Fine-resolution Model Research Articles

Sweating the small stuff: microclimatic exposure and species habitat associations inform climate vulnerability in a grassland songbird community.

Assessment of species' vulnerability to climate change has been limited by mismatch between coarse macroclimate data and the fine scales at which species select habitat. Habitat mediates climate conditions, and fine-scale habitat features may permit species to exploit favourable microclimates, but habitat preferences can also constrain their ability to do so. We leveraged fine-resolution models of near-surface temperature and humidity in grasslands to understand how microclimates affect climatic exposure and demographics in a grassland bird community. We asked: (i) Do species select favourable nest-site microclimates? (ii) Do habitat preferences limit the ability of species to access microclimates? (iii) What are the demographic consequences of microclimatic exposure? We found limited evidence that grassland birds select cooler microclimates, which may buffer eggs and nestlings from extreme heat. Instead, many species appeared constrained by vegetation preferences. While facultative generalists displayed flexibility to use denser vegetation that provided buffering from high temperatures (>39°C), most obligate species nested in more exposed microclimates. Nesting success in facultative species was not well explained by microclimate, but success in specialized grassland obligates declined with increasing microclimate temperatures. These findings illustrate how microclimate and habitat use can interact to influence the potential vulnerability of species to future climate change.

Improved Orographic Gravity Wave Drag Parameterization Accounting for the Nonhydrostatic Effect in the Weather Research and Forecasting Model: Tests for Short-Range Forecast of Northeast China Cold Vortices

Abstract The parameterization of orographic gravity wave drag (OGWD) is essential for accurate numerical weather prediction in regions of complex terrain. Current OGWD schemes assume hydrostatic orographic gravity waves (OGWs) but the parameterized OGWs in fine-resolution models with narrow subgrid-scale orography can be significantly affected by the nonhydrostatic effects (NHE). In our recent work, the OGWD scheme in the Model for Prediction Across Scales (MPAS) was revised by accounting for the NHE on the surface wave momentum flux of upward-propagating OGWs. Herein, the revised OGWD scheme is implemented in the Weather Research and Forecasting (WRF) Model to evaluate its performance in short-range weather forecast. Two sets of 36-h WRF simulations are conducted for nine Northeast China cold vortices (NECVs) that occurred in the warm season of 2011 using the original and revised OGWD schemes. Results show that the WRF Model tends to underestimate the intensity of the NECVs, producing too high geopotential height. When accounting for the NHE in the OGWD scheme, the NECV intensity biases are significantly reduced. Analyses reveal that the NHE act to weaken the lower-tropospheric OGWD by decreasing the surface wave momentum flux, which strengthens the NECV in the lower troposphere. Consequently, the strengthened low-level cyclonic circulation increases the posttrough cold advection to the southwest of the NECV which in turn enhances the NECV in the mid–upper troposphere with reduced geopotential height. The NHE are found to increase as the model horizontal resolution increases, suggesting greater importance of NHE in the OGWD parameterization of high-resolution numerical models.

Insights into the Australian mid-Holocene climate using downscaled climate models

Abstract. The mid-Holocene climate of Australia and the equatorial tropics of the Indonesian–Australian monsoon region is investigated using the Community Earth System Model (CESM) and the Weather Research and Forecasting (WRF) model. Each model is used to simulate the pre-industrial (1850) and the mid-Holocene (6000 years before 1950) climate. The results of these four simulations are compared to existing bioclimatic modelling of temperature and precipitation. The finer-resolution WRF simulations reduce the bias between the model and bioclimatic data results for three of the four variables available in the proxy data set. The model results show that temperatures over southern Australia at the mid-Holocene and in the pre-industrial period were similar, and temperatures were slightly warmer during the mid-Holocene over northern Australia and into the tropics compared to the pre-industrial. During the mid-Holocene, precipitation was generally reduced over northern Australia and in the Indonesian–Australian monsoon region, particularly during summertime. The results highlight the improved value of using finer-resolution models such as WRF to simulate the palaeoclimate.

Comparing the interactions between particulate matter and cloud properties over two populated cities in Texas using WRF-Chem fine-resolution modeling

Accurate modeling of aerosol-cloud interactions is essential for reliable weather and air quality simulations, given their significant impact on precipitation patterns, cloud dynamics, and aerosol distributions. This study employed the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to examine the impact of enhanced meteorological simulations, achieved through advanced microphysics parameterization supported by data assimilation techniques, on air quality across Texas on August 19 and 20, 2022. We tested four distinct configurations: (1) the Morrison two-moment bulk microphysics scheme, (2) Morrison's with observation nudging, (3) the Spectral Bin Microphysics (SBM), and (4) SBM with observation nudging. While the SBM scheme is known for its detailed representation of aerosol-cloud interactions, our focus was on how improvements in meteorological accuracy translate to more precise air quality simulations. Our findings demonstrated a progressive improvement in simulation accuracy, starting with the Morrison's scheme and further enhanced by adopting the SBM scheme, complemented by incorporating observation nudging. Specifically, the combination of the SBM scheme and the nudging substantially enhanced the model's ability to capture convective precipitation events, as shown by better alignment with NEXRAD radar reflectivity, with R increasing from −0.21 to 0.82, IOA from 0.10 to 0.87, and NMB decreasing from 99% to 34% in Houston. The enhanced meteorology translated into more accurate PM2.5 concentration simulations, particularly through the more accurate representation of aerosol washout during precipitation events. In Houston, the SBM scheme with nudging improved the model's PM2.5 simulations significantly, with NMB decreasing from −20% to 5% and IOA improving from 0.43 to 0.61. In San Antonio, improvements were also notable, with NMB improved from −27% to −22%, R increased from 0.48 to 0.82, and IOA increased from 0.66 to 0.86. Our results underscore the crucial role of accurate meteorological simulations in refining our understanding of aerosol behaviors in relation to precipitation patterns, directly enhancing the reliability and effectiveness of air quality modeling.

A high resolution hydrodynamic-biogeochemical-oyster-filtration model predicts that the presence of oysters (Crassostrea virginica) can improve, or reduce, water quality depending upon oyster abundance and location

The eastern oyster (Crassostrea virginica) provides numerous ecosystem services such as building reef habitat, clarifying the water by filtering seston, and reducing excess nitrogen when their biodeposits are denitrified. Some of these ecosystem services have been extensively studied using two-dimensional and three-dimensional (3D) models. Yet, the relationship between oyster abundance, their filtration and biodeposition rates, and associated water quality metrics has not been estimated with high-resolution models that include biodeposit resuspension as well as simulation of 3D processes in the sub-tributaries where oysters are abundant. To undertake these estimates, a 3D Regional Ocean Modeling System (ROMS) framework, comprised of a coupled hydrodynamic-water quality model with an oyster filtration and biodeposition sub-model, was implemented over a fine-resolution model grid (120–150 m) of the Choptank River on the eastern shore of Chesapeake Bay, U.S.A. After validation with data from 20 cruises between May and September 2010, the model was used to predict seven variables associated with water quality in simulations with zero, recent, and 50x recent abundances of oysters. Results indicated that improvement in the seven water quality variables differed in response to the abundance of oysters and between regions in the Choptank River. In line with expectations, the water quality metrics improved with increasing oyster abundance in the shallow and retentive sub-tributaries of Broad and Harris Creeks. In contrast, in the deeper and more flushed mainstem of the Choptank River, some water quality metrics deteriorated when recent abundances of oysters were added to the model compared to the simulation with zero oysters and the same metrics improved when oyster abundances were increased by 50x. Overall, model results indicated that the many complex physical and biogeochemical processes that influence the effect of oysters on water quality resulted in differing responses to oysters across different systems. In addition, this study introduces a high-resolution, predictive tool that could be used to estimate the number of oysters needed to meet water quality thresholds in specific systems in support of ecosystem management.

A Path Toward Understanding Regional Sea Level Rise

Finer-resolution models, as well as an improved understanding of ocean shelf–sea processes, are key to understanding the way different coastlines will be affected by rising waters, extreme storm surges, and waves.

Big data in Earth science: Emerging practice and promise.

Improvements in the number and resolution of Earth- and satellite-based sensors coupled with finer-resolution models have resulted in an explosion in the volume of Earth science data. This data-rich environment is changing the practice of Earth science, extending it beyond discovery and applied science to new realms. This Review highlights recent big data applications in three subdisciplines-hydrology, oceanography, and atmospheric science. We illustrate how big data relate to contemporary challenges in science: replicability and reproducibility and the transition from raw data to information products. Big data provide unprecedented opportunities to enhance our understanding of Earth's complex patterns and interactions. The emergence of digital twins enables us to learn from the past, understand the current state, and improve the accuracy of future predictions.

An urban drainage scheme for large-scale flood models

As flood modeling spatial resolutions get finer, physical processes normally neglected, such as urban drainage, must be accounted for. Here, we describe and evaluate an urban drainage scheme for large-scale flood models. The parameterization accounts for urban imperviousness, and water flow over streets and through a prescribed urban drainage network. A parameter sensitivity analysis is performed during three major extreme floods over Rio de Janeiro city, Brazil, at ∼200 m spatial resolution. Results show that, compared to a hypothetical case without urban drainage, representing a drainage network decreases urban flooding during selected extreme events across Rio de Janeiro by 31–53 %. Such a decrease is caused by an underground water storage of up to 2.5 billion m3 across the city during flood peaks. Underground water storage and transport smooth out and delay peak flows by a few hours over major rivers and channels draining the city. Simulations also indicate that the number of residents exposed to flooding drops by 60–80 %, from ∼5 million to 1–2 million, when an urban drainage system is considered during extreme events. Similar proportions are found for social infrastructure (i.e., schools and hospitals) exposed to flooding. Results reveal that racial minority and low-income populations could disproportionally be exposed to extreme floodings across the city. We conclude that representing urban drainage has a substantial impact on flood exposure and should be accounted for in fine resolution modeling. The proposed scheme is particularly useful in poorly monitored cities and where extreme floods are a frequent hazard yet to be tackled.

Toward large-scale fine resolution DEM landslide simulations: periodic granular box for efficient modeling of excavatable slope

Understanding landslide hazards and developing mitigation measures is critical to protecting lives and property. The Discrete Element Method (DEM) has demonstrated its ability to simulate landslides and estimate impact forces on mitigation structures. Such simulations often model slope surfaces as bedrock on which a fixed amount of earth mass is released and slides. Mass entrainment due to local failure of an excavatable slope surface is rarely modeled with sufficient resolution. Fine-resolution modeling of an excavatable slope for simulation is necessary and non-trivial to improve DEM simulations of large-scale landslides. In this study, we present a method based on Periodic Granular (PG) boxes for efficient modeling of excavatable slopes. A PG box is a packing of particles in a quasi-static state inside a virtual unit box, where the periodic boundary conditions are satisfied at the box surfaces. The procedures used to construct a PG box are presented. The quality of PG boxes is analyzed both at the particle contact scale and at the laboratory specimen scale by measuring contact orientation statistics and by performing numerical triaxial tests. As a demonstration, a centimeter resolution slope model constructed using the PG box-based method is presented using topographic data for the Aso Bridge landslide. In addition, landslide simulations to verify the excavation effect were performed on sub-meter resolution slope models. Our simulations show that landscape shape and particle size have a direct effect on the movement and deposition of earth masses. This highlights the need for detailed 3D terrain modeling and further study of particle size effects. By constructing the first excavatable DEM slope model with billions of centimeter-sized particles, this study can be considered a solid step toward fine-resolution DEM simulations of large-scale landslides, which can contribute to both scientific understanding and engineering countermeasures to mitigate the catastrophic consequences of large-scale landslides.

Statistical downscaling of GRACE TWSA estimates to a 1-km spatial resolution for a local-scale surveillance of flooding potential

The Gravity Recovery and Climate Experiment (GRACE) paved the way for large-scale monitoring of the hydrological extremes. However, local scale analysis is aslo challenging due to the coarse resolution of the GRACE estimates. The feasibility of the downscaled GRACE data for the flood monitoring in the Kizilirmak Basin (KB) in Türkiye is investigated in this study by integrating the GRACE and hydrological model outputs of a random forest approach. Results suggest that the TWSA, over the Asagi Kizilirmak Basin (AKB), is ascending with an annual rate of +3.51mm/yr; while the Orta Kizilirmak Basin (OKB), Yukari Kizilirmak Basin (YKB), Delice Basin (DB), Develi Kapali Basin (DKB), and Seyfe Kapali Basin (SKB) showed descending trend respectively as -1.15mm/yr, -1.58mm/yr, -1.14mm/yr, -2.34mm/yr, and -1.31mm/yr. The hydrological status of the basin showed that in 2003, 2005, 2010–2013, and 2015–2016 periods the study area was prone to the inundation. Hence, by validating the Flood Potential Index (FPI) rates acquired from the downscaled GRACE data, it was shown that the best correlation coefficient (0.73) between FPI and streamflow (Q) is associated with the SKB. It is also concluded that the downscaled TWSA associated with the fine-resolution models depicts acceptable accuracy in determination of the flood potential at local scales.

Maternal exposure to ambient air pollution mixture and premature rupture of membranes: Evidence from a large cohort in Southern California (2008–2018)

BackgroundThere is minimal evidence of relationships between maternal air pollution exposure and spontaneous premature rupture of membranes (SPROM), a critical obstetrical problem that can significantly increase maternal and fetal mortality and morbidity. No prior study has explored the PROM risk related to specific components of particulate matter with aerodynamic diameters of ≤ 2.5 µm (PM2.5). We examined associations between maternal exposure to nitrogen dioxide (NO2), ozone (O3), PM2.5, PM10, and PM2.5 constituents and SPROM. MethodsA large retrospective cohort study was conducted and included 427,870 singleton live births from Kaiser Permanente Southern California during 2008–2018. Monthly averages of NO2, O3 (8-h daily maximum), PM2.5, and PM10 were measured using empirical Bayesian kriging based on measurements from monitoring stations. Data on PM2.5 sulfate, nitrate, ammonium, organic matter, and black carbon were obtained from a fine-resolution model. A discrete time approach with pooled logistic regressions was used to estimate associations throughout the pregnancy and based on trimesters and gestational months. The quantile-based g-computation models were fitted to examine the effects of 1) the air pollution mixture of four pollutants of interest and 2) the mixture of PM2.5 components. ResultsThere were 37,857 SPROM cases (8.8%) in our study population. We observed relationships between SPROM and maternal exposure to NO2, O3, and PM2.5. PM2.5 sulfate, nitrate, ammonium, and organic matter were associated with higher SPROM risks in the single-pollutant model. Mixture analyses demonstrated that the overall effects of the air pollution mixture and PM2.5 mixture in this study were mainly driven by O3 and PM2.5 nitrate, respectively. Underweight mothers had a significantly higher risk of SPROM associated with NO2. ConclusionOur findings add to the literature on associations between air pollution exposure and SPROM. This is the first study reporting the impact of PM2.5 constituents on SPROM.

Biophysical models resolution affects coral connectivity estimates

Estimating connectivity between coral reefs is essential to inform reef conservation and restoration. Given the vastness of coral reef ecosystems, connectivity can only be simulated with biophysical models whose spatial resolution is often coarser than the reef scale. Here, we assess the impact of biophysical models resolution on connectivity estimates by comparing the outputs of five different setups of the same model with resolutions ranging from 250 m to 4 km. We show that increasing the model resolution around reefs yields more complex and less directional dispersal patterns. With a fine-resolution model, connectivity graphs have more connections but of weaker strength. The resulting community structure therefore shows larger clusters of well-connected reefs. Virtual larvae also tend to stay longer close to their source reef with a fine-resolution model, leading to an increased local retention and self-recruitment for species with a short pre-competency period. Overall, only about half of the reefs with the largest connectivity indicator values are similar for the finest and coarsest resolution models. Our results suggest that reef management recommendations should only be made at scales coarser than the model resolution. Reef-scale recommendations can hence only be made with models not exceeding about 500 m resolution.

Forest tree species distribution for Europe 2000-2020: mapping potential and realized distributions using spatiotemporal machine learning.

This article describes a data-driven framework based on spatiotemporal machine learning to produce distribution maps for 16 tree species (Abies alba Mill., Castanea sativa Mill., Corylus avellana L., Fagus sylvatica L., Olea europaea L., Picea abies L. H. Karst., Pinus halepensis Mill., Pinus nigra J. F. Arnold, Pinus pinea L., Pinus sylvestris L., Prunus avium L., Quercus cerris L., Quercus ilex L., Quercus robur L., Quercus suber L. and Salix caprea L.) at high spatial resolution (30 m). Tree occurrence data for a total of three million of points was used to train different algorithms: random forest, gradient-boosted trees, generalized linear models, k-nearest neighbors, CART and an artificial neural network. A stack of 305 coarse and high resolution covariates representing spectral reflectance, different biophysical conditions and biotic competition was used as predictors for realized distributions, while potential distribution was modelled with environmental predictors only. Logloss and computing time were used to select the three best algorithms to tune and train an ensemble model based on stacking with a logistic regressor as a meta-learner. An ensemble model was trained for each species: probability and model uncertainty maps of realized distribution were produced for each species using a time window of 4 years for a total of six distribution maps per species, while for potential distributions only one map per species was produced. Results of spatial cross validation show that the ensemble model consistently outperformed or performed as good as the best individual model in both potential and realized distribution tasks, with potential distribution models achieving higher predictive performances (TSS = 0.898, R2logloss = 0.857) than realized distribution ones on average (TSS = 0.874, R2logloss = 0.839). Ensemble models for Q. suber achieved the best performances in both potential (TSS = 0.968, R2logloss = 0.952) and realized (TSS = 0.959, R2logloss = 0.949) distribution, while P. sylvestris (TSS = 0.731, 0.785, R2logloss = 0.585, 0.670, respectively, for potential and realized distribution) and P. nigra (TSS = 0.658, 0.686, R2logloss = 0.623, 0.664) achieved the worst. Importance of predictor variables differed across species and models, with the green band for summer and the Normalized Difference Vegetation Index (NDVI) for fall for realized distribution and the diffuse irradiation and precipitation of the driest quarter (BIO17) being the most frequent and important for potential distribution. On average, fine-resolution models outperformed coarse resolution models (250 m) for realized distribution (TSS = +6.5%, R2logloss = +7.5%). The framework shows how combining continuous and consistent Earth Observation time series data with state of the art machine learning can be used to derive dynamic distribution maps. The produced predictions can be used to quantify temporal trends of potential forest degradation and species composition change.

The potential impact of model horizontal resolution on the simulation of atmospheric cloud radiative effect in CMIP6 models

The simulations of atmospheric cloud-radiative effect (ACRE) from 54 Coupled Model Intercomparison Project phase 6 (CMIP6) models during the historical period of 2000/03–2014/12 are compared and evaluated against the satellite-based Clouds and the Earth’s Radiant Energy System (CERES) products. For ease of comparison, all CMIP6 models are divided into high-, medium-, and low-resolution groups to examine the potential impact of model horizontal resolution change on the simulations of ACRE distribution over the tropical oceans. The results show that ACRE is positive inside the ITCZs but negative in the subtropics and cold tongue areas, owing to the very different radiative forcing between deep and shallow clouds. Simulations of ACRE are sensitive to the model horizontal resolution used and the finer resolution models generally produce a better performance of ACRE simulations against the CERES observations. The reduced ACRE biases in finer resolution models are mainly contributed by the improved longwave ACRE (i.e., LWACRE) simulations, especially over the Pacific and Atlantic cold tongue areas where shallow stratocumulus clouds prevail.

Numerical Study of Circulation and Seasonal Variability in the Southwestern Yellow Sea

A nested-grid ocean circulation modelling system (NGMS-swYS) is used for examining the impact of tides and winds on the three-dimensional (3D) circulation, hydrography and seasonal variability over the southwestern Yellow Sea (swYS). The modelling system is based on the Princeton Ocean Model (POM) and uses a nested-grid setup, with a fine-resolution (~2.7 km) inner model nested inside a coarse-resolution (~9.0 km) outer model. The domain of the outer model covers the China Seas and adjacent deep ocean waters. The domain of the fine-resolution inner model covers the swYS and adjacent waters. The NGMS-swYS is driven by a suite of external forcings, including the atmospheric forcing, tides, freshwater discharge and currents specified at the lateral open boundaries. A comparison of model results with observations and previous numerical studies demonstrates the satisfactory performance of the NGMS-swYS in simulating tides, seasonal mean circulation and distribution of temperature and salinity. Five additional numerical experiments were conducted using NGMS-swYS with different combinations of external forcing. Analysis of model results demonstrates that the monthly mean circulation over the swYS is affected significantly by tides and winds, with large seasonal variability. The northward Subei Shoal Current occurred in both winter and summer months in 2015, with persistent strong southeastward mean currents induced by tides along the 50 m isobath. Model results also demonstrated that strong wind-induced currents occurred with large sea surface cooling during Typhoon Chan-Hom.

A fine spatial resolution modeling of urban carbon emissions: a case study of Shanghai, China

Quantification of fossil fuel carbon dioxide emissions (CEs) at fine space and time resolution is a critical need in climate change research and carbon cycle. Quantifying changes in spatiotemporal patterns of urban CEs is important to understand carbon cycle and development carbon reduction strategies. The existing spatial data of CEs have low resolution and cannot distinguish the distribution characteristics of CEs of different emission sectors. This study quantified CEs from 15 types of energy sources, including residential, tertiary, and industrial sectors in Shanghai. Additionally, we mapped the CEs for the three sectors using point of interest data and web crawler technology, which is different from traditional methods. At a resolution of 30 m, the improved CEs data has a higher spatial resolution than existing studies. The spatial distribution of CEs based on this study has higher spatial resolution and more details than that based on traditional methods, and can distinguish the spatial distribution characteristics of different sectors. The results indicated that there was a consistent increase in CEs during 2000–2015, with a low rate of increase during 2009–2015. The intensity of CEs increased significantly in the outskirts of the city, mainly due to industrial transfer. Moreover, intensity of CEs reduced in city center. Technological progress has promoted the improvement of energy efficiency, and there has been a decoupling between the economic development and CEs in the city was observed during in 2000–2015.

Improvements and persistent biases in the southeast tropical Atlantic in CMIP models

State-of-the-art climate models simulate warmer than observed sea surface temperatures (SST) in eastern boundary upwelling systems (EBUS), generating biases with profound implications for the simulation of present-day climate and its future projections. Amongst all EBUS, the bias is largest in the southeastern tropical Atlantic (SETA). Here, we provide a comprehensive evaluation of the performance in the SETA of the Coupled Model Intercomparison Project phase 6 (CMIP6), including fine resolution (HighResMIP) and ocean-forced (OMIP) models. We show that biases in the SETA remain large in CMIP6 models but are reduced in HighResMIP, with OMIP models giving the best performance. The analysis suggests that, once local forcing errors have been reduced, the major source of the SETA biases lies in the equatorial Atlantic. This study shows that finer model resolution has helped reduce the local origin of the SETA SST bias but further developments of model physics schemes will be required to make progress.

Surface and aloft NO2 pollution over the greater Tokyo area observed by ground-based and MAX-DOAS measurements bridged by kilometer-scale regional air quality modeling

To advance our understanding of surface and aloft nitrogen dioxide (NO2) pollution, this study extensively evaluated NO2 concentrations simulated by the regional air quality modeling system with a 1.3 km horizontal grid resolution using the Atmospheric Environmental Regional Observation System ground-based observation network and aloft measurements by multi-axis differential optical absorption spectroscopy (MAX-DOAS) over the greater Tokyo area. Observations are usually limited to the surface level, and gaps remain in our understanding of the behavior of air pollutants above the near-surface layer, particularly within the planetary boundary layer (PBL). Therefore, MAX-DOAS measurement was used, which observes scattered sunlight in the ultraviolet/visible range at several elevation angles between the horizon and zenith to determine the aloft NO2 pollution averaged over 0–1 km. In total, four MAX-DOAS measurement systems at Chiba University (35.63°N, 140.10°E) systematically covered the north, east, west, and south directions to capture the aloft NO2 pollution over the greater Tokyo area. The target period was Chiba-Campaign 2015 conducted during 9–23 November 2015. The evaluations showed that the air quality modeling system can generally capture the observed behavior of both surface and aloft NO2 pollution in terms of spatial and temporal coverage. The diurnal variation, which typically showed an increase from evening to early morning without daylight and a decrease during the daytime, was also captured by the model. During Chiba-Campaign 2015, two cases of episodic higher NO2 concentration were identified: one during the nighttime and another during the daytime as different diurnal patterns. These were related to a stagnant wind field, with the latter also connected to a lower PBL height in cloudy conditions. Comparison of the modeled daily-averaged surface and aloft NO2 concentrations showed that aloft NO2 concentration exhibited a strong linear correlation with surface NO2 concentration, with the aloft (0–1 km) value scaled to 0.4–0.5-fold the surface value, irrespective of whether the day was clean or polluted. This scaling value was lower during the nighttime and higher during the daytime. Based on this synergetic analysis of surface and aloft observation bridged by a kilometer-scale fine-resolution modeling simulation, this study contributes to fostering understanding of aloft NO2 pollution.

Ocean shelf exchange, NW European shelf seas: Measurements, estimates and comparisons

Transports across the continental shelf edge enhance shelf-sea production, remove atmospheric carbon and imply an active boundary to ocean circulation. We estimate relatively large overall transport across three contrasted sectors of north-west European shelf edge: the Celtic Sea south-west of Britain, the Malin-Hebrides shelf west of Scotland, the West Shetland shelf north of Scotland. The estimates derive from measurements in the project FASTNEt (Fluxes across sloping topography of the North East Atlantic): drifters, moored current meters, effective “diffusivity” from drifter dispersion and salinity surveys, other estimates of velocity variance contributing to exchange. Process contributions include transport by along-slope flow, internal waves and their Stokes drift, tidal pumping, eddies, Ekman transports in the wind-driven surface layer and bottom boundary layer.Overall exchange across the shelf edge is estimated as several m2s−1: if extrapolated globally even 1 m2s−1 is large compared with oceanic transports and potentially important to shelf-sea and adjacent oceanic budgets. In our context, most exchange is in tides, and other motion with periods ∼ one day or less, and so effective only for water properties that evolve on such short time-scales. Nevertheless, cross-slope fluxes, and exchange by low-frequency motion (periods > two days), are large by global standards and also very variable. Deployment-mean fluxes nearest the shelf break were in the range 0.3–4 m2s−1; mean exchanges from low-frequency motion were 0.8–3 m2s−1. Deeper longer-term moorings and drifters crossing 500 m depth gave much larger fluxes and exchanges up to 20 m2s−1. These transports’ significance depends on distinctive properties of the water, or its contents, and on internal shelf-sea circulation affecting further transport. For the NW European shelf, transports across the shelf edge enable its disproportionately strong CO2 “pump”.The complex context, and small scales of numerous processes enabling cross-slope transports, imply a need for models. Measurements remain limited in extent and duration, but widely varied contexts, particular conditions, events, processes and behaviours are now available to support model validation, especially around the north-west European continental shelf edge. Variability still renders observations insufficient for stable estimates of transports and exchanges, especially if partitioned by sector and season; indeed, there may be significant inter-annual differences. Validated fine-resolution models give the best prospect of spatial and temporal coverage and of estimating present-day and potential future shelf-sea sensitivities to the adjacent ocean.

Downscaling a national hydrological model to subgrid scale

The resolution of nation scale hydrological models is often considered a limitation with respect to providing useful management information on local scale issues. Use of these coarse scale models to inform both the structure and parameterisation of local scale models could potentially enhance their utility and in some cases provide initial estimates to local scale water resource issues such as nitrate transport. This study explores whether a finer resolution model, whose conceptualization and parameterisation are based upon a nation scale model, can replicate local scale water balance observations and in this way expand the utility of large-scale models. We use the Danish National Water Resources model (DK-model) as a template to model a small 5 km2 subcatchment in Denmark and compare the flows at four transect locations to those observed in a previous field investigation. The resolution of the DK-model is 500 m, and this model was refined to create four additional models of the subcatchment with 100 m, 50 m, 20 m, and 10 m resolutions. Results suggest that none of the refined models could accurately capture the daily overland and groundwater flow conditions. However, for the finer resolution models (10 m and 20 m), the annual overland and groundwater flows agreed well with the observed flows.