Huug van den Dool

The NCEP–NCAR 50-Year Reanalysis: Monthly Means CD-ROM and Documentation

The National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) have cooperated in a project (denoted “reanalysis”) to produce a retroactive record of more than 50 years of global analyses of atmospheric fields in support of the needs of the research and climate monitoring communities. This effort involved the recovery of land surface, ship, rawinsonde, pibal, aircraft, satellite, and other data. These data were then quality controlled and assimilated with a data assimilation system kept unchanged over the reanalysis period. This eliminated perceived climate jumps associated with changes in the operational (real time) data assimilation system, although the reanalysis is still affected by changes in the observing systems. During the earliest decade (1948–57), there were fewer upper-air data observations and they were made 3 h later than the current main synoptic times (e.g., 0300 UTC), and primarily in the Northern Hemisphere, so that the reanalysis is less reliable than for th later 40 years. The reanalysis data assimilation system continues to be used with current data in real time (Climate Data Assimilation System or CDAS), so that its products are available from 1948 to the present. The products include, in addition to the gridded reanalysis fields, 8-day forecasts every 5 days, and the binary universal format representation (BUFR) archive of the atmospheric observations. The products can be obtained from NCAR, NCEP, and from the National Oceanic and Atmospheric Administration/ Climate Diagnostics Center (NOAA/CDC). (Their Web page addresses can be linked to from the Web page of the NCEP–NCAR reanalysis at http:// wesley.wwb.noaa.gov/Reanalysis.html.) This issue of the Bulletin includes a CD-ROM with a documentation of the NCEP–NCAR reanalysis (Kistler et al. 1999). In this paper we present a brief summary and some highlights of the documentation (also available on the Web at http://atmos.umd.edu/ ~ekalnay/). The CD-ROM, similar to the one issued with the March 1996 issue of the Bulletin, contains 41 yr (1958–97) of monthly means of many reanalysis variables and estimates of precipitation derived from satellite and in situ observations (see the appenThe NCEP–NCAR 50-Year Reanalysis: Monthly Means CD-ROM and Documentation

The NCEP Climate Forecast System Reanalysis

The NCEP Climate Forecast System Reanalysis (CFSR) was completed for the 31-yr period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high-resolution coupled atmosphere–ocean–land surface–sea ice system to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real-time product into the future. New features of the CFSR include 1) coupling of the atmosphere and ocean during the generation of the 6-h guess field, 2) an interactive sea ice model, and 3) assimilation of satellite radiances by the Gridpoint Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is ~38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global oceans latitudinal spacing is 0.25° at the equator, extending to a global 0.5° beyond the tropics, with 40 levels to a depth of 4737 m. The global land surface model has four soil levels and the global sea ice m...

The NCEP Climate Forecast System Version 2

AbstractThe second version of the NCEP Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled reanalysis was made over a 32-yr period (1979–2010), which provided the initial conditions to carry out a comprehensive reforecast over 29 years (1982–2010). This was done to obtain consistent and stable calibrations, as well as skill estimates for the operational subseasonal and seasonal predictions at NCEP with CFSv2. The operational implementation of the full system ensures a continuity of the climate record and provides a valuable up-to-date dataset to study many aspects of predictability on the seasonal and subseasonal scales. Evaluation of the reforecasts show that the CFSv2 increases the length of skillful MJO forecasts from 6 to 17 days (dramatically improving subseasonal forecasts), nearly doubles the skill of seasonal forecasts of 2-m temperatures over the ...

The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction

The recent U.S. National Academies report, Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2...

Long-Lead Seasonal Forecasts—Where Do We Stand?

Abstract The National Weather Service intends to begin routinely issuing long-lead forecasts of 3-month mean U.S. temperature and precipitation by the beginning of 1995. The ability to produce useful forecasts for certain seasons and regions at projection times of upto 1 yr is attributed to advances in data observing and processing, computer capability, and physical understanding-particularly, for tropical ocean-atmosphere phenomena. Because much of the skill of the forecasts comes from anomalies of tropical SST related to ENSO, we highlight here long-lead forecasts of the tropical Pacific SST itself, which have higher skill than the U.S forecasts that are made largely on their basis. The performance of five ENSO prediction systems is examined: Two are dynamical [the Cane-Zebiak simple coupled model of Lamont-Doherty Earth Observatory and the nonsimpie coupled model of the National Centers for Environmental Prediction (NCEP)]; one is a hybrid coupled model (the Scripps Institution for Oceanography-Max Pla...

Present-Day Capabilities of Numerical and Statistical Models for Atmospheric Extratropical Seasonal Simulation and Prediction

Abstract A statistical model and extended ensemble integrations of two atmospheric general circulation models (GCMs) are used to simulate the extratropical atmospheric response to forcing by observed SSTs for the years 1980 through 1988. The simulations are compared to observations using the anomaly correlation and root-mean-square error of the 700-hPa height field over a region encompassing the extratropical North Pacific Ocean and most of North America. On average, the statistical model is found to produce considerably better simulations than either numerical model, even when simple statistical corrections are used to remove systematic errors from the numerical model simulations. In the mean, the simulation skill is low, but there are some individual seasons for which all three models produce simulations with good skill. An approximate upper bound to the simulation skill that could be expected from a GCM ensemble, if the models response to SST forcing is assumed to be perfect, is computed. This perfect...

An analysis of multimodel ensemble predictions for seasonal climate anomalies

[1] In this paper the potential advantages and relative performances of different techniques for constructing multimodel ensemble seasonal predictions are examined. Two commonly used methods of constructing multimodel ensemble predictions are analyzed. Particular emphasis is placed on the analysis of the schemes themselves. In the first technique--simple multimodel ensemble (SME) predictions-equal weights are assigned to the ensemble mean predictions of each of the atmospheric general circulation models (AGCM). In the second approach-optimal multimodel ensemble (OME) predictions the weights are obtained using a multiple linear regression. A theoretical analysis of these techniques is complemented by the analyses based on seasonal climate simulations for 45 January-February-March seasons over the 1950-1994 period. A comparison of seasonal simulation skill scores between SME and OME indicates that for the bias corrected data, i.e., when the seasonal anomalies of each of the AGCMs are computed with respect to its own mean state, the performance of seasonal predictions based on the simpler SME approach is comparable to that of the more complex OME approach. A major reason for this result is that the data record of historical predictions may not be long enough for a stable estimate of weights at individual geographical locations to be obtained. This problem can be reduced by extending the multiple linear regression approach to include the spatial domain. However, even with this algorithm change, the performance of OME in seasonal predictions does not improve over that using the SME approach. Results, therefore, indicate that the use of more sophisticated techniques for constructing multimodel ensembles may not be any more advantageous than the use of simpler approaches. Results also show that on average the skill scores for the predictions based on multimodel ensemble prediction techniques are only marginally better than those of the best AGCM. However, an advantage of multimodel ensemble prediction techniques may be that they retain the best performance of each AGCM on a regional basis in the merged forecasts.

A Degeneracy in Cross-Validated Skill in Regression-based Forecasts

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, a...

Predictability and Forecast Skill in NMME

AbstractForecast 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...

NCEP Forecasts of the El Niño of 1997–98 and Its U.S. Impacts

Abstract The strong El Nino of 1997-98 provided a unique opportunity for National Weather Service, National Centers for Environmental Prediction, Climate Prediction Center (CPC) forecasters to apply several years of accumulated new knowledge of the U.S. impacts of El Nino to their long-lead seasonal forecasts with more clarity and confidence than ever previously. This paper examines the performance of CPCs official forecasts, and its individual component forecast tools, during this event. Heavy winter precipitation across California and the southern plains-Gulf coast region was accurately forecast with at least six months of lead time. Dryness was also correctly forecast in Montana and in the southwestern Ohio Valley. The warmth across the northern half of the country was correctly forecast, but extended farther south and east than predicted. As the winter approached, forecaster confidence in the forecast pattern increased, and the probability anomalies that were assigned reached unprecedented levels in ...