CGD 2009 Profiles in Science: Dr. Gokhan Danabasoglu

Summary of achievements

The general subjects of my research are understanding the role of the oceans in the earth's climate system and computational modeling of the ocean as geophysical fluid. In FY08, I devoted about half of my research effort to developing, implementing, testing, and evaluating subgridscale parameterizations within the ocean component of the Community Climate System Model (CCSM). This work supports in approximately equal balance both my personal research and collaborations with university and U.S. Government scientists involved in the CCSM Ocean Model Working Group (OMWG) and the U.S. CLIVAR Climate Process Teams (CPTs) on ocean mixing and gravity currents. As a cochair of the CCSM OMWG, I have been spending time both actively helping and coordinating various efforts to finalize the CCSM4 ocean component for use in the next IPCC assessment studies. I spent the other half of my research effort focusing on curiosity driven research projects that included investigating the driving mechanisms and potential predictability associated with the multidecadal variability of the North Atlantic Meridional Overturning Circulation, as well as multidisciplinary research that reef bleaching and adaptation.

Publications

Griffies, S. M., A. Biastoch, C. Böning, F. Bryan, G. Danabasoglu, E. Chassignet, M. England, R. Gerdes, H. Haak, R. Hallberg, W. Hazeleger, J. Jungclaus, W. Large, G. Madec, A. Pirani, B. Samuels, M. Scheinert, A. Gupta, C. Severijns, H. Simmons, A. Treguier, M. Winton, S. Yeager, and J. Yin. 2009: Coordinated Oceanice Reference Experiments (COREs). Ocean. Modell., 26, 146, doi:10.1016/j.ocemod.2008.08.007.



Figure 1: High resolution figure

Abstract: Coordinated Oceanice Reference Experiments (COREs) are presented as a tool to explore the behaviour of global oceanice models under forcing from a common atmospheric dataset. We highlight issues arising when designing coupled global ocean and sea ice experiments, such as difficulties formulating a consistent forcing methodology and experimental protocol. Particular focus is given to the hydrological forcing, the details of which are key to realizing simulations with stable meridional overturning circulations.

The atmospheric forcing from [Large, W., Yeager, S., 2004. Diurnal to decadal global forcing for ocean and seaice models: the data sets and flux climatologies. NCAR Technical Note: NCAR/TN460+STR. CGD Division of the National Center for Atmospheric Research] was developed for coupledocean and sea ice models. We found it to be suitable for our purposes, even though its evaluation originally focussed more on the ocean than on the seaice. Simulations with this atmospheric forcing are presented from seven global oceanice models using the COREI design (repeating annual cycle of atmospheric forcing for 500 years). These simulations test the hypothesis that global oceanice models run under the same atmospheric state produce qualitatively similar simulations. The validity of this hypothesis is shown to depend on the chosen diagnostic. The CORE simulations provide feedback to the fidelity of the atmospheric forcing and model configuration, with identification of biases promoting avenues for forcing dataset and/or model development.

Figure caption: Northward heat transport for the global ocean as determined by the ocean models. Results are decadal means from model years 491500, and the values include both resolved advective and SGS transport contributions. Units are PW=1015Watts.

Hurrell, J.W., T. Delworth, G. Danabasoglu, H. Drange, S. Griffies, N. Holbrook, B. Kirtman, N. Keenlyside, M. Latif, J. Marotzke, G.A. Meehl, T. Palmer, H. Pohlmann, T. Rosati, R. Seager, D. Smith, R. Sutton, A. Timmermann, K.E. Trenberth, and J. Tribbia, 2009: Decadal Climate Prediction: Opportunities and Challenges. Community White Paper, OceanObs '09. [article]



Figure 2: High resolution figure

Introduction: The scientific understanding of Earth's climate system is now sufficiently developed to show that climate change from anthropogenic greenhouse gas forcing is already upon us, and the rate of change as projected exceeds anything seen in nature in the past 10,000 years. Uncertainties remain, however, especially regarding how climate will change at regional and local scales where the signal of natural variability is large. Decision makers in diverse arenas, from water managers in the U.S. Southwest to public health experts in Asia, need to know the extent to which the climate events they are seeing are the product of natural variability, and hence can be expected to reverse at some point, or are the result of potentially irreversible anthropogenic climate change.

Figure caption: The global number of temperature observations per month as a function of depth. The data sources are XBTs, fixed tropical moorings (TAO (Pacific), TRITON (Pacific), PIRATA (Atlantic), and the developing Indian Ocean array) and ARGO floats. The apparent horizontal strata reflect the successive influence of 450 m XBTs, 750 m XBTs, 500 m TAOclass moorings and 1000 m and 2000 m Argo floats.

Danabasoglu, G. and P. Gent, 2009: Equilibrium Climate Sensitivity: Is It Accurate to Use a Slab Ocean Model? Journal of Climate, 22, 24942499, doi:10.1175/2008JCLI2596.1..



Figure 3: High resolution figure

Abstract: The equilibrium climate sensitivity of a climate model is usually defined as the globally averaged equilibrium surface temperature response to a doubling of carbon dioxide. This is virtually always estimated in a version with a slab model for the upper ocean. The question is whether this estimate is accurate for the full climate model version, which includes a fulldepth ocean component. This question has been answered for the lowresolution version of the Community Climate System Model, version 3 (CCSM3). The answer is that the equilibrium climate sensitivity using the fulldepth ocean model is 0.14°C higher than that using the slab ocean model, which is a small increase. In addition, these sensitivity estimates have a standard deviation of nearly 0.1°C because of interannual variability. These results indicate that the standard practice of using a slab ocean model does give a good estimate of the equilibrium climate sensitivity of the full CCSM3. Another question addressed is whether the effective climate sensitivity is an accurate estimate of the equilibrium climate sensitivity. Again the answer is yes, provided that at least 150 yr of data from the doubled carbon dioxide run are used.

Figure Caption: (a) Annualmean, globally averaged surface temperature vs time from the 2 × CO2 (black line) and control (gray line) runs, and (b) the difference between the annualmean surface temperature in the 2 × CO2 run and the years 10011500 control run average.

Eden, C., M. Jochum and G. Danabasoglu. 2009: Effects of different closures for thickness diffusivity. Journal of Climate, 26, 4759, doi:doi:10.1016/j.ocemod.2008.08.004.



Figure 4: High resolution figure

Abstract: The effects of spatial variations of the thickness diffusivity (K) appropriate to the parameterisation of [Gent, P.R. and McWilliams, J.C., 1990. Isopycnal mixing in ocean circulation models. J. Phys. Oceanogr., 20, 150155.] are assessed in a coarse resolution global ocean general circulation model. Simulations using three closures yielding different lateral and/or vertical variations in K are compared with a simulation using a constant value. Although the effects of changing K are in general small and all simulations remain biased compared to observations, we find systematic local sensitivities of the simulated circulation on K. In particular, increasing K near the surface in the tropical ocean lifts the depth of the equatorial thermocline, the strength of the Antarctic Circumpolar Current decreases while the subpolar and subtropical gyre transports in the North Atlantic increase by increasing K locally. We also find that the lateral and vertical structure of K given by a recently proposed closure reduces the negative temperature biases in the western North Atlantic by adjusting the pathways of the Gulf Stream and the North Atlantic Current to a more realistic position.

Figure Caption: Annual mean temperature difference between simulation and the climatology of Levitus and Boyer (1994) in experiment CONST (a), VMHS (b), NSQR (c) and EG (d) after 500 years integration at 200 m depth. Also shown are contour lines of temperature (2 °C contour spacing). Note that the data have been interpolated from the model grid to a regular rectangular grid of similar resolution prior to plotting. The land mask in the figure (taken from Smith and Sandwell (1997)) differs therefore slightly from the model's land mask.

Legg, S., B. Briegleb, Y. Chang, E.P. Chassignet, G. Danabasoglu, T. Ezer, A.L. Gordon, S. Griffies, R. Hallberg, L. Jackson, W. Large, T.M. Özgökmen, H. Peters, J. Price, U. Riemenschneider, W. Wu, X. Xu and J. Yang. 2009: Improving Oceanic Overflow Representation in Climate Models: The Gravity Current Entrainment Climate Process Team. Bulletin of the American Meteorological Society, 90, 657670, doi:doi:10.1175/2008BAMS2667.1.



Figure 5: High resolution figure

Abstract: Oceanic overflows are bottomtrapped density currents originating in semienclosed basins, such as the Nordic seas, or on continental shelves, such as the Antarctic shelf. Overflows are the source of most of the abyssal waters, and therefore play an important role in the largescale ocean circulation, forming a component of the sinking branch of the thermohaline circulation. As they descend the continental slope, overflows mix vigorously with the surrounding oceanic waters, changing their density and transport significantly. These mixing processes occur on spatial scales well below the resolution of ocean climate models, with the result that deep waters and deep western boundary currents are simulated poorly. The Gravity Current Entrainment Climate Process Team was established by the U.S. Climate Variability and Prediction (CLIVAR) Program to accelerate the development and implementation of improved representations of overflows within largescale climate models, bringing together climate model developers with those conducting observational, numerical, and laboratory process studies of overflows. Here, the organization of the Climate Process Team is described, and a few of the successes and lessons learned during this collaboration are highlighted, with some emphasis on the wellobserved Mediterranean overflow. The Climate Process Team has developed several different overflow parameterizations, which are examined in a hierarchy of ocean models, from comparatively wellresolved regional models to the largestscale global climate models.

Figure Caption: Salinity in the Mediterranean outflow plume shown as a function of latitude and depth along a section at 8.5°W from (a) the World Ocean Circulation Experiment (WOCE) observations and (b) HY COM regional simulation at 0.08° horizontal resolution with 28 layers in the vertical. The regional model was integrated for six months, and salinity is shown for the end of the integration.

Danabasoglu, G., S. Peacock, K. Lindsay and D. Tsumune. 2009: Sensitivity of CFC11 uptake to physical initial conditions and interannually varying surface forcing in a global ocean model. Ocean Modelling, 29, 5865, doi:doi:10.1016/j.ocemod.2009.02.011.



Figure 6: High resolution figure

Abstract: Sensitivity of the oceanic chlorofluorocarbon CFC11 uptake to physical initial conditions and surface dynamical forcing (heat and salt fluxes and wind stress) is investigated in a global ocean model used in climate studies. Two different initial conditions are used: a solution following a short integration starting with observed temperature and salinity and zero velocities, and the quasiequilibrium solution of an independent integration. For surface dynamical forcing, recently developed normalyear and interannually varying (19582000) data sets are used. The model CFC11 global and basin inventories, particularly in the normalyear forcing case, are below the observed mean estimates, but they remain within the observational error bars. Column inventory spatial distributions indicate nontrivial differences due to both initial condition and forcing changes, particularly in the northern North Atlantic and Southern Ocean. These differences are larger between forcing sensitivity experiments than between the initial condition cases. The comparisons along the A16N and SR3 WOCE sections also show differences between cases. However, comparisons with observations do not clearly favor a particular case, and modelobservation differences remain much larger than modelmodel differences for all simulations. The choice of initial condition does not significantly change the CFC11 distributions. Both because of locally large differences between normalyear and interannually varying simulations and because the dynamical and CFC11 forcing calendars are synchronized, we favor using the more realistic interannually varying forcing in future simulations, given the availability of the forcing data sets.

Figure Caption: CFC11 column inventory in moles km2 for 1994 from (a) GLODAP and (b) IAF2; the model difference distributions for (c) IAF2 IAF1 and (d) IAF2 NYF2. Panels (a and b) and (c and d) share the same color bars, respectively.

Meehl, G.A., L. Goddard, J. Murphy, R.J. Stouffer, G. Boer, G. Danabasoglu, K. Dixon, M.A. Giorgetta, A.M. Greene, E. Hawkins, g. Hegerl, D. Karoly, N. Keenlyside, M. Kimoto, B. Kirtman, A. Navarra, R. Pulwarty, D. Smith and T. Stockdale. 2009: Decadal Prediction: Can it be skillful? Bulletin of the American Meteorological Society, 36, L08703, doi:10.1175/2009BAMS2778.1, (early online release).



Figure 7: High resolution figure

Capsule: A new field called "decadal prediction" will use initialized climate models to produce timeevolving predictions of regional climate that will bridge ENSO forecasting and future climate change projections.

Abstract: A new field of study, "decadal prediction", is emerging in climate science. Decadal prediction lies between seasonal/interannual forecasting and longer term climate change projections, and focuses on timeevolving regional climate conditions over the next 1030 years. Numerous assessments of climate information user needs have identified this timescale as being important to infrastructure planners, water resource managers, and many others. It is central to the information portfolio required to adapt effectively to and through climatic changes. At least three factors influence timeevolving regional climate at the decadal timescale: 1) climate change commitment (further warming as the coupled climate system comes into adjustment with increases of greenhouse gases that have already occurred), 2) external forcing, particularly from future increases of greenhouse gases and recovery of the ozone hole, and 3) internallygenerated variability. Some decadal prediction skill has been demonstrated to arise from the first two of these factors, and there is evidence that initialized coupled climate models can capture mechanisms of internallygenerated decadal climate variations, thus increasing predictive skill globally and particularly regionally. Several methods have been proposed for initializing global coupled climate models for decadal predictions, all of which involve global timeevolving threedimensional ocean data, including temperature and salinity. An experimental framework to address decadal predictability/prediction is described in this paper and has been incorporated into the coordinated CMIP5 experiments, some of which will be assessed for the IPCC AR5. These experiments will likely guide work in this emerging field over the next five years.

Figure caption: (An optimal perturbation for the Atlantic domain from the HadCM3 model, using a Linear Inverse Modelling approach (from Hawkins and Sutton 2009b). The panels show integrated temperature (left) and salinity (right) from the surface to a depth of 1800m. The coloured regions indicate where the ocean is sensitive to small anomalies, and are thus the optimal regions for initial condition perturbations, and for targeted observations to improve forecast skill.

Griffies, S.M., A.J. Adcroft, H. Banks, C.W. Boning, E.P. Chassignet, G. Danabasoglu, S. Danilov, E. Deleersnijder, B. FoxKemper, R. Gerdes, A. Gnanadesikan, R.J. Greatbatch, R.W. Hallberg, E. Hanert, M.J. Harrison, S.A. Legg, C.M. Little, M. Nikurashin, A. Pirani, H.L. Simmons, J. Schroter, B.L. Samuels, J.R. Toggweiler, H. Tsujino, G.K. Vallis, and L. White. 2009: Problems and prospects in largescale ocean circulation models. Community white paper, Ocean Obs.

Abstract: Numerical ocean circulation models provide an increasingly realistic analog to the natural ocean. Such models support oceanography and climate science by providing tools to mechanistically interpret ocean observations, to experimentally investigate hypotheses for ocean phenemona, to consider future scenarios such as those associated with humaninduced climate warming, and to forecast ocean conditions on weekly to decadal time scales using dynamical modeling systems. We anticipate that the already significant role models play in ocean and climate science will increase in prominence as models improve, observational datasets grow, and the impacts of climate change become more tangible...

Danabasoglu, G. 2008: On Multidecadal Variability of the Atlantic Meridional Overturning Circulation in the Community Climate System Model Version 3. Journal of Climate, 21, 55245544, doi:10.1175/2008JCLI2019.1.



Figure 8: High resolution figure

Abstract: Multidecadal variability of the Atlantic meridional overturning circulation (MOC) is investigated diagnostically in the NCAR Community Climate System Model version 3 (CCSM3) presentday simulations, using the highest (T85 × 1) resolution version. This variability has a 21yr period and is present in many other ocean fields in the North Atlantic. In MOC, the oscillation amplitude is about 4.5 Sv (1 Sv = 106 m3 s-1), corresponding to 20% of the mean maximum MOC transport. The northward heat transport (NHT) variability has an amplitude of about 0.12 PW, representing 10% of the mean maximum NHT. In sea surface temperature (SST) and sea surface salinity (SSS), the peaktopeak changes can be as large as 6°7°C and 3 psu, respectively. The Labrador Sea region is identified as the deepwater formation (DWF) site associated with the MOC oscillations. In contrast with some previous studies, temperature and salinity contributions to the total density in this DWF region are almost equal and in phase. The heat and freshwater budget analyses performed for the DWF site indicate a complex relationship between the DWF, MOC, North Atlantic Oscillation (NAO), and subpolar gyre circulation anomalies. Their complicated interactions appear to be responsible for the maintenance of this multidecadal oscillation. In these interactions, the atmospheric variability associated with the model's NAO plays a prominent role. In particular, the NAO modulates the subpolar gyre strength and contributes to the formation of the temperature and salinity anomalies that lead to positive/negative density anomalies at the DWF site. In addition, the wind stress curl anomalies occurring during the transition phase between the positive and negative NAO states produce fluctuations of the subtropicalsubpolar gyre boundary, thus creating midlatitude SST and SSS anomalies. Comparisons with observations show that neither the pattern nor the magnitude of this dominant SST variability is realistic.

Figure caption: (a) Power spectrum of the time series of the BSF subtropical–subpolar gyre boundary north–south shifts along 30°W (shown by the dashed line in Fig. 5e ), (b) simultaneous WSC regressions with the time series of the BSF subtropical–subpolar gyre boundary shifts (10-8 N m-3 per degree); NORTH–SOUTH (see text) difference distributions for (c) SST (°C) and (d) SSS (psu). In (a), the Hanning window is applied, and the reference red noise spectrum with the same total variance is given by the thick solid line, and the dashed and dotted lines show its 95% and 99% confidence limits, respectively. In (b)–(d), the solid and dotted lines show the most northward and southward mean positions of the zero BSF contour line, respectively. In (b), the thin solid and pink lines show the 95% significance level, using the twosided Student's t test, and the zero contour line for the timemean WSC from Fig. 5f , respectively. In (c)–(d), the zero contour lines are drawn.