Prediction Skill and Bias of Tropical Pacific Sea Surface Temperatures in the NCEP Climate Forecast System Version 2

2013 ◽  
Vol 26 (15) ◽  
pp. 5358-5378 ◽  
Author(s):  
Yan Xue ◽  
Mingyue Chen ◽  
Arun Kumar ◽  
Zeng-Zhen Hu ◽  
Wanqiu Wang

Abstract The prediction skill and bias of tropical Pacific sea surface temperature (SST) in the retrospective forecasts of the Climate Forecast System, version 2 (CFSv2), of the National Centers for Environmental Prediction were examined. The CFSv2 was initialized from the Climate Forecast System Reanalysis (CFSR) over 1982–2010. There was a systematic cold bias in the central–eastern equatorial Pacific during summer/fall. The cold bias in the Niño-3.4 index was about −2.5°C in summer/fall before 1999 but suddenly changed to −1°C around 1999, related to a sudden shift in the trade winds and equatorial subsurface temperature in the CFSR. The SST anomaly (SSTA) was computed by removing model climatology for the periods 1982–98 and 1999–2010 separately. The standard deviation (STD) of forecast SSTA agreed well with that of observations in 1982–98, but in 1999–2010 it was about 200% too strong in the eastern Pacific and 50% too weak near the date line during winter/spring. The shift in STD bias was partially related to change of ENSO characteristics: central Pacific (CP) El Niños were more frequent than eastern Pacific (EP) El Niños after 2000. The composites analysis shows that the CFSv2 had a tendency to delay the onset phase of the EP El Niños in the 1980s and 1990s but predicted their decay phases well. In contrast, the CFSv2 predicted the onset phase of the CP El Niños well but prolonged their decay phase. The hit rate for both El Niño and La Niña was lower in the later period than in the early period, and the false alarm for La Niña increased appreciably from the early to the later period.

2019 ◽  
Vol 32 (4) ◽  
pp. 1307-1326 ◽  
Author(s):  
Douglas E. Miller ◽  
Zhuo Wang

The representation of ENSO and NAO are examined in the Climate Forecast System, version 2 (CFSv2), reforecasts with a focus on the physical processes related to teleconnections and predictability. CFSv2 predicts ENSO well, but an eastward shift of the tropical Pacific sea surface temperature (SST) anomalies is evident. Although it appears minor on the global scale, the shift in convection and the large-scale wave train affects the model prediction of regional climate. In contrast, NAO is predicted poorly. The anomaly correlation coefficient (ACC) between the model ensemble mean and the observation is 0.27 during 1982–2010, and the ensemble spread is large. The representation of three sources of NAO predictability—SST, the stratospheric polar vortex, and the Arctic sea ice concentration—is investigated. It is found that the link between tropical Pacific SST and NAO is not well represented in CFSv2, and that the tropospheric–stratospheric interactions are too weak, both contributing to the poor prediction of NAO. Additionally, the impact of ENSO and NAO on prediction skill of CFSv2 in boreal winter is analyzed in terms of the spatial ACC of geopotential height. Active ENSO events exhibit larger prediction skill than neutral years, especially during the ENSO+/NAO− and ENSO−/NAO+ winters. Spatial patterns of prediction skill are also examined, and larger skill of geopotential height and 2-m air temperature is found outlined by the nodes of the PNA pattern, consistent with the large signal-to-noise ratios associated with the ENSO teleconnection.


2012 ◽  
Vol 140 (9) ◽  
pp. 3003-3016 ◽  
Author(s):  
A. Kumar ◽  
M. Chen ◽  
L. Zhang ◽  
W. Wang ◽  
Y. Xue ◽  
...  

Abstract For long-range predictions (e.g., seasonal), it is a common practice for retrospective forecasts (also referred to as the hindcasts) to accompany real-time predictions. The necessity for the hindcasts stems from the fact that real-time predictions need to be calibrated in an attempt to remove the influence of model biases on the predicted anomalies. A fundamental assumption behind forecast calibration is the long-term stationarity of forecast bias that is derived based on hindcasts. Hindcasts require specification of initial conditions for various components of the prediction system (e.g., ocean, atmosphere) that are generally taken from a long reanalysis. Trends and discontinuities in the reanalysis that are either real or spurious can arise due to several reasons, for example, the changing observing system. If changes in initial conditions were to persist during the forecast, there is a potential for forecast bias to depend over the period it is computed, making calibration even more of a challenging task. In this study such a case is discussed for the recently implemented seasonal prediction system at the National Centers for Environmental Prediction (NCEP), the Climate Forecast System version 2 (CFS.v2). Based on the analysis of the CFS.v2 for 1981–2009, it is demonstrated that the characteristics of the forecast bias for sea surface temperature (SST) in the equatorial Pacific had a dramatic change around 1999. Furthermore, change in the SST forecast bias, and its relationship to changes in the ocean reanalysis from which the ocean initial conditions for hindcasts are taken is described. Implications for seasonal and other long-range predictions are discussed.


2011 ◽  
Vol 24 (17) ◽  
pp. 4676-4694 ◽  
Author(s):  
Scott J. Weaver ◽  
Wanqiu Wang ◽  
Mingyue Chen ◽  
Arun Kumar

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.


2021 ◽  
Author(s):  
Kristina Fröhlich ◽  
Katharina Isensee ◽  
Sascha Brandt ◽  
Sebastian Brune ◽  
Andreas Paxian ◽  
...  

<p>In November 2020, the new version of the German Climate Forecast System, GCFS2.1, became operational at Deutscher Wetterdienst (DWD), providing new seasonal forecasts every month. The system <strong>is based</strong><strong> </strong>on the Max Planck Institute for Meteorology Earth-System Model <strong>(MPI-ESM-HR)</strong> and is developed jointly by DWD, the Max Planck Institute for Meteorology and Universität Hamburg.</p><p>In GCFS2.1, ERA5 and ORAS5 reanalyses are assimilated using atmospheric, oceanic and sea ice nudging, respectively. From the assimilation, 50-member 6-month forecast ensembles are initialized at the start of each month. Prediction skill is assessed with a 30-member 6-month hindcast ensemble covering the time period 1982-2019 for February, May, August and November start months, and 1990-2019 for the remaining start months. Both the forecast and hindcast ensembles are generated by oceanic bred vectors with additional physical perturbations applied to the upper atmospheric model layers.</p><p>Here, we investigate the performance of GCFS2.1 summer and winter forecasts over Europe. While our main focus is on the prediction of large scale patterns that control the weather regimes during these two seasons, e.g. European blockings, special emphasis is paid on the impact of the January 2021 sudden stratospheric warming (SSW) event on the performance of GCFS2.1. The inclusion of the early phases of the January 2021 SSW event in the forecast initialisation significantly changes the GCFS2.1 forecast for February 2021 European surface climate. Prediction skill of GCFS2.1 for summer European blocking events will be also compared to the previous version GCFS2.0.</p>


2020 ◽  
Author(s):  
Samir Pokhrel ◽  
Hasibur Rahaman ◽  
Hemantkumar Chaudhari ◽  
Subodh Kumar Saha ◽  
Anupam Hazra

<p>IITM provides seasonal monsoon rainfall forecast using modified CGCM CFSv2. The present operational CFSv2 initilized with the INCOIS-GODAS ocean analysis based on MOM4p0d and 3DVar assimilation schemes. Recently new Ocean analysis GODAS-Mom4p1 using Moduler Ocean Model (MOM) upgraded physical model MOM4p1 is generated. This analysis has shown improvement in terms of subsurface temperature, salinity , current as well as sea surface temperature (SST), sea surface salinity (SSS) and surface currents over the Indian Ocean domain with respect to present operational INCOIS-GODAS analysis (Rahaman et al. 2017;Rahman et al. 2019). This newly generated ocean analysis is used to initialize NCEP Climate Forecast System (CFSv2) for the retrospective run from 2011 to 2018. The simulated coupled run has shown improvement in both oceanic as well atmospheric parameters. The more realistic nature of coupled simulations across the atmosphere and ocean may be promising to get better forecast skill.</p>


2010 ◽  
Vol 25 (3) ◽  
pp. 950-969 ◽  
Author(s):  
Wanqiu Wang ◽  
Mingyue Chen ◽  
Arun Kumar

Abstract This study assesses the real-time seasonal forecasts for 2005–08 with the current National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). The forecasts are compared with retrospective forecasts (or hindcasts) for 1981–2004 to examine the consistency of the forecast system, and with the Atmospheric Model Intercomparison Project (AMIP) simulations forced with observed sea surface temperatures (SSTs) to contrast the realized skill against the potential predictability due to the specification of the observed sea surface temperatures. The analysis focuses on the forecasts of SSTs, 2-m surface air temperature (T2M), and precipitation. The CFS forecasts maintained a good level of prediction skill for SSTs in the tropical Pacific, the western Indian Ocean, and the northern Atlantic. The SST forecast skill is within the range of hindcast skill levels calculated with 4-yr windows, which can vary greatly associated with the interannual El Niño–Southern Oscillation (ENSO) variability. Overall, the SST forecast skill over the globe is comparable to the average of the hindcast skill. For the tropical eastern Pacific, however, the forecast skill at lead times longer than 2 months is less than the average hindcast skill due to the relatively weaker ENSO variability during the forecast period (2005–08). The forecasts and hindcasts show a similar level of precipitation skill over most of the globe. For T2M, the spatial distribution of skill differs substantially between the forecasts and hindcasts. In particular, the T2M skill of the forecasts for the Northern Hemisphere during its warm seasons is lower than that of the hindcasts. Comparison with the AMIP simulations shows similar levels of precipitation skill over the tropical Pacific. Over the tropical Indian Ocean, the CFS forecasts show a substantially higher level of skill than the AMIP simulations for a large part of the period. This conforms with the results from previous studies that while interannual variability in the tropical Pacific atmosphere is slaved to the underlying SST anomalies, specification of SSTs (as for the AMIP simulations) in the Indian Ocean may lead to incorrect simulation of the atmospheric variability. Over the tropical Atlantic, the precipitation skill of both the CFS forecasts and AMIP simulations is low, suggesting that SSTs have less control over the atmospheric anomalies and the predictability is low. The analysis reveals several deficiencies in the current CFS that need to be corrected for improved seasonal forecasting. For example, the CFS tends to consistently forecast larger ENSO amplitude and delayed transition between the ENSO phases. Forecasts of T2M also have a strong cold bias in Northern Hemisphere mid- to high latitudes during warm seasons. This error is due to initial soil moisture anomalies, which appear to be too wet compared with two other observational analyses. The strong impacts of soil moisture on the seasonal forecasts, and large discrepancies among the soil moisture analyses, call for more accurate specification of soil moisture. Furthermore, average forecast SST and T2M anomalies for 2005–08 show a cold bias over the entire globe, indicating that the model is unable to maintain the observed long-term warming trend.


2014 ◽  
Vol 27 (12) ◽  
pp. 4451-4472 ◽  
Author(s):  
Song Yang ◽  
Xingwen Jiang

Abstract The eastern Pacific (EP) El Niño–Southern Oscillation (ENSO) and the central Pacific (CP) ENSO exert different influences on climate. In this study, the authors analyze the hindcasts of the NCEP Climate Forecast System, version 2 (CFSv2), and assess the skills of predicting the two types of ENSO and their impacts on East Asian climate. The possible causes of different prediction skills for different types of ENSO are also discussed. CFSv2 captures the spatial patterns of sea surface temperature (SST) related to the two types of ENSO and their different climate impacts several months in advance. The dynamical prediction of the two types of ENSO by the model, whose skill is season dependent, is better than the prediction based on the persistency of observed ENSO-related SST, especially for summer and fall. CFSv2 performs well in predicting EP ENSO and its impacts on the East Asian winter monsoon and on the Southeast Asian monsoon during its decaying summer. However, for both EP ENSO and CP ENSO, the model overestimates the extent of the anomalous anticyclone over the western North Pacific Ocean from the developing autumn to the next spring but underestimates the magnitude of climate anomalies in general. It fails to simulate the SST pattern and climate impact of CP ENSO during its developing summer. The model’s deficiency in predicting CP ENSO may be linked to a warm bias in the eastern Pacific. However, errors in simulating the climate impacts of the two types of ENSO should not be solely ascribed to the bias in SST simulation.


2012 ◽  
Vol 40 (11-12) ◽  
pp. 2745-2759 ◽  
Author(s):  
Zeng-Zhen Hu ◽  
Arun Kumar ◽  
Bohua Huang ◽  
Wanqiu Wang ◽  
Jieshun Zhu ◽  
...  

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