2.Increasing diabatic cooling in outer rainbands suppresses convective activity in outer rainbands and is unfavorable for the SEF.3.When the area-averaged diabatic heating rate in outer rainbands and the eyewall become similar in magnitude, the secondary eyewall forms.

Future projection of tropical cyclone genesis in the Western North pacific using high-resolution GCMs and genesis potential indices

The study employed high-resolution atmospheric general circulation models (AGCM) to simulate tropical cyclones (TCs) and evaluated two TC genesis potential indices in reflecting projected TC changes in the western North Pacific (WNP) under a warming scenario. Both indices accurately represented the seasonal variation of TC genesis frequency (TCGF) and its spatial distribution in historical simulations and observation data. The widely-used TC genesis potential index (χGPI) projected a significant increase in TCGF in response to a warmer ocean surface. However, this projection conflicted with the significant reduction in the model projection due to the dominant control of SST on the χGPI. Higher SST in remote ocean basins often over dominated the destabilization effect of in-situ warmer SST and caused more stable atmospheric conditions in the WNP, resulting in fewer TC occurrences. By contrast, the revised index (χMqGPI), which considers gross moisture condensation, projected a TCGF decrease that more accurately reflected the decreasing trend of TCGF in the warming simulations by AGCM, although the degree of reduction was smaller than that derived directly from TC detection scheme. The results suggest the plausibility of using χMqGPI, based on the results of multimodel coarse-resolution CMIP6 climate models, to project future changes in TCGF in the WNP.

Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes

Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northward-propagating mode that determines the active and break phases of the monsoon and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the portion of the rainfall corresponding to MISO rather than the full rainfall signal. Here, we combine state-of-the-art ensemble precipitation forecasts from a high-resolution atmospheric model with data-driven forecasts of MISO. The ensemble members of the detailed atmospheric model are projected onto a lower-dimensional subspace corresponding to the MISO dynamics and are then weighted according to their distance from the data-driven MISO forecast in this subspace. We thereby achieve improvements in rainfall forecasts over India, as well as the broader monsoon region, at 10- to 30-d lead times, an interval that is generally considered to be a predictability gap. The temporal correlation of rainfall forecasts is improved by up to 0.28 in this time range. Our results demonstrate the potential of leveraging the predictability of intraseasonal oscillations to improve extended-range forecasts; more generally, they point toward a future of combining dynamical and data-driven forecasts for Earth system prediction.

An Evaluation of NOAA Modeled and In Situ Soil Moisture Values and Variability across the Continental United States

Abstract Estimates of soil moisture from two National Oceanic and Atmospheric Administration (NOAA) models are compared to in situ observations. The estimates are from a high-resolution atmospheric model with a land surface model [High-Resolution Rapid Refresh (HRRR) model] and a hydrologic model from the NOAA Climate Prediction Center (CPC). Both models produce wetter soils in dry regions and drier soils in wet regions, as compared to the in situ observations. These soil moisture differences occur at most soil depths but are larger at the deeper depths below the surface (100 cm). Comparisons of soil moisture variability are also assessed as a function of soil moisture regime. Both models have lower standard deviations as compared to the in situ observations for all soil moisture regimes. The HRRR model’s soil moisture is better correlated with in situ observations for drier soils as compared to wetter soils—a trend that was not present in the CPC model comparisons. In terms of seasonality, soil moisture comparisons vary depending on the metric, time of year, and soil moisture regime. Therefore, consideration of both the seasonality and soil moisture regime is needed to accurately determine model biases. These NOAA soil moisture estimates are used for a variety of forecasting and societal applications, and understanding their differences provides important context for their applications and can lead to model improvements. Significance Statement Soil moisture is an essential variable coupling the land surface to the atmosphere. Accurate estimates of soil moisture are important for forecasting near-surface temperature and moisture, predicting where clouds will form, and assessing drought and fire risks. There are multiple estimates of soil moisture available, and in this study, we compare soil moisture estimates from two different National Oceanic and Atmospheric Administration (NOAA) models to in situ observations. These comparisons include both soil moisture amount and variability and are conducted at several soil depths, in different soil moisture regimes, and for different seasons and years. This comprehensive assessment allows for an accurate assessment of biases within these models that would be missed when conducting analyses more broadly.

Response of the Current Climate to Land‐Ocean Contrasts in Parameterized Cumulus Entrainment

AbstractCumulus entrainment substantially regulates the earth's climate but remains poorly constrained in global climate models. Recent studies have shown that cumulus bulk entrainment (or dilution) is particularly sensitive to continentality, with the entrainment rate in simulated maritime cumuli nearly double that of continental cumuli. This study examines the impacts of such land–ocean entrainment contrasts on the current climate using 21‐year simulations with the Geophysical Fluid Dynamics Laboratory's High‐Resolution Atmospheric Model (HIRAM). In response to a 25% reduction in the HIRAM entrainment parameter c0 over land, precipitation over tropical land regions increases by up to 40%. Along with directly facilitating enhanced convective precipitation, this c0 reduction induces an increase in soil moisture, which may contribute to a further enhancement of convective precipitation over land. A 25% c0 reduction over the oceans leads to more widespread modifications of convection patterns, with the strongest signal in the tropical Pacific. Deep convection shifts upstream (eastward) there, inducing enhanced large‐scale ascent over the central Pacific with compensating subsidence and reduced humidity and precipitation over the western Pacific (WP). Land–ocean variations in c0 project onto the Pacific Walker circulation, with the 25% land reduction strengthening it by 4% and the 25% ocean reduction weakening it by 14%. These changes are driven by variations in convective and large‐scale stratiform heating over the Pacific. While reduced c0 over land enhances diabatic heating in the Maritime Continent to strengthen the Walker circulation, reduced c0 over the oceans decreases diabatic heating in the WP to weaken the Walker circulation.

Intense Tropical Cyclones in the Western North Pacific Under Global Warming: A Dynamical Downscaling Approach

AbstractThis study aims to assess the global warming's impact on intense tropical cyclones (TCs) over the western North Pacific (WNP) through dynamical downscaling. 379 and 179 TCs reaching Category 1 in the High‐Resolution Atmospheric Model (HiRAM) are downscaled for use in the Weather Research and Forecasting (WRF) model at 5‐km horizontal resolution in the current climate (1979–2015) and for use in the Representative Concentration Pathways 8.5 (RCP8.5) in the future climate (2074–2100) scenarios, respectively. Inclusion of the downscaling simulations by WRF helps better reproduce the probability distribution of the TC's lifetime maximum intensity (LMI). In the warmer climate, the LMI of very intense TCs in WNP are projected to be stronger. Such an increase in intensity is statistically significant, and can be primarily explained by enhanced intensification rate. Meanwhile, TCs among the top 5% in LMI can reach higher intensities which cannot be attained in the current climate. After downscaling, the probability of WNP TCs reaching Category 4–5 increases by 6.5 percentage points in the late 21st century. This increase is 1.7 percentage points higher than the increase projected exclusively by HiRAM. Moreover, for TCs among the top 5% in LMI, a 233‐km and 300‐km westward shift of LMI locations is identified in the late 21st century for simulations with and without the downscaling approach, respectively. Both results suggest that very intense TCs would pose a higher threat to the WNP lands under global warming, as they become substantially stronger, and as their LMI locations migrate toward the coast.

The role of sea surface temperature in shaping the characteristics of future convective afternoon rainfall in Taiwan

Convective afternoon rainfall (CAR) is a significant summer rainfall feature in Taiwan. This study investigates the projected uncertainties in summer CAR in Taiwan, using a dynamical downscaling approach with the Weather Research and Forecasting Model (WRF) and the High-Resolution Atmospheric Model (HiRAM). The projections were driven by four different sea surface temperature (SST) categories derived from CMIP5 model simulations under the RCP8.5 scenario. All projections indicate a reduced frequency but increased intensity of CAR over Taiwan by the end of the 21st century. However, notable differences in the degree of CAR changes were observed among simulations with varying degrees of Pacific SST warming. These variations in potential future changes in CAR frequency and intensity can be attributed to the variations in daytime thermal instability, local inland wind convergence, and moisture flux convergence over Taiwan. Furthermore, this study explored the linkage between local thermodynamic conditions and projected large-scale circulation patterns.

The generation of EarthCARE L1 test data sets using atmospheric model data sets

Abstract. The Earth Cloud, Aerosol and Radiation Explorer mission (EarthCARE) is a multi-instrument cloud–aerosol–radiation process study mission embarking a high spectral resolution lidar, a cloud profiling radar, a multi-spectral imager, and a three-view broadband radiometer. An important aspect of the EarthCARE mission is its focus on instrument synergy. Many L2 products are the result of L1 inputs from one or more instruments. Since no existing complete observational proxy data sets comprised of co-located and co-temporal “EarthCARE-like” data exists, it has been necessary to create synthetic data sets for the testing and development of various retrieval algorithms and the data processing chain. Given the synergistic nature of the processing chain, it is important that the test data are physically consistent across the various instruments. Within the EarthCARE project, a version of the EarthCARE simulator multi-instrument framework (ECSIM) has been used to create unified realistic test data frames. These simulations have been driven using high-resolution atmospheric model data (described in a companion paper). In this paper, the methods used to create the test data scenes are described. In addition, the simulated L1 data corresponding to each scene are presented and discussed.

Influence of synoptic weather on aerosol variability over East Asia: Present and future

Aerosol variability is influenced by changes in emissions and atmospheric dynamics. The aim of this study is to identify the role of synoptic weather systems in affecting the aerosol variation over East Asia under future changes in atmospheric dynamics. First we investigate the influence of synoptic weather on aerosol optical depth (AOD) spatiotemporal variability using long-term observations from Moderate Resolution Imaging Spectroradiometer (MODIS). We demonstrate that the major synoptic weather patterns of each season are among the dominant factors that affect AOD variations. During spring and autumn, AOD variability is significantly influenced by dynamic transport under northeasterly (winter monsoon) or southwesterly (summer monsoon) conditions. In summer, the maximum anomaly of the AOD variability was associated with the occurrence of fronts and tropical cyclones near Taiwan. While in winter, cold surge was the major event controlling the AOD variability. As atmospheric dynamics are well connected with AOD interannual variability, the AOD predictability over East Asia is associated with the predictability of synoptic weather systems. We apply the high-resolution atmospheric model (HiRAM) to identify these weather systems in present climate and under future warming scenarios. Under future climate based on HiRAM, the occurrence of summer tropical cyclones decreases by 32%, and the autumn southwesterly pattern increases by 50%. Considering the frequency change in summer tropical cyclones and autumn southwestlies under future scenarios, this may lead to the accumulation of AOD over China during summer and autumn through dynamic transport. The results suggest that AOD variations in East Asia may change along with future changes in the large-scale circulation under global warming.

Evaluation of Simulated CO2 Point Source Plumes from High-Resolution Atmospheric Transport Model

Coal-fired power plants, as major anthropogenic CO2 emission sources, constitute one of the largest contributors to global greenhouse gas emissions. Accurately calculating the dispersion process of CO2 emissions from these point sources is crucial, as it will aid in quantifying CO2 emissions using remote sensing measurements. Employing the Lagrangian Particle Dispersion Theory Model (LPDTM), our study involves modeling CO2 diffusion from point sources. Firstly, we incorporated high-resolution DEM (Digital Elevation Model) and artificial building elements obtained through the Adaptive Deep Learning Location Matching Method, which is involved in CO2 simulation. The accuracy of the results was verified using meteorological stations and aircraft measurements. Additionally, we quantitatively analyzed the influence of terrain and artificial building characteristics on high spatial resolution atmospheric CO2 diffusion simulations, revealing the significance of surface characteristics in dispersion modeling. To validate the accuracy of the LPDTM in high-resolution CO2 diffusion simulation, a comparative experiment was conducted at a power plant in Yangzhou, Jiangsu Province, China. The simulated result was compared with observation from aerial flights, yielding the R2 (Correlation Coefficient) of 0.76, the RMSE (Root Mean Square Error) of 0.267 ppm, and the MAE (Mean Absolute Error) of 0.2315 ppm for the comparison of 73 pixels where the plume intersected with flight trajectories. The findings demonstrate a high level of consistency between the modeled CO2 point source plume morphology and concentration quantification and the actual observed outcomes. This study carried out a quantitative assessment of the influence of surface features on high-resolution atmospheric CO2 point source diffusion simulations, resulting in an enhanced accuracy of the simulated CO2 concentration field. It offers essential technological and theoretical foundations for the accurate quantification of anthropogenic CO2 emissions using top-down approaches.

The High-resolution Intermediate Complexity Atmospheric Research (HICAR v1.1) model enables fast dynamic downscaling to the hectometer scale

Abstract. High-resolution (< 1 km) atmospheric modeling is increasingly used to study precipitation distributions in complex terrain and cryosphere–atmospheric processes. While this approach has yielded insightful results, studies over annual timescales or at the spatial extents of watersheds remain unrealistic due to the computational costs of running most atmospheric models. In this paper we introduce a high-resolution variant of the Intermediate Complexity Atmospheric Research (ICAR) model, HICAR. We detail the model development that enabled HICAR simulations at the hectometer scale, including changes to the advection scheme and the wind solver. The latter uses near-surface terrain parameters which allow HICAR to simulate complex topographic flow features. These model improvements clearly influence precipitation distributions at the ridge scale (50 m), suggesting that HICAR can approximate processes dependent on particle–flow interactions such as preferential deposition. A 250 m HICAR simulation over most of the Swiss Alps also shows monthly precipitation patterns similar to two different gridded precipitation products which assimilate available observations. Benchmarking runs show that HICAR uses 594 times fewer computational resources than the Weather Research and Forecasting (WRF) atmospheric model. This gain in efficiency makes dynamic downscaling accessible to ecohydrological research, where downscaled data are often required at hectometer resolution for whole basins at seasonal timescales. These results motivate further development of HICAR, including refinement of parameterizations used in the wind solver and coupling of the model with an intermediate-complexity snow model.

Can we use atmospheric CO2 measurements to verify emission trends reported by cities? Lessons from a 6-year atmospheric inversion over Paris

Abstract. Existing CO2 emissions reported by city inventories usually lag in real-time by a year or more and are prone to large uncertainties. This study responds to the growing need for timely and precise estimation of urban CO2 emissions to support present and future mitigation measures and policies. We focus on the Paris metropolitan area, the largest urban region in the European Union and the city with the densest atmospheric CO2 observation network in Europe. We performed long-term atmospheric inversions to quantify the citywide CO2 emissions, i.e., fossil fuel as well as biogenic sources and sinks, over 6 years (2016–2021) using a Bayesian inverse modeling system. Our inversion framework benefits from a novel near-real-time hourly fossil fuel CO2 emission inventory (Origins.earth) at 1 km spatial resolution. In addition to the mid-afternoon observations, we attempt to assimilate morning CO2 concentrations based on the ability of the Weather Research and Forecasting model with Chemistry (WRF-Chem) transport model to simulate atmospheric boundary layer dynamics constrained by observed layer heights. Our results show a long-term decreasing trend of around 2 % ± 0.6 % per year in annual CO2 emissions over the Paris region. The impact of the COVID-19 pandemic led to a 13 % ± 1 % reduction in annual fossil fuel CO2 emissions in 2020 with respect to 2019. Subsequently, annual emissions increased by 5.2 % ± 14.2 % from 32.6 ± 2.2 Mt CO2 in 2020 to 34.3 ± 2.3 Mt CO2 in 2021. Based on a combination of up-to-date inventories, high-resolution atmospheric modeling and high-precision observations, our current capacity can deliver near-real-time CO2 emission estimates at the city scale in less than a month, and the results agree within 10 % with independent estimates from multiple city-scale inventories.

Towards hyperlocal source identification of pollutants in cities by combining mobile measurements with atmospheric modeling

While mobile, vehicle-based sampling of air quality has provided insights into pollutant distributions within cities, previous research has been limited in using observed pollution hotspots to identify and localize unknown emission sources away from the road. Such “hyperlocal source identification” requires hotspots identified through mobile sampling to be linked to emission sources by tracing atmospheric transport upwind from observed hotspots. Here we analyze almost one year's worth of pollutant observations from two Google Street View cars driven around the Salt Lake area, with repeated sampling in select neighborhoods. We then present a method to bridge the gap in hyperlocal source identification by using a high-resolution atmospheric transport model, as part of a new Stochastic Lagrangian Monte Carlo (SLMC) method to identify emission sources based on mobile observations. Case studies with both known and unknown sources are presented to illustrate the efficacy of the SLMC method in hyperlocal source identification. With the availability of techniques like SLMC, we envision mobile air quality monitoring vehicles can serve as “roving sentinels” in identifying unknown pollution sources.

Towards the Accurate Automatic Detection of Mesoscale Convective Systems in Remote Sensing Data: From Data Mining to Deep Learning Models and Their Applications

Mesoscale convective systems (MCSs) and associated hazardous meteorological phenomena cause considerable economic damage and even loss of lives in the mid-latitudes. The mechanisms behind the formation and intensification of MCSs are still not well understood due to limited observational data and inaccurate climate models. Improving the prediction and understanding of MCSs is a high-priority area in hydrometeorology. One may study MCSs either employing high-resolution atmospheric modeling or through the analysis of remote sensing images which are known to reflect some of the characteristics of MCSs, including high temperature gradients of cloud-top, specific spatial shapes of temperature patterns, etc. However, research on MCSs using remote sensing data is limited by inadequate (in size) databases of satellite-identified MCSs and poorly equipped automated tools for MCS identification and tracking. In this study, we present (a) the GeoAnnotateAssisted tool for fast and convenient visual identification of MCSs in satellite imagery, which is capable of providing AI-generated suggestions of MCS labels; (b) the Dataset of Mesoscale Convective Systems over the European Territory of Russia (DaMesCoS-ETR), which we created using this tool, and (c) the Deep Convolutional Neural Network for the Identification of Mesoscale Convective Systems (MesCoSNet), constructed following the RetinaNet architecture, which is capable of identifying MCSs in Meteosat MSG/SEVIRI data. We demonstrate that our neural network, optimized in terms of its hyperparameters, provides high MCS identification quality (mAP=0.75, true positive rate TPR=0.61) and a well-specified detection uncertainty (false alarm ratio FAR=0.36). Additionally, we demonstrate potential applications of the GeoAnnotateAssisted labelling tool, the DaMesCoS-ETR dataset, and the MesCoSNet neural network in addressing MCS research challenges. Specifically, we present the climatology of axisymmetric MCSs over the European territory of Russia from 2014 to 2020 during summer seasons (May to September), obtained using MesCoSNet with Meteosat MSG/SEVIRI data. The automated identification of MCSs by the MesCoSNet artificial neural network opens up new avenues for previously unattainable MCS research topics.

Future impacts of O3 on respiratory hospital admission in the UK from current emissions policies

Exposure to ambient ozone (O3) O3 is associated with impacts on human health. O3 is a secondary pollutant whose concentrations are determined inter alia by emissions of precursors such as oxides of nitrogen (NOx) and volatile organic compounds (VOCs), and thus future health burdens depend on policies relating to climate and air quality. While emission controls are expected to reduce levels of PM2.5 and NO2 and their associated mortality burdens, for secondary pollutants like O3 the picture is less clear. Detailed assessments are necessary to provide quantitative estimates of future impacts to support decision-makers. We simulate future O3 across the UK using a high spatial resolution atmospheric chemistry model with current UK and European policy projections for 2030, 2040 and 2050, and use UK regional population-weighting and latest recommendations on health impact assessment to quantify respiratory emergency hospital admissions associated with short-term effects of O3. We estimate 60,488 admissions in 2018, increasing by 4.2%, 4.5% and 4.6% by 2030, 2040 and 2050 respectively (assuming a fixed population). Including future population growth, estimated emergency respiratory hospital admissions are 8.3%, 10.3% and 11.7% higher by 2030, 2040 and 2050 respectively. Increasing O3 concentrations in future are driven by reduced nitric oxide (NO) in urban areas due to reduced emissions, with increases in O3 mainly occurring in areas with lowest O3 concentrations currently. Meteorology influences episodes of O3 on a day-to-day basis, although a sensitivity study indicates that annual totals of hospital admissions are only slightly impacted by meteorological year. While reducing emissions results in overall benefits to population health (through reduced mortality due to long-term exposure to PM2.5 and NO2), due to the complex chemistry, as NO emissions reduce there are associated local increases in O3 close to population centres that may increase harms to health.

Modes of Operation and Forcing in Oil Spill Modeling: State-of-Art, Deficiencies and Challenges

Oil spills may have devastating effects on marine ecosystems, public health, the economy, and coastal communities. As a consequence, scientific literature contains various up-to-date, advanced oil spill predictive models, capable of simulating the trajectory and evolution of an oil slick generated by the accidental release from ships, hydrocarbon production, or other activities. To predict in near real time oil spill transport and fate with increased reliability, these models are usually coupled operationally to synoptic meteorological, hydrodynamic, and wave models. The present study reviews the available different met-ocean forcings that have been used in oil-spill modeling, simulating hypothetical or real oil spill scenarios, worldwide. Seven state-of-the-art oil-spill models are critically examined in terms of the met-ocean data used as forcing inputs in the simulation of twenty-three case studies. The results illustrate that most oil spill models are coupled to different resolution, forecasting meteorological and hydrodynamic models, posing, however, limited consideration in the forecasted wave field (expressed as the significant wave height, the wave period, and the Stokes drift) that may affect oil transport, especially at the coastal areas. Moreover, the majority of oil spill models lack any linkage to the background biogeochemical conditions; hence, limited consideration is given to processes such as oil biodegradation, photo-oxidation, and sedimentation. Future advancements in oil-spill modeling should be directed towards the full operational coupling with high-resolution atmospheric, hydrodynamic, wave, and biogeochemical models, improving our understanding of the relative impact of each physical and oil weathering process.

Impacts of emissions policies on future UK mortality burdens associated with air pollution

Air pollution is the greatest environmental risk to public health. Future air pollution concentrations are primarily determined by precursor emissions, which are driven by environmental policies relating to climate and air pollution. Detailed health impact assessments (HIA) are necessary to provide quantitative estimates of the impacts of future air pollution to support decision-makers developing environmental policy and targets. In this study we use high spatial resolution atmospheric chemistry modelling to simulate future air pollution concentrations across the UK for 2030, 2040 and 2050 based on current UK and European policy projections. We combine UK regional population-weighted concentrations with the latest epidemiological relationships to quantify mortality associated with changes in PM2.5 and NO2 air pollution. Our HIA suggests that by 2050, population-weighted exposure to PM2.5 will reduce by 28% to 36%, and for NO2 by 35% to 49%, depending on region. The HIA shows that for present day (2018), annual mortality attributable to the effects of long-term exposure to PM2.5 and NO2 is in the range 26,287 – 42,442, and that mortality burdens in future will be substantially reduced, being lower by 31%, 35%, and 37% in 2030, 2040 and 2050 respectively (relative to 2018) assuming no population changes. Including population projections (increases in all regions for 30+ years age group) slightly offsets these health benefits, resulting in reductions of 25%, 27%, and 26% in mortality burdens for 2030, 2040, 2050 respectively. Significant reductions in future mortality burdens are estimated and, importantly for public health, the majority of benefits are achieved early on in the future timeline simulated, though further efforts are likely needed to reduce impacts of air pollution to health.

Future projection of the African easterly waves in a high-resolution atmospheric general circulation model

Simulating the features of the African Easterly Waves (AEWs), such as their westward propagation off the east Atlantic coast, is challenging for coarse-resolution climate models. In this study, we use High-Resolution Atmospheric Model (HiRAM) to simulate AEWs and analyze their future projections by the end of the twenty first century. The simulations are performed globally at a horizontal resolution of sim 25 km. The model uses shallow convective parameterization for moist convection and stratiform cloudiness. Future projections are conducted using representative concentration pathway 8.5. The AEWs are separated with respect to their periods as 3–5- and 6–9-day period AEWs, and bandpass filtering is used to filter the waves from the mean flow. HiRAM simulates structure and propagation of the waves well; however, it tends to overestimate the associated precipitation. In the future, the AEW precipitation and intensity of the circulation will considerably increase. The northward extent of the AEW track also shows a significant increase in the future. Enhanced baroclinic overturning and eddy available potential energy generated due to diabatic heating is also observed in the future.