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Mechanisms of Low-Frequency Variability of the Atmospheric Circulation Over the 20th Century

Principal Investigator(s): Brian Soden, University of Miami Rosenstiel School of Marine and Atmospheric Science

Year Initially Funded: 2010

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


All climate models predict a weakening of the tropical atmospheric circulation in response to anthropogenic increases in greenhouse gases. Analysis of climate model simulations from the CMIP4 archive indicate that the atmospheric circulation may weaken by as much as 25% by the end of the century (Vecchi and Soden, 2006). However, observed variability of the Walker circulation over the past few decades appears dominated by unforced internal variability (Burgman et al. 2008). This decadal variability in the atmospheric circulation also appears to be amplified by the response of low-level clouds (Clement et al. 2009), and hence these clouds may be an important component of decadal variability. Circulation changes can have significant impacts on cloud feedbacks in response to anthropogenic warming, particularly marine stratocumulus clouds, which have been identified as one of the main sources of uncertainty in global warming projections. While our prior work has identified patterns of decadal variability in the tropical circulation, the causes of these changes and their implications for climate are still not known. For example, what are the relative contributions of internally-generated variability and external forcing? What role does the ocean play in generating decadal atmospheric variability? What role do low clouds play on decadal and longer-timescales? We propose to address these questions with a modeling and diagnostic study that is focused on three separate tasks:

• Task I: Determine what aspects of low-frequency changes in the atmospheric circulation over recent decades can be attributed to unforced internal variability, or to external (anthropogenic and natural) climate forcings using both observations and CMIP5 climate model simulations under different forcing scenarios.

• Task II: Investigate the mechanisms of variability in the tropical atmospheric circulation using idealized climate model simulations

• Task III: Evaluate the impacts of atmospheric circulation changes on cloud feedback from marine stratocumulus clouds.

The proposed work will address the FY 2010 priority of 'understanding the causes of climate variability over the observational record,' and we will 'attempt to quantify the roles of radiative forcing and natural climate variability for explaining the observed climate record.'

Understanding and Predicting Interannual to Multi-Decadal Variability of Atlantic Hurricane Activity

Principal Investigator(s): Kerry Emanuel, Massachusetts Institute of Technology; Gabriel Vecchi, NOAA/Geophysical Fluid Dynamics Laboratory

Year Initially Funded: 2009

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


The applications of two very different methods for deducing (downscaling) tropical cyclone activity from NCAR/NCEP reanalysis data explain, respectively, 60% and 65% of interannual variations in Atlantic tropical cyclone frequency during the period 1980-2006. Yet, when one of these methods is applied to the output of simulations using a global climate model forced by observed sea surface temperature over the same period, far less variance is accounted for, and the upward trend seen in both the observations and the downscaled NCAR/NCEP reanalysis is largely absent. Moreover, when this downscaling technique is applied to ERA40 re-analysis data, the amount of variance explained is comparable to that of the global climate model, and again the upward trend is largely absent. 

This proposal seeks support for an effort to understand the physical reasons for these discrepancies, and by so doing to advance our understanding of environmental control of tropical cyclone activity and its relationship with climate change. We propose to undertake a comprehensive analysis of the physical causes of the variability and trends seen in various downscaled tropical cyclone metrics, focusing on the disparity among the reanalysis-driven and global climate model-driven results. We here present a few hypotheses for the discrepancies and a plan to test these, with the goal of identifying model and/or reanalysis biases that may be affecting the results. To the extent we are successful, we can begin to assess the ability of climate models to predict future variations in tropical cyclone activity resulting from natural and anthropogenic climate variability, while at the same time increasing our understanding of the fundamental environmental controls on tropical cyclone activity. 

Diagnosing Decadal-Scale Climate Variability in Current Generation Coupled Models for Informing Near-term Climate Change Impacts

Principal Investigator(s): Lisa Goddard and Arthur Greene, International Research Institute for Climate and Society (IRI); Gokhan Danabasoglu, National Center for Atmospheric Research; Keith Dixon, NOAA/Geophysical Fluid Dynamics Laboratory; Doug Smith, UK Met Office Hadley Centre

Year Initially Funded: 2009

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


As the relevance of climate change information grows, demand for that information, in particular covering the next 1ΓÇÉ2 decades increases. On the decadal timescale, both natural and anthropogenic factors will influence the evolution of the climate. The scientific community, particularly the international modeling community, has been working towards predictions/projections that consider both the changes in atmospheric composition, relevant to climate change projections, and initial oceanic conditions, relevant to decadalΓÇÉscale climate variability predictions. Initialization of dynamical models, while a very new effort, is considered crucial to reducing uncertainty in the nearΓÇÉterm climate projections. Even with initialized models, questions still exist on the degree to which they exhibit realistic variability on decadal time scales. It is imperative that we examine and document the characteristics of decadalΓÇÉscale variability in CGCMs, particularly in the context of initialized predictions, in order to prepare for the experimental decadal predictions that are starting to emerge from modeling centers. The three objectives that this proposal will address are: 

1) Determine the fidelity of the surface expression of oceanic decadal variability, and the associated climate teleconnections, in several stateΓÇÉofΓÇÉthe-art CGCMs, with particular emphasis on the impact of initialization. 

2) Develop metrics and baselines for estimating the quality of decadal predictions 

3) Design climate information products for climate risk management and planning. 

We have configured a team of researchers containing scientists from the top international modeling centers involved in the generation of climate projections and experimental decadal predictions, and scientists who focus on assessing and designing information that can benefit regional climate risk management. We will assess decadal predictability through the use of baselines, and by examining the impact of initialization. In any prediction system, one must know the quality of the models, particularly in a prediction context. One must also know how to handle biases that may be masking predictability, how to appropriately quantify uncertainty, and ultimately how to communicate that information in a meaningful, yet compact and flexible manner. The results of our project will contribute to progress in all these areas through work with existing and anticipated model simulations and hindcasts from state-ofΓÇÉthe-art CGCMs. 

Toward a North American Decadal Climate Prediction for the 2011-2020

Principal Investigator(s): James W. Hurrell, National Center for Atmospheric Research; Martin P. Hoerling, NOAA/Earth System Research Laboratory; Arun Kumar, NOAA/Climate Prediction Center; and Xiaowei Quan, University of Colorado

Year Initially Funded: 2009

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


We propose to generate a probabilistic decadal prediction of North American climate for the period 2011- 2020. The methodology will involve large ensemble integrations from multiple atmospheric general circulation models (AGCMs) driven by various, plausible trajectories of global sea surface temperature (SST) over the next decade. The latter will be derived from both uninitialized and initialized coupled climate model experiments. We are motivated by evidence that initial state information from the oceans is a key skill source in nascent attempts at decadal prediction. Furthermore, attribution studies have established that key features of observed regional decadal climate variability have been largely driven by variations in global SST. Multimodel large ensemble methods are proposed in order to generate meaningful statistics of regional climate change on decadal timescales, thereby overcoming current limitations of coupled model prediction efforts resulting from small ensemble size. 

A focus of the project will be to derive an estimate of the potential skill for predicting the evolution of North American decadal climate. We will perform a comprehensive analysis of North American decadal variability from 1900 to present using existing large ensembles of AGCMs driven by observed SST variability and coupled models driven by estimates of observed changes in greenhouse gas, aerosol, solar and volcanic forcing. These will be diagnosed to quantify the variances of North American climate driven by external forcing, internal coupled ocean-atmosphere variations, and internal atmospheric variations alone. As a prelude to our proposed 2011-2020 predictions, we will analyze “decadal hindcasts” over the past twenty years using initialized coupled models and AGCMs to assess perfect-model skill and sources of uncertainty in decadal predictions and predictability.

Investigating the Role of Noise in Decadal Climate Predictability Using a Hierarchy of Coupled Ocean-Atmosphere Models

Principal Investigator(s): Ping Chang and R. Saravanan, Texas A&M University

Year Initially Funded: 2009

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


This is a proposal focusing on exploring climate predictability on decadal or longer timescales. The proposed research builds upon our currently NOAA-sponsored projects in the tropical Atlantic and Pacific Oceans. These research projects have produced a set of coupled ocean-atmosphere models with novel features that we believe are well suited for understanding predictable dynamics at decadal or longer time scales. We propose to use this hierarchy of the coupled climate models to shed light on the intricate interplay among natural modes of climate variability, anthropogenic forcing and weather noise in decadal climate predictability. Our model hierarchy includes 1) an atmospheric general circulation model (CAM3) coupled to a slab ocean (CAM3-ML), 2) an atmospheric general circulation model coupled to a reduced gravity ocean (CAM3-RGO), and 3) an atmospheric general circulation model coupled to a general circulation ocean (CAM3-MOM3). These are equipped with noise filtering algorithms capable of suppressing weather noise, including the signal-noise optimization noise filter developed by Chang et al. (2007) and the interactive coupled ensemble developed by Kirtman and Shukla (2002). These noisefiltering methodologies have proven to be extremely valuable in the understanding of the role of weather noise in ENSO dynamics and its predictability. We anticipate that the same will be true in the understanding of decadal climate predictability. Large ensembles of prediction experiments will be conducted using each of these models. The experiments will be analyzed systematically to test a set of scientific hypotheses aiming at providing insight into physical mechanisms that give rise to any decadal scale predictability – one of the major objectives of this year's NOAA CVP program. An important concern in the design of decadal prediction experiments is the prohibitive computational cost of ensemble forecasts using a high-resolution, global coupled climate model for lead times of a decade or longer. Understanding the role of weather noise, and developing techniques to mitigate its effects, can help minimize the size of the ensembles needed for operational decadal prediction and substantially reduce the computational costs. 

Assessment of Decadal Prediction and Predictability Using Empirical Models

Principal Investigator(s): Matthew Newman and Michael Alexander, NOAA/Earth System Research Laboratory

Year Initially Funded: 2009

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


Given the relatively slow evolution of the ocean, it likely holds the key to North American climate predictions on sub-decadal and longer time scales. We plan to use empirical models trained on multiple variables (SST, thermocline depth, MOC strength, etc.) from ocean assimilation products to forecast the global ocean and its impact on North America. The forecast system will also include surface air temperature and winds, both to improve ocean forecasts and to predict societally relevant quantities. The same approach will also be applied to coupled climate model simulations to identify model errors, determine the processes responsible for predictability, and investigate the extent to which global climate change influences the predictability of the oceans. Our primary forecast method will be linear inverse models (LIMs), which are currently used operationally to predict SSTs in the tropical oceans. We have recently extended the LIM prediction system to include thermocline depth and surface winds, which has improved ENSO predictions at longer leads and encouraged us to explore predictions at decadal time scales. Forecasts will be made on a seasonal basis for at least two years ahead, and on an annual basis for at least five years ahead. In addition to providing skillful forecasts, LIM also allows exploration of important aspects of the dynamical system, including processes that give rise to rapidly growing and/or persistent anomalies and limits to predictability. This information is particularly useful for decadal prediction, since not only does it help determine the construction of an initial ensemble for climate model runs, but it also helps show where observations are most needed to reduce forecast error growth. We will also evaluate the dependence of North American forecasts on information from different regions, investigating linkages between the Atlantic and the Pacific, and tropics and the extratropics. 

The impact of Systematic Biases in Pacific Ocean SSTs on Predictability of the Hydrological Cycle Over North America in Decadal Climate Prediction Studies

Principal Investigator(s): Amy Solomon, NOAA/Earth System Research Laboratory

Year Initially Funded: 2009

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


The World Climate Research Program’s Working Group on Coupled Modeling will be carrying out a coordinated set of model experiments that includes, for the first time, simulations of decadal climate prediction. The ultimate goal of these simulations will be to provide policymakers with information on decadal timescales to assess possible consequences of climate change. To what extent these experiments will be useful to stakeholders and policymakers will depend upon whether there is a predictable signal of climate change and to what extent this signal varies on regional scales. In this proposed research we will focus on systematic errors in the predictable signal forced by sea surface temperature (SST) biases in the coupled model’s response to external forcing. In addition, we will investigate how these model biases limit predictability by impacting the spatial and temporal structure of natural variability. An active hypothesis is that the predictable signal of climate change comes from low-frequency ocean variability and it’s forcing of the atmosphere. We will explore this hypothesis by studying how systematic biases in Pacific Ocean SSTs impact the decadal predictability of the hydrological cycle over North America, focused primarily on the following two questions: 

1. To what extent is the predictable decadal signal over North America related to the spatial pattern of SST anomalies in the Indian and Pacific Ocean basins? 

2. Do systematic biases in Indian and Pacific Ocean SSTs impact potential predictability over North America by forcing regional variations in the climate signal, as well as, biases in the spatial and temporal structure of natural variability? 

Based on the results of previous studies, we will use model output from coupled climate model simulations of the 20th Century as unassimilated decadal climate predictions. We will then use AGCM model studies forced by SSTs output from these simulations to determine how biases in the models’ response to radiative forcing (through lowfrequency ocean variability) impact the decadal predictability of the hydrological cycle over North America. We will focus on identifying physical mechanisms that cause biases in predictability over North America, such as biases in the structure of the PDO. We will study the decadal predictability of the hydrological cycle by focusing our analysis on the variability of rainfall, surface temperature, and circulation patterns over North America. We will investigate strategies to correct for model biases in SSTs thereby improving probabilistic projections of decadal climate forecasts.

Sea-Ice Variability and the North Atlantic Oscillation on Interannual to Decadal Timescales

Principal Investigator(s): Gudrun Magnusdottir, University of California - Irvine

Year Initially Funded: 2009

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


The North Atlantic Oscillation/Northern Annular Mode (NAO/NAM, hereafter NAO) is the most important global mode of atmospheric variability in the northern extratropics especially in winter. It is expressed as a seesaw in mass between high- and mid to subtropical latitudes. This relation is especially dominant in the North Atlantic basin. Sea-ice concentration is to first order forced by the atmosphere and observations show that sea-ice variability in the North Atlantic sector of the Arctic is closely tied to the NAO. The primary mode of variability in sea ice is a dipole with nodes in the Labrador and Barents Seas, respectively. A positive NAO is associated with increased sea-ice concentration in the Labrador Sea from NAO induced wind forcing and decreased sea-ice concentration in the Barents Sea from NAO-induced, positive, oceanic heat-flux anomalies. 

The NAO was in its strong positive polarity from the 1960s to the mid 1990s and during this time sea-ice concentrations decreased in the Barents Sea and increased in the Labrador Sea. When we asked the question in Atmosperic Global Climate Model (AGCM) simulations, is there a feedback from this spatial pattern of change in sea ice back onto the NAO (or atmospheric circulation), we found a clear negative feedback in the equilibrium winter response. We have recently examined the transient response to this sea-ice forcing to determine what processes control the evolution to a negative NAO. We found that the initial modest circulation response from the change in surface fluxes allows a changed configuration of Rossby wave breaking and it is the latter effect that leads to the more prominent and larger scale (equilibrium) response of a negative NAO. Thus internal (or natural) variability indirectly sets the stage for the prominent response to a changed sea ice distribution. It is the interaction of the short time-scale internal variability with the forced initial response that sets the stage for the evolution of the amplified large-scale change. 

We are now entering an unchartered era in sea-ice variability. In addition to the NAO related mode of sea-ice variability (the Labrador-Barents Sea dipole), rapid anthropogenic sea-ice loss, even in winter, is an even more prominent mode of variability. Sea-ice observations are beginning to show this effect, but the clearest signature may be seen in climate model projections. Interestingly, the climate model projection show the NAO related dipole of variability as the second leading mode, the overall sea-ice decline is the first leading mode. 

In this research we seek to identify, understand and quantify the dynamical feedback processes between 1)the atmospheric circulation, 2)sea-ice concentrations and 3)the oceanic heat flux, from observations and a hierarchy of numerical models with the ultimate goal of facilitating prediction of North Atlantic climate on interannual to decadal timescales. The models range from linear stochastic equations linking the NAO index and sea-ice concentration, to AGCMs, to coupled (atmosphere, sea ice, ocean) climate models with a simplified ocean that allow for easier identification of processes, to output from fully coupled state of the art climate models. With coupled reanalysis products on the horizon, the research is timely and holds great potential in the quest for decadal prediction of climate. 

Predicting North American Hydroclimate Change and Variability on the Interannual to Multidecadal Timescale

Principal Investigator(s): Richard Seager, Yochanan Kushnir, Mark Cane and Naomi Naik of Columbia University LamontΓÇôDoherty Earth Observatory

Year Initially Funded: 2009

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


Modeling work has shown that persistent droughts in Southwestern North America are forced by multiyear La Niñas in the tropical Pacific Ocean with a warm subtropical North Atlantic also playing a role in some cases. These persistent droughts, including the severe one that began after the 1997/98 El Niño, place colossal strain on regional water resources, impact agriculture, fires, ecosystems and the regional economy leading to billions of dollars in expenses in disaster relief. In addition the most recent generation of Intergovernmental Panel on Climate Change (IPCC) model climate projections (the Assessment Report 4, AR4) robustly predicts that Southwestern North America will become more arid as part of a general subtropical drying caused by an intensifying hydrological cycle and a poleward shift of the Hadley Cell border and mid-latitude storm tracks. This drying is projected to become comparable in amplitude to naturally occurring drought by mid-century. Prediction of hydroclimate variability and change on the interannual to decadal timescale, if skillful, would allow advance planning across water-sensitive parts of the region’s economic and social systems. 

 

In a collaborative effort with the NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL) we propose to 1) examine the mechanisms and predictability of tropical SST-forced drought on interannual to decadal timescales and 2) examine anthropogenic-induced regional drying in models and observations to determine its mechanisms, if this is occurring and when it provides a useful predictable signal that needs to be adapted to. This work will rely on the GFDL Climate Model 2.1 (CM2.1) which realistically produces multiyear La Niñas that force drought in Southwestern North America. The predictability of these will be examined in a perfect model environment allowing assessment of potential predictability with uncertainty estimates. Similar predictability experiments will be performed for multidecadal changes in tropical Pacific climate within CM2.1 that appear analogous to the 1976/77 climate shift. To determine actual predictability we will examine initialized (from the observed atmosphere-ocean state) climate change projections that will be performed as part of IPCC AR5. These experiments will include changes in radiative forcing and include hindcasts and predictions of the next years to decades. They will be examined for actual predictive skill that comes from the initial conditions as well as the relative amplitudes and character of natural variability and forced climate change. 

The influence of atmospheric stochastic noise on the decadal predictability of tropical and North Pacific SST

Principal Investigator(s): Cristiana Stan, Center for Ocean-Land-Atmosphere Studies

Year Initially Funded: 2009

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


We propose to investigate the role of atmospheric noise (due to internal dynamics) at the air-sea interface on the limit of decadal predictability of tropical and North Pacific regions using the NOAA-NCEP Climate Forecast System (CFS). There is increasing evidence from observations and modeling studies that the Earth’s climate system possesses natural variability on decadal timescales. Numerous physical mechanisms have been proposed for decadal variability in the tropical and North Pacific areas. However, it is not well understood which of these mechanisms underpins the decadal predictability and if the state-of-the-art climate models show any decadal forecast skill. One of the ingredients of the physical mechanisms is the stochastic weather noise (due to internal atmospheric dynamics) randomly forcing the ocean through the surface turbulent fluxes. From a climate modeling perspective, the problem is further complicated because it has to be understood as a problem of separating the predictable signal from the unpredictable background noise. We propose to use the interactive ensemble coupling strategy, which is designed to filter out the noise, to investigate the role of noise on the limit of decadal predictability. 

The CFS has been exploited mostly as a monthly and seasonal forecast tool. It has also great potential for forecasts of the longer timescales, which recommends it as a suitable candidate of a multi-model ensemble forecast system. This proposed project has the following main objectives: 

1. investigate the role of weather noise on the internal decadal predictability of tropical and North Pacific SST; 

2. produce a set of ensemble decadal hindcasts with CFS between 1981 and 2001; 

3. evaluate the effects of systematic errors on the decadal forecast skill. 

We expect that the results of this study will unify the three elements currently competing to explain factors which limit the decadal predictability of the SST variations. Initial conditions, boundary conditions and weather noise might all be required to explain the reality. The proposed directly contributes to the Climate Variability and Predictability (CVP) in the main priority areas of (i) understanding the limits of decadal predictability, and (ii) developing a decadal climate prediction system. 



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