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Anthony G. Barnston
and
Yuxiang He

Abstract

Statistical short-term climate predictive skills and their sources for 3-month mean local surface climate (temperature and precipitation) in Hawaii and Alaska have been explored at lead times of up to one year using a canonical correlation analysis (CCA). Four consecutive 3-month predictor periods are followed by a variable lead time and then a single 3-month predictand period. Predictor fields are quasi-global sea surface temperature, Northern Hemisphere 700-mb height, and prior values of the predictand field itself Forecast skill is estimated using cross-validation.

Short-term global climate fluctuations such as the El Niño–Southern Oscillation (ENSO) phenomenon are found to play an important role in the climate variability in Hawaii and the southern half of Alaska. During the late winter and spring of mature warm (cold) ENSO events, Hawaii tends to be anomalously warm and dry (wet and cool), while southern Alaska tends to be warm (cold). Hawaii's responses occur more strongly the year after a mature ENSO event rather than the year of the event, even if the opposite phase of ENSO has already begun. Persistence is the best seasonal temperature prediction for Hawaii at short leads. Winter and spring temperature (precipitation) can be predicted up to one year (a few months) in advance with modest but usable skill for Hawaii, where temperature forecasts are generally more skillful. Southern Alaska has temperature prediction possibilities up to 7–10 months in advance. While Alaskan seasonal precipitation prediction is poor on the large spatial scale, forecasts on terrain-dependent local scales may he more fruitful using methods other than CCA.

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Yuxiang He
and
Anthony G. Barnston

Abstract

A potentially operational system for 3-month total precipitation forecasts for island stations in the tropical Pacific has been developed at NOAA's Climate Prediction Center using the statistical method of canonical correlation analysis (CCA). Routine issuance of the forecasts could begin during 1996, presently they are issued experimentally. The levels and sources of predictive skills have been estimated at lead times of up to one year, using a cross-validation design. The predictor fields, in order of their predictive value, are quasi-global sea surface temperature, Northern Hemisphere 700-mb height, and prior values of the predictand precipitation itself. Four consecutive 3-nionth predictor periods are used to detect evolving as well as steady-state conditions.

Modest forecast skills are realized for most seasons of the year; however, moderate skills (correlation <0.5) are found for certain stations in the northern Tropics at lead times of 3 months or less in late northern winter, especially in the western Pacific. CCA generally outperforms persistence, even at short leads. The El Niñto-Southern Oscillation (ENSO) phenomenon is found to play the dominant role in the precipitation variability at many tropical Pacific islands. During especially the late northern winter of mature warm (cold) episodes, pre- cipitation is suppressed (enhanced) in a horwshoe-shaped region surrounding (to the north, west, south) the central and eastern equatorial zone. which is anomalously wet (dry).

A secondary source of predictive skill, most important for northern summer, is a pattern with like-signed SST anomalies over the Tropics of all three ocean basins. While this pattern may encompass ENSO episodes, it varies at lower frequencies than the ENSO phenomenon on its own.

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Anthony G. Barnston
,
Michael H. Glantz
, and
Yuxiang He

Critical reviews of forecasts of ENSO conditions, based on a set of 15 dynamical and statistical models, are given for the 1997–98 El Niño event and the initial stages of the 1998–99 La Nina. While many of the models forecasted some degree of warming one to two seasons prior to the onset of the El Niño in boreal spring of 1997, none predicted its strength until the event was already becoming very strong in late spring. Neither the dynamical nor the statistical models, as groups, performed significantly better than the other during this episode. The best performing statistical models and dynamical models forecast SST anomalies of about +1°C (vs 2.5°–3° observed) in the Niño 3.4 region prior to any observed positive anomalies. The most comprehensive dynamical models performed better than the simple dynamical models. Once the El Niño had developed in mid-1997, a larger set of models was able to forecast its peak in late 1997 and dissipation and reversal to cold conditions in late spring/early summer 1998. Overall, however, skill for these recent two years does not appear greater than that found over an earlier (1982–93) period. In both cases, median model correlation skill averaged over lead times of one to three seasons is near or just above 0.6.

Because ENSO extremes usually develop in boreal spring or early summer and persist through the following winter, forecasting impact tendencies in extratropical North America for winter (when impacts are most pronounced) at 5 months of lead time is not difficult, requiring only good observations of the summer ENSO state and knowledge of the winter teleconnections. Because of the strength of the 1997–98 El Niño and the consequent skill of 5-month lead forecasts of U.S. winter 1997–98 impacts, the success of these forecasts was noticed to an unprecedented extent by the general public. However, forecasting impacts in austral winter that occur simultaneously with the initial appearance of an ENSO extreme (e.g., in Chile, Uruguay, Kiribati, Ecuador, and Peru) require forecasting the boreal spring/summer onset of ENSO events themselves at several months of lead time. This latter task is formidable, as evidenced by the fact that formal announcements of an El Niño did not occur until May, leaving little time for users in the above regions to prepare.

Verbal summaries of ENSO forecasts issued to users worldwide during the 1997–98 El Niño event contained ambiguities. To address the needs for forecasts to be expressed verbally for nontechnical users and also to be precise enough for meaningful utility and verification, a simple numerically based verbal classification system for describing ENSO-related forecasts is presented.

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Anthony G. Barnston
,
Yuxiang He
, and
David A. Unger

The prediction of seasonal climate anomalies at useful lead times often involves an unfavorable signal-to-noise ratio. The forecasts, while consequently tending to have modest skill, nonetheless have significant utility when packaged in ways to which users can relate and respond appropriately. This paper presents a reasonable but unprecedented manner in which to issue seasonal climate forecasts and illustrates how implied “tilts of the odds” of the forecasted climate may be used beneficially by technical as well as nontechnical clients.

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Xin-Zhong Liang
,
Min Xu
,
Xing Yuan
,
Tiejun Ling
,
Hyun I. Choi
,
Feng Zhang
,
Ligang Chen
,
Shuyan Liu
,
Shenjian Su
,
Fengxue Qiao
,
Yuxiang He
,
Julian X. L. Wang
,
Kenneth E. Kunkel
,
Wei Gao
,
Everette Joseph
,
Vernon Morris
,
Tsann-Wang Yu
,
Jimy Dudhia
, and
John Michalakes

The CWRF is developed as a climate extension of the Weather Research and Forecasting model (WRF) by incorporating numerous improvements in the representation of physical processes and integration of external (top, surface, lateral) forcings that are crucial to climate scales, including interactions between land, atmosphere, and ocean; convection and microphysics; and cloud, aerosol, and radiation; and system consistency throughout all process modules. This extension inherits all WRF functionalities for numerical weather prediction while enhancing the capability for climate modeling. As such, CWRF can be applied seamlessly to weather forecast and climate prediction. The CWRF is built with a comprehensive ensemble of alternative parameterization schemes for each of the key physical processes, including surface (land, ocean), planetary boundary layer, cumulus (deep, shallow), microphysics, cloud, aerosol, and radiation, and their interactions. This facilitates the use of an optimized physics ensemble approach to improve weather or climate prediction along with a reliable uncertainty estimate. The CWRF also emphasizes the societal service capability to provide impactrelevant information by coupling with detailed models of terrestrial hydrology, coastal ocean, crop growth, air quality, and a recently expanded interactive water quality and ecosystem model.

This study provides a general CWRF description and basic skill evaluation based on a continuous integration for the period 1979– 2009 as compared with that of WRF, using a 30-km grid spacing over a domain that includes the contiguous United States plus southern Canada and northern Mexico. In addition to advantages of greater application capability, CWRF improves performance in radiation and terrestrial hydrology over WRF and other regional models. Precipitation simulation, however, remains a challenge for all of the tested models.

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