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Gabriel A. Vecchi and Matthew J. Harrison

Abstract

An integrated in situ Indian Ocean observing system (IndOOS) is simulated using a high-resolution ocean general circulation model (OGCM) with daily mean forcing, including an estimate of subdaily oceanic variability derived from observations. The inclusion of subdaily noise is fundamental to the results; in the mixed layer it is parameterized as Gaussian noise with an rms of 0.1°C; below the mixed layer a Gaussian interface displacement with an rms of 7 m is used. The focus of this assessment is on the ability of an IndOOS—comprising a 3° × 3° Argo profiling float array, a series of frequently repeated XBT lines, and an array of moored buoys—to observe the interannual and subseasonal variability of subsurface Indian Ocean temperature. The simulated IndOOS captures much of the OGCM interannual subsurface temperature variability.

A fully deployed Argo array with 10-day sampling interval is able to capture a significant part of the Indian Ocean interannual temperature variability; a 5-day sampling interval degrades its ability to capture variability. The proposed moored buoy array and frequently repeated XBT lines provide complementary information in key regions, particularly the Java/Sumatra and Somali upwelling and equatorial regions. Since the subdaily noise is of the same order as the subseasonal signal and since much of the variability is submonthly, a 5-day sampling interval does not drastically enhance the ability of Argo to capture the OGCM subseasonal variability. However, as sampling intervals are decreased, there is enhanced divergence of the Argo floats, diminished ability to quality control data, and a decreased lifetime of the floats; these factors argue against attempting to resolve subseasonal variability with Argo by shortening the sampling interval. A moored array is essential to capturing the subseasonal and near-equatorial variability in the model, and the proposed moored buoy locations span the region of strong subseasonal variability. On the whole, the proposed IndOOS significantly enhances the ability to capture both interannual and subseasonal variability in the Indian Ocean.

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Eli Galanti, Eli Tziperman, Matthew Harrison, Anthony Rosati, and Ziv Sirkes

Abstract

An experimental ENSO prediction system is presented, based on an ocean general circulation model (GCM) coupled to a statistical atmosphere and the adjoint method of 4D variational data assimilation. The adjoint method is used to initialize the coupled model, and predictions are performed for the period 1980–99. The coupled model is also initialized using two simpler assimilation techniques: forcing the ocean model with observed sea surface temperature and surface fluxes, and a 3D variational data assimilation (3DVAR) method, similar to that used by the National Centers for Environmental Prediction (NCEP) for operational ENSO prediction. The prediction skill of the coupled model initialized by the three assimilation methods is then analyzed and compared. The effect of the assimilation period used in the adjoint method is studied by using 3-, 6-, and 9-month assimilation periods. Finally, the possibility of assimilating only the anomalies with respect to observed climatology in order to circumvent systematic model biases is examined.

It is found that the adjoint method does seem to have the potential for improving over simpler assimilation schemes. The improved skill is mainly at prediction intervals of more than 6 months, where the coupled model dynamics start to influence the model solution. At shorter prediction time intervals, the initialization using the forced ocean model or the 3DVAR may result in a better prediction skill. The assimilation of anomalies did not have a substantial effect on the prediction skill of the coupled model. This seems to indicate that in this model the climatology bias, which is compensated for by the anomaly assimilation, is less significant for the predictive skill than the bias in the model variability, which cannot be eliminated using the anomaly assimilation. Changing the optimization period from 6 to 3 to 9 months showed that the period of 6 months seems to be a near-optimal choice for this model.

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Eli Galanti, Eli Tziperman, Matthew Harrison, Antony Rosati, Ralf Giering, and Ziv Sirkes

Abstract

One of the major factors determining the strength and extent of ENSO events is the instability state of the equatorial Pacific coupled ocean–atmosphere system and its seasonal variations. This study analyzes the coupled instability in a hybrid coupled model of the Indo–Pacific region, using the adjoint method for sensitivity studies.

It is found that the seasonal changes in the ocean–atmosphere instability strength in the model used here are related to the outcropping of the thermocline in the east equatorial Pacific. From July to December, when the thermocline outcrops over a wide area in the east Pacific, there is a strong surface–thermocline connection and anomalies that arrive as Kelvin waves from the west along the thermocline can reach the surface and affect the SST and thus the coupled system. Conversely, from February to June, when the thermocline outcropping is minimal, the surface decouples from the thermocline and temperature anomalies in the thermocline depth range do not affect the surface and dissipate within the thermocline. The role of vertical mixing rather than upwelling in linking vertical thermocline movements to SST changes is emphasized.

It is therefore suggested that the seasonal ocean–atmosphere instability strength in the equatorial Pacific is strongly influenced by the thermocline outcropping and its seasonal modulation, a physical mechanism that is often neglected in intermediate coupled models and that can be represented properly only in models that employ the full dynamics of the mixed layer.

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Colm Sweeney, Anand Gnanadesikan, Stephen M. Griffies, Matthew J. Harrison, Anthony J. Rosati, and Bonita L. Samuels

Abstract

The impact of changes in shortwave radiation penetration depth on the global ocean circulation and heat transport is studied using the GFDL Modular Ocean Model (MOM4) with two independent parameterizations that use ocean color to estimate the penetration depth of shortwave radiation. Ten to eighteen percent increases in the depth of 1% downwelling surface irradiance levels results in an increase in mixed layer depths of 3–20 m in the subtropical and tropical regions with no change at higher latitudes. While 1D models have predicted that sea surface temperatures at the equator would decrease with deeper penetration of solar irradiance, this study shows a warming, resulting in a 10% decrease in the required restoring heat flux needed to maintain climatological sea surface temperatures in the eastern equatorial Atlantic and Pacific Oceans. The decrease in the restoring heat flux is attributed to a slowdown in heat transport (5%) from the Tropics and an increase in the temperature of submixed layer waters being transported into the equatorial regions. Calculations were made using a simple relationship between mixed layer depth and meridional mass transport. When compared with model diagnostics, these calculations suggest that the slowdown in heat transport is primarily due to off-equatorial increases in mixed layer depths. At higher latitudes (5°–40°), higher restoring heat fluxes are needed to maintain sea surface temperatures because of deeper mixed layers and an increase in storage of heat below the mixed layer. This study offers a way to evaluate the changes in irradiance penetration depths in coupled ocean–atmosphere GCMs and the potential effect that large-scale changes in chlorophyll a concentrations will have on ocean circulation.

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Mitchell Bushuk, Xiaosong Yang, Michael Winton, Rym Msadek, Matthew Harrison, Anthony Rosati, and Rich Gudgel

ABSTRACT

Dynamical prediction systems have shown potential to meet the emerging need for seasonal forecasts of regional Arctic sea ice. Observationally constrained initial conditions are a key source of skill for these predictions, but the direct influence of different observation types on prediction skill has not yet been systematically investigated. In this work, we perform a hierarchy of observing system experiments with a coupled global data assimilation and prediction system to assess the value of different classes of oceanic and atmospheric observations for seasonal sea ice predictions in the Barents Sea. We find notable skill improvements due to the inclusion of both sea surface temperature (SST) satellite observations and subsurface conductivity–temperature–depth (CTD) measurements. The SST data are found to provide the crucial source of interannual variability, whereas the CTD data primarily provide climatological and trend improvements. Analysis of the Barents Sea ocean heat budget suggests that ocean heat content anomalies in this region are driven by surface heat fluxes on seasonal time scales.

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Chaojiao Sun, Michele M. Rienecker, Anthony Rosati, Matthew Harrison, Andrew Wittenberg, Christian L. Keppenne, Jossy P. Jacob, and Robin M. Kovach

Abstract

Two global ocean analyses from 1993 to 2001 have been generated by the Global Modeling and Assimilation Office (GMAO) and Geophysical Fluid Dynamics Laboratory (GFDL), as part of the Ocean Data Assimilation for Seasonal-to-Interannual Prediction (ODASI) consortium efforts. The ocean general circulation models (OGCM) and assimilation methods in the analyses are different, but the forcing and observations are the same as designed for ODASI experiments. Global expendable bathythermograph and Tropical Atmosphere Ocean (TAO) temperature profile observations are assimilated. The GMAO analysis also assimilates synthetic salinity profiles based on climatological TS relationships from observations (denoted “TS scheme”). The quality of the two ocean analyses in the tropical Pacific is examined here. Questions such as the following are addressed: How do different assimilation methods impact the analyses, including ancillary fields such as salinity and currents? Is there a significant difference in interpretation of the variability from different analyses? How does the treatment of salinity impact the analyses? Both GMAO and GFDL analyses reproduce the time mean and variability of the temperature field compared with assimilated TAO temperature data, taking into account the natural variability and representation errors of the assimilated temperature observations. Surface zonal currents at 15 m from the two analyses generally agree with observed climatology. Zonal current profiles from the analyses capture the intensity and variability of the Equatorial Undercurrent (EUC) displayed in the independent acoustic Doppler current profiler data at three TAO moorings across the equatorial Pacific basin. Compared with independent data from TAO servicing cruises, the results show that 1) temperature errors are reduced below the thermocline in both analyses; 2) salinity errors are considerably reduced below the thermocline in the GMAO analysis; and 3) errors in zonal currents from both analyses are comparable. To discern the impact of the forcing and salinity treatment, a sensitivity study is undertaken with the GMAO assimilation system. Additional analyses are produced with a different forcing dataset, and another scheme to modify the salinity field is tested. This second scheme updates salinity at the time of temperature assimilation based on model TS relationships (denoted “T scheme”). The results show that both assimilated field (i.e., temperature) and fields that are not directly observed (i.e., salinity and currents) are impacted. Forcing appears to have more impact near the surface (above the core of the EUC), while the salinity treatment is more important below the surface that is directly influenced by forcing. Overall, the TS scheme is more effective than the T scheme in correcting model bias in salinity and improving the current structure. Zonal currents from the GMAO control run where no data are assimilated are as good as the best analysis.

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John P. Dunne, Jasmin G. John, Elena Shevliakova, Ronald J. Stouffer, John P. Krasting, Sergey L. Malyshev, P. C. D. Milly, Lori T. Sentman, Alistair J. Adcroft, William Cooke, Krista A. Dunne, Stephen M. Griffies, Robert W. Hallberg, Matthew J. Harrison, Hiram Levy, Andrew T. Wittenberg, Peter J. Phillips, and Niki Zadeh

Abstract

The authors describe carbon system formulation and simulation characteristics of two new global coupled carbon–climate Earth System Models (ESM), ESM2M and ESM2G. These models demonstrate good climate fidelity as described in part I of this study while incorporating explicit and consistent carbon dynamics. The two models differ almost exclusively in the physical ocean component; ESM2M uses the Modular Ocean Model version 4.1 with vertical pressure layers, whereas ESM2G uses generalized ocean layer dynamics with a bulk mixed layer and interior isopycnal layers. On land, both ESMs include a revised land model to simulate competitive vegetation distributions and functioning, including carbon cycling among vegetation, soil, and atmosphere. In the ocean, both models include new biogeochemical algorithms including phytoplankton functional group dynamics with flexible stoichiometry. Preindustrial simulations are spun up to give stable, realistic carbon cycle means and variability. Significant differences in simulation characteristics of these two models are described. Because of differences in oceanic ventilation rates, ESM2M has a stronger biological carbon pump but weaker northward implied atmospheric CO2 transport than ESM2G. The major advantages of ESM2G over ESM2M are improved representation of surface chlorophyll in the Atlantic and Indian Oceans and thermocline nutrients and oxygen in the North Pacific. Improved tree mortality parameters in ESM2G produced more realistic carbon accumulation in vegetation pools. The major advantages of ESM2M over ESM2G are reduced nutrient and oxygen biases in the southern and tropical oceans.

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John P. Dunne, Jasmin G. John, Alistair J. Adcroft, Stephen M. Griffies, Robert W. Hallberg, Elena Shevliakova, Ronald J. Stouffer, William Cooke, Krista A. Dunne, Matthew J. Harrison, John P. Krasting, Sergey L. Malyshev, P. C. D. Milly, Peter J. Phillipps, Lori T. Sentman, Bonita L. Samuels, Michael J. Spelman, Michael Winton, Andrew T. Wittenberg, and Niki Zadeh

Abstract

The physical climate formulation and simulation characteristics of two new global coupled carbon–climate Earth System Models, ESM2M and ESM2G, are described. These models demonstrate similar climate fidelity as the Geophysical Fluid Dynamics Laboratory’s previous Climate Model version 2.1 (CM2.1) while incorporating explicit and consistent carbon dynamics. The two models differ exclusively in the physical ocean component; ESM2M uses Modular Ocean Model version 4p1 with vertical pressure layers while ESM2G uses Generalized Ocean Layer Dynamics with a bulk mixed layer and interior isopycnal layers. Differences in the ocean mean state include the thermocline depth being relatively deep in ESM2M and relatively shallow in ESM2G compared to observations. The crucial role of ocean dynamics on climate variability is highlighted in El Niño–Southern Oscillation being overly strong in ESM2M and overly weak in ESM2G relative to observations. Thus, while ESM2G might better represent climate changes relating to total heat content variability given its lack of long-term drift, gyre circulation, and ventilation in the North Pacific, tropical Atlantic, and Indian Oceans, and depth structure in the overturning and abyssal flows, ESM2M might better represent climate changes relating to surface circulation given its superior surface temperature, salinity, and height patterns, tropical Pacific circulation and variability, and Southern Ocean dynamics. The overall assessment is that neither model is fundamentally superior to the other, and that both models achieve sufficient fidelity to allow meaningful climate and earth system modeling applications. This affords the ability to assess the role of ocean configuration on earth system interactions in the context of two state-of-the-art coupled carbon–climate models.

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Anand Gnanadesikan, Keith W. Dixon, Stephen M. Griffies, V. Balaji, Marcelo Barreiro, J. Anthony Beesley, William F. Cooke, Thomas L. Delworth, Rudiger Gerdes, Matthew J. Harrison, Isaac M. Held, William J. Hurlin, Hyun-Chul Lee, Zhi Liang, Giang Nong, Ronald C. Pacanowski, Anthony Rosati, Joellen Russell, Bonita L. Samuels, Qian Song, Michael J. Spelman, Ronald J. Stouffer, Colm O. Sweeney, Gabriel Vecchi, Michael Winton, Andrew T. Wittenberg, Fanrong Zeng, Rong Zhang, and John P. Dunne

Abstract

The current generation of coupled climate models run at the Geophysical Fluid Dynamics Laboratory (GFDL) as part of the Climate Change Science Program contains ocean components that differ in almost every respect from those contained in previous generations of GFDL climate models. This paper summarizes the new physical features of the models and examines the simulations that they produce. Of the two new coupled climate model versions 2.1 (CM2.1) and 2.0 (CM2.0), the CM2.1 model represents a major improvement over CM2.0 in most of the major oceanic features examined, with strikingly lower drifts in hydrographic fields such as temperature and salinity, more realistic ventilation of the deep ocean, and currents that are closer to their observed values. Regional analysis of the differences between the models highlights the importance of wind stress in determining the circulation, particularly in the Southern Ocean. At present, major errors in both models are associated with Northern Hemisphere Mode Waters and outflows from overflows, particularly the Mediterranean Sea and Red Sea.

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Thomas L. Delworth, Anthony J. Broccoli, Anthony Rosati, Ronald J. Stouffer, V. Balaji, John A. Beesley, William F. Cooke, Keith W. Dixon, John Dunne, K. A. Dunne, Jeffrey W. Durachta, Kirsten L. Findell, Paul Ginoux, Anand Gnanadesikan, C. T. Gordon, Stephen M. Griffies, Rich Gudgel, Matthew J. Harrison, Isaac M. Held, Richard S. Hemler, Larry W. Horowitz, Stephen A. Klein, Thomas R. Knutson, Paul J. Kushner, Amy R. Langenhorst, Hyun-Chul Lee, Shian-Jiann Lin, Jian Lu, Sergey L. Malyshev, P. C. D. Milly, V. Ramaswamy, Joellen Russell, M. Daniel Schwarzkopf, Elena Shevliakova, Joseph J. Sirutis, Michael J. Spelman, William F. Stern, Michael Winton, Andrew T. Wittenberg, Bruce Wyman, Fanrong Zeng, and Rong Zhang

Abstract

The formulation and simulation characteristics of two new global coupled climate models developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) are described. The models were designed to simulate atmospheric and oceanic climate and variability from the diurnal time scale through multicentury climate change, given our computational constraints. In particular, an important goal was to use the same model for both experimental seasonal to interannual forecasting and the study of multicentury global climate change, and this goal has been achieved.

Two versions of the coupled model are described, called CM2.0 and CM2.1. The versions differ primarily in the dynamical core used in the atmospheric component, along with the cloud tuning and some details of the land and ocean components. For both coupled models, the resolution of the land and atmospheric components is 2° latitude × 2.5° longitude; the atmospheric model has 24 vertical levels. The ocean resolution is 1° in latitude and longitude, with meridional resolution equatorward of 30° becoming progressively finer, such that the meridional resolution is 1/3° at the equator. There are 50 vertical levels in the ocean, with 22 evenly spaced levels within the top 220 m. The ocean component has poles over North America and Eurasia to avoid polar filtering. Neither coupled model employs flux adjustments.

The control simulations have stable, realistic climates when integrated over multiple centuries. Both models have simulations of ENSO that are substantially improved relative to previous GFDL coupled models. The CM2.0 model has been further evaluated as an ENSO forecast model and has good skill (CM2.1 has not been evaluated as an ENSO forecast model). Generally reduced temperature and salinity biases exist in CM2.1 relative to CM2.0. These reductions are associated with 1) improved simulations of surface wind stress in CM2.1 and associated changes in oceanic gyre circulations; 2) changes in cloud tuning and the land model, both of which act to increase the net surface shortwave radiation in CM2.1, thereby reducing an overall cold bias present in CM2.0; and 3) a reduction of ocean lateral viscosity in the extratropics in CM2.1, which reduces sea ice biases in the North Atlantic.

Both models have been used to conduct a suite of climate change simulations for the 2007 Intergovernmental Panel on Climate Change (IPCC) assessment report and are able to simulate the main features of the observed warming of the twentieth century. The climate sensitivities of the CM2.0 and CM2.1 models are 2.9 and 3.4 K, respectively. These sensitivities are defined by coupling the atmospheric components of CM2.0 and CM2.1 to a slab ocean model and allowing the model to come into equilibrium with a doubling of atmospheric CO2. The output from a suite of integrations conducted with these models is freely available online (see http://nomads.gfdl.noaa.gov/).

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