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We use a hierarchy of climate models (from simple theoretical models to comprehensive Earth System Models) and leverage advanced statistics and machine learning techniques to model and understand the dynamics of climate and its impact on weather risk. 

Department of Atmospheric Sciences, University of Hawai‘i at Mānoa

I ka wā ma mua, I ka wā ma hope

(look to the past to move forward)

 

"The real voyage of discovery consists not in seeking new landscapes but in having new eyes."

Marcel Proust, 1871-1922

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WELCOME!

Our research group is based at the Department of Atmospheric Sciences at the University of Hawaiʻi at Mānoa.

 

The group is led by Prof. Christina Karamperidou [pronunciationand focuses on ENSO dynamics and predictability, extreme events in response to large-scale climate variability and change, paleoclimate, and machine learning applications in environmental science and climate model optimization.

News & Highlights

RESEARCH THEMES

DYNAMICS & PREDICTABILITY OF ENSO DIVERSITY AND ITS IMPACTS

The mechanisms behind ENSO strength and pattern diversity remain an open question: our studies utilize a hierarchy of models to better understand the response of ENSO diversity and its impacts in past, present, and future climates.

INTERACTING DYNAMICS OF TROPICAL AND EXTRATROPICAL CLIMATE

Extratropical atmospheric circulation is influenced by the tropics, and in turn it may influence tropical ocean-atmosphere variability: our studies look into the interactions between tropical climate variability and midlatitude atmospheric dynamics, including blocking events, atmosperic response to volcanic eruptions, surface temperature contrasts etc.

MULTI-RESOLUTION PALEOCLIMATE MODEL-PROXY SYNTHESIS

Paleoclimate proxy records reflect large-scale signals as they are modulated by regional processes and terrain influences: our studies combine the use of global climate models, high-resolution regional models and statistical downscaling methods to improve interpretation of proxies recording ENSO variability.

CONSTRAINTS FOR CLIMATE CHANGE PROJECTIONS

Model uncertainty is a main source of uncertainty in climate projections, especially at multidecadal scales: our studies focus on using our understanding of tropical climate process to identify model biases and constrain intermodel spread of future projections of tropical and global climate.

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