The mean and variability of the Atlantic meridional overturning circulation (AMOC), as represented in six ocean reanalysis products, are analyzed over the period 1960–2007. Particular focus is on ...multi-decadal trends and interannual variability at 26.5°N and 45°N. For four of the six reanalysis products, corresponding reference simulations obtained from the same models and forcing datasets but without the imposition of subsurface data constraints are included for comparison. An emphasis is placed on identifying general characteristics of the reanalysis representation of AMOC relative to their reference simulations without subsurface data constraints. The AMOC as simulated in these two sets are presented in the context of results from the Coordinated Ocean-ice Reference Experiments phase II (CORE-II) effort, wherein a common interannually varying atmospheric forcing data set was used to force a large and diverse set of global ocean-ice models. Relative to the reference simulations and CORE-II forced model simulations it is shown that (1) the reanalysis products tend to have greater AMOC mean strength and enhanced variance and (2) the reanalysis products are
less
consistent in their year-to-year AMOC changes. We also find that relative to the reference simulations (but not the CORE-II forced model simulations) the reanalysis products tend to have enhanced multi-decadal trends (from 1975–1995 to 1995–2007) in the mid to high latitudes of the northern hemisphere.
PREDICTING NEAR-TERM CHANGES IN THE EARTH SYSTEM Yeager, S. G.; Danabasoglu, G.; Rosenbloom, N. A. ...
Bulletin of the American Meteorological Society,
09/2018, Letnik:
99, Številka:
9
Journal Article
Recenzirano
Odprti dostop
The objective of near-term climate prediction is to improve our fore-knowledge, from years to a decade or more in advance, of impactful climate changes that can in general be attributed to a ...combination of internal and externally forced variability. Predictions initialized using observations of past climate states are tested by comparing their ability to reproduce past climate evolution with that of uninitialized simulations in which the same radiative forcings are applied. A new set of decadal prediction (DP) simulations has recently been completed using the Community Earth System Model (CESM) and is now available to the community. This new large-ensemble (LE) set (CESM-DPLE) is composed of historical simulations that are integrated forward for 10 years following initialization on 1 November of each year between 1954 and 2015. CESM-DPLE represents the “initialized” counterpart to the widely studied CESM Large Ensemble (CESM-LE); both simulation sets have 40-member ensembles, and they use identical model code and radiative forcings. Comparing CESM-DPLE to CESM-LE highlights the impacts of initialization on prediction skill and indicates that robust assessment and interpretation of DP skill may require much larger ensembles than current protocols recommend. CESM-DPLE exhibits significant and potentially useful prediction skill for a wide range of fields, regions, and time scales, and it shows widespread improvement over simpler benchmark forecasts as well as over a previous initialized system that was submitted to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The new DP system offers new capabilities that will be of interest to a broad community pursuing Earth system prediction research.
Coupled climate models initialized from historical climate states and subject to anthropogenic forcings can produce skillful decadal predictions of sea surface temperature change in the subpolar ...North Atlantic. The skill derives largely from initialization, which improves the representation of slow changes in ocean circulation and associated poleward heat transport. We show that skillful predictions of decadal trends in Arctic winter sea ice extent are also possible, particularly in the Atlantic sector. External radiative forcing contributes to the skill of retrospective decadal sea ice predictions, but the spatial and temporal accuracy is greatly enhanced by the more realistic representation of ocean heat transport anomalies afforded by initialization. Recent forecasts indicate that a spin‐down of the thermohaline circulation that began near the turn of the century will continue, and this will result in near‐neutral decadal trends in Atlantic winter sea ice extent in the coming years, with decadal growth in select regions.
Key Points
Ocean thermohaline circulation variations drove recent decadal Arctic winter sea ice trends
Initialized climate model ensembles can skillfully predict Arctic winter sea ice trends
Decadal trends in Atlantic winter sea ice will be neutral or positive in the near future
Least squares algorithms for data assimilation require estimates of both background error covariances and observational error covariances. The specification of these errors is an essential part of ...designing an assimilation system; the relative sizes of these uncertainties determine the extent to which the state variables are drawn toward the observational information. Observational error covariances are typically computed as the sum of measurement/instrumental errors and “representativeness error.” In a coarse-resolution ocean general circulation model the errors of representation are the dominant contribution to observational error covariance over large portions of the globe, and the size of these errors will vary by the type of observation and the geographic region. They may also vary from model to model. A straightforward approach for estimating model-dependent, spatially varying observational error variances that are suitable for least squares ocean data assimilating systems is presented here. The author proposes an ensemble-based estimator of the true observational error variance and outlines the assumptions necessary for the estimator to be unbiased. The author also presents the variance (or uncertainty) associated with the estimator under certain conditions. The analytic expressions for the expected value and variance of the estimator are validated with a simple autoregressive model and illustrated for the nominal 1° resolution POP2 global ocean general circulation model.
The assimilation of sea surface temperature (SST) anomalies into a coupled ocean–atmosphere model of the tropical Pacific is investigated using an ensemble adjustment Kalman filter (EAKF). The ...intermediate coupled model used here is the operational version of the Zebiak–Cane model, called LDEO5. The assimilation is applied as a means of estimating the true state of the system in the presence of incomplete observations of the state.
In the first part of this study assimilation is performed under the “perfect model” assumption, where SST observations are synthetically derived from a trajectory of the model. The focus is on how and why changes in the filter parameters (ensemble size, covariance localization, and covariance inflation) affect the quality of the analysis. It is shown that isotropic covariance localization does not benefit the analysis even when a small number of ensemble members are used. These results suggest that destruction of the “balance” between variables caused by localization is more detrimental than spurious correlation due to small ensemble size.
In the second part of this study the EAKF is used to assimilate an independent dataset of SST observations. The EAKF/Zebiak–Cane assimilation system is able to correctly estimate the phase and intensity of ENSO, as measured by the average SST anomaly in the eastern equatorial Pacific. A comparison of the analysis herein to independent wind stress and thermocline depth datasets suggests that even with the assimilation of only SST observations it is possible to reproduce over 70% of the interannual variability of thermocline depth in the eastern equatorial Pacific and off the coast of the Philippine Islands. The interannual variability of zonal wind stress in the central and western equatorial Pacific is also well correlated with independent observations (R> 0.75).
The authors report on the implementation and evaluation of a 48-member ensemble adjustment Kalman filter (EAKF) for the ocean component of the Community Climate System Model, version 4 (CCSM4). The ...ocean assimilation system described was developed to support the eventual generation of historical ocean-state estimates and ocean-initialized climate predictions with the CCSM4 and its next generation, the Community Earth System Model (CESM). In this initial configuration of the system, daily subsurface temperature and salinity data from the 2009 World Ocean Database are assimilated into the ocean model from 1 January 1998 to 31 December 2005. Each ensemble member of the ocean is forced by a member of an independently generated CCSM4 atmospheric EAKF analysis, making this a loosely coupled framework. Over most of the globe, the time-mean temperature and salinity fields are improved relative to an identically forced ocean model simulation without assimilation. This improvement is especially notable in strong frontal regions such as the western and eastern boundary currents. The assimilation system is most effective in the upper 1000m of the ocean, where the vast majority of in situ observations are located. Because of the shortness of this experiment, ocean variability is not discussed. Challenges that arise from using an ocean model with strong regional biases, coarse resolution, and low internal variability to assimilate real observations are discussed, and areas of ongoing improvement for the assimilation system are outlined.
This paper presents a description of the CESM/DART ensemble coupled data assimilation (DA) system based on the Community Earth System Model (CESM) and the Data Assimilation Research Testbed (DART) ...assimilation software. The CESM/DART should be viewed as a flexible system to support the DA needs of the CESM research community and not as a static reanalysis product.
In this implementation of the CESM/DART, conventional insitu observations of the ocean and atmosphere are assimilated into the respective component models of the CESM using a 30‐member ensemble adjustment Kalman filter (EAKF). CESM/DART is run in a “weakly coupled” configuration wherein observations native to each climate system component only directly impact the state vector for that component. Information is passed between components indirectly through the short‐term coupled model forecasts that provide the EAKF background ensemble. This system leverages previous ensemble DA development for the Community Atmosphere Model and Parallel Ocean Program models using the DART EAKF. The CESM/DART project is a step towards providing increasingly useful DA capabilities for the CESM research community.
Results are presented for our prototype 12‐year reanalysis, run from 1970 to mid 1982. Multiple lines of evidence demonstrate that the system is capable of constraining the CESM coupled model to simulate the historical variability of the climate system in the well‐observed Northern Hemisphere. A collection of monthly average variables, climate mode indices, observation diagnostics and snapshots of synoptic weather in the ocean and atmosphere are compared to established datasets, showing especially good agreement in the Northern Hemisphere. A discussion of the CESM/DART as a modular, community facility and the benefits and challenges associated with this vision is also included.
This work describes the newly developed ensemble coupled data assimilation system for the Community Earth System Model using the Data Assimilation Research Testbed (CESM/DART) and evaluates the performance of a 12‐year experiment assimilating conventional oceanic and atmospheric observations. Multiple lines of evidence demonstrate that the system is capable of constraining the CESM coupled model to simulate the historical variability of the climate system over much of the globe and especially in the well‐observed Northern Hemisphere. The Niño3 index (average SST from 5°N to 5°S and 90 to 150°W) as simulated in the 12‐year experimental ensemble coupled assimilation using the CESM model. For comparison, NoAssim is a free‐running coupled integration of the CESM model (initialized from the same ocean/atmosphere state). The HADISST dataset is not ingested in the data assimilation system.
An ensemble seasonal hindcast approach is used to investigate the development of the equatorial Pacific Ocean cold sea surface temperature (SST) bias and its characteristic annual cycle in the ...Community Earth System Model, version 1 (CESM1). In observations, eastern equatorial Pacific SSTs exhibit a warm phase during boreal spring and a cold phase during late boreal summer–autumn. The CESM1 climatology shows a cold bias during both warm and cold phases. In our hindcasts, the cold bias during the cold phase develops in less than 6 months, whereas the cold bias during the warm phase takes longer to emerge. The fast-developing cold-phase cold bias is associated with too-strong vertical advection and easterly wind stress over the eastern equatorial region. The antecedent boreal summer easterly wind anomalies also appear in atmosphere-only simulations, indicating that the errors are intrinsic to the atmosphere component. For the slower-developing warm-phase cold bias, we find that the too-cold SSTs over the equatorial region are associated with a slowly evolving upward displacement of subsurface ocean zonal currents and isotherms that can be traced to the ocean component.
DECADAL CLIMATE PREDICTION Meehl, Gerald A.; Goddard, Lisa; Boer, George ...
Bulletin of the American Meteorological Society,
02/2014, Letnik:
95, Številka:
2
Journal Article
Recenzirano
Odprti dostop
This paper provides an update on research in the relatively new and fast-moving field of decadal climate prediction, and addresses the use of decadal climate predictions not only for potential users ...of such information but also for improving our understanding of processes in the climate system. External forcing influences the predictions throughout, but their contributions to predictive skill become dominant after most of the improved skill from initialization with observations vanishes after about 6–9 years. Recent multimodel results suggest that there is relatively more decadal predictive skill in the North Atlantic, western Pacific, and Indian Oceans than in other regions of the world oceans. Aspects of decadal variability of SSTs, like the mid-1970s shift in the Pacific, the mid-1990s shift in the northern North Atlantic and western Pacific, and the early-2000s hiatus, are better represented in initialized hindcasts compared to uninitialized simulations. There is evidence of higher skill in initialized multimodel ensemble decadal hindcasts than in single model results, with multimodel initialized predictions for near-term climate showing somewhat less global warming than uninitialized simulations. Some decadal hindcasts have shown statistically reliable predictions of surface temperature over various land and ocean regions for lead times of up to 6–9 years, but this needs to be investigated in a wider set of models. As in the early days of El Niño–Southern Oscillation (ENSO) prediction, improvements to models will reduce the need for bias adjustment, and increase the reliability, and thus usefulness, of decadal climate predictions in the future.
The seasonal and interannual predictability of ENSO variability in a version of the Zebiak–Cane coupled model is examined in a perturbation experiment. Instead of assuming that the model is ...“perfect,” it is assumed that a set of optimal initial conditions exists for the model. These states, obtained through a nonlinear minimization of the misfit between model trajectories and the observations, initiate model forecasts that correlate well with the observations. Realistic estimates of the observational error magnitudes and covariance structures of sea surface temperatures, zonal wind stress, and thermocline depth are used to generate ensembles of perturbations around these optimal initial states, and the error growth is examined. The error growth in response to subseasonal stochastic wind forcing is presented for comparison.
In general, from 1975 to 2002, the large-scale uncertainty in initial conditions leads to larger error growth than continuous stochastic forcing of the zonal wind stress fields. Forecast ensemble spread is shown to depend most on the calendar month at the end of the forecast rather than the initialization month, with the seasons of greatest spread corresponding to the seasons of greatest anomaly variance. It is also demonstrated that during years with negative (and rapidly decaying) Niño-3 SST anomalies (such as the time period following an El Niño event), there is a suppression of error growth. In years with large warm ENSO events, the ensemble spread is no larger than in moderately warm years. As a result, periods with high ENSO variance have greater potential prediction utility.
In the realistic range of observational error, the ensemble spread has more sensitivity to the initial error in the thermocline depth than to the sea surface temperature or wind stress errors. The thermocline depth uncertainty is the principal reason why initial condition uncertainties are more important than wind noise for ensemble spread.
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