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Examining the Predictability of the Tropical Atlantic Variability using Coupled Prediction Models

Principal Investigator(s): Bohua Huang, Center for Ocean-Land-Atmosphere

Year Initially Funded: 2007

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


Understanding the predictability of the tropical Atlantic variability (TAV) is crucial for short-term climate prediction in the Atlantic sector. Two major TAV mechanisms are regional air-sea interaction and remote El Niño/Southern Oscillation (ENSO) influence, both potentially predictable on the seasonal-to-interannual time scales. Mid-latitude seasonal atmospheric anomalies over the North and South Atlantic may also be useful precursors for the tropical anomalies in subsequent seasons. In a dynamical forecast model, these mechanisms and their interactions should be represented realistically and initialized accurately. This study examines the TAV predictability using a coupled ocean-atmosphere general circulation model (CGCM) with realistic ocean/atmosphere initial states. In particular, we would like to understand what kind of initial surface and subsurface anomalies within the Atlantic Ocean can damp or amplify the remote ENSO influences, and vice versa. We will also examine under what conditions the midlatitude anomalies can stimulate major tropical air-sea feedback on seasonal time scales. 

Our previous studies have shown that this CGCM can simulate the major TAV features realistically. For this study, we propose further improvements in its simulation and initialization. We will conduct an empirical CGCM error correction that prescribes observational climatological monthly mean low-cloud amount in the model while allowing the anomalous model sea surface temperature (SST)-low cloud feedback. The mean cloud correction targets specifically the inadequate simulation of low cloud fraction over the southeastern tropical Atlantic, which is a major component of the current CGCM systematic bias and unrealistic annual cycle in this region. The preserved anomalous SST-low cloud feedback is important for the interannual variability. To minimize the CGCM initial shock, a new model initialization technique will be tested. We will conduct a series of coupled “initialization runs”, in which the coupled system is nudged continuously toward the observed climate anomalies derived from the oceanatmosphere analyses. Initial conditions from these runs should be more in balance between the ocean and atmosphere and likely represent the low-frequency signals better than the instantaneous initial ocean-atmosphere states separately generated by uncoupled atmospheric and oceanic data assimilation systems. To take into account the potentially significant uncertainty of current oceanic analysis in the tropical Atlantic Ocean, we will use several different ocean analysis products to generate an ensemble of oceanic states with sufficient spread. Using the improved CGCM, we will conduct a set of hindcast experiments for 1981-2005 to establishing its predictive skill in the tropical Atlantic. Further sensitivity case studies will be conducted using our regional coupling strategy with the observed or climatological SST anomalies prescribed in the tropical Pacific to study the relative roles of the ENSO forcing and regional air-sea interaction. To link this study more directly to operational prediction, we plan to conduct some experiments with the NCEP Climate Forecast System (CFS), which has been successfully installed at COLA.

Mechanisms and Predictability of Interannual to Interdecadal Climate Variability

Principal Investigator(s): Geoffrey Vallis, Princeton University

Year Initially Funded: 2007

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


We propose a study of the mechanisms and predictability of interannual to interdecadal climate variability. Our general goals are to to understand the underlying mechanisms for climate variability on these timescales, to identify processes that might lead to predictability, and to understand what the intrinsic limits to climate predictability are. Our main tool is a novel hierarchy of climate models, developed using the Flexible Modeling System at GFDL. At the top of the hierarchy is the state-of-the-art, IPCC-class model at GFDL. Our models directly connect to that, but are simplified by using simpler and more economical physics packages, and/or by making simplifications in the geometry. Use of such models allows more experiments to be performed, including ensemble experiments, and mechanisms to be identified. We will focus on extra-tropical variability in the Atlantic sector, including the interannual and decadal variability of the NAO, although somes aspects of the proposed work are more general. The specific topics we propose to investigate include the timescales on which the atmosphere ocean system may be regarded as truly coupled, the timescales on which the atmosphere forces the ocean, the generation and persistence of sea-surface temperature anomalies and the effects of such anomalies on the atmosphere. As appropriate, we shall use still simpler theoretical tools and analyses to try to abstract the mechanisms to their esssentials. We shall also compare and validate our models against the full coupled climate model to ensure that we are in a realistic parameter regime that is relevant to reality.

Examining Oceanic Tropical Biases in Climate Models

Principal Investigator(s): Paul Schopf, George Mason University

Year Initially Funded: 2007

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


Coupled climate models used for studying climate variability and change have evolved dramatically over the past years, with increased resolution, improved numerics, and additional complexity. Coupled GCMs such as the GDFL CM2 coupled model and the NCAR CCSM are playing a major role in the IPCC assessment process, seasonal to interannual climate prediction, paleoclimate and many other studies that depend on the models’ ability to faithfully reproduce the observed climate as a pre-condition for being able to draw strong conclusions about climate variability and change. 

These coupled GCMs seem to have persistent and pervasive biases in their representation of current observed climate states that have proven difficult to resolve. Particularly troubling has been a large bias in the tropics, characterized by a "double ITCZ", cold equatorial Pacific SST, and warm SST along the eastern boundaries of the tropical oceans. This bias has been known since the mid-90s, and has been the subject of studies implicating the low-level stratus clouds in the region (Ma, et al, 1996), the poor resolution of the surface wind fields in AGCMs, the influences of the poorly resolved topography, and ocean resolution. The US CLIVAR program has sponsored workshops (May 2003, September, 2005, and June, 2006 Tropical Biases workshops), and the biases are a prime target of work at NCAR and NOAA GFDL. We have participated actively in all these studies and endorse the premise that a correction of these biases is crucial for the advancement of understanding and prediction of the climate. It is through these community efforts that we seek to contribute to the overall improvement in coupled GCM simulations of climate and its variability. We seek to examine oceanographic aspects of the problem and in particular, we want to examine the processes that set the equilibrium subsurface properties in the tropical ocean upwelling regions and how they might be connected. 

Decadal Climate Predictability and Predictions - Focus on the Atlantic

Principal Investigator(s): Thomas Delworth, NOAA/Geophysical Fluid Dynamics Laboratory

Year Initially Funded: 2007

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


There is currently limited understanding of the mechanisms of decadal climate variability, and of the potential predictability of the climate system on decadal time scales. Models currently used for decadal and longer climate change projections do not start their projections from the observed state of the ocean. Therefore, a potential source of skill for decadal climate change simulations is neglected. On the decadal scale, the relative roles of forced climate change and internal natural variability may be comparable. Thus, an improved understanding of decadal variability and predictability could lead to significant improvements of decadal scale climate projections. One potentially important region is the Atlantic, where multi-decadal scale warming has apparently led to increased hurricane activity. The relative contributions of anthropogenic forcing and internal variability to that increase of hurricanes is unknown, but it is precisely this question that is crucial for future estimates of hurricane activity. 

We describe a systematic program of research activities whose aim is to (i) improve our understanding of the mechanisms of Atlantic decadal variability, (ii) evaluate potential predictability of the climate system, (iii) develop the necessary tools to make decadal climate predictions starting from observed ocean states, and (iv) conduct ensembles of decadal climate predictions starting from estimates of the observed state of the ocean. This research will be primarily conducted using GFDL s CM2.1 global climate model, as well as future climate models currently under development at GFDL. A crucial component of the research will be the further development and use of a novel assimilation technique recently developed at GFDL. The outcome of the research should be (i) an improved understanding of the mechanisms of Atlantic decadal variability, (ii) an evaluation of decadal scale predictability, (iii) a prototype system for making decadal climate predictions, including a newly developed assimilation system that will make state of the art estimates of the ocean from modern observational networks, and (iv) several ensembles of experimental decadal scale forecasts. 

A Multi-model Approach Toward the Attribution of U.S. Climate Variation and Change

Principal Investigator(s): James W. Hurrell, National Center for Atmospheric Research; Martin P. Hoerling and Jon Eischeid, NOAA/Earth System Research Laboratory

Year Initially Funded: 2007

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


Key aspects of regional U.S. climate variability and change during the past century lack explanation. What, for example, are the processes and causes responsible for the observed strong seasonality in U.S. surface temperature changes as well as for the spatially inhomogeneous warming? The western U.S. has been the epicenter for warming in recent decades, particularly in spring and summer, and this has led to early snowmelt and premature maximum streamflow. At the same time, there has been a lack of warming in the central U.S., especially during summer, in spite of the warming expected in the interior continent from increasing levels of greenhouse gases in the atmosphere. 

Strong decadal variations of U.S. climate during the last century have confounded both the detection and the attribution of regional climate trends. Prominent among these is the relatively abrupt shift in Pacific-North American climate in the mid-1970s. Other features include the decadal swings between U.S wet regimes (1910s, 1980s-90s) and dry regimes (1930s, 1950s, 2000s). Do these events reflect internal atmospheric variability? Are they the response to decadal variations in the state of the global ocean? What has been the role of anthropogenic forcing? Identifying the factors responsible for the observed low frequency variability is a necessary step toward implementing a credible decadal prediction system and for improving climate information for decision makers. 

Our proposal will increase understanding of observed U.S. climate variability and change through parallel development and analysis of observational and model-generated datasets, and through systematic numerical experimentation to allow attribution of observed variability to processes and causes. In particular, we seek to identify those factors driving fluctuations in U.S. surface temperature and precipitation on the regional scale by employing a hierarchy of existing climate model simulations, as well as new experiments targeted specifically to elucidate the role of oceanic variability. We will employ a multi-model architecture and make resulting data available to the broader research community. 

Diagnosing and Improving Convective Processes in Large-Scale Ocean-Atmosphere-Land Interaction

Principal Investigator(s): David Neelin and Ben Lintner, University of CaliforniaΓÇôLos Angeles

Year Initially Funded: 2008

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


Despite the key role of precipitation in climate and climate impacts, it remains one of the most poorly modeled climate variables. In addition to well-known biases in the simulated climatology of tropical precipitation, there are also biases in tropical precipitation sensitivity to climate perturbations. For example, even if a model has its convection zone in the proper mean location vis a vis the observations, it does not necessarily follow that the sensitivity of the convection to variations in temperature, wind, or inflow water vapor is correct. Under the previous grant, we have developed a number of tools that we propose can contribute to identifying and addressing the biases in convective processes. In particular, we outline new diagnostic methods to understand the transition to strong convection, presenting preliminary examples using satellite observations of precipitation and column water vapor. We propose to apply these diagnostics to better constrain the temperature and moisture dependences of the onset of precipitation required for climate model convective parameterizations, and to contrast these measures in observations to current model simulations. We propose to complement the satellite observations with in situ sounding data for vertical structure in strongly convecting regions, beginning with the high temporal coverage Atmospheric Radiation Measurement (ARM) Program site at Nauru Island. We will focus first on quantifying the convective threshold and its dependences on thermodynamic variables. The water vapor-temperature dependence of the onset of convection can be important to mechanisms by which large-scale processes, such as the wave dynamics occurring in teleconnections, or inflow air masses from dry regions into the convective margins, interact with the small-scale convective processes. We propose to examine how variations in inflow from the dry Southeastern Pacific into the South Pacific Convergence Zone (SPCZ) interact with the threshold for the the onset of convection in models compared to observations. This will help quantify the contribution of this interaction to the mean and sensitivity biases of the convectivemargin in this region. We propose to analyze similar impacts of the variations in the moisture relative to the convective threshold in ENSO variations. We will aim to formulate our diagnostics of observed and modeled convective processes in terms that can be directly useful to modeling groups. 

Understanding ENSO Biases in GCMs and Their Relation to Mean State Biases

Principal Investigator(s): Daniel Vimont, University of WisconsinΓÇôMadison; David Battisti, University of Washington

Year Initially Funded: 2008

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


El Niño / Southern Oscillation (ENSO) variability represents the leading source of interannual variability in the tropical Pacific and globally. Our understanding of ENSO developed rapidly in the 1980’s and 1990’s with the development of intermediate coupled models in which ENSO variability operates around a prescribed mean state. This was a useful approach, as it has been found that ENSO characteristics are very sensitive to details of the tropical Pacific mean state and seasonal cycle. At the same time, global climate models (GCMs) have improved to the point that ENSO variability exists, in some form, in many of the current generation of GCMs. Unfortunately, large, and even small, biases in GCM simulations of the tropical mean state lead to large biases in simulations of ENSO variability. While attempts have been made to relate biases in ENSO variability to biases in the mean state of the tropical climate, analysis has been limited to analysis of existing GCM output, qualitative comparisons between GCM output and coupled dynamical theory, and analysis of modal characteristics using very simple models. 

The present proposal outlines a research plan aimed at quantitatively estimating the influence of mean state biases on ENSO biases in the present generation of GCMs. This will be accomplished by development and application of a linearized version of the intermediate coupled models described above. This linear ocean / atmosphere model (LOAM) can be tuned around observed or modeled mean states, and once having done so, has been shown to reproduce characteristics of the respective observed or modeled ENSO variability (e.g. amplitude, stability, period, seasonal phase-locking, regularity). An advantage to the linear model is that it can be used to investigate the sensitivity of ENSO characteristics to specific features in the mean state by tuning model parameters to, say, observations, and substituting individual parameters derived from a model. A research strategy is described that uses this model to: 1. Characterize (quantitatively) the spatial and temporal structure of modeled ENSO variability, and ENSO characteristics when the LOAM is linearized around each model’s mean state. 2. Using the LOAM, conduct sensitivity studies to quantify how mean state biases affect bias in ENSO simulation. 3. Using the LOAM, conduct sensitivity studies to understand changes in ENSO behavior under future climate scenarios. 

This proposal directly addresses CVP’s focus area in two ways: (1) it proposes a strategy for understanding the source of bias in simulated interannual ENSO variability, and (2) it identifies specific biases in the mean state that produce those biases. By identifying ENSO sensitivity to specific mean state biases, the work will provide quantitative guidance for modeling groups trying to improve ENSO simulation. 

Toward Reducing Climate Model Biases in the Equatorial Atlantic and Adjacent Continents

Principal Investigator(s): Shang-Ping Xie, University of Hawaii

Year Initially Funded: 2008

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


In the latest model intercomparison, most coupled ocean-atmosphere general circulation models (GCMs) continue to suffer serious errors in their simulations of tropical Atlantic climate. Two errors common to all the models are 1) the failure to develop an eastern cold tongue on the equator, associated with a westerly surface wind bias and 2) an erroneous southward shift of the intertropical convergence zone (ITCZ) associated with a warm bias south of the equator. Such errors in the mean state seriously limit the models' skills in seasonal prediction and future climate projection. 

Recent analyses of simulations in the IPCC Fourth Assessment Report (AR4) data archive hint that tropical Atlantic biases in coupled models originate from their atmospheric component. Specifically, the westerly wind error on the equator and the double ITCZ bias are already present during boreal spring in atmospheric simulations forced by observed SST. The spring westerly error depresses the thermocline and prevents the cold tongue from developing in the equatorial Atlantic in the subsequent season. Furthermore, studies show that simulated spring rainfall is deficient and excessive over equatorial South America and Africa, respectively, suggesting that continental precipitation biases are key to the westerly wind error over the equatorial Atlantic. 

The PIs propose to identify the sources of tropical Atlantic biases and investigate how they develop in coupled GCMs using a suite of diagnostic and modeling studies. First they will develop metrics to evaluate coupled model simulations of tropical Atlantic climate and identify common sources of error. They will apply them to the AR4 output as well as the upcoming AR5 simulations as the latter become available by early 2010. They will use the NOAA/GFDL Climate Model CM 2.1 and its atmospheric component to test the hypothesis that continental rainfall biases cause the models failure to develop the equatorial cold tongue, by perturbing convective heating over tropical South America. 

Diagnosing Local and Remote Coupling Errors in the Tropics

Principal Investigator(s): Prashant Sardeshmukh, NOAA/Earth System Research Laboratory

Year Initially Funded: 2008

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


The primary goal of this project is to gain a better understanding of the local and remote sources of tropical biases in climate models through an analysis of local and remote dynamical interactions. The PIs will attempt this by diagnosing both local coupled interactions and remote interactions among 8 geographically localized tropical areas (four in the tropical Pacific, two each in the Indian and Atlantic basins) in observational datasets, in the NCEP/CFS, NCAR/CCSM3, and all available IPCC model simulations, and through additional model integrations of the PIs. 

The project will have substantial diagnostic and modeling phases. The plan is to estimate local and remote coupling matrices from observational data and all available climate model simulations, and to perform extensive intercomparisons among them. In the modeling phase of the project, they will attempt to reproduce the results obtained in the diagnostic phase with additional integrations of the atmospheric components of the NCAR (CAM3) and NCEP (GFS) models, but now coupled to a mixed layer ocean with simple parameterized Ekman dynamics. 

Seasonal Biases in the Tropical Atlantic Sector in Climate Models: Causes and Impact on Interannual Variability

Principal Investigator(s): James Carton, University of Maryland

Year Initially Funded: 2008

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


The PIs propose to complete a diagnostic examination of the relationship between bias in the representation of the seasonal cycle and CGCM simulation of climate variability, and secondly a climate modeling study using the bias-corrected seasonal cycle. They focus on the Atlantic sector partly because the bias is more severe there than in the Pacific. This proposal will extend their previous study to a multi-model analysis in order to look at the impact of this seasonal bias on errors in representation of climate variability. The PIs will also apply these results to improve representation of climate variability in CGCMs, focusing on the NOAA/GFDL CM2.1 model in cooperation with members of the GFDL climate group. 

The current plan is to continue a diagnostic examination of bias in climate variability in the tropical Atlantic sector of NCEP, NCAR, and GFDL CGCMs. The analysis includes the relative roles of local dynamic and thermodynamic air-sea interactions and the remote influences of ENSO and extratropics on the tropical Atlantic sector. For CM2.1, they already have access to an interesting suite of experiments including experiments in which SST in the tropical Pacific and Indian sectors are replaced with climatological monthly SST and a flux-corrected experiment with an "improved" climatological seasonal cycle. They will design sensitivity experiments to examine the response of simplified and full atmospheric models to a bias correcting forcing. The ultimate goal of these sensitivity studies is to formulate suggestions for the improvement of coupled models. 



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