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  • Author or Editor: Susan E. Wijffels x
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Alexander Sen Gupta
,
Les C. Muir
,
Jaclyn N. Brown
,
Steven J. Phipps
,
Paul J. Durack
,
Didier Monselesan
, and
Susan E. Wijffels

Abstract

Even in the absence of external forcing, climate models often exhibit long-term trends that cannot be attributed to natural variability. This so-called climate drift arises for various reasons including the following: perturbations to the climate system on coupling component models together and deficiencies in model physics and numerics. When examining trends in historical or future climate simulations, it is important to know the error introduced by drift so that action can be taken where necessary. This study assesses the importance of drift for a number of climate properties at global and local scales. To illustrate this, the present paper focuses on simulated trends over the second half of the twentieth century. While drift in globally averaged surface properties is generally considerably smaller than observed and simulated twentieth-century trends, it can still introduce nontrivial errors in some models. Furthermore, errors become increasingly important at smaller scales. The direction of drift is not systematic across different models or variables, as such drift is considerably reduced in the multimodel mean. Despite drift being primarily associated with ocean adjustment, it is also apparent in atmospheric variables. For example, most models have local drift magnitudes in surface air and ocean temperatures that are typically between 15% and 35% of the twentieth-century simulation trend magnitudes for 1950–2000. Below depths of 1000–2000 m, drift dominates over any forced trend in most regions. As such steric sea level is strongly affected and for some models and regions the sea level trend direction is reversed. Thus depending on the application, drift may be negligible or may make up an important part of the simulated trend.

Full access
Susan E. Wijffels
,
Josh Willis
,
Catia M. Domingues
,
Paul Barker
,
Neil J. White
,
Ann Gronell
,
Ken Ridgway
, and
John A. Church

Abstract

A time-varying warm bias in the global XBT data archive is demonstrated to be largely due to changes in the fall rate of XBT probes likely associated with small manufacturing changes at the factory. Deep-reaching XBTs have a different fall rate history than shallow XBTs. Fall rates were fastest in the early 1970s, reached a minimum between 1975 and 1985, reached another maximum in the late 1980s and early 1990s, and have been declining since. Field XBT/CTD intercomparisons and a pseudoprofile technique based on satellite altimetry largely confirm this time history. A global correction is presented and applied to estimates of the thermosteric component of sea level rise. The XBT fall rate minimum from 1975 to 1985 appears as a 10-yr “warm period” in the global ocean in thermosteric sea level and heat content estimates using uncorrected data. Upon correction, the thermosteric sea level curve has reduced decadal variability and a larger, steadier long-term trend.

Full access
Véronique Lago
,
Susan E. Wijffels
,
Paul J. Durack
,
John A. Church
,
Nathaniel L. Bindoff
, and
Simon J. Marsland

Abstract

The ocean’s surface salinity field has changed over the observed record, driven by an intensification of the water cycle in response to global warming. However, the origin and causes of the coincident subsurface salinity changes are not fully understood. The relationship between imposed surface salinity and temperature changes and their corresponding subsurface changes is investigated using idealized ocean model experiments. The ocean’s surface has warmed by about 0.5°C (50 yr)−1 while the surface salinity pattern has amplified by about 8% per 50 years. The idealized experiments are constructed for a 50-yr period, allowing a qualitative comparison to the observed salinity and temperature changes previously reported. The comparison suggests that changes in both modeled surface salinity and temperature are required to replicate the three-dimensional pattern of observed salinity change. The results also show that the effects of surface changes in temperature and salinity act linearly on the changes in subsurface salinity. Surface salinity pattern amplification appears to be the leading driver of subsurface salinity change on depth surfaces; however, surface warming is also required to replicate the observed patterns of change on density surfaces. This is the result of isopycnal migration modified by the ocean surface warming, which produces significant salinity changes on density surfaces.

Full access
Abhishek Savita
,
Catia M. Domingues
,
Tim Boyer
,
Viktor Gouretski
,
Masayoshi Ishii
,
Gregory C. Johnson
,
John M. Lyman
,
Josh K. Willis
,
Simon J. Marsland
,
William Hobbs
,
John A. Church
,
Didier P. Monselesan
,
Peter Dobrohotoff
,
Rebecca Cowley
, and
Susan E. Wijffels

Abstract

The Earth system is accumulating energy due to human-induced activities. More than 90% of this energy has been stored in the ocean as heat since 1970, with ∼60% of that in the upper 700 m. Differences in upper-ocean heat content anomaly (OHCA) estimates, however, exist. Here, we use a dataset protocol for 1970–2008—with six instrumental bias adjustments applied to expendable bathythermograph (XBT) data, and mapped by six research groups—to evaluate the spatiotemporal spread in upper OHCA estimates arising from two choices: 1) those arising from instrumental bias adjustments and 2) those arising from mathematical (i.e., mapping) techniques to interpolate and extrapolate data in space and time. We also examined the effect of a common ocean mask, which reveals that exclusion of shallow seas can reduce global OHCA estimates up to 13%. Spread due to mapping method is largest in the Indian Ocean and in the eddy-rich and frontal regions of all basins. Spread due to XBT bias adjustment is largest in the Pacific Ocean within 30°N–30°S. In both mapping and XBT cases, spread is higher for 1990–2004. Statistically different trends among mapping methods are found not only in the poorly observed Southern Ocean but also in the well-observed northwest Atlantic. Our results cannot determine the best mapping or bias adjustment schemes, but they identify where important sensitivities exist, and thus where further understanding will help to refine OHCA estimates. These results highlight the need for further coordinated OHCA studies to evaluate the performance of existing mapping methods along with comprehensive assessment of uncertainty estimates.

Open 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
Molly Baringer
,
Mariana B. Bif
,
Tim Boyer
,
Seth M. Bushinsky
,
Brendan R. Carter
,
Ivona Cetinić
,
Don P. Chambers
,
Lijing Cheng
,
Sanai Chiba
,
Minhan Dai
,
Catia M. Domingues
,
Shenfu Dong
,
Andrea J. Fassbender
,
Richard A. Feely
,
Eleanor Frajka-Williams
,
Bryan A. Franz
,
John Gilson
,
Gustavo Goni
,
Benjamin D. Hamlington
,
Zeng-Zhen Hu
,
Boyin Huang
,
Masayoshi Ishii
,
Svetlana Jevrejeva
,
William E. Johns
,
Gregory C. Johnson
,
Kenneth S. Johnson
,
John Kennedy
,
Marion Kersalé
,
Rachel E. Killick
,
Peter Landschützer
,
Matthias Lankhorst
,
Tong Lee
,
Eric Leuliette
,
Feili Li
,
Eric Lindstrom
,
Ricardo Locarnini
,
Susan Lozier
,
John M. Lyman
,
John J. Marra
,
Christopher S. Meinen
,
Mark A. Merrifield
,
Gary T. Mitchum
,
Ben Moat
,
Didier Monselesan
,
R. Steven Nerem
,
Renellys C. Perez
,
Sarah G. Purkey
,
Darren Rayner
,
James Reagan
,
Nicholas Rome
,
Alejandra Sanchez-Franks
,
Claudia Schmid
,
Joel P. Scott
,
Uwe Send
,
David A. Siegel
,
David A. Smeed
,
Sabrina Speich
,
Paul W. Stackhouse Jr.
,
William Sweet
,
Yuichiro Takeshita
,
Philip R. Thompson
,
Joaquin A. Triñanes
,
Martin Visbeck
,
Denis L. Volkov
,
Rik Wanninkhof
,
Robert A. Weller
,
Toby K. Westberry
,
Matthew J. Widlansky
,
Susan E. Wijffels
,
Anne C. Wilber
,
Lisan Yu
,
Weidong Yu
, and
Huai-Min Zhang
Free access