This presentation focuses on migration of the most influential mammal species: humans! For humans, migration is one of the most drastic adaptation strategies against unfavorable conditions. This model is named after the factors it includes to capture migration probability by humans, namely CH = Changing mindset, A = Agglomeration, S = Social ties, and E = the Environment. Because many of these factors are not typically included in migration models of other non-human species, the CHASE model has the potential to give rise to different dynamics and patterns, which may in turn be useful for understanding and modeling migration of other species. Here we performed numerical experiments on the model by subjecting the human agents in the model to two different kinds of disturbances: sudden shocks and gradual changes. Preliminary results on the dynamics and patterns will be reported, compared, and discussed. Discussion with other presenters and comparison to other presentations in this session should lead to new ideas useful for modeling migration of humans and other species alike.

How to cite: Muneepeerakul, R.: CHASE: a model of human migration under environmental changes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1685, https://doi.org/10.5194/egusphere-egu23-1685, 2023.

The climate is changing most rapidly in the Arctic because of Arctic amplification, influencing migratory bird species that depend on the short, but productive Arctic summer climate. A potential increase in climate variability can lead to reduced reproductive success and even be a major source of mortality for these birds. Most studies so far, focus on mean changes in climate, telling part of the story. However, along with changes in the mean, changes in the variability of climate will occur. These two combined (changes in mean and variability) can lead to more/less frequent extreme events such as heatwaves, droughts and excessive snowfall or melt.

Here we focus on changes in variability and extremes of Arctic bird-related climatic variables, such as temperature, precipitation, snow cover, primary productivity, solar radiation, and soil moisture. We investigate how strongly these climatic variables vary on a daily, monthly, annual and decadal basis. Furthermore, we infer changes in variability between four distinct climate states (0.5x, 1x, 2x & 4x CO₂ level): will the variability and probability for extreme events change in warmer or colder climates? How will this potentially affect Arctic migratory birds? For example, snowfall and ground snow cover are expected to decrease in a warmer climate, resulting in more areas available for nesting. However, snowfall variability is projected to increase, making conditions more unpredictable on an annual basis.

To this end, we carried out four long (500 years), steady-state runs (constant CO₂ level), using the state-of-the-art Earth System Model EC-Earth3. We used two versions of the model (EC-Earth3-Veg & EC-Earth3-CC) and 4 CO₂ levels: 0.5x, 1x, 2x & 4x CO₂ concentration of the year 2022. The end result is 4,000 years of model output data, allowing us to study climate-related changes in climate variability of Arctic bird-related variables.

How to cite: Skyllas, N. and Bintanja, R.: Changes in Arctic climate variability and extremes: effects on migratory birds, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6838, https://doi.org/10.5194/egusphere-egu23-6838, 2023.

This study considers the potential shift of biomes due to simulated changes in climatic drivers up until the end of this century, and how these changes effect the frequency of disturbances which in turn may affect the ranges of vegetation life zones. The study area is mainly the Anatolian Peninsula and its immediate surroundings, a unique location harboring high species diversity and high rates of endemism. Forcing a global to regional dynamic vegetation model with five Global Circulation Model contributions to Coupled Model Intercomparison Project (CMIP6, bias-corrected with ERA5-Land), we looked not only at the changes in the distribution and composition of key forest taxa, but the range shifts of vegetation formations from a biome perspective (classified per The International Geosphere–Biosphere Programme’s nomenclature) focusing on transition zones. Our results simulated a potential increase in the ranges of all 4 woody biomes: forest, transitional woodland, woody grassland and shrubland, with a potential retreat in grasslands. This shift is continuous throughout the simulation period of 1961-2099, with the Central Anatolian grasslands being taken over by tree taxa – comprised mostly of pines and oaks – even for the historical simulation period (1961-2021), but more significantly towards the end of the century. From a biome perspective, the increase in forest biomass and the retreat in grasslands is somewhat contrary to expectations that dryland mechanisms will become more common even in mesic environments as climate change progresses, however in line when we look at the overall picture from a taxon-specific perspective, as species that make up the composition of the simulated woody grasslands in Central Anatolia are mainly drought resistant taxa. One potential reason behind this woody plant encroachment may be the changes in fire frequency and intensity in the absence of anthropogenic interference. Our ongoing research is focusing on this curious pattern as we further analyze this phenomenon with more detailed climate input data with different time windows and with a special focus on disturbances.

How to cite: Ekberzade, B., Yetemen, O., Sen, O. L., and Dalfes, H. N.: Transitioning: the role of disturbances on instigating cross-overs of vegetation zones (a biome perspective), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-141, https://doi.org/10.5194/egusphere-egu23-141, 2023.

Migratory insects may move in very large numbers, even surpassing migratory vertebrates in biomass. However, the extent of aerial flows of insects circulating around the planet, as well as their impact on ecosystems and biogeography, remain almost unstudied because of methodological challenges associated with tracking small, short-lived, organisms. In this presentation, I will show how a novel integrative approach allows reconstructing long-range insect movements, through a combination of tools on genetics, isotope ecology, ecological niche modelling, pollen metabarcoding, field ecology, and citizen science.

I will show the latest discoveries on the migrations of the Painted Lady butterfly (Vanessa cardui). This butterfly species is the most cosmopolitan of all butterflies, and it is known by its regular trans-Saharan migrations, that entail distances of >4000 km, similar to those of some birds. First, we track a migratory outbreak of V. cardui butterflies taking place at a continental scale in Europe, the Middle East, and Africa from March 2019 to November 2019. We use DNA metabarcoding to identify plants from pollen transported by the insects. From 265 butterflies collected in 14 countries over 7 months, we molecularly identify 398 plants. We develop a novel geolocation approach based on combining probability rasters from species distribution modelling of each identified plant, and thus trace back the location of the outbreak’s origin and the origin of each of the subsequent generations. We show a strong representation of plants of Middle Eastern distribution in butterfly swarms collected in Eastern Europe in early spring. Swarms collected in Northern Europe in late spring were highly represented by plants of Mediterranean origin, and swarms collected in the summer in the Mediterranean likely originated in central and Northern Europe.

Second, we report the first proven transatlantic crossing by individual insects, a journey of at least 4,200 km from West Africa to South America. This discovery was possible through gathering evidence from multiple sources, including coastal field surveys, wind trajectory modelling, phylogeography, pollen metabarcoding, and multi-isotope geolocation of natal origins. Wind trajectories were exceptionally favourable for the butterflies to disperse across the Atlantic from West Africa. Population genetic analyses clustered the butterflies collected in South America with the European-African population, ruling out the possibility that the migrants originated in America. Pollen metabarcoding showed highly represented plants endemic to the Sahelian region. Finally, a dual isotope analysis of hydrogen (δ²H) and strontium (⁸⁷Sr/⁸⁶Sr) combined with a spatio-temporal niche model of suitable reproductive habitat geolocated the natal origins of the migrants to regions in Mali, Morocco, or Portugal, and thus not discarding a journey also involving a trans-Saharan crossing.

In summary, this work contributes new methodological avenues to advance our understanding of the dispersal and migration of insects. The findings here reported suggest that we may be underestimating long-range dispersal in insects, and highlight the importance of aerial highways connecting continents by trade winds. Overall, we will discuss the scale and potential implications that insect migratory movements represent for ecosystems and nature conservation worldwide.

How to cite: Talavera, G., Gorki, L., Toro-Delgado, E., López-Mañas, R., Reich, M., Menchetti, M., Domingo-Marimon, C., Sáez, L., Pierce, N., Vila, R., Bataille, C., and Suchan, T.: Migration ecology in insects: integrative approaches to trace long-distance movements of the Painted Lady butterfly (Vanessa cardui), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11884, https://doi.org/10.5194/egusphere-egu23-11884, 2023.

The Greenland ice-sheet surface melt has increased substantially in intensity and spatial extent over the recent decades. The rapid migration of melt towards upstream areas of Greenland ice sheet is expected to incur major changes in hydrological behaviour of the ice-sheet and outlet glaciers along with changes in export fluxes of carbon, methane, and other nutrient fluxes, which, in turn, will further affect the downstream ecosystem of rivers, fjords and oceans. Subglacial environments are emerging as ecological hotspots, urging detailed understanding of interaction between subglacial-hydrology and biogeochemistry. However, due to their inaccessibility, the hydrology and biogeochemistry of subglacial environment thus far lacks a detailed understanding. Numerical models are, in combination with observational data, ideal tools to advance our understanding.

Here, we developed a novel process-based model to investigate the interplay between subglacial-hydrology and (passive and active) tracer dynamics underneath the rapidly changing Greenland ice sheet on seasonal, inter-annual and climate warming relevant timescales. We set up the subglacial-hydrology model GlaDS (Glacier Drainage System model) to simulate seasonal and interannual evolution of distributed and channelized subglacial water flow for Leverett glacier (Southwest Greenland) to explore the geometry, connectivity, and flow dynamics in the seasonally evolving drainage system.

We then use the GlaDS results to inform a reaction-transport model (RTM) of Leverett’s subglacial system following the GlaDS set-up. The RTM is run to conduct a series of idealized tracer experiments with the aim of disentangling the transport and reaction controls on subglacial tracer distribution and outflow. Tracers are injected to the system through moulins with the surface meltwater and are either passively transported (passive) or also consumed/produced (active) during their transit through the system. Model results are validated with long-term measurements in this area. Results show that the tracer transport is primarily controlled by subglacial drainage system efficiency, which is regulated by discharge magnitude, topography and moulin locations. The spatial and temporal variation in tracer concentration is further dependent on hydrological interaction between different subglacial components (cavities and channels), location and type of branching of channels, and bed properties.

In the future, we will extend the model to wider area of Greenland ice sheet and couple it to multi-component biogeochemical reaction networks with the. aim to understand the evolution of biogeochemical process along with the evolution of hydrology in warming climate.

How to cite: Pramanik, A., Arndt, S., Werder, M., and Pattyn, F.: Simulating hydrology and tracer dynamics in a subglacial environment underneath the Greenland ice sheet, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10571, https://doi.org/10.5194/egusphere-egu23-10571, 2023.

Atmospheric circulations at the mid-latitudes are marked by circulation regimes, structures evolving in space very slowly and persisting over time. Their persistence and duration in a context such as Europe's, could lead to weather patterns, such as heat waves and drought, that have a­­ major impact on many socio-economic sectors. Forecasts at seasonal timescale are becoming then crucial to plan or give relevant indicators for societal applications. Predictability of such events could be of great use in further applications related to energy and management of water supplies. Also, this may provide useful insights to understanding the increase in frequency and intensity of these extreme events and their location.

The late purpose of this study is to investigate the predictability of European droughts in a forecast range of 1-3 months. To this aim, drought events are firstly identified, and state-of-the-art seasonal forecast products are analysed to compute the skill for targeted drought-related climate variables and/or circulation patterns. Observational datasets, high-resolution reanalysis and latest generation satellite observations will be used for the characterization of drought events and the forecast validation.

How to cite: Dal Monte, T., Cherchi, A., Alessandri, A., and Gaetani, M.: Assessing the predictability of droughts through seasonal forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14731, https://doi.org/10.5194/egusphere-egu23-14731, 2023.

To optimize the performance of seasonal climate forecasts we used a Grand Multi-Model Ensemble (MME) approach. The Grand MME consists of five Seasonal Prediction Systems (SPSs) provided by the European Copernicus Climate Change Service (C3S) and of other six SPSs independently developed by centres outside Europe, five by the North American (NMME) plus the SPS by the Japan Meteorological Agency (JMA).

All the possible Grand MME combinations have been evaluated for temperature and precipitation, for different geographical regions. Results show that, in general, only a limited number of SPSs is required to maximize the skill. Although the selection of models that optimize performance is usually different depending on the region, variable and season, it is shown that the performance of the Grand-MME seasonal predictions is enhanced with the increase of the independence of the contributing SPSs.

Independence is measured by using  a novel metric developed here, named the Brier score covariance (BScov), which estimates the relative independence of the SPSs. Together with probabilistic skill metrics, BScov is used to develop a strategy for an effective identification of the combinations of SPSs that optimize the probabilistic performance of the predictions, thus avoiding the inefficient and ineffective use of all SPSs available.

How to cite: Alessandri, A., Catalano, F., Nielsen, K., and Troccoli, A.: On the optimization of grand multi-model probabilistic performance and the independence of the contributing seasonal prediction systems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9190, https://doi.org/10.5194/egusphere-egu23-9190, 2023.

The northward/northwestward propagation of boral summer intraseasonal oscillation (BSISO) modulates the subtropical variability ad typhoon activity and has significant impacts on the extreme weather and climate events in Asia. BSISO strongly interacts with background mean fields and tends to be stronger and longer in its northward propagation during La Nina than El Nino summers. It is further found that BSISO-related convections are stronger and more organized with northward propagation on 30-60-day timescales during El Nino developing than decaying summers over the western Pacific. Thus, for skillful subseasonal prediction of extreme events in Asia, it is crucial for climate models to well represent BSISO and its interaction with the background mean state and synoptic variability. Our case study shows that the rare extreme flooding event in Henan Province, China, during July 2021 (referred to as the “21.7” flooding event) was a result of scale interactions between the background mean field associated with the weak La Nina condition, intraseasonal oscillations, and synoptic disturbances. The two distinct modes of the BSISO (10-30- and 30-90-day modes) unusually had a crucial combined role in moisture convergence, aided by the increased seasonal-mean moisture content, maintaining persistent rainfall during the 21.7 event. Synoptic-scale moisture convergence was also contributed to the extreme values in the peak day of the event. The five state-of-the art subseasonal-to-seasonal prediction models showed limited skills in predicting this extreme event one to two weeks in advance, partly because of their biases in representing the BSISO and multiscale interactions. Our results highlight that an accurate prediction of subseasonal perturbations and their interactions with the background moisture content is crucial for improving the extended-range forecast skill of extreme precipitation events.

How to cite: Lee, J.-Y., Hsu, P.-C., Lee, D.-Y., Yang, Y.-M., and Xie, J.: The role of multi-scale interaction on subseasonal prediction of extreme events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17423, https://doi.org/10.5194/egusphere-egu23-17423, 2023.

The capacity factor (CF) is a critical indicator for quantifying wind turbine efficiency, and therefore has been widely used to measure the impact of interannual wind variability on wind energy production. Using the seasonal prediction products from GFDL’s Seamless System for Predicton and Earth System (SPEAR), we assess the seasonal prediction skill of CF over North America. SPEAR shows high skill in predicting winter CF over the western United States. The seasonal wind speed and CF variations associated with large-scale circulation anomalies are examined to understand the predictability mechanism of CF. The source of the skillful seasonal CF prediction can be attributed to year-to-year variations of ENSO and North Pacific Oscillation, which produce large-scale anomalous wind patterns over North America. The skillful seasonal prediction of CF is potentially beneficial to various stakeholders in the energy sector, including wind energy production, trading, and transmission.  

How to cite: Yang, X., Delworth, T., Jia, L., Johnson, N., Lu, F., and McHugh, C.: Seasonal prediction and predictability of wind power potential over North America, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10719, https://doi.org/10.5194/egusphere-egu23-10719, 2023.

In the SADC region of Eastern Africa the onset and duration of the rainy season is of high importance to the agriculture and general water resource management. The planting time, selection of crops and success of different crops is linked to how skillfully this date can be forecasted.  
 
As part of the Horizon 2020 project called FOCUS-Africa, in order to forecast this specific onset-date and duration for a specific location in Tanzania, we have constructed a statistical model utilizing the Random Forest algorithm. This is being trained using a mix of observation of past teleconnection indices such as IOD and ENSO3.4 from recent months that from earlier studies have shown to be connected to the onset date and dynamical seasonal forecast of precipitation with a daily temporal resolution. At this stage three dynamical models are included. Finally, the observed precipitation of the previous months is being used as predictors as well.  
 
The first results have shown an improvement of the statistical model over using climatic information such as mean onset date as the reference forecast. This can be achieved 2-3 months ahead of the onset date. Furthermore, a relatively large importance of the seasonal forecast systems and the teleconnection indices seems to be present several months ahead of the observed onset date. This also indicates the importance of mixing observations and dynamical models in order to optimize the best possible overall skill of the system in predicting the onset date of the rainy season and thereby supporting local agriculture. 

How to cite: Nielsen, K., Troccoli, A., Roy, I., and Mliwa, M.: Random Forest approach to forecast onset date and duration of rainy season in Tanzania, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16200, https://doi.org/10.5194/egusphere-egu23-16200, 2023.