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Bin Wang and Yan Xue

Abstract

The effects of nonlinear (positive only or conditional) heating on moist Kelvin waves are examined with a simple equatorial zonal-plane model describing the gravest baroclinic mode.

The unstable perturbation subject to nonlinear beating emerges as a wave packet. A typical amplifying, eastward-moving wave packet is characterized by an asymmetric structure: 1) the ascending branch (wet region) is much narrower than the two descending ones (dry regions); and 2) the circulation cell to the east of the wet region center is smaller and stronger than its counterpart to the west of the center. The wet-dry asymmetry is primarily caused by the nonlinear beating effect, while the east-west asymmetry is a result of the movement of the wave packet relative to mean flow. The existence of Newtonian cooling and Rayleigh friction enhances the structural asymmetries.

The unstable wave packet is characterized by two zonal length scales: the ascending branch length (ABL) and total circulation extent (TCE). For a given basic state, the growth rate of a wave packet increases with decreasing ABL or TCE. However, up to a moderate growth rate (order of day−1) the energy spectra of all wave packets are dominated by zonal wavenumber one regardless of ABL size. In particular, the slowly growing (low frequency) wave packets normally exhibit TCEs of planetary scale and ABLs of synoptic scale.

Observed equatorial intraseasonal disturbances often display a narrow convection region in between two much broader dry regions and a total circulation of planetary scale. These structure and scale characteristics are caused by the effects of nonlinear heating and the cyclic geometry of the equator. It is argued that the unstable disturbance found in numerical experiments (e.g., Lau and Peng; Hayashi and Sumi) is a manifestation of the nonlinear wave packet.

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Yan Xue, Ants Leetmaa, and Ming Ji

Abstract

A series of seasonally varying linear Markov models are constructed in a reduced multivariate empirical orthogonal function (MEOF) space of observed sea surface temperature, surface wind stress, and sea level analysis. The Markov models are trained in the 1980–95 period and are verified in the 1964–79 period. It is found that the Markov models that include seasonality fit to the data better in the training period and have a substantially higher skill in the independent period than the models without seasonality. The authors conclude that seasonality is an important component of ENSO and should be included in Markov models. This conclusion is consistent with that of statistical models that take seasonality into account using different methods.

The impact of each variable on the prediction skill of Markov models is investigated by varying the weightings among the three variables in the MEOF space. For the training period the Markov models that include sea level information fit the data better than the models without sea level information. For the independent 1964–79 period, the Markov models that include sea level information have a much higher skill than the Markov models without sea level information. The authors conclude that sea level contains the most essential information for ENSO since it contains the filtered response of the ocean to noisy wind forcing.

The prediction skill of the Markov model with three MEOFs is competitive for both the training and independent periods. This Markov model successfully predicted the 1997/98 El Niño and the 1998/99 La Niña.

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Caihong Wen, Yan Xue, and Arun Kumar

Abstract

The NCEP Climate Forecast System Reanalysis (CFSR) represents a new effort with the first guess from a high-resolution coupled system and offers prospects for improved simulation of mesoscale air–sea coupled variability. This study aims to describe the characteristics of ocean–atmosphere covariability associated with tropical instability waves (TIWs) in the Pacific for the CFSR, and to assess how well they agree with in situ and satellite observations.

Multiyear daily high-resolution CFSR data are used to describe variability associated with TIWs. Results show that TIW-induced SST variations exhibit pronounced seasonal and interannual variability that are tightly connected with cold tongue variations. The analysis illustrates coherent patterns associated with TIWs, both in the ocean and the atmosphere. Moisture and air temperature maximums are located west of SST maximums, leading to downstream displacement of surface pressure minimums relative to SST maximums. Surface winds accelerate (decelerate) over warm (cold) water, and a thermally direct circulation is created. Significant signals are observed in low-level cloud cover, which are closely in phase with surface wind convergences. The magnitudes of TIW-induced surface wind, surface pressure, and cloud cover perturbations agree well with in situ and satellite observations. Further analysis shows that surface net heat flux perturbations are dominated by latent heat fluxes and have a large negative feedback on TIW SST variability (~40 W m−2 °C−1). Water vapor perturbation is the primary factor contributing to changes in latent heat fluxes, while SST-induced wind perturbation plays a secondary role. The analysis presented here highlights that the CFSR provides an unprecedented opportunity to study the physical mechanisms for the TIWs, as well as their influences on climate variability.

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Caihong Wen, Yan Xue, and Arun Kumar

Abstract

Seasonal prediction skill of North Pacific sea surface temperature anomalies (SSTAs) and the Pacific decadal oscillation (PDO) in the NCEP Climate Forecast System (CFS) retrospective forecasts is assessed. The SST forecasts exhibit significant skills over much of the North Pacific for two seasons in advance and outperform persistence over much of the North Pacific except near the Kuroshio–Oyashia Extension. Similar to the “spring barrier” feature in the El Niño–Southern Oscillation forecasts, the central North Pacific SST experiences a faster drop in prediction skill for forecasts initialized from November to February than those from May to August. Forecasts for the PDO displayed a constant phase shift from the observation with respect to lead time. The PDO skill has a clear seasonality with highest skill for forecasts initialized in boreal spring.

The impact of ENSO on the PDO and North Pacific SST prediction was investigated. The analysis revealed that seasonal prediction skill in the central North Pacific mainly results from the skillful prediction of ENSO. As a result, the PDO is more skillful than persistence at all lead times during ENSO years. On the other hand, persistence is superior to the CFS forecast during ENSO-neutral conditions owing to errors in initial conditions and deficiencies in model physics. Examination of seasonal variance and predictability (signal-to-noise ratio) further articulates the influence of ENSO on the PDO skill. The results suggest that improvement of ENSO prediction as well as reduction in model biases in the western North Pacific will lead to improvements in the PDO and North Pacific SST predictions.

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Yan Xue, Thomas M. Smith, and Richard W. Reynolds

Abstract

SST predictions are usually issued in terms of anomalies and standardized anomalies relative to a 30-yr normal: climatological mean (CM) and standard deviation (SD). The World Meteorological Organization (WMO) suggests updating the 30-yr normal every 10 yr. In complying with the WMO's suggestion, a new 30-yr normal for the 1971–2000 base period is constructed. To put the new 30-yr normal in historical perspective, all the 30-yr normals since 1871 are investigated, starting from the beginning of each decade (1871–1900, 1881–1910, … , 1971–2000). Using the extended reconstructed sea surface temperature (ERSST) on a 2° grid for 1854–2000 and the Hadley Centre Sea Ice and SST dataset (HadISST) on a 1° grid for 1870–1999, eleven 30-yr normals are calculated, and the interdecadal changes of seasonal CM, seasonal SD, and seasonal persistence (P) are discussed. The interdecadal changes of seasonal CM are prominent (0.3°–0.6°) in the tropical Indian Ocean, the midlatitude North Pacific, the midlatitude North Atlantic, most of the South Atlantic, and the sub-Antarctic front. Four SST indices are used to represent the key regions of the interdecadal changes: the Indian Ocean (“INDIAN”; 10°S–25°N, 45°–100°E), the Pacific decadal oscillation (PDO; 35°–45°N, 160°E–160°W), the North Atlantic Oscillation (NAO; 40°–60°N, 20°–60°W), and the South Atlantic (SATL; 22°S–2°N, 35°W–10°E). Both INDIAN and SATL show a warming trend that is consistent between ERSST and HadISST. Both PDO and NAO show a multidecadal oscillation that is consistent between ERSST and HadISST except that HadISST is biased toward warm in summer and cold in winter relative to ERSST. The interdecadal changes in Niño-3 (5°S–5°N, 90°–150°W) are small (0.2°) and are inconsistent between ERSST and HadISST. The seasonal SD is prominent in the eastern equatorial Pacific, the North Pacific, and North Atlantic. The seasonal SD in Niño-3 varies interdecadally: intermediate during 1885–1910, small during 1910–65, and large during 1965–2000. These interdecadal changes of ENSO variance are further verified by the Darwin sea level pressure. The seasonality of ENSO variance (smallest in spring and largest in winter) also varies interdecadally: moderate during 1885–1910, weak during 1910–65, and strong during 1965–2000. The interdecadal changes of the seasonal SD of other indices are weak and cannot be determined well by the datasets. The seasonal P, measured by the autocorrelation of seasonal anomalies at a two-season lag, is largest in the eastern equatorial Pacific, the tropical Indian, and the tropical North and South Atlantic Oceans. It is also seasonally dependent. The “spring barrier” of P in Niño-3 (largest in summer and smallest in winter) varies interdecadally: relatively weak during 1885–1910, moderate during 1910–55, strong during 1955–75, and moderate during 1975–2000. The interdecadal changes of SD and P not only have important implications for SST forecasts but also have significant scientific values to be explored.

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Viva F. Banzon, Richard W. Reynolds, Diane Stokes, and Yan Xue

Abstract

A new sea surface temperature (SST) climatological mean was constructed using the first 30 years (1982–2011) of the NOAA daily optimum interpolation (OI) SST. The daily analysis blends in situ and satellite data on a ¼° (~25 km) spatial grid. Use of an analysis allows computation of a climatological value for all ocean grid points, even those without observations. Comparisons were made with a monthly, 1°-spatial-resolution climatology produced by the National Centers for Environmental Prediction, computed primarily from the NOAA weekly OISST. Both climatologies were found to provide a good representation of major oceanic features and the annual temperature cycle. However, the daily climatology showed tighter gradients along western boundary currents and better resolution along coastlines. The two climatologies differed by over 0.6°C in high-SST-gradient regions because of resolution differences. The two climatologies also differed at very high latitudes, where the sea ice processing differed between the OISST products. In persistently cloudy areas, the new climatology was generally cooler by approximately 0.4°C, probably reflecting differences between the input satellite SSTs to the two analyses. Since the new climatology represents mean conditions at scales that match the daily analysis, it would be more appropriate for computing the corresponding daily anomalies.

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Yan Xue, M. A. Cane, and S. E. Zebiak

Abstract

The fastest initial error growth (optimal growth) in the Zebiak and Cane (ZC) forecast model for the El Niño–Southern Oscillation (ENSO) is analyzed by singular value decomposition of a forward tangent model along a trajectory in a reduced EOF space. In this paper (Part I of II), optimal growth about the seasonally varying background and ENSO cycles from a long model run are discussed.

Among the many forms of nonlinearity in ZC, the discontinuity of the slope in subsurface temperature at zero thermocline depth and the nonlinear advection of SST are the most significant. That positive perturbations grow much faster than negative perturbations around the seasonally varying background is first attributable to the discontinuity and, second, attributable to nonlinear advection.

About the seasonally varying background, 6-month optimal growth is largest for early (boreal) spring starts, which is related to the enhanced atmospheric heating due to equatorward movement of the ITCZ. One dominant growing structure is found, characterized by north–south and east–west SST dipoles, convergent winds on the equator in the eastern Pacific, and a deepened thermocline in the whole equatorial belt. This structure is insensitive to start month and optimization time.

Optimal growth about ENSO cycles in a long model run is generally much smaller than that about the seasonally varying background. As before, one dominant growing structure, insensitive to start time and optimization time, is found. During the warm phase of ENSO, optimal growth is modulated by season as is that about the seasonal varying background. During the onset and mature phases of ENSO, the final pattern of the optimal structure in 6 months is confined to the eastern Pacific; during the decay phase of ENSO, it spreads to the western Pacific as well. During the cold phase of ENSO, optimal growth has two maxima in a year—early spring and fall; the optimal perturbation propagates westward associated with surface layer–wind interaction.

The authors also compare the singular vector analysis in EOF space and the standard one in physical space. The importance of norm definition to optimal growth and optimal structure is discussed.

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Yan Xue, Lian-Ping Wang, and Wojciech W. Grabowski

Abstract

An open question in cloud physics is how rain forms in warm cumulus as rapidly as it is sometimes observed. In particular, the growth of cloud droplets across the size gap from 10 to 50 μm in radius has not been fully explained. In this paper, the authors investigate the growth of cloud droplets by collision–coalescence, taking into account both the gravitational mechanism and several enhancements of the collision–coalescence rate due to air turbulence. The kinetic collection equation (KCE) is solved with an accurate bin integral method and a newly developed parameterization of turbulent collection kernel derived from direct numerical simulation of droplet-laden turbulent flows. Three other formulations of the turbulent collection kernel are also considered so as to assess the dependence of the rain initiation time on the nature of the collection kernel. The results are compared to the base case using the Hall hydrodynamical–gravitational collection kernel. Under liquid water content and eddy dissipation rate values typical of small cumulus clouds, it is found that air turbulence has a significant impact on the collection kernel and thus on the time required to form drizzle drops. With the most realistic turbulent kernel, the air turbulence can shorten the time for the formation of drizzle drops by about 40% relative to the base case, applying measures based on either the radar reflectivity or the mass-weighted drop size. A methodology is also developed to unambiguously identify the three phases of droplet growth, namely, the autoconversion phase, the accretion phase, and the larger hydrometeor self-collection phase. The important observation is that even a moderate enhancement of collection kernel by turbulence can have a significant impact on the autoconversion phase of the growth.

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Lian-Ping Wang, Orlando Ayala, Yan Xue, and Wojciech W. Grabowski
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Stephen Baxter, Scott Weaver, Jon Gottschalck, and Yan Xue

Abstract

Lagged pentad composites of surface air temperature and precipitation are analyzed for the winter season (December–February) to assess the influence of the Madden–Julian oscillation (MJO) on the climate of the contiguous United States. Composites are based on the Wheeler and Hendon MJO index as well as an index developed and maintained at NOAA’s Climate Prediction Center (CPC), which is based on extended empirical orthogonal function analysis of upper-level velocity potential. Significant positive temperature anomalies develop in the eastern United States 5–20 days following Wheeler and Hendon MJO index phase 3, which corresponds to enhanced convection centered over the eastern Indian Ocean. At the same lag, positive precipitation anomalies are observed from the southern Plains to the Great Lakes region. Negative temperature anomalies appear in the central and eastern United States 10–20 days following Wheeler and Hendon MJO phase 7. These impacts are supported by an analysis of the evolution of 200-hPa geopotential height and zonal wind anomalies. Composites based on the CPC velocity potential MJO index generally yield similar results; however, they capture more cases since the index contains both interannual and subseasonal variability. There are some cases where the CPC index differs from that of WH in both MJO phase identification and its North American impacts, especially near the West Coast. This analysis suggests that MJO-related velocity potential anomalies can be used without the Wheeler and Hendon MJO index to predict MJO impacts.

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