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

2019 ◽  
Vol 32 (4) ◽  
pp. 1307-1326 ◽  
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.

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
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.

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

2021 ◽  
Author(s):  
Kristina Fröhlich ◽  
Katharina Isensee ◽  
Sascha Brandt ◽  
Sebastian Brune ◽  
Andreas Paxian ◽  
...  
Keyword(s):  
Large Scale ◽  
Atmospheric Model ◽  
Prediction Skill ◽  
Climate Forecast ◽  
Seasonal Forecasts ◽  
Max Planck ◽  
Forecast System ◽  
Climate Forecast System ◽  
Forecast Performance ◽  
The Impact

<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>

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

2014 ◽  
Vol 27 (11) ◽  
pp. 4263-4272 ◽  
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.

scholarly journals SEAS5: The new ECMWF seasonal forecast system

2018 ◽  
Author(s):  
Stephanie J. Johnson ◽  
Timothy N. Stockdale ◽  
Laura Ferranti ◽  
Magdalena Alonso Balmaseda ◽  
Franco Molteni ◽  
...  
Keyword(s):  
Sea Ice ◽  
Decadal Variability ◽  
Seasonal Forecast ◽  
Arctic Sea Ice ◽  
Prediction Skill ◽  
The Arctic ◽  
Western Boundary ◽  
Forecast System ◽  
Enso Prediction ◽  
Ice Model

Abstract. In this paper we describe SEAS5, ECMWF’s fifth generation seasonal forecast system, which became operational in November 2017. Compared to its predecessor, System 4, SEAS5 is a substantially changed forecast system. It includes upgraded versions of the atmosphere and ocean models at higher resolutions, and adds a prognostic sea ice model. Here, we describe the configuration of SEAS5 and summarise the most noticeable results from a set of diagnostics including biases, variability, teleconnections and forecast skill. An important improvement in SEAS5 is the reduction of the Equatorial Pacific cold tongue bias, which is accompanied by a more realistic ENSO amplitude and an improvement in ENSO prediction skill over the central-west Pacific. Improvements in two-metre temperature skill are also clear over the tropical Pacific. SST biases in the northern extratropics change due to increased ocean resolution, especially in regions associated with western boundary currents. The increased ocean resolution exposes a new problem in the northwest Atlantic, where SEAS5 fails to capture decadal variability of the North Atlantic subpolar gyre, resulting in a degradation of DJF two-metre temperature prediction skill in this region. The prognostic sea ice model improves seasonal predictions of sea ice cover, although some regions and seasons suffer from biases introduced by employing a fully dynamical model rather than the simple, empirical scheme used in System 4. There are also improvements in two-metre temperature skill in the vicinity of the Arctic sea-ice edge. Cold temperature biases in the troposphere improve, but increase at the tropopause. Biases in the extratropical jets are larger than in System 4: extratropical jets are too strong, and displaced northwards in summer. In summary, development and added complexity since System 4 has ensured SEAS5 is a state-of-the-art seasonal forecast system which continues to display a particular strength in ENSO prediction.

scholarly journals Evaluation of the Climate Forecast System Reanalysis weather data for the hydrological model in the Arctic watershed Målselv

2021 ◽  
Author(s):  
Minh Tuan Bui ◽  
Jinmei Lu ◽  
Linmei Nie
Keyword(s):  
High Resolution ◽  
Hydrological Model ◽  
The Arctic ◽  
Annual Rainfall ◽  
Climate Forecast ◽  
Hydrological Models ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis

Abstract The high-resolution Climate Forecast System Reanalysis (CFSR) data have recently become an alternative input for hydrological models in data-sparse regions. However, the quality of CFSR data for running hydrological models in the Arctic is not well studied yet. This paper aims to compare the quality of CFSR data with ground-based data for hydrological modeling in an Arctic watershed, Målselv. The QSWAT model, a coupling of the hydrological model SWAT (soil and water assessment tool) and the QGIS, was applied in this study. The model ran from 1995 to 2012 with a 3-year warm-up period (1995–1997). Calibration (1998–2007), validation (2008–2012), and uncertainty analyses were conducted by the model for each dataset at five hydro-gauging stations within the watershed. The objective function Nash–Sutcliffe coefficient of efficiency for calibration is 0.65–0.82 with CFSR data and 0.55–0.74 with ground-based data, which indicate higher performance of the high-resolution CFSR data than the existing scattered ground-based data. The CFSR weather grid points showed higher variation in precipitation than the ground-based weather stations across the whole watershed. The calculated average annual rainfall by CFSR data for the whole watershed is approximately 24% higher than that by ground-based data, which results in some higher water balance components. The CFSR data also demonstrate its high capacities to replicate the streamflow hydrograph, in terms of timing and magnitude of peak and low flow. Through examination of the uncertainty coefficients P-factors (≥0.7) and R-factors (≤1.5), this study concludes that CFSR data are a reliable source for running hydrological models in the Arctic watershed Målselv.

scholarly journals The Transient and Equilibrium Climate Response to Rapid Summertime Sea Ice Loss in CCSM4

2016 ◽  
Vol 29 (2) ◽  
pp. 401-417 ◽  
Author(s):  
Russell Blackport ◽  
Paul J. Kushner
Keyword(s):  
Standard Deviation ◽  
Sea Ice ◽  
Transient Response ◽  
Geopotential Height ◽  
General Circulation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Climate System Model ◽  
Study Results ◽  
The Impact

Abstract The impact that disappearing Arctic sea ice will have on the atmospheric circulation and weather variability remains uncertain. In this study, results are presented from a sea ice perturbation experiment using the coupled Community Climate System Model, version 4 (CCSM4). By decreasing the albedo of the sea ice, the impact of an ice-free summertime Arctic on the coupled ocean–atmosphere system is isolated in an idealized but energetically self-consistent way. The multicentury equilibrium response is examined, as well as the transient response in an initial condition ensemble. The perturbation drives pronounced year-round sea ice thinning, Arctic warming, Arctic amplification, and moderate global warming. Even in the almost complete absence of summertime sea ice, the atmospheric general circulation response is very weak and the transient response is small compared to the internal variability. Surface temperature variability is reduced on all time scales over most of the middle and high latitudes with a 50% reduction in the standard deviation of temperature over the Arctic Ocean. The reduction is attributed to decreased temperature gradients and increased maritime influence once the sea ice melts. This reduced variability extends weakly into the variability of the midlatitude and free tropospheric geopotential height (less than 10% reduction in the standard deviation). Consistently, eddy geopotential height variability is found to decrease while geopotential isopleth meandering, which reflects Arctic amplified warming, increases moderately. The sign of these changes is consistent with recent observations, but the size of these changes is relatively small.

Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system

2021 ◽  
Vol 40 (10) ◽  
pp. 65-75
Author(s):  
Qi Shu ◽  
Fangli Qiao ◽  
Jiping Liu ◽  
Zhenya Song ◽  
Zhiqiang Chen ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration

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

2008 ◽  
Vol 21 (15) ◽  
pp. 3755-3775 ◽  
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.

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

2011 ◽  
Vol 24 (9) ◽  
pp. 2319-2334 ◽  
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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