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Malaquias Peña
and
Huug van den Dool

Abstract

The performance of ridge regression methods for consolidation of multiple seasonal ensemble prediction systems is analyzed. The methods are applied to predict SST in the tropical Pacific based on ensembles from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) models, plus two of NCEP’s operational models. Strategies to increase the ratio of the effective sample size of the training data to the number of coefficients to be fitted are proposed and tested. These strategies include objective selection of a smaller subset of models, pooling of information from neighboring grid points, and consolidating all ensemble members rather than each model’s ensemble average. In all variations of the ridge regression consolidation methods tested, increased effective sample size produces more stable weights and more skillful predictions on independent data. While the scores may not increase significantly as the effective sampling size is increased, the benefit is seen in terms of consistent improvements over the simple equal weight ensemble average. In the western tropical Pacific, most consolidation methods tested outperform the simple equal weight ensemble average; in other regions they have similar skill as measured by both the anomaly correlation and the relative operating curve. The main obstacles to progress are a short period of data and a lack of independent information among models.

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Emily Becker
and
Huug van den Dool

Abstract

The North American Multimodel Ensemble (NMME) forecasting system has been continuously producing seasonal forecasts since August 2011. The NMME, with its suite of diverse models, provides a valuable opportunity for characterizing forecast confidence using probabilistic forecasts. The current experimental probabilistic forecast product (in map format) presents the most likely tercile for the seasonal mean value, chosen out of above normal, near normal, or below normal categories, using a nonparametric counting method to determine the probability of each class. The skill of the 3-month-mean probabilistic forecasts of 2-m surface temperature (T2m), precipitation rate, and sea surface temperature is assessed using forecasts from the 29-yr (1982–2010) NMME hindcast database. Three forecast configurations are considered: a full six-model NMME; a “mini-NMME” with 24 members, four each from six models; and the 24-member CFSv2 alone. Skill is assessed on the cross-validated hindcasts using the Brier skill score (BSS); forecast reliability and resolution are also assessed. This study provides a baseline skill assessment of the current method of creating probabilistic forecasts from the NMME system.

For forecasts in the above- and below-normal terciles for all variables and geographical regions examined in this study, BSS for NMME forecasts is higher than BSS for CFSv2 forecasts. Niño-3.4 forecasts from the full NMME and the mini-NMME receive nearly identical BSS that are higher than BSS for CFSv2 forecasts. Even systems with modest BSS, such as T2m in the Northern Hemisphere, have generally high reliability, as shown in reliability diagrams.

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Emily Becker
,
Huug van den Dool
, and
Qin Zhang

Abstract

Forecast skill and potential predictability of 2-m temperature, precipitation rate, and sea surface temperature are assessed using 29 yr of hindcast data from models included in phase 1 of the North American Multimodel Ensemble (NMME) project. Forecast skill is examined using the anomaly correlation (AC); skill of the bias-corrected ensemble means (EMs) of the individual models and of the NMME 7-model EM are verified against the observed value. Forecast skill is also assessed using the root-mean-square error. The models’ representation of the size of forecast anomalies is also studied. Predictability was considered from two angles: homogeneous, where one model is verified against a single member from its own ensemble, and heterogeneous, where a model’s EM is compared to a single member from another model. This study provides insight both into the physical predictability of the three fields and into the NMME and its contributing models.

Most of the models in the NMME have fairly realistic spread, as represented by the interannual variability. The NMME 7-model forecast skill, verified against observations, is equal to or higher than the individual models’ forecast ACs. Two-meter temperature (T2m) skill matches the highest single-model skill, while precipitation rate and sea surface temperature NMME EM skill is higher than for any single model. Homogeneous predictability is higher than reported skill in all fields, suggesting there may be room for some improvement in model prediction, although there are many regional and seasonal variations. The estimate of potential predictability is not overly sensitive to the choice of model. In general, models with higher homogeneous predictability show higher forecast skill.

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Wilbur Y. Chen
and
Huug M. Van den Dool

Abstract

The characteristics of extratropical low-frequency variability are examined using a comprehensive atmospheric general circulation model. A large experiment consisting of 13 45-yr-long integrations forced by prescribed sea surface temperature (SST) variations is analyzed. The predictability of timescales of seasonal to decadal averages is evaluated. The variability of a climate mean contains not only climate signal arising from external boundary forcing but also climate noise due to the internal dynamics of the climate system, resulting in various levels of predictability that are dependent on the forcing boundary conditions and averaging timescales. The focus of this study deviates from the classic predictability study of Lorenz, which is essentially initial condition sensitive. This study can be considered to be a model counterpart of Madden’s “potential” predictability study.

The tropical SST anomalies impact more on the predictability over the Pacific/North America sector than the Atlantic/Eurasia sector. In the former sector, more significant and positive impacts are found during El Niño and La Niña phases of the ENSO cycle than during the ENSO inactive period of time. Furthermore, the predictability is significantly higher during El Niño than La Niña phases of the ENSO cycle. The predictability of seasonal means exhibits large seasonality for both warm and cold phases of the ENSO cycle. During the warm phases, a high level of predictability is observed from December to April. During the cool phases, the predictability rapidly drops to below normal from November to March. The spring barrier in the atmospheric predictability is therefore a distinct phenomenon for the cold phase, not the warm phase, of the ENSO cycle. The cause of the barrier can be traced to the smaller climate signal and larger climate noise generated during cold events, which in turn can be traced back to the rapidly weakening negative SST anomalies in the tropical Pacific east of the date line.

Due to the fact that the signal to noise ratio of this model climate system is very small, an upper bound in atmospheric predictability is present, even when a perfect model atmosphere is considered and large ensemble mean predictions are exploited. The outstanding issues of the dynamical short-term climate prediction employing an atmospheric general circulation model are examined, the current model deficiencies identified, and continuing efforts in model development addressed.

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Wilbur Y. Chen
and
Huug van den Dool

Abstract

A low-resolution version of the National Meteorological Center's global spectral model was used to generate a 10-year set of simulated daily meteorological data. Wintertime low-frequency large-amplitude anomalies were examined and compared with those observed in the real atmosphere. The geographical distributions of the mean and variance of model and real atmosphere show some resemblance. However, careful comparisons reveal distinct regions where short-term climate anomalies prefer to develop. The model's low-frequency anomalies (LFAS) over the North Pacific (North Atlantic) tend to occur about 1500 miles east (southeast) of those observed, locating themselves much closer to the western continents. Because of the Displacement of the model's LFA centers, their associated circulation patterns deviate substantially from those observed.

The frequency distributions of the LFAs for both the model and reality display large skewness. The positive and negative large LFAs were, therefore, examined separately, and four-way intercomparisons were conducted between the model, the observed, the positive, and the negative LFAS. The separate analyses resulted in distinguishable circulation patterns between the positive and negative large LFAS, which cannot possibly be identified if a linear analysis tool, such as an empirical orthogonal function analysis, were used to extract the most dominant mode of the circulations. Despite pronounced misplacement of large LFAs of both polarities and a general underestimation of their magnitudes, the model dm have the capability of persisting its short-term climate anomaly at certain geographical locations. Over the North Pacific, the model's positive LFAs persist as long or longer than those found in reality, while its negative LFAs persist only one-fourth as long (10 versus 40 days).

The principal storm tracks and mean zonal wind at 250 mb (U250) were also examined to supplement the low-frequency anomaly investigation. Contrasting with observations, the model's U250s display considerable eastward extension and its storm tracks near the jet exit show substantial equatorward displacement over both the North Pacific and the North Atlantic oceans. These model characteristics are consistent with the behavior that the model's large LFAs also prefer to develop over the regions far east and southeast of those observed in the real atmosphere.

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Kshudiram Saha
,
Huug van den Dool
, and
Suranjana Saha

Abstract

The authors have investigated the climatological annual cycle in surface pressure on the Tibetan Plateau in relation to the annual cycle in surface pressure at the lower surroundings (India and China). It is found that surface pressure on the plateau is low (high) when the surrounding Asian continent has high (low) pressure. This out-of-phase relationship is evident in the NMC analyses and in long runs made with the NMC's global model. The authors have also found a few station observations on the plateau that have partially confirmed these opposing annual cycles in surface pressure. The authors believe this contrast to be real and operative over other parts of the globe as well. Near mean sea level, the surface pressure is low (high) when the temperature is high (low) (relative to its surroundings). At higher elevations, pressure is low (high) when temperatures are low (high). Also, in the datasets studied, the authors found no evidence for a thermal low on top of the plateau in summer.

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Anthony G. Barnston
and
Huug M. van den Dool

Abstract

The field of standard deviation of monthly mean 700-mb geopotential height in the Northern Hemisphere for each of the 12 months over the 1950–1991 period, among other auxiliary statistics, is compiled in an atlas to which this paper is companion. Some of the major features found in the atlas are highlighted and extended here. A comparison is also made to the same statistics derived from a 10-year run of the NMC model.

There are three distinct regions of peak standard deviation (up to 85 geopotential meters in winter), all of which are located over water. Two of them remain positionally relatively stationary throughout the year in the high-latitude Pacific and Atlantic oceans, respectively. A portion of the Pacific region's winter variability comes from interdecadal fluctuations. The third region is over the Arctic Ocean and exhibits some large seasonal changes in location. A roughly north-to-south troughlike minimum in standard deviation (down to less than 20 geopotential meters in summer) is found in west central North America throughout most of the year.

The standard deviation maxima (minima) coincide largely with areas with a high (low) frequency of occurrence of height anomaly centers of both signs. Many of these anomaly centers occur in spatial coherence with other centers, forming familiar teleconnection and principal component patterns. While the high (low) standard deviation areas invest greater (lesser) amounts of variance in these coherent variability clusters than the surrounding regions, their involvement in terms of the strength of the relationships is not substantially greater (smaller). The standard deviation field does not move north and south with the changes in season as do the jets, storm tracks, and the mean flow. In summer the standard deviation peaks are largely detached from spatially coherent variability patterns, suggesting that they may be caused in large part by local interactions related to permanent (spatially fixed) features of the lower boundary at all times of the year.

The observed monthly mean 700-mb flow and the quasi-stationary locations of its interannual standard deviation maxima and minima are reproduced in approximate form in a 10-year run of the NMC medium-range forecast model. This helps provide evidence that the field of standard deviation is related, directly or indirectly, to some of the geographically fixed boundary conditions across the globe such SST, ocean-land interfaces, and terrain.

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Huug M. van den Dool
and
Suranjana Saha

Abstract

A 10-year run was made with a reduced resolution (T40) version of NMC's medium range forecast model. The 12 monthly mean surface pressure fields averaged over 10 years are used to study the climatological seasonal redistribution of mass associated with the annual cycle in heating in the model. The vertically integrated divergent mass flux required to account for the surface pressure changes is presented in 2D vector form. The primary outcome is a picture of mass flowing between land and sea on planetary scales. The divergent mass fluxes are small in the Southern Hemisphere and tropics but larger in the midlatitudes of the Northern Hemisphere, although, when expressed as a velocity, nowhere larger than a few millimeters per second. Although derived from a model, the results are interesting because we have described aspects of the global monsoon system that are very difficult to determine from observations.

Two additional features are discussed, one physical, the other due to postprocessing. First, we show that the local imbalance between the mass of precipitation and evaporation implies a divergent water mass flux that is large in the aforementioned context (i.e., cm s−1). Omission of surface pressure tendencies due to the imbalance of evaporation and precipitation (order 10–30 mb per month) may therefore be a serious obstacle in the correct simulation of the annual cycle. Within the context of the model world it is also shown that the common conversion from surface to sea level pressure creates very large errors in the mass budget over land. In some areas the annual cycles of surface and sea level pressure are 180° out of phase.

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Anthony G. Barnston
and
Huug M. van den Dool

Abstract

Highly negative skill scores may occur in regression-based experimental forecast trials in which the data being forecast are withheld in turn from a fixed sample, and the remaining data are used to develop regression relationships-that is, exhaustive cross-validation methods. A small negative bias in skill is amplified when forecasts are verified using the correlation between forecasts and actual data. The same outcome occurs when forecasts are amplitude-inflated in conversion to a categorical system and scored in a “number of hits” framework. The effect becomes severe when predictor-predictand relationships are weak, as is often the case in climate prediction. Some basic characteristics of this degeneracy are explored for regression-based cross-validation.

Simulations using both randomized and designed datasets indicate that the correlation skill score degeneracy becomes important when nearly all of the available sample is used to develop forecast equations for the remaining (very few) points, and when the predictability in the full dependent sample falls short of the conventional requirement for statistical significance for the sample size. The undesirable effects can be reduced with one of the following methodological adjustments: 1) excluding more than a very small portion of the sample from the development group for each cross-validation forecast trial or 2) redefining the “total available sample” within one cross-validation exercise. A more complete elimination of the effects is achieved by 1) downward adjusting the magnitude of negative correlation skills in proportion to forecast amplitude, 2) regarding negative correlation skills as zero, or 3) using a forecast verification measure other than correlation such as root-mean-square error.

When the correlation skill score degeneracy is acknowledged and treated appropriately, cross-validation remains an effective and valid technique for estimating predictive skill for independent data.

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Jin Huang
and
Huug M. van den Dool

Abstract

The monthly mean precipitation-air temperature (MMP-MMAT) relation over the United States has been examined by analyzing the observed MMP and MMAT during the period of 1931–87. The authors’ main purpose is to examine the possibility of using MMP as a second predictor in addition to the MMAT itself in predicting the next month's MMAT and to shed light on the physical relationship between MMP and MMAT. Both station and climate division data are used.

It was found that the lagged MMP-MMAT correlation with MMP leading by a month is generally negative, with the strongest negative correlation in summer and in the interior United States continent. Over large areas of the interior United States in summer, predictions of MMAT based on either antecedent MMP alone or on a combination of antecedent MMP and MMAT are better than a Prediction scheme based on MMAT alone. On the whole, even in the interior United States though, including MMP as a second predictor does not improve the skill of MMAT forecasts on either dependent or independent data dramatically because the first predictor (temperature persistence) has accounted for most of the MMP's predictive variance. For a verification performed separately for antecedent wet and dry months, much larger skill was found following wet than dry Julys for both one- and two-predictor schemes. Upon further analysis, we attribute this to the differences in the climate between the dependent (1931–60) and independent (1961–87) periods (the second being considerably colder in August) rather than to a true wetness dependence in the predictability.

We found some evidence for the role of soil moisture in explaining negative MMP-MMAT and positive MMAT-MMAT lagged correlations both from observed data and from output of multiyear runs with the National Meteorological Center model. This suggests that we should use some direct measure of soil moisture to improve MMAT forecasts instead of using the MMP as a proxy.

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