Search Results

You are looking at 1 - 4 of 4 items for :

  • Author or Editor: Jennifer E. Kay x
  • Bulletin of the American Meteorological Society x
  • Refine by Access: All Content x
Clear All Modify Search
Jonathan J. Day
,
Gunilla Svensson
,
Ian M. Brooks
,
Cecilia Bitz
,
Lina Broman
,
Glenn Carver
,
Matthieu Chevallier
,
Helge Goessling
,
Kerstin Hartung
,
Thomas Jung
,
Jennifer E. Kay
,
Erik W. Kolstad
,
Don Perovich
,
James Screen
,
Stephan Siemen
, and
Filip Váňa
Full access
James W. Hurrell
,
M. M. Holland
,
P. R. Gent
,
S. Ghan
,
Jennifer E. Kay
,
P. J. Kushner
,
J.-F. Lamarque
,
W. G. Large
,
D. Lawrence
,
K. Lindsay
,
W. H. Lipscomb
,
M. C. Long
,
N. Mahowald
,
D. R. Marsh
,
R. B. Neale
,
P. Rasch
,
S. Vavrus
,
M. Vertenstein
,
D. Bader
,
W. D. Collins
,
J. J. Hack
,
J. Kiehl
, and
S. Marshall

The Community Earth System Model (CESM) is a flexible and extensible community tool used to investigate a diverse set of Earth system interactions across multiple time and space scales. This global coupled model significantly extends its predecessor, the Community Climate System Model, by incorporating new Earth system simulation capabilities. These comprise the ability to simulate biogeochemical cycles, including those of carbon and nitrogen, a variety of atmospheric chemistry options, the Greenland Ice Sheet, and an atmosphere that extends to the lower thermosphere. These and other new model capabilities are enabling investigations into a wide range of pressing scientific questions, providing new foresight into possible future climates and increasing our collective knowledge about the behavior and interactions of the Earth system. Simulations with numerous configurations of the CESM have been provided to phase 5 of the Coupled Model Intercomparison Project (CMIP5) and are being analyzed by the broad community of scientists. Additionally, the model source code and associated documentation are freely available to the scientific community to use for Earth system studies, making it a true community tool. This article describes this Earth system model and its various possible configurations, and highlights a number of its scientific capabilities.

Full access
Tristan S. L’Ecuyer
,
Brian J. Drouin
,
James Anheuser
,
Meredith Grames
,
David S. Henderson
,
Xianglei Huang
,
Brian H. Kahn
,
Jennifer E. Kay
,
Boon H. Lim
,
Marian Mateling
,
Aronne Merrelli
,
Nathaniel B. Miller
,
Sharmila Padmanabhan
,
Colten Peterson
,
Nicole-Jeanne Schlegel
,
Mary L. White
, and
Yan Xie

Abstract

Earth’s climate is strongly influenced by energy deficits at the poles that emit more thermal energy than they receive from the sun. Energy exchanges between the surface and atmosphere influence the local environment while heat transport from lower latitudes drives midlatitude atmospheric and oceanic circulations. In the Arctic, in particular, local energy imbalances induce strong seasonality in surface–atmosphere heat exchanges and an acute sensitivity to forced climate variations. Despite these important local and global influences, the largest contributions to the polar atmospheric and surface energy budgets have not been fully characterized. The spectral variation of far-infrared radiation that makes up 60% of polar thermal emission has never been systematically measured impeding progress toward consensus in predicted rates of Arctic warming, sea ice decline, and ice sheet melt. Enabled by recent advances in sensor miniaturization and CubeSat technology, the Polar Radiant Energy in the Far Infrared Experiment (PREFIRE) mission will document, for the first time, the spectral, spatial, and temporal variations of polar far-infrared emission. Selected under NASA’s Earth Ventures Instrument (EVI) program, PREFIRE will utilize new lightweight, low-power, ambient temperature detectors capable of measuring at wavelengths up to 50 μm to quantify Earth’s far-infrared spectrum. Estimates of spectral surface emissivity, water vapor, cloud properties, and the atmospheric greenhouse effect derived from these measurements offer the potential to advance our understanding of the factors that modulate thermal fluxes in the cold, dry conditions characteristic of the polar regions.

Full access
Mitchell Bushuk
,
Sahara Ali
,
David A. Bailey
,
Qing Bao
,
Lauriane Batté
,
Uma S. Bhatt
,
Edward Blanchard-Wrigglesworth
,
Ed Blockley
,
Gavin Cawley
,
Junhwa Chi
,
François Counillon
,
Philippe Goulet Coulombe
,
Richard I. Cullather
,
Francis X. Diebold
,
Arlan Dirkson
,
Eleftheria Exarchou
,
Maximilian Göbel
,
William Gregory
,
Virginie Guemas
,
Lawrence Hamilton
,
Bian He
,
Sean Horvath
,
Monica Ionita
,
Jennifer E. Kay
,
Eliot Kim
,
Noriaki Kimura
,
Dmitri Kondrashov
,
Zachary M. Labe
,
WooSung Lee
,
Younjoo J. Lee
,
Cuihua Li
,
Xuewei Li
,
Yongcheng Lin
,
Yanyun Liu
,
Wieslaw Maslowski
,
François Massonnet
,
Walter N. Meier
,
William J. Merryfield
,
Hannah Myint
,
Juan C. Acosta Navarro
,
Alek Petty
,
Fangli Qiao
,
David Schröder
,
Axel Schweiger
,
Qi Shu
,
Michael Sigmond
,
Michael Steele
,
Julienne Stroeve
,
Nico Sun
,
Steffen Tietsche
,
Michel Tsamados
,
Keguang Wang
,
Jianwu Wang
,
Wanqiu Wang
,
Yiguo Wang
,
Yun Wang
,
James Williams
,
Qinghua Yang
,
Xiaojun Yuan
,
Jinlun Zhang
, and
Yongfei Zhang

Abstract

This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multimodel dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–20 for predictions of pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on 1 June, 1 July, 1 August, and 1 September. This diverse set of statistical and dynamical models can individually predict linearly detrended pan-Arctic SIE anomalies with skill, and a multimodel median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and central Arctic sectors. The skill of dynamical and statistical models is generally comparable for pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least 3 months in advance.

Open access