Search Results

You are looking at 1 - 2 of 2 items for :

  • Author or Editor: Danielle R. B. Coleman x
  • Bulletin of the American Meteorological Society x
  • Refine by Access: All Content x
Clear All Modify Search
Judith Berner
,
Ulrich Achatz
,
Lauriane Batté
,
Lisa Bengtsson
,
Alvaro de la Cámara
,
Hannah M. Christensen
,
Matteo Colangeli
,
Danielle R. B. Coleman
,
Daan Crommelin
,
Stamen I. Dolaptchiev
,
Christian L. E. Franzke
,
Petra Friederichs
,
Peter Imkeller
,
Heikki Järvinen
,
Stephan Juricke
,
Vassili Kitsios
,
François Lott
,
Valerio Lucarini
,
Salil Mahajan
,
Timothy N. Palmer
,
Cécile Penland
,
Mirjana Sakradzija
,
Jin-Song von Storch
,
Antje Weisheimer
,
Michael Weniger
,
Paul D. Williams
, and
Jun-Ichi Yano

Abstract

The last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to represent model inadequacy better and to improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing long-standing climate biases and is relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups that show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface, and cryosphere of comprehensive weather and climate models 1) gives rise to more reliable probabilistic forecasts of weather and climate and 2) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics, and turbulence is reviewed; its relevance for the climate problem is demonstrated; and future research directions are outlined.

Full access
Eric D. Maloney
,
Andrew Gettelman
,
Yi Ming
,
J. David Neelin
,
Daniel Barrie
,
Annarita Mariotti
,
C.-C. Chen
,
Danielle R. B. Coleman
,
Yi-Hung Kuo
,
Bohar Singh
,
H. Annamalai
,
Alexis Berg
,
James F. Booth
,
Suzana J. Camargo
,
Aiguo Dai
,
Alex Gonzalez
,
Jan Hafner
,
Xianan Jiang
,
Xianwen Jing
,
Daehyun Kim
,
Arun Kumar
,
Yumin Moon
,
Catherine M. Naud
,
Adam H. Sobel
,
Kentaroh Suzuki
,
Fuchang Wang
,
Junhong Wang
,
Allison A. Wing
,
Xiaobiao Xu
, and
Ming Zhao

Abstract

Realistic climate and weather prediction models are necessary to produce confidence in projections of future climate over many decades and predictions for days to seasons. These models must be physically justified and validated for multiple weather and climate processes. A key opportunity to accelerate model improvement is greater incorporation of process-oriented diagnostics (PODs) into standard packages that can be applied during the model development process, allowing the application of diagnostics to be repeatable across multiple model versions and used as a benchmark for model improvement. A POD characterizes a specific physical process or emergent behavior that is related to the ability to simulate an observed phenomenon. This paper describes the outcomes of activities by the Model Diagnostics Task Force (MDTF) under the NOAA Climate Program Office (CPO) Modeling, Analysis, Predictions and Projections (MAPP) program to promote development of PODs and their application to climate and weather prediction models. MDTF and modeling center perspectives on the need for expanded process-oriented diagnosis of models are presented. Multiple PODs developed by the MDTF are summarized, and an open-source software framework developed by the MDTF to aid application of PODs to centers’ model development is presented in the context of other relevant community activities. The paper closes by discussing paths forward for the MDTF effort and for community process-oriented diagnosis.

Full access