scholarly journals The Met Office Global Coupled model 2.0 (GC2) configuration

2015 ◽  
Vol 8 (5) ◽  
pp. 1509-1524 ◽  
Author(s):  
K. D. Williams ◽  
C. M. Harris ◽  
A. Bodas-Salcedo ◽  
J. Camp ◽  
R. E. Comer ◽  
...  
Keyword(s):  
Southern Oscillation ◽  
Coupled Model ◽  
Diagnostic Techniques ◽  
Seasonal Forecast ◽  
West African ◽  
Madden Julian Oscillation ◽  
Forecast System ◽  
Projection System ◽  
Modes Of Variability ◽  
Model Components

Abstract. The latest coupled configuration of the Met Office Unified Model (Global Coupled configuration 2, GC2) is presented. This paper documents the model components which make up the configuration (although the scientific description of these components is detailed elsewhere) and provides a description of the coupling between the components. The performance of GC2 in terms of its systematic errors is assessed using a variety of diagnostic techniques. The configuration is intended to be used by the Met Office and collaborating institutes across a range of timescales, with the seasonal forecast system (GloSea5) and climate projection system (HadGEM) being the initial users. In this paper GC2 is compared against the model currently used operationally in those two systems. Overall GC2 is shown to be an improvement on the configurations used currently, particularly in terms of modes of variability (e.g. mid-latitude and tropical cyclone intensities, the Madden–Julian Oscillation and El Niño Southern Oscillation). A number of outstanding errors are identified with the most significant being a considerable warm bias over the Southern Ocean and a dry precipitation bias in the Indian and West African summer monsoons. Research to address these is ongoing.

scholarly journals The Met Office Global Coupled model 2.0 (GC2) configuration

2015 ◽  
Vol 8 (1) ◽  
pp. 521-565 ◽  
Author(s):  
K. D. Williams ◽  
C. M. Harris ◽  
A. Bodas-Salcedo ◽  
J. Camp ◽  
R. E. Comer ◽  
...  
Keyword(s):  
Coupled Model ◽  
Systematic Errors ◽  
Diagnostic Techniques ◽  
Seasonal Forecast ◽  
Unified Model ◽  
Climate Projection ◽  
Forecast System ◽  
Projection System ◽  
Model Components ◽  
Global Coupled Model

Abstract. The latest coupled configuration of the Met Office Unified Model (Global Coupled configuration 2, GC2) is presented. This paper documents the model components which make up the configuration (although the scientific description of these components is detailed elsewhere) and provides a description of the coupling between the components. The performance of GC2 in terms of its systematic errors is assessed using a variety of diagnostic techniques. The configuration is intended to be used by the Met Office and collaborating institutes across a range of timescales, with the seasonal forecast system (GloSea5) and climate projection system (HadGEM) being the initial users. In this paper GC2 is compared against the model currently used operationally in those two systems.

scholarly journals The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2)

2020 ◽  
Vol 35 (4) ◽  
pp. 1317-1343 ◽  
Author(s):  
Hai Lin ◽  
William J. Merryfield ◽  
Ryan Muncaster ◽  
Gregory C. Smith ◽  
Marko Markovic ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Southern Oscillation ◽  
Weather Prediction ◽  
Coupled Model ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Boreal Winter ◽  
Prediction System ◽  
Sea Ice Concentration

AbstractThe second version of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2) was implemented operationally at Environment and Climate Change Canada (ECCC) in July 2019. Like its predecessors, CanSIPSv2 applies a multimodel ensemble approach with two coupled atmosphere–ocean models, CanCM4i and GEM-NEMO. While CanCM4i is a climate model, which is upgraded from CanCM4 of the previous CanSIPSv1 with improved sea ice initialization, GEM-NEMO is a newly developed numerical weather prediction (NWP)-based global atmosphere–ocean coupled model. In this paper, CanSIPSv2 is introduced, and its performance is assessed based on the reforecast of 30 years from 1981 to 2010, with 10 ensemble members of 12-month integrations for each model. Ensemble seasonal forecast skill of 2-m air temperature, 500-hPa geopotential height, precipitation rate, sea surface temperature, and sea ice concentration is assessed. Verification is also performed for the Niño-3.4, the Pacific–North American pattern (PNA), the North Atlantic Oscillation (NAO), and the Madden–Julian oscillation (MJO) indices. It is found that CanSIPSv2 outperforms the previous CanSIPSv1 system in many aspects. Atmospheric teleconnections associated with the El Niño–Southern Oscillation (ENSO) are reasonably well captured by the two CanSIPSv2 models, and a large part of the seasonal forecast skill in boreal winter can be attributed to the ENSO impact. The two models are also able to simulate the Northern Hemisphere teleconnection associated with the tropical MJO, which likely provides another source of skill on the subseasonal to seasonal time scale.

scholarly journals Simulation of ENSO in the New NCEP Coupled Forecast System Model (CFS03)

2005 ◽  
Vol 133 (6) ◽  
pp. 1574-1593 ◽  
Author(s):  
Wanqiu Wang ◽  
Suranjana Saha ◽  
Hua-Lu Pan ◽  
Sudhir Nadiga ◽  
Glenn White
Keyword(s):  
Southern Oscillation ◽  
Coupled Model ◽  
Forecast Model ◽  
Seasonal Prediction ◽  
Ocean Model ◽  
System Model ◽  
Enso Cycle ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Forecast System ◽  
Enso Events

Abstract A new global coupled atmosphere–ocean forecast system model (CFS03) has recently been developed at the National Centers for Environmental Prediction (NCEP). The new coupled model consists of a T62L64 version of the operational NCEP Atmospheric Global Forecast System model and the Geophysical Fluid Dynamics Laboratory Modular Ocean Model version 3, and is expected to replace the current NCEP operational coupled seasonal forecast model. This study assesses the performance of the new coupled model in simulating El Niño–Southern Oscillation (ENSO), which is considered to be a desirable feature for models used for seasonal prediction. The diagnoses indicate that the new coupled model simulates ENSO variability with realistic frequency. The amplitude of the simulated ENSO is similar to that of the observed strong events, but the ENSO events in the simulation occur more regularly than in observations. The model correctly simulates the observed ENSO seasonal phase locking with the peak amplitude near the end of the year. On average, however, simulated warm events tend to start about 3 months earlier and persist longer than observed. The simulated ENSO is consistent with the delayed oscillator, recharge oscillator, and advective–reflective oscillator theories, suggesting that each of these mechanisms may operate at the same time during the ENSO cycle. The diagnoses of the simulation indicate that the model may be suitable for real-time prediction of ENSO.

scholarly journals Assessment of Modes of Interannual Variability of Southern Hemisphere Atmospheric Circulation in CMIP5 Models

2014 ◽  
Vol 27 (21) ◽  
pp. 8107-8125 ◽  
Author(s):  
Simon Grainger ◽  
Carsten S. Frederiksen ◽  
Xiaogu Zheng
Keyword(s):  
Interannual Variability ◽  
Southern Hemisphere ◽  
Southern Oscillation ◽  
Slow Component ◽  
Coupled Model ◽  
Reanalysis Data ◽  
Cmip5 Models ◽  
Noise Component ◽  
Modes Of Variability ◽  
Multimodel Ensembles

Abstract An assessment is made of the modes of interannual variability in the seasonal mean summer and winter Southern Hemisphere (SH) 500-hPa geopotential height in the twentieth century in models from the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) dataset. Modes of variability of both the slow (signal) and intraseasonal (noise) components in the CMIP5 models are evaluated against those estimated from reanalysis data. There is general improvement in the leading modes of the slow (signal) component in CMIP5 models compared with the CMIP phase 3 (CMIP3) dataset. The largest improvement is in the spatial structures of the modes related to El Niño–Southern Oscillation variability in SH summer. An overall score metric is significantly higher for CMIP5 over CMIP3 in both seasons. The leading modes in the intraseasonal noise component are generally well reproduced in CMIP5 models, and there are few differences from CMIP3. A new total overall score metric is used to rank the CMIP5 models over both seasons. Weighting the seasons by the relative spread of overall scores is shown to be suitable for generating multimodel ensembles for further analysis of interannual variability. In multimodel ensembles, it is found that an ensemble of size 5 or 6 is sufficient in SH summer to reproduce well the dominant modes. In contrast, about 13 models are typically are required in SH winter. It is shown that it is necessary that the selected models individually reproduce well the leading modes of the slow component.

scholarly journals Sources of Subseasonal Predictability over CONUS during Boreal Summer

2021 ◽  
pp. 1-52
Author(s):  
V. Krishnamurthy ◽  
Jessica Meixner ◽  
Lydia Stefanova ◽  
Jiande Wang ◽  
Denise Worthen ◽  
...  
Keyword(s):  
Time Scale ◽  
Southern Oscillation ◽  
Intraseasonal Oscillation ◽  
Coupled Model ◽  
Warming Trend ◽  
Adaptive Method ◽  
Horizontal Wind ◽  
Boreal Summer ◽  
Forecast Errors ◽  
Modes Of Variability

AbstractThe predictability of the Unified Forecast System (UFS) Coupled Model Prototype 2 developed by the National Centers for Environmental Prediction is assessed for the boreal summer over the continental United States (CONUS). The retrospective forecasts of low-level horizontal wind, precipitation and 2m temperature for 2011–2017 are examined to determine the predictability at subseasonal time scale. Using a data-adaptive method, the leading modes of variability are obtained and identified to be related to El Niño-Southern Oscillation (ENSO), intraseasonal oscillation (ISO) and warming trend. In a new approach, the sources of enhanced predictability are identified by examining the forecast errors and correlations in the weekly averages of the leading modes of variability. During the boreal summer, the ISO followed by the trend in UFS are found to provide better predictability in weeks 1–4 compared to the ENSO mode and the total anomaly. The western CONUS seems to have better predictability on weekly time scale in all the three modes.

scholarly journals Representation of Modes of Variability in Six U.S. Climate Models

2020 ◽  
Vol 33 (17) ◽  
pp. 7591-7617 ◽  
Author(s):  
Clara Orbe ◽  
Luke Van Roekel ◽  
Ángel F. Adames ◽  
Amin Dezfuli ◽  
John Fasullo ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Climate Modeling ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Southern Annular Mode ◽  
Quasi Biennial Oscillation ◽  
Annular Mode ◽  
Modes Of Variability ◽  
The Pacific

AbstractWe compare the performance of several modes of variability across six U.S. climate modeling groups, with a focus on identifying robust improvements in recent models [including those participating in phase 6 of the Coupled Model Intercomparison Project (CMIP)] compared to previous versions. In particular, we examine the representation of the Madden–Julian oscillation (MJO), El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), the quasi-biennial oscillation (QBO) in the tropical stratosphere, and the dominant modes of extratropical variability, including the southern annular mode (SAM), the northern annular mode (NAM) [and the closely related North Atlantic Oscillation (NAO)], and the Pacific–North American pattern (PNA). Where feasible, we explore the processes driving these improvements through the use of “intermediary” experiments that utilize model versions between CMIP3/5 and CMIP6 as well as targeted sensitivity experiments in which individual modeling parameters are altered. We find clear and systematic improvements in the MJO and QBO and in the teleconnection patterns associated with the PDO and ENSO. Some gains arise from better process representation, while others (e.g., the QBO) from higher resolution that allows for a greater range of interactions. Our results demonstrate that the incremental development processes in multiple climate model groups lead to more realistic simulations over time.

scholarly journals Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation

2021 ◽  
Author(s):  
Mark D. Risser ◽  
Michael F. Wehner ◽  
John P. O’Brien ◽  
Christina M. Patricola ◽  
Travis A. O’Brien ◽  
...  
Keyword(s):  
Climate Variability ◽  
Extreme Precipitation ◽  
Southern Oscillation ◽  
Statistical Significance ◽  
In Situ Measurements ◽  
Climate Indices ◽  
Natural Climate Variability ◽  
Modes Of Variability ◽  
Natural Climate

AbstractWhile various studies explore the relationship between individual sources of climate variability and extreme precipitation, there is a need for improved understanding of how these physical phenomena simultaneously influence precipitation in the observational record across the contiguous United States. In this work, we introduce a single framework for characterizing the historical signal (anthropogenic forcing) and noise (natural variability) in seasonal mean and extreme precipitation. An important aspect of our analysis is that we simultaneously isolate the individual effects of seven modes of variability while explicitly controlling for joint inter-mode relationships. Our method utilizes a spatial statistical component that uses in situ measurements to resolve relationships to their native scales; furthermore, we use a data-driven procedure to robustly determine statistical significance. In Part I of this work we focus on natural climate variability: detection is mostly limited to DJF and SON for the modes of variability considered, with the El Niño/Southern Oscillation, the Pacific–North American pattern, and the North Atlantic Oscillation exhibiting the largest influence. Across all climate indices considered, the signals are larger and can be detected more clearly for seasonal total versus extreme precipitation. We are able to detect at least some significant relationships in all seasons in spite of extremely large (> 95%) background variability in both mean and extreme precipitation. Furthermore, we specifically quantify how the spatial aspect of our analysis reduces uncertainty and increases detection of statistical significance while also discovering results that quantify the complex interconnected relationships between climate drivers and seasonal precipitation.

scholarly journals Re-emergence of North Atlantic subsurface ocean temperature anomalies in a seasonal forecast system

2019 ◽  
Vol 53 (7-8) ◽  
pp. 4799-4820 ◽  
Author(s):  
Jeremy P. Grist ◽  
Bablu Sinha ◽  
Helene. T. Hewitt ◽  
Aurélie Duchez ◽  
Craig MacLachlan ◽  
...  
Keyword(s):  
North Atlantic ◽  
Seasonal Forecast ◽  
Ocean Temperature ◽  
Forecast System ◽  
Temperature Anomalies ◽  
Subsurface Ocean

Climate Index Weighting Schemes for NWS ESP-Based Seasonal Volume Forecasts

2004 ◽  
Vol 5 (6) ◽  
pp. 1076-1090 ◽  
Author(s):  
Kevin Werner ◽  
David Brandon ◽  
Martyn Clark ◽  
Subhrendu Gangopadhyay
Keyword(s):  
Nearest Neighbor ◽  
Southern Oscillation ◽  
Climate Index ◽  
Climate Information ◽  
Streamflow Prediction ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Weighting Schemes ◽  
Temperature And Precipitation ◽  
Spring Runoff

Abstract This study compares methods to incorporate climate information into the National Weather Service River Forecast System (NWSRFS). Three small-to-medium river subbasins following roughly along a longitude in the Colorado River basin with different El Niño–Southern Oscillation signals were chosen as test basins. Historical ensemble forecasts of the spring runoff for each basin were generated using modeled hydrologic states and historical precipitation and temperature observations using the Ensemble Streamflow Prediction (ESP) component of the NWSRFS. Two general methods for using a climate index (e.g., Niño-3.4) are presented. The first method, post-ESP, uses the climate index to weight ensemble members from ESP. Four different post-ESP weighting schemes are presented. The second method, preadjustment, uses the climate index to modify the temperature and precipitation ensembles used in ESP. Two preadjustment methods are presented. This study shows the distance-sensitive nearest-neighbor post-ESP to be superior to the other post-ESP weighting schemes. Further, for the basins studied, forecasts based on post-ESP techniques outperformed those based on preadjustment techniques.

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