An Ensemble Adjustment Kalman Filter for the CCSM4 Ocean Component

2013 ◽  
Vol 26 (19) ◽  
pp. 7392-7413 ◽  
Author(s):  
Alicia R. Karspeck ◽  
Steve Yeager ◽  
Gokhan Danabasoglu ◽  
Tim Hoar ◽  
Nancy Collins ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Model Simulation ◽  
World Ocean ◽  
Internal Variability ◽  
Ocean Model ◽  
Ensemble Member ◽  
System Model ◽  
Climate System Model ◽  
Ensemble Adjustment Kalman Filter ◽  
Assimilation System

Abstract 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 1000 m 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.

scholarly journals The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation of the physical climate

2013 ◽  
Vol 6 (3) ◽  
pp. 687-720 ◽  
Author(s):  
M. Bentsen ◽  
I. Bethke ◽  
J. B. Debernard ◽  
T. Iversen ◽  
A. Kirkevåg ◽  
...  
Keyword(s):  
Model Performance ◽  
Internal Variability ◽  
Horizontal Resolution ◽  
Ocean Model ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Climate System Model ◽  
Model Stability ◽  
The University

Abstract. The core version of the Norwegian Climate Center's Earth System Model, named NorESM1-M, is presented. The NorESM family of models are based on the Community Climate System Model version 4 (CCSM4) of the University Corporation for Atmospheric Research, but differs from the latter by, in particular, an isopycnic coordinate ocean model and advanced chemistry–aerosol–cloud–radiation interaction schemes. NorESM1-M has a horizontal resolution of approximately 2° for the atmosphere and land components and 1° for the ocean and ice components. NorESM is also available in a lower resolution version (NorESM1-L) and a version that includes prognostic biogeochemical cycling (NorESM1-ME). The latter two model configurations are not part of this paper. Here, a first-order assessment of the model stability, the mean model state and the internal variability based on the model experiments made available to CMIP5 are presented. Further analysis of the model performance is provided in an accompanying paper (Iversen et al., 2013), presenting the corresponding climate response and scenario projections made with NorESM1-M.

scholarly journals The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation

2012 ◽  
Vol 5 (3) ◽  
pp. 2843-2931 ◽  
Author(s):  
M. Bentsen ◽  
I. Bethke ◽  
J. B. Debernard ◽  
T. Iversen ◽  
A. Kirkevåg ◽  
...  
Keyword(s):  
Model Performance ◽  
Internal Variability ◽  
Horizontal Resolution ◽  
Ocean Model ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Climate System Model ◽  
Model Stability ◽  
The University

Abstract. The core version of the Norwegian Climate Center's Earth System Model, named NorESM1-M, is presented. The NorESM-family of models are based on the Community Climate System Model version 4 (CCSM4) of the University Corporation for Atmospheric Research, but differs from the latter by, in particular, an isopycnic coordinate ocean model and advanced chemistry-aerosol-cloud-radiation interaction schemes. NorESM1-M has a horizontal resolution of approximately 2° for the atmosphere and land components and 1° for the ocean and ice components. NorESM is also available in a lower resolution version (NorESM1-L) and a version that includes prognostic biogeochemical cycling (NorESM1-ME). The latter two model configurations are not part of this paper. Here, a first-order assessment of the model stability, the mean model state and the internal variability based on the model experiments made available to CMIP5 are presented. Further analysis of the model performance is provided in an accompanying paper (Iversen et al., 2012), presenting the corresponding climate response and scenario projections made with NorESM1-M.

Satellite observation and climate system model simulation of the St. Lawrence Island polynya

1997 ◽  
Vol 49 (2) ◽  
pp. 277-297 ◽  
Author(s):  
AMANDA H. LYNCH ◽  
MARY F. GLUECK ◽  
WILLIAM L. CHAPMAN ◽  
DAVID A. BAILEY ◽  
JOHN E. WALSH
Keyword(s):  
Model Simulation ◽  
Satellite Observation ◽  
Climate System ◽  
System Model ◽  
Climate System Model

Coupling the Earth system model INMCM with the biogeochemical flux model

2018 ◽  
Vol 33 (6) ◽  
pp. 325-331
Author(s):  
Ilya A. Chernov ◽  
Nikolay G. Iakovlev
Keyword(s):  
Climate Model ◽  
World Ocean ◽  
Coupled Model ◽  
Horizontal Resolution ◽  
Ocean Model ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Time Step ◽  
The Earth

Abstract In the present paper we consider the first results of modelling the World Ocean biogeochemistry system within the framework of the Earth system model: a global atmosphere-ocean-ice-land-biogeochemistry model. It is based on the INMCM climate model (version INMCM39) coupled with the pelagic ecosystem model BFM. The horizontal resolution was relatively low: 2∘ × 2.5∘ for the ‘longitude’ and ‘latitude’ in transformed coordinates with the North Pole moved to land, 33 non-equidistant σ-horizons, 1 hour time step. We have taken into account 54 main rivers worldwide with run–off supplied by the atmosphere submodel. The setup includes nine plankton groups, 60 tracers in total. Some components sink with variable speed. We discuss challenges of coupling the BFM with the σ-coordinate ocean model. The presented results prove that the model output is realistic in comparison with the observed data, the numerical efficiency is high enough, and the coupled model may serve as a basis for further simulations of the long-term climate change.

Satellite observation and climate system model simulation of the St. Lawrence Island polynya

1997 ◽  
Vol 49 (2) ◽  
pp. 277-297 ◽  
Author(s):  
Amanda H. Lynch ◽  
Mary F. Glueck ◽  
William L. Chapman ◽  
David A. Bailey ◽  
John E. Walsh
Keyword(s):  
Model Simulation ◽  
Satellite Observation ◽  
Climate System ◽  
System Model ◽  
Climate System Model

scholarly journals An ensemble Kalman filter data assimilation system for the whole neutral atmosphere

2020 ◽  
Vol 13 (7) ◽  
pp. 3145-3177
Author(s):  
Dai Koshin ◽  
Kaoru Sato ◽  
Kazuyuki Miyazaki ◽  
Shingo Watanabe
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
General Circulation ◽  
Lower Thermosphere ◽  
Localization Length ◽  
Circulation Model ◽  
Ensemble Member ◽  
Neutral Atmosphere ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A data assimilation system with a four-dimensional local ensemble transform Kalman filter (4D-LETKF) is developed to make a new analysis dataset for the atmosphere up to the lower thermosphere using the Japanese Atmospherics General Circulation model for Upper Atmosphere Research. The time period from 10 January to 20 February 2017, when an international radar network observation campaign was performed, is focused on. The model resolution is T42L124, which can resolve phenomena at synoptic and larger scales. A conventional observation dataset provided by the National Centers for Environmental Prediction, PREPBUFR, and satellite temperature data from the Aura Microwave Limb Sounder (MLS) for the stratosphere and mesosphere are assimilated. First, the performance of the forecast model is improved by modifying the vertical profile of the horizontal diffusion coefficient and modifying the source intensity in the non-orographic gravity wave parameterization by comparing it with radar wind observations in the mesosphere. Second, the MLS observational bias is estimated as a function of the month and latitude and removed before the data assimilation. Third, data assimilation parameters, such as the degree of gross error check, localization length, inflation factor, and assimilation window, are optimized based on a series of sensitivity tests. The effect of increasing the ensemble member size is also examined. The obtained global data are evaluated by comparison with the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis data covering pressure levels up to 0.1 hPa and by the radar mesospheric observations, which are not assimilated.

scholarly journals Internal Model Variability of the Regional Coupled System Model GCOAST-AHOI

Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 227 ◽  
Author(s):  
Ha Thi Minh Ho-Hagemann ◽  
Stefan Hagemann ◽  
Sebastian Grayek ◽  
Ronny Petrik ◽  
Burkhardt Rockel ◽  
...  
Keyword(s):  
Internal Model ◽  
Climate Model ◽  
Extreme Event ◽  
Regional Climate ◽  
Coupled System ◽  
Internal Variability ◽  
Atmospheric Model ◽  
Ocean Model ◽  
System Model ◽  
Model Variability

Simulations of a Regional Climate Model (RCM) driven by identical lateral boundary conditions but initialized at different times exhibit the phenomenon of so-called internal model variability (or in short, Internal Variability—IV), which is defined as the inter-member spread between members in an ensemble of simulations. Our study investigates the effects of air-sea coupling on IV of the regional atmospheric model COSMO-CLM (CCLM) of the new regional coupled system model GCOAST-AHOI (Geesthacht Coupled cOAstal model SysTem: Atmosphere, Hydrology, Ocean and Sea Ice). We specifically address physical processes parameterized in CCLM, which may cause a large IV during an extreme event, and where this IV is affected by the air-sea coupling. Two six-member ensemble simulations were conducted with GCOAST-AHOI and the stand-alone CCLM (CCLM_ctr) for a period of 1 September–31 December 2013 over Europe. IV is expressed by spreads within the two sets of ensembles. Analyses focus on specific events during this period, especially on the storm Christian occurring from 27 to 29 October 2013 in northern Europe. Results show that simulations of CCLM_ctr vary largely amongst ensemble members during the storm. By analyzing two members of CCLM_ctr with opposite behaviors, we found that the large uncertainty in CCLM_ctr is caused by a combination of two factors (1) uncertainty in parameterization of cloud-radiation interaction in the atmospheric model. and (2) lack of an active two-way air-sea interaction. When CCLM is two-way coupled with the ocean model, the ensemble means of GCOAST-AHOI and CCLM_ctr are relatively similar, but the spread is reduced remarkably in GCOAST-AHOI, not only over the ocean where the coupling is done but also over land due to the land-sea interactions.

scholarly journals The influence of internal variability on Earth's energy balance framework and implications for estimating climate sensitivity

2018 ◽  
Vol 18 (7) ◽  
pp. 5147-5155 ◽  
Author(s):  
Andrew E. Dessler ◽  
Thorsten Mauritsen ◽  
Bjorn Stevens
Keyword(s):  
Energy Balance ◽  
Solar Energy ◽  
Climate Sensitivity ◽  
Internal Variability ◽  
Ensemble Member ◽  
System Model ◽  
Temperature Record ◽  
Max Planck ◽  
The Earth ◽  
Historical Temperature

Abstract. Our climate is constrained by the balance between solar energy absorbed by the Earth and terrestrial energy radiated to space. This energy balance has been widely used to infer equilibrium climate sensitivity (ECS) from observations of 20th-century warming. Such estimates yield lower values than other methods, and these have been influential in pushing down the consensus ECS range in recent assessments. Here we test the method using a 100-member ensemble of the Max Planck Institute Earth System Model (MPI-ESM1.1) simulations of the period 1850–2005 with known forcing. We calculate ECS in each ensemble member using energy balance, yielding values ranging from 2.1 to 3.9 K. The spread in the ensemble is related to the central assumption in the energy budget framework: that global average surface temperature anomalies are indicative of anomalies in outgoing energy (either of terrestrial origin or reflected solar energy). We find that this assumption is not well supported over the historical temperature record in the model ensemble or more recent satellite observations. We find that framing energy balance in terms of 500 hPa tropical temperature better describes the planet's energy balance.

scholarly journals A weighting scheme to incorporate large ensembles in multi-model ensemble projections

2019 ◽  
Author(s):  
Anna Louise Merrifield ◽  
Lukas Brunner ◽  
Ruth Lorenz ◽  
Reto Knutti
Keyword(s):  
Internal Variability ◽  
Ensemble Member ◽  
Earth System Model ◽  
System Model ◽  
Initial Condition ◽  
Earth System ◽  
Model Ensemble ◽  
Single Model ◽  
Large Ensembles ◽  
Model Ensembles

Abstract. Multi-model ensembles can be used to estimate uncertainty in projections of regional climate, but this uncertainty often depends on the constituents of the ensemble. The dependence of uncertainty on ensemble composition is clear when single model initial condition large ensembles (SMILEs) are included within a multi-model ensemble. SMILEs introduce new information into a multi-model ensemble by representing region-scale internal variability, but also introduce redundant information, by virtue of a single model being represented by 50–100 outcomes. To preserve the contribution of internal variability and ensure redundancy does not overwhelm uncertainty estimates, a weighting approach is used to incorporate 50-members of the Community Earth System Model (CESM1.2.2), 50-members of the Canadian Earth System Model (CanESM2), and 100-members of the MPI Grand Ensemble (MPI-GE) into an 88-member Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble. The weight assigned to each multi-model ensemble member is based on the member's ability to reproduce observed climate (performance) and scaled by a measure of redundancy (dependence). Surface air temperature (SAT) and sea level pressure (SLP) diagnostics are used to determine the weights, and relationships between present and future diagnostic behavior are discussed. A new diagnostic, estimated forced trend, is proposed to replace a diagnostic with no clear emergent relationship, 50-year regional SAT trend. The influence of the weighting is assessed in estimates of Northern European winter and Mediterranean summer end-of-century warming in the CMIP5 and combined SMILE-CMIP5 multi-model ensembles. The weighting is shown to recover uncertainty obscured by SMILE redundancy, notably in Mediterranean summer. For each SMILE, the independence weight of each ensemble member as a function of the number of SMILE members included in the CMIP5 ensemble is assessed. The independence weight increases linearly with added members with a slope that depends on SMILE, region, and season. Finally, it is shown that the weighting method can be used to guide SMILE member selection if a subsetted ensemble with one member per model is sought. The weight a SMILE receives within a subsetted ensemble depends on which member is used to represent it, reinforcing the advantage of weighting and incorporating all initial condition ensemble members in multi-model ensembles.

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