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June 2013

New CIRES Director: Waleed Abdalati

New CIRES Director: Waleed Abdalati

The Council of Fellows and University of Colorado Boulder have selected Waleed Abdalati, Ph.D., as the new director of the Cooperative Institute for Research in Environmental Sciences (CIRES). The CIRES Fellow and Former NASA Chief Scientist starts July 1.  To learn more about Dr. Abdalati and CIRES, click here

New CIRES Director: Waleed Abdalati

New CIRES Director: Waleed Abdalati Read More »

CIFAR Research Used by National Weather Service to Better Predict Flooding

CIFAR Research Used by National Weather Service to Better Predict Flooding

Recent Yukon River flooding underscores the importance of accurately predicting snowmelt and river ice breakup in Alaska. Residents of Galena were evacuated with little warning as their community of 400 was inundated with water and ice on Memorial Day weekend. Cooperative Institute for Alaska Research (CIFAR) researchers Katrina Bennett and Jessica Cherry are working with the National Weather Service’s Alaska-Pacific River Forecast Center (APRFC) to improve the accuracy of snowmelt processes in the models used by the APRFC. The High Latitude Proving Ground, an effort by National Oceanic and Atmospheric Administration (NOAA)-National Environmental Satellite, Data and Information Service (NESDIS), supports this project to develop and implement next-generation remote sensing products into weather and river forecast offices’ modeling protocol.

Background:  Bennett, a PhD student at the University of Alaska Fairbanks, is working with the APRFC to update snow conditions in the temperature-index snow accumulation and melt model SNOW-17 using snow cover data from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. Currently, snow cover in the model is generated using a sparse observational network and algorithms that calculate mean areal temperature and precipitation. This and other forms of data assimilation with remote sensing products are one way to update information in our modeling systems. Cherry and other members of her research team are trying to balance progress in assimilation and observational data management with a move toward more physically based land surface and systems models.    

Significance:  As hydro-climate systems regimes shift and extreme events become more frequent, it’s increasingly important that model predictions simulate the Earth system response accurately. In the Far North, spring snowmelt is the most dramatic hydrologic event of the year, and the mostly likely time for flooding to occur. Given Alaska’s vast territory and sparse ground-based observing networks, remote sensing is an obvious place to turn to improve the prediction of spring snowmelt and river ice breakup. CIFAR’s work relates to NOAA’s Climate Adaptation and Mitigation goal.

Contact: Dr. Jessica Cherry (jcherry@iarc.uaf.edu) 

Summary

Recent Yukon River flooding underscores the importance of accurately predicting snowmelt and river ice breakup in Alaska. 

CIFAR Research Used by National Weather Service to Better Predict Flooding Read More »

Recent CICS-P Study Examines Controls of Global Snow Under Climate Change

Recent CICS-P Study Examines Controls of Global Snow Under Climate Change

Using an idealized experiment where atmospheric carbon dioxide doubles from its 1990 value over 70 years, a new Geophysical Fluid Dynamics Laboratory (GFDL) high-resolution climate model, CM2.5, predicts an increase in snowfall for the Earth’s polar regions and highest mountains, but an overall drop in snowfall for the globe.  A recent study currently in press and available online in the Journal of Climate, led by Cooperative Institute for Climate Science – Princeton (CICS-P) Scientist Sarah Kapnick and coauthored with Thomas Delworth, a senior physical scientist at GFDL and Princeton lecturer; found that CM2.5 better captures various snow variables when compared to previous models.

Background:

CM2.5 is unique in that its resolution allows it to capture complex mountain ranges. This makes a difference in places like the western U.S. where a single plateau in coarse models is transformed into several distinct mountain ranges in CM2.5 (e.g. California Sierras, Cascades, and Rockies).  This is important for having snow fall in the correct regions and collect as snowpack (seasonal snow on the ground).

Significance:

Over the United States, the future climate experiment exhibits significant reductions in average annual snowfall. Figure 1 represents the geographic distribution of snowfall change in response to CO2 doubling. The vast majority of the U.S. experiences snowfall loss, with the greatest percentages occurring in the south, along the eastern coast, and the Pacific Northwest. This will lead to fundamental changes in the availability of water from spring and summer snowmelt. The continental interior experiences fewer reductions. While this study focuses on mean annual snow variables, future work will focus on exploring changes in extreme snowfall events, such as the frequency and strength of blizzards, and changes in seasonality.  This work relates to the National Oceanic and Atmospheric’s (NOAA) Climate Goal: Understand Climate Variability and Change to Enhance Society’s Ability to Plan and Respond.

Contact Information:  Sarah Kapnick at (609) 452-6548 or skapnick@princeton.edu:

Summary

Using an idealized experiment where atmospheric carbon dioxide doubles from its 1990 value over 70 years, a new Geophysical Fluid Dynamics Laboratory (GFDL) high-resolution climate model, CM2.5, predicts an increase in snowfall for the Earth’s polar regions and highest mountains, but an overall drop in snowfall for the globe.  

Recent CICS-P Study Examines Controls of Global Snow Under Climate Change Read More »