Results are described from a large sample of coupled ocean-atmosphere retrospective forecasts during 1982-98. The prediction system is based on the National Center for Atmospheric Research (NCAR) ...Community Climate System Model, version 3 (CCSM3.0), and a state-of-the-art ocean data assimilation system made available by the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL). The retrospective forecasts are initialized in January, April, July, and November of each year, and ensembles of 6 forecasts are run for each initial month, yielding a total of 408 1-yr predictions. In generating the ensemble members, perturbations are added to the atmospheric initial state only. The skill of the prediction system is analyzed from both a deterministic and a probabilistic perspective, it is then compared to the operational NOAA Climate Forecast System (CFS), and the forecasts are combined with CFS to produce a multimodel prediction system. While the skill scores for each model are highly dependent on lead time and initialization month, the overall level of skill of the individual models is quite comparable. The multimodel combination (i.e., the unweighted average of the forecast), while not always the most skillful, is generally as skillful as the best model, using either deterministic or probabilistic skill metrics. PUBLICATION ABSTRACT
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THE SUBSEASONAL EXPERIMENT (SubX) Pegion, Kathy; Kirtman, Ben P.; Becker, Emily ...
Bulletin of the American Meteorological Society,
10/2019, Volume:
100, Issue:
10
Journal Article
Peer reviewed
Open access
The Subseasonal Experiment (SubX) is a multimodel subseasonal prediction experiment designed around operational requirements with the goal of improving subseasonal forecasts. Seven global models have ...produced 17 years of retrospective (re)forecasts and more than a year of weekly real-time forecasts. The reforecasts and forecasts are archived at the Data Library of the International Research Institute for Climate and Society, Columbia University, providing a comprehensive database for research on subseasonal to seasonal predictability and predictions. The SubX models show skill for temperature and precipitation 3 weeks ahead of time in specific regions. The SubX multimodel ensemble mean is more skillful than any individual model overall. Skill in simulating the Madden–Julian oscillation (MJO) and the North Atlantic Oscillation (NAO), two sources of subseasonal predictability, is also evaluated, with skillful predictions of the MJO 4 weeks in advance and of the NAO 2 weeks in advance. SubX is also able to make useful contributions to operational forecast guidance at the Climate Prediction Center. Additionally, SubX provides information on the potential for extreme precipitation associated with tropical cyclones, which can help emergency management and aid organizations to plan for disasters.
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Series of forecast experiments for two seasons investigate the impact of specifying realistic initial states of the land in conjunction with the observed states of the ocean and atmosphere while ...using the National Center for Atmospheric Research (NCAR) Community Climate System Model, version 3 (CCSM3.0). Since direct soil moisture observations adequate for initialization of the land surface do not exist, this study considers proxy data. The authors are able to successfully initialize all components of the CCSM3.0 and produce a good representation of the mean land surface climate in the first season’s forecast. In comparison with a previous set of forecast experiments that had initialized only the observed ocean state, there is firm evidence that this study produces a better representation of the interannual variability of the soil surface. The representation of soil moisture in the fully initialized seasonal forecasts as measured against the reanalysis is improved, due in part to the ability of the CCSM3.0 to persist large-scale anomalies present in the initial soil state. The improvement in the representation of the land surface, in conjunction with the atmospheric initialization, contributes to a skillful seasonal forecast of surface temperature. There is little evidence of an improved forecast of precipitation over land. Results from this study support the use of the CCSM, originally designed for use as a climate model, as a fully initialized seasonal forecast model. The authors suggest that initialization of the land surface state is crucial for skillful seasonal forecasts made with fully coupled models.
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Diagnosing how much low frequency climate variability is due to intrinsic coupled (i.e., interactions among the components of the climate system) modes and how much is stochastically forced by ...internal dynamics (e.g., weather noise forcing ocean variability or ocean dynamics associated with western boundary current forcing atmospheric variability) remains a challenge in climate research. Here we present a methodology for separating the intrinsic coupled modes from the stochastically forced variability that can be applied at the air‐sea, air‐land, air‐ice or ice‐ocean interface. In the results presented here, we focus on the air‐sea interface and apply the approach to the National Center for Atmospheric Research Community Climate System Model. We find that coupled ocean‐atmosphere feedbacks contribute to a significant fraction of the sea surface temperature variability worldwide with increasing importance in the tropics. One of the by‐products of the experiments presented here is an improved diagnostic tool for understanding atmospheric teleconnections. In this regard, we find that the mid‐latitude atmospheric response to tropical forcing is not simply a function of the magnitude of the forcing.
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A newly developed Atmospheric General Circulation Model (AGCM) at T62 spectral truncation with 28 terrainfollowing
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σ
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p
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levels coupled to the Modular Ocean Model version 3.0 (MOM3.0) is ...evaluated for its simulation of El Ni˜no and the Southern Oscillation (ENSO). It is also compared with an older version of the AGCM coupled to the same ocean model. A dozen features of ENSO are validated. These characteristics of ENSO highlight its influence on global climate at seasonal to interannual scales.
The major improvements of the ENSO simulation from this new coupled climate model are the seasonal phase locking of the ENSO variability to a realistic annual cycle of the eastern equatorial Pacific Ocean, the duration of the ENSO events and its evolution that is comparable to the ocean data assimilation. The two apparent drawbacks of this new model are its relatively weak ENSO variability and the presence of erroneous split ITCZ.
The improvement of the ENSO simulation in the new coupled model is attributed to realistic thermocline variability and wind stress simulation.
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THE NORTH AMERICAN MULTIMODEL ENSEMBLE Kirtman, Ben P.; Min, Dughong; Infanti, Johnna M. ...
Bulletin of the American Meteorological Society,
04/2014, Volume:
95, Issue:
4
Journal Article
Peer reviewed
Open access
The recent U.S. National Academies report,Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North ...American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users.
The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model.
Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2011), a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data are readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (www.cpc.ncep.noaa.gov/products/NMME/). Moreover, the NMME forecast is already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, and presents an overview of the multimodel forecast quality and the complementary skill associated with individual models.
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The new interactive ensemble modeling strategy is used to diagnose how noise due to internal atmospheric dynamics impacts the forced climate response during the twentieth century (i.e., 1870–1999). ...The interactive ensemble uses multiple realizations of the atmospheric component model coupled to a single realization of the land, ocean and ice component models in order to reduce the noise due to internal atmospheric dynamics in the flux exchange at the interface of the component models. A control ensemble of so-called climate of the twentieth century simulations of the Community Climate Simulation Model version 3 (CCSM3) are compared with a similar simulation with the interactive ensemble version of CCSM3. Despite substantial differences in the overall mean climate, the global mean trends in surface temperature, 500 mb geopotential and precipitation are largely indistinguishable between the control ensemble and the interactive ensemble. Large differences in the forced response; however, are detected particularly in the surface temperature of the North Atlantic. Associated with the forced North Atlantic surface temperature differences are local differences in the forced precipitation and a substantial remote rainfall response in the deep tropical Pacific. We also introduce a simple variance analysis to separately compare the variance due to noise and the forced response. We find that the noise variance is decreased when external forcing is included. In terms of the forced variance, we find that the interactive ensemble increases this variance relative to the control.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
SubX is a multi-model subseasonal prediction experiment designed around operational requirements with the goal of improving subseasonal forecasts. Seven global models have produced seventeen years of ...retrospective (re-) forecasts and more than a year of weekly real-time forecasts. The re-forecasts and forecasts are archived at the Data Library of the International Research Institute for Climate and Society, Columbia University, providing a comprehensive database for research on subseasonal to seasonal predictability and predictions. The SubX models show skill for temperature and precipitation three weeks ahead of time in specific regions. The SubX multi-model ensemble mean is more skillful than any individual model overall. Skill in simulating the Madden-Julian Oscillation (MJO) and the North Atlantic Oscillation (NAO), two sources of subseasonal predictability, is also evaluated with skillful predictions of the MJO four weeks in advance and of the NAO 2 weeks in advance. SubX is also able to make useful contributions to operational forecast guidance at the Climate Prediction Center. Additionally, SubX provides information on the potential for extreme precipitation associated with tropical cyclones which can help emergency management and aid organizations to plan for disasters. (Capsule Summary) A research to operations project in service of developing better operational subseasonal forecasts.
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10.
Monsoon Regimes in the CCSM3 Meehl, Gerald A.; Arblaster, Julie M.; Lawrence, David M. ...
Journal of climate,
06/2006, Volume:
19, Issue:
11
Journal Article
Peer reviewed
Open access
Simulations of regional monsoon regimes, including the Indian, Australian, West African, South American, and North American monsoons, are described for the T85 version of the Community Climate System ...Model version 3 (CCSM3) and compared to observations and Atmospheric Model Intercomparison Project (AMIP)-type SST-forced simulations with the Community Atmospheric Model version 3 (CAM3) at T42 and T85. There are notable improvements in the regional aspects of the precipitation simulations in going to the higher-resolution T85 compared to T42 where topography is important (e.g., Ethiopian Highlands, South American Andes, and Tibetan Plateau). For the T85 coupled version of CCSM3, systematic SST errors are associated with regional precipitation errors in the monsoon regimes of South America and West Africa, though some aspects of the monsoon simulations, particularly in Asia, improve in the coupled model compared to the SST-forced simulations. There is very little realistic intraseasonal monsoon variability in the CCSM3 consistent with earlier versions of the model. Teleconnections to the tropical Pacific are well simulated for the South Asian monsoon.
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