CL5.3 | Challenges in climate prediction: multiple time-scales and the Earth system dimensions

CL5.3

Challenges in climate prediction: multiple time-scales and the Earth system dimensions

A big challenge in Earth system science is providing reliable climate predictions on sub-seasonal, seasonal, decadal and longer timescales. Resulting data can potentially be translated into climate information for better assessment of global and regional climate-related risks. Latest developments and progress in climate forecasting on different timescales will be discussed and evaluated, including predictions for different time horizons from dynamical ensemble and statistical/empirical forecast systems, and the aspects required for their application: forecast quality assessment, multi-model combination, bias adjustment, downscaling, etc. Contributions on initialization methods that use observations from different Earth system components, on assessing and mitigating impacts of model errors on skill and on ensemble methods will be included, much as contributions on the use of climate predictions for climate impact assessment, demonstrations of end-user value for climate risk applications and climate-change adaptation and development of early warning systems.
Another focus is on the use of operational climate predictions (C3S, NMME, S2S), results from CMIP5-CMIP6 decadal prediction experiments, and climate-prediction research and application projects. Since an important part of climate forecast is to apply appropriate downscaling methods -dynamic, statistical or a combination- to generate time series and fields with appropriate spatial or temporal resolution, this will be covered by the session, which aims to bring together scientists from all geoscientific disciplines working on the prediction and application problems. Following the new WCRP strategic plan for 2019-2029, prediction enhancements are also sought that embrace climate forecasting from an Earth system science perspective, including study of coupled processes between atmosphere, land, ocean and sea-ice components, and the impacts of coupling and feedbacks in physical, chemical, biological and human dimensions including migration. On migration, the focus is on migratory species or those that are forced to migrate due to a change in the frequency and severity of climatic disturbances or human intervention, i.e. land use land cover change. This part of the session is for researchers working on terrestrial, marine or freshwater species and studies covering all aspects of migration including trait and behavioral changes as a response to sudden or gradual environmental changes, at all temporal scales.

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Co-organized by BG9/CR7/NP5/OS4

Convener: Andrea Alessandri | Co-conveners: Yoshimitsu Chikamoto, Tatiana Ilyina, June-Yi Lee, Xiaosong Yang, Bikem EkberzadeECSECS, Nomikos SkyllasECSECS

Orals

| Wed, 26 Apr, 08:30–10:15 (CEST)

Room 0.49/50

Posters on site

| Attendance Tue, 25 Apr, 10:45–12:30 (CEST)

Hall X5

Posters virtual

| Attendance Tue, 25 Apr, 10:45–12:30 (CEST)

vHall CL

Orals: Wed, 26 Apr | Room 0.49/50

The oral presentations are given in a hybrid format supported by a Zoom meeting featuring on-site and virtual presentations. The button to access the Zoom meeting appears just before the time block starts.

Chairpersons: Andrea Alessandri, Tatiana Ilyina, Bikem Ekberzade

08:30–08:31

Introduction

08:31–08:41

EGU23-3388

solicited

Highlight

Virtual presentation

Multiyear predictability of atmospheric angular momentum and its implications.

Adam Scaife

08:41–08:45

Extra time for solicited speaker

08:45–08:55

EGU23-3433

Virtual presentation

The relative role of the subsurface Southern Ocean in driving negative Antarctic Sea ice extent anomalies in 2016-2021

Liping Zhang, Thomas L. Delworth, Xiaosong Yang, Fanrong Zeng, feiyu lu, Yushi Morioka, and Mitchell Bushuk

08:55–09:05

EGU23-5446

On-site presentation

Effect of initialisation within a 20yr multi-annual climate prediction system

André Düsterhus and Sebastian Brune

Decadal climate predictions use state-of-the-art climate models and combine them with initialisation procedures to create information about our future. Their development has proven successful in the past years and offer a wide range of applications. One of them is the option to learn about the used climate models. With predictions usually aiming at time periods up to ten lead years it is often assumed that initialisation will wear off over time and the model will regress to results comparable to uninitialised simulations.

This contribution investigates decadal predictions over lead times of up to twenty years. The decadal prediction system is based on the Max Planck Institute Earth system model (MPI-ESM), uses atmospheric nudging and an oceanic Ensemble Kalman filter for initialisation and is applied for periods from 1960 onwards. We demonstrate that the effect of initialisation within the prediction can be found for long lead years and does not necessarily regresses back to the uninitialised simulation.

We show that in some areas the prediction skill varies over time, while in others it persists or drops quickly. Examples are a consistently increased prediction skill compared to historical simulations in the North East Pacific or decreased prediction skill for lead years longer than ten in the South Atlantic. We also take a look at the Atlantic Meridional Overturning Circulation (AMOC) and its development over time. We show that the AMOC drifts on short time scales towards a new state, which is reached after about ten lead years. For decadal predictions with MPI-ESM we find that for large areas of the globe the correct determination of future developments of external forcings plays an important role. This asks the question whether the current approach to hindcasts is appropriate to determine our capability to predict the future.

How to cite: Düsterhus, A. and Brune, S.: Effect of initialisation within a 20yr multi-annual climate prediction system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5446, https://doi.org/10.5194/egusphere-egu23-5446, 2023.

09:05–09:15

EGU23-14765

On-site presentation

Variations of the CO2 fluxes and atmospheric CO2 in multi-model predictions with an interactive carbon cycle

Hongmei Li, Aaron Spring, istvan Dunkl, Sebastian Brune, Raffaele Bernardello, Laurent Bopp, William Merryfield, Juliette Mignot, Reinel Sospedra-Alfonso, Etienne Tourigny, Michio Watanabe, and Tatiana Ilyina

Variable fluxes of anthropogenic CO₂ emissions into the land and the ocean and the remaining proportion in the atmosphere reflect on the global carbon budget variations and further modulate global climate change. A more accurate reconstruction of the global carbon budget in the past decades and a more reliable prediction of the variations in the next years are crucial for assessing the effectiveness of climate change mitigation policies and supporting global carbon stocktaking and monitoring in compliance with the goals of the Paris Agreement.

In this study, we investigate reconstructions and predictions of the CO₂ fluxes and atmospheric CO₂ growth from ensemble prediction simulations using 5 Earth System Model (ESM) - based decadal prediction systems. These novel prediction systems driven by CO₂ emissions with an interactive carbon cycle enable prognostic atmospheric CO₂ and represent atmospheric CO₂ growth variations in response to the strength of CO₂ fluxes into the ocean and the land, which are missing in the conventional concentration-driven decadal prediction systems with prescribed atmospheric CO₂ concentration.

The reconstructions generated by assimilating physical ocean and atmosphere data products into the prediction systems are able to reproduce the annual mean historical variations of the CO₂ fluxes and atmospheric CO₂ growth. Multi-model ensemble means best match the assessments of CO₂ fluxes and atmospheric CO₂ growth rate from the Global Carbon Project with correlations of 0.79, 0.82, and 0.98 for atmospheric CO₂ growth rate, air-land CO₂ fluxes, and air-sea CO₂ fluxes, respectively. The CO₂ emission-driven prediction systems with an interactive carbon cycle still maintain the predictive skill of CO₂ fluxes and atmospheric CO₂ growth as found in conventional concentration-driven prediction systems, i.e., about 2 years for the air-land CO₂ fluxes and atmospheric CO₂ growth, the air-sea CO₂ fluxes have higher skill up to 5 years. The ESM-based prediction systems are capable to reconstruct and predict the variations in the global carbon cycle and hence are powerful tools for supporting carbon budgeting and monitoring, especially in the decarbonization processes. Furthermore, we investigate the contribution of uncertainty in the predictions of CO₂ fluxes and atmospheric CO₂ growth rate from internal climate variability, different model responses, and emission-forcing reductions to identify the prominent challenge in limiting the skill of CO₂ predictions.

How to cite: Li, H., Spring, A., Dunkl, I., Brune, S., Bernardello, R., Bopp, L., Merryfield, W., Mignot, J., Sospedra-Alfonso, R., Tourigny, E., Watanabe, M., and Ilyina, T.: Variations of the CO2 fluxes and atmospheric CO2 in multi-model predictions with an interactive carbon cycle, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14765, https://doi.org/10.5194/egusphere-egu23-14765, 2023.

09:15–09:25

EGU23-13998

ECS

On-site presentation

Variability in ENSO-induced carbon flux patterns

István Dunkl and Tatiana Ilyina

El Niño-Southern Oscillation (ENSO) is not only a driver of global carbon cycle variability, but it also provides several mechanisms of predictability. Although most Earth system models (ESMs) can reproduce the relationship between ENSO and atmospheric CO₂ concentrations, the question remains whether the ESMs agree on the origins of these ENSO-related GPP anomalies. We analyse the patterns of ENSO-induced GPP anomalies in 17 ESMs to determine from which regions these GPP anomalies come from, and whether the differences among the models are driven by climate forcing or biochemistry. While most of the GPP anomalies originate from Southeast Asia and northern South America, there are large deviations among the ESMs. The combined GPP anomaly of these two regions ranges between 26% and 75% of the global anomaly among the ESMs. To find out what causes the differences, we examined two major drivers of the GPP anomalies: the size of the ENSO-induced climate anomalies, and the sensitivity of GPP to climate. On the global average, ENSO-induced climate anomalies and GPP sensitivity have similar uncertainty among the ESMs, contributing equally to the variations in ENSO-induced GPP anomaly patterns. This analysis reveals model biases in teleconnection patterns and biochemistry. Addressing these biases is a tangible goal for model developers to decrease the uncertainty in the reproduction of the global carbon cycle, and to increase its predictability.

How to cite: Dunkl, I. and Ilyina, T.: Variability in ENSO-induced carbon flux patterns, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13998, https://doi.org/10.5194/egusphere-egu23-13998, 2023.

09:25–09:35

EGU23-12428

ECS

On-site presentation

Effects of the realistic vegetation cover on predictions at seasonal and decadal time scales

Emanuele Di Carlo, Andrea Alessandri, Fransje van Oorschot, Annalisa Cherchi, Susanna Corti, Giampaolo Balsamo, Souhail Boussetta, and Timothy Stockdale

Vegetation is a relevant and highly dynamic component of the Earth System controlling, amongst others, surface roughness, albedo and evapotranspiration; its variability shows changes in seasons, interannual, decadal and longer timescales. In this study, we investigate the effects of improved representation of vegetation dynamics on climate predictions at different timescales: seasonal and decadal. To this aim, the latest generation satellite datasets of vegetation characteristics have been exploited, and a novel and improved parameterization of the effective vegetation cover has been developed. The new parameterization is implemented in the land surface scheme HTESSEL shared by two state-of-the-art Earth system models: ECMWF SEAS5 and EC-Earth3. The former model is used for sensitivity at seasonal timescale, while the latter is used for sensitivity at decadal timescale.

Both seasonal and decadal experiments show considerable sensitivity of models' surface climate bias with large effects on December-January-February (DJF) T2M, mean sea level pressure and zonal wind over middle-to-high latitudes. Consistently, a significant improvement in the skill for DJF T2M is found, especially over Euro-Asian Boreal forests. In seasonal experiments, this improvement displays a strong interannual coupling with the local surface albedo. From the region with the most considerable T2M improvement, over Siberia, originates a large-scale effect on circulation encompassing Northern Hemisphere middle-to-high latitudes from Siberia to the North Atlantic. As a result, in seasonal experiments, the correlation between the model NAO index against the ERA5 NAO index improves significantly.

These results show a non-negligible effect of the vegetation cover on the general circulation, especially for the northern hemisphere and on the prediction skill.

How to cite: Di Carlo, E., Alessandri, A., van Oorschot, F., Cherchi, A., Corti, S., Balsamo, G., Boussetta, S., and Stockdale, T.: Effects of the realistic vegetation cover on predictions at seasonal and decadal time scales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12428, https://doi.org/10.5194/egusphere-egu23-12428, 2023.

09:35–09:45

EGU23-15373

On-site presentation

DWD’s operational climate predictions – towards a seamless climate prediction website - towards a seamless climate prediction website

Birgit Mannig, Andreas Paxian, Miriam Tivig, Klaus Pankatz, Kristina Fröhlich, Sabrina Wehring, Alexander Pasternack, Philip Lorenz, Amelie Hoff, Katharina Isensee, Saskia Buchholz, and Barbara Früh

Germany's National Meteorological Service, Deutscher Wetterdienst (DWD), is working on an operational seamless climate prediction approach: What started in 2016 with operational seasonal climate predictions, was later complemented with decadal climate predictions. Since 2022, DWD publishes decadal, seasonal, and subseasonal climate predictions on one single, comprehensive climate prediction website www.dwd.de/climatepredictions [1].

While global simulations of decadal and seasonal predictions are produced by DWD’s climate prediction systems, global subseasonal predictions are retrieved from the European Centre of Medium-Range Weather Forecast (ECMWF). The next step in the operational processing chain is the empirical-statistical downscaling EPISODES [2], which results in high-resolution climate predictions (approx. 5 km) for Germany.

Both global and regional climate predictions are evaluated using the Meteorological Analyzation and Visualization System MAVIS, a fork of the FREVA system (Free Evaluation System Framework for Earth System Modeling) [3]. We evaluate ensemble mean predictions using the Mean Squared Error Skill Score (MSESS) and the Pearson Correlation Coefficient. Probabilistic climate predictions are evaluated using the Ranked Probability Skill Score (RPSS).

Ensemble mean and probabilistic climate prediction results of global and downscaled simulations, as well as the evaluation results are jointly published on DWD’s climate prediction website. The user-friendly graphical presentation is consistent for all displayed regions (global, Europe, Germany, and German cities) and across all time scales and was developed as a co-design between DWD and various national users.

We work on several extensions of the website: multi-year seasonal predictions (e.g. 5-year summer means), the prediction of drought indices and El Nino Southern Oscillation predictions. In addition, a seamless time series combining observations, climate predictions and climate projections is in preparation.

[1] A. Paxian, B. Mannig, M. Tivig, K. Reinhardt, K. Isensee, A. Pasternack, A. Hoff, K. Pankatz, S. Buchholz, S. Wehring, P. Lorenz, K. Fröhlich, F. Kreienkamp, B. Früh (2023). The DWD climate predictions website: towards a seamless outlook based on subseasonal, seasonal and decadal predictions. Manuscript in review.

[2] Kreienkamp, F., Paxian, A., Früh, B., Lorenz, P., Matulla, C., 2018. Evaluation of the Empirical-Statistical Downscaling method EPISODES. Clim. Dyn. 52, 991–1026 (2019). https://doi.org/10.1007/s00382-018-4276-2.

[3] Kadow, C., Illing, S., Lucio-Eceiza, E.E., Bergemann, M., Ramadoss, M., Sommer, P.S., Kunst, O., Schartner, T., Pankatz, K., Grieger, J., Schuster, M., Richling, A., Thiemann, H., Kirchner, I., Rust, H.W., Ludwig, T., Cubasch, U. and Ulbrich, U., 2021. Introduction to Freva – A Free Evaluation System Framework for Earth System Modeling. Journal of Open Research Software, 9(1), p.13. DOI: http://doi.org/10.5334/jors.253

How to cite: Mannig, B., Paxian, A., Tivig, M., Pankatz, K., Fröhlich, K., Wehring, S., Pasternack, A., Lorenz, P., Hoff, A., Isensee, K., Buchholz, S., and Früh, B.: DWD’s operational climate predictions – towards a seamless climate prediction website - towards a seamless climate prediction website, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15373, https://doi.org/10.5194/egusphere-egu23-15373, 2023.

09:45–09:55

EGU23-14401

On-site presentation

A case study to investigate the role of aerosols reduction on the East Asian summer monsoon seasonal prediction

Annalisa Cherchi, Etienne Tourigny, Juan C Acosta Navarro, Pablo Ortega, Paolo Davini, Andrea Alessandri, Franco Catalano, and Twan van Noije

In the late 20^th century, both the East Asian and the South Asian summer monsoons weakened because of increased emissions of anthropogenic aerosols over Asia, counteracting the warming effect of increased greenhouse gases (GHGs). During the spring 2020, when restrictions to contain the spread of the coronavirus were implemented worldwide, reduced emissions of gases and aerosols were detected and found to be quite extended over Asia.

Following on from the above and using the EC-Earth3 coupled model, a case-study forecast for summer 2020 (May 1st start date) has been designed and produced with and without the reduced atmospheric forcing due to covid-19 related restrictions in the SSP2-4.5 baseline scenario, as estimated and adopted within CMIP6 DAMIP covidMIP experiments (hereinafter “covid-19 forcing”). The forecast ensembles (sensitivity and control experiments, meaning with and without covid-19 forcing) consist of 60 members each to better account for the internal variability (noise) and to maximize the capability to identify the effects of the reduced emissions.

The analysis focuses on the effects of the covid-19 forcing on the forecasted evolution of the monsoon, with a specific focus on the performance in predicting the summer precipitation over India and over other parts of South and East Asia. The results indicate that in 2020 a more realistic representation of the atmospheric forcing in the spring preceding the core monsoon season improves the skill of the predicted summer precipitation, mostly over East Asia. Beyond the testbed considered in this analysis, the result helps improving the understanding of the processes at work over the Asian monsoons regions, with positive implications on the usefulness of seasonal predictions products.

How to cite: Cherchi, A., Tourigny, E., Acosta Navarro, J. C., Ortega, P., Davini, P., Alessandri, A., Catalano, F., and van Noije, T.: A case study to investigate the role of aerosols reduction on the East Asian summer monsoon seasonal prediction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14401, https://doi.org/10.5194/egusphere-egu23-14401, 2023.

09:55–10:05

EGU23-17300

Highlight

Virtual presentation

Skillful subseasonal prediction of rainfall and extreme events in East China based on deep learning

Pang-Chi Hsu and Jiehong Xie

The subseasonal prediction with a lead time of 10–30 days is the gap between weather (<10 days) and climate (>30 days) predictions. Improving the forecast skill of extreme weather events at the subseasonal range is crucial for risk management of disastrous events. In this study, three deep-learning (DL) models based on the methods of convolutional neural network and gate recurrent unit are constructed to predict the rainfall anomalies and associated extreme events in East China at the lead times of 1–6 pentads. All DL models show skillful prediction of the temporal variation of rainfall anomalies (in terms of temporal correlation coefficient skill) over most regions in East China beyond 4 pentads, outperforming the dynamical models from the China Meteorological Administration (CMA) and the European Centre for Medium Range Weather Forecasts (ECMWF). The spatial distribution of the rainfall anomalies is also better predicted by the DL models than the dynamical models; and the DL models show higher pattern correlation coefficients than the dynamical models at lead times of 3 to 6 pentads. The higher skill of DL models in predicting the rainfall anomalies will help to improve the accuracy of extreme-event predictions. The Heidke skill scores of the extreme rainfall event forecast performed by the DL models are also superior to those of the dynamical models at a lead time beyond about 4 pentads. Heat map analysis for the DL models shows that the predictability sources are mainly the large-scale factors modulating the East Asian monsoon rainfall.

How to cite: Hsu, P.-C. and Xie, J.: Skillful subseasonal prediction of rainfall and extreme events in East China based on deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17300, https://doi.org/10.5194/egusphere-egu23-17300, 2023.

10:05–10:15

EGU23-11922

ECS

On-site presentation

Is your ensemble of CMIP6 models consistent with IPCC AR6?

Vincent Humphrey, Anna Merrifield, and Reto Knutti

The Intergovernmental Panel on Climate Change (IPCC) assesses the sensitivity of the climate system to increases in greenhouse gas concentrations using multiple lines of evidence, covering paleoclimate data, historical observations, and numerical Earth system model (ESM) simulations. Within IPCC’s latest Assessment Report (AR6), there is, for the first time, a non-negligible difference between the most likely rate of warming estimated in the report and the average warming rate simulated by the ESMs that participated in the Coupled Model Intercomparison Project (CMIP6). This discrepancy occurs because a large number of CMIP6 models have projected future warming rates that are higher than previously reported but quite unlikely according to historical observations. The consequence is that using a random selection of CMIP6 simulations is likely to overestimate historical and future warming (compared to what is assessed in the IPCC report), potentially leading to avoidable inconsistencies when compared to observations or greater projected changes compared to what could be inferred from CMIP5.

As this constitutes a wide-spread obstacle and limitation to using CMIP6 simulations ‘out of the box’, we propose here a simple model weighting method with the objective to address this problem. Our approach can be used to 1) evaluate the extent to which any given set of CMIP6 simulations is consistent with IPCC-assessed warming rates and 2) calculate the appropriate model weights so that potential inconsistencies are reduced as much as possible. The calculation of the weights is solely based on the user’s selection of a CMIP6 subset and does not require any data manipulation. The weights can then be easily implemented in existing analyses to calculate weighted (i.e. instead of just arithmetic) multi-model means, weighted quantiles, etc. We demonstrate the interest and flexibility of the method with some examples, including global to regional assessments of historical and projected changes in temperature and precipitation. We illustrate the extent to which applying model weights can reconcile otherwise divergent scientific results and provide assessments that are more robust across CMIP generations.

How to cite: Humphrey, V., Merrifield, A., and Knutti, R.: Is your ensemble of CMIP6 models consistent with IPCC AR6?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11922, https://doi.org/10.5194/egusphere-egu23-11922, 2023.

Posters on site: Tue, 25 Apr, 10:45–12:30 | Hall X5

The posters scheduled for on-site presentation are only visible in the poster hall in Vienna. If authors uploaded their presentation files, these files are linked from the abstracts below, but only on the day of the poster session.

Chairpersons: Andrea Alessandri, Bikem Ekberzade, Nomikos Skyllas

X5.250

EGU23-1685

solicited

On-site presentation

CHASE: a model of human migration under environmental changes

Rachata Muneepeerakul

X5.251

EGU23-6838

ECS

Highlight

On-site presentation

Changes in Arctic climate variability and extremes: effects on migratory birds

Nomikos Skyllas and Richard Bintanja

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 CO₂level): 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 CO₂concentration 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.

X5.252

EGU23-141

ECS

Highlight

On-site presentation

Transitioning: the role of disturbances on instigating cross-overs of vegetation zones (a biome perspective)

Bikem Ekberzade, Omer Yetemen, Omer Lutfi Sen, and H. Nuzhet Dalfes

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.

X5.253

EGU23-11884

On-site presentation

Migration ecology in insects: integrative approaches to trace long-distance movements of the Painted Lady butterfly (Vanessa cardui)

Gerard Talavera, Luise Gorki, Eric Toro-Delgado, Roger López-Mañas, Megan Reich, Mattia Menchetti, Cristina Domingo-Marimon, Llorenç Sáez, Naomi Pierce, Roger Vila, Clément Bataille, and Tomasz Suchan

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.

X5.254

EGU23-14304

ECS

Highlight

On-site presentation

Decadal prediction along the Western Irish Coast

Catherine O'Beirne, Gerard McCarthy, and André Düsterhus

Over the last decade there have been vast improvements in the field of global decadal climate prediction; however, on a regional scale there is still limited confidence. Previous studies with the Max Plank Institute Earth System Model (MPI-ESM) have demonstrated that it can replicate water properties on a regional scale in the North Sea and Barents Sea.

In this study we investigate the prediction skill at depth along the Western Irish Coast using the MPI-ESM. For this we compare Hindcast simulations with Historical simulations. The employed Hindcast simulations consists of an ensemble mean of 16 members over the time frame 1961-2008 with a 2-to-5-year lead time. The Historical simulations over the same time frame also consist of an ensemble mean of 16 members.

For this contribution we investigate further the MPI-ESM predictability at depth for temperature and salinity along three transects that influence the Western Irish Coast at the Extended Ellet Line northwest, Galway Transect west, and Goban Spur southwest. A lead time analysis determines the improvement of prediction skill by initialisation. We discuss potential applications for this work in areas such as fisheries, coastal security, and marine leisure, for Ireland and its surrounding seas.

How to cite: O'Beirne, C., McCarthy, G., and Düsterhus, A.: Decadal prediction along the Western Irish Coast, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14304, https://doi.org/10.5194/egusphere-egu23-14304, 2023.

X5.255

EGU23-10571

On-site presentation

Simulating hydrology and tracer dynamics in a subglacial environment underneath the Greenland ice sheet

Ankit Pramanik, Sandra Arndt, Mauro Werder, and Frank Pattyn

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.

X5.256

EGU23-14731

ECS

On-site presentation

Assessing the predictability of droughts through seasonal forecasts

Thomas Dal Monte, Annalisa Cherchi, Andrea Alessandri, and Marco Gaetani

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.

X5.257

EGU23-9190

On-site presentation

On the optimization of grand multi-model probabilistic performance and the independence of the contributing seasonal prediction systems

Andrea Alessandri, Franco Catalano, Kristian Nielsen, and Alberto Troccoli

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.

Posters virtual: Tue, 25 Apr, 10:45–12:30 | vHall CL

The posters scheduled for virtual presentation are visible in Gather.Town. Attendees are asked to meet the authors during the scheduled attendance time for live video chats. If authors uploaded their presentation files, these files are also linked from the abstracts below, but only on the day of the poster session. The button to access Gather.Town appears just before the time block starts.

Chairpersons: Yoshimitsu Chikamoto, June-Yi Lee, Xiaosong Yang

vCL.16

EGU23-17423

Virtual presentation

The role of multi-scale interaction on subseasonal prediction of extreme events

June-Yi Lee, Pang-Chi Hsu, Doo-Young Lee, Young-Min Yang, and Jinhui Xie

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.

vCL.17

EGU23-10719

Highlight

Virtual presentation

Seasonal prediction and predictability of wind power potential over North America

Xiaosong Yang, Thomas Delworth, Liwei Jia, Nathaniel Johnson, Feiyu Lu, and Colleen McHugh

vCL.18

EGU23-16200

Virtual presentation

Random Forest approach to forecast onset date and duration of rainy season in Tanzania

Kristian Nielsen, Alberto Troccoli, Indrani Roy, and Meshack Mliwa

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.

vCL.19

EGU23-17225

Virtual presentation

Exploring the Role of Hybrid Energy Systems for Enhancing Green Energy Potential in Urban Areas

Deepak Kumar and Nick P. Bassill

Hybrid energy systems for improving sustainable urban energy attempt to combine energy supply, public transport modernization, and residential/commercial energy demand reduction. Due to reduced nonrenewable resources, alternative and augmented energy sources are required everywhere. The development of science and industry has increased the energy required to achieve environmental goals with reduced gas emissions. Solar and wind energy are cleaner, more efficient alternatives to polluting energy sources, so the attention is now on large-scale hybrid energy systems. Lots of attempts have been made to show technological advancement and research has analyzed the functionality of energy systems, but urban applications have received little attention. The proposed work imitates the feasibility analysis of hybrid urban energy systems. The research acknowledged the development of research purpose, methodology, research, and data collection approach to reporting the technological, scientific, and industrial developments. This research explains a typical urban environment to determine the hourly load profile for any urban region to exhibit the role of a hybrid energy system to raise energy potential. It summarizes past, present, and future trends in energy system design, development, and implementation. The design can be enlarged to implementations with several other combinations to provide cleaner and cheaper energy.

How to cite: Kumar, D. and Bassill, N. P.: Exploring the Role of Hybrid Energy Systems for Enhancing Green Energy Potential in Urban Areas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17225, https://doi.org/10.5194/egusphere-egu23-17225, 2023.