Christina Karamperidou's Avatar

Christina Karamperidou

@drxtinakar.bsky.social

Climate Scientist, Professor of Atmospheric Sciences at University of Hawaiʻi at Mānoa. El Niño, extremes, paleoclimate, ML/AI in weather and climate

473 Followers  |  224 Following  |  12 Posts  |  Joined: 21.09.2023  |  1.846

Latest posts by drxtinakar.bsky.social on Bluesky

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El Niño research brings global experts to UH Mānoa | University of Hawaiʻi System News This year marks the 50th anniversary of key milestones in ENSO research.

I’m honored to have chaired the ENSO Winter School 2025
@uhmanoa.bsky.social, where we brought together bright minds from across the globe to deepen our understanding of El Niño–Southern Oscillation (ENSO) and build invaluable professional networks. www.hawaii.edu/news/2025/03...

26.03.2025 05:41 — 👍 3    🔁 0    💬 0    📌 0
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Open MS/PhD & postdoctoral researcher positions:

29.12.2024 22:39 — 👍 12    🔁 4    💬 0    📌 0
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SOEST POSTDOCTORAL RESEARCHER (ML/AI FOR CLIMATE & WEATHER EXTREMES) - ID# 224896 - Honolulu, HI Please apply directly on the RCUH website to be considered for the position. SOEST POSTDOCTORAL RESEARCHER (ML/AI FOR CLIMATE & WEATHER EXTREMES) - ID# 224896. CLOSING DATE: January 17, 2025, or until...

Seeking a Postdoctoral Researcher on the topic of ML/AI for climate and weather extremes. Apply here: tinyurl.com/2yksva7b

29.12.2024 22:34 — 👍 8    🔁 0    💬 0    📌 0

I want to thank the reviewers and the editor for making the review process a constructive, pleasant, and exciting experience! I am grateful! n/n

30.09.2024 20:15 — 👍 3    🔁 0    💬 0    📌 0

Another “unintended” conclusion from this study (thank you Reviewer #3) is that the DL blocking reconstruction lends support to tropical Pacific gradient reconstructions that are based on very sparse networks. 8/n

30.09.2024 20:15 — 👍 3    🔁 0    💬 1    📌 0

The deep learning reconstruction also shows an increased influence of ENSO post-1600 coinciding with a weaker tropical Pacific zonal temperature gradient: so much to unpack here! 7/n

30.09.2024 20:15 — 👍 2    🔁 0    💬 1    📌 0

A weaker gradient is consistent with hemispherically reduced blocking as CMIP models project, but if GHG forcing eventually leads to an increased gradient, we may also see increased atmospheric blocking and subsequent extremes. 6/n

30.09.2024 20:14 — 👍 2    🔁 0    💬 1    📌 0

This underscores how important it is for climate models to simulate trends in the tropical Pacific surface temperature gradient accurately. 5/n

30.09.2024 20:14 — 👍 3    🔁 0    💬 1    📌 0

The results provide paleoclimatic evidence supporting modeling studies that show that changes in the pattern of mean surface temperature in the tropical Pacific are an important factor in explaining patterns of change in blocking frequencies. 4/n

30.09.2024 20:14 — 👍 2    🔁 0    💬 1    📌 0

This new way to infer synoptic signals from low temporal resolution paleoclimate records is a flexible temporal downscaling method that can be used to derive variables with complex/unknown relationships to proxies that are not typically targeted in paleo reconstructions. 3/n

30.09.2024 20:14 — 👍 2    🔁 0    💬 1    📌 0

The study shows that even though the DL model ingests paleodata assimilation products and has no information on the location of paleoclimate proxies, it is implicitly constrained by them. 2/n

30.09.2024 20:13 — 👍 2    🔁 0    💬 1    📌 0
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Extracting paleoweather from paleoclimate through a deep learning reconstruction of Last Millennium atmospheric blocking - Communications Earth & Environment The tropical Pacific’s weakened zonal temperature gradient during the Little Ice Age significantly impacted blocking variability, resulting in reduced blocking frequency and altered regional patterns,...

I am excited to see this study published at Nature Comms Earth & Environment! It uses a deep learning model to infer summertime atmospheric blocking frequencies from seasonally averaged surface temperature reconstructions over the Last Millennium. www.nature.com/articles/s43...

30.09.2024 20:13 — 👍 28    🔁 10    💬 4    📌 1

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