Seasonal prediction is essentially an initial value problem, where as the climate projections of the IPCC are primarily a boundary value problem. Decadal prediction is both. As such, closing the current gap between seasonal prediction and climate change projections is important not only because of the clear socio-economic benefit, but also through contributing to reducing uncertainties in climate change projections.Presented here are seasonal-to-decadal hindcasts performed with the IPCC version of the MPI-OM/ECHAM5 climate model, and covering the period 1960-2005. Initial conditions for all hindcasts are obtained from coupled simulations in which model SST are relaxed towards observations. Radiative forcing is as observed or following the IPCC A1B scenario. Tropical Pacific and Indian Ocean SST variations are well predicted out to 6

An assessment of the ability of 20th-century simulations from the WCRP/CMIP3 models in reproducing the seasonal, intraseasonal and year-to-year climate variations in South America will be presented. In addition, climate change projections over South America and the associated uncertainty issues will be also discussed. Models are able to reproduce in some extent the basic features of the precipitation seasonal cycle over South America; although the precipitation amounts in the SACZ, monsoon core, and La Plata Basin regions are not well represented. The spatial patterns of precipitation variability on interannual and intraseasonal time scales are somewhat represented by some of the models, although they lack in describing correctly the remote influence of forcing like ENSO, AAO. There is a generalized consensus among models that seasonal precipitation changes projected for the second half of 21C are mainly an increase of precipitation over southeastern subtropical South America and reduction along the southern Andes. However, current climate model projections over South America still exhibit a considerably large range of uncertainties that need to be reduced. Moreover, the physical explanation of those climate changes is not clear yet. Changes in the storm tracks as well as in the mean conditions of the tropical regions might have a role in explaining them.

An operational Global Ocean Data Assimilation System (GODAS) has been developed at the National Centers for Environmental Prediction (NCEP). A retrospective global ocean reanalysis for 1979- present is accessible at the GODAS web site (http://www.cpc.ncep.noaa.gov/products/GODAS/), which provides the public an easy access to the documentations, model data, model validation and oceanic monitoring products of GODAS. The operational GODAS assimilates temperature profiles from XBT, Argo profiling floats and TAO moorings and synthetic salinity that are constructed from temperature and a local T-S climatology. The operational GODAS was updated in April 2007 with inclusion of the Altimetry sea surface height, but not the Argo salinity. This is because assimilation of the Argo salinity had large impacts on the quality of the ocean analysis, which would likely influence CFS forecast, initialized with the operational GODAS. Detailed validation of the GODAS ocean analysis against independent observations and analysis of impacts of Argo salinity will be presented. To maximize the utility of the GODAS ocean analysis for real time global oceanic monitoring, climate attributions and climate nowcasting, NOAA’s Climate Prediction Center initiated “Monthly Ocean Briefing” in May 2007. The briefing aims to provide a monthly assessment of how the state of the global ocean evolved recently, what is the interaction with atmosphere, and how recent CFS model predictions verify. The briefing consists of a PPT presentation and conference call, and is becoming a valuable product for both research and operational communities. The first part of the briefing describes the recent evolutions and current conditions of the ocean in each basin. A SST heat budget analysis is used to explain the SST tendencies for the major air-sea coupled modes such as ENSO. The influences of MJO-related winds on oceanic Kevin waves and ENSO are discussed. The impacts of extra-tropical winds on the ocean and coastal upwelling along the western coast of North America are monitored. The second part of the briefing discusses the biases in GODAS and their potential impacts on the recent performance of the CFS ENSO forecast.

In N.H. summer, the influence of ENSO SST on N.H. atmospheric circulation is significantly weaker than in the winter season. Hence seasonal predictions by coupled global climate models show notably lower skill over the N.H. in summer than in winter, especially over land. Research over the past decade or more has indicated that proper land surface physics, land characteristics and land-state initialization (soil moisture, snowpack) is important for improving N.H. summer seasonal predictions with coupled global models.

In this study, we investigate the impact of different land models and different sources of land initial conditions on summer season predictions of the NCEP global coupled Forecast System (CFS). Specifically, 20-25 years of ensemble CFS summer forecasts are executed from late April initial conditions for four configurations of the CFS with two land models and two sources of initial land states. We present the precipitation and temperature prediction skill of these CFS summer forecasts, with a focus on prediction skill over CONUS. Additionally, we present some assessments over the Asian monsoon region (provided by CPC collaborators). The two land models in the above experiments are 1) the older OSU LSM used in the presently operational CFS and 2) the newer Noah LSM used operationally in NCEP's medium-range Global Forecast System (GFS) since June 2005. The two sources of initial land states are 1) the NCEP/DOE Global Reanalysis 2 (GR2), which utilizes the OSU LSM and 2) the new NCEP Global Land Data Assimilation System (GLDAS), which utilizes the new Noah LSM. Results from our experiments show that merely upgrading the land component of a global climate model for seasonal forecasting without providing initial land states that are selfconsistent with the land model upgrade can actually degrade the performance of the global model.

The influence of Indian Ocean Dipole (IOD) on poleward propagation of boreal summer intraseasonal oscillations (BSISO) is examined using observed datasets and coupled model outputs. The study finds that coherent (incoherent) poleward propagation of precipitation anomalies from 5S to 25N are observed during negative (positive) IOD years. Disorganized poleward propagation of BSISO in the south equatorial Indian Ocean is observed during positive IOD years. The rationale behind such an anomaly in poleward propagation of BSISO in contrasting IOD years are identified based on the theory of northward propagating BSISO, which suggests the influential role of air-sea interaction on the genesis and propagation of BSISO. We find that the mean structure of moisture convergence and meridional specific humidity distribution undergoes radical changes in contrasting IOD years, which in turn influences the meridional propagation of BSISO. This study assumes significance, considering the critical role of BSISO in modulating the seasonal mean summer monsoon rainfall.

The Madden-Julian Oscillation (MJO) is the leading mode of intraseasonal climate variability across the global tropics and results in substantial areas and periods of enhanced / suppressed tropical rainfall, modulates tropical cyclone activity and monsoon systems and often impacts the extratropical circulation including over the US. CPC is committed to comprehensively monitoring, assessing and predicting the MJO in realtime operations. CPC is particularly interested in and actively pursuing methods to better understand the MJO and include its potential predictability more effectively into CPC operations -- both to improve the current forecasting capability in the week 2-4 time frame but also in the tropics through weekly hazard assessments. A description of current CPC MJO monitoring and assessment activities is given as background and includes a description of the CPC global tropics benefits/hazards assessment. Later, the use of the current version of the Climate Forecast System (CFS) in both operational and research-related MJO activities at CPC is described. Examples shown include the development of experimental realtime MJO forecast tools and its inclusion as part of an MJO objective consolidation effort at CPC. Verification statistics from these tools are shown and preliminary results indicate useful skill out to approximately 10 days on average for calculation of an MJO index. Also, a brief review is given on how the MJO activity during the winter of 2007-2008 affected CFS prediction of the current La Nina event. Finally, a few key messages are given about how COLA-CPC CFS related MJO research may be focused and applied to best and most efficiently aid CPC forecast operations .

Drought in the Sahel has long been a fascinating topic in climate research. For decades, since inception of the drying trend in this region in the late 1960's, scientists have debated its nature. Some hypothesized a bio-geophysical feedback between the anthropogenic disturbance associated with overgrazing and the extension of agricultural activity into marginal lands, driven by population growth, and the regional circulation. Others held
that the monsoon was sensitive to the imprint of the oceans on the large-scale circulation. I will review the modeling research of the past 5 years that demonstrates the dominance of the latter view: drought in the Sahel
was caused by a warming of the oceans, most noticeable in the equatorial
Indian Ocean and in the southern compared to the northern Atlantic.

The Asian summer monsoon exhibits pronounced variability on various time scales. Among them, an irregular propagating intraseasonal oscillation (ISO) with a period of 30–60 days is now considered as one of the most important phenomena to be challenged. Also, the interannual variability of seasonal mean monsoon circulation and its relationship with ENSO has been well recognized and intensively studied. While the ISO and ENSO-monsoon relationships have conventionally been considered separately, several recent studies have pointed to parallels in spatial structure, suggesting that interannual monsoon variability may be a residue of alteration of ISO phases. The relationship between the ISO and interannual variability is one of the key outstanding issues in the Asian monsoon studies.

In the present study, intraseasonal and interannual variability of summer monsoon rainfall in the pentad precipitation data is examined in a simple but flexible probabilistic model. In contrast to most previous analyses, a probabilistic model that explicitly accounts for the stochastic aspect of atmospheric variability, and can be used to generate a large number of stochastic rainfall simulations for hypothesis testing. The modeling framework is provided by a Hidden Markov Model (HMM), in which a latent state variable (hidden state) is introduced to enable a simplified factorization of the probability distribution function (PDF) of pentad rainfall.

The HMM selected consecutive phases of ISO as distinctive states and the transition probabilities among the states clearly support a cyclic transition of ISO phase. The transition probability of hidden states also suggests that re-initiation of ISO is more variable than other phases. In the stochastic simulation, the canonical ISO propagation and the level of irregularity are reasonably simulated by HMM up to 20 days time lag. The interannual variation of ISO associated with ENSO is assessed by employing a nonhomogeneous HMM (NHMM). The NHMM results showed some noticeable aspects of interannual variability of ISO. The influence of ENSO onto the ISO is reflected as preferences of particular ISO phases depending on the ENSO condition. In the presence of seasonal mean interannual variability, El-Nino related seasonal mean state is able to be distinguished from the ISO states whereas La-Nina related state is not clearly identified or shares the spatial pattern with ISO states. Statistical post-processing of dynamical seasonal prediction is also attempted by using NHMM with GCM simulation as an input variable.

Recent studies indicate that freshwater flux (FWF) forcing and its directly related changes in salinity can play an active role in maintaining the Pacific climate and its low-frequency variability. However, most previous modeling studies have focused on the roles of forcing components of atmospheric winds and heat flux; the effect of   FWF forcing and induced feedback have been examined mostly in ocean-alone modeling studies.   The impacts of FWF forcing on interannual variability in the tropical Pacific climate system are investigated using a hybrid coupled model (HCM), constructed from an oceanic general circulation model (OGCM) and a simplified atmospheric model, whose forcing fields to the ocean consist of three components. Interannual anomalies of wind stress and precipitation minus evaporation, (P-E), are calculated respectively by their statistical feedback models that are constructed from a singular value decomposition (SVD) analysis of their historical data. Heat flux is calculated using an advective atmospheric mixed layer (AML) model. The constructed HCM can well reproduce interannual variability associated with El Niño-Southern Oscillation (ENSO) in the tropical Pacific.

Historically, only the thermodynamic processes (e.g., water vapor, cloud, surface albedo, and atmospheric lapse rate) that directly influence the top of the atmosphere (TOA) radiative energy flux balance are considered in climate feedback analysis.   One of my recent research areas is to develop a new framework for climate feedback analysis that explicitly takes into consideration not only the thermodynamic processes that the directly influence the TOA radiative energy flux balance but also the local dynamical (e.g., evaporation, surface sensible heat flux, vertical convections etc) and non-local dynamical (large-scale horizontal energy transport) processes in aiming to explain the warming asymmetry between high and low latitudes, between ocean and land, and between the surface and atmosphere.   

In this talk, I will begin with a brief review on the partial radiative perturbation (PRP) method, the primary climate feedback analysis method used in the IPCC AR4 report.   To demonstrate the need for developing a new framework, I will present a theoretical evidence showing the change in the atmospheric poleward energy transport is one of the leading factors causing the polar warming amplification.   The theoretical proof resolves the seemingly paradox, namely, "how can the warming in high latitude be greater than the low latitude warming by the atmospheric poleward heat transport given the fact the atmospheric poleward heat transport itself is driven by the poleward decreasing temperature profile?"

Next, I will propose a coupled atmosphere-surface climate feedback-response analysis method (CFRAM) as a new framework for estimating climate feedback and sensitivity in coupled general circulation models with a full physical parameterization package. The formulation of the CFRAM is based on the energy balance in an atmosphere-surface column.   In the CFRAM, the isolation of partial temperature changes due to an external forcing alone or an individual feedback is achieved by solving the linearized infrared radiation transfer model subject to individual energy flux perturbations (external or due to feedbacks).   The partial temperature changes are addable and their sum is equal to the (total) temperature change (in the linear sense).   The decomposition of feedbacks is based on the thermodynamic and dynamical processes that directly affect individual energy flux terms.    Therefore, not only the feedbacks that directly affect the TOA radiative fluxes but also those that do not directly affect the TOA radiation are explicitly included in the CFRAM. The differences between the CFRAM and PRP will be illustrated using a radiative-convective climate model.

In the end, I will present some results obtained with an idealized GCM model that does not include the hydrological cycle (therefore, cloud and ice-albedo feedbacks are not included). The CFRAM is used to isolate the partial temperature changes due to the external forcing, due to water vapor feedback, local vertical convection and non-local dynamical feedbacks. The sum of these partial temperature changes is responsible for the (total) atmospheric and surface warming patterns derived from the GCM climate perturbation simulations.   The feedback analysis using the CFRAM shows that the stronger polar warming in this idealized GCM climate simulation is solely due to the non-local dynamical feedback, as in the theoretical model.

The influence of atmospheric stochastic forcing and uncertainty in initial conditions on the limit of predictability of the NCEP Climate Forecast System (CFS) is quantified based on comparisons of idealized identical twin prediction experiments using two different coupling strategies. In the first method, called the interactive ensemble, a single oceanic general circulation model (GCM, Modular Ocean Model version 3 of GFDL) is coupled to the ensemble average of multiple realizations (in this case six ensemble members) of an atmospheric GCM (NCEP Global Forecast System). In the second method the standard CFS is used. The interactive ensemble is specifically designed to reduce the internal atmospheric dynamic fluctuations that are unrelated to the sea surface temperature anomalies via ensemble averaging at the air-sea interface, whereas in the standard CFS, the atmospheric noise (i.e., stochastic forcing) plays an active role in the evolution of the coupled system.

In the identical twin experiments presented here, we take the perfect model approach, thereby explicitly excluding the impact of model error on the estimate of the limit of predictability. The experimental design and the analysis separately consider how uncertainty in the ocean initial conditions (i.e., initial condition noise) versus uncertainty as the forecast evolves (i.e., noise due to internal dynamics of the atmosphere) impact estimates of the limit of predictability. Estimates of the limit of predictability are based on both deterministic measures (ensemble spread and root mean square error) and probabilistic measures (relative operating characteristics). The analysis examines both oceanic and atmospheric variables in the tropical Pacific. The overarching result is that noise in the initial condition is the primary factor limiting predictability, whereas noise as the forecast evolves is of secondary importance.

The South American Monsoon (SAM) will be used as a kaleidoscope to understand the global monsoons. In this talk I will briefly introduce the observed South American Monsoon (SAM) climate and its variability. The talk will then dwell on the role of airsea interactions and land-atmosphere coupling on the variability of the SAM precipitation. But these interactions are indeed not unique to SAM. A panoramic view of such coupled interactions existing in other monsoon regions of the globe will also be presented.

After demonstrating that coupled interactions are an integral part of the monsoons, the challenging issue of seasonal coupled prediction will be discussed. This will be done in the context of the ENSO predictions. ENSO is regarded to be the largest known natural coupled ocean-atmosphere phenomenon. Since ENSO has a bearing on the interannual variations of the monsoons, it is important to realize the difficulties in predicting this phenomenon. The deleterious role of the “initialization shock” in coupled model predictions of ENSO will be demonstrated.