Outstanding Issues in Seasonal to Interannual Climate Prediction III
Presiding: G P Compo, NOAA-CIRES Climate Diagnostics Center; A W Robertson, International Research Institute for Climate Prediction, Columbia University
A43B-01 13:35h
Interactions Between ENSO and Tropical Landcover Change
Recent research has indicated that historical landcover changes in the tropics and in southeast Asia in particular cause similar magnitude circulation changes as seen in the alternating phases of climatological ENSO. By affecting the tropical divergent circulation, these changes have global reach. Because most observed changes in climate in recent decades are associated with shifts in circulation regime and because landcover change in the tropics appears to have the ability to generate similar magnitude circulation changes as those observed, the non-linear interactions between ENSO and landcover change generated circulation shifts is an important question. We will present results from ensemble general climate model simulations showing circulation changes due to tropical landcover changes; changes due to El Nino-like SST anomalies and circulation changes due to a combination of both effects. We address the possibility that landcover changes have made the El Nino phase of ENSO more likely and whether landcover changes have affected ENSO/Asian monsoon dynamics.
A43B-02 13:50h
Seasonal Predictions over the Southeast U.S. using the FSU Regional Spectral Model
We have developed a regional spectral model for weather and climate studies and prediction. The regional model is embedded in the FSU Global Spectral Coupled Model (FSUGCM), though in principle it can be coupled to other models or analyses, or nested within the regional model itself. The Florida State University Regional Spectral Model (FSUNRSM) utilizes the spectral method in the horizontal direction using Double Fourier trigonometric series. The regional model is a perturbation model, meaning that only deviations from the global, or base, solution are represented by the spectral functions. The regional model was designed to be compatible with the FSUGSCM. As a result, the regional model has available to it the same array of physical parameterizations, including six convection schemes, the FSU physics package and most of the NCAR CCM3.6 atmospheric physics package. The regional model also shares the same sigma-coori dinate vertical structure with Charney-Phillips staggering. The FSUNRSM is very similar in concept to the NCEP Regional Spectral Model, though with some significant differences in implementation. We will provide a brief overview of the model, including some recent enhancements, such as the inclusion of the Community Land Model version 2. We will also present some results for seasonal predictions of rainfall over the Southeast U.S. In these experiments, we ran 12 4-month integrations for the years 1986-1997 starting November 1 of each year. The seasonal precipitation anomalies (December-February) were reasonably well simulated by both the global and regional models, with the regional model performing somewhat better. More importantly, the regional model was better able to simulate the frequency of rainfall events than the global model, and in reasonable agreement with cooperative station data.
A43B-03 14:05h
Variability, predictability and prediction of DJF climate in NCEP Climate Forecast System (CFS)
A big achievement of NCEP in 2004 is the implementation of a new climate forecast system (CFS) for seasonal and inter-annual climate forecast. The CFS is a fully coupled global ocean, land and atmosphere model. Its modest climate drift makes "1-tiered" climate prediction practical. A set of 24-year retrospective forecasts was made for evaluating the CFS forecast skill. With this retrospective forecast data, we have further investigated the following issues: (1) the extent of CFS climate drift in seasonal forecast and the tropics-extratropic connections in the climate drift; (2) variability and predictability of seasonal climate in the coupled system; (3) characteristics of ENSO related climate anomalies in CFS forecasts; (4) the actual and the potential (or upper limit) of the CFS forecast skill. The results of this study not only provide a comprehensive evaluation of the CFS, but also help to advance our understanding of climate variability and predictability. Some results are even valuable to indicate avenues of model improvement.
A43B-04 14:20h
Pros and Cons of 1-tiered versus 2-tiered Seasonal Forecast Systems
All reasonable seasonal forecast systems have advantages and disadvantages.Some advantages/disadvantages may be theoretical; others may be practical. A clear understanding of where the limitations of a particular forecast system lie is helpful in making the most of the tool(s) in hand. In this presentation we examine the good, the bad and the ugly in both 1-tiered forecast systems (i.e. coupled ocean-atmosphere general circulation models or CGCMs) and 2-tiered forecast systems (i.e. atmospheric general circulation models or AGCMs). AGCMs are potentially hindered by unphysical air-sea fluxes in the mid-latitudes and warm pool regions, where the observations suggest that the atmosphere forces changes in the ocean, rather than the other way around. In CGCMs the ocean and atmosphere evolve harmoniously, but that is no guarantee that their air-sea fluxes are correct.And indeed, CGCMs have problems with climate drift and large-scale systematic biases because of difficulties in getting the proper air-sea fluxes. To what extent these physical limitations limit skill in seasonal climate prediction will be presented. Suggestions will be offered for how one might capitalize on the strengths of both types of dynamical forecast systems, while minimizing the weaknesses, in constructing a seasonal climate forecast.
A43B-05 14:35h
ENACT : Advanced ocean data assimilation for seasonal forecasting
A comprehensive set of multi-decadal ocean analysis is now available as part of the recently completed European Commission ENACT (Enhanced ocean data Assimilation and Climate predicTion) programme. These were produced using different ocean models and data assimilation techniques but the same atmospheric fluxes and observations. The aim was to produce a comprehensive assessment of the mean ocean state and its variability and assess the benefit of using advanced assimilation schemes (3D and 4D variational methods and ensemble Kalman filters) for initialising coupled seasonal forecast models. The ability of several of these analyses to initialise ensemble based CGCM seasonal forecasting systems has been assessed. Whilst benefits can be found in some regions and for some seasons, it is clear that forecast model errors can overshadow improvements gained from the advanced ocean assimilation. This has implications for the tuning of the ocean data assimilation schemes but also calls for a better understanding and handling of model initialisation shock and CGCM forecast drift.
http://www.lodyc.jussieu.fr/ENACT/
A43B-06 14:50h
Atmospheric response to North Pacific Oceanic Variability in an Coupled Model
Atmospheric response to a mid-latitude winter SST anomaly is studied in a coupled ocean-atmosphere general circulation model. The role of ocean-atmosphere coupling is examined with ensemble experiments of different coupling configurations. The atmospheric response is found to depend critically on ocean-atmosphere coupling. The full coupling experiment produces the strongest warm-ridge response and agrees the best with a statistic estimation of the atmospheric response. The fixed SST experiment and the thermodynamic coupling experiment also generate a warm-ridge response, but with a substantially weaker magnitude. This weaker warm-ridge response is associated with an excessive heat flux into the atmosphere, which tends to force an anomalous warm-low response and therefore weakens the warm-ridge response of the full coupling experiment. Our study suggests that the atmospheric response is associated with both the SST and heat flux. The SST forcing favors a warm-ridge response, while the heat flux forcing tends to be associated with a warm-low response. The correct atmospheric response is generated in the fully coupled model which produces the correct combination of SST and heat flux naturally.