Abstract

Previous observational studies indicated that local sea surface temperatures (SSTs) near the west coast of the United States, in the Gulf of California, and in the Gulf of Mexico have strong impacts on the North American monsoon (NAM) system. Simulations of the NAM by numerical models are also found to be sensitive to the specification of SSTs. Accordingly, a reliable SST dataset is essential for improving the understanding, simulation, and prediction of the NAM system. In this study, a new fine-resolution SST analysis is constructed by merging in situ observations from ships and buoys with retrievals from National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-16 and NOAA-17), Geostationary Operational Environmental Satellites (GOES), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer (AMSR). Called the multiplatform-merged (MPM) SST analysis, this new product of 3-hourly SST is defined on a 0.25° × 0.25° latitude–longitude grid over the Western Hemisphere (30°S–60°N, 180°–30°W). The analysis for the period of 15 May–30 September 2004 shows that the MPM is capable of capturing small-scale disturbances such as those associated with the tropical instability waves. It also depicts local sharp gradients around Baja California and the Gulf Stream with reasonable accuracy compared with the existing analyses. Experiments have been conducted to examine the impacts of the addition of satellite observations on the quality of the MPM analysis. Results showed that inclusion of observations from more satellites progressively improves the quantitative accuracy, especially for diurnal amplitude of the analysis, indicating the importance of accommodating observations from multiple platforms in depicting critical details in an SST analysis with high temporal and spatial resolutions.

Abstract

The subsurface ocean temperature response to El Niño–Southern Oscillation (ENSO) is examined based on 31-yr (1981–2011) simulations with the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) coupled model. The model sea surface temperature (SST) in the tropical Pacific is relaxed to observations to ensure realistic ENSO variability in the simulations.

In the tropical Pacific, the subsurface temperature response to the ENSO SST is closely related to the variability of thermocline. The subsurface response is stronger and deeper in the tropical Indian Ocean than in the tropical Atlantic. The analysis at three selected locations reveals that the peak response of the subsurface temperature to ENSO lags the Niño-3.4 SST by 3, 6, and 6 months, respectively, in the southern tropical Indian Ocean, the northern tropical Atlantic, and the North Pacific, where SSTs are also known to be strongly influenced by ENSO. The ENSO-forced temperature anomalies gradually penetrate to the deeper ocean with time in the North Pacific and the tropical Atlantic, but not in the tropical Indian Ocean where the subsurface response at different depths peaks almost at the same time (i.e., at about 3–4 months following ENSO). It is demonstrated that the ENSO-induced surface wind stress plays an important role in determining the time scale and strength of the subsurface temperature response to ENSO in the North Pacific and the northern tropical Atlantic. Additionally, the ENSO-related local surface latent heat flux also contributes to the subsurface response to ENSO in these two regions.

Abstract

An analysis of lagged ensemble seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), is presented. The focus of the analysis is on the construction of lagged ensemble forecasts with increasing lead time (thus allowing use of larger ensemble sizes) and its influence on seasonal prediction skill. Predictions of seasonal means of sea surface temperature (SST), 200-hPa height (z200), precipitation, and 2-m air temperature (T2m) over land are analyzed. Measures of prediction skill include deterministic (anomaly correlation and mean square error) and probabilistic [rank probability skill score (RPSS)]. The results show that for a fixed lead time, and as one would expect, the skill of seasonal forecast improves as the ensemble size increases, while for a fixed ensemble size the forecast skill decreases as the lead time becomes longer. However, when a forecast is based on a lagged ensemble, there exists an optimal lagged ensemble time (OLET) when positive influence of increasing ensemble size and negative influence due to an increasing lead time result in a maximum in seasonal prediction skill. The OLET is shown to depend on the geographical location and variable. For precipitation and T2m, OLET is relatively longer and skill gain is larger than that for SST and tropical z200. OLET is also dependent on the skill measure with RPSS having the longest OLET. Results of this analysis will be useful in providing guidelines on the design and understanding relative merits for different configuration of seasonal prediction systems.

Abstract

The focus of this investigation is how the relationship at intraseasonal time scales between sea surface temperature and precipitation (SST–P) varies among different reanalyses. The motivation for this work was spurred by a recent report that documented that the SST–P relationship in Climate Forecast System Reanalysis (CFSR) was much closer to that in the observation than it was for the older generation of reanalyses [i.e., NCEP–NCAR reanalysis (R1) and NCEP–Department of Energy (DOE) reanalysis (R2)]. Further, the reason was attributed either to the fact that the CFSR is a partially coupled reanalysis, while R1 and R2 are atmospheric-alone reanalyses, or that R1 and R2 use the observed weekly-averaged SST.

The authors repeated the comparison of the SST–P relationship among R1, R2, and CFSR, as well as two recent generations of atmosphere-alone reanalyses, the Modern-Era Retrospective Analysis for Research and Applications (MERRA) and the ECMWF Re-Analysis Interim (ERA-Interim). The results clearly demonstrate that the differences in the SST–P relationship at intraseasonal time scales across different reanalyses are not due to whether the reanalysis system is coupled or atmosphere alone, but are due to the specification of different SSTs. The SST–P relationship in different reanalyses, when computed against a single SST for the benchmark, demonstrates a relationship that is common across all of the reanalyses and observations.

Abstract

While fully coupled atmosphere–ocean models have been used to study the seasonal predictability of sea ice variations within the context of models’ own variability, their capability in predicting the observed sea ice at the seasonal time scales is not well assessed. In this study, sea ice predictions from the recently developed NCEP Climate Forecast System, version 2 (CFSv2), a fully coupled atmosphere–ocean model including an interactive dynamical sea ice component, are analyzed. The focus of the analysis is the performance of CFSv2 in reproducing observed Northern Hemisphere sea ice extent (SIE). The SIE climatology, long-term trend, interannual variability, and predictability are assessed. CFSv2 contains systematic biases that are dependent more on the forecast target month than the initial month, with a positive SIE bias for the forecast for January–September and a negative SIE bias for the forecast for October–December. A large source of seasonal prediction skill is from the long-term trend, which is underestimated in the CFSv2. Prediction skill of interannual SIE anomalies is found to be primarily within the first three target months and is largest in the summer and early fall. The performance of the prediction of sea ice interannual variations varies from year to year and is found to be related to initial sea ice thickness. Potential predictability based on the forecast ensemble, its dependence on model deficiencies, and implications of the results from this study for improvements in the seasonal sea ice prediction are discussed.

Abstract

Numerical experiments are conducted using the University of Illinois, Urbana–Champaign (UIUC), 11-layer atmospheric general circulation model (GCM) to investigate the dependence of the simulated tropical intraseasonal oscillation (TIO) on convection parameterization. Three convection parameterizations have been tested: 1) the UIUC GCM’s original cumulus–convection parameterization, which includes a modified version of the penetrative–convection parameterization and a middle-level convection parameterization, 2) the parameterization of , and (3) the moist convective adjustment parameterization of For each parameterization a relative humidity criterion (RH_c) for convection or convective heating to occur is used, as in many GCMs. Perpetual-March simulations with these convection parameterizations have been performed for different values of RH_c. It is found that the simulated TIO is highly dependent on RH_c. As RH_c increases, the oscillation in the simulations becomes stronger for all three parameterizations. This dependence of the amplitude of the simulated oscillation on RH_c appears to explain the differences in the TIO among previous simulations by different GCMs.

The analysis of the simulations suggests that a certain degree of nonlinear dependence of the condensational heating on large-scale moisture convergence is required to give a reasonable simulation of the TIO. When large values of RH_c are used, the triggering of convective activity requires the moist static energy in the lower troposphere to be accumulated to a certain amount through moisture convergence. This requirement of accumulation of the moist static energy to trigger convection leads to the weakening of the interaction between the circulation and the heating for perturbations of small amplitudes and small scales, and allows the initiation of the TIO to occur at lower frequencies. In the simulations that produce relatively strong intraseasonal oscillations, the frictional wave-CISK (conditional instability of the second kind) appears to contribute to the amplification of the TIO.

Abstract

Using the retrospective forecasts from the National Centers for Environmental Prediction (NCEP) coupled atmosphere–ocean Climate Forecast System (CFS) and the Atmospheric Model Intercomparison Project (AMIP) simulations from its uncoupled atmospheric component, the NCEP Global Forecast System (GFS), the relative roles of atmospheric and land initial conditions and the lower boundary condition of sea surface temperatures (SSTs) for the prediction of monthly-mean temperature are investigated. The analysis focuses on the lead-time dependence of monthly-mean prediction skill and its asymptotic value for longer lead times, which could be attributed the atmospheric response to the slowly varying SST. The results show that the observed atmospheric and land initial conditions improve the skill of monthly-mean prediction in the extratropics but have little influence in the tropics. However, the influence of initial atmospheric and land conditions in the extratropics decays rapidly. For 30-day-lead predictions, the global-mean forecast skill of monthly means is found to reach an asymptotic value that is primarily determined by the SST anomalies. The lead time at which initial conditions lose their influence varies spatially. In addition, the initial atmospheric and land conditions are found to have longer impacts in northern winter and spring than in summer and fall. The relevance of the results for constructing lagged ensemble forecasts is discussed.

Abstract

The connection between the local SST and precipitation (SST–P) correlation and the prediction skill of precipitation on a seasonal time scale is investigated based on seasonal hindcasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). The results demonstrate that there is good correspondence between the two: precipitation skill is generally high only over the regions where SST–P correlation is positive and is low where SST–P correlation is small or weakly negative. This result has fundamental implications for understanding the limits of precipitation predictability on seasonal time scale and helps explain spatial variations in the skill of seasonal mean precipitation. Over the regions where atmospheric variability drives the ocean variability (and consequently the local SST–P correlation is weakly negative), the inherently unpredictable nature of atmospheric variability leads to low predictability for seasonal precipitation. On the other hand, over the regions where slow time scale ocean variability drives the atmosphere (and the local SST–P correlation is large positive), the predictability of seasonal mean precipitation is also high.

Abstract

The observed Madden–Julian oscillation (MJO) tends to propagate eastward across the Maritime Continent from the eastern equatorial Indian Ocean to the western Pacific. However, numerical simulations present different levels of fidelity in representing the propagation, especially for the tropical convection associated with the MJO. This study conducts a series of coupled simulations using the NCEP CFSv2 to explore the impacts of SST feedback and convection parameterization on the propagation simulations. First, two simulations differing in the model horizontal resolutions are conducted. The MJO propagation in these two simulations is found generally insensitive to the resolution change. Further, based on the CFSv2 with a lower resolution, two additional experiments are performed with model SSTs nudged to climatologies with different time scales representing different air–sea coupling strength. It is demonstrated that weakening the air–sea coupling strength significantly degrades the MJO propagation simulation, suggesting the critical role of SST feedback in maintaining MJO propagation. Last, the sensitivity to convection parameterization is explored by comparing two simulations with different convection parameterization schemes. Analyses of these simulations indicate that including air–sea coupling alone in a dynamical model does not result in realistic maintenance of the MJO eastward propagation without the development of favorable SST conditions in the western Pacific. In both observations and one simulation with realistic MJO propagations, the preconditioning of SSTs is strongly affected by surface latent heat fluxes that are modulated by surface wind anomalies in both zonal and meridional directions. The diagnostics highlight the critical contribution from meridional winds in wind speed variations, which has been neglected in most MJO studies.

Abstract

This study investigates the capability for simulating the Madden–Julian oscillation (MJO) in a series of atmosphere–ocean coupled and uncoupled simulations using NCEP operational general circulation models. The effect of air–sea coupling on the MJO is examined by comparing long-term simulations from the coupled Climate Forecast System (CFS T62) and the atmospheric Global Forecast System (GFS T62) models. Another coupled simulation with a higher horizontal resolution model (CFS T126) is performed to investigate the impact of model horizontal resolution. Furthermore, to examine the impact on a deep convection scheme, an additional coupled T126 run (CFS T126RAS) is conducted with the relaxed Arakawa–Schubert (RAS) scheme. The most important factors for the proper simulation of the MJO are investigated from these runs.

The empirical orthogonal function, lagged regression, and spectral analyses indicated that the interactive air–sea coupling greatly improved the coherence between convection, circulation, and other surface fields on the intraseasonal time scale. A higher horizontal resolution run (CFS T126) did not show significant improvements in the intensity and structure. However, GFS T62, CFS T62, and CFS T126 all yielded the 30–60-day variances that were not statistically distinguishable from the background red noise spectrum. Their eastward propagation was stalled over the Maritime Continent and far western Pacific. In contrast to the model simulations using the simplified Arakawa–Schubert (SAS) cumulus scheme, CFS T126RAS produced statistically significant spectral peaks in the MJO frequency band, and greatly improved the strength of the MJO convection and circulation. Most importantly, the ability of MJO convection signal to penetrate into the Maritime Continent and western Pacific was demonstrated. In this simulation, an early-stage shallow heating and moistening preconditioned the atmosphere for subsequent intense MJO convection and a top-heavy vertical heating profile was formed by stratiform heating in the upper and middle troposphere, working to increase temperature anomalies and hence eddy available potential energy that sustains the MJO. The stratiform heating arose from convective detrainment of moisture to the environment and stratiform anvil clouds. Therefore, the following factors were analyzed to be most important for the proper simulation of the MJO rather than the correct simulations of basic-state precipitation, sea surface temperature, intertropical convergence zone, vertical zonal wind shear, and lower-level zonal winds: 1) an elevated vertical heating structure (by stratiform heating), 2) a moisture–stratiform instability process (a positive feedback process between moisture and convective–stratiform clouds), and 3) the low-level moisture convergence to the east of MJO convection (through the appropriate moisture and convective–stratiform cloud processes–circulation interactions). The improved MJO simulation did improve the global circulation response to the tropical heating and may extend the predictability of weather and climate over Asia and North America.