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  • Author or Editor: D. W. Wang x
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Jayarathna W. N. D. Sandaruwan
,
Wen Zhou
,
Paxson K. Y. Cheung
,
Yan Du
, and
Xuan Wang

Abstract

Marine heatwaves (MHWs) are extreme climatic events that can have a significant impact on marine ecosystems and their services across the world. We examine the spatiotemporal variation of summer MHWs in the north Indian Ocean (NIO) and find that the whole NIO Basin exhibits a pronounced spatial variability as well as a significant increasing trend in MHW frequency. We show that the NIO has two leading MHW modes linked to two distinct sea surface temperature (SST) patterns during summer. The first MHW mode is associated with basinwide warming, which is preconditioned by a decaying El Niño–Southern Oscillation (ENSO) and sustained throughout the summer by anomalous northeasterlies extending from the anticyclonic circulation of the western North Pacific subtropical high (WNPSH). The combined effect of thermocline warming due to downwelling oceanic planetary waves, decreased wind-induced evaporative cooling, and enhanced insolation cause basinwide summer MHWs. The second MHW mode exhibits a zonal dipole pattern, which has unfavorable cooling conditions in the previous seasons. The second MHW mode is associated with a phase change of ENSO and is greatly influenced by the formation of an interhemispheric pressure difference (IHPD) due to strengthening of the Australian high (AH) and weakening of the WNPSH. The IHPD induces cross-equatorial southerly winds across the eastern Indian Ocean. These winds favor the transformation of basinwide cooling conditions into zonal SST patterns via wind–evaporation–SST and thermocline–SST feedback, causing MHWs with a zonal dipole pattern. These MHW modes have a significant influence on the distribution and intensity of summer precipitation in the NIO.

Free access
D. Kim
,
K. Sperber
,
W. Stern
,
D. Waliser
,
I.-S. Kang
,
E. Maloney
,
W. Wang
,
K. Weickmann
,
J. Benedict
,
M. Khairoutdinov
,
M.-I. Lee
,
R. Neale
,
M. Suarez
,
K. Thayer-Calder
, and
G. Zhang

Abstract

The ability of eight climate models to simulate the Madden–Julian oscillation (MJO) is examined using diagnostics developed by the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group. Although the MJO signal has been extracted throughout the annual cycle, this study focuses on the boreal winter (November–April) behavior. Initially, maps of the mean state and variance and equatorial space–time spectra of 850-hPa zonal wind and precipitation are compared with observations. Models best represent the intraseasonal space–time spectral peak in the zonal wind compared to that of precipitation. Using the phase–space representation of the multivariate principal components (PCs), the life cycle properties of the simulated MJOs are extracted, including the ability to represent how the MJO evolves from a given subphase and the associated decay time scales. On average, the MJO decay (e-folding) time scale for all models is shorter (∼20–29 days) than observations (∼31 days). All models are able to produce a leading pair of multivariate principal components that represents eastward propagation of intraseasonal wind and precipitation anomalies, although the fraction of the variance is smaller than observed for all models. In some cases, the dominant time scale of these PCs is outside of the 30–80-day band.

Several key variables associated with the model’s MJO are investigated, including the surface latent heat flux, boundary layer (925 hPa) moisture convergence, and the vertical structure of moisture. Low-level moisture convergence ahead (east) of convection is associated with eastward propagation in most of the models. A few models are also able to simulate the gradual moistening of the lower troposphere that precedes observed MJO convection, as well as the observed geographical difference in the vertical structure of moisture associated with the MJO. The dependence of rainfall on lower tropospheric relative humidity and the fraction of rainfall that is stratiform are also discussed, including implications these diagnostics have for MJO simulation. Based on having the most realistic intraseasonal multivariate empirical orthogonal functions, principal component power spectra, equatorial eastward propagating outgoing longwave radiation (OLR), latent heat flux, low-level moisture convergence signals, and vertical structure of moisture over the Eastern Hemisphere, the superparameterized Community Atmosphere Model (SPCAM) and the ECHAM4/Ocean Isopycnal Model (OPYC) show the best skill at representing the MJO.

Full access
S. Saha
,
S. Nadiga
,
C. Thiaw
,
J. Wang
,
W. Wang
,
Q. Zhang
,
H. M. Van den Dool
,
H.-L. Pan
,
S. Moorthi
,
D. Behringer
,
D. Stokes
,
M. Peña
,
S. Lord
,
G. White
,
W. Ebisuzaki
,
P. Peng
, and
P. Xie

Abstract

The Climate Forecast System (CFS), the fully coupled ocean–land–atmosphere dynamical seasonal prediction system, which became operational at NCEP in August 2004, is described and evaluated in this paper. The CFS provides important advances in operational seasonal prediction on a number of fronts. For the first time in the history of U.S. operational seasonal prediction, a dynamical modeling system has demonstrated a level of skill in forecasting U.S. surface temperature and precipitation that is comparable to the skill of the statistical methods used by the NCEP Climate Prediction Center (CPC). This represents a significant improvement over the previous dynamical modeling system used at NCEP. Furthermore, the skill provided by the CFS spatially and temporally complements the skill provided by the statistical tools. The availability of a dynamical modeling tool with demonstrated skill should result in overall improvement in the operational seasonal forecasts produced by CPC.

The atmospheric component of the CFS is a lower-resolution version of the Global Forecast System (GFS) that was the operational global weather prediction model at NCEP during 2003. The ocean component is the GFDL Modular Ocean Model version 3 (MOM3). There are several important improvements inherent in the new CFS relative to the previous dynamical forecast system. These include (i) the atmosphere–ocean coupling spans almost all of the globe (as opposed to the tropical Pacific only); (ii) the CFS is a fully coupled modeling system with no flux correction (as opposed to the previous uncoupled “tier-2” system, which employed multiple bias and flux corrections); and (iii) a set of fully coupled retrospective forecasts covering a 24-yr period (1981–2004), with 15 forecasts per calendar month out to nine months into the future, have been produced with the CFS.

These 24 years of fully coupled retrospective forecasts are of paramount importance to the proper calibration (bias correction) of subsequent operational seasonal forecasts. They provide a meaningful a priori estimate of model skill that is critical in determining the utility of the real-time dynamical forecast in the operational framework. The retrospective dataset also provides a wealth of information for researchers to study interactive atmosphere–land–ocean processes.

Full access
Will Hobbs
,
Paul Spence
,
Amelie Meyer
,
Serena Schroeter
,
Alexander D. Fraser
,
Philip Reid
,
Tian R. Tian
,
Zhaohui Wang
,
Guillaume Liniger
,
Edward W. Doddridge
, and
Philip W. Boyd

Abstract

In recent years, the Southern Ocean has experienced extremely low sea ice cover in multiple summers. These low events were preceded by a multidecadal positive trend that culminated in record high ice coverage in 2014. This abrupt transition has led some authors to suggest that Antarctic sea ice has undergone a regime shift. In this study we analyze the satellite sea ice record and atmospheric reanalyses to assess the evidence for such a shift. We find that the standard deviation of the summer sea ice record has doubled from 0.31 million km2 in 1979–2006 to 0.76 million km2 for 2007–22. This increased variance is accompanied by a longer season-to-season sea ice memory. The atmosphere is the primary driver of Antarctic sea ice variability, but using a linear predictive model we show that sea ice changes cannot be explained by the atmosphere alone. Identifying whether a regime shift has occurred is difficult without a complete understanding of the physical mechanism of change. However, the statistical changes that we demonstrate (i.e., increased variance and autocorrelation, and a changed response to atmospheric forcing), as well as the increased spatial coherence noted by previous research, are indicators based on dynamical systems theory of an abrupt critical transition. Thus, our analysis is further evidence in support of a changed Antarctic sea ice system.

Significance Statement

In recent years, there have been several summers with extremely low Antarctic sea ice cover, including consecutive record lows in February 2022 and February 2023. Since then, the 2023 winter has seen a remarkably low sea ice growth with an anomaly far below expected climatology. This has led researchers to question whether there has been a regime shift, and we assess the observational evidence for such a shift. In the last decade or so, the variability of summer sea ice has almost doubled, accompanied by a much longer sea ice memory from season to season. These statistical changes, as well an increased spatial coherence noted by other researchers, are consistent with theoretical indicators of a critical transition, or regime shift.

Open access