Huvudinnehåll

Pasha Karami

Updated

Published

Ph.D., Researcher.

Porträtt Pasha Karami.

Pasha Karami

Fields of work

Global climate modelling, Regional and global ocean modelling, Arctic ocean and climate dynamics, Decadal climate prediction, Interannual and interdecadal climate variability, Physical Oceanography.

Research interests

  • The role of Arctic sea ice and fresh water export in the North Atlantic Ocean circulation and climate
  • Investigating the interannual and interdecadal climate variability processes for better representation of future climate
  • Understanding the ocean and climate dynamics by applying high resolution global and regional climate models

Special competences

Perform and analyse climate and ocean model simulations. Current projects: FORMAS-funded project- Extreme events in the coastal zone of Baltic Sea, Vinnova-funded project concerning infectious disease risks related to climate(CLAIRE), co-leading the SMHI’s decadal climate prediction simulations.

Past projects: ARCPATH, InterDec, ArcTrain, Paleoclimate modelling as part of ERC-funded project.

Latest publications

Exploring Storm Tides Projections and Their Return Levels Around the Baltic Sea Using a Machine Learning Approach

Kevin Dubois, Erik Nilsson, Morten Drews Dahl Andreas, Martin larsen, Magnus Hieronymus, Pasha Karami, Anna Rutgersson

In: Tellus. Series A, Dynamic meteorology and oceanography, Vol. 77, No. 1

2025

DOI: 10.16993/tellusa.4101

Extreme sea levels are a major global concern due to their potential to cause fatalities and significant economic losses in coastal areas. Consequently, accurate projections of these extremes for the coming century are crucial for effective coastal planning. While it is well established that relative sea level rise driven by ongoing climate change is a key factor influencing future extreme sea levels, changes in storm surges resulting from shifts in storm climatology may also play a critical role. In this study, we project future daily maximum storm tides (the combination of storm surge and tides) using a random forest machine learning approach for 59 stations around the Baltic Sea, based on atmospheric variables such as surface pressure, wind speed, and wind direction derived from climate datasets. The results suggest both positive and negative changes, with sub-regional variations, in 50-year storm tide return levels across the Baltic Sea when comparing the period of 2070-2099 to 1850-1879. Localized increases of up to 10 cm are projected along the west coast of Sweden and the northern Baltic Sea, while decreases of up to 6 cm are anticipated along the south coast of Sweden, the Gulf of Riga, and the mouth of the Gulf of Finland. Negligible levels of change are expected in other parts of the Baltic Sea. The variability in atmospheric drivers across the four climate models contributes to a high degree of uncertainty in future climate projections.