Climate Forecast System Version Research Articles

The impact of the MJO on clusters of wintertime circulation anomalies over the North American region

Recent studies have shown that the Madden–Julian Oscillation (MJO) impacts the leading modes of intraseasonal variability in the northern hemisphere extratropics, providing a possible source of predictive skill over North America at intraseasonal timescales. We find that a k-means cluster analysis of mid-level geopotential height anomalies over the North American region identifies several wintertime cluster patterns whose probabilities are strongly modulated during and after MJO events, particularly during certain phases of the El Nino-Southern Oscillation (ENSO). We use a simple new optimization method for determining the number of clusters, k, and show that it results in a set of clusters which are robust to changes in the domain or time period examined. Several of the resulting cluster patterns resemble linear combinations of the Arctic Oscillation (AO) and the Pacific/North American (PNA) teleconnection pattern, but show even stronger responses to the MJO and ENSO than clusters based on the AO and PNA alone. A cluster resembling the positive (negative) PNA has elevated probabilities approximately 8–14 days following phase 6 (phase 3) of the MJO, while a negative AO-like cluster has elevated probabilities 10–20 days following phase 7 of the MJO. The observed relationships are relatively well reproduced in the 11-year daily reforecast dataset from the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). This study statistically links MJO activity in the tropics to common intraseasonal circulation anomalies over the North American sector, establishing a framework that may be useful for improving extended range forecasts over this region.

Relative Merit of Model Improvement versus Availability of Retrospective Forecasts: The Case of Climate Forecast System MJO Prediction

Abstract Retrospective forecasts of the new NCEP Climate Forecast System (CFS) have been analyzed out to 45 days from 1999 to 2009 with four members (0000, 0600, 1200, and 1800 UTC) each day. The new version of CFS [CFS, version 2 (CFSv2)] shows significant improvement over the older CFS [CFS, version 1 (CFSv1)] in predicting the Madden–Julian oscillation (MJO), with skill reaching 2–3 weeks in comparison with the CFSv1’s skill of nearly 1 week. Diagnostics of experiments related to the MJO forecast show that the systematic error correction, possible only because of the enormous hindcast dataset and the ensemble aspects of the prediction system (4 times a day), do contribute to improved forecasts. But the main reason is the improvement in the model and initial conditions between 1995 and 2010.

Prediction skill of monthly SST in the North Atlantic Ocean in NCEP Climate Forecast System version 2

This work evaluates the skill of retrospective predictions of the second version of the NCEP Climate Forecast System (CFSv2) for the North Atlantic sea surface temperature (SST) and investigates the influence of El Nino-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) on the prediction skill over this region. It is shown that the CFSv2 prediction skill with 0–8 month lead displays a “tripole”-like pattern with areas of higher skills in the high latitude and tropical North Atlantic, surrounding the area of lower skills in the mid-latitude western North Atlantic. This “tripole”-like prediction skill pattern is mainly due to the persistency of SST anomalies (SSTAs), which is related to the influence of ENSO and NAO over the North Atlantic. The influences of ENSO and NAO, and their seasonality, result in the prediction skill in the tropical North Atlantic the highest in spring and the lowest in summer. In CFSv2, the ENSO influence over the North Atlantic is overestimated but the impact of NAO over the North Atlantic is not well simulated. However, compared with CFSv1, the overall skills of CFSv2 are slightly higher over the whole North Atlantic, particularly in the high latitudes and the northwest North Atlantic. The model prediction skill beyond the persistency initially presents in the mid-latitudes of the North Atlantic and extends to the low latitudes with time. That might suggest that the model captures the associated air-sea interaction in the North Atlantic. The CFSv2 prediction is less skillful than that of SSTA persistency in the high latitudes, implying that over this region the persistency is even better than CFSv2 predictions. Also, both persistent and CFSv2 predictions have relatively low skills along the Gulf Stream.

Ensemble ENSO hindcasts initialized from multiple ocean analyses

In this study, the impact of ocean initial conditions (OIC) on the prediction skill in the tropical Pacific Ocean is examined. Four sets of OIC are used to initialize the 12‐month hindcasts of the tropical climate from 1979 to 2007, using the Climate Forecast System, version 2 (CFSv2), the current operational climate prediction model at the National Centers for Environmental Predictions (NCEP). These OICs are chosen from four ocean analyses produced by the NCEP and the European Center for Medium Range Weather Forecasts (ECMWF). For each hindcast starting from a given OIC, four ensemble members are generated with different atmosphere and land initial states. The predictive skill in the tropical Pacific Ocean is assessed based on the ensemble mean hindcasts from each individual as well as multiple oceanic analyses. To reduce the climate drift from various oceanic analyses, an anomaly initialization strategy is used for all hindcasts. The results indicate that there exists a substantial spread in the sea surface temperature (SST) prediction skill with different ocean analyses. Specifically, the ENSO prediction skill in terms of the anomaly correlation of Niño‐3.4 index can differ by as much as 0.1–0.2 at lead times longer than 2 months. The ensemble mean of the predictions initialized from all four ocean analyses gives prediction skill equivalent to the best one derived from the individual ocean analysis. It is suggested that more accurate OIC can improve the ENSO prediction skill and an ensemble ocean initialization has the potential of enhancing the skill at the present stage.

Representation of MJO Variability in the NCEP Climate Forecast System

The Madden–Julian oscillation (MJO) is arguably the most important intraseasonal mode of climate variability, given its significant modulation of global climate variations and attendant societal impacts. Advancing the current understanding and simulation of the MJO using state-of-the-art climate data and modeling systems is thus a necessary goal for improving MJO prediction capability. MJO variability is assessed in NOAA/NCEP reanalyses and two versions of the Climate Forecast System (CFS), CFS version 1 (CFSv1) and its update version 2 (CFSv2). The analysis leans on a variety of diagnostic procedures and includes MJO sensitivity to varying El Niño–Southern Oscillation (ENSO) phases. It is found that significant improvements have been realized in the representation of MJO variations in the new NCEP Climate Forecast System reanalysis (CFSR) as evidenced by outgoing longwave radiation (OLR) power spectral analysis and more coherent propagation characteristics of precipitation and 850-hPa zonal winds over the Eastern Hemisphere in CFSR-only depictions. Conversely, while modest improvements are realized in the CFSv2 as compared to CFSv1, in general the simulation of the MJO continues to be a challenge. Both versions produce strong eastward propagating variance of convection and wind fields in the intraseasonal frequency band. However, the simulated MJO propagates slower than the observed with difficulties traversing the Maritime Continent into the western Pacific, as noted in many previous modeling studies. The CFS shows robust intraseasonal simulations over the west Pacific during El Niño years with diminished simulation capability over the Indian Ocean during La Niña years. This is likely a manifestation of the preference for La Niña MJO activity to occur over the Indian Ocean and the simulation challenges over that domain.

A first look at Climate Forecast System version 2 (CFSv2) for hydrological seasonal prediction

[1] NOAA's National Centers for Environmental Prediction (NCEP) has transitioned to operationally use the second generation of their coupled ocean-atmosphere-land model, the Climate Forecast System version 2 (CFSv2), with advanced physics, increased resolution and refined initialization to improve the seasonal climate forecasts. We present a first look at the capability of CFSv2 on surface air temperature and precipitation predictions based on analyzing the 28-year (1982–2009) reforecasts. These variables are primary inputs to hydrological seasonal forecast procedures. Averaged globally, the CFSv2 increases the predictive skill for month-1 land surface air temperature and precipitation from the CFSv1 by 37% and 29%, respectively. The CFSv2 has comparable performance to the latest ECMWF model, the best among the current European seasonal forecast models. The soil moisture produced by CFSv2 also provides useful information in identifying several major drought events, especially over tropical regions. Though there is limited skill beyond month-1, the CFSv2 does show promising features for advancing hydrological forecast and application studies.