The German Climate Forecast System 2.1: seasonal forecast performance over Europe

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 is based on the Max Planck Institute for Meteorology Earth-System Model (MPI-ESM-HR) and is developed jointly by DWD, the Max Planck Institute for Meteorology and Universit&#228;t Hamburg.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.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.

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Assessing Seasonal Predictability Sources and Windows of High Predictability in the Climate Forecast System, Version 2

Journal of Climate ◽

10.1175/jcli-d-18-0389.1 ◽

2019 ◽

Vol 32 (4) ◽

pp. 1307-1326 ◽

Cited By ~ 1

Author(s):

Douglas E. Miller ◽

Zhuo Wang

Keyword(s):

Geopotential Height ◽

Tropical Pacific ◽

Arctic Sea Ice ◽

Prediction Skill ◽

The Arctic ◽

Climate Forecast ◽

Forecast System ◽

Climate Forecast System ◽

Enso Events ◽

The Impact

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.

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Prediction Skill of North Pacific Variability in NCEP Climate Forecast System Version 2: Impact of ENSO and Beyond

Journal of Climate ◽

10.1175/jcli-d-13-00633.1 ◽

2014 ◽

Vol 27 (11) ◽

pp. 4263-4272 ◽

Cited By ~ 23

Author(s):

Zeng-Zhen Hu ◽

Arun Kumar ◽

Bohua Huang ◽

Jieshun Zhu ◽

Yuanhong Guan

Keyword(s):

North Pacific ◽

Phase Relation ◽

Southern Oscillation ◽

Phase Relationship ◽

Prediction Skill ◽

Climate Forecast ◽

Forecast System ◽

Climate Forecast System ◽

The Impact ◽

Ncep Climate Forecast System

Abstract This work examines the impact of El Niño–Southern Oscillation (ENSO) on the prediction skill of North Pacific variability (NPV) in retrospective predictions of the NCEP Climate Forecast System, version 2. It is noted that the phase relationship between ENSO and NPV at initial conditions (ICs) affects the prediction skill of NPV. For average lead times of 0–6 months, the prediction skills of sea surface temperature anomalies (SSTAs) in NPV (defined as the NPV index) increase from 0.42 to 0.63 from the cases of an out-of-phase relation between the Niño-3.4 and NPV indices in ICs to the cases of an in-phase relation. It is suggested that when ENSO and NPV are in phase in ICs, ENSO plays a constructive role in the NPV development and enhances its signals. Nevertheless, when ENSO and NPV are out of phase, some pronounced positive NPV events are still predictable. In these cases, the North Pacific is dominated by strong positive SSTAs, which may overcome the opposing influence from the tropical Pacific and display predictability.

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Do CFSv2 Seasonal Forecasts Help Improve the Forecast of Meteorological Drought over Mainland China?

Water ◽

10.3390/w12072010 ◽

2020 ◽

Vol 12 (7) ◽

pp. 2010

Author(s):

Yang Lang ◽

Lifeng Luo ◽

Aizhong Ye ◽

Qingyun Duan

Keyword(s):

Mainland China ◽

Meteorological Drought ◽

Lead Times ◽

Climate Forecast ◽

Seasonal Forecasts ◽

Dynamical Models ◽

Standard Precipitation Index ◽

Forecast System ◽

Climate Forecast System ◽

Drought Prediction

Seasonal forecasts from dynamical models are expected to be useful for drought predictions in many regions. This study investigated the usefulness of the Climate Forecast System version 2 (CFSv2) in improving meteorological drought prediction in China based on its 25-year reforecast. The six-month standard precipitation index (SPI6) was used as the drought indicator, and its persistence forecast served as the benchmark against which CFSv2 forecasts were evaluated. The analysis found that the SPI6 persistence forecast shows good skills in all regions at short lead times, and CFSv2 forecast can further improve those skills in most regions. The improvement is particularly pronounced at longer lead times and over the humid regions in the southeast. This study also examined the seasonality and regionality of persistence forecast skills and CFSv2 contributions, and reveals regions where CFSv2 forecast shows no or sometimes even negative contributions.

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

Journal of Climate ◽

10.1175/jcli-d-12-00600.1 ◽

2013 ◽

Vol 26 (15) ◽

pp. 5358-5378 ◽

Cited By ~ 73

Author(s):

Yan Xue ◽

Mingyue Chen ◽

Arun Kumar ◽

Zeng-Zhen Hu ◽

Wanqiu Wang

Keyword(s):

Tropical Pacific ◽

Sea Surface ◽

Eastern Pacific ◽

Prediction Skill ◽

Cold Bias ◽

Climate Forecast ◽

Forecast System ◽

Climate Forecast System ◽

La Nina ◽

Onset Phase

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.

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Simulations and Seasonal Prediction of the Asian Summer Monsoon in the NCEP Climate Forecast System

Journal of Climate ◽

10.1175/2008jcli1961.1 ◽

2008 ◽

Vol 21 (15) ◽

pp. 3755-3775 ◽

Cited By ~ 118

Author(s):

Song Yang ◽

Zuqiang Zhang ◽

Vernon E. Kousky ◽

R. Wayne Higgins ◽

Soo-Hyun Yoo ◽

...

Keyword(s):

Summer Monsoon ◽

Asian Monsoon ◽

Asian Summer Monsoon ◽

Monsoon Circulation ◽

Climate Forecast ◽

Forecast System ◽

Climate Forecast System ◽

Asian Summer ◽

The Impact ◽

Ncep Climate Forecast System

Abstract Analysis of the retrospective ensemble predictions (hindcasts) of the NCEP Climate Forecast System (CFS) indicates that the model successfully simulates many major features of the Asian summer monsoon including the climatology and interannual variability of major precipitation centers and atmospheric circulation systems. The model captures the onset of the monsoon better than the retreat of the monsoon, and it simulates the seasonal march of monsoon rainfall over Southeast Asia more realistically than that over South Asia. The CFS predicts the major dynamical monsoon indices and monsoon precipitation patterns several months in advance. It also depicts the interactive oceanic–atmospheric processes associated with the precipitation anomalies reasonably well at different time leads. Overall, the skill of monsoon prediction by the CFS mainly comes from the impact of El Niño–Southern Oscillation (ENSO). The CFS produces weaker-than-observed large-scale monsoon circulation, due partially to the cold bias over the Asian continent. It tends to overemphasize the relationship between ENSO and the Asian monsoon, as well as the impact of ENSO on the Asian and Indo-Pacific climate. A higher-resolution version of the CFS (T126) captures the climatology and variability of the Asian monsoon more realistically than does the current resolution version (T62). The largest improvement occurs in the simulations of precipitation near the Tibetan Plateau and over the tropical Indian Ocean associated with the zonal dipole mode structure. The analysis suggests that NCEP’s next operational model may perform better in simulating and predicting the monsoon climate over Asia and the Indo-Pacific Oceans.

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Summer-Season Forecast Experiments with the NCEP Climate Forecast System Using Different Land Models and Different Initial Land States

Journal of Climate ◽

10.1175/2010jcli3797.1 ◽

2011 ◽

Vol 24 (9) ◽

pp. 2319-2334 ◽

Cited By ~ 13

Author(s):

Rongqian Yang ◽

Kenneth Mitchell ◽

Jesse Meng ◽

Michael Ek

Keyword(s):

Air Temperature ◽

Land Surface ◽

Land Surface Model ◽

Summer Season ◽

Department Of Energy ◽

Surface Model ◽

Climate Forecast ◽

Forecast System ◽

Climate Forecast System ◽

The Impact

Abstract To examine the impact from land model upgrades and different land initializations on the National Centers for Environmental Prediction (NCEP)’s Climate Forecast System (CFS), extensive T126 CFS experiments are carried out for 25 summers with 10 ensemble members using the old Oregon State University (OSU) land surface model (LSM) and the new Noah LSM. The CFS using the Noah LSM, initialized in turn with land states from the NCEP–Department of Energy Global Reanalysis 2 (GR-2), Global Land Data System (GLDAS), and GLDAS climatology, is compared to the CFS control run using the OSU LSM initialized with the GR-2 land states. Using anomaly correlation as a primary measure, the summer-season prediction skill of the CFS using different land models and different initial land states is assessed for SST, precipitation, and 2-m air temperature over the contiguous United States (CONUS) on an ensemble basis. Results from these CFS experiments indicate that upgrading from the OSU LSM to the Noah LSM improves the overall CONUS June–August (JJA) precipitation prediction, especially during ENSO neutral years. Such an enhancement in CFS performance requires the execution of a GLDAS with the very same Noah LSM as utilized in the land component of the CFS, while improper initializations of the Noah LSM using the GR-2 land states lead to degraded CFS performance. In comparison with precipitation, the land upgrades have a relatively small impact on both of the SST and 2-m air temperature predictions.

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The NCEP Climate Forecast System Version 2

Journal of Climate ◽

10.1175/jcli-d-12-00823.1 ◽

2014 ◽

Vol 27 (6) ◽

pp. 2185-2208 ◽

Cited By ~ 1314

Author(s):

Suranjana Saha ◽

Shrinivas Moorthi ◽

Xingren Wu ◽

Jiande Wang ◽

Sudhir Nadiga ◽

...

Keyword(s):

Energy Use ◽

Initial Conditions ◽

Forecast Model ◽

Seasonal Prediction ◽

The United States ◽

Climate Forecast ◽

Seasonal Forecasts ◽

Forecast System ◽

Climate Forecast System ◽

Ncep Climate Forecast System

Abstract The 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 United States, and significantly improves global SST forecasts over its predecessor. The CFSv2 not only provides greatly improved guidance at these time scales but also creates many more products for subseasonal and seasonal forecasting with an extensive set of retrospective forecasts for users to calibrate their forecast products. These retrospective and real-time operational forecasts will be used by a wide community of users in their decision making processes in areas such as water management for rivers and agriculture, transportation, energy use by utilities, wind and other sustainable energy, and seasonal prediction of the hurricane season.

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The NCEP Climate Forecast System

Journal of Climate ◽

10.1175/jcli3812.1 ◽

2006 ◽

Vol 19 (15) ◽

pp. 3483-3517 ◽

Cited By ~ 778

Author(s):

S. Saha ◽

S. Nadiga ◽

C. Thiaw ◽

J. Wang ◽

W. Wang ◽

...

Keyword(s):

Weather Prediction ◽

Seasonal Prediction ◽

Ocean Model ◽

Climate Forecast ◽

Seasonal Forecasts ◽

Forecast System ◽

Coupled Modeling ◽

Dynamical Modeling ◽

Climate Forecast System ◽

Fully Coupled

Abstract The Climate Forecast System (CFS), the fully coupled ocean–land–atmosphere dynamical seasonal prediction system, which became operational at NCEP in August 2004, is described and evaluated in this paper. The CFS provides important advances in operational seasonal prediction on a number of fronts. For the first time in the history of U.S. operational seasonal prediction, a dynamical modeling system has demonstrated a level of skill in forecasting U.S. surface temperature and precipitation that is comparable to the skill of the statistical methods used by the NCEP Climate Prediction Center (CPC). This represents a significant improvement over the previous dynamical modeling system used at NCEP. Furthermore, the skill provided by the CFS spatially and temporally complements the skill provided by the statistical tools. The availability of a dynamical modeling tool with demonstrated skill should result in overall improvement in the operational seasonal forecasts produced by CPC. The atmospheric component of the CFS is a lower-resolution version of the Global Forecast System (GFS) that was the operational global weather prediction model at NCEP during 2003. The ocean component is the GFDL Modular Ocean Model version 3 (MOM3). There are several important improvements inherent in the new CFS relative to the previous dynamical forecast system. These include (i) the atmosphere–ocean coupling spans almost all of the globe (as opposed to the tropical Pacific only); (ii) the CFS is a fully coupled modeling system with no flux correction (as opposed to the previous uncoupled “tier-2” system, which employed multiple bias and flux corrections); and (iii) a set of fully coupled retrospective forecasts covering a 24-yr period (1981–2004), with 15 forecasts per calendar month out to nine months into the future, have been produced with the CFS. These 24 years of fully coupled retrospective forecasts are of paramount importance to the proper calibration (bias correction) of subsequent operational seasonal forecasts. They provide a meaningful a priori estimate of model skill that is critical in determining the utility of the real-time dynamical forecast in the operational framework. The retrospective dataset also provides a wealth of information for researchers to study interactive atmosphere–land–ocean processes.

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The Madden–Julian Oscillation Simulated in the NCEP Climate Forecast System Model: The Importance of Stratiform Heating

Journal of Climate ◽

10.1175/2010jcli2983.1 ◽

2010 ◽

Vol 23 (18) ◽

pp. 4770-4793 ◽

Cited By ~ 63

Author(s):

Kyong-Hwan Seo ◽

Wanqiu Wang

Keyword(s):

General Circulation ◽

General Circulation Models ◽

Horizontal Resolution ◽

Western Pacific ◽

Climate Forecast ◽

Madden Julian Oscillation ◽

Maritime Continent ◽

Forecast System ◽

Climate Forecast System ◽

The Impact

Abstract This study investigates the capability for simulating the Madden–Julian oscillation (MJO) in a series of atmosphere–ocean coupled and uncoupled simulations using NCEP operational general circulation models. The effect of air–sea coupling on the MJO is examined by comparing long-term simulations from the coupled Climate Forecast System (CFS T62) and the atmospheric Global Forecast System (GFS T62) models. Another coupled simulation with a higher horizontal resolution model (CFS T126) is performed to investigate the impact of model horizontal resolution. Furthermore, to examine the impact on a deep convection scheme, an additional coupled T126 run (CFS T126RAS) is conducted with the relaxed Arakawa–Schubert (RAS) scheme. The most important factors for the proper simulation of the MJO are investigated from these runs. The empirical orthogonal function, lagged regression, and spectral analyses indicated that the interactive air–sea coupling greatly improved the coherence between convection, circulation, and other surface fields on the intraseasonal time scale. A higher horizontal resolution run (CFS T126) did not show significant improvements in the intensity and structure. However, GFS T62, CFS T62, and CFS T126 all yielded the 30–60-day variances that were not statistically distinguishable from the background red noise spectrum. Their eastward propagation was stalled over the Maritime Continent and far western Pacific. In contrast to the model simulations using the simplified Arakawa–Schubert (SAS) cumulus scheme, CFS T126RAS produced statistically significant spectral peaks in the MJO frequency band, and greatly improved the strength of the MJO convection and circulation. Most importantly, the ability of MJO convection signal to penetrate into the Maritime Continent and western Pacific was demonstrated. In this simulation, an early-stage shallow heating and moistening preconditioned the atmosphere for subsequent intense MJO convection and a top-heavy vertical heating profile was formed by stratiform heating in the upper and middle troposphere, working to increase temperature anomalies and hence eddy available potential energy that sustains the MJO. The stratiform heating arose from convective detrainment of moisture to the environment and stratiform anvil clouds. Therefore, the following factors were analyzed to be most important for the proper simulation of the MJO rather than the correct simulations of basic-state precipitation, sea surface temperature, intertropical convergence zone, vertical zonal wind shear, and lower-level zonal winds: 1) an elevated vertical heating structure (by stratiform heating), 2) a moisture–stratiform instability process (a positive feedback process between moisture and convective–stratiform clouds), and 3) the low-level moisture convergence to the east of MJO convection (through the appropriate moisture and convective–stratiform cloud processes–circulation interactions). The improved MJO simulation did improve the global circulation response to the tropical heating and may extend the predictability of weather and climate over Asia and North America.

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