Influence of different weather aspects on breeding performance, food supply and nest-space use in hoopoe offspring

In this study, we investigated the influence of different weather aspects on breeding performance, food supply and nest-space use in hoopoe offspring (Upupa epops). Camera recordings of 88 nests were used to examine how ambient environmental conditions influence food supply, offspring nest-space use and the trade-off nestlings face regarding the two mainly used locations in the nest. Therefore, we provide a comprehensive analysis involving different factors including weather parameters together with food provisioned to nestlings on different temporal scales to identify the factors having the most influence on nest-space use. We found that different breeding conditions significantly influenced how nestlings used the nest. During excessively humid weather, nestlings spent more time under the entrance hole when small food was delivered. However, nestlings supplied with large prey more often remained hidden in the distant area, despite the adverse weather situation. In all three aspects and temporal scales, our analysis confirmed that prey was the most important factor influencing offspring nest-space use, suggesting a crucial role of large insects for hoopoes. Finally, we found that long-term effects of weather affect overall food provisioned to nestlings and thus offspring behaviour. We provide evidence that parental feeding location and prey size, which are in turn influenced by weather conditions, are the most influential factors for nest-space use. This study expands our knowledge of parent–offspring communication and how environmental factors may lead to differential nest-space use, which may be regarded as the earliest form of habitat preference in birds. Significance statement Nests are usually constrained in space but designed to protect offspring from the environment while giving them limited possibilities to express behavioural diversity. This is particularly true for bird nests, where nestlings are usually packed in close contact with one another and without much space for movement, except begging. Here we demonstrate that nest features, such as available nest space together with environmental conditions surrounding a nest, influence nestling strategies and behaviours, including social interactions between nest mates, which further leads to fitness consequences. Our results seem to be of great importance for habitat selection theory in birds, in particular regarding the early development of habitat preferences (imprinting) and use. On the other hand, the result may also have important implications for conservation issues given that nestling behaviour may be used as a determinant of environmental quality.

Artificial intelligence to link environmental endocrine disruptors (EEDs) with bone diseases

Our endocrine system is not only complex, but is also enormously sensitive to the imbalances caused by the environmental stressors, extreme weather situation, and other geographical factors. The endocrine disruptions are associated with the bone diseases. Osteoporosis is a bone disorder that occurs when bone mineral density and bone mass decrease. It affects women and men of all races and ethnic groups, causing bone weakness and the risk of fractures. Environmental stresses are referred to physical, chemical, and biological factors that can impact species productivity. This research aims to examine the impact of environmental stresses on bone diseases like osteoporosis and low bone mass (LBM) in the United States (US). For this purpose, we use an artificial neural network model to evaluate the correlation between the data. A multilayer neural network model is constructed using the Levenberg–Marquardt training algorithm, and its performance is evaluated by mean absolute error and coefficient of correlation. The data of osteoporosis and LBM cases in the US are divided into three groups, including gender group, age group, and race/ethnicity group. Each group shows a positive correlation with environmental stresses and thus the endocrinology.

Improving efficiency of block-level agrometeorological advisory system by exploiting reuse: A study in Telangana

India Meteorological Department (IMD) has started block-level level agromet advisory (AA) service from the year 2015 and is currently operating in a few blocks of each state across India. In a block-level AA service, on every Tuesday and Friday, AA is being prepared for each block based on the block-level Medium Range weather Forecast (MRF). In this paper, we propose a framework to improve the preparation of blocklevel AA by modeling a weather situation as “Category-based Weather Condition (CWC)” and exploiting both “temporal reuse” and “spatial reuse” of AA based on the similarity among CWCs. The weather data analysis for 12 blocks of Telangana by considering the phenophase-specific CWCs of Rice crop showed that there is a scope to improve the efficiency of block-level AA bulletin preparation process by exploiting reuse.

The role of emission reductions and the meteorological situation for air quality improvements during the COVID-19 lockdown period in central Europe

The lockdown measures taken to prevent a rapid spreading of the coronavirus in Europe in spring 2020 led to large emission reductions, particularly in road traffic and aviation. Atmospheric concentrations of NO2 and PM2.5 were mostly reduced when compared to observations taken for the same time period in previous years; however, concentration reductions may not only be caused by emission reductions but also by specific weather situations. In order to identify the role of emission reductions and the meteorological situation for air quality improvements in central Europe, the meteorology chemistry transport model system COSMO-CLM/CMAQ was applied to Europe for the period 1 January to 30 June 2020. Emission data for 2020 were extrapolated from most recent reported emission data, and lockdown adjustment factors were computed from reported activity data changes, e.g. Google mobility reports. Meteorological factors were investigated through additional simulations with meteorological data from previous years. The results showed that lockdown effects varied significantly among countries and were most prominent for NO2 concentrations in urban areas with 2-week-average reductions up to 55 % in the second half of March. Ozone concentrations were less strongly influenced (up to ±15 %) and showed both increasing and decreasing concentrations due to lockdown measures. This depended strongly on the meteorological situation and on the NOx / VOC emission ratio. PM2.5 revealed 2 %–12 % reductions of 2-week-average concentrations in March and April, which is much less than a different weather situation could cause. Unusually low PM2.5 concentrations as observed in northern central Europe were only marginally caused by lockdown effects. The lockdown can be seen as a big experiment about air quality improvements that can be achieved through drastic traffic emission reductions. From this investigation, it can be concluded that NO2 concentrations can be largely reduced, but effects on annual average values are small when the measures last only a few weeks. Secondary pollutants like ozone and PM2.5 depend more strongly on weather conditions and show a limited response to emission changes in single sectors.

Development of a forecast-oriented km-resolution ocean-atmosphere coupled system for Western Europe and evaluation for a severe weather situation

To improve high-resolution numerical environmental prediction, it is essential to represent ocean-atmosphere interactions properly, which is not the case in current operational regional forecasting systems used in Western Europe. The objective of this paper is to present a new forecast-oriented coupled ocean-atmosphere system and its evaluation. This system uses the state-of-the-art numerical models AROME (cy43t2) and NEMO (v3.6) with a horizontal resolution of 2.5 km. The OASIS coupler (OASIS3MCT-4.0), implemented in the SurfEX surface scheme and in NEMO, is used to perform the communications between models. The evaluation of this system is carried out using 7-day simulations from 12 to 19 October 2018, characterised by extreme weather events (storms and heavy precipitation event) in the area of interest. Comparisons with in-situ and L3 satellite observations show that the fully coupled simulation reproduces quantitatively well the spatial and temporal evolution of the sea surface temperature and 10 m wind speed. Sensitivity analysis to OA coupling show that the use of an interactive and high resolution SST, in contrast to actual NWP where SST is persistent and at low resolution, modifies the atmospheric circulation and the location of heavy precipitation. When compared to the operational-like ocean forecast, simulated oceanic fields show a large sensitivity to coupling. Forced ocean simulations highlight that this sensitivity is mainly controlled by the change in the atmospheric model used to drive NEMO (AROME vs. ECMWF IFS operational forecast). The oceanic boundary layer depths can vary by more than 40%. This impact is amplified by the interactive coupling and is attributed to positive feedback between sea surface cooling and evaporation.

Objective Synoptic Weather Classification on Air Pollution during Winter Seasons in Hangzhou

Using the 2015-2018 Hangzhou city PM2.5, PM10, SO2, CO, NO2 and O3 mass concentration data, ERA5 reanalysis data and ground observation data, through the PCT classification method, the objective analysis of the winter air pollution weather situation in Hangzhou was obtained. The results showed that the winter air quality concentration in Hangzhou continued to be high from 2015 to 2018, and the air pollution was the most significant. Through objective classification, it is concluded that the main weather conditions affecting the region in winter are divided into 6 types, namely high pressure control, high pressure bottom control equalizing field, L-shaped high pressure control, high pressure front control equalizing field, low pressure control, low pressure front control Equalizing field. Among them, when high pressure control, high pressure bottom control equalizing field, L high pressure control, low pressure control are affected by local sources, the impact of external sources has a greater impact on the air quality in Hangzhou, and air pollution is prone to occur; before low pressure When the pressure equalization field is controlled by the Ministry and the pressure equalization field is controlled by the high pressure front, the local wind and precipitation in Hangzhou are relatively high, which is not conducive to the accumulation of air pollutants. The probability of occurrence of air pollution is small, and air pollution is not easy to occur.

Evaluating Short-Term Spatio-Temporal Tropospheric Variability in Multi-Temporal SAR Interferograms Using LES Models

Atmospheric delay has a significant impact on synthetic aperture radar (SAR) interferometry, inducing spatial phase errors and decorrelation in extreme weather condition. For Low Earth Orbit (LEO) SAR missions, the atmosphere can be considered as being spatio-temporally frozen due to the short integration time. Geosynchronous (GEO) SAR missions, however, have short revisit times and extensive imaging coverage but with a longer integration time. As a result, GEOSAR interferograms can provide continuous deformation monitoring and integrated refractivity for weather forecasting. However, as the troposphere may vary significantly within the integration time, this may lead to a degradation during focusing and decorrelation of the InSAR pair. Here we simulate a time-series refractivity distribution with a high spatio-temporal resolution, for a fair-weather situation using an advanced Large Eddy Simulation (LES) model, to show the spatio-temporal variability of the troposphere on short time scales. Given GEO orbit parameters with different viewing angles along both azimuth and range directions, corresponding time-series of tropospheric interferograms are obtained based on the SAR geometry, and the impacts of different parameters are compared. Tropospheric delay is found to vary rapidly and a lead to phase gradient exceeding one cycle within a few minutes. Yet, for periods of less than ~15 minutes, a frozen-flow approximation may be successful to mitigate atmospheric decorrelation. Consequently, GEOSAR imaging should be iterative to compensate the atmospheric effects.

The benefits of using TV-meteorologists as climate change communicators

<p>User surveys indicate that the Norwegian Meteorological Institute (MET Norway) enjoys a very high level of trust in the Norwegian population, being the state agency with the best reputation in Norway 15 years in a row. In our latest annual polling close to 85 percent say they have a high degree of trust in our climate research, while 82 percent have a high degree of trust in our TV meteorologists as climate change communicators. The combined climate science and communication expertise within MET Norway can capitalize on these high levels of trust to advance climate communication for Norwegian society. This is the backdrop for the project TV meteorologists as climate communicators initiated in spring 2019, inspired by similar projects in other countries, especially Climate Matters in the USA.</p><p>MET Norway supplies meteorologists to forecast the weather at NRK, the Norwegian national public broadcaster. The TV-meteorologist's assets is that they use language that people understand, and that they can talk about climate change from a local point of view and in relation to the weather situation at a given time. The main objective of this project was therefore to regularly include climate information in the weather forecasts on NRK radio and television. </p><p>In 2019 and 2020 we had 40 stories in the weather forecasts on NRK television. Most of the stories have been related to temperature changes, but also about Arctic sea-ice, flash-floods, and changes in snow cover. The TV-meteorologists involved in the project report that they mainly get positive feedback from the audience. The majority of the stories have also been shared through social media and through press releases to reach other audiences than those that watch TV. Most of these stories have been shared (e.g. retweeted) by multiple users, and we estimate a reach of about 20 percent of Norway's population as realistic.</p><p>The TV-meteorologists have also received training in both climate research and climate communication on a monthly basis since the project started. After the project ended in december 2020, 40 percent of the TV-meteorologists said that they feel more comfortable to communicate climate information than before the project started. </p><p>The project is now operationalized in a climate editorial desk on MET, consisting of several climate researchers, TV-meteorologists and communication advisors that meets on a weekly basis. </p>

How is uncertainty represented best to reduce complexity or cognitive cost in high-impact events.

<p>Within the forecasters community, it is a common approach to make use of meteorological « conceptual models » to synthetise weather situation key processes. These "models" come along with general physical rules that can help, to understand options, particularly in a high-impact situation with low predictability. At the beginning, it helps to build an appropriate scenario in the decision-process.</p><p>Forecasting uncertainty is most of the time considered through a "multi-model" approach, taking into account synoptic and convective scale model outputs, to hightlight the main patterns of uncertainty sources. This subset of model solutions is often called « poor-man-ensemble ». Ensembles provide plenty of relevant information : not only classical probabilities, quantiles, meteograms, but also more subjective visualisations like post-stamps or spaghetti, easy to interpret for forecasters. In the end, all of these informations are mixed to produce or to design mentally the most likely scenario.</p><p>Forecasters experience includes a kind of a subjective pseudo climatology, where each one can find references, making use of analogs to understand the meteorological context, the behaviour of the numerical systems in terms of systematic errors, but also to overcome the consequences linked with vulnerability, or to learn from the reaction of our different end-users facing uncertainty. Especially with civil protection, forecasters get trained to develop practical strategies in strong uncertainty contexts.</p><p>Within an operational forecasting team, real-time decisions can be affected by high stress levels, as well as collective or individual cognitive biases in uncertainty interpretation. We give here some illustrations from past high impact forecasting situations.</p>

Learning of weather-type transitions for risk assessment

<p>Persistence or sequences of critical weather patterns over Europe can trigger seasonally extreme hydroclimatic conditions in certain regions. In order to better estimate return periods of extremes across Europe, existing time series of sequences of weather-types over Europe were used to train monthly rules for the transition from one situation to another and their duration behaviour. This can be efficiently realized and tested by setting up decision trees and generating up to 10,000 year time series of weather-type sequences.</p><p>In an experiment carried out, large-scale weather situation types according to Hess/Brezowsky available from 1961 to 2020 were divided into two time periods and rules for the transition were derived for both by training decision trees. Based on the trained rules of transistions for the periods 1961-1990 and 1991-2020, 10,000-year weather-type sequences were then generated and analysed.</p><p>The comparison of the probability density functions of persistence for the 30 different large-scale weather situation types show that omega-like circultion patterns over Europe have a higher tendency to persist in the present time period. In connection with this, the risks of prolonged dry phases in Central Europe have increased. For the translation of different weather-types into local weather-type characteristics, long-term monthly mean daily precipitation values per weather-type was assigned from ERA5 reanalysis data and rearranged in a post-processing step according to the generated weather-type sequences. The analysis of the maximum duration of consecutive dry and wet months in Europe was the main focus and the identified long-term changes in hydroclimatic quantities can be thus exclusively attributed to dynamic factors.</p>