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Chunlüe Zhou
,
Aiguo Dai
,
Junhong Wang
, and
Deliang Chen
Open access
Aiguo Dai
,
Thomas R. Karl
,
Bomin Sun
, and
Kevin E. Trenberth

Automated Surface Observation Systems (ASOS) were widely introduced to replace manned weather stations around the mid- 1990s over North America and other parts of the world. While laser beam ceilometers of the ASOS in North America measure overhead clouds within the lower 3.6 km of the atmosphere, they do not contain cloud-type and opacity information and are not comparable with previous cloud records. However, a network of 124 U.S. military weather stations with continuous human observations provides useful information of total cloud cover over the contiguous United States, thus lessening the disruption caused by the ASOS. Analyses of the military cloud data suggest an increasing trend (~1.4% of the sky cover per decade) in U.S. total cloud cover from 1976 to 2004, with increases over most of the country except the Northwest, although large uncertainties exist because of sparse spatial sampling. Thus, inadequacies exist in surface observations of global cloud amounts and types, especially over the oceans, Canada, and the United States since the mid- 1990s. The problem is compounded by inhomogeneities in satellite cloud data. Reprocessing of satellite data has the potential for improvements if priority is given to the improved continuity of records.

Full access
Chunlüe Zhou
,
Deliang Chen
,
Kaicun Wang
,
Aiguo Dai
, and
Dan Qi
Free access
Aiguo Dai
,
Gerald A. Meehl
,
Warren M. Washington
,
Tom M. L. Wigley
, and
Julie M. Arblaster

Natural variability of the climate system imposes a large uncertainty on future climate change signals simulated by a single integration of any coupled ocean–atmosphere model. This is especially true for regional precipitation changes. Here, these uncertainties are reduced by using results from two ensembles of five integrations of a coupled ocean–atmosphere model forced by projected future greenhouse gas and sulfate aerosol changes. Under a business-as-usual scenario, the simulations show a global warming of ~1.9°C over the twenty-first century (continuing the trend observed since the late 1970s), accompanied by a ~3% increase in global precipitation. Stabilizing the CO2 level at 550 ppm reduces the warming only moderately (by ~0.4°C in 2100). The patterns of seasonal-mean temperature and precipitation change in the two cases are highly correlated (r ≈ 0.99 for temperature and r ≈ 0.93 for precipitation). Over the midlatitude North Atlantic Ocean, the model produces a moderate surface cooling (1°–2°C, mostly in winter) over the twenty-first century. This cooling is accompanied by changes in atmospheric lapse rates over the region (i.e., larger warming in the free troposphere than at the surface), which stabilizes the surface ocean. The resultant reduction in local oceanic convection contributes to a 20% slowdown in the thermohaline circulation.

Full access
Kevin E. Trenberth
,
Aiguo Dai
,
Roy M. Rasmussen
, and
David B. Parsons

From a societal, weather, and climate perspective, precipitation intensity, duration, frequency, and phase are as much of concern as total amounts, as these factors determine the disposition of precipitation once it hits the ground and how much runs off. At the extremes of precipitation incidence are the events that give rise to floods and droughts, whose changes in occurrence and severity have an enormous impact on the environment and society. Hence, advancing understanding and the ability to model and predict the character of precipitation is vital but requires new approaches to examining data and models. Various mechanisms, storms and so forth, exist to bring about precipitation. Because the rate of precipitation, conditional on when it falls, greatly exceeds the rate of replenishment of moisture by surface evaporation, most precipitation comes from moisture already in the atmosphere at the time the storm begins, and transport of moisture by the storm-scale circulation into the storm is vital. Hence, the intensity of precipitation depends on available moisture, especially for heavy events. As climate warms, the amount of moisture in the atmosphere, which is governed by the Clausius–Clapeyron equation, is expected to rise much faster than the total precipitation amount, which is governed by the surface heat budget through evaporation. This implies that the main changes to be experienced are in the character of precipitation: increases in intensity must be offset by decreases in duration or frequency of events. The timing, duration, and intensity of precipitation can be systematically explored via the diurnal cycle, whose correct simulation in models remains an unsolved challenge of vital importance in global climate change. Typical problems include the premature initiation of convection, and precipitation events that are too light and too frequent. These challenges in observations, modeling, and understanding precipitation changes are being taken up in the NCAR “Water Cycle Across Scales” initiative, which will exploit the diurnal cycle as a test bed for a hierarchy of models to promote improvements in models.

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
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
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