DANIEL TOPAL
Climate research and music
BIO
I’ve had a passion for science and music since I was a child. I graduated from Eötvös University, Budapest in 2019 and now I am a junior Research Fellow at the Institute for Geological and Geochemical Research Research Centre for Astronomy and Earth Sciences, Budapest, Hungary. I recently won a Fulbright Fellowship, which will allow me to broaden my research at the University of California, Santa Barbara in 2021. Keep scrolling to learn more about my work and music by checking out my current projects and past publications.
ARCTIC CLIMATE CHANGE
The Arctic has experienced amplified warming over the past decades, which has kept it in the focus on climate research. Despite tremendous scientific advances, the underlying physical mechanisms and their representation in climate models are still uncertain. My research focuses on atmospheric drivers of summer sea-ice variability.
A NEW FRAMEWORK FOR UNDERSTANDING FUTURE CLIMATIC VARIATIONS
One consequence of anthropogenic forcing requires to reconsider previous frameworks of climate diagnostics and turn to the so-called snapshot framework when studying future climate change. I further explore this research avenue in collaboration with members of the Theoretical Physics Research Group at Eötvös University, Budapest.
PALEOCLIMATE
To make more precise projections of future Arctic sea-ice or Greenland melt, we need to understand past climate changes better. At the Institute for Geological and Geochemical Research, Research Centre for Astronomy and Earth Sciences we explore past drivers of climate changes.
RESEARCH PAPERS
Future hydroclimate projections of global climate models for East-Central Europe diverge to a great extent, thus, constrain adaptation strategies. To reach a more comprehensive understanding of this regional spread in model projections, we make use of the CMIP5 multi-model ensemble and six single-model initial condition large ensemble (SMILE) simulations to separate the effects of model structural differences and internal variability, respectively, on future hydroclimate projection uncertainty. Our results help refine the relative contribution of structural differences between models in affecting future hydroclimate uncertainty in the presence of irreducible internal variability in East-Central Europe.
Topal et al.
September 7, 2020, Theor Appl Clim
We show that all existing climate models exhibit limitations in replicating the magnitude of the observed local Arctic atmosphere-sea ice coupling and its sensitivity to remote tropical SST variability in the past four decades. These biases call for caution in the interpretation of existing models’ simulations and fresh thinking about models’ credibility in simulating interactions of sea ice variability with the Arctic and global climate systems.
Topal et al.
June 25, 2020, Journal of Climate
Haszpra et al.
September 1, 2020, Variations
The Arctic Oscillation and its related wintertime phenomena are investigated under climate change by 2099 in an ensemble approach using the CESM1 Large Ensemble and the MPI-ESM Grand Ensemble with different RCP scenarios. This study emphasizes the importance of the snapshot framework when studying changes in the climate system.
Haszpra et al.
April 15, 2020, Journal of Climate
Observational and model evidence shows that the changes in summer sea ice since the 2000s reflect a continuous anthropogenically forced melting masked by interdecadal variability of Arctic atmospheric circulation. This variation is partially driven by teleconnections originating from sea surface temperature (SST) changes in the east-central tropical Pacific via a Rossby wave train propagating into the Arctic [herein referred to as the Pacific–Arctic teleconnection (PARC)], which represents the leading internal mode connecting the pole to lower latitudes.
Baxter et al.
November 20, 2019, Journal of Climate
In the present study, artificially generated time series with white and red noise structures are analyzed using three recently developed breakpoint detection methods. Utilizing this experience can help solving breakpoint detection problems in real-life data sets, as is demonstrated with two examples taken from the fields of paleoclimate research and petrology.
Topal et al.
February 15, 2016, Open Geosciences
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