Global Modeling and Assimilation Office Annual Report and Research Highlights 2011-2012

Over the last year, the Global Modeling and Assimilation Office (GMAO) has continued to advance our GEOS-5-based systems, updating products for both weather and climate applications. We contributed hindcasts and forecasts to the National Multi-Model Ensemble (NMME) of seasonal forecasts and the suite of decadal predictions to the Coupled Model Intercomparison Project (CMIP5)

Frontogenesis in the North Pacific Oceanic Frontal Zones--A Numerical Simulation

A primitive equation model [Geophysical Fluid Dynamics Laboratory\u27s (GFDL\u27s) MOM 2] with one degree horizontal resolution is used to simulate the seasonal cycle of frontogenesis in the subarctic frontal zone (SAFZ) and the subtropical frontal zone (STFZ) of the North Pacific Ocean. The SAFZ in the model contains deep (greater than 500 m in some places) regions with seasonally varying high gradients in temperature and salinity. The gradients generally weaken toward the east. The STFZ consists of a relatively shallow (less than 200 m in most places) region of high gradient in temperature that disappears in the summer/fall. The high gradient in salinity in the STFZ maintains its strength year round and extends across almost the entire basin. The model simulates the location and intensity of the frontal zones in good agreement with climatological observations: generally to within two degrees of latitude and usually at the same or slightly stronger intensity. The seasonal cycle of the frontal zones also marches observations well, although the subarctic front is stronger than observed in winter and spring. The model balances are examined to identify the dominant frontogenetic processes. The seasonal cycle of temperature frontogenesis in the surface level of the model is governed by both the convergence of the wind-driven Ekman transport and differential heating/cooling. In the STFZ, the surface Ekman convergence is frontogenetic throughout the year as opposed to surface heating, which is frontogenetic during winter and strongly frontolytic during late spring and summer. The subarctic front at 40 degrees N in the central Pacific (not the maximum wintertime gradient in the model, but its location in summer and the location where variability is in best agreement with the observations) undergoes frontogenesis during spring and summer due to surface Ekman convergence and differential horizontal shear. The frontolysis during winter is due to the joint influence of differential heat flux and vertical convection in opposition to frontogenetic Ekman convergence. The seasonal cycle of salinity frontogenesis in the surface level is governed by Ekman convergence, differential surface freshwater flux, and differential vertical convection (mixing). For salinity, the differential convection is primarily forced by Ekman convergence and differential cooling, thereby linking the salinity and temperature frontogenesis/frontolysis. Below the surface level, the seasonal frontogenesis/frontolysis is only significant in the western and central SAFZ where ii is due primarily to differential mixing (mostly in winter and early spring) with contributions from convergence and shearing advection during fall and winter. The shearing advection in the model western SAFZ is likely a result of the Kuroshio overshooting its observed separation latitude. The model\u27s vertical mixing through convective adjustment is found to be very important in controlling much of the frontogenesis/frontolysis. Thus, the seasonal cycle of the surface frontal variability depends strongly on the subsurface structure

A note on frictional effects in Taylor\u27s problem

Taylor\u27s tidal problem of the reflection of a Kelvin wave in a semi-infinite rotating channel is modified here by considering the effect of the inclusion of fri ction in the analysis. Results are obtained using Galerkin and Collocation methods to satisfy the end boundary condition, and these are compared with results given by other authors for the nonfriction case

Ensemble Kalman filter assimilation of temperature and altimeter data with bias correction and application to seasonal prediction

To compensate for a poorly known geoid, satellite altimeter data is usually analyzed in terms of anomalies from the time mean record. When such anomalies are assimilated into an ocean model, the bias between the climatologies of the model and data is problematic. An ensemble Kalman filter (EnKF) is modified to account for the presence of a forecast-model bias and applied to the assimilation of TOPEX/Poseidon (T/P) altimeter data. The online bias correction (OBC) algorithm uses the same ensemble of model state vectors to estimate biased-error and unbiased-error covariance matrices. Covariance localization is used but the bias covariances have different localization scales from the unbiased-error covariances, thereby accounting for the fact that the bias in a global ocean model could have much larger spatial scales than the random error.The method is applied to a 27-layer version of the Poseidon global ocean general circulation model with about 30-million state variables. Experiments in which T/P altimeter anomalies are assimilated show that the OBC reduces the RMS observation minus forecast difference for sea-surface height (SSH) over a similar EnKF run in which OBC is not used. Independent in situ temperature observations show that the temperature field is also improved. When the T/P data and in situ temperature data are assimilated in the same run and the configuration of the ensemble at the end of the run is used to initialize the ocean component of the GMAO coupled forecast model, seasonal SSH hindcasts made with the coupled model are generally better than those initialized with optimal interpolation of temperature observations without altimeter data. The analysis of the corresponding sea-surface temperature hindcasts is not as conclusive

The Simulation and Assimilation of Doppler Wind Lidar Observations in Support of Future Instruments

With the launch of the European Space Agency's Atmospheric Dynamics Mission (ADM-Aeolus) in 2011 and the call for the 3D-Winds mission in National Research Council's decadal survey, direct spaceborne measurements of vertical wind profiles are imminent via Doppler wind lidar technology. Part of the preparedness for such missions is the development of the proper data assimilation methodology for handling such observations. Since no heritage measurements exist in space, the Joint Observing System Simulation Experiment (Joint OSSE) framework is being utilized to generate a realistic proxy dataset as a precursor to flight. These data are being used for the development of the Gridpoint Statistical Interpolation (GSI) data assimilation system utilized at a number of centers through the United States including the Global Modeling and Assimilation Office (GMAO) at NASA/Goddard Space Flight Center and at the National Centers for Environmental Prediction (NOAA/NWS/NCEP). This effort will be presented, including the methodology of proxy data generation, the handling of line-of-sight wind measurements within the GSI, and the impact on both analyses and forecasts with the addition of the new data type

Ensemble Data Assimilation Without Ensembles: Methodology and Application to Ocean Data Assimilation

Two methods to estimate background error covariances for data assimilation are introduced. While both share properties with the ensemble Kalman filter (EnKF), they differ from it in that they do not require the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The first method is referred-to as SAFE (Space Adaptive Forecast error Estimation) because it estimates error covariances from the spatial distribution of model variables within a single state vector. It can thus be thought of as sampling an ensemble in space. The second method, named FAST (Flow Adaptive error Statistics from a Time series), constructs an ensemble sampled from a moving window along a model trajectory. The underlying assumption in these methods is that forecast errors in data assimilation are primarily phase errors in space and/or time

Creation of a homogeneous plasma column by means of hohlraum radiation for ion-stopping measurements

In this work, we present the results of two-dimensional radiation-hydrodynamics simulations of a hohlraum target whose outgoing radiation is used to produce a homogeneously ionized carbon plasma for ion-beam stopping measurements. The cylindrical hohlraum with gold walls is heated by a frequency-doubled ($\lambda_l = 526.5$ $\mu m$) $1.4$ $ns$ long laser pulse with the total energy of $E_l = 180$ $J$. At the laser spot, the peak matter and radiation temperatures of, respectively, $T \approx 380$ $eV$ and $T_r \approx 120$ $eV$ are observed. X-rays from the hohlraum heat the attached carbon foam with a mean density of $\rho_C = 2$ $mg/cm^3$ to a temperature of $T \approx 25$ $eV$. The simulation shows that the carbon ionization degree ($Z \approx 3.75$) and its column density stay relatively stable (within variations of about $\pm7\%$) long enough to conduct the ion-stopping measurements. Also, it is found that a special attention should be paid to the shock wave, emerging from the X-ray heated copper support plate, which at later times may significantly distort the carbon column density traversed by the fast ions.Comment: 12 pages, 12 figure

An Assessment of the Skill of GEOS-5 Seasonal Forecasts

The seasonal forecast skill of the NASA Global Modeling and Assimilation Office coupled global climate model (CGCM) is evaluated based on an ensemble of 9-month lead forecasts for the period 1993 to 2010. The results from the current version (V2) of the CGCM consisting of the GEOS-5 AGM coupled to the MOM4 ocean model are compared with those from an earlier version (V1) in which the AGCM (the NSIPP model) was coupled to the Poseidon Ocean Model. It was found that the correlation skill of the Sea Surface Temperature (SST) forecasts is generally better in V2, especially over the sub-tropical and tropical central and eastern Pacific, Atlantic, and Indian Ocean. Furthermore, the improvement in skill in V2 mainly comes from better forecasts of the developing phase of ENSO from boreal spring to summer. The skill of ENSO forecasts initiated during the boreal winter season, however, shows no improvement in terms of correlation skill, and is in fact slightly worse in terms of root mean square error (RMSE). The degradation of skill is found to be due to an excessive ENSO amplitude. For V1, the ENSO amplitude is too strong in forecasts starting in boreal spring and summer, which causes large RMSE in the forecast. For V2, the ENSO amplitude is slightly stronger than that in observations and V1 for forecasts starting in boreal winter season. An analysis of the terms in the SST tendency equation, shows that this is mainly due to an excessive zonal advective feedback. In addition, V2 forecasts that are initiated during boreal winter season, exhibit a slower phase transition of El Nino, which is consistent with larger amplitude of ENSO after the ENSO peak season. It is found that this is due to weak discharge of equatorial Warm Water Volume (WWV). In both observations and V1, the discharge of equatorial WWV leads the equatorial geostrophic easterly current so as to damp the El Nino starting in January. This process is delayed by about 2 months in V2 due to the slower phase transition of the equatorial zonal current from westerly to easterly

Global tidal impacts of large-scale ice-sheet collapses

Tide model output for "Wilmes et al., (2017), Global tidal impacts of large-scale ice-sheet collapses, JGR Oceans" together with the Matlab files needed to read the model binary files Please refer to the publications for details on the run setup. h0.* contains elevation output; M2 elevations can be read in Matlab using [h,th_lim,ph_lim] = h_in(filename,1); where h is tidal elevation (abs(h) gives amplitudes and angle(h) gives phase), th_lim gives latitude limits in degs N and ph_lim longitude limits in degs E u0.* contains tidal transport output; M2 transports can be read in Matlab using [u,v,th_lim,ph_lim] = u_in(filename,1); where u and v are transports in x and y direction (real(u)/hz gives tidal current strength) grid* contains the bathymetry; can be read in Matlab using [ll_lims,hz,mz,iob] = grd_in(filename); where ll_lims gives lon and lat limits, hz is water depth, mz is the land-sea mask (0 is land, 1 is water), and iob are open boundary nodes *.it_m2_k1_00.0kyrBP_ish_no0.1sal_191322_sal4 - CTRL; bathymetry: grid_etssib_1_8_paleo_glob_ice_shelves *.it_m2_k1_00.0kyrBP_ish_5mSLR_vw_no0.1sal_191333_sal4 - 5m SLR; bathymetry: grid_etssib_1_8_paleo_glob_ice_shelves_5mSLR_vw *.it_m2_k1_00.0kyrBP_ish_7mSLR_vw_no0.1sal_191336_sal4 - 7m SLR; bathymetry: grid_etssib_1_8_paleo_glob_ice_shelves_7mSLR_vw *.it_m2_k1_1_8th_00.0kyrBP_12mSLR_vw_7048752_sal4 - 12m SLR; bathymetry: grid_etssib_1_8_paleo_glob_ice_shelves_12mSLR_vw *.it_m2_k1_00.0kyrBP_no_wais_fp_5mSLR_vw_no0.1sal_191326_sal4 - No WAIS; bathymetry: grid_etssib_1_8_glob_no_wais_SLR_fingerprint_5m_EEV_vw *.it_m2_k1_00.0kyrBP_no_gris_fp_7mSLR_vw_no0.1sal_191331_sal4 - No GIS; bathymetry: grid_etssib_1_8_glob_no_gris_SLR_fingerprint_7m_EEV_vw *.it_m2_00.0kyrBP_no_wais_gis_fp_vw_375526_sal4 - No WAIS & No GIS; bathymetry: grid_etssib_1_8_glob_no_wais_gris_SLR_fingerprint_12m_EEV_v