bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of simulations of the Last Glacial Maximum with fire-enabled vegetation models from the FireMIP intercomparison project 
Authors: Lincoln Paul , Harrison Sandy P. , Forrest Matthew , Kaplan Jed , Yue Chao
Published: March 2022

Fire-enabled vegetation models are an important component of earth system modelling. Understanding the sensitivity of vegetation and wildfire to climate change benefits from out-of-sample experiments, of which the Last Glacial Maximum (LGM; 21 ka BP) is a preferred test. Here, we compared wildfire simulations for the LGM made with four fire-enabled vegetation models using a standardized protocol and driven by a climate-model simulation of the response to known LGM changes in ice-sheet extent, atmospheric composition and insolation. We compare the resulting model output with inferred changes in fire based on charcoal records from the Reading Palaeofire Database (RPD).

All four models show a global decrease in fire at the LGM compared to the present day, consistent with the charcoal records which also record less fire. The simulated change in fire is driven principally by changes in vegetation cover at the LGM, particularly the shift from forest to more open vegetation. The simulated reduction in forest cover is consistent with pollen-based reconstructions of LGM vegetation. Despite this general agreement among models, there are differences between the simulated fire anomalies at a regional scale. The largest differences between the models occur in equatorial Africa, South America and East Asia where the amplitude and spatial extent of regions of increased fire (driven principally by the replacement of tropical trees by grassland); in some regions even the direction of change is not consistent. Comparison of the simulated changes with charcoal records from these regions identifies which model(s) perform best, but also make it clear that there is no one model that simulates observed patterns of change in fire across all of the regions.
Information flow in complex high-dimensional systems
Authors: Ratas Mart , van Leeuwen Peter Jan
Published: March 2022

Knowledge on how information flows in complex Earth system models would be of great benefit for our understanding of the system Earth and its components. In principle the Kolmogorov or Fokker-Planck equation can be used to estimate the evolution of the probability density. However, this is not very practical since this equation can only be solved in very low dimensional systems. Because of that, mutual information and information flow have been used to infer information in complex systems. This usually involves integration over all state variables, which is generally numerically too expensive. Here we introduce an exact but much simpler way to find how information flows in numerical solutions that only involves integrations over the local state variables. It allows to infer both magnitude and direction of the information flow. The method is based on ensemble integrations of the system, but because the calculations are local the ensemble size can remain small, of  O(100). 

In this talk we will explain the methodology and demonstrate its use on the highly nonlinear Kumamoto-Sivashinsky model using a range of model sizes and exploring both 1-dimensional and multi-dimensional configurations. 
New insights into the last glacial cycle in the south-eastern European Alps from the glacial geomorphological record of the Monte Cavallo (NE Italy)
Authors: Rettig Lukas , Hajdas Irka , Monegato Giovanni , Mozzi Paolo , Spagnolo Matteo
Published: March 2022

Recent studies have shown that during the last glacial cycle the extent, timing and style of glaciation was not uniform across the European Alps but influenced by local topographic or climatic factors. In the south-eastern part of the mountain range, for example, glaciers not only developed in the inner-Alpine sectors but also along the pre-Alpine chains, probably fuelled by high orographic precipitation in these regions. Despite their high climatic sensitivity, the evolution of these glaciers throughout the last glacial cycle is still not fully understood and more field data are needed to enable comparisons among different sites. To address this issue, we present new results from the Monte Cavallo Group (Venetian Prealps, NE-Italy), based on detailed geomorphological mapping, glacier reconstructions and Equilibrium Line Altitude (ELA) modelling; then we compare our findings to other paleoglaciers that existed along the fringe of the southern Alps.

The oldest sediments in the Monte Cavallo Group are deposits of a small lake basin, rich in organic macrofossils such as branches and bark remains. These sediments likely date back to at least the earliest part of MIS 3, or potentially even previous interglacial periods. As climate deteriorated towards the Last Glacial Maximum (LGM), glacier tongues advanced from the peak regions into the main valleys. While towards the west, some small tributaries merged with the large Piave glacier, most of the glacial system of the Monte Cavallo remained independent. Its maximum extent is marked by prominent lateral and frontal moraine ridges that allowed reconstructing the geometry and ELA of the glaciers during the LGM. Besides the valley glaciers, also mid-altitude plateaus were at least temporarily covered by ice, however these plateau glaciers probably quickly vanished after the LGM acme, due to their restricted elevation range. Glacial retreat in the valleys, on the other hand, was intermitted by phases of stagnancy or readvance, as indicated by smaller moraine ridges up-valley. Comparing these Late Glacial moraines with other regional records may reveal important patterns regarding the early stages of post-LGM deglaciation in the south-eastern Alps.
k-Means Clustering of geophysical tomographic data for landfill characterization
Authors: Piegari Ester , De Donno Giorgio , Paoletti Valeria
Published: March 2022

The detection and imaging of landfills is a challenging task for geophysical methods because major pitfalls may arise, in such complex areas, from the speculative interpretation of geophysical anomalies as geological or antrophic features. In fact, when we face a multi-layered scenario, with numerous resistive to conductive transitions (that is the case of landfills), the actual shape and position of the anomalies (e.g. due to leachate accumulation) can be biased. The use of electrical resistivity tomography (ERT) in combination with the induced-polarization (IP) method, can help in this sense, even though may be not sufficient to completely remove ambiguities in interpretation of inverted models.

In this work, we present an application of an unsupervised machine learning k-means algorithm to ERT and IP data acquired in two urban waste disposal sites. The aim of the cluster analysis is to reduce the ambiguity on geophysical model interpretation and to improve the accuracy on detection of anomalous zones related to leachate accumulation. Experimental 2D field data were firstly inverted separately for resistivity and chargeability, using a Gauss-Newton algorithm. Then, joint 2D sections were obtained using k-means clustering of electrical resistivity, chargeability and normalized chargeability (chargeability divided by the resistivity) data. The retrieved model sections provide a quantitative integration of distinct geophysical data, which can offer new perspectives for the characterization of leachate distribution in landfills.
Gender in EU project.
Authors: Dehant Véronique
Published: March 2022

In EU project (being H2020 or ERC), we are asked to do a “Gender Equality Plan” and to take care about gender issues. I think that this is very important. We definitely need to take care about gender at recruitment level and to take care about gender balance. However, to care about gender inequality does not only refer to gender balance in the teams in charge of carrying out the projects but also to plan research and innovation activities with the goal of inspiring a new generation of women in space.  Gender dimension brings a vision that goes beyond the line of sight. It is not only about statistics on the number of male and female researchers in a specific field or gender balance in the Consortium but here are some paths to follow: (1) becoming gender-sensitive, (2) integrating an in-depth understanding of both genders’ needs, behaviours, and attitudes, (3) promoting our sector to young girls at the level of schools or universities, and (4) being aware about unconscious gender biases. Gender bias is the tendency to prefer one gender over another gender. Several studies found that both men and women prefer male job candidates. So much so that, in general, I have found on the web that a man is 1.5 times more likely to be hired than a woman when both are equal-performing candidates. Bias is a systemic prejudice for, or against something or someone, based on for instance stereotypes. When preparing newsletters for my EU project about that, I came through several examples about ways in which women and men are often held to different standards in the workplace. I think that this is interesting to know. This is what I propose to share with you.
Spatio-temporal Variablity of Arctic Sea Ice Freeboard, Thickness and Volume from CryoSat-2 and Its Possible Drivers
Authors: Zhang Yanze
Published: March 2022

Investigating the temporal and spatial changes in Arctic sea ice freeboard, thickness and volume is crucial for climate and environmental research. In this study, the freeboard, thickness and volume of the Arctic sea ice between 2011 and 2020 were acquired from CryoSat-2 data and it was compared with NASA Operation IceBridge and Alfred Wegener Institue AWI datasets. The effect of wind field and temperature and itscontribution on Arctic sea ice were also investigated. The main steps of our research are as follows. 1) Selecting data points above 66 degrees north latitude and filtering out sea ice by flag value and mask; Using OSI SAF sea ice concentration (SIC) data to further constrict the area; Interpolating the latest mean sea surface data DTU21 into CryoSat-2 data; Calculating the sea ice freeboard via spatial altimetry relationship. 2) Combining with snow density, snow depth, multi-year ice density and first-year ice density to estimate the sea ice thickness respectively from freeboard according to the assumption of hydrostatic equilibrium. 3) Using the area and extent provided by NSIDC to interpret the volume. 4) Utilizing Seasonal and Trend decomposition using Loess(STL) to analyse the seasonal and interannual variations of sea ice. 5) Dividing the Arctic Ocean by its marginal sea, coupled with the HY-2B microwave scatterometer data and NCEP/NCAR reanalysis data, the impact of the Beaufort Sea, the Chukchi Sea, the East Siberian Sea, the Laptev Sea, the Kara Sea, the Barents Sea, the Greenland Sea, and the Baffin Bay wind field on the sea ice were studied. 6) Exploring the correlation between the sea surface temperature and the sea ice freeboard, thickness and volume. The result indicated that (1) the freeboard and thickness was decreasing about 9.748% and 8.80% during 2011-2020,respectively; (2) there are interlunar variations in sea ice freeboard and thickness, the freeboard and thickness of sea ice reach the minimum in August to September each year and the maximum appears in March to April; (3) Arctic sea ice is affected by both thermodynamics and dynamics, the reduction of Arctic sea ice was largely due to the sea surface wind field, one of the dynamic factors is the wind field on the sea surface. The sea ice changes in the various sea areas of the Arctic Ocean are related to the wind field to varying degrees. The changes in the sea surface wind field in recent years have further promoted the reduction of sea ice, making it possible for the Arctic to open to navigation in the summer. Fully understanding the evolution of sea ice change in the Arctic Ocean is helpful for humans to better protect the earth.
The detection of the magnetic fields induced by ocean circulation - An observing system simulation experiment
Authors: Hornschild Aaron , Saynisch-Wagner Jan , Baerenzung Julien , Irrgang Christopher , Thomas Maik
Published: March 2022

The movement of conductive seawater through Earth's magnetic field leads to electromagnetic induction processes in the oceans. The resulting radial magnetic fields have been successfully modeled and, in the case of tidal-induced magnetic fields, also identified in satellite magnetometer data. However, the magnetic signals caused by ocean circulation have still remained unobserved.
We introduce a new method to detect these signals using an observing system simulation experiment. Our approach relies on a Kalman filter-based assimilation of satellite magnetometer data. A key aspect of identifying ocean-induced signals is separating them from other magnetic contributions. For this separation, we used both estimations of the temporal behavior and spatial constraints as prior information. The observing system simulation experiment allows us to evaluate the proposed method.  
We present the results of this evaluation and report on the detectability of magnetic fields induced by ocean circulation.
A feature-based perspective on upscale error growth.
Authors: Schmidt Sören , Riemer Michael , de Heuvel Jorge
Published: March 2022

Atmospheric predictability is fundamentally limited by the upscale growth of initial small-scale, small-amplitude errors. To better understand this
fundamental limitation, it is essential to study upscale growth mechanisms. Upscale error growth is often investigated by spectral analysis. By design,
however, spectral analysis is not able to provide detailed spatial information of locally coherent error „features“. The flow dependence of error growth
suggests that such local error features play an important role in limitingpredictability at later forecasts times.

We here take an approach complementary to spectral analysis and apply a feature-based perspective. We have developed an automated algorithm to
identify error features in gridded data and track their spatial and temporal evolution. Errors are considered in terms of potential vorticity (PV) and we
evaluate a previously derived PV-error tendencies equation to characterize the growth of error features. We examine previously published upscale error
growth experiments with the global prediction Model ICON from the German Weather Service, in which the only difference between various realizations is in the seeding of a stochastic convection scheme. Complementing previous work, we here focus on low- to mid-tropospheric errors.

Spatial composites centered on the centroid of error features indicate that features are primarily generated ahead of an upper-tropospheric trough. The
environment surrounding the features at the time of their first detection is characterized by locally enhanced lower to mid tropospheric moisture, latent
heat release, and upper tropospheric divergence. Subsequently, this moist-diabatic nature of the error-feature environment becomes gradually less
prominent. The development of error features in their early stage is governed by non-conservative processes. Very early after detection, the convective
scheme has the strongest contribution to feature amplification. Subsequently, the influence of the convective scheme diminishes. Error features are thus generated by the convective scheme, consistent with the design of the experiments, but further amplification due to that scheme is not diagnosed.
Longwave radiation, in contrast, has a persistent and dominant amplifying impact throughout the early stages of the features. PV-error features are
found to also exhibit significant moisture errors and the moisture-radiation feedback occurs as a dominant error growth mechanism in our experiments.
In addition to the mean perspective of error-feature evolution, the presentation will discuss differences between fast and slowly growing errorfeatures and the interaction of low- to mid-tropospheric errors with errors at tropopause level.
Soil compaction imaging through Pedophysical Joint Inversion: a Northeastern Italy case study
Authors: Carrera Alberto , Pavoni Mirko , Piccoli Ilaria , Boaga Jacopo , Cassiani Giorgio , Morari Francesco
Published: March 2022

 

One of the major threats to global arable lands is represented by soil compaction, mainly due to agricultural traffic. Modern heavy agricultural machinery and unsuitable soil moisture conditions might irreversibly induce soil compaction that, in turn, adversely affects soil quality and ecosystem. Restriction to root penetration alongside impaired water and air fluxes are a few principal drawbacks of compacted soils, resulting in significant ecological and economic damage to society.
However, traditional methods to study soil compaction are limited by punctual nature and not-in-situ conditions.
In this context, the purpose of this work was to combine different non-invasive geophysical techniques, with a joint inversion approach of the acquired datasets, to study the complexity of the soil structure. In detail, we tried to adapt the petrophysical joint inversion developed in permafrost systems, combining geoelectrical and seismic soundings to characterize the subsoil structure in compacted and non-compacted soils. This methodology rests on the conjunction of seismic refraction tomography (RST) and electrical resistivity tomography (ERT), acquired on the very line, through a representative pedophysical model, able to quantitatively estimate the fractions of investigated soil phases (e.g., air, water, and matrix fractions).
The survey was conducted on an arable field of “L. Toniolo” Padova University experimental farm. The reliability of the obtained models was compared with direct measurements of volumetric water content, bulk density, and penetration resistance along the survey line. Preliminary results showed that the methodology might be a promising tool for spatio-temporal evaluation of soil structure evolution as related to soil compaction.
Cloud detection from IASI radiance for climate analysis purposes
Authors: Whitburn Simon , Clarisse Lieven , Coheur Pierre , Clerbaux Cathy
Published: March 2022

Clouds are an essential component in our Earth system because of their importance for the weather, the water cycle and the Earth radiation budget. To better understand the climate, its past and future evolution, the development of long coherent time series of cloud properties is needed. In addition, as the clouds strongly impact the radiance at the top of the atmosphere, the detection of clear-sky scenes is a major preprocessing step for most climate and atmospheric satellite applications, such as trace gas retrieval or to derive the Earth Outgoing Longwave Radiation.  

The Infrared Atmospheric Sounding Interferometer (IASI), flying on board the suite of Metop satellites for more than 15 years, has shown an excellent stability over its entire lifespan and a very good consistency between the three instruments (on board Metop-A, -B and -C). This makes the IASI dataset an excellent climate data record. For the detection and the characterization of clouds, the current IASI operational Level 2 product is highly performant. However, since it was first released in 2007, the L2 cloud data have undergone a series of updates which have not yet been reprocessed back in time. This leads to discontinuities in the data record which makes it very difficult for use in long-term studies. Even in the event of a complete reprocessing of the L2, there would also be no guarantee on the homogeneity of the futures versions. Other cloud products exist (e.g. the AVHRR-L1C, the cloud_cci, the CIRS-LMD) but those are usually either less accurate or sensitive to cloud detection or are not available in near-real-time. These limitations in the existing products triggered the development of a sensitive and coherent IASI cloud detection dataset.

Here we present a new cloud detection algorithm for the IASI measurements based on a Neural Network (NN). The input data consists of a set of 45 IASI channels. Those were selected outside the regions affected by CO2, CFC-11 and CFC-12 absorptions to avoid any long-term bias in the detection as their concentrations are evolving over time in the atmosphere. As a reference dataset, we use the current version (v6.6) of the IASI L2 cloud product. The IASI-derived NN cloud product appears to be both accurate in the cloud detection and coherent over the whole IASI period and between the three versions of the instrument. To illustrate this, we show global distributions and time series of the cloud fractions and we assess the quality of the cloud mask by comparing the NN product against several other cloud products. We also evaluate the capabilities of our NN cloud detection product to correctly distinguish cloud from dust plumes.
Xylem porosity shapes sapwood characteristics and stem water use of temperate and boreal tree species
Authors: Pappas Christoforos , Bélanger Nicolas , Bastien-Beaudet Gabriel , Couture Catherine , D'Orangeville Loïc , Duchesne Louis , Gennaretti Fabio , Houle Daniel , Hurley Alexander G. , Klesse Stefan , Lebel Desrosiers Simon , Montoro Girona Miguel , Peters Richard L. , Rossi Sergio , St-Amand Karel , Kneeshaw Daniel
Published: March 2022

Sapwood characteristics, such as sapwood area as well as thermal and hydraulic conductivity, are linked to species-specific hydraulic function and resource allocation to water transport tissues (xylem). These characteristics are often unknown and thus a major source of uncertainty in sap flow data processing and transpiration estimates because bulk rather than species-specific values are usually applied. Here, we analyzed the sapwood characteristics of fifteen common tree species in eastern North America from different taxa (i.e., angiosperms and gymnosperms) and xylem porosity groups (i.e., tracheid-bearing, diffuse- or ring-porous species). We quantified their sapwood area changes with stem diameter (allometric scaling) and thermal conductivity. We combined these measurements with species-specific values of wood density and hydraulic conductivity found in literature and assessed the role of wood anatomy in orchestrating their covariation. Angiosperms (ring- and diffuse-porous species), with specialized vessels for water transport, showed steeper relation (scaling) between tree size and sapwood area in comparison to gymnosperms (tracheid-bearing species). Despite the variability in thermal conductivity between species, gymnosperms (angiosperms) were characterized by lower (higher) wood density and higher (lower) sapwood moisture content, resulting in non-significant differences in sapwood thermal conductivity between taxa and xylem porosity groups. Clustering of species sapwood characteristics based on taxa or xylem porosity could facilitate more accurate parameterizations of these attributes. When combined with an increasing number of sap flow observations, these findings can improve tree- and landscape-level transpiration estimates, leading to more robust partitioning of terrestrial water fluxes.
The seasonal teleconnections of the Indian Ocean Dipole to the North Atlantic region
Authors: Hempel Tim , Weisheimer Antje , Palmer Tim
Published: March 2022

The Indian Ocean Dipole (IOD) is a major source of seasonal climate variability in the
Indian Ocean. This dipole has major impacts on the Indian Ocean region and through
teleconnections can influence the seasonal climate of remote regions as well. In late 2019 a
major IOD event contributed to a strong positive North Atlantic Oscillation (NAO) of that
winter. Thus, a good understanding of the mechanism that transports information from
the Indian Ocean to the North Atlantic is desirable. In this contribution we investigate
the special teleconnection of the winter of 2019 and analyse the transport mechanism.

In model experiments with the OpenIFS from ECMWF we show that the NAO in the
winter 2019 is influenced by the Indian Ocean Dipole. We use hindcast ensemble model
experiments to analyse the behaviour of the IOD and its impact on the NAO. These
seasonal hindcast experiments are started from the 01. November 2019 and run for the
DJF season 2019/2020. Since the OpenIFS is uncoupled we change the Sea Surface
Temperature (SST) boundary conditions in regions of importance to the NAO (like the
ENSO region, the North Atlantic, and also the Indian Ocean). With these perturbations
we identify the relative importance of individual ocean regions to the state of the NAO
in the winter of 2019.

We contrast the experiments with the perturbed SST conditions to a control forecast and
ERA5 reanalysis. We find that removing the IOD has a significant impact on the NAO of
the 2019/2020 DJF season, pushing the NAO to a more negative state. Additionally the
contrast between control forecast and model experiments shows Rossby Waves emanating
from the Indian Ocean over the North Pacific and the Arabian Peninsular.

Experiments with perturbations in other ocean regions show that some signals, like ENSO,
can suppress the impact of the IOD on the NAO, but in their absence the positive IOD
event of 2019 did likely contribute to the strong positive NAO of 2019/2020.
Coupled Model Simulations of Carbon Dioxide Removal via Ocean Alkalinity Enhancement and Large-scale Afforestation and Reforestation
Authors: Wey Hao-wei , Kemena Tronje , Keller David , Oschlies Andreas
Published: March 2022

All Shared Socioeconomic Pathways (SSP) of future climate scenarios that are well below 2 °C warming require the application of carbon dioxide removal (CDR) technologies. While the mitigation potentials of different CDR methods have been proposed, the climate impacts have only been studied to a limited extent with the Earth System Models (ESMs). As part of the CDR Model Intercomparison Project (CDRMIP), we utilize here the land-ocean-atmosphere coupled FOCI-MOPS model to study the potential reversibility and impacts of different proposed CDR methods. FOCI-MOP is an integration of the marine biogeochemical model, Model of Oceanic Pelagic Stoichiometry (MOPS), in the Flexible Ocean and Climate Infrastructure (FOCI) ESM. Two CDR methods are studied under highly-idealized scenarios: a marine-based CDR of ocean alkalinity enhancement, and a land-based CDR of afforestation and reforestation, given their large theoretical mitigation potentials. In both experiments, the CDR methods are applied under the high CO2 emission scenario (SSP5-8.5). In the experiment of ocean alkalinity enhancement, alkalinity is added to ice-free ocean at a rate of roughly 0.14 petamole per year. In the experiment of afforestation and reforestation, the land use follows the scenario with high levels of afforestation and reforestation (SSP1-2.6). We look into the efficiency and the side-effects of CDR methods. In addition, we investigate whether the hysteresis behavior exists as well as the non-reversible aspects of the applied CDR, including ocean deoxygenation as well as the respective impacts on both terrestrial and marine primary production. Finally, as models are largely different in their structures and representations of terrestrial and marine biogeochemistry, we compare our results to results from other models participating in CDRMIP for assessing the modeling uncertainty. The results presented here are helpful for a more realistic application of CDR portfolio and provide insights on a mitigation pathway toward a net-zero world in the future.
Observation of the direct effect of aerosols in the South-East Atlantic at high temporal resolution from MSG/SEVIRI
Authors: Peers Fanny , Haywood Jim , Allan Richard , Francis Pete , Johnson Ben , Myhre Gunnar , Schulz Michael
Published: March 2022

The direct radiative forcing due to aerosols remains highly uncertain in the South-East Atlantic. During the fire season, absorbing aerosols from the African biomass burning are transported towards the ocean where they are frequently observed above the semi-permanent layer of stratocumulus. Biomass burning aerosols above an ocean surface will exert a negative radiative forcing, while over a highly reflectant cloud, will exert a positive radiative forcing. As a result, the direct forcing of aerosols is expected to be highly influenced by the strong diurnal cycle of the clouds in this region.

Until recently, passive satellite observations of aerosols were limited to cloud-free scenes as aerosol and cloud retrievals used to be mutually exclusive. Also, cloud properties retrieved from shortwave passive sensors are expected to be biased in the South-East Atlantic because operational retrievals do not account for the impact of above-cloud aerosol absorption on the measured signal. In the last decade, methods have been developed to retrieve simultaneously above-cloud aerosol and corrected-cloud properties. However, these methods have been mostly applied to polar orbiting instruments, which provide one or two observations a day. Consequently, the study of aerosol-radiation interaction in the South-East Atlantic would benefit from the high-temporal-resolution observations provided by geostationary satellite platforms.

In this study, we develop a novel and comprehensive approach to estimate the direct forcing of aerosols in the South-East Atlantic by combining cloudy and cloud-free retrievals at high temporal resolution from the geostationnary satellite MSG/SEVIRI. These observations allows to properly evaluate climate models participating in CMIP-6 and AeroCom phase III. The reasons for the differences between the observed and the modelled direct effects will be discussed.
Approximation error correction for drizzle formation in bulk microphysical parameterizations
Authors: Tonttila Juha , Romakkaniemi Sami , Kokkola Harri
Published: March 2022

The so-called autoconversion is a key numerical process used to describe the coalescence growth of cloud droplets to drizzle and rain in atmospheric models. Together with further growth of drizzle through accretion, these processes are typically represented by relatively simple two-moment bulk parameterizations. One of the shortcomings of this approach is that most often the parameterizations do not explicitly consider the impact of coarse mode aerosol and giant cloud condensation nuclei (GCCN), even though they are known to be important in marine cloud regimes. More elaborate sectional models, such as the Sectional Aerosol Module for Large-Scale Applications (SALSA) solve the coalescence growth equations and are able to account for the effect of large aerosol particles on droplet growth and drizzle formation.

In this work, the autoconversion and accretion rates diagnosed from SALSA are compared with the process rates from bulk parameterizations run simultaneously within a large-eddy simulation model (UCLALES-SALSA). The model is used to create an ensemble of simulations comprising varying aerosol conditions in terms of the coarse mode particles in marine stratocumulus and shallow cumulus regimes. The difference between SALSA and bulk process rates is taken as the approximation error in the bulk parameterizations. The dependence of this error term on the coarse mode aerosol concentration is shown by a multivariate sensitivity analysis based on the ensemble data. Further, machine learning methods, notably the neural networks, are used to represent the approximation error term. The trained networks are shown to successfully capture the main features of the model-based approximation error. As an outlook, the machine learning-based representation of the approximation error allows to enhance the existing bulk microphysical parameterizations for drizzle formation and to introduce an explicit dependence on coarse model aerosol and GCCN.
Effect of different tree species on soil moisture and temperature. Early-stage view of continuous forest soil regime monitoring.
Authors: Kuželková Marta , Jačka Lukáš , Kovář Martin , Hradilek Václav
Published: March 2022

Due to the spatial heterogeneity, root presence and other specific properties, measurement of forest soil hydraulic properties is difficult. Forests are generally hydrologically important systems that can mitigate negative climate change impact, and specifically, forest soil represents crucial water reservoir. A common forest management strategy is to plant monocultural stands of trees. Due to the differences in trees characteristics, e.g., root system, litter and leaf area, the development of soil undergoes specific changes according to the planted species. The main aim of this study is to investigate the connection between the tree species and hydro-physical properties of forest soil with focus on long term soil moisture and temperature regime monitoring. This research brings an early-stage view to data obtained from May 2021 up to nowadays.

A set of 55 TDT (time domain transmission) soil moisture and temperature sensors were installed into three nearby locations. In each of those a monoculture stand of beech (Fagus sylvatica), spruce (Picea Abies), and larch (Larix Decidua) are planted. Half of the sensors are used for measuring the mineral soil moisture in depth of -15 to -29 cm below soil surface and point temperature of -23, -15, +5 cm relative to the surface, the rest is used for measuring the topsoil moisture from the surface to the depth of -14 cm and point temperatures in levels of -8, 0 and +15 cm.

Results shows distinct differences in soil moisture among tested tree species. After longer period without precipitation (period of soil-water loss), the highest differences in volumetric water content (VWC) were observed. After one-month period without rain in early summer, mean values of VWC for topsoil were 35% for beach, 28% for larch, and 21% for spruce. Overall, the beech stands showed the highest ability to maintain soil water after periods of soil water loss and therefore, potentially exhibited the strongest resistance towards soil drought. By contrast, spruce tends to lose water relatively fast which can be problematic especially in events of long-term drought. For the surface temperature during vegetation season, the highest values were observed in larch stands followed by spruce and the lowest in beach. These findings probably corresponding to different solar radiation permeability of tree canopies. The observed effects of tree species on soil moisture and temperature should be considered for hydrological modelling, future forest planning, and water management improvement of forest soil.
Offshore windfarm modelling over the North Sea with COSMO-CLM: model evaluation and application at kilometer-scale resolution
Authors: Borgers Ruben , Meyers Johan , Van Lipzig Nicole
Published: March 2022

The rapid growth of the offshore wind energy sector emphasizes the need for realistic projections of the lifetime energy yield of existing and planned offshore windfarms. Analyses of CMIP5 data show that, even though near-future wind speed changes over Europe and the North Sea are uncertain across models, these changes should be taken into account by wind industries due to the potentially large impact on the energy. Thanks to advances in model design and the increase in computing resources over the past decades, regional climate models (RCMs) can be used for multi-decadal simulations and that at a spatial resolution of a couple of kilometers or less. These high-resolution simulations not only allow for an improved representation of weather systems, but also allow to represent the interactions between windfarms and the atmosphere through a wind farm parametrization. In this way, RCMs can be employed for a detailed wind resource assessment for the coming decades that takes into account the wakes and energy losses induced by upwind arrays and clusters.

In our wind resource assessment, we use the regional climate model COSMO-CLM, extended with the Fitch wind farm parametrization. An evaluation of a 10-year, ERA5-driven simulation at 2.8km resolution against in-situ anemometers, lidar measurements and satellite-borne ASCAT measurements that has been performed for the North Sea will be discussed. For the year 2019, the spatially-variable bias in the mean wind speed was found to be generally within +- 0.4 m/s and the overlap in the wind speed distributions generally larger than 92%. Next, array- and cluster-scale wakes, modelled at horizontal resolutions of 2.8km and 1km were analysed and compared for a present and near-future windfarm layout over the southern North Sea, providing valuable information on how regional changes to the wind farm layout will impact the farm-specific energy yields due to additional upwind wake generation under different atmospheric conditions. This research frames within the FREEWIND project of KU Leuven (freewind-project.eu), which aims to contribute to the scientific research of offshore wind energy and plans to provide wind farm planning and forecasting tools on multiple time scales over the coming years.
Impact of local climate change on groundwater resources and surface water availability in headwater catchments
Authors: Munir Muhammad Usman , Frei Sven
Published: March 2022

About 90% of rivers and streams are originating in headwater catchments. Headwater catchments are more susceptible to climate change and sensitive to drought conditions. Extreme weather events (such as extended drought periods) are affecting water availability in headwater catchments which are important source areas for downstream river networks. Climate change is predicted to affect water availability for surface water bodies and groundwater alike. Future projections indicate that increasing temperature will affect the water balance with higher evapotranspiration rates. Headwater streams are often dependent on groundwater input specifically during baseflow conditions in summer/late summer. It is therefore important to investigate the potential impact of climate change on the interaction between surface water and groundwater in headwater catchments. As part of this study, we investigated the impact of local climate change on small scale surface/groundwater interactions for a small headwater catchment (Grosse Ohe) located in the Bavarian Forest (Germany). We used a the fully integrated hydrological model HydroGeoSphere (HGS) to represent surface groundwater interactions for the catchment. Simulations include data from regional climate change models (RCM) as input to represent future scenarios up to the year 2100. Results showed that increasing temperature causes higher evapotranspiration rates which significantly affects the water availability in headwater streams. Simulations indicate that climate change is responsible for more frequent drought periods during summer where groundwater inflow into the streams declines by up to 35% compared to the past (2002 to 2018). We also evaluated local exchange fluxes between groundwater and stream for the entire catchment. Here, simulations indicate that formerly gaining stream sections in future are more frequently turn into losing sections especially during extended baseflow conditions in summer. This may have severe consequences for the ecosystem as stream sections in future are prone to lose their entire water to the subsurface.
Introducing the eWaterCycle Python package for open and FAIR hydrological modelling
Authors: Kalverla Peter , Hut Rolf , Drost Niels , Verhoeven Stefan , Alidoost Fakhereh , Vreede Barbara , Andela Bouwe , Smeets Stef , Camphuijsen Jaro , Dzigan Yifat , Pelupessy Inti , van den Oord Gijs , van Werkhoven Ben , Aerts Jerom , van de Giesen Nick
Published: March 2022

The eWaterCycle platform (https://www.ewatercycle.org/) is a fully Open Source system designed explicitly to advance the state of Open and FAIR Hydrological modelling. It allows scientists to set up experiments in a standardized way and run them interactively in a Jupyter environment.

 

Previously we have presented various components that constitute the system: a preprocessing pipeline using ESMValTool (https://www.esmvaltool.org/) to generate meteorological forcing data, containerized models implementing the Basic Model Interface (https://bmi.readthedocs.io/), gRPC4BMI (https://github.com/eWaterCycle/grpc4bmi) to communicate with these containers from a Python environment, a visual explorer that lets the user set up an experiment with a few clicks and automatically generates a notebook based on the selected settings, and utilities to work with observations and analyse results.

 

Recently we have officially released the eWaterCycle Python package (https://ewatercycle.readthedocs.io/en/latest/) that connects these components to provide a simple and clean user interface. The core of the package is modelled after PyMT (https://pymt.readthedocs.io/en/latest/index.html), extended with convenience functions to make the interface more user friendly, e.g. using xarray (http://xarray.pydata.org/en/stable/index.html) for spatial data and providing more user-friendly time accessors. Separate modules are available to load forcing data and parameter sets from the system and configure them correctly for the target model.

 

The package comes with comprehensive documentation, including a suite of example notebooks. It also includes setup instructions for system administrators and guidance for incorporating new models. Currently, the following models are available through the eWaterCycle system: wflow, lisflood, marrmot m01, marrmot m14 and pcrglobwb.

 

The release of the package marks a milestone in the development towards our goal of fully reproducible, open, and FAIR Hydrological modelling.
Exploiting the combined GRACE/GRACE-FO solutions to determine gravimetric excitation of polar motion 
Authors: Śliwińska Justyna , Wińska Małgorzata , Nastula Jolanta
Published: March 2022

Interpretation of variations in polar motion (PM) excitation due to global mass redistribution of atmosphere, oceans, hydrosphere and cryosphere is an important task that takes place on the boundary between geodesy and geophysics. The role of Earth’s surficial fluids in PM excitation is usually assessed by analysing time series of atmospheric, oceanic, hydrological and cryospheric angular momentum (AAM, OAM, HAM and CAM, respectively). With the launch of the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions, a new era of using global gravity data to determine gravimetric excitation of PM has begun. This can be done due to linear relationship between degree-2 order-1 coefficients of geopotential and equatorial components of PM excitation.

A number of institutes around the world routinely process and deliver GRACE/GRACE-FO solutions; however, the non-negligible differences between PM excitation  estimates derived from data provided by various data centres exist. The choice of most appropriate GRACE/GRACE-FO solution for the purpose of HAM and CAM determination is a very complex task and depends on several factors like considered time period, analysed oscillation and assumed criterion of evaluation. The use of a combination of multiple GRACE/GRACE-FO solutions is a very common approach in various research tasks.

In this work, we create combined GRACE/GRACE-FO solutions to determine the total effect of HAM and CAM (HAM/CAM) on PM excitation. To do so, we use three-cornered hat method to estimate noise level of single GRACE/GRACE-FO solutions. We also consider unweighted and weighted mean of GRACE/GRACE-FO datasets as well as publicly available combined solutions such as that provided by the International Combination Service for Time-variable Gravity Field (COST-G). Our estimates of HAM/CAM based on the combined data are compared to the HAM/CAM determined from single solutions and to the hydrological plus cryospheric signal in observed (geodetic) excitation called geodetic residuals (GAO). As data form Satellite Laser Ranging (SLR) are also recommended to determine HAM/CAM, especially in the period of lack of GRACE or GRACE-FO measurements, we include SLR solutions to our analyses. Our work aims to check whether the combination of multiple solutions from different computing centres noticeable increases the compliance between HAM/CAM and GAO in several spectral bands like seasonal and non-seasonal oscillations.
Data-driven time series modeling to support groundwater model development for the Grazer Feld Aquifer, Austria
Authors: Kokimova Ainur , Collenteur Raoul , Birk Steffen
Published: March 2022

Alluvial aquifers and springs play a vital role in the water supply of Austria as the main source of drinking water. One such example is the Grazer Feld Aquifer, located in an urban and semi-urban setting in southeast Austria. Urbanisation imposes stresses on aquifers leading to quantitative and qualitative groundwater problems. These problems are commonly addressed by numerical groundwater models. In these models, natural and anthropogenic processes need to be carefully represented. Calibration to groundwater level data disturbed by human activities will fail or produce erroneous parameter estimates if the disturbance is not adequately considered by the model. As a consequence, such model likely result in erroneous predictions and assessments. To account for relevant drivers in the system and examine groundwater level data for the calibration of a numerical groundwater model, we test the application of the time series analysis as an additional and preliminary step in a general numerical groundwater modeling framework. The results of time series models (TSM) contribute to the understanding of spatiotemporal aquifer dynamics and main driving forces as advocated by Bakker and Schaars (2019).

The objective of this study is twofold. First, time series models were set up and calibrated for each monitoring well in the aquifer, using the stresses identified in the initial hydrogeological assessment (precipitation, evapotranspiration, and river levels). Second, we create a calibration data set that flags groundwater level observations that were caused by human temporal activities (e.g., pumping, irrigation, and/or dam construction). This is achieved by constructing TSMs and conducting a visual and spatial investigation on model results. The process differentiates good fit models from no-good fit models. Then, models, not delivering a good fit, are checked for missing driving forces by engaging local stakeholders. Once the process is characterized, the period with unexplained groundwater level change is marked. The groundwater level fluctuations of 115 out of 149 observation wells are found to be reasonably simulated by considering recharge from precipitation and, if applicable, river stages as driving forces. For 34 observation wells, however, the models perform less accurately, suggesting other factors, such as construction activities and temporary groundwater abstraction, influencing groundwater level fluctuations during the part or the entire simulation period. Estimated recharge is found to be higher in urban and semi-urban areas compared to agricultural fields. The results from this study will be used in the future development of a numerical groundwater model for the entire aquifer.