The sensitivity of pCO₂ reconstructions in the Southern Ocean to sampling scales: a semi-idealized model sampling and reconstruction approach

The Southern Ocean is a complex system yet is sparsely sampled in both space and time. These factors raise questions about the confidence in present sampling strategies and associated machine learning (ML) reconstructions. Previous studies have not yielded a clear understanding of the origin of uncertainties and biases for the reconstructions of the partial pressure of carbon dioxide (pCO2) at the surface ocean (pCO2ocean). Here, we examine these questions by investigating the sensitivity of pCO2ocean reconstruction uncertainties and biases to a series of semi-idealized observing system simulation experiments (OSSEs) that simulate spatio-temporal sampling scales of surface ocean pCO2 in ways that are comparable to ocean CO2 observing platforms (Ship, Waveglider, Carbon-float, Saildrone). These experiments sampled a high spatial resolution (±10 km) coupled physical and biogeochemical model (NEMO-PISCES) within a sub-domain representative of the Sub-Antarctic and Polar Frontal Zones in the Southern Ocean. The reconstructions were done using a two-member ensemble approach that consisted of two machine learning (ML) methods, (1) the feed-forward neural network and (2) the gradient boosting machines. With the baseline observations being from the simulated ships mimicking observations from the Surface Ocean CO2 Atlas (SOCAT), we applied to each of the scale-sampling simulation scenarios the two-member ensemble method ML2, to reconstruct the full sub-domain pCO2ocean and assess the reconstruction skill through a statistical comparison of reconstructed pCO2ocean and model domain mean. The analysis shows that uncertainties and biases for pCO2ocean reconstructions are very sensitive to both the spatial and temporal scales of pCO2 sampling in the model domain. The four key findings from our investigation are the following: (1) improving ML-based pCO2 reconstructions in the Southern Ocean requires simultaneous high resolution observations of the meridional and the seasonal cycle (< 3 days) of pCO2ocean; (2) Saildrones stand out as the optimal platforms to simultaneously address these requirements; (3) Wavegliders with hourly/daily resolution in pseudo-mooring mode improve on Carbon-floats (10-day period), which suggests that sampling aliases from the low temporal frequency have a greater negative impact on their uncertainties, biases and reconstruction means; and (4) the present summer seasonal sampling biases in SOCAT data in the Southern Ocean may be behind a significant winter bias in the reconstructed seasonal cycle of pCO2ocean.

Imaging structure and geometry of slabs in the greater Alpine area – a P-wave travel-time tomography using AlpArray Seismic Network data

We perform a teleseismic P-wave travel-time tomography to examine the geometry and structure of subducted lithosphere in the upper mantle beneath the Alpine orogen. The tomography is based on waveforms recorded at over 600 temporary and permanent broadband stations of the dense AlpArray Seismic Network deployed by 24 different European institutions in the greater Alpine region, reaching from the Massif Central to the Pannonian Basin and from the Po Plain to the river Main. Teleseismic travel times and travel-time residuals of direct teleseismic P waves from 331 teleseismic events of magnitude 5.5 and higher recorded between 2015 and 2019 by the AlpArray Seismic Network are extracted from the recorded waveforms using a combination of automatic picking, beamforming and cross-correlation. The resulting database contains over 162 000 highly accurate absolute P-wave travel times and travel-time residuals. For tomographic inversion, we define a model domain encompassing the entire Alpine region down to a depth of 600 km. Predictions of travel times are computed in a hybrid way applying a fast TauP method outside the model domain and continuing the wave fronts into the model domain using a fast marching method. We iteratively invert demeaned travel-time residuals for P-wave velocities in the model domain using a regular discretization with an average lateral spacing of about 25 km and a vertical spacing of 15 km. The inversion is regularized towards an initial model constructed from a 3D a priori model of the crust and uppermost mantle and a 1D standard earth model beneath. The resulting model provides a detailed image of slab configuration beneath the Alpine and Apenninic orogens. Major features are a partly overturned Adriatic slab beneath the Apennines reaching down to 400 km depth still attached in its northern part to the crust but exhibiting detachment towards the southeast. A fast anomaly beneath the western Alps indicates a short western Alpine slab whose easternmost end is located at about 100 km depth beneath the Penninic front. Further to the east and following the arcuate shape of the western Periadriatic Fault System, a deep-reaching coherent fast anomaly with complex internal structure generally dipping to the SE down to about 400 km suggests a slab of European origin limited to the east by the Giudicarie fault in the upper 200 km but extending beyond this fault at greater depths. In its eastern part it is detached from overlying lithosphere. Further to the east, well-separated in the upper 200 km from the slab beneath the central Alps but merging with it below, another deep-reaching, nearly vertically dipping high-velocity anomaly suggests the existence of a slab beneath the eastern Alps of presumably the same origin which is completely detached from the orogenic root. Our image of this slab does not require a polarity switch because of its nearly vertical dip and full detachment from the overlying lithosphere. Fast anomalies beneath the Dinarides are weak and concentrated to the northernmost part and shallow depths. Low-velocity regions surrounding the fast anomalies beneath the Alps to the west and northwest follow the same dipping trend as the overlying fast ones, indicating a kinematically coherent thick subducting lithosphere in this region. Alternatively, these regions may signify the presence of seismic anisotropy with a horizontal fast axis parallel to the Alpine belt due to asthenospheric flow around the Alpine slabs. In contrast, low-velocity anomalies to the east suggest asthenospheric upwelling presumably driven by retreat of the Carpathian slab and extrusion of eastern Alpine lithosphere towards the east while low velocities to the south are presumably evidence of asthenospheric upwelling and mantle hydration due to their position above the European slab.

Optimization of Groundwater Pumping and River-Aquifer Exchanges for Management of Water Resources

Groundwater pumping influences the rate of River-Aquifer (R-A) exchanges and alters the water budget of the aquifer. Therefore, fulfilling the total water demand of the area, with an optimal pumping rate of wells and optimal R-A exchanges rate, is important for the sustainable management of water resources and aquatic ecosystems. Meanwhile, comparison of the output of different simulation-optimization techniques, which is used for the solution of water resource management problems, is a very challenging task where different Pareto fronts are compared to identify the best results. In the present work, mathematical models were developed to simulate the R-A exchanges for the lower part of the River Ain, France. The developed models were coupled with optimization models in MATLAB environment and were executed to solve the multi-objective optimization problem based on the maximization of pumping rates of wells and maximization of groundwater input into the river Ain through R-A exchanges. The Pareto front developed by different simulation-optimization models was compared and analyzed. The Pareto fronts were juxtaposed based on the convergence, total diversity, and uniformity with the help of different performance metrics like hypervolume, generational distance, inverted generational distance, etc. The impact of different groundwater models based on domain size and boundary conditions was also examined. Results show the dominance of MOPSO over other optimization algorithms and concluded that the maximization of pumping rates significantly changes after considering the R-A exchanges-based objective function. It is observed that the model domain also alters the output of simulation-optimization, therefore the model domain and corresponding boundary conditions should be selected carefully for the field application of management models. ANN models were also developed to deal with the computationally expensive simulation model by reducing the processing time and were found efficient. Keywords: Simulation-Optimization, Multi-Objective optimization, Artificial Neural Network, River-Aquifer exchanges.

Using Task Farming to Optimise a Street-Scale Resolution Air Quality Model of the West Midlands (UK)

High resolution air quality models combining emissions, chemical processes, dispersion and dynamical treatments are necessary to develop effective policies for clean air in urban environments, but can have high computational demand. We demonstrate the application of task farming to reduce runtime for ADMS-Urban, a quasi-Gaussian plume air dispersion model. The model represents the full range of source types (point, road and grid sources) occurring in an urban area at high resolution. Here, we implement and evaluate the option to automatically split up a large model domain into smaller sub-regions, each of which can then be executed concurrently on multiple cores of a HPC or across a PC network, a technique known as task farming. The approach has been tested for a large model domain covering the West Midlands, UK (902 km2), as part of modelling work in the WM-Air (West Midlands Air Quality Improvement Programme) project. Compared to the measurement data, overall, the model performs well. Air quality maps for annual/subset averages and percentiles are generated. For this air quality modelling application of task farming, the optimisation process has reduced weeks of model execution time to approximately 35 h for a single model configuration of annual calculations.

Multi-Objective Model-Based Assessment of Green-Grey Infrastructures for Urban Flood Mitigation

This paper presents the performance quantification of different green-grey infrastructures, including rainfall-runoff and infiltration processes, on the overland flow and its connection with a sewer system. The present study suggests three main components to form the structure of the proposed model-based assessment. The first two components provide the optimal number of green infrastructure (GI) practices allocated in an urban catchment and optimal grey infrastructures, such as pipe and storage tank sizing. The third component evaluates selected combined green-grey infrastructures based on rainfall-runoff and infiltration computation in a 2D model domain. This framework was applied in an urban catchment in Dhaka City (Bangladesh) where different green-grey infrastructures were evaluated in relation to flood damage and investment costs. These practices implemented separately have an impact on the reduction of damage and investment costs. However, their combination has been shown to be the best action to follow. Finally, it was proved that including rainfall-runoff and infiltration processes, along with the representation of GI within a 2D model domain, enhances the analysis of the optimal combination of infrastructures, which in turn allows the drainage system to be assessed holistically.

Modelling rain heterogeneities in urban areas

&lt;p&gt;Urbanisation modifies the local climate and results in the so-called urban climate. Within the urban boundary layer, the average wind speed is reduced, while gustiness is increased. Buildings induce vertical winds. Heterogeneities in the rain pattern around buildings are the consequence. Human discomfort in street canyons may be one result. In addition, sealed urban surfaces lead to large rainwater run-off, which is a cause for flash floods in urban areas.&lt;/p&gt;&lt;p&gt;Increased computational power allows high-resolution modelling in urban areas with a horizontal resolution well below 10 m. The consideration of more meteorological processes like cloud and rain microphysics is possible. This allows us to estimate the impact of rain events, especially heavy rain events and flash floods, to urban neighbourhoods. Nevertheless, the domain size with these high-resolution models is restricted and the cloud and rain development passes the domain without full development of rain. To overcome this challenge, high-resolution information about rain events in urban areas are necessary.&lt;/p&gt;&lt;p&gt;In the area of Hamburg, Germany, measurements of a X-band weather radar at a 100-metre-scale and a vertically pointing micro rain radar are available for several years. These high-resolution measurement data are used to develop a forcing method for the microscale, obstacle resolving transport and stream model MITRAS (Salim et al. 2019). The forcing method samples 2D and 3D information about the rain rate to the model domain. The nudging approach adds the information about the rain rate to the top and the lateral boundaries of the model domain. Model simulations with different synoptic situations evaluate the forcing methodology.&lt;/p&gt;&lt;p&gt;In this contribution, the forcing method will be presented and results from different test cases in a test area in Hamburg will be shown.&lt;/p&gt;&lt;p&gt;&lt;br&gt;Salim M.H, Schl&amp;#252;nzen K.H., Grawe D., Boettcher M., Gierisch A.M.U., Fock B.H. (2018): The microscale obstacle-resolving meteorological model MITRAS v2.0: model theory. Geosci. Model Dev., 11, 3427&amp;#8211;3445, https://doi.org/10.5194/gmd-11-3427-2018.&lt;/p&gt;

Evaluation of daily forecasts by the coupled Terrestrial Systems Modelling Platform (TSMP) over a small convection-permitting model domain in Central Europe

&lt;p&gt;In many of today&amp;#8217;s resource management and climate change adaptation challenges, versatile and&amp;#160; reliable numerical model simulations are the basis for informed decision making. The integration of multiple compartmental&amp;#160; models into simulation platforms allows us to reproduce interacting geosystem processes and thereby solve a wide range of problems in a variety of applications. The Terrestrial System Modelling Platform (TSMP, https://www.terrsysmp.org) is an integrated regional Earth system model that simulates processes from the groundwater across the land surface to the top of the atmosphere on multiple spatio-temporal scales. TSMP consists of the COSMO (Consortium for Small-scale Modeling) atmospheric model, the CLM (Community Land Model), and the hydrologic model ParFlo, coupled through OASIS3-MCT. TSMP is used in various studies from climate change simulations to near-real time forecasting and monitoring. Here we present the results of the evaluation of the TSMP in a monitoring setup, providing daily forecasts with a lead time of 10days of the atmospheric, surface, and groundwater states and fluxes for a heterogeneous mid mountain-ranges area in Western Germany. The model domain covers an area of 150km x 150km at 1km (atmosphere) and 0.5km (land surface and subsurface) resolution. The simulated data is compared with observations from the TERENO (Terrestrial Environmental Observatories, https://www.tereno.net) Eifel/Lower Rhine Valley network. This TERENO observatory comprises a total area of 2354 km&amp;#178; and provides data from a very dense measurement network of 12 climate stations, 6 eddy covariance stations, 6 lysimeter stations, and 13 cosmic-ray neutron stations. To assess the quality and suitability of the TSMP as a monitoring system of the geosystem&amp;#8217;s state and evolution with agricultural applications in mind,&amp;#160; forecasts from July 2019 to October 2020 are analyzed with reference to the observations. Results show that the TSMP can well represent the main subsurface hydrological and relevant meteorological features.&lt;/p&gt;

The reduction of systematic temperature biases in soil moisture-limited regimes by stochastic root depth variations

In soil moisture-limited evapotranspiration regimes, near-surface temperatures are strongly affected by the available soil water amount for evapotranspiration. Its spurious representation in climate models consequently results in an inaccurately simulated turbulent heat flux partitioning and associated temperature biases.Since the physical reasons for soil moisture induced temperature biases are different in every region and model, a new method is presented to reduce these biases systematically. To achieve this, a stochastic root depth variation is applied, whereby the root depths in each grid-box of the model domain are uniformly perturbed. Thus, the soil water supply for evapotranspiration is increased for 50 % of the grid-boxes in the model domain and reduced for the other 50 %. In energy-limited regimes, where soil moisture just slightly affects the near-surface temperatures, the turbulent heat flux partitioning is not affected. In moisture-limited regimes, the method has an asymmetric effect on evapotranspiration. In cases of overestimated supplies, the reduced root depths in 50 % of the model domain result in an overall evapotranspiration reduction. In cases of underestimated supplies, the opposite is the case. In cases of correctly simulated supplies, the evapotranspiration reduction in 50 % of the model domain and the evapotranspiration increase in the other 50 % balance each other on a climatological mean. In this way, the method affects the turbulent heat flux partitioning only if soil moisture is spuriously simulated in the model. The associated biases are then systematically reduced, independently of the underlying physical process, which caused the soil moisture deficiencies.