Search Results

You are looking at 1 - 6 of 6 items for :

  • Author or Editor: A. Gettelman x
  • Bulletin of the American Meteorological Society x
  • Refine by Access: All Content x
Clear All Modify Search
A. Gettelman
,
G. R. Carmichael
,
G. Feingold
,
A. M. Da Silva
, and
S. C. van den Heever
Full access
V. Eyring
,
N. R. P. Harris
,
M. Rex
,
T. G. Shepherd
,
D. W. Fahey
,
G. T. Amanatidis
,
J. Austin
,
M. P. Chipperfield
,
M. Dameris
,
P. M. De F. Forster
,
A. Gettelman
,
H. F. Graf
,
T. Nagashima
,
P. A. Newman
,
S. Pawson
,
M. J. Prather
,
J. A. Pyle
,
R. J. Salawitch
,
B. D. Santer
, and
D. W. Waugh

Accurate and reliable predictions and an understanding of future changes in the stratosphere are major aspects of the subject of climate change. Simulating the interaction between chemistry and climate is of particular importance, because continued increases in greenhouse gases and a slow decrease in halogen loading are expected. These both influence the abundance of stratospheric ozone. In recent years a number of coupled chemistry–climate models (CCMs) with different levels of complexity have been developed. They produce a wide range of results concerning the timing and extent of ozone-layer recovery. Interest in reducing this range has created a need to address how the main dynamical, chemical, and physical processes that determine the long-term behavior of ozone are represented in the models and to validate these model processes through comparisons with observations and other models. A set of core validation processes structured around four major topics (transport, dynamics, radiation, and stratospheric chemistry and microphysics) has been developed. Each process is associated with one or more model diagnostics and with relevant datasets that can be used for validation. This approach provides a coherent framework for validating CCMs and can be used as a basis for future assessments. Similar efforts may benefit other modeling communities with a focus on earth science research as their models increase in complexity.

Full access
J. David Neelin
,
John P. Krasting
,
Aparna Radhakrishnan
,
Jessica Liptak
,
Thomas Jackson
,
Yi Ming
,
Wenhao Dong
,
Andrew Gettelman
,
Danielle R. Coleman
,
Eric D. Maloney
,
Allison A. Wing
,
Yi-Hung Kuo
,
Fiaz Ahmed
,
Paul Ullrich
,
Cecilia M. Bitz
,
Richard B. Neale
,
Ana Ordonez
, and
Elizabeth A. Maroon

Abstract

Process-oriented diagnostics (PODs) aim to provide feedback for model developers through model analysis based on physical hypotheses. However, the step from a diagnostic based on relationships among variables, even when hypothesis driven, to specific guidance for revising model formulation or parameterizations can be substantial. The POD may provide more information than a purely performance-based metric, but a gap between POD principles and providing actionable information for specific model revisions can remain. Furthermore, in coordinating diagnostics development, there is a trade-off between freedom for the developer, aiming to capture innovation, and near-term utility to the modeling center. Best practices that allow for the former, while conforming to specifications that aid the latter, are important for community diagnostics development that leads to tangible model improvements. Promising directions to close the gap between principles and practice include the interaction of PODs with perturbed physics experiments and with more quantitative process models as well as the inclusion of personnel from modeling centers in diagnostics development groups for immediate feedback during climate model revisions. Examples are provided, along with best-practice recommendations, based on practical experience from the NOAA Model Diagnostics Task Force (MDTF). Common standards for metrics and diagnostics that have arisen from a collaboration between the MDTF and the Department of Energy’s Coordinated Model Evaluation Capability are advocated as a means of uniting community diagnostics efforts.

Open access
Xubin Zeng
,
Lincoln Alves
,
Marie-Amélie Boucher
,
Annalisa Cherchi
,
Charlotte DeMott
,
A.P. Dimri
,
Andrew Gettelman
,
Edward Hanna
,
Takeshi Horinouchi
,
Jin Huang
,
Chris Lennard
,
L. Ruby Leung
,
Yali Luo
,
Meloth Thamban
,
Hindumathi Palanisamy
,
Sara C. Pryor
,
Marion Saint-Lu
,
Stefan P. Sobolowski
,
Detlef Stammer
,
Jakob Steiner
,
Bjorn Stevens
,
Stefan Uhlenbrook
,
Michael Wehner
, and
Paquita Zuidema

Abstract

The future state of the global water cycle and prediction of freshwater availability for humans around the world remain among the challenges of climate research and are relevant to several United Nations Sustainable Development Goals. The Global Precipitation EXperiment (GPEX) takes on the challenge of improving the prediction of precipitation quantity, phase, timing and intensity, characteristics that are products of a complex integrated system. It will achieve this by leveraging existing World Climate Research Programme (WCRP) activities and community capabilities in satellite, surface-based, and airborne observations, modeling and experimental research, and by conducting new and focused activities. It was launched in October 2023 as a WCRP Lighthouse Activity. Here we present an overview of the GPEX Science Plan that articulates the primary science questions related to precipitation measurements, process understanding, model performance and improvements, and plans for capacity development. The central phase of GPEX is the WCRP Years of Precipitation for 2-3 years with coordinated global field campaigns focusing on different storm types (atmospheric rivers, mesoscale convective systems, monsoons, and tropical cyclones, among others) over different regions and seasons. Activities are planned over the three phases (before, during, and after the Years of Precipitation) spanning a decade. These include gridded data evaluation and development, advanced modeling, enhanced understanding of processes critical to precipitation, multi-scale prediction of precipitation events across scales, and capacity development. These activities will be further developed as part of the GPEX Implementation Plan.

Open 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
Greg M. McFarquhar
,
Christopher S. Bretherton
,
Roger Marchand
,
Alain Protat
,
Paul J. DeMott
,
Simon P. Alexander
,
Greg C. Roberts
,
Cynthia H. Twohy
,
Darin Toohey
,
Steve Siems
,
Yi Huang
,
Robert Wood
,
Robert M. Rauber
,
Sonia Lasher-Trapp
,
Jorgen Jensen
,
Jeffrey L. Stith
,
Jay Mace
,
Junshik Um
,
Emma Järvinen
,
Martin Schnaiter
,
Andrew Gettelman
,
Kevin J. Sanchez
,
Christina S. McCluskey
,
Lynn M. Russell
,
Isabel L. McCoy
,
Rachel L. Atlas
,
Charles G. Bardeen
,
Kathryn A. Moore
,
Thomas C. J. Hill
,
Ruhi S. Humphries
,
Melita D. Keywood
,
Zoran Ristovski
,
Luke Cravigan
,
Robyn Schofield
,
Chris Fairall
,
Marc D. Mallet
,
Sonia M. Kreidenweis
,
Bryan Rainwater
,
John D’Alessandro
,
Yang Wang
,
Wei Wu
,
Georges Saliba
,
Ezra J. T. Levin
,
Saisai Ding
,
Francisco Lang
,
Son C. H. Truong
,
Cory Wolff
,
Julie Haggerty
,
Mike J. Harvey
,
Andrew R. Klekociuk
, and
Adrian McDonald

Abstract

Weather and climate models are challenged by uncertainties and biases in simulating Southern Ocean (SO) radiative fluxes that trace to a poor understanding of cloud, aerosol, precipitation, and radiative processes, and their interactions. Projects between 2016 and 2018 used in situ probes, radar, lidar, and other instruments to make comprehensive measurements of thermodynamics, surface radiation, cloud, precipitation, aerosol, cloud condensation nuclei (CCN), and ice nucleating particles over the SO cold waters, and in ubiquitous liquid and mixed-phase clouds common to this pristine environment. Data including soundings were collected from the NSF–NCAR G-V aircraft flying north–south gradients south of Tasmania, at Macquarie Island, and on the R/V Investigator and RSV Aurora Australis. Synergistically these data characterize boundary layer and free troposphere environmental properties, and represent the most comprehensive data of this type available south of the oceanic polar front, in the cold sector of SO cyclones, and across seasons. Results show largely pristine environments with numerous small and few large aerosols above cloud, suggesting new particle formation and limited long-range transport from continents, high variability in CCN and cloud droplet concentrations, and ubiquitous supercooled water in thin, multilayered clouds, often with small-scale generating cells near cloud top. These observations demonstrate how cloud properties depend on aerosols while highlighting the importance of dynamics and turbulence that likely drive heterogeneity of cloud phase. Satellite retrievals confirmed low clouds were responsible for radiation biases. The combination of models and observations is examining how aerosols and meteorology couple to control SO water and energy budgets.

Full access