bluuebunny/crossref_metadata_2025 huggingface dataset:
Coastal-breeze simulation with the WRF model: analysing the sensitivity to land/sea surface temperature changes
Authors: Román-Cascón Carlos , Mulero-Martínez Roberto , Bruno Miguel , Izquierdo Alfredo , Yagüe Carlos , Álvarez Oscar , Gómez-Enri Jesús , Mañanes Rafael , Adame José Antonio
Published: June 2022

Coastal breezes are mesoscale winds formed in mid-latitude regions under fair-weather synoptic conditions, i.e., weak surface-pressure gradient and low winds. They are characterized by a typical wind-direction reversal twice per day, with onshore winds during daytime and offshore during the night. The impacts of these winds are broad and varied: they transport humidity and pollutants (among other physical properties) in the coastal region; they can initiate convection (and even trigger the formation of storms), and they also drive some of the surface oceanic currents formed close to the coast, among others. In this sense, recent surface current observations collected by the Gulf of Cádiz radar system point at the coastal breezes as important contributors to explain the behaviour of the surface currents variability in this region. From a societal point of view, the coastal breezes are crucial for wind power industry, air-quality forecasts, maritime sports, and simply for the refreshing impact that they cause in some warm areas in summer. Hence, a correct understanding of their physical characteristics is needed to correctly forecast them and to be able to investigate their future trends.

These phenomena are formed due to the pressure gradient associated with the temperature difference between the sea and the land surfaces. Therefore, both systems (the ocean and the atmosphere) are involved on the final characteristics that specific breeze events will have, impacting on the variability observed in the strength, duration, and vertical/horizontal extension of the breezes. In this work, we use the Weather Research and Forecasting (WRF) mesoscale model to investigate how the changes in the surface affect the features of this phenomenon through the design of different sensitivity experiments. These include artificial changes in the land and sea surface temperature, but also the use of more realistic (and higher resolution) data to initialise the model. Besides, other experiments are designed to investigate the impact of different technical aspects of the model on the correct simulation of the physical processes, such as the use of different vertical and horizontal resolution, the choice of the planetary-boundary-layer scheme, or the activation or not of typical filters (smoothing) within the model.
Small and Lightweight Gas Measurement System for Unmanned Fixed-Wing Research Aircrafts
Authors: Mashni Hasan , Buechau Yann-Georg , Boventer Jakob , Platis Andreas , Bange Jens
Published: June 2022

 

The ability to reduce CO2 and other gas emissions to meet global emission targets and air quality standards also requires measuring gas sources and sinks and gas transport mechanisms with the highest possible spatial and temporal resolution.

For a higher spatially resolution of gas distributions and concentrations in the sub-atmosphere unmanned aerial systems (UAS) can be used for in-situ measurements in conjunction with stationary ground measurements. In particular, the use of fixed-wing UAS due to their inability to flight longer times and ranges with only a minor disturbance to the measured volume compared to widely used multicopter UASs.

This study presents a lightweight, small, cost-effective nondispersive infrared CO2 gas sensor system for universal use on board fixed wing UASs. The gas sensor is integrated into an aerodynamic 3D-printed housing, so-called "EGG-Pod". This housing is designed as a gas measurement system that acts as a passive pump which maintains and measures a constant volumetric flow that feeds a gas measurement chamber during flight. This cost-effective approach can be transferred to other mobile platforms, especially due to its simple structure, and its scalability to fit other gas sensors. 

To characterize this measurement system, the nocturnal CO2 stratification effect was measured. The ground-level build-up effect of CO2 is caused by ground cooling in radiant and anticyclonic weather conditions. The resulting CO2 vertical gradient was used to define the resolution of the system. For this purpose, flight measurements were compared with the CO2 measurements from the ICOS-Tower (Integrated Carbon Observation System) near Lindenberg Meteorological Observatory - Richard Aßmann Observatory (German Weather Service DWD).
A radar-based quantitative precipitation estimation algorithm to overcome the impact of vertical gradients of warm-rain precipitation: the flood in western Germany on 14 July 2021
Authors: Chen Ju-Yu , Reinoso-Rondinel Ricardo , Trömel Silke , Ryzhkov Alexander , Simmer Clemens
Published: June 2022

The importance of accurate and near real-time radar-based quantitative precipitation estimation (QPE) and derived nowcasting products, which are key to feed hydrological models and enable reliable flash flood predictions, was illustrated again by the disastrous flooding in West Germany after a long-lasting intense stratiform precipitation event on 14 July 2021. We applied three state-of-the-art rainfall algorithms to four operational polarimetric C-band radars of the German Meteorological Service (DWD, Deutscher Wetterdienst): one is based on radar reflectivities Z only, while two hybrid algorithms use specific differential phase KDP in heavy rain combined with Z or specific attenuation A in light rain, respectively. Since large vertical variability of the precipitation flux was observed below the melting layer during the warm-rain process, all QPE products showed significant underestimation. To mitigate this impact on the accuracy of QPE, two approaches have been proposed in this work: i) the inclusion of a local X-band radar, JUXPOL, with lower-altitude observations, and ii) a vertical profile correction for Z and KDP using so-called range-defined quasi-vertical profiles (RD-QVP) to quantify vertical changes of these variables. When evaluated with DWD rain gauge measurements, JUXPOL and vertical profile correction have considerably improved the accuracy of the rainfall estimates by reducing at least 8% and 30% of normalized mean bias, respectively. QPEs with vertical profile correction even beat DWD’s operational rainfall product, which is based on Z only but hourly adjusted to rain gauge measurements. This greatly increases the value of radar-based QPE algorithms for warm-rain events and potential flood alerts.
Creating a coupled multi-model hydrometeorological forecasting and decision support system
Authors: Wetterhall Fredrik , Modigliani Umberto , Moschini Francesca , Kasic Bojan , Ferri Stefano
Published: June 2022

The project “South-East European Multi-Hazard Early Warning Advisory System” (SEE-MHEWS-A) is a collaborative effort to strengthen the existing early warning capacity in south-eastern Europe. The project was initiated in 2016 by the World Meteorological Organization (WMO) and has been supported by the U.S. Agency for International Development (USAID), World Bank and the European Commission and has now developed from a concept into implementation of a quasi-operational multi-hazard forecasting  and decision making system. The SEE-MHEWS-A project will benefit the national meteorological and hydrological services of WMO Members from the region - that is Albania, Bosnia-Herzegovina, Bulgaria, Croatia, Cyprus, Greece, Hungary, Israel, Jordan, Lebanon, North Macedonia, Republic of Moldova, Montenegro, Romania, Serbia, Slovenia, Turkey and Ukraine. The system contains a full multi-model hydrometeorological chain with four limited area numerical weather prediction models (LAM) that provide input to three hydrological models. The LAM's are COSMO, ICON, NMMB and ALARO. The hydrological models are LISFLOOD, HBV and WFLOW. The pilot also consists of a meteorological nowcasting system, and the outputs are visualized on a web-based interactive platform. SEE-MHEWS-A will provide operational forecasters with effective and tested tools for forecasting hazardous weather events and their possible impacts in order to improve the accuracy of warnings and their relevance to stakeholders and users. The project has so far led to an increase in sharing of observations, information and expertise between the countries in the area. It has also allowed for development of innovative ways of presenting the hydrometeorological information to support forecasting and decision-support activities in the region.
Varying precipitation variability over Europe in observed data and climate models
Authors: Plavcová Eva , Huth Radan , Beranová Romana , Lhotka Ondřej
Published: June 2022

Since long-term changes in climate variability are an important aspect of climate change, it is necessary to know whether and how they will change in the future. To this end, it is also important that they are simulated correctly by climate models. Despite its importance to the society and relevance in various impacts, short-term precipitation variability was given very little attention even in the last IPCC report. In our analysis, we aim to answer these questions: (1) What long-term changes in day-to-day precipitation variability have been observed? (2) Do the short-term precipitation variability and its long-term trends differ between various datasets, and how much? (3) How successful are climate models in reproducing precipitation variability? Is it possible to identify biases and errors common to all or majority of models, or to groups of models? (4) What changes of short-term precipitation variability do climate models project in future? Are the observed changes consistent with the projected changes, that is, does the anthropogenic climate change already manifest in the recently observed changes of variability?

We analyze day-to-day variability in precipitation in North Atlantic and European sector. We consider wet-to-wet and dry-to-dry transition probabilities as a measure of short-term precipitation variability, focusing on winter (DJF) and summer (JJA) seasons separately. We compare results obtained from stations data, gridded observed data (E-OBS), various reanalyzes, and outputs of an ensemble of CMIP6 global climate models. We analyze historical model runs starting in 1950 and possible climate change over the 21st century in the simulations under the SSP585 scenarios.
The analog-based gridded data post-processing
Authors: Odak Plenkovic Iris , Schicker Irene , Dabernig Markus , Kann Alexander , Atencia Aitor
Published: June 2022

Analogies between similar past forecasts, measurements, or analyses are a potentially useful tool when the training dataset is long enough, thus enabling an adequate identification of true analogs. Reducing the number of degrees of freedom in the matching procedure makes the analog-based method an excellent candidate for point-based post-processing. However, accurate forecasts at remote locations are used to drive many user-specific applications (e.g., road temperature forecasts along an entire roadway or wind speed for windfarms). For that reason, besides the point-based post-processing for the measuring sites, there is also an increasing demand for gridded (2D) products. The latter is a direct motivation for the adaptation of analog-based method to produce gridded output based on an analysis.

In this work, the control member of the ECMWF ensemble forecast is used as a raw forecast as input to the analog method, whereas the gridded INCA analysis fields are used similar to the observations in the point-based analog approach. All experiments use wind speed and direction variables as predictors, normalized by standard deviation. The domain is defined by ECMWF resolution and INCA domain size, covering Austria. The first experiment (EX1) is based on the simplest transfer from point-based to gridded products: treating every grid point as an independent location. Alternatively, an average error on the entire field is used to choose the most similar historical field (EX2). The latter generates comparable results to the EX1 when using training datasets of the same length. In addition to these experiments, a simplification of the procedure for choosing the best analogs using empirical orthogonal functions (EOF) is also tested (EX3). The training data is used to calculate EOFs for EX3, and training is thus saved as EOFs and corresponding principal components (PCs) time series. Results show that using only a few EOFs might capture the majority of the variance in the training. The best match to the current raw forecast is determined using PCs. Even though EX3 is not as skillful as EX1 and EX2, the approach using EOFs is notably more computationally efficient. For that reason, it is possible to include longer training than for EX1 and EX2. Moreover, the setup can be further optimized to improve results (e.g., increasing the number of predictors or implementing a predictor-weighting strategy), which is a natural next step in future work.
The role of warm conveyor belts for medium-range forecast error growth
Authors: Pickl Moritz , Grams Christian M , Quinting Julian F
Published: June 2022

Warm Conveyor Belts (WCBs) are moist air streams that ascend from the warm sector of extratropical cyclones into the upper troposphere. Cloud-condensational processes along their ascent lead to the release of latent heat which further accelerates the ascending motion and results in cross-isentropic transport of lower-tropospheric air with low values of potential vorticity (PV) to the upper troposphere. This diabatically enhanced outflow intensifies the PV-gradient along the dynamical tropopause, accelerates the jet stream and ultimately modulates the upper-tropopsheric Rossby wave pattern through ridge amplification and downstream development. In numerical weather prediction (NWP) models, it is crucial to correctly represent this interaction of WCBs with the large-scale flow, as mis-representations thereof can lead to poor forecast skill in downstream regions. At the same time, this multi-scale interaction poses a major challenge for global NWP-models, as the governing moist-dynamical processes in WCBs are parameterized and introduce uncertainty into the forecast.

In this study, we investigate the role of WCBs for the error growth in medium-range forecasts over the North Atlantic in a systematic way. This is done by analyzing the relationship between commonly used error metrics of variables that characterize the large-scale flow and imprints of WCBs detected with trajectory analysis in three years of operational ECMWF ensemble forecasts.

Forecasts with high WCB activity are on average characterized by an amplified Rossby wave pattern and anticyclonic flow anomalies downstream. We find that the forecast skill is generally reduced when the WCB-activity is high, and that the WCB-activity is particularly increased around forecast times when the error growth is largest. To establish a relationship between the occurrence of WCBs and error growth, composites of normalized forecast error fields centered on WCB objects are computed. The composites reveal that anomalously high forecast errors associated with mis-representations of the Rossby wave pattern emerge along and downstream of WCB ascent and outflow. While there is considerable case-to-case variability in the mid-tropospheric error patterns, the structure of the errors associated with the upper-level jet stream is robust.

The WCB-relative errors are largest when the WCB-event occurs on forecast days 3-5, whereas WCBs at earlier leadtimes have a substantially reduced impact on the forecast errors, pointing towards an amplification of pre-existing errors by WCBs. Future work will attempt to further distinguish between situations in which WCBs amplify forecast errors, and in which WCBs are the source of forecast errors.
Using Doppler lidar observations to verify wind profile forecasts from the ECMWF IFS model
Authors: Pentikäinen Pyry , O'Connor Ewan
Published: June 2022

Long-range scanning Doppler lidars can provide the vertical profile of the horizontal wind within the boundary layer. We used Doppler lidar wind profiles from six locations around the globe to verify the wind profile forecasts generated by ECMWF from the operational IFS model. The six locations selected cover a variety of surface types (rural, marine, mountainous urban, coastal urban).

We first validated the Doppler lidar observations at four locations by comparison with collocated radiosonde profiles to ensure that the Doppler lidar observations were of sufficient quality. The two observation types agree well, with the mean absolute error (MAE) in wind speed almost always less than 1 m s-1. Large deviations in the wind direction were usually seen only for low wind speeds. This is due to the ambiguity in wind direction at low wind speed and that the relative uncertainty in the wind direction measurement increases as the wind speeds decrease.

The Doppler lidar observations are at sufficient temporal resolution for us to generate time-height composites of the wind verification with one-hour resolution so that we can investigate the diurnal cycle. Verification of the model winds showed that the IFS model performs best over marine (ocean) locations. Larger errors were seen in locations where the surface was more complex, especially in the wind direction. For example, in Granada, which is near a high mountain range, the IFS model failed to capture a commonly occurring mountain breeze, a feature which is highly dependent on the orography at sub grid-scale.

At one location, we conditionally performed the wind verification based on the presence or absence of a low-level-jet diagnosed from the Doppler lidar observations. The IFS model was able to reproduce the presence of the low-level-jet but the wind speed maximum was about 2 m s-1 lower than observed. This is attributed to the effective vertical resolution of the model being too coarse to create the strong gradients in wind speed observed.
Assessing the potential of assimilating wind turbine power output and smartphone data for future reanalysis
Authors: Kelbch Alexander , Valmassoi Arianna , Keller Jan
Published: June 2022

Improving the representation of lower boundary layer winds and temperatures in weather forecasting is still a major task, despite the increase in the spatial resolution. As applications strongly rely on high-quality data from weather and climate models, systematic errors and biases in relevant areas, e.g., boundary layer wind speed or near-surface temperatures, degrade the quality of such applications. In this regard, new observing systems can provide information to enhance the model state representation through data assimilation. Smartphones are widely used and are already equipped with build-in sensors, e.g., for air pressure, thus providing a potentially valuable source of meteorological information. We further see wind power data obtained as wind turbine power output as potential implicit measurements for boundary layer wind speeds. Through the FAIR project, examplary data from smartphopnes is provided by the University Duisburg-Essen and for wind power data from the industry partners BayWa r.e. In this study, we aim to assimilate these observations into the operational NWP model ICON-LAM to assess the potential benefit with respect to NWP and future regional reanalyses. A new forward operator was developed for the LETKF scheme to assimilate wind power output. To investigate the benefit of assimilating wind power and air pressure data from smartphones tuning experiments have been performed. The sensitivity of various parameter settings, such as horizontal localization scale, and appropriate observation error estimation using the Desroziers metric is tested. The simulation results are evaluated and compared against independent observations for different synoptical situations and they indicate a potential for improvement.
How do Euro-Atlantic regimes respond to climate change?
Authors: Dorrington Josh , Strommen Kristian , Fabiano Federico , Palmer Tim
Published: June 2022

The regional impacts of anthropogenic climate change over Europe are still very unclear, in large part due to uncertainties in the dynamical response of the large-scale circulation. Particularly in Boreal winter, the structure of the Euro-Atlantic circulation is strongly nonlinear, and is dominated by Rossby wave breaking, persistent atmospheric blocks and latitudinal meandering of the eddy driven jet stream. In the face of this complexity, adopting a regime approach – where the large scale circulation is decomposed into a small number of representative flow state – can shed much needed light on the internal variability and forced response of Euro-Atlantic dynamics.

 

One major open question is how exactly nonlinear regimes respond to a warming climate. While it has been hypothesised that climate forcing should primarily result in a shift in the probability of historically observed regimes, rather than inducing new flow configurations, this has never been tested in complex earth system models. In this work we fill this gap, by analysing regime statistics in 20 CMIP6 models under the SSP5-8.5 scenario. We decompose the forced response into two components: one representing the degree to which the prevailing regime patterns alter, and another representing their changing probability of occurrence and persistence. We show that while robust changes in regime patterns can be detected, these are minor in comparison to changes in the regime life-cycles. The overall picture tends towards a less regime dominated future: we find that anticyclonic regimes associated with persistent blocking are projected to become less persistent in the future and that central jet positions, associated with zonal flows, become more common. However considerable intermodel variability, even in the sign of the prevailing regime trends, shows the need for further investigation, and an improved representation of Euro-Atlantic regimes in state-of-the-art models.
Urban meteorological networks in Europe: A review
Authors: Dunjic Jelena , Savic Stevan , Milosevic Dragan , Secerov Ivan
Published: June 2022

Urban meteorological networks in Europe: A review

Assessment of climate in urban environments strongly relies on the quality and resolution of meteorological data. Intensive urbanization processes in urban environments affect and modify local climates so the traditional weather stations located on the outskirts of the urban areas can hardly be considered in the analysis of local climate. Technological progress led to major improvements in sensor development, which increases the number and density of urban meteorological networks. This article presents a review of urban meteorological networks in Europe, including an assessment of their spatial distribution and density, type and density of the sensors, data resolution, and main barriers and challenges in their development and maintenance. The analysis includes urban meteorological networks whose characteristics or data are reported in scientific journals referred in the Web of Science database. Also, the networks reported during the data collection of urban and rural networks from partners participating in the COST Action project entitled FAIRNESS “FAIR NEtwork of micrometeorological measurements” (CA20108) are included in this review. This COST Action aims to develop synergy between stakeholders, researchers, and civil society to promote open data and enhance data visibility and usage. In conclusion, recommendations for future urban meteorological networks development emerged from identified gaps and challenges in measurement logistics, methods, and data assimilation of the existing networks, along with the discussion regarding the possibility of establishing an open data platform for long-term data availability.

Acknowledgment: This research is supported by the COST Action project entitled “FAIR NEtwork of micrometeorological measurements”, no. CA20108.
Machine learning for postprocessing ensemble forecasts of wind gusts with a focus on European winter storms
Authors: Schulz Benedikt , Lerch Sebastian
Published: June 2022

Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only few recent studies have focused on ensemble postprocessing of wind gust forecasts, despite its importance for severe weather warnings, e.g. in European winter storms. First, we provide a comprehensive review and systematic comparison of several statistical and machine learning methods for probabilistic wind gust forecasting via ensemble postprocessing, then we assess the performance of selected methods within winter storms. The methods can be divided in three groups: State of the art postprocessing techniques from statistics (ensemble model output statistics (EMOS), member-by-member postprocessing, isotonic distributional regression), established machine learning methods (gradient-boosting extended EMOS, quantile regression forests) and neural network-based approaches (distributional regression network, Bernstein quantile network, histogram estimation network). The different approaches are systematically compared using six years of data from a high-resolution, convection-permitting ensemble prediction system run operationally at the German weather service, and hourly observations at 175 surface weather stations in Germany. While all postprocessing methods yield calibrated forecasts and are able to correct the systematic errors of the raw ensemble predictions, incorporating information from additional meteorological predictor variables beyond wind gusts as well as estimating locally adaptive neural networks leads to significant improvements in forecast skill. Assessing the performance of EMOS and neural network-based postprocessing for selected winter storms, we find that the networks better adapt to the extreme conditions than the statistical benchmark and thus yield a superior predictive performance. However, results suggest that the performance can still be further improved, e.g. via regime-dependent postprocessing.
Analyzing the influence of irrigation on convection – Case study for Northern Italy using convection-permitting simulations
Authors: Asmus Christina , Hoffmann Peter , Pietikäinen Joni-Pekka , Böhner Jürgen , Rechid Diana
Published: June 2022

Land management practices modify the land surface by changing its biogeophysical and biogeochemical properties, leading to heterogeneous surface and soil conditions. One example for this is irrigation, which is widely used to overcome unsuitable climatic conditions in agriculture to protect plants from water stress. Irrigation leads to a heterogeneous and time-varying soil moisture distribution. Soil moisture conditions are known to feedback with the atmosphere. Previous studies analyzed the soil moisture-precipitation feedback and focused on the development conditions of convection. However, uncertainties in the magnitude and sign of this feedback remain due to the representation of convection in the climate model. Therefore, convection-permitting simulations are crucial to understanding the link between soil moisture and convection.

In our study, we investigate this link by taking into account sudden soil moisture changes through irrigation and the resulting heterogeneity of soil moisture distribution. Our aim is to understand how irrigation influences development conditions for convection in the atmosphere. For this purpose, we conduct convection-permitting simulations at high resolution with a focus on the Po valley in Northern Italy. We use the non-hydrostatic version of the regional climate model REMO which in our setup is interactively coupled to its mosaic-based vegetation module iMOVE. For REMO-iMOVE we developed and implemented a new irrigation parameterization based on a fractional approach which makes it suitable for the representation of heterogeneous soil moisture distribution.

We will present the effects of irrigation on the development of convection in the Po valley by analyzing selected atmospheric variables from simulations with and without irrigation.
Neural network-based methods for generating synthetic satellite images in the solar spectral range
Authors: Scheck Leonhard , Baur Florian , Stumpf Christina , Köpken-Watts Christina
Published: June 2022

Solar satellite channels of instruments onboard geostationary or polar orbiting satellites provide high resolution information on clouds and aerosols that is valuable for numerical weather prediction. The solar channels are sensitive to the microphysical properties of cloud and aerosol particles and contain better information on water content than the thermal channels. The direct assimilation of solar satellite images or their application for the evaluation of numerical weather prediction (NWP) models requires sufficiently fast and accurate forward operators, which solve radiative transfer (RT) problems to compute synthetic images from the NWP model output. As multiple scattering complicates the solution of radiative transfer problems in the solar spectral range, standard RT methods are too slow for this purpose. Faster methods have been developed for cloud-affected visible channels, but are limited to non-absorbing channels and do not take aerosols into account. Machine learning methods provide a promising way to accelerate the complex radiative transfer computations in satellite forward operators and to overcome the limitations of previous approaches. Here we report on experiments based on deep feed forward neural network. It is demonstrated that using neural networks the amount of training data that has to be computed with standard radiative transfer methods can be reduced by several orders of magnitude, compared to previous approaches, while increasing the speed by an order of magnitude and improving accuracy. Moreover, tangent linear and adjoint versions required for variational data assimilation can easily be implemented and do not have to be adapted when network structure or training data are changed. We discuss optimizations to reduce the computational effort and provide examples for applications that require more input parameters than cloud-affected visible channels and have only become feasible with the new approach.
An Improved Version of the Localized Mixture Coefficients Particle Filter Applied to the Regional ICON-LAM Model at DWD
Authors: Schenk Nora , Walter Anne , Potthast Roland
Published: June 2022

Standard algorithms for data assimilation in the application of numerical weather prediction in high-dimensional spaces are in general ensemble methods where the ensemble describes the sample of an underlying distribution. With the forecast ensemble, the uncertainty of the forecast state is estimated. A widely used ensemble method is the ensemble Kalman filter but the filter is based on normality assumptions which is not satisfied in general in case of nonlinear models. In future, the dynamical models used in numerical weather prediction will get even more nonlinear due to higher resolution and more complex physical schemes. Nonlinear ensemble data assimilation methods like particle filters aim to improve the numerical weather prediction and the uncertainty quantification in a nonlinear setting.

At the German Meteorological Service (DWD), two particle filter methods were developed which are structured in a way that a consistent implementation in existing LETKF code is possible. The localized adaptive particle filter (LAPF), introduced by R. Potthast, A. Walter and A. Rhodin in 2019, overcomes filter collapse in a high-dimensional framework. This particle filter was further developed by Walter et al. (2022) to the local mixture coefficients particle filter (LMCPF) which was tested within the global ICON model. In the LMCPF method the background distribution is approximated by Gaussian mixtures and a Gaussian distributed observation error is assumed. Recently, we have shown that an improved version of the LMCPF is able to outperform the LETKF within an experimental design reflecting a standard NWP setup and standard NWP scores applied to the dynamical systems Lorenz 1963 and 1996.

In this study, we explore the potential of the improved version of the LMCPF applied in the kilometre-scale ensemble data assimilation (KENDA) system with the limited area mode of the ICON model (ICON-LAM) to estimate the state and the state's uncertainty. Further, we compare the particle filter method to the localized ensemble transform Kalman filter (LETKF) which is operationally used at DWD. Both methods describe four-dimensional data assimilation schemes if the observation operators are applied during the model forward integration at the exact observation times and not only at analysis time. This leads to four-dimensional background error covariance matrices at times and locations of the observations which are employed to derive the analysis ensemble. Besides a mathematical introduction of the LMCPF method and the description of the changes in the improved version, we present experimental results in comparison with the LETKF method in KENDA used at DWD for the ICON-LAM model.
Changes in tropical and extratropical hydroclimate during the Last Millennium
Authors: Roldán-Gómez Pedro José , González-Rouco Jesús Fidel , Smerdon Jason , Melo-Aguilar Camilo
Published: June 2022

Reconstructions of the Last Millennium (LM) suggest coordinated changes in the hydroclimate of separate regions. This happens in regions of North America, North Africa and Europe for the periods of the Medieval Climate Anomaly (MCA; ca. 950–1250 CE) and the Little Ice Age (LIA; ca. 1450–1850 CE), but also in tropical areas, where consistent changes are found in regions of South America, East Africa, Southeast Asia and the Indo-Pacific basin. For extratropical areas, changes in the hydroclimate have been mostly linked to changes in variability modes like the Northern (NAM) and Southern Annular Modes (SAM), while in tropical areas they are mostly associated to alterations in the position and intensity of the Intertropical Convergence Zone (ITCZ).

To assess these large-scale changes in the hydroclimate of the LM and the mechanisms explaining them, climate simulations from the Community Earth System Model - Last Millennium Ensemble (CESM-LME) have been analysed. To assess the consistency of the results for different regions, these analyses have been also compared with the information provided by proxy-based datasets, including the Drought Atlases for Europe (OWDA), North America (NADA), Asia (MADA), Mexico (MXDA), Eastern Australia and New Zealand (ANZDA), the Paleo Hydrodynamics Data Assimilation product (PHYDA) and the Last Millennium Reanalysis (LMR).

Simulations show that changes in the hydroclimate of extratropical and tropical regions of the Atlantic Ocean basin are mainly linked to changes in external forcing factors, while for the tropical areas of the Pacific and Indian Ocean basins, there is a significant contribution of internal variability. The understanding of the mechanisms behind these large-scale changes could help not only to better characterize the evolution of the hydroclimate of tropical and extratropical regions, but also the ability of the model simulations to reproduce the behavior of hydroclimate depending on the region.
ICON-Seamless, the development of a novel Earth System Model based on ICON for time scales from weather to climate
Authors: Früh Barbara , Potthast Roland , Müller Wolfgang , Korn Peter , Brienen Susanne , Fröhlich Kristina , Helmert Jürgen , Köhler Martin , Lorenz Stephan , Pham Trang Van , Pohlmann Holger , Schlemmer Linda , Schnur Reiner , Schulz Jan-Peter , Sgoff Christine , Vogel Bernhard , Wirth Roland , Zängl Günther
Published: June 2022

Within the project ICON-Seamless a new Earth System Model is developed for weather forecasts, seasonal and decadal climate predictions, as well as climate projections. In doing so, we use the expertise from the numerical weather prediction (NWP), which operates and maintains ICON-NWP, as well as the experience with the first ICON-Earth System Model version based on the physics of the MPI-M atmosphere model (ECHAM). The goal is to use common components for all time scales. As a first step we develop a model for seasonal and decadal predictions.

ICON-Seamless builds on the coupling of the atmosphere (ICON-NWP) and ocean (ICON-O) components via the coupling software YAC. Sea ice as a further important component is thereby included. Furthermore, to have a closed hydrological cycle and to represent the carbon and other biogeochemical cycles comprehensively, a suitable soil model based on the ICON-Land framework as well as the TERRA and JSBACH/QUINCY land models, are or will be coupled to ICON-NWP. In addition, transient external fields for aerosol, greenhouse gases, ozone and solar irradiance are implemented in ICON-NWP to be able to simulate historical time periods and scenarios of the future. In parallel, the ART modules (Aerosol and Reactive Trace gases), which allow a dynamic treatment of gases and aerosol, are adapted to the modified model physics. Intensive model evaluation supports the tuning. For future use in the field of (weather and) climate predictions, coupled data assimilation is being developed as well.

We give an overview of the current state of the development, experiments and potential areas of application.
Dunkelflauten in Germany: Climatology and Relation to Weather Regimes
Authors: Mockert Fabian , Grams Christian M. , Brown Tom , Neumann Fabian , Fallon James
Published: June 2022

It is scientific consensus that the Paris Agreement (COP21) can only be achieved by drastically reducing the emissions of anthropogenic greenhouse gases, in particular those related to burning fossil energy sources. Succesfully providing the required energy with renewable energy sources, such as wind and solar power, is dependant on the weather conditions. Dunkelflauten, periods during which only little energy is available by wind and solar power, can put strain on the energy system based on high shares of variable renewables. Particularly, cold Dunkelflauten with coinciding low renewable energy generation and high heat demand are critical. To mitigate the risk of such critical conditions, energy storage systems are an important component in the future energy system. The appropriate dimensioning of energy storage requires knowledge about the typical duration and frequency of Dunkelflauten.

This study presents a climatological analysis of Dunkelflauten that would have occurred in Germany from 1979 until 2018, with the assumption of a 100% renewable energy scenario. On average four Dunkelflauten are detected per year, with durations of up to nine days. To forecast the chances of a Dunkelflauten event several weeks ahead of time, it is helpful to unveil the long-lasting weather conditions, called weather regimes, during which Dunkelflauten occur. We show that Dunkelflauten are more likely to occur during blocked regimes than during cyclonic regimes. The particularly critical cold Dunkelflaute occurs predominantly during the Greenland blocking. Accordingly, accurate predictions of weather regimes are key to manage energy storage systems and to eventually avoid temporary shortages in energy supply.
Kinematic processes contributing to the intensification of anomalously-strong North Atlantic jets
Authors: Winters Andrew
Published: June 2022

An intense extratropical cyclone impacted western Europe on 9 February 2020 with wind gusts in excess of 35 m s–1, heavy rainfall, and considerable inland and coastal flooding. This extratropical cyclone developed beneath the poleward-exit region of an anomalously strong North Atlantic jet streak that featured wind speeds in excess of 100 m s–1 and enabled a British Airways 747 to break the record for a subsonic commercial flight between New York City and London at the time. Anomalously strong jet streaks, such as that which accompanied the aforementioned extratropical cyclone, also feature strong lower-tropospheric baroclinicity and strong horizontal and vertical wind shear favorable for the development of clear-air turbulence. Consequently, improved understanding of the processes that influence the development anomalously strong jet streaks is necessary not only because of their influence on the production of high-impact weather, but also their influence on aviation.

This presentation focuses on the kinematic processes that contribute to the development of anomalously strong jet streaks over the North Atlantic with wind speeds in excess of 100 m s–1 during September-May 1979–2018, and how those processes vary across a large sample of cases identified within the NCEP Climate Forecast System Reanalysis. A composite analysis demonstrates that anomalously strong North Atlantic jets are most frequent during the winter compared with the fall and spring, and that their development is preceded by low-level warm-air advection, poleward moisture advection, and moist ascent within the warm conveyor belt of a surface cyclone beneath the equatorward jet-entrance region. A diagnosis of the irrotational and nondivergent components of the ageostrophic wind within the near-jet environment reveals that both wind components facilitate jet intensification via their nonnegligible contributions to negative potential vorticity (PV) advection and PV frontogenesis in the vicinity of the dynamic tropopause. The foregoing results indicate that a diagnosis of jet intensification during anomalously strong jet events is strongly dependent on an accurate representation of the cumulative effects of latent heating within the near-jet environment.
Trends in the indices of precipitation phases under the background of current warming in Poland, 1966-2020
Authors: Łupikasza Ewa , Małarzewski Łukasz
Published: June 2022

Precipitation phase depends on air temperature; therefore, the variabilities in snowfall and rainfall are counted among the indicators of current climate change. The shifts in the structure of precipitation phases have significant environmental implications and influence public perception of climate change. Based on data from 42 synoptic stations for the period 1966-2020, this study discusses trends in the indices of liquid (Lq), mixed (Mx) and solid (Sd) precipitation describing their totals (T), frequencies (Nd) and contribution to overall precipitation totals (T%) and to a number of days with overall precipitation (Nd%) from a seasonal perspective. Moreover, the influence of seasonal air temperature on long-term variability in the indices and changes in the probability of precipitation phases in 1 °C  wide ranges of daily air temperatures between  1966-1985 and 2001-2020 were also analysed. Precipitation phases were discriminated based on the observations of weather phenomena noted as current and past weather in SYNOP messages, considering days with precipitation ≥0.1mm.

In Poland, precipitation phases significantly reacted to current warming. Trend directions corresponded to warming; Thus, trends in liquid precipitation indices were positive, while trends in mixed and solid precipitation indices were negative, both resulting from redistribution of particular phases. The strongest and widespread were increasing trends in winter rains (+1.74 for LqNd, +5.35 mm for LqT, +3.51% for LqNd%, +4.08% for LqT% per decade) and decreasing trends in winter snowfalls (–1.92 days for SdNd, –3.12 mm for SdT, –3.65% for SdNd%, –3.26% for SdT% per decade). In winter, air temperature explained 60–80% variance in the frequency indices. In spring and autumn, increasing trends were found in LqNd% due to the transformation of solid to liquid phase, and in autumn due to melting of mixed and solid phases. In spring, air temperature explained at most 30–50% of the variance in the indices of snowfall frequency. Negative trends in mixed precipitation were significant in transitional seasons, being more prominent in MAM than in SON. A diminished percentage of winter mixed precipitation and summer rains on days with air temperature below 0 °C and 15 °C, respectively, was compensated by an increase in their frequency above these thresholds, thus producing no long-term trends. In most cases, trends in air temperature and indices of precipitation phases diminished the correlations between these variables, particularly in transitional seasons in the case of mixed and solid phases. Solid and mixed phases are becoming sporicidal phases in Polish transitional seasons.
Jet Steam Waviness Response to Polar Amplification on an Aquaplanet
Authors: Batelaan Thomas J. , Sinclair Victoria , van Heerwaarden Chiel
Published: June 2022

In recent years the surface temperature at high latitudes – especially in the Arctic region – warmed faster than the global average. This process called Polar amplification is a key characteristic of anthropogenic global warming. Polar amplification will reduce the equator-to-pole temperature gradient of the lower troposphere, but the consequences to the atmospheric general circulation are not fully understood. Here, we study the effect of the reduced meridional temperature gradient by enhanced Sea Surface Temperatures (SSTs), primarily at the Polar regions, on the atmospheric general circulation by executing idealised aquaplanet simulations with the Open Integrated Forecast System (OpenIFS) from the European Centre for Medium-Range Weather Forecasts (ECMWF).

Firstly, we show the global atmospheric response to Polar amplification by comparing various atmospheric climatologies of the control and Polar amplification simulations. We found that Polar amplification influences the Hadley cell circulation and jet stream: both key features of the atmospheric general circulation are strongly reduced in strength. Secondly, we studied the influence of Polar amplification on jet stream waviness. In this study, waviness is quantified by the Sinuosity Index. Sinuosity decreases in a statistically significant manner under Polar amplification: the jet stream becomes less wavy and the meridional extend of the zonal flow decreases as well. The perturbed SSTs around the Polar edge of the Hadley cell and in the baroclinic zone found to be key. Our result contradicts results found in other studies on the consequences of Polar amplification: potential reasons for this and the implication of our results will be discussed in this study.
Investigating plant’s stomatal and non-stomatal responses to water stress via STEMMUS-SCOPE model
Authors: Song Zengjing , Zeng Yijian , Ma Mingguo , Su Bob , Gracia Monica
Published: June 2022

Water stress factor is utilized to describe drought effects on plant growth in land surface models (LSMs). Accurately representing water stress is critical to understand the impact of climate change on plant and ecosystem. Models use various approaches to describe the responses of vegetation to water stress. Some models assumed water stress causes stomata closure to attenuate gas exchange process, while others assumed water stress reduces the maximum rate of carboxylation (Vcmax) to slow photosynthesis. Only a few models considered both constraints. However, which parameterization can better capture the dry condition is still controversial. A reliable detection and attribution of the impact of water stress on plant is necessary for understanding the consequence of climate change on the ecosystem from a mechanism aspect. In this study, an empirical stomatal conductance scheme (proposed by Ball et al. in1987, called “BB_gs”) and a unified stomatal conductance model (proposed by Medlyn et al. 2011, called “ME_gs”) were coupled into STEMMUS-SCOPE model to explore the discrepancy between empirical and optimal approaches. Three scenarios were designed to represent the effect of water stress on gas exchange (gs_w), photosynthesis (Vcmax_w) and both processes (gs & Vcmax_w). The coupled model was implemented for three sites with different plant function types, including C3 grassland, C3 shrub, and C4 cropland. Results showed that the optimal stomatal conductance scheme has better performance than the empirical approach because the optimal method considers the realistic stomata regulation. The Vcmax_w scheme captured the drought effects better than other schemes. The results improved our understanding on regional ecosystem functioning under the context of climate change.   