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Xin Huang
,
Tianjun Zhou
,
Andrew Turner
,
Aiguo Dai
,
Xiaolong Chen
,
Robin Clark
,
Jie Jiang
,
Wenmin Man
,
James Murphy
,
John Rostron
,
Bo Wu
,
Lixia Zhang
,
Wenxia Zhang
, and
Liwei Zou

Abstract

The Indian summer monsoon (ISM) rainfall affects a large population in South Asia. Observations show a decline in ISM rainfall from 1950 to 1999 and a recovery from 1999 to 2013. While the decline has been attributed to global warming, aerosol effects, deforestation, and a negative-to-positive phase transition of the interdecadal Pacific oscillation (IPO), the cause for the recovery remains largely unclear. Through analyses of a 57-member perturbed-parameter ensemble of model simulations, this study shows that the externally forced rainfall trend is relatively weak and is overwhelmed by large internal variability during both 1950–99 and 1999–2013. The IPO is identified as the internal mode that helps modulate the recent decline and recovery of the ISM rainfall. The IPO induces ISM rainfall changes through moisture convergence anomalies associated with an anomalous Walker circulation and meridional tropospheric temperature gradients and the resultant anomalous convection and zonal moisture advection. The negative-to-positive IPO phase transition from 1950 to 1999 reduces what would have been an externally forced weak upward rainfall trend of 0.01 to −0.15 mm day−1 decade−1 during that period, while the rainfall trend from 1999 to 2013 increases from the forced value of 0.42 to 0.68 mm day−1 decade−1 associated with a positive-to-negative IPO phase transition. Such a significant modulation of the historical ISM rainfall trends by the IPO is confirmed by another 100-member ensemble of simulations using perturbed initial conditions. Our findings highlight that the interplay between the effects of external forcing and the IPO needs be considered for climate adaptation and mitigation strategies in South Asia.

Open access
Shuangmei Ma
,
Tianjun Zhou
,
Dáithí A. Stone
,
Debbie Polson
,
Aiguo Dai
,
Peter A. Stott
,
Hans von Storch
,
Yun Qian
,
Claire Burke
,
Peili Wu
,
Liwei Zou
, and
Andrew Ciavarella

Abstract

Changes in precipitation characteristics directly affect society through their impacts on drought and floods, hydro-dams, and urban drainage systems. Global warming increases the water holding capacity of the atmosphere and thus the risk of heavy precipitation. Here, daily precipitation records from over 700 Chinese stations from 1956 to 2005 are analyzed. The results show a significant shift from light to heavy precipitation over eastern China. An optimal fingerprinting analysis of simulations from 11 climate models driven by different combinations of historical anthropogenic (greenhouse gases, aerosols, land use, and ozone) and natural (volcanic and solar) forcings indicates that anthropogenic forcing on climate, including increases in greenhouse gases (GHGs), has had a detectable contribution to the observed shift toward heavy precipitation. Some evidence is found that anthropogenic aerosols (AAs) partially offset the effect of the GHG forcing, resulting in a weaker shift toward heavy precipitation in simulations that include the AA forcing than in simulations with only the GHG forcing. In addition to the thermodynamic mechanism, strengthened water vapor transport from the adjacent oceans and by midlatitude westerlies, resulting mainly from GHG-induced warming, also favors heavy precipitation over eastern China. Further GHG-induced warming is predicted to lead to an increasing shift toward heavy precipitation, leading to increased urban flooding and posing a significant challenge for mega-cities in China in the coming decades. Future reductions in AA emissions resulting from air pollution controls could exacerbate this tendency toward heavier precipitation.

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
Gabriele C. Hegerl
,
Emily Black
,
Richard P. Allan
,
William J. Ingram
,
Debbie Polson
,
Kevin E. Trenberth
,
Robin S. Chadwick
,
Phillip A. Arkin
,
Beena Balan Sarojini
,
Andreas Becker
,
Aiguo Dai
,
Paul J. Durack
,
David Easterling
,
Hayley J. Fowler
,
Elizabeth J. Kendon
,
George J. Huffman
,
Chunlei Liu
,
Robert Marsh
,
Mark New
,
Timothy J. Osborn
,
Nikolaos Skliris
,
Peter A. Stott
,
Pier-Luigi Vidale
,
Susan E. Wijffels
,
Laura J. Wilcox
,
Kate M. Willett
, and
Xuebin Zhang

Abstract

Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time series over land, but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols and because of the large climate variability presently limits confidence in attribution of observed changes.

Full access
Siegfried Schubert
,
David Gutzler
,
Hailan Wang
,
Aiguo Dai
,
Tom Delworth
,
Clara Deser
,
Kirsten Findell
,
Rong Fu
,
Wayne Higgins
,
Martin Hoerling
,
Ben Kirtman
,
Randal Koster
,
Arun Kumar
,
David Legler
,
Dennis Lettenmaier
,
Bradfield Lyon
,
Victor Magana
,
Kingtse Mo
,
Sumant Nigam
,
Philip Pegion
,
Adam Phillips
,
Roger Pulwarty
,
David Rind
,
Alfredo Ruiz-Barradas
,
Jae Schemm
,
Richard Seager
,
Ronald Stewart
,
Max Suarez
,
Jozef Syktus
,
Mingfang Ting
,
Chunzai Wang
,
Scott Weaver
, and
Ning Zeng

Abstract

The U.S. Climate Variability and Predictability (CLIVAR) working group on drought recently initiated a series of global climate model simulations forced with idealized SST anomaly patterns, designed to address a number of uncertainties regarding the impact of SST forcing and the role of land–atmosphere feedbacks on regional drought. The runs were carried out with five different atmospheric general circulation models (AGCMs) and one coupled atmosphere–ocean model in which the model was continuously nudged to the imposed SST forcing. This paper provides an overview of the experiments and some initial results focusing on the responses to the leading patterns of annual mean SST variability consisting of a Pacific El Niño–Southern Oscillation (ENSO)-like pattern, a pattern that resembles the Atlantic multidecadal oscillation (AMO), and a global trend pattern.

One of the key findings is that all of the AGCMs produce broadly similar (though different in detail) precipitation responses to the Pacific forcing pattern, with a cold Pacific leading to reduced precipitation and a warm Pacific leading to enhanced precipitation over most of the United States. While the response to the Atlantic pattern is less robust, there is general agreement among the models that the largest precipitation response over the United States tends to occur when the two oceans have anomalies of opposite signs. Further highlights of the response over the United States to the Pacific forcing include precipitation signal-to-noise ratios that peak in spring, and surface temperature signal-to-noise ratios that are both lower and show less agreement among the models than those found for the precipitation response. The response to the positive SST trend forcing pattern is an overall surface warming over the world’s land areas, with substantial regional variations that are in part reproduced in runs forced with a globally uniform SST trend forcing. The precipitation response to the trend forcing is weak in all of the models.

It is hoped that these early results, as well as those reported in the other contributions to this special issue on drought, will serve to stimulate further analysis of these simulations, as well as suggest new research on the physical mechanisms contributing to hydroclimatic variability and change throughout the world.

Full access
Francina Dominguez
,
Roy Rasmussen
,
Changhai Liu
,
Kyoko Ikeda
,
Andreas Prein
,
Adam Varble
,
Paola A. Arias
,
Julio Bacmeister
,
Maria Laura Bettolli
,
Patrick Callaghan
,
Leila M. V. Carvalho
,
Christopher L. Castro
,
Fei Chen
,
Divyansh Chug
,
Kwok Pan (Sun) Chun
,
Aiguo Dai
,
Luminita Danaila
,
Rosmeri Porfírio da Rocha
,
Ernani de Lima Nascimento
,
Erin Dougherty
,
Jimy Dudhia
,
Trude Eidhammer
,
Zhe Feng
,
Lluís Fita
,
Rong Fu
,
Julian Giles
,
Harriet Gilmour
,
Kate Halladay
,
Yongjie Huang
,
Angela Maylee Iza Wong
,
Miguel Ángel Lagos-Zúñiga
,
Charles Jones
,
Jorge Llamocca
,
Marta Llopart
,
J. Alejandro Martinez
,
J. Carlos Martinez
,
Justin R. Minder
,
Monica Morrison
,
Zachary L. Moon
,
Ye Mu
,
Richard B. Neale
,
Kelly M. Núñez Ocasio
,
Sujan Pal
,
Erin Potter
,
German Poveda
,
Franciano Puhales
,
Kristen L. Rasmussen
,
Amanda Rehbein
,
Rosimar Rios-Berrios
,
Christoforus Bayu Risanto
,
Alan Rosales
,
Lucia Scaff
,
Anton Seimon
,
Marcelo Somos-Valenzuela
,
Yang Tian
,
Peter Van Oevelen
,
Daniel Veloso-Aguila
,
Lulin Xue
, and
Timothy Schneider
Open access